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ALL RIGHT.

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WELL, THANKS, EVERYBODY, FOR

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COMING OUT TODAY.

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WELCOME TO THE PEOPLE ONLINE.

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I HAVE THE DISTINCT PLSH YOU'RE

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OF -- PLEASURE OF INTRODUCING

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OUR SPEAKER TODAY FOR THE

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SPECIAL SEMINAR TO THE SINGLE

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CELL AND SPATIAL BIOLOGY USERS

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GROUP.

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DR. JAS MINIMUM PLUMMER IS

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CURRENTLY THE DIRECTOR OF THE

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CENTER FOR SPATIAL OMICS AT ST.

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JUDE CHILDREN'S RESEARCH

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HOSPITAL.

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SHE'S ORIGINALLY FROM CANADA,

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SHE TOLD ME THIS MORNING, WHICH

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IN MOST YEARS WOULD MEAN WE'RE

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REALLY ROOTING FOR HER BUT IN A

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SUMMER OLYMPIC YEAR PERHAPS WE

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SHOULD BE THINKING OF HER AS

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COMPETITION THIS YEAR.

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SHE COMPLETED HER UNDERGRADUATE

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AND Ph.D. WORK AT RAILROAD

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UNIVERSITY OF TORONTO, ANDS

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DOING RESEARCH AT THE CHILDREN'S

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HOSPITAL OF LOS ANGELES.

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AFTER THAT, SHE MOVED TO THE

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SRNLT FOR BIOINFORMATICS AT

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FUNCTIONAL GENOMICS AT

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CEDARS-SINAI MEDICAL CENTER.

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OVER THE COURSE OF HER WORK AND

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WHAT SHE'LL SHOW MANUFACTURE

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TODAY SHE GAINED A DEEP

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EXPERTISE IN FUNCTIONAL GENOMICS

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AND SPECIFICALLY NEURAL

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DEVELOPMENT HER OBSERVATIONS

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INCLUDED MANY WORKS THAT

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DEMONSTRATE HOW TUMORS ARISE DUE

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TO ALTERATIONS BOTH GENETIC 1K3

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EPIGENETIC AND CAN RUPT NORMAL

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DEVELOPMENTAL PROGRAMS,

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IMPORTANT TO THE PEDIATRIC

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ONCOLOGISTS IN THE ROOM.

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SHE NOW LEADS A GROUP AT SAINT

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JUDZ USING CUTTING EDGE

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SPATIAL -- ST. JUDE'S USING

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CUTTING EAJ SPATIAL TECHNOLOGIES

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AND MACHINE LEARNING AND

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ARTIFICIAL INTELLIGENCE TO

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PROFILE COMPLEX CELLULAR

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INTERACTIONS THAT DRIVE

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DEVELOPMENT AND IN SOME CASES

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TUMOR GENESIS AND PATHOGENESIS.

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HER APPROACH TO RESEARCH IS

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DEEPLY ROOTED IN INNOVATION 1K3

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THOROUGHNESS AND CURRENTLY HER

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LAB IS FOCUSED ON UNRAVELING THE

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MYSTERIES OF GENETIC INFLUENCE

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IN CANCER SUSCEPTIBILITY IN

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PATHOGENESIS.

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THE TITLE OF HER LECTURE TODAY

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IS SINGLE CELL AND SPATIAL

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TECHNOLOGIES, APPLICATIONS TO

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DISEASE.

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DR. PLUMMER, THANK YOU FOR BEING

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WITH US TODAY.

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[APPLAUSE]

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>> THANK YOU VERY MUCH

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ESPECIALLY TO THE PEDIATRIC

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ONCOLOGY BRANCH FOR COORDINATING

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MY VISIT.

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I LOOK FORWARD TO MEETING WITH

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ALL OF YOU.

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FIRST GLADGES ARE I TOOK THE

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ASSIGNMENT, I PICKED UP THE

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ASSIGNMENT OF KIND OF GIVING

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BROAD STROKES BUT VERY SPECIFIC

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TO HYPOTHESIS WE'RE INTERESTED

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IN IN MY LAB.

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THE IDEA THAT MAYBE YOU GUYS ALL

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COME FROM DIFFERENT DOMAINS, BUT

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MAYBE YOU CAN SEE HOW YOUR

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PROJECTS WILL BE OF USE USING

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SOME OF THESE THOLINGS.

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-- USING SOME OF THESE

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TECHNOLOGIES.

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AGAIN FROM THE BEAUTIFUL

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INTRODUCTION, THANK YOU FOR

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THAT.

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MY LAB IS INTERESTED IN

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SOMETHING VERY PRINCIPAL.

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I'M A GENETICIST AND REALLY

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INTERESTED IN HOW WE CAN USE THE

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CELLS OF ORIGIN, KIND OF IF YOU

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WANT TO BE A

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NEURODEVELOPMENTALLIST, YOU WANT

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TO BE A CANCER BIOLOGIST, TO

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REALLY UNDERSTAND WHERE CELLS

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CAME FROM AND UNDER OUR CONTEXT,

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REALLY UNDERSTANDING HOW GENETIC

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PERTUBATIONS CAN DRIVE DISEASE

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PATHOLOGY.

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SO YOU'LL KIND OF 1R A BIG SCOPE

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RIGHT -- SO OUR BIG SCOPE MOW TO

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THINK ABOUT WHEN DO WE ACTUALLY

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DECIDE TO USE SINGLE CELL AND

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WHEN DO WE DECIDE TO USE SPATIAL

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TECHNOLOGIES.

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I'M NOT A TECHNOLOGIST, I'M

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STILL A BIOLOGIST REALLY

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INTERESTED IN CERTAIN QUESTIONS.

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SO IN THIS CASE, ONE OF THE

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QUESTIONS MY LAB IS INTERESTED

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IN BACK TO THE IDEA OF CELLS OF

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ORIGIN IS REALLY TAKING YOU

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THRAW A CASE STUDY -- THROUGH A

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CASE STUDY OF MULTIOMICS AND

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NEURODEVELOPMENTAL DISORDERS,

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THIS IS ACTUALLY AN UNDERLYING

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PICTURE OF USING A TECHNOLOGY

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CALLED GEOMIX TO LOOK -- ARE G

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HE OMICS.

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NEURAL DEVELOPMENT AFFECTS MORE

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THAN 25% OF THE WORLD GLOBALLY.

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IT'S UNDER A LARGER BRANCH OF

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THE IDEA THAT WE HAVE NEURAL

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DEVELOPMENTAL PATHOLOGIES IN

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MANY DISEASES WHETHER THAT'S

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DEPRESSION, ADHD, AUTISM,

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SCHIZOPHRENIA, AND BIPOLAR.

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SO THE TREE ON THE RIGHT IS

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SHOWING YOU THAT IN A FAMILY,

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YOU MAY HAVE AN AFFECTED

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INDIVIDUAL WHO IS IN ORANGE AND

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UNAFFECTED INDIVIDUALS, AND OVER

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SEVERAL LINEAGES YOU'VE PROBABLY

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HEARD OF LIKE AN UNCLE THAT MAY

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HAVE HAD SCILTS FREN YA BUT --

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SCITSZ FREN YA BUT THAT

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SOMEONE'S NEPHEW OR NIECE MAY

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HAVE AUTISM.

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THIS WAS KIND OF THE STUDY THAT

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SHOULD HAVE BEEN -- HAS A REALLY

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PROFOUND FINDING FROM SOMETHING

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VERY SIMPLISTIC, YOU'LL SEE I

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USE A LOFT DIFFERENT TYPES OF

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DATA TO REALLY GET TO THE IDEA

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OF WHERE WE TEST THE HYPOTHESIS.

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SO THE STUDY WAS REALLY SIMPLE

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IN THAT THE PREFRONTAL CORTEX IS

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THE PLACE BY WHICH A LOT OF

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THESE ISSUES ARISE.

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THIS IS YOUR HIGH LEVEL

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COGNITIVE FUNCTION CENTER.  SO

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THEY JUST MUSHED UP DIFFERENT

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PARTS OF THE BRAINS THAT ARE

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AFFECTED BY AUTISM OR

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NEUROPSYCHIATRIC DISEASE, SO ON

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THE LEFT ARE KIND OF REGIONS OF

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THE BRAIN THAT HAVE TYPICALLY UP

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REGULATED GENES OR DOWN

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REGULATED GENES.

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PREFRONTAL CORTEX ARE BEING

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DEFINED BY THESE GENES, THE RED

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OR BLUE ONES.

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WHAT WAS INTERESTING WHEN YOU

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DID THAT IN AUTISM YOU JUST GOT

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RID OF THAT PATTERNING, SO NOT

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NECESSARILY THAT ALL GENES ARE

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GOTTEN RID OF, THEY'VE GOTTEN

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RID OF THAT KIND OF BEAUTIFUL

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SPATIAL TEMPORAL PATTERN.

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SO WHAT DOES THAT ACTUALLY LOOK

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LIKE?

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SO FROM THAT VERY BASIC

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PRINCIPLE THAT OBVIOUSLY THERE'S

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A DISREGULATING OF GENE

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EXPRESSION.

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SO IT'S NOT AS SIMPLISTIC AS

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KIND OF ONCOLOGY WHERE YOU KIND

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OF HAVE ONE OR TWO DRIVER GENES

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OR FUSION PROTEINS IN THE CASE

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OF PEDIATRIC CANCERS.

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WHAT REALLY HAPPENS IN NEURO

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DEVELOPMENT DISORDERS IS WE

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DON'T HAVE A ONE HIT WONDER SO

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FROM THIS CONTEXT YOU CAN SEE

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THE YELLOW ARE THE DISORDERS.

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IN AUTISMS ARE THE BLUE,

191
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SYNDROMIC, THESE ARE REALLY BIG

192
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HITS, IF YOU HAVE THESE, YOU

193
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KIND OF HAVE VERY SEVERE NEURO

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DEVELOPMENT DISORDERS, MOST OF

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THEM ARE MENDELIAN DISORDERS.

196
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WHAT YOU SHOULD SEE IN THE

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MIDDLE ARE THINGS THAT ARE VERY

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DIFFERENT BOTH PHENOTYPICALLY

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HAVE A LOT OF GENES THAT ARE

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INTERCONNECTED, ALMOST HALF OF

201
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THESE GENES ARE ACTUALLY -- THAT

202
00:06:08,517 --> 00:06:09,618
ARE ACTUALLY ASSOCIATED ACROSS

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00:06:09,618 --> 00:06:11,486
MORE THAN TWO PSYCHIATRIC

204
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CATEGORIES.

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SO WE TOOK THIS VERY BASIC

206
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PRINCIPLE, RIGHT, SO IF ALL

207
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THESE GENES ARE CONNECTED BUT

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THEY'RE NOT NECESSARILY

209
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CONNECTED BY THE EXACTED

210
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MUTATIONS THEY HAVE, MAYBE

211
00:06:18,927 --> 00:06:20,362
THEY'RE CONNECTED IN TERMS OF

212
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THEIR GENE REGULATORY NETWORK,

213
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RIGHT?

214
00:06:22,197 --> 00:06:23,398
BACK TO THAT VERY IDEA OF

215
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MUSHING UP A BRAIN AND IN

216
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TYPICALLY-DEVELOPING CHILDREN

217
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YOU HAVE THINGS THAT GO UP AND

218
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THINGS THAT GO DOWN, MAYBE IT'S

219
00:06:29,571 --> 00:06:30,772
THE UNDERLYING GENES, THAT

220
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PATTERN OF THINGS BEING

221
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CONNECTED ARE THE GENES THAT ARE

222
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BEING REGULATED IN SIMILAR WAYS.

223
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SO THIS IS NOT A VERY FANCY MATH

224
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PROBLEM.

225
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ESSENTIAL WE BUILD THESE

226
00:06:41,149 --> 00:06:42,718
NETWORKS VERY SIMPLISTICALLY.

227
00:06:42,718 --> 00:06:43,752
SO IF YOU IMAGINE THAT YOU HAVE

228
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GENE NUMBER ONE BEING BLUE, GENE

229
00:06:45,387 --> 00:06:47,022
NUMBER TWO BEING RED, GENE

230
00:06:47,022 --> 00:06:48,557
NUMBER THREE BEING GREEN, IT'S

231
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REALLY THE TRANSCRIPTION FACTORS

232
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THAT BIND THEM, RIGHT, THAT GIVE

233
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KIND OF THIS OVERLAPPING

234
00:06:55,564 --> 00:06:56,932
PHENOTYPE.

235
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IS ONE TRANSCRIPTION FACTOR IN

236
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THE CASE OF TRANSCRIPTION FACTOR

237
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SIX THAT BINDS THREE DIFFERENT

238
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TYPES OF GENES AND MAYBE THAT'S

239
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THE OVERLAPPINGNESS.

240
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SO AS YOU BUILD THAT NETWORK IS

241
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THE BOTTOM RIGHT-HAND SIDE.

242
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SO YOU SEE NUMBER ONE AUTO

243
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REGULATES ITSELF, NUMBER TWO

244
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REGULATES ITSELF AND REGULATES

245
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GENE NUMBER FIVE AND YOU CAN SEE

246
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HOW THEN YOU GET TO THE MIDDLE

247
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PATTERN.

248
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SO OVER THE ENTIRE GENOME YOU

249
00:07:18,353 --> 00:07:20,055
CAN SEE HOW TRANSCRIPTION

250
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FACTORS START TO REGULATE BOTH

251
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THEMSELVES AND COMMON GENES.

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THAT LOOKS VERY MUCH, IT'S

253
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REMINISCENT TO THAT FIRST SLIDE

254
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I SHOWED YOU WHICH IS REALLY THE

255
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PATTERN BY WHICH WE SEE IN TERMS

256
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OF THESE NEUROPSYCHIATRIC

257
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DISORDERS.

258
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SO WE TOOK A LARGE GENOME

259
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APPROACH TO THIS.

260
00:07:36,705 --> 00:07:38,974
WE ESSENTIALLY WORKED AT A GROUP

261
00:07:38,974 --> 00:07:41,843
AT UMASS MED WHICH HAVE TAKEN

262
00:07:41,843 --> 00:07:44,346
ALMOST EVERY PUNITIVE GIENGD

263
00:07:44,346 --> 00:07:46,548
PROTEIN WHICH IS -- BINDING

264
00:07:46,548 --> 00:07:47,749
PROTEIN WHICH IS OVER 1200

265
00:07:47,749 --> 00:07:49,084
TRANSCRIPTION FACTORS AND

266
00:07:49,084 --> 00:07:52,888
ESSENTIALLY DID A YEAST 100

267
00:07:52,888 --> 00:07:53,121
GREEN.

268
00:07:53,121 --> 00:07:55,524
THOSE THAT ARE OLD ENOUGH MIGHT

269
00:07:55,524 --> 00:07:57,726
REMEMBER YEAST 200, LIKE MYSELF.

270
00:07:57,726 --> 00:07:59,127
PROTEIN-PROTEIN, ASSOCIATION,

271
00:07:59,127 --> 00:08:02,798
TULL IT DOWN, TURNS BLUE, WE USE

272
00:08:02,798 --> 00:08:04,266
FANCY MOLECULAR TRICKS FOR THIS.

273
00:08:04,266 --> 00:08:07,235
YEAST ONE HYBRID DENOTES DNA ON

274
00:08:07,235 --> 00:08:07,769
PROTEIN.

275
00:08:07,769 --> 00:08:09,204
REMEMBER I SAID THERE ARE THESE

276
00:08:09,204 --> 00:08:10,172
NEURODEVELOPMENTAL DISORDERS,

277
00:08:10,172 --> 00:08:11,473
SAY YOU HAVE A DISORDER, MAJOR

278
00:08:11,473 --> 00:08:13,241
MUTATION, YOU GET ONE PHENOTYPE.

279
00:08:13,241 --> 00:08:14,810
BUT THE IDEA OF THIS IS THAT YOU

280
00:08:14,810 --> 00:08:16,411
JUST NEED SOME TEMPORAL

281
00:08:16,411 --> 00:08:17,079
REGULATION, RIGHT?

282
00:08:17,079 --> 00:08:18,380
IT MIGHT NOT BE ALL OR NOTHING.

283
00:08:18,380 --> 00:08:19,815
SO IF YOU HAD ONE GENE AND YOUR

284
00:08:19,815 --> 00:08:21,883
GENE WAS A TRANSCRIPTION FACTOR

285
00:08:21,883 --> 00:08:24,219
THEN YOU COULD DO CHIP SEQ WHICH

286
00:08:24,219 --> 00:08:25,754
IS KIND OF THE GENE CENTRIC

287
00:08:25,754 --> 00:08:28,690
APPROACH YOU SEE WHERE THERE'S

288
00:08:28,690 --> 00:08:30,759
PURPLE AND YOU SAY YOU HAVE TCF4

289
00:08:30,759 --> 00:08:32,394
AND THAT GETS BOUND BY MANY

290
00:08:32,394 --> 00:08:34,029
DMINGZ IS THAT WOULD BE COOL

291
00:08:34,029 --> 00:08:34,896
THEN YOU WOULD KNOW THAT

292
00:08:34,896 --> 00:08:37,299
EVERYTHING THAT'S TCF4 BOUND BUT

293
00:08:37,299 --> 00:08:39,401
IF YOU WANT TO KNOW MANY GENES

294
00:08:39,401 --> 00:08:41,603
AND THAT THERE'S SHARES

295
00:08:41,603 --> 00:08:42,737
TRANSCRIPTION FACTORS YOU HAVE

296
00:08:42,737 --> 00:08:44,739
TO TAKE THIS APPROACH THE TWO

297
00:08:44,739 --> 00:08:47,175
TAGS HERE, FANCY TAGS, ONE IS

298
00:08:47,175 --> 00:08:49,711
FOR HISS SO WE CAN SELECT FOR

299
00:08:49,711 --> 00:08:51,546
HISTONE MARKERS, SO WE CAN

300
00:08:51,546 --> 00:08:54,049
SELECT FOR HISTIDINE AND SEE IF

301
00:08:54,049 --> 00:08:55,383
THEY'RE GROWING, THAT'S FIRST

302
00:08:55,383 --> 00:08:57,252
THING, THEN WE CHECK TO SEE IF

303
00:08:57,252 --> 00:08:58,787
THEY'RE DONE.

304
00:08:58,787 --> 00:09:03,358
THIS IS DONE IN TRIPLICATE, MANY

305
00:09:03,358 --> 00:09:04,593
TRANSCRIPTION FACTOR IS PINNED

306
00:09:04,593 --> 00:09:05,560
FOUR TIMES SO YOU CAN IMAGINE

307
00:09:05,560 --> 00:09:07,329
THIS IS A REALLY AUTOMATED

308
00:09:07,329 --> 00:09:11,133
SCREEN, WE DO IT BY ROBOT.

309
00:09:11,133 --> 00:09:13,468
THE GENE REGULATORY NETWORK

310
00:09:13,468 --> 00:09:15,871
LOOKS FOR 50 PUNITIVE

311
00:09:15,871 --> 00:09:16,538
NEURODEVELOPMENTAL DISORDER

312
00:09:16,538 --> 00:09:17,405
GENES.

313
00:09:17,405 --> 00:09:19,040
IT EX-TRAP LATES QUICKLY.

314
00:09:19,040 --> 00:09:21,009
THOSE 50 GENES ARE IN THE

315
00:09:21,009 --> 00:09:21,243
YELLOW.

316
00:09:21,243 --> 00:09:22,244
IN BLUE ARE ALL THE

317
00:09:22,244 --> 00:09:23,245
TRANSCRIPTION FACTORS THAT

318
00:09:23,245 --> 00:09:23,912
COMBINE IT.

319
00:09:23,912 --> 00:09:25,380
I HOPE YOU APPRECIATE THAT IT'S

320
00:09:25,380 --> 00:09:28,917
HIGHLY REGULATED, RIGHT, AND

321
00:09:28,917 --> 00:09:29,384
INTERCONNECTED.

322
00:09:29,384 --> 00:09:30,852
SO THAT IDEA THAT YOU HAVE A

323
00:09:30,852 --> 00:09:32,187
SINGLE TRANSCRIPTION FACTOR AND

324
00:09:32,187 --> 00:09:33,522
IT'S YOUR MASTER TRANSCRIPTION

325
00:09:33,522 --> 00:09:34,689
FACTOR AND IT DOES EVERYTHING

326
00:09:34,689 --> 00:09:36,124
AND ANYTHING IS ACTUALLY THE

327
00:09:36,124 --> 00:09:36,892
NUANCE OF WHEN THOSE

328
00:09:36,892 --> 00:09:38,193
TRANSCRIPTION FACTORS ARE BOUND

329
00:09:38,193 --> 00:09:39,528
AND WHAT COMBINATIONS AND NOW WE

330
00:09:39,528 --> 00:09:41,163
HAVE KIND OF CLOSER TO THE

331
00:09:41,163 --> 00:09:42,797
GROUND TRUTH OF THAT COMPLEX

332
00:09:42,797 --> 00:09:45,967
THAT CAN BIND A GIVEN

333
00:09:45,967 --> 00:09:46,535
NEURODEVELOPMENTAL DISORDER

334
00:09:46,535 --> 00:09:46,735
GENE.

335
00:09:46,735 --> 00:09:48,170
HOW DO WE USE THAT?

336
00:09:48,170 --> 00:09:50,038
THAT'S KIND OF THE FUNCTIONAL

337
00:09:50,038 --> 00:09:51,206
GENOMIC SYSTEMS BIOLOGY APPROACH

338
00:09:51,206 --> 00:09:53,308
TO KIND OF HAVE AN IDEA OF WHAT

339
00:09:53,308 --> 00:09:55,277
THE TRANSCRIPTION FACTORS ARE.

340
00:09:55,277 --> 00:09:57,145
AND ARE WE RIGHT ABOUT THIS?

341
00:09:57,145 --> 00:09:59,648
SO WE GO BACK TO LIKE AND I ALSO

342
00:09:59,648 --> 00:10:01,516
SAY THE DATA PARASITES, THERE'S

343
00:10:01,516 --> 00:10:02,918
SO MUCH DATA OUT THERE TO ANY OF

344
00:10:02,918 --> 00:10:04,786
THE TRAINEES, THIS ISN'T MY

345
00:10:04,786 --> 00:10:06,421
DATA, IT'S COMPUTATIONALLY

346
00:10:06,421 --> 00:10:08,757
ANALYZED, BUT THERE'S A

347
00:10:08,757 --> 00:10:13,862
BEAUTIFUL ATLAS CALLED THE

348
00:10:13,862 --> 00:10:15,664
YALLAN BRAIN ATLAS, THESE ARE

349
00:10:15,664 --> 00:10:17,799
ALL THE REGIONS OF THE BRAIN

350
00:10:17,799 --> 00:10:18,700
ACROSS DEVELOPMENTAL TIME.

351
00:10:18,700 --> 00:10:20,135
THERE'S SOME EXPRESSION TO SOME

352
00:10:20,135 --> 00:10:22,070
DEGREE OF EVERY SINGLE GENE IS

353
00:10:22,070 --> 00:10:23,305
EXPRESSED IN THE BRAIN AT SOME

354
00:10:23,305 --> 00:10:24,506
POINT IN DEVELOPMENT.

355
00:10:24,506 --> 00:10:26,775
YOU CAN SEE WHEN WE USE OUR

356
00:10:26,775 --> 00:10:27,742
NEURODEVELOPMENTAL GENE

357
00:10:27,742 --> 00:10:29,010
PROMOTERS THAT YOU GET KIND OF A

358
00:10:29,010 --> 00:10:30,579
HIGHER LEVEL OF WE CAN START TO

359
00:10:30,579 --> 00:10:31,580
TEASE OUT DIFFERENT REGIONS OF

360
00:10:31,580 --> 00:10:32,113
THE BRAIN.

361
00:10:32,113 --> 00:10:33,782
SO REMEMBERING THIS IS BULK

362
00:10:33,782 --> 00:10:35,750
DATA, SO USE THE

363
00:10:35,750 --> 00:10:36,284
NEURODEVELOPMENTAL STORES

364
00:10:36,284 --> 00:10:38,286
THEMSELVES WE CAN START TO TEASE

365
00:10:38,286 --> 00:10:40,789
OUT DIFFERENT REGIONS OF THE

366
00:10:40,789 --> 00:10:42,324
GENES, CEREBELLUM FALLS OFF THE

367
00:10:42,324 --> 00:10:44,326
BACK OF YOUR HEAD, VERY DISTINCT

368
00:10:44,326 --> 00:10:46,494
FROM THESE, WE GET MORE

369
00:10:46,494 --> 00:10:47,395
DIFFERENTIATION THERE.

370
00:10:47,395 --> 00:10:49,598
I HOPE YOU CAN APPRECIATE 158

371
00:10:49,598 --> 00:10:50,432
TRANSCRIPTION FACTORS WE

372
00:10:50,432 --> 00:10:51,533
IDENTIFIED ON THE SCREEN

373
00:10:51,533 --> 00:10:52,934
ACTUALLY IN BULK DATA CAN GET

374
00:10:52,934 --> 00:10:54,336
YOU INTO DEEPER LAYERS OF THE

375
00:10:54,336 --> 00:10:55,670
BRAIN THAT WE WOULDN'T NORMALLY

376
00:10:55,670 --> 00:10:56,738
BE ABLE TO UNCOVER.

377
00:10:56,738 --> 00:10:58,006
IN THE BULK DATA WE'RE ACTUALLY

378
00:10:58,006 --> 00:10:59,841
GETTING TO SOME SPATIAL

379
00:10:59,841 --> 00:11:01,142
SPECIFICITY USING THESE KIND OF

380
00:11:01,142 --> 00:11:03,878
LARGE REGULATORY APPROACHES.

381
00:11:03,878 --> 00:11:05,847
SO THAT WAS A PRETTY STORY, IT'S

382
00:11:05,847 --> 00:11:08,383
TYING OFF, WE HAVE LOTS OF DATA

383
00:11:08,383 --> 00:11:10,452
FOR IT, WE EVEN HAVE SOME SICKLE

384
00:11:10,452 --> 00:11:12,854
CELL, SO WHY -- WE ACTUALLY HAVE

385
00:11:12,854 --> 00:11:14,189
SOME SINGLE CELL.

386
00:11:14,189 --> 00:11:17,892
WHY DID WE ACTUALLY TURN TO

387
00:11:17,892 --> 00:11:18,226
SPATIAL OMICS?

388
00:11:18,226 --> 00:11:19,761
EVERYBODY HAS SEEN A VERSION OF

389
00:11:19,761 --> 00:11:21,963
THIS, IT'S A PIE, I REALLY LIKE

390
00:11:21,963 --> 00:11:22,731
THIS PICTURE.

391
00:11:22,731 --> 00:11:28,270
I SHOWED YOU BULK WHY SEQ.

392
00:11:28,270 --> 00:11:30,071
YOU'VE SEEN THIS WHERE YOU CAN

393
00:11:30,071 --> 00:11:31,573
BREAK THAT OFF AND FIND THAT

394
00:11:31,573 --> 00:11:32,540
BULK DATA ISN'T THAT IMPORTANT,

395
00:11:32,540 --> 00:11:34,643
YOU REALLY WANT TO KNOW THE CELL

396
00:11:34,643 --> 00:11:34,843
TYPES.

397
00:11:34,843 --> 00:11:36,278
I WANT TO MAKE AN ARGUMENT THAT

398
00:11:36,278 --> 00:11:38,780
YOU'RE JUST LOOKING AT CELL

399
00:11:38,780 --> 00:11:39,080
PROPORTIONS.

400
00:11:39,080 --> 00:11:40,649
SO YOU'VE MUSHED UP A BRAIN AND

401
00:11:40,649 --> 00:11:41,750
HAVEN'T REALLY IDENTIFIED HOW

402
00:11:41,750 --> 00:11:43,385
THEY'RE CONNECTED AND REALLY

403
00:11:43,385 --> 00:11:45,453
IT'S THE SPATIAL TRANSCRIPT TOE

404
00:11:45,453 --> 00:11:47,222
MECHANICS THAT WILL ALLOW YOU

405
00:11:47,222 --> 00:11:48,657
HOW THOSE LEGO PIECES COME

406
00:11:48,657 --> 00:11:50,191
TOGETHER TO FORM A FUNCTIONING

407
00:11:50,191 --> 00:11:51,626
BRAIN BUT THIS IS REALLY

408
00:11:51,626 --> 00:11:52,727
TRUTHFULLY MY LAND AND I SAY

409
00:11:52,727 --> 00:11:54,362
THIS BECAUSE I'M VERY GUILTY OF

410
00:11:54,362 --> 00:11:55,664
LIVING IN THE MIDDLE LAND, I

411
00:11:55,664 --> 00:11:57,966
SPEND A LOT OF TIME AND MONEY AS

412
00:11:57,966 --> 00:12:00,602
A PI AND IN THE LAB CONTRIBUTING

413
00:12:00,602 --> 00:12:03,238
TO EVEN THE HUMAN CELL ATLAS.

414
00:12:03,238 --> 00:12:05,740
SO WHAT BULK RNA SEQ DATA IS

415
00:12:05,740 --> 00:12:07,275
TAKING THE ENTIRE BRAIN, MUSHING

416
00:12:07,275 --> 00:12:09,210
IT UP AND HAVING A PELLET AND

417
00:12:09,210 --> 00:12:10,745
WRITING IT ON A SEQUENCER.

418
00:12:10,745 --> 00:12:11,946
I WOULD PROPOSE WE DIDN'T DO

419
00:12:11,946 --> 00:12:13,281
THAT MUCH BETTER WITH SINGLE

420
00:12:13,281 --> 00:12:15,917
CELL, SO YOU JUST DISASSOCIATE

421
00:12:15,917 --> 00:12:18,420
AN ENTIRE BRAIN AND NOW HAVE A

422
00:12:18,420 --> 00:12:19,521
CLOUD OF CELLS AND PUTTING THAT

423
00:12:19,521 --> 00:12:20,488
ON A SEQUENCER.

424
00:12:20,488 --> 00:12:22,157
FOR THOSE THAT KNOW NOTHING

425
00:12:22,157 --> 00:12:23,591
ABOUT CANCER ESPECIALLY ADULT

426
00:12:23,591 --> 00:12:25,427
CANCER IN THIS ROOM, I CAN HOPE

427
00:12:25,427 --> 00:12:26,394
THAT YOU APPRECIATE THAT THESE

428
00:12:26,394 --> 00:12:27,829
TWO THINGS ARE VERY APPRECIATE

429
00:12:27,829 --> 00:12:29,698
BY EYE, THEY'RE VERY DIFFERENT,

430
00:12:29,698 --> 00:12:31,566
SO WHAT I'VE DONE IN THE PAST IS

431
00:12:31,566 --> 00:12:32,867
TAKING BREAST CANCER AND MUSHED

432
00:12:32,867 --> 00:12:35,070
IT UP AND PUT IT INTO SINGLE

433
00:12:35,070 --> 00:12:36,471
CLOUD, WHEN THAT LOOKS VERY

434
00:12:36,471 --> 00:12:38,106
DIFFERENT THAN ADJACENT NORMAL.

435
00:12:38,106 --> 00:12:39,507
SO WHAT DOES THIS LACK LIKE?

436
00:12:39,507 --> 00:12:40,442
THIS IS REAL DATA.

437
00:12:40,442 --> 00:12:42,477
THESE ARE ACTUALLY PLOTS THAT I

438
00:12:42,477 --> 00:12:43,812
THINK I'VE CONTRIBUTED TO SO

439
00:12:43,812 --> 00:12:45,213
GUILTY OF AND I'M SURE OTHER IN

440
00:12:45,213 --> 00:12:48,850
THE ROOM THAT THOSE 44 CELLS

441
00:12:48,850 --> 00:12:51,252
IDENTIFY THAT LOBULE IN BREAST

442
00:12:51,252 --> 00:12:51,920
CANCER.

443
00:12:51,920 --> 00:12:53,755
THOSE 44 CELLS IT DOESN'T MATTER

444
00:12:53,755 --> 00:12:54,923
HOW MUCH COMPUTATION I PUT

445
00:12:54,923 --> 00:12:56,391
BEHIND IT, I'M NEVER GOING TO

446
00:12:56,391 --> 00:12:58,727
IDENTIFY AND MAKE THEM A UNIQUE

447
00:12:58,727 --> 00:13:00,128
CELL ANNOTATION BECAUSE IT'S

448
00:13:00,128 --> 00:13:00,995
ACTUALLY THEIR SPATIAL LOCATION

449
00:13:00,995 --> 00:13:04,933
THAT MAKES THEM UNIQUE.

450
00:13:04,933 --> 00:13:07,168
SO WHY SPATIAL?

451
00:13:07,168 --> 00:13:08,803
I THINK I HELPED TO KIND OF SAY

452
00:13:08,803 --> 00:13:11,439
LIKE WHERE YOU ARE IN CON -- AND

453
00:13:11,439 --> 00:13:12,974
CONTEXT MATTERS AS BIOLOGIST I

454
00:13:12,974 --> 00:13:14,976
THINK IT'S THE EASIEST THING WHO

455
00:13:14,976 --> 00:13:16,811
EVEN MY HUSBAND WHO THINKS TUNA

456
00:13:16,811 --> 00:13:18,079
AND CHICKEN IS PROTEIN, AND HE'S

457
00:13:18,079 --> 00:13:19,514
NOT WRONG, HE CAN LOOK AT

458
00:13:19,514 --> 00:13:20,615
PICTURES AND REALLY UNDERSTAND

459
00:13:20,615 --> 00:13:22,917
THE DIFFERENCES WHICH I THINK IS

460
00:13:22,917 --> 00:13:26,187
WHY WE ALL FUNDAMENTALLYALLY GOT

461
00:13:26,187 --> 00:13:26,755
INTO OUR JOBS.

462
00:13:26,755 --> 00:13:28,223
THE IDEA OF OUR TALK AND IN

463
00:13:28,223 --> 00:13:29,924
GENERAL IS A BROADER VIEW

464
00:13:29,924 --> 00:13:31,126
BECAUSE I KNOW A LOT OF THESE

465
00:13:31,126 --> 00:13:33,161
TECHNOLOGIES ARE COMING IT YOUR

466
00:13:33,161 --> 00:13:34,629
CAMPUS WHICH IS REALLY EXCITING

467
00:13:34,629 --> 00:13:36,164
IS WE REALLY THINK ABOUT THESE

468
00:13:36,164 --> 00:13:38,366
IN FOUR CATEGORIES.

469
00:13:38,366 --> 00:13:40,435
GENOME, EPIGENOME, TRANSCRIPTOME

470
00:13:40,435 --> 00:13:41,403
AND PROTEOME.

471
00:13:41,403 --> 00:13:43,071
WE ALL KNOW THOSE WORLDS VERY

472
00:13:43,071 --> 00:13:43,271
WELL.

473
00:13:43,271 --> 00:13:44,906
IN THE GENOME PLACE AND SPACE WE

474
00:13:44,906 --> 00:13:46,241
ARE NOT VERY WELL VADGESSED IN

475
00:13:46,241 --> 00:13:47,876
THAT, THOSE ARE KIND OF

476
00:13:47,876 --> 00:13:49,077
TECHNOLOGIES WE'LL NOT BE

477
00:13:49,077 --> 00:13:51,946
TALKING ABOUT TODAY BUT THEY

478
00:13:51,946 --> 00:13:52,781
EXIST.

479
00:13:52,781 --> 00:13:54,682
EPIGENOME COMING FAST AND

480
00:13:54,682 --> 00:13:59,053
FURIOUS, TEND TO BE D BIT SEQ,

481
00:13:59,053 --> 00:14:00,822
MAJORITY OF THE TALK WE'LL BE

482
00:14:00,822 --> 00:14:03,758
TALKING ABOUT TRANSCRIPTOME AND

483
00:14:03,758 --> 00:14:04,959
PROTEOME, MAINLY BECAUSE I KNOW

484
00:14:04,959 --> 00:14:08,363
THESE TECHNOLOGIES LIE HERE OR

485
00:14:08,363 --> 00:14:09,464
THEY'RE BEING PURCHASED AND

486
00:14:09,464 --> 00:14:09,731
AVAILABLE.

487
00:14:09,731 --> 00:14:11,833
I'M NOT THE ONLY ONE.

488
00:14:11,833 --> 00:14:13,067
I LOVE THIS REVIEW BECAUSE I'VE

489
00:14:13,067 --> 00:14:14,502
BEEN IN SEQUENCING FOR A VERY

490
00:14:14,502 --> 00:14:15,703
LONG TIME AND THIS IS SINCE LAST

491
00:14:15,703 --> 00:14:17,572
YEAR AND YOU CAN SEE THE

492
00:14:17,572 --> 00:14:18,473
EXPLOSION, I DON'T THINK MANY

493
00:14:18,473 --> 00:14:21,309
FIELDS HAVE SEEN THIS LEVEL OF

494
00:14:21,309 --> 00:14:22,510
EXPLOSION AND THAT'S BEING

495
00:14:22,510 --> 00:14:23,812
REALLY I THINK ALL OF US HAVE

496
00:14:23,812 --> 00:14:25,780
WANTED TO DO SPATIAL AT A HIGHER

497
00:14:25,780 --> 00:14:26,314
LEVEL.

498
00:14:26,314 --> 00:14:27,649
OMICS IS JUST PLEXING.

499
00:14:27,649 --> 00:14:29,184
BY NO MEANS IMAGINE THAT THIS IS

500
00:14:29,184 --> 00:14:30,385
A NEW THING, RIGHT?

501
00:14:30,385 --> 00:14:32,153
WE DIDN'T INVENTED SPATIAL

502
00:14:32,153 --> 00:14:34,789
BIOLOGY, IT'S BEEN AROUND FOR A

503
00:14:34,789 --> 00:14:35,190
VERY LONG TIME.

504
00:14:35,190 --> 00:14:37,592
BUT REALLY THE EXPLOSION.

505
00:14:37,592 --> 00:14:39,027
COMMERCIALIZATION OF THESE

506
00:14:39,027 --> 00:14:40,128
PRODUCTS HAVE ACTUALLY MADE IT

507
00:14:40,128 --> 00:14:40,795
EASIER FOR US.

508
00:14:40,795 --> 00:14:42,063
SO YOU CAN GO SEE A FEW OVER

509
00:14:42,063 --> 00:14:43,198
HERE THAT WE'LL BE RUNNING

510
00:14:43,198 --> 00:14:47,335
THROUGH TODAY, BUT ON CAMPUS YOU

511
00:14:47,335 --> 00:14:55,543
HAVE XAN I UM -- WE HAVE XENIUM,

512
00:14:55,543 --> 00:14:57,612
1K3 -- DID NOT COME OUT IN 2019,

513
00:14:57,612 --> 00:15:00,048
IF CAME OUT THIS PAST YEAR.

514
00:15:00,048 --> 00:15:01,149
THE ORIGINAL SLIDE I SHOWED

515
00:15:01,149 --> 00:15:02,617
EARLIER ABOUT THE GENOME MOST OF

516
00:15:02,617 --> 00:15:03,485
THE TARGET TYPES ARE REALLY ON

517
00:15:03,485 --> 00:15:05,286
THE RNA, I'LL SHOW YOU SOME RNA

518
00:15:05,286 --> 00:15:07,388
BUT WE'LL TRY TO VENTURE INTO A

519
00:15:07,388 --> 00:15:08,223
LITTLE PROTEIN LAND.

520
00:15:08,223 --> 00:15:09,657
I WOULD SAY MOST OF THE TYPES

521
00:15:09,657 --> 00:15:11,226
FOR ANYBODY IN THIS ROOM THAT IS

522
00:15:11,226 --> 00:15:12,160
NOT COMFORTABLE IN SPATIAL YET

523
00:15:12,160 --> 00:15:13,361
TO REALLY BE THINKING ABOUT SOME

524
00:15:13,361 --> 00:15:14,896
OF THE NEXT GENERATION

525
00:15:14,896 --> 00:15:17,999
SEQUENCING, SO KIND OF THE

526
00:15:17,999 --> 00:15:20,034
VIZEUM OR COREO FOR PEOPLE THAT

527
00:15:20,034 --> 00:15:21,569
DON'T HAVE CAPITAL EQUIPMENT IS

528
00:15:21,569 --> 00:15:23,338
A REALLY NICE ASSAY.

529
00:15:23,338 --> 00:15:25,206
AGAIN, EVERYBODY ON THE SAME

530
00:15:25,206 --> 00:15:27,075
MAP, THIS IS KIND OF AN IDEA OF

531
00:15:27,075 --> 00:15:29,410
SEQUENCING BASED TECHNOLOGY.

532
00:15:29,410 --> 00:15:32,447
I'M GOING TO WALK YOU THROUGH

533
00:15:32,447 --> 00:15:34,516
VISIUM BUT IT'S NOT THAT

534
00:15:34,516 --> 00:15:35,383
DIFFERENT, IT'S WHETHER OR NOT

535
00:15:35,383 --> 00:15:37,085
YOU DNA BARCODED ON THE SPOT IN

536
00:15:37,085 --> 00:15:38,453
A DIFFERENT WAY.

537
00:15:38,453 --> 00:15:39,787
ESSENTIALLY YOU CAN TAKE ANY

538
00:15:39,787 --> 00:15:40,121
SLIDES.

539
00:15:40,121 --> 00:15:44,259
THE NICE THING ABOUT THE

540
00:15:44,259 --> 00:15:46,861
VISIUM HD PLATFORM IS YOU CAN

541
00:15:46,861 --> 00:15:49,297
TAKE FROZE TEN OR FFP, COME TALK

542
00:15:49,297 --> 00:15:51,799
TO ME IF YOU WANT TO KNOW

543
00:15:51,799 --> 00:15:53,301
SPECIFICITY.

544
00:15:53,301 --> 00:15:55,003
FFP IS NICE IN TERMS OF

545
00:15:55,003 --> 00:15:55,436
ARCHITECTURE.

546
00:15:55,436 --> 00:15:57,605
THERE'S ONLY A CAPTURED AREA FOR

547
00:15:57,605 --> 00:15:59,908
HD, WHICH IS 6.5, NOT LAUNCHED

548
00:15:59,908 --> 00:16:01,910
YET IN 11 MILLIMETERS SO WHAT IT

549
00:16:01,910 --> 00:16:03,945
IS FOR ALL THE TECHNOLOGIES

550
00:16:03,945 --> 00:16:07,448
WHETHER THAT'S LIVE SEQ VERSION

551
00:16:07,448 --> 00:16:11,386
TWO WHICH IS CURE YOA, STEREO OR

552
00:16:11,386 --> 00:16:13,254
VISIUM IS WE PUT LITTLE DOTS

553
00:16:13,254 --> 00:16:16,257
WHETHER THAT'S NANO BEAD IN THIS

554
00:16:16,257 --> 00:16:17,759
CASE, IT'S NOT THAT DIFFERENT

555
00:16:17,759 --> 00:16:19,360
THAN SINGLE CELL, WE IDENTIFY

556
00:16:19,360 --> 00:16:20,295
THE SPATIAL LOCATION.

557
00:16:20,295 --> 00:16:22,664
SO THE TISSUE IS PUT ON THIS, IT

558
00:16:22,664 --> 00:16:24,432
FITS ON IT, WE MAKE LIBRARIES

559
00:16:24,432 --> 00:16:25,800
OFF AND TOSS IT ON A SEQUENCER

560
00:16:25,800 --> 00:16:27,201
AND NOW YOU HAVE THE CORD

561
00:16:27,201 --> 00:16:27,435
NATALIE.

562
00:16:27,435 --> 00:16:28,536
I HOPE YOU CAN APPRECIATE THAT

563
00:16:28,536 --> 00:16:31,806
NOW THAT SINGLE CELL DATA THAT

564
00:16:31,806 --> 00:16:34,309
WAS JUST A RED CLUSTER VERSUS

565
00:16:34,309 --> 00:16:36,044
GREEN CLUSTER NOW IS A SPATIAL

566
00:16:36,044 --> 00:16:37,178
LOCALIZATION THAT LOOKS VERY

567
00:16:37,178 --> 00:16:40,448
SIMILAR TO THE CYTOARCHITECTURE

568
00:16:40,448 --> 00:16:40,782
UNDERNEATH.

569
00:16:40,782 --> 00:16:42,650
SO THIS IS REAL DATA FROM MY LAB

570
00:16:42,650 --> 00:16:44,819
WHERE WE TOOK HUMAN DEVELOPING

571
00:16:44,819 --> 00:16:46,254
BRAIN WITH THE QUESTION, RIGHT?

572
00:16:46,254 --> 00:16:48,122
BACK TO THAT IDEA THAT IT REALLY

573
00:16:48,122 --> 00:16:49,991
IS KIND OF WHERE AND WHEN YOU

574
00:16:49,991 --> 00:16:51,726
ARE IN TIME AND PLACE OF WHEN

575
00:16:51,726 --> 00:16:52,927
THESE TRANSCRIPTION FACTORS GET

576
00:16:52,927 --> 00:16:54,462
TURNED ON THAT REALLY START TO

577
00:16:54,462 --> 00:16:56,297
MAKE YOUR BRAIN CONNECT AND DO

578
00:16:56,297 --> 00:16:59,267
DIFFERENT PROBLEM-SOLVING

579
00:16:59,267 --> 00:17:00,468
METHODS.

580
00:17:00,468 --> 00:17:02,003
SO WHICH WE ARE, THIS IS GUILTY,

581
00:17:02,003 --> 00:17:04,305
SO HERE IS OUR SINGLE NUCLEI

582
00:17:04,305 --> 00:17:06,641
DATA, WE'VE DONE ATAC ON THIS,

583
00:17:06,641 --> 00:17:08,610
MUSHED UP THAT BRAIN, TAKEN FOUR

584
00:17:08,610 --> 00:17:10,111
PIECES, ALL OF THEM DIERCHT

585
00:17:10,111 --> 00:17:11,846
FUNCTIONS, PREFRONTAL --

586
00:17:11,846 --> 00:17:14,482
DIFFERENT FUNCTIONS, PREFRONTAL

587
00:17:14,482 --> 00:17:17,485
CORTEX, THALAMUS, OCCIPITAL, WE

588
00:17:17,485 --> 00:17:19,954
TAKE THESE AND WE MAP THEM.

589
00:17:19,954 --> 00:17:22,790
SO YOU CAN SEE LIKE I

590
00:17:22,790 --> 00:17:25,093
FUNDAMENTALLY CALL THESE GLUE

591
00:17:25,093 --> 00:17:27,061
N4, IT'S SO ARBITRARY, WE'RE ALL

592
00:17:27,061 --> 00:17:29,130
DOING T I'M SUPER-GUILTY OF IT,

593
00:17:29,130 --> 00:17:31,299
BUT LOOK AT GLUE N4 RIGHT HERE,

594
00:17:31,299 --> 00:17:32,967
IT IS JUST THESE CELLS.

595
00:17:32,967 --> 00:17:33,735
SO IF YOU KNOW ANYTHING ABOUT

596
00:17:33,735 --> 00:17:36,604
THE BRAIN, THIS IS THE NEURAL

597
00:17:36,604 --> 00:17:37,338
PROGENITOR POPULATION.

598
00:17:37,338 --> 00:17:39,007
FROM THE YELLOW TO THIS KIND OF

599
00:17:39,007 --> 00:17:42,944
DARK GREEN IT'S CALLED THE SUB

600
00:17:42,944 --> 00:17:44,379
VENTRICULAR ZONE, WHERE ALL

601
00:17:44,379 --> 00:17:46,814
NEURONS ARE BORN, THEY BEGIN TO

602
00:17:46,814 --> 00:17:48,182
DIFFERENTIATED IN THIS ORANGE

603
00:17:48,182 --> 00:17:48,416
ZONE.

604
00:17:48,416 --> 00:17:50,151
IF I WERE TO GO HERE AND LOOK AT

605
00:17:50,151 --> 00:17:52,120
IT IT'S JUST A ORANGE BLOB BUT

606
00:17:52,120 --> 00:17:52,887
OBVIOUSLY CONTEXT MATTERS.

607
00:17:52,887 --> 00:17:54,989
SO IN THIS PINK DOT THOSE ARE

608
00:17:54,989 --> 00:17:56,090
PARASITES, THIS IS WHERE

609
00:17:56,090 --> 00:17:57,525
VASCULATURE IS STARTING TO FORM.

610
00:17:57,525 --> 00:17:58,926
SO EVEN THOUGH WE HAVE THINGS

611
00:17:58,926 --> 00:18:00,361
HERE, IT'S ACTUALLY CONTEXT OF

612
00:18:00,361 --> 00:18:02,563
KIND OF WHERE THEY'RE

613
00:18:02,563 --> 00:18:02,964
INTERLAYING.

614
00:18:02,964 --> 00:18:03,665
DOES IT MATTER?

615
00:18:03,665 --> 00:18:04,732
YES, IT DOES.

616
00:18:04,732 --> 00:18:06,734
IN THAT SUB VENTRICULAR ZONE I

617
00:18:06,734 --> 00:18:08,336
TOLD YOU ABOUT, IT'S THIS ZONE

618
00:18:08,336 --> 00:18:09,837
RIGHT HERE, THAT ZONE IS

619
00:18:09,837 --> 00:18:10,772
ACTUALLY DEFINED FROM HERE TO

620
00:18:10,772 --> 00:18:12,974
HERE BUT YOU CAN SEE IN CERTAIN

621
00:18:12,974 --> 00:18:14,042
NEURONAL SUBTYPES THAT WE

622
00:18:14,042 --> 00:18:15,376
ACTUALLY HAVE SPATIAL RESOLUTION

623
00:18:15,376 --> 00:18:20,014
THAT WE WERE NEVER ABLE TO SEE

624
00:18:20,014 --> 00:18:20,448
BEFORE.

625
00:18:20,448 --> 00:18:25,153
SO IT'S ONLY IN THIS LAYER.

626
00:18:25,153 --> 00:18:30,525
THAT GLUE -- THIS IS NOT YET A

627
00:18:30,525 --> 00:18:30,892
NEURON.

628
00:18:30,892 --> 00:18:31,092
OKAY.

629
00:18:31,092 --> 00:18:32,293
I CARE ABOUT JEBLGHTS, RIGHT?

630
00:18:32,293 --> 00:18:34,228
I SHOWED YOU -- I CARE ABOUT

631
00:18:34,228 --> 00:18:34,796
GENETIC, RIGHT?

632
00:18:34,796 --> 00:18:38,533
I SHOWED YOU THIS WHOLE PROBLEM

633
00:18:38,533 --> 00:18:40,501
ABOUT HAVING SHARED GENETIC, I

634
00:18:40,501 --> 00:18:43,671
KNOW ALL OF US ARE REALLY BORED

635
00:18:43,671 --> 00:18:44,939
ABOUT CELL ATLASSING.

636
00:18:44,939 --> 00:18:46,607
HOW DO WE MOVE THAT BACK INTO

637
00:18:46,607 --> 00:18:48,042
OUR HYPOTHESIS IN TERMS OF WHAT

638
00:18:48,042 --> 00:18:49,544
WE THINK ABOUT IN TERM OF THIS

639
00:18:49,544 --> 00:18:49,744
FIELD?

640
00:18:49,744 --> 00:18:51,646
BACK TO THAT DIAGRAM.

641
00:18:51,646 --> 00:18:52,280
RIGHT?

642
00:18:52,280 --> 00:18:53,915
THAT DIAGRAM, GENES ASSOCIATED

643
00:18:53,915 --> 00:18:54,882
WITH NEURODEVELOPMENTAL

644
00:18:54,882 --> 00:18:55,516
DISORDERS.

645
00:18:55,516 --> 00:18:57,852
SO WE JUST TAKE ONE BRANCH OF

646
00:18:57,852 --> 00:18:58,052
THEM.

647
00:18:58,052 --> 00:18:59,220
FOR SAKE OF TIME I'LL JUST WALK

648
00:18:59,220 --> 00:19:00,188
YOU THROUGH ONE BRANCH.

649
00:19:00,188 --> 00:19:01,055
THIS IS AUTISM.

650
00:19:01,055 --> 00:19:03,558
WE TOOK THE TOP AUTISM GENES,

651
00:19:03,558 --> 00:19:04,992
THEY'RE ALL SHOWN HERE, THEY'RE

652
00:19:04,992 --> 00:19:07,395
ALL MAPPED HERE, AND THEN WE

653
00:19:07,395 --> 00:19:10,131
LOOKED AT OUR SPATIAL DATA.

654
00:19:10,131 --> 00:19:11,566
IN OUR SPATIAL DATA WE ACTUALLY

655
00:19:11,566 --> 00:19:12,967
SEE, SO REMEMBER BACK TO THIS

656
00:19:12,967 --> 00:19:13,835
PICTURE, RIGHT?

657
00:19:13,835 --> 00:19:15,403
THIS PICTURE, THIS IS ALL SINGLE

658
00:19:15,403 --> 00:19:17,472
CELL ANALYSIS.

659
00:19:17,472 --> 00:19:19,006
EACH LAYER DIFFERENT PROPORTIONS

660
00:19:19,006 --> 00:19:19,674
OF CELLS.

661
00:19:19,674 --> 00:19:23,277
SO IN THIS LAYER, WE HAVE HIGH

662
00:19:23,277 --> 00:19:24,746
LEVEL NEURAL PROGENITOR CELLS.

663
00:19:24,746 --> 00:19:26,380
WHEN YOU LOOK AT THOSE

664
00:19:26,380 --> 00:19:27,048
NEURODEVELOPMENTAL DISORDERS,

665
00:19:27,048 --> 00:19:29,117
NOTHING IS REALLY GOING ON IN

666
00:19:29,117 --> 00:19:30,084
EARLY DEVELOPMENTAL TIME.

667
00:19:30,084 --> 00:19:31,285
WHAT REALLY STARTS TO HAPPEN IS

668
00:19:31,285 --> 00:19:33,955
WE START TO GET ENRICH MENTD AND

669
00:19:33,955 --> 00:19:35,690
INTERPLAY OF -- ENRICHMENT AND

670
00:19:35,690 --> 00:19:37,525
INTERPLAY OF THESE CELL TYPES AS

671
00:19:37,525 --> 00:19:39,694
THESE NEURONS HERE ARE STARTING

672
00:19:39,694 --> 00:19:40,394
TO DIVIDE.

673
00:19:40,394 --> 00:19:42,029
NOW WE HAVE ACTUAL CONTEXT TO

674
00:19:42,029 --> 00:19:43,765
THE CELL TYPES THAT WE

675
00:19:43,765 --> 00:19:45,066
DISCOVERED IN SINGLE CELL DATA

676
00:19:45,066 --> 00:19:46,501
OF WHERE THEY COULD ACTUALLY

677
00:19:46,501 --> 00:19:50,972
START TO CAUSE DISEASE.

678
00:19:50,972 --> 00:19:52,707
SO I WILL WALK YOU THROUGH REAL

679
00:19:52,707 --> 00:19:52,907
LIFE.

680
00:19:52,907 --> 00:19:54,175
THIS IS A REAL LIFE EXAMPLE OF

681
00:19:54,175 --> 00:19:56,244
HOW I'VE DONE DATA VERY

682
00:19:56,244 --> 00:19:56,544
INCORRECTLY.

683
00:19:56,544 --> 00:19:58,412
PLEASE LEARN FROM A LOT OF

684
00:19:58,412 --> 00:19:59,981
WASTED MONEY, NOT WASTED, IT

685
00:19:59,981 --> 00:20:01,382
WILL STILL GO IN THE PAPER.

686
00:20:01,382 --> 00:20:03,117
SORMING BACK TO THAT DIAGRAM

687
00:20:03,117 --> 00:20:04,218
WHERE I SHOWED -- SO REMEMBERING

688
00:20:04,218 --> 00:20:05,787
BACK TO THAT DIAGRAM WHERE I

689
00:20:05,787 --> 00:20:07,622
SHOWED YOU HERE SYNDROMIC GENES,

690
00:20:07,622 --> 00:20:09,090
ONE OF THOSE IF YOU HIT IT VERY

691
00:20:09,090 --> 00:20:10,958
HARD AND YOU HAVE A MUTATION IN

692
00:20:10,958 --> 00:20:12,326
AN AXON YOU CAN ACTUALLY CAUSE

693
00:20:12,326 --> 00:20:19,267
THIS PHENOTYPE CALLED TCF4 HAS A

694
00:20:19,267 --> 00:20:22,203
VERY IF PHENOTYPICAL PHENOTYPE

695
00:20:22,203 --> 00:20:23,404
WHERE THEY'RE DEVELOPMENTALLY

696
00:20:23,404 --> 00:20:25,573
DELAYED, IT'S ALSO IN GWAS AND

697
00:20:25,573 --> 00:20:28,142
ALSO COME UP IN AUTISM.

698
00:20:28,142 --> 00:20:28,976
EARLIER IT WAS THOUGHT TO BE

699
00:20:28,976 --> 00:20:31,479
JUST A SYNDROMIC GENE, NOW THAT

700
00:20:31,479 --> 00:20:32,680
WE HAVE MORE CASES AND CONTROLS

701
00:20:32,680 --> 00:20:35,383
WE CAN START TO AUGMENT IT.

702
00:20:35,383 --> 00:20:37,018
THERE'S EVEN MICE MODELS NOW,

703
00:20:37,018 --> 00:20:40,054
WHERE YOU MAKE A MUTANT AND HAVE

704
00:20:40,054 --> 00:20:42,356
PHENOTYPES HA CORRELATE REALLY

705
00:20:42,356 --> 00:20:42,824
WELL.

706
00:20:42,824 --> 00:20:43,858
INCLUDING HYPERVENTILATION.

707
00:20:43,858 --> 00:20:44,225
THIS IS COOL.

708
00:20:44,225 --> 00:20:45,993
GOES BACK TO THE IDEA THAT

709
00:20:45,993 --> 00:20:46,861
SPATIAL MATTERS.

710
00:20:46,861 --> 00:20:47,061
RIGHT?

711
00:20:47,061 --> 00:20:49,530
WE CAN FIND A BRAIN PATTERN BUT

712
00:20:49,530 --> 00:20:52,333
WHAT IS THE SAME GENE DOING IN

713
00:20:52,333 --> 00:20:53,201
SOMETHING RESPIRATORY.

714
00:20:53,201 --> 00:20:53,835
STAY TUNED.

715
00:20:53,835 --> 00:20:55,069
THAT'S ANOTHER TALK.

716
00:20:55,069 --> 00:20:56,804
BACK TO BULK DATA, PARASITE,

717
00:20:56,804 --> 00:20:57,905
THINGS WE'VE DONE RIGHT AND

718
00:20:57,905 --> 00:21:00,541
THINGS WE'VE DONE WRONG.

719
00:21:00,541 --> 00:21:03,044
WE'VE ALWAYS KNOWN IN THE FIELD

720
00:21:03,044 --> 00:21:04,679
TCF4 IN THE FIELD IT'S A

721
00:21:04,679 --> 00:21:05,479
TRANSCRIPTION FACTOR.

722
00:21:05,479 --> 00:21:08,950
YOU TAKE IT OUT, CAUSE A SEVERE

723
00:21:08,950 --> 00:21:10,718
PHENOTYPE.

724
00:21:10,718 --> 00:21:11,819
S A LITTLE BIT OVER TIME MAYBE

725
00:21:11,819 --> 00:21:12,987
YOU GET AUTISM.

726
00:21:12,987 --> 00:21:15,556
IN BULK DATA WE CAN SEE SOX9

727
00:21:15,556 --> 00:21:17,525
OVER DEVELOPMENT OF TIME HAS

728
00:21:17,525 --> 00:21:19,060
THIS BUSM ANTI-REGULATION TO

729
00:21:19,060 --> 00:21:20,628
EACH OTHER -- BEAUTIFUL

730
00:21:20,628 --> 00:21:21,629
ANTI-REGULATION TO EACH OTHER.

731
00:21:21,629 --> 00:21:22,797
WHAT DOES THAT LOOK LIKE IN

732
00:21:22,797 --> 00:21:24,565
SINGLE CELL 1234R BACK TO THAT

733
00:21:24,565 --> 00:21:26,868
PLOT I SHOWED YOU BEFORE, LOTS

734
00:21:26,868 --> 00:21:28,703
OF MONEY, MASTER TRANSCRIPTION

735
00:21:28,703 --> 00:21:29,270
FACTOR.

736
00:21:29,270 --> 00:21:30,671
SOX9 MIGHT BE IN THIS, BUT IT

737
00:21:30,671 --> 00:21:32,106
WASN'T ENOUGH TO DEFINE THE CELL

738
00:21:32,106 --> 00:21:34,108
TYPE WE CAN'T SAY ANYTHING ABOUT

739
00:21:34,108 --> 00:21:34,442
IT.

740
00:21:34,442 --> 00:21:35,509
BETTER YET WE WOULDN'T HAVE

741
00:21:35,509 --> 00:21:36,477
FOUND IT, RIGHT?

742
00:21:36,477 --> 00:21:38,246
HERE WE FOUND IT IN BULK DATA

743
00:21:38,246 --> 00:21:39,714
WHERE THERE'S A STRIATION

744
00:21:39,714 --> 00:21:40,014
PATTERN.

745
00:21:40,014 --> 00:21:41,782
HERE THERE'S NO RECIPROCITY.

746
00:21:41,782 --> 00:21:43,851
BUT WHEN WE LOOK SPATIALLY AND I

747
00:21:43,851 --> 00:21:45,653
DIDN'T PUT THE STATISTICS IN BUT

748
00:21:45,653 --> 00:21:47,555
THERE IS STATISTICS TO SHOW THAT

749
00:21:47,555 --> 00:21:50,324
EVERY PLACE TCF4 MASTER

750
00:21:50,324 --> 00:21:51,492
TRANSCRIPTION FACTOR IT'S ON

751
00:21:51,492 --> 00:21:54,295
EVERYWHERE, EVERYWHERE TCF4 IS,

752
00:21:54,295 --> 00:21:56,464
SOX9 IS OFF AND VICE VERSA.

753
00:21:56,464 --> 00:22:00,034
SO WHEN IT HITS THE OUTER

754
00:22:00,034 --> 00:22:03,037
CORTEX, TCF4 GOES OFF BUT GOES

755
00:22:03,037 --> 00:22:05,539
SUPER HIGH IN NEURONS THAT SOX9

756
00:22:05,539 --> 00:22:06,374
IS NOT PRESENT IN.

757
00:22:06,374 --> 00:22:07,608
FOR THE SAKE OF TIME I WOULD

758
00:22:07,608 --> 00:22:08,843
WALK YOU THROUGH KIND OF OUR

759
00:22:08,843 --> 00:22:10,378
CASE STUDY ALZHEIMER'S, THIS IS

760
00:22:10,378 --> 00:22:11,913
BACK TO KIND OF THE IDEA THAT IT

761
00:22:11,913 --> 00:22:13,247
GOES ALONG ALL DISEASE.

762
00:22:13,247 --> 00:22:14,782
IC EVERYBODY WANTS TO BE IN THE

763
00:22:14,782 --> 00:22:16,651
NEWEST TECHNOLOGY AND I WILL SAY

764
00:22:16,651 --> 00:22:18,119
EVEN OLDER TECHNOLOGIES WORK SO

765
00:22:18,119 --> 00:22:21,122
THIS IS THE IDEA BACK TO VISIUM

766
00:22:21,122 --> 00:22:22,423
THAT YOU CAN TAKE EXISTING

767
00:22:22,423 --> 00:22:23,991
DATASETS OF SINGLE CELL, WE DID

768
00:22:23,991 --> 00:22:26,394
ESSENTIALLY THE SAME IDEA, SO

769
00:22:26,394 --> 00:22:28,129
TAKING TEMPORAL LOBE WHERE WE

770
00:22:28,129 --> 00:22:30,197
KNOW ALZHEIMER'S IS MOST

771
00:22:30,197 --> 00:22:31,499
AFFECTED, HAVE SINGLE CELL DATA,

772
00:22:31,499 --> 00:22:36,003
SO EVEN IN OLDER CLASSIC VISIUM

773
00:22:36,003 --> 00:22:37,204
WE CAN SEE DIFFERENCES BETWEEN

774
00:22:37,204 --> 00:22:39,073
CONTROL AND AUTISM, AND REALLY

775
00:22:39,073 --> 00:22:40,942
LOOKING AT THOSE WE SEE ACTUALLY

776
00:22:40,942 --> 00:22:41,575
LAYER SPECIFIC DIFFERENCE.

777
00:22:41,575 --> 00:22:43,311
SO THE DIFFERENCES ARE NOT

778
00:22:43,311 --> 00:22:44,779
COMING BACK FROM MUSHING UP A

779
00:22:44,779 --> 00:22:46,914
BRAIN AND FINDING A SINGLE CELL

780
00:22:46,914 --> 00:22:47,281
PHENOTYPE.

781
00:22:47,281 --> 00:22:48,683
WE ACTUALLY SEE DIFFERENCES IN

782
00:22:48,683 --> 00:22:50,518
THESE GENES BASED ON WHETHER

783
00:22:50,518 --> 00:22:52,520
THEY'RE IN WHITE MATTER SO THOSE

784
00:22:52,520 --> 00:22:54,488
ARE MAINLY GLIAL CELLS AND IF

785
00:22:54,488 --> 00:22:55,890
THEY'RE IN NEURONS WE ACTUALLY

786
00:22:55,890 --> 00:22:56,924
HAVE THESE KIND OF SPATIAL

787
00:22:56,924 --> 00:22:58,326
PATTERNS TO ETCH AFTER THESE

788
00:22:58,326 --> 00:22:59,827
GEERNZ TO DEFINE THESE LAYERS.

789
00:22:59,827 --> 00:23:01,495
-- GENES TO DEFINE THESE LAYERS.

790
00:23:01,495 --> 00:23:02,296
LAYERS MATTER.

791
00:23:02,296 --> 00:23:03,664
SO BACK TO THE IDEA THAT IT

792
00:23:03,664 --> 00:23:05,666
DOESN'T ALWAYS HAVE TO BE BIG

793
00:23:05,666 --> 00:23:07,101
MULTIOMIC DATA, HOW DO YOU

794
00:23:07,101 --> 00:23:08,936
REFINE IT AND GET INTO A SPACE

795
00:23:08,936 --> 00:23:10,805
AND PLACE WHERE LABORATORIES CAN

796
00:23:10,805 --> 00:23:12,006
USE THIS MOVING FORWARD?

797
00:23:12,006 --> 00:23:14,942
SO NOW THAT WE'VE USED KIND OF

798
00:23:14,942 --> 00:23:17,611
HIGH LEVEL VISIUM DATA WE CAN

799
00:23:17,611 --> 00:23:19,347
TAKE THOSE INTO LARGER DATASETS,

800
00:23:19,347 --> 00:23:21,015
DO MOUSE MODELS AND DO THOSE

801
00:23:21,015 --> 00:23:23,317
KIND OF DHINGZ AND REALLY

802
00:23:23,317 --> 00:23:24,952
ESTABLISH -- THINGS AND REALLY

803
00:23:24,952 --> 00:23:26,821
ESTABLISH IT WAS AN ASTROCYTE

804
00:23:26,821 --> 00:23:28,022
PHENOTYPE WE SAW IN THE

805
00:23:28,022 --> 00:23:29,457
DIFFERENCE BETWEEN CONTROL IN

806
00:23:29,457 --> 00:23:30,658
ALZHEIMER'S NOW WE HAVE A

807
00:23:30,658 --> 00:23:30,891
MARKER.

808
00:23:30,891 --> 00:23:33,928
SO FROM USING VISIUM WE NOW HAVE

809
00:23:33,928 --> 00:23:36,330
A MARKER TO SHOW THOSE ASTRO

810
00:23:36,330 --> 00:23:39,600
SIETSZ, SEE THOSE WHITE ONES ARE

811
00:23:39,600 --> 00:23:41,268
ACTUALLY INFORMULA ITED IN

812
00:23:41,268 --> 00:23:42,136
ALZHEIMER'S COMPARED TO CONTROL

813
00:23:42,136 --> 00:23:43,571
AND WE HAVE A LOCALIZATION FOR

814
00:23:43,571 --> 00:23:43,804
THEM.

815
00:23:43,804 --> 00:23:44,772
WE WOULDN'T HAVE BEEN ABLE TO

816
00:23:44,772 --> 00:23:46,407
DISCOVER THAT AND KNOW THIS GENE

817
00:23:46,407 --> 00:23:48,609
HAD WE NOT DONE KIND OF WHOLE

818
00:23:48,609 --> 00:23:51,812
TRANSMIT TOMORROW.

819
00:23:51,812 --> 00:23:52,980
SO -- TRANSCRIPTOME.

820
00:23:52,980 --> 00:23:54,215
THAT'S FIRST HERE, KNOWING THERE

821
00:23:54,215 --> 00:23:55,616
ARE SOME CANCER PEOPLE, I'M NOT

822
00:23:55,616 --> 00:23:57,151
GOING TO TALK PEDIATRIC CANCER

823
00:23:57,151 --> 00:23:58,819
THIS TALK BUT MAYBE WHEN I COME

824
00:23:58,819 --> 00:23:59,020
BACK.

825
00:23:59,020 --> 00:24:00,654
BUT I'M SHOWING THIS VERY

826
00:24:00,654 --> 00:24:02,189
PARTICULAR EXAMPLE BECAUSE THE

827
00:24:02,189 --> 00:24:03,457
NUANCES DID NOT COME OUT IN ANY

828
00:24:03,457 --> 00:24:05,593
OF THE APROCHES THAT WE EXISTED

829
00:24:05,593 --> 00:24:06,794
TODAY -- APPROACHES THAT WE

830
00:24:06,794 --> 00:24:07,461
EXISTED TODAY.

831
00:24:07,461 --> 00:24:08,863
ONE OF THE IDEAS IN OUR GROUP IS

832
00:24:08,863 --> 00:24:10,498
BACK TO CELLS OF ORIGIN, SOME OF

833
00:24:10,498 --> 00:24:12,366
THE CELLS OF ORIGIN THAT WE'RE

834
00:24:12,366 --> 00:24:13,934
INTERESTED IN ACTUALLY ARE TO

835
00:24:13,934 --> 00:24:15,669
ADDRESS HEALTH DISPARITIES.

836
00:24:15,669 --> 00:24:17,505
SO THE CASE STUDY FOR THIS IS

837
00:24:17,505 --> 00:24:18,706
THAT THIS IS ONE GRAPH THAT

838
00:24:18,706 --> 00:24:20,808
KEEPS ME UP LATE AT NIGHT, AND

839
00:24:20,808 --> 00:24:22,710
I'M GUILTY OF IT.

840
00:24:22,710 --> 00:24:24,645
I THINK DR. ELLIS KNOWS THAT I

841
00:24:24,645 --> 00:24:27,782
PARTICIPATE IN A LOT OF GWAS

842
00:24:27,782 --> 00:24:29,550
CHARACTERIZATIONS, SO GENOMICS

843
00:24:29,550 --> 00:24:31,786
HAS HAD THIS 81% EXPLOSION, THIS

844
00:24:31,786 --> 00:24:34,822
IS PUBLISHED IN THE END OF '22,

845
00:24:34,822 --> 00:24:35,723
I'M HAPPY TO HAVE PARTICIPATED

846
00:24:35,723 --> 00:24:38,092
IN THAT.

847
00:24:38,092 --> 00:24:39,527
BUT 86% OF THIS EXPLOSION HAS

848
00:24:39,527 --> 00:24:43,330
BEEN IN CAUCASIAN OR EUROPEAN

849
00:24:43,330 --> 00:24:43,998
ANCESTRY.

850
00:24:43,998 --> 00:24:45,866
SO YOU CAN SEE THAT WE'VE HAD

851
00:24:45,866 --> 00:24:47,968
THIS HUGE EXPLOSION SINCE 2016,

852
00:24:47,968 --> 00:24:49,804
BUT THE RED IS REALLY DEMARKED

853
00:24:49,804 --> 00:24:52,440
BY EUROPEAN AND WE'RE MISSING

854
00:24:52,440 --> 00:24:53,107
ALL OF THESE.

855
00:24:53,107 --> 00:24:54,942
SO BEING A GENETICIST, YOU KNOW,

856
00:24:54,942 --> 00:24:56,777
WE HAVE THIS DISPARITY AND WE

857
00:24:56,777 --> 00:25:00,014
ARE AT NIH AND NCI AND I KNOW

858
00:25:00,014 --> 00:25:01,315
THIS HAS BEEN A BIG MISSION FOR

859
00:25:01,315 --> 00:25:03,384
THE NEXT KIND OF STRATEGIC PLANS

860
00:25:03,384 --> 00:25:04,785
IS TO REALLY INCORPORATE THE

861
00:25:04,785 --> 00:25:08,189
IDEA OF HOW ARE THESE -- HOW ARE

862
00:25:08,189 --> 00:25:10,157
THESE ANCESTRIES ACTUALLY

863
00:25:10,157 --> 00:25:11,926
AFFECTED HOW PATHOGENICITY COMES

864
00:25:11,926 --> 00:25:14,862
IN CANCER AND HOW THESE DISEASES

865
00:25:14,862 --> 00:25:16,697
ARE AFFECTED IN TERMS OF

866
00:25:16,697 --> 00:25:18,132
PREVALENCE, IMMUNOTHERAPIES, ET

867
00:25:18,132 --> 00:25:18,365
CETERA.

868
00:25:18,365 --> 00:25:21,735
I'VE BEEN LUCKY ENOUGH TO ACTUAL

869
00:25:21,735 --> 00:25:25,339
GET FUNDING IN THIS, SO WE

870
00:25:25,339 --> 00:25:27,308
CONTRIBUTE IN THE SINGLE CELL

871
00:25:27,308 --> 00:25:28,375
AFRICAN CARIBBEAN NETWORK.

872
00:25:28,375 --> 00:25:29,844
THE IDEA IS THAT THERE'S A

873
00:25:29,844 --> 00:25:31,145
DIFFERENT RATE OF CANCER IN ALL

874
00:25:31,145 --> 00:25:33,013
OF THESE BUT THAT REALLY

875
00:25:33,013 --> 00:25:34,215
PROFILING KIND OF THE CELLS OF

876
00:25:34,215 --> 00:25:35,616
ORIGIN TO THESE DISEASES SO IN

877
00:25:35,616 --> 00:25:37,585
THE CASE OF OVARIAN CANCER IT'S

878
00:25:37,585 --> 00:25:40,121
ACTUALLY NOT THE OVARY, IT'S THE

879
00:25:40,121 --> 00:25:41,589
FALLOPIAN TUBE THAT IS THE CELL

880
00:25:41,589 --> 00:25:42,857
OF ORIGIN.

881
00:25:42,857 --> 00:25:44,158
PROSTATE CANCER AND BREAST

882
00:25:44,158 --> 00:25:45,493
REALLY GETTING AN UNDERSTANDING

883
00:25:45,493 --> 00:25:49,396
OF KIND OF HOW THESE DIFFERENT

884
00:25:49,396 --> 00:25:50,965
POPULATIONS, HOW THEIR CELLS OF

885
00:25:50,965 --> 00:25:52,266
ORIGIN LOOK LIKE IN ORDER TO

886
00:25:52,266 --> 00:25:54,668
REALLY APPLY THEM TO HOW THOSE

887
00:25:54,668 --> 00:25:57,171
DISEASES ARISE.

888
00:25:57,171 --> 00:25:58,606
SO THIS IS A SLIDE THAT I

889
00:25:58,606 --> 00:25:59,707
PURPOSELY PUT HERE BECAUSE I

890
00:25:59,707 --> 00:26:01,208
KNOW A LOT OF PEOPLE HERE ARE

891
00:26:01,208 --> 00:26:02,510
INTERESTED IN DOING SINGLE CELL

892
00:26:02,510 --> 00:26:04,845
AND IT'S NOT JUST A SPATIAL

893
00:26:04,845 --> 00:26:05,146
TALK.

894
00:26:05,146 --> 00:26:06,714
SO THIS CAME FROM REAL LIFE.

895
00:26:06,714 --> 00:26:09,550
I'M NOT A METHOD ON GIST, BUT

896
00:26:09,550 --> 00:26:11,185
BECAUSE YOU CAN IMAGINE, WE HAVE

897
00:26:11,185 --> 00:26:14,989
FUNDING TO DO THIS IN AFRICA,

898
00:26:14,989 --> 00:26:16,557
WE'VE BOUGHT -- WE CAN DO IT

899
00:26:16,557 --> 00:26:17,725
WELL, WE ARE LEADERS IN THE

900
00:26:17,725 --> 00:26:19,960
FIELD OF NUCLEI PREP BUT I'LL BE

901
00:26:19,960 --> 00:26:22,163
REALLY HONEST, YOU STILL HAVE TO

902
00:26:22,163 --> 00:26:23,330
GO THROUGH CUSTOMS AND THE

903
00:26:23,330 --> 00:26:24,965
MINUTE THERE'S A STICKER THAT

904
00:26:24,965 --> 00:26:26,934
HAS BIOHAZARD ON IT, THERE'S AN

905
00:26:26,934 --> 00:26:28,435
INCREASED PRICE IN AFRICA.

906
00:26:28,435 --> 00:26:29,970
SO I APPROACHED ONE OF MY

907
00:26:29,970 --> 00:26:33,007
COLLEAGUES AND SAID LIKE WE CAN

908
00:26:33,007 --> 00:26:36,410
DO THE FIX AND CUT, THAT'S A 10X

909
00:26:36,410 --> 00:26:37,845
PROTOCOL THEY'VE BORROWED FROM

910
00:26:37,845 --> 00:26:40,281
US, AND COULD WE DO IT IN FFP?

911
00:26:40,281 --> 00:26:41,815
THE IDEA OF THIS WAS REALLY TO

912
00:26:41,815 --> 00:26:43,851
TAKE FFP BLOCKS AND MAKE SINGLE

913
00:26:43,851 --> 00:26:46,387
CELL AVAILABLE ACROSS OUR

914
00:26:46,387 --> 00:26:46,720
REPOSITORIES.

915
00:26:46,720 --> 00:26:48,122
SO I'LL WALK YOU THROUGH, WE'VE

916
00:26:48,122 --> 00:26:49,523
DONE A LOT OF DIFFERENT BREAST

917
00:26:49,523 --> 00:26:51,325
TYPE, A LOT OF DIFFERENT TISSUE

918
00:26:51,325 --> 00:26:53,961
TYPES, WE'VE DONE BOTH NUCLEI

919
00:26:53,961 --> 00:26:56,564
PREP BOTH SNAP FROZEN AND FFP,

920
00:26:56,564 --> 00:26:58,999
THIS IS UNDER REVIEW BUT 10X HAS

921
00:26:58,999 --> 00:27:02,169
OUR PROTOCOL SO THAT'S OUR MAIN

922
00:27:02,169 --> 00:27:04,705
CREDITCISM IS THAT IT'S ALREADY

923
00:27:04,705 --> 00:27:04,972
PUBLISHED.

924
00:27:04,972 --> 00:27:10,411
THIS IS WORK WITH DOCTORS IN

925
00:27:10,411 --> 00:27:10,678
AUSTRALIA.

926
00:27:10,678 --> 00:27:12,713
WE TOOK THE SAME BREAST CANCER

927
00:27:12,713 --> 00:27:15,416
AND TOOK A METASTASIS AND TOOK

928
00:27:15,416 --> 00:27:16,650
BREAST CANCER AND MUSHED IT UP

929
00:27:16,650 --> 00:27:17,952
LIKE EVERYBODY DOES AND THE DATA

930
00:27:17,952 --> 00:27:19,153
LOOKS BEAUTIFUL SO WE CAN FIND

931
00:27:19,153 --> 00:27:21,021
ALL THE SAME CELL TYPES THAT YOU

932
00:27:21,021 --> 00:27:24,858
CAN FIND IN FRESH FROZEN IN OUR

933
00:27:24,858 --> 00:27:26,594
FFP METHOD.

934
00:27:26,594 --> 00:27:29,463
SO THAT MAP EVERYBODY KIND OF

935
00:27:29,463 --> 00:27:31,232
KNOWS MAPS LIKE THIS WHERE WE

936
00:27:31,232 --> 00:27:32,600
CAN SEE EVERY CELL TYPE.

937
00:27:32,600 --> 00:27:34,835
WE CAN ACTUALLY -- WE ACTUALLY

938
00:27:34,835 --> 00:27:37,438
HAVE HIGHER FIBROBLAST CONTENT

939
00:27:37,438 --> 00:27:39,873
ACTUALLY IN THE FFP THAN IN THE

940
00:27:39,873 --> 00:27:40,975
FRESH FROZEN.

941
00:27:40,975 --> 00:27:42,810
BUT I GO BACK TO THIS MAP.

942
00:27:42,810 --> 00:27:44,778
SO THIS IS WHAT WE'RE ABLE TO DO

943
00:27:44,778 --> 00:27:46,880
IN FRESH FROZEN OR FFP, WE CAN

944
00:27:46,880 --> 00:27:49,283
DO IT IN NUCLEI, BUT LOOK AT

945
00:27:49,283 --> 00:27:50,584
WHAT THAT MAP ACTUALLY LOOKS

946
00:27:50,584 --> 00:27:51,919
LIKE IN THE TISSUE.

947
00:27:51,919 --> 00:27:55,756
THIS IS AN -- THIS ISN'T EVEN

948
00:27:55,756 --> 00:27:56,023
VISIUM HD.

949
00:27:56,023 --> 00:27:57,858
THIS IS JUST US TAKING SINGLE

950
00:27:57,858 --> 00:27:59,226
CELL MUCH LIKE I SHOWED YOU IN

951
00:27:59,226 --> 00:28:01,262
THE BRAIN BUT NOW DIRECTLY INTO

952
00:28:01,262 --> 00:28:01,495
TUMORS.

953
00:28:01,495 --> 00:28:02,596
I HOPE YOU CAN APPRECIATE THAT

954
00:28:02,596 --> 00:28:04,465
THIS LOOKS VERY DIFFERENT THAN

955
00:28:04,465 --> 00:28:04,999
THIS.

956
00:28:04,999 --> 00:28:06,867
WHILE THIS TUMOR IS METASTATIC

957
00:28:06,867 --> 00:28:08,636
AND THIS IS NOT, THIS IS

958
00:28:08,636 --> 00:28:11,905
PRIMARY, YOU CAN SEE THAT THE

959
00:28:11,905 --> 00:28:12,773
INFILTRATION OF THE SINGLE CELL

960
00:28:12,773 --> 00:28:15,276
DATA IS VERY DIFFERENT ONCE IT'S

961
00:28:15,276 --> 00:28:16,844
BECOME METASTATIC TUMOR.

962
00:28:16,844 --> 00:28:18,445
WE ARE NOT RESOLVING THAT.

963
00:28:18,445 --> 00:28:20,214
IN THE SINGLE CELL DATA ALL

964
00:28:20,214 --> 00:28:21,649
WE'RE RESOLVING IS CELL

965
00:28:21,649 --> 00:28:24,018
PROPORTIONS THAT ARE CELL PRO

966
00:28:24,018 --> 00:28:25,519
POURINGS OF THESE CELL TYPES

967
00:28:25,519 --> 00:28:27,888
HAVE CHANGED BETWEEN PRIMARY AND

968
00:28:27,888 --> 00:28:29,123
METASTASIS BUT WE HAVEN'T

969
00:28:29,123 --> 00:28:30,724
ACTUALLY LOOKED AT HOW THOSE

970
00:28:30,724 --> 00:28:32,059
NEIGHBORHOODS ACTUALLY LOOK

971
00:28:32,059 --> 00:28:32,426
DIFFERENT.

972
00:28:32,426 --> 00:28:35,562
AND THE INFLUENCES OF THE

973
00:28:35,562 --> 00:28:38,932
MICROENVIRONMENT FROM PRIMARY TO

974
00:28:38,932 --> 00:28:39,266
METASTATIC.

975
00:28:39,266 --> 00:28:40,901
SO I'M WALKING YOU THROUGH HOW

976
00:28:40,901 --> 00:28:43,103
WE KIND OF GOT THE METHODOLOGIES

977
00:28:43,103 --> 00:28:44,605
UP AND RUNNING, HOW THAT WOULD

978
00:28:44,605 --> 00:28:46,040
BE AVAILABLE TO YOU TO THINK

979
00:28:46,040 --> 00:28:47,574
ABOUT KIND OF THE REPOSITORIES

980
00:28:47,574 --> 00:28:49,643
THAT YOU'RE ABLE TO HAVE AT NIH

981
00:28:49,643 --> 00:28:51,445
TO REALLY START TO PLOW THROUGH

982
00:28:51,445 --> 00:28:52,613
THAT DATA NOW THAT YOU DON'T

983
00:28:52,613 --> 00:28:54,782
HAVE TO COLLECT IT FRESH.

984
00:28:54,782 --> 00:28:56,550
BUT HOW DID WE GO BACK TO THAT

985
00:28:56,550 --> 00:28:57,651
ORIGINAL QUESTION OF WHY WE

986
00:28:57,651 --> 00:28:59,053
DEVELOPED THAT ME ODDOLOGY?

987
00:28:59,053 --> 00:29:01,388
SO THE METHODOLOGY REALLY ARISE

988
00:29:01,388 --> 00:29:03,991
FROM THIS QUESTION, BACK TO THAT

989
00:29:03,991 --> 00:29:06,827
THING THAT LEAVES ME AWAKE AT

990
00:29:06,827 --> 00:29:09,463
NIGHT IS THIS IS DATA FROM KIND

991
00:29:09,463 --> 00:29:11,098
OF OUR GROUP WHERE WE LOOKED

992
00:29:11,098 --> 00:29:12,566
ACROSS KIND OF THOSE THREE

993
00:29:12,566 --> 00:29:13,600
POPULATIONS, RIGHT?

994
00:29:13,600 --> 00:29:15,936
SO AFRICAN ANCESTRY BEING IN THE

995
00:29:15,936 --> 00:29:17,771
U.S., AFRICAN ANCESTRY BEING IN

996
00:29:17,771 --> 00:29:19,773
CARIBBEAN AND AFRICAN ANCESTRY

997
00:29:19,773 --> 00:29:20,841
BEING IN AFRICA.

998
00:29:20,841 --> 00:29:22,710
SO IT WAS REALLY HARD ESPECIALLY

999
00:29:22,710 --> 00:29:26,347
FROM NIH TO GET FUNDING BECAUSE

1000
00:29:26,347 --> 00:29:28,849
WHENEVER WE COMPARED THE U.S.

1001
00:29:28,849 --> 00:29:30,384
POPULATION TO THE AFRICAN

1002
00:29:30,384 --> 00:29:33,354
POPULATION, IT REALLY BECAME

1003
00:29:33,354 --> 00:29:34,788
ABOUT -- AND TRUE TO THEIR POINT

1004
00:29:34,788 --> 00:29:36,557
IS THAT IT'S A MORE AGGRESSIVE

1005
00:29:36,557 --> 00:29:36,890
DISEASE.

1006
00:29:36,890 --> 00:29:38,158
SO WHILE WE HAVE A HIGH

1007
00:29:38,158 --> 00:29:40,127
INCIDENCE HERE, WE'RE VEG AT

1008
00:29:40,127 --> 00:29:41,462
CURING IT IN THE U.S., THAT'S

1009
00:29:41,462 --> 00:29:43,964
THE BLUE, BUT IN AFRICA, THEY

1010
00:29:43,964 --> 00:29:46,133
DON'T REALLY HAVE A HIGH

1011
00:29:46,133 --> 00:29:47,901
INCIDENCE BUT YOU DIE.

1012
00:29:47,901 --> 00:29:49,570
IT'S VERY VERY AGGRESSIVEMENT

1013
00:29:49,570 --> 00:29:51,972
THAT COULD BE KIND OF THEY COME

1014
00:29:51,972 --> 00:29:53,807
AT LATE STAGE, AGE MATCH, STAGE

1015
00:29:53,807 --> 00:29:55,008
MATCH THE BEST WE COULD.

1016
00:29:55,008 --> 00:29:56,210
WE COULDN'T ALWAYS TREATMENT

1017
00:29:56,210 --> 00:29:56,410
MATCH.

1018
00:29:56,410 --> 00:29:57,945
BUT IT WAS WHEN WE ADDED, WHAT

1019
00:29:57,945 --> 00:29:59,980
CHANGED THE GAME FOR US AND

1020
00:29:59,980 --> 00:30:01,248
PROBABLY CHANGED THE GAME FOR

1021
00:30:01,248 --> 00:30:03,617
FUNDING IS WHEN WE ADDED AFRICAN

1022
00:30:03,617 --> 00:30:04,218
CARIBBEAN.

1023
00:30:04,218 --> 00:30:06,153
REMEMBER AFRICAN CARIBBEAN CAN

1024
00:30:06,153 --> 00:30:08,489
BE MAN TORD IN TERMS OF ANCESTRY

1025
00:30:08,489 --> 00:30:11,625
BASED ON WHEN SLAVE SHIPS CAME.

1026
00:30:11,625 --> 00:30:13,293
THEY HAVE ASIAN, THEY HAVE

1027
00:30:13,293 --> 00:30:14,561
HISPANIC AND WHAT'S SURPRISING

1028
00:30:14,561 --> 00:30:16,997
TO US IS IN THE AFRICAN

1029
00:30:16,997 --> 00:30:18,365
CARIBBEAN NOT ONLY DO THEY HAVE

1030
00:30:18,365 --> 00:30:20,734
HIGH INCIDENCE, THEY HAVE A

1031
00:30:20,734 --> 00:30:21,101
HIGH MORTALITY.

1032
00:30:21,101 --> 00:30:23,137
THAT GETS AT THE IDEA THAT

1033
00:30:23,137 --> 00:30:24,605
CRAPPY HOSPITALS, CRAPPY ACCESS

1034
00:30:24,605 --> 00:30:27,107
TO CARE, IT'S KIND OF EQUIVOCAL

1035
00:30:27,107 --> 00:30:28,776
ACROSS AFRICA AND CARIBBEAN BUT

1036
00:30:28,776 --> 00:30:31,311
IT'S LITERALLY THEIR ANCESTRY

1037
00:30:31,311 --> 00:30:32,946
THAT IS MOVING THE NEEDLE

1038
00:30:32,946 --> 00:30:33,180
FORWARD.

1039
00:30:33,180 --> 00:30:35,215
WE SEE THAT IN OVARY AND

1040
00:30:35,215 --> 00:30:37,851
PROSTATE BUT IN DIFFERENT

1041
00:30:37,851 --> 00:30:38,252
DIRECTIONS.

1042
00:30:38,252 --> 00:30:39,253
ACTUALLY INTERESTING, WE GET

1043
00:30:39,253 --> 00:30:41,121
DIFFERENT SUBTYPES.

1044
00:30:41,121 --> 00:30:43,090
SO OVARIAN WE GET WEIRD SUBTYPES

1045
00:30:43,090 --> 00:30:44,391
THAT WE DON'T SEE IN THE U.S.

1046
00:30:44,391 --> 00:30:46,026
SO BACK TO THE IDEA OF

1047
00:30:46,026 --> 00:30:47,361
INFRASTRUCTURE AND CAPACITY

1048
00:30:47,361 --> 00:30:47,761
BUILDING.

1049
00:30:47,761 --> 00:30:49,563
WE DIDN'T START AS TUMOR PEOPLE

1050
00:30:49,563 --> 00:30:51,198
BUT BECAUSE WE BUILT THE

1051
00:30:51,198 --> 00:30:51,799
INFRASTRUCTURE THERE TO DO

1052
00:30:51,799 --> 00:30:54,468
SINGLE CELL, WE HAVE KIND OF THE

1053
00:30:54,468 --> 00:30:55,769
INFRASTRUCTURE TO MOVE IT

1054
00:30:55,769 --> 00:30:57,304
FORWARD INTO TUMORS, SO THIS IS

1055
00:30:57,304 --> 00:31:00,574
LED BY THE SAME GROUP,

1056
00:31:00,574 --> 00:31:01,775
DR. GEORGE OUT OF MIAMI AND WE

1057
00:31:01,775 --> 00:31:03,677
TAKE THOSE 32 SITES, DO THE

1058
00:31:03,677 --> 00:31:05,078
SURGICAL RESECTIONS THERE, WE

1059
00:31:05,078 --> 00:31:06,580
TRY AND MATCH SO WE'RE TRYING TO

1060
00:31:06,580 --> 00:31:09,883
BRING THEM UP INTO KIND OF U.S.

1061
00:31:09,883 --> 00:31:12,052
STANDARDS OF COLLECTING CLINICAL

1062
00:31:12,052 --> 00:31:13,387
INFORMATION AND ACTUALLY THE

1063
00:31:13,387 --> 00:31:15,889
HARDEST PART IS MERGING THE

1064
00:31:15,889 --> 00:31:17,357
METADATA BECAUSE THEY ARE STILL

1065
00:31:17,357 --> 00:31:18,759
IN PAPER SOMETIMES, THEY DON'T

1066
00:31:18,759 --> 00:31:20,527
EVEN HAVE RECORDED NOTES.

1067
00:31:20,527 --> 00:31:22,162
SO WE KIND OF DO THAT BOTH HERE

1068
00:31:22,162 --> 00:31:24,097
AND THERE AND TRY AND MERGE IT

1069
00:31:24,097 --> 00:31:25,732
AS BEST AS POSSIBLE.

1070
00:31:25,732 --> 00:31:27,734
WE COLLECT AND TRY TO DO TISSUE

1071
00:31:27,734 --> 00:31:28,869
PROCESSING AS MUCH AS POSSIBLE

1072
00:31:28,869 --> 00:31:29,803
THERE BECAUSE WE'RE TRYING TO

1073
00:31:29,803 --> 00:31:31,538
MAKE SURE THAT THEY KIND OF CAN

1074
00:31:31,538 --> 00:31:33,073
KEEP THE BALL ROLLING AND GIVE

1075
00:31:33,073 --> 00:31:34,608
THEM NEW INFRASTRUCTURE, BUT

1076
00:31:34,608 --> 00:31:37,044
UNFORTUNATELY THIS IS WHERE THE

1077
00:31:37,044 --> 00:31:38,111
BIFURCATION HAPPENS, SO THAT

1078
00:31:38,111 --> 00:31:40,314
MOST OF THE DATA ACQUISITION IS

1079
00:31:40,314 --> 00:31:41,648
BEING -- AND PROCESSING IS BEING

1080
00:31:41,648 --> 00:31:42,349
DONE IN THE U.S.

1081
00:31:42,349 --> 00:31:44,017
I THINK WE ALL KNOW THAT FOR

1082
00:31:44,017 --> 00:31:46,620
KIND OF CAVEATS OF THE PRICING

1083
00:31:46,620 --> 00:31:48,422
AND HOW MANY TOOLS ARE REQUIRED.

1084
00:31:48,422 --> 00:31:51,492
BUT WE'RE ALSO TRYING TO USE

1085
00:31:51,492 --> 00:31:55,095
PATHOLOGY ACROSS SITES BECAUSE

1086
00:31:55,095 --> 00:31:58,932
CANYON -- BECAUSE A CANADIAN

1087
00:31:58,932 --> 00:32:00,567
PATHOLOGIST CALLS CANCER

1088
00:32:00,567 --> 00:32:02,202
DIFFERENT THAN U.S. AND WE'VE

1089
00:32:02,202 --> 00:32:04,605
MERGED THAT AND MADE 966

1090
00:32:04,605 --> 00:32:07,140
ANTIBODY TRIPLE NEGATIVE BREAST

1091
00:32:07,140 --> 00:32:10,644
CANCER WITH ACOYA, I WAS LUCKY

1092
00:32:10,644 --> 00:32:12,713
NUF TO HELP DESIGN T PART OF

1093
00:32:12,713 --> 00:32:13,580
THEIR DISCOVERY PANEL, THAT

1094
00:32:13,580 --> 00:32:14,915
WOULD BE AVAILABLE TO YOU GUYS

1095
00:32:14,915 --> 00:32:16,884
HERE WHO HAVE CODEX AND

1096
00:32:16,884 --> 00:32:18,619
OBVIOUSLY A LOT OF THEM ARE

1097
00:32:18,619 --> 00:32:19,920
BREAST SPECIFIC, CAWNTD HAVE A

1098
00:32:19,920 --> 00:32:21,121
TRIPLE NEGATIVE BREAST CANCER

1099
00:32:21,121 --> 00:32:22,422
PANEL UNLESS YOU INCLUDE THINGS

1100
00:32:22,422 --> 00:32:25,192
LIKE ER HER 2 AND PR AND THEN

1101
00:32:25,192 --> 00:32:26,126
TRYING TO GIVE ADDITIONAL

1102
00:32:26,126 --> 00:32:28,662
STRUCTURE MARKERS AND METABOLIC

1103
00:32:28,662 --> 00:32:29,763
MARKERS WE THINK MIGHT BE

1104
00:32:29,763 --> 00:32:31,064
IMPORTANT TO CELL TYPING.

1105
00:32:31,064 --> 00:32:32,733
OH AND JUST TO RUN THROUGH VERY

1106
00:32:32,733 --> 00:32:34,401
BASICALLY WHAT THAT IS, SO

1107
00:32:34,401 --> 00:32:35,836
REMEMBERING THAT TRANSCRIPTOMICS

1108
00:32:35,836 --> 00:32:38,038
TENDS TO BE USING HIJACKING THE

1109
00:32:38,038 --> 00:32:39,573
SINGLE CELL WHERE HE WITH PUT A

1110
00:32:39,573 --> 00:32:42,309
UMI ON IT AND WE CAN KIND OF

1111
00:32:42,309 --> 00:32:43,243
PLEX LOTS OF THINGS.

1112
00:32:43,243 --> 00:32:44,578
IN THE CASE OF ANTIBODIES WE'RE

1113
00:32:44,578 --> 00:32:45,779
TAKING THE SAME APPROACH.

1114
00:32:45,779 --> 00:32:47,548
IN PROTEIN WE'RE TAKING THE SAME

1115
00:32:47,548 --> 00:32:49,816
APPROACH, TAKING EMI'S INSTEAD

1116
00:32:49,816 --> 00:32:51,451
OF RNA PROBE SET, HIJACKING IT

1117
00:32:51,451 --> 00:32:54,054
AND PUTTING IT ONTO ANTIBODY,

1118
00:32:54,054 --> 00:32:55,822
YOU CAN DO IN-HOUSE CONJUGATION

1119
00:32:55,822 --> 00:32:58,191
WHICH IS WHAT 80% OF MY PANEL S

1120
00:32:58,191 --> 00:33:00,794
WE TAKE THOSE AND IT'S NOT SUPER

1121
00:33:00,794 --> 00:33:02,329
FANCY ALTHOUGH THE MACHINE

1122
00:33:02,329 --> 00:33:03,864
PROBABLY COSTS A LOT SU JUST

1123
00:33:03,864 --> 00:33:04,331
CYCLE IT.

1124
00:33:04,331 --> 00:33:06,133
YOU PUT ON FOUR OR FIVE AT A

1125
00:33:06,133 --> 00:33:08,468
TIME AND IT CAN ALLOW YOU,

1126
00:33:08,468 --> 00:33:10,003
THERE'S OVER REPORTS OF OVER 100

1127
00:33:10,003 --> 00:33:11,038
NOW OF PLEXING.

1128
00:33:11,038 --> 00:33:12,706
YOU CAN IMAGINE WITH 100

1129
00:33:12,706 --> 00:33:15,309
ANTIBODIES OR 66, YOU CAN GET TO

1130
00:33:15,309 --> 00:33:18,178
A SIMILAR LEVEL, NOT QUIF CAL

1131
00:33:18,178 --> 00:33:20,047
LEVEL OF -- NOT EQUIVOCAL LEVEL

1132
00:33:20,047 --> 00:33:22,449
OF BEING ABLE TO CELL ANNOTATE

1133
00:33:22,449 --> 00:33:23,417
YOUR DATA.

1134
00:33:23,417 --> 00:33:25,852
I LOVE TO SHOW, I MUSHED THESE

1135
00:33:25,852 --> 00:33:27,387
UP FOR A VERY LONG TIME.

1136
00:33:27,387 --> 00:33:29,623
THOSE DHEENT KNOW CANCER

1137
00:33:29,623 --> 00:33:31,458
ASSOCIATED FIBROBLAST ARE

1138
00:33:31,458 --> 00:33:32,993
THOUGHT TO HELP TO CONTRIBUTE TO

1139
00:33:32,993 --> 00:33:33,961
AGGRESSION OF DISEASE.

1140
00:33:33,961 --> 00:33:35,195
IT'S LITERALLY ONE TO FIVE

1141
00:33:35,195 --> 00:33:37,831
PERCENT OF ALL SINGLE CELL

1142
00:33:37,831 --> 00:33:40,000
DATASETS BECAUSE FIBROBLAST

1143
00:33:40,000 --> 00:33:42,502
DON'T LIKE TO GO MANY A DROPLET

1144
00:33:42,502 --> 00:33:42,736
SYSTEM.

1145
00:33:42,736 --> 00:33:44,905
THE THINGS IN YELLOW ARE VERY,

1146
00:33:44,905 --> 00:33:46,106
VERY, VERY DIFFERENT.

1147
00:33:46,106 --> 00:33:47,441
THEY'RE LITERALLY LOOK AT THESE

1148
00:33:47,441 --> 00:33:49,309
GUYS OVER HERE THAT ARE

1149
00:33:49,309 --> 00:33:50,711
LITERALLY INFILTRATED BY

1150
00:33:50,711 --> 00:33:51,244
VASCULATURE.

1151
00:33:51,244 --> 00:33:51,545
RIGHT?

1152
00:33:51,545 --> 00:33:53,113
THEY'RE ALMOST FULLY SURROUNDED

1153
00:33:53,113 --> 00:33:54,748
AND THEY HAVE DIFFERENT IMMUNE

1154
00:33:54,748 --> 00:33:57,050
CELL COMPONENTS COMPARED TO THE

1155
00:33:57,050 --> 00:33:57,818
ONES OVER HERE.

1156
00:33:57,818 --> 00:34:02,189
SO EVEN IN CALF DATA, THEY HAVE

1157
00:34:02,189 --> 00:34:03,190
DIFFERENT COMPONENTS WITHIN THE

1158
00:34:03,190 --> 00:34:03,390
TUMOR.

1159
00:34:03,390 --> 00:34:06,159
SO MUSHING IT UP AND JUST

1160
00:34:06,159 --> 00:34:07,461
CALLING -- YOU'RE NOT EVEN ABLE

1161
00:34:07,461 --> 00:34:09,196
TO REALLY CALL EVEN THE YELLOW

1162
00:34:09,196 --> 00:34:10,664
BECAUSE THEY'RE SO LOW

1163
00:34:10,664 --> 00:34:12,032
EXPRESSING, BUT EVEN CALLING IT

1164
00:34:12,032 --> 00:34:14,001
THAT HAS NO CONTEXT TO WHAT

1165
00:34:14,001 --> 00:34:15,102
THEIR ACTIVITY COULD BE.

1166
00:34:15,102 --> 00:34:16,970
SO HOW DO YOU DO THIS?

1167
00:34:16,970 --> 00:34:18,472
IT'S GOING TO BE THE LAST PART

1168
00:34:18,472 --> 00:34:19,906
OF THE TALK TO THINK ABOUT HOW

1169
00:34:19,906 --> 00:34:22,342
YOU COMPUTATIONALLY DO THIS.

1170
00:34:22,342 --> 00:34:25,612
SO SING -- SO SINGLE CELL --

1171
00:34:25,612 --> 00:34:26,913
SINGLE CELL MIGHT BE ONE HURDLE

1172
00:34:26,913 --> 00:34:27,581
TO OVERCOME.

1173
00:34:27,581 --> 00:34:28,915
I WOULD SAY PROTEIN AND IMAGING

1174
00:34:28,915 --> 00:34:30,217
BASE IS ANOTHER HURDLE.

1175
00:34:30,217 --> 00:34:31,852
WE TAKE A VERY SINGLE CELL

1176
00:34:31,852 --> 00:34:32,619
APPROACH TO THIS.

1177
00:34:32,619 --> 00:34:34,087
YOU IMAGINE WE KIND OF CODE ALL

1178
00:34:34,087 --> 00:34:35,989
THE CELLS AND THEN EVERY CELL

1179
00:34:35,989 --> 00:34:37,758
BECOMES KIND OF A CIRCLE AND IN

1180
00:34:37,758 --> 00:34:39,960
THAT CIRCLE SO THIS IS MIR SCOPE

1181
00:34:39,960 --> 00:34:41,495
DATA, IMAGINING THIS CAN CROTION

1182
00:34:41,495 --> 00:34:42,829
OVER AND I WANTED TO SHOW YOU

1183
00:34:42,829 --> 00:34:44,531
THAT, THAT THESE ARE REALLY

1184
00:34:44,531 --> 00:34:45,866
ROBUST SYSTEMS IN THE SENSE THAT

1185
00:34:45,866 --> 00:34:48,201
THESE ARE NOT -- THESE ARE

1186
00:34:48,201 --> 00:34:49,436
PROCESSED IN UNDERDEVELOPED

1187
00:34:49,436 --> 00:34:50,137
COUNTRIES AND YOU CAN IMAGINE AT

1188
00:34:50,137 --> 00:34:52,706
THAT OUR TUMORS DON'T LOOK

1189
00:34:52,706 --> 00:34:53,040
AMAZING.

1190
00:34:53,040 --> 00:34:54,741
SO YOU CAN STILL KIND OF GET RNA

1191
00:34:54,741 --> 00:34:55,842
DATA OUT OF IT.

1192
00:34:55,842 --> 00:34:57,811
BUT WHAT WE DO IS WE ASSIGN

1193
00:34:57,811 --> 00:34:59,546
EVERY GENE, SO IF YOU'RE A GENE

1194
00:34:59,546 --> 00:35:00,981
HERE THAT LIVES IN THIS CELL,

1195
00:35:00,981 --> 00:35:02,616
EVERY TIME WE SEE THAT GENE IN

1196
00:35:02,616 --> 00:35:04,951
THAT CELL YOU BECOME KIND OF A

1197
00:35:04,951 --> 00:35:06,153
COUNT, VERY SIMILAR TO SINGLE

1198
00:35:06,153 --> 00:35:07,120
CELL DATA.

1199
00:35:07,120 --> 00:35:08,522
AND WHAT DOES THIS LOOK LIKE?

1200
00:35:08,522 --> 00:35:10,390
THIS IS ACTUALLY OUT OF DATE NOW

1201
00:35:10,390 --> 00:35:12,259
BUT WE'RE AT 8 MILLION CELLS.

1202
00:35:12,259 --> 00:35:14,928
SO IN MY ONE LITTLE STUDY WE

1203
00:35:14,928 --> 00:35:15,862
HAVE 8 MILLION CELLS AND

1204
00:35:15,862 --> 00:35:16,129
COUNTING.

1205
00:35:16,129 --> 00:35:18,265
IN THE HUMAN CELL ATLAS WAS

1206
00:35:18,265 --> 00:35:22,269
PUBLISHED IN MAY 2022 AND IT HAD

1207
00:35:22,269 --> 00:35:22,769
1 MILLION CELLS.

1208
00:35:22,769 --> 00:35:23,970
THIS BEAUTIFUL ATLAS WHICH WAS

1209
00:35:23,970 --> 00:35:25,806
THE SECOND BIGGEST AT LAS TO

1210
00:35:25,806 --> 00:35:28,575
PUBLISH WAS LAST YEAR IN 2023

1211
00:35:28,575 --> 00:35:29,776
WITH 2.4 AND YOU CAN IMAGINE

1212
00:35:29,776 --> 00:35:32,279
JUST IN THAT DATASET FROM MY

1213
00:35:32,279 --> 00:35:34,781
SMALL DATASET IS 8 MILLION AND

1214
00:35:34,781 --> 00:35:36,349
YOU CAN SEE KIND OF YOU DON'T

1215
00:35:36,349 --> 00:35:37,784
GET EVERY CELL TYPE BUT IF YOU

1216
00:35:37,784 --> 00:35:39,319
CHOOSE THE RIGHT MARKERS YOU CAN

1217
00:35:39,319 --> 00:35:41,288
KIND OF GET ALL THE SIMILAR CELL

1218
00:35:41,288 --> 00:35:43,023
TYPES, YOU USE YOUR SINGLE CELL

1219
00:35:43,023 --> 00:35:44,591
DATA TO KIND OF INFORM WHAT

1220
00:35:44,591 --> 00:35:46,426
MARKERS YOU WANT TO DO.

1221
00:35:46,426 --> 00:35:48,595
AND THIS IS REALLY WHAT I'M

1222
00:35:48,595 --> 00:35:49,396
PROUD ABOUT.

1223
00:35:49,396 --> 00:35:51,898
SO WE'RE ACTIVELY DOING THIS

1224
00:35:51,898 --> 00:35:52,566
THROUGH CLINICAL TRIALS.

1225
00:35:52,566 --> 00:35:54,101
I SPENT PRETTY MUCH EIGHT MONTHS

1226
00:35:54,101 --> 00:35:55,302
TRYING TO MAKE SURE THAT WE

1227
00:35:55,302 --> 00:35:56,937
COULD DO BIOPSIES AND MAKE

1228
00:35:56,937 --> 00:35:58,004
BIOPSIES DON'T ALWAYS LIKE TO

1229
00:35:58,004 --> 00:35:59,539
SIT ON A -- THEY DON'T HAVE A

1230
00:35:59,539 --> 00:36:01,441
LOT OF SURFACE AREA AND TENSION

1231
00:36:01,441 --> 00:36:03,910
ON THESE SLIDES, SO WE SPEND A

1232
00:36:03,910 --> 00:36:05,345
LOT OF TIME TRYING TO MAKE SURE

1233
00:36:05,345 --> 00:36:07,748
WE COULD DO BIOPSIES SO WE COULD

1234
00:36:07,748 --> 00:36:10,951
DO ACTIVE CLAIN CAL TRIALS.

1235
00:36:10,951 --> 00:36:12,753
-- CLINICAL TRIALS.

1236
00:36:12,753 --> 00:36:15,288
IN THE U.S., BLACK WOMEN, MORE

1237
00:36:15,288 --> 00:36:16,823
AGGRESSIVE DISEASE THAN WHITE

1238
00:36:16,823 --> 00:36:17,491
WOMEN.

1239
00:36:17,491 --> 00:36:18,925
EPITHELIAL CELLS RELATIVE TO

1240
00:36:18,925 --> 00:36:20,327
STROMA, YOU CAN SEE THAT

1241
00:36:20,327 --> 00:36:21,661
COMPOSITION IS COMPLETELY

1242
00:36:21,661 --> 00:36:21,928
DIFFERENT.

1243
00:36:21,928 --> 00:36:23,196
SO REMEMBERING WHEN I MUSHED

1244
00:36:23,196 --> 00:36:25,165
THIS UP, I WOULD BE ABLE TO SAY

1245
00:36:25,165 --> 00:36:28,668
WELL THERE'S PROBABLY MORE E CAD

1246
00:36:28,668 --> 00:36:35,809
HERE ENS CELL -- E-CADHERENCE,

1247
00:36:35,809 --> 00:36:37,644
BUT THEY HAVE DIFFERENT TUMORS,

1248
00:36:37,644 --> 00:36:39,379
THIS IS MORE AGGRESSIVEMENT

1249
00:36:39,379 --> 00:36:41,081
THESE HAVE MORE TUMOR CELLS BUT

1250
00:36:41,081 --> 00:36:42,349
THIS IS MORE AGGRESSIVE.

1251
00:36:42,349 --> 00:36:43,650
BUT YOU CAN STILL DO THE SAME

1252
00:36:43,650 --> 00:36:46,987
THINGS YOU DID WITH SINGLE CELL,

1253
00:36:46,987 --> 00:36:48,822
FIND ENDOTHELIAL CELL MARKERS,

1254
00:36:48,822 --> 00:36:51,324
MILE NOID MARKERS AND EVEN IN

1255
00:36:51,324 --> 00:36:52,392
EPITHELIAL CELLS I CAN ADD

1256
00:36:52,392 --> 00:36:53,827
ANOTHER MARKER AND FIND WHICH

1257
00:36:53,827 --> 00:36:58,331
CELLS ARE TUMOR VERSUS NORMAL.

1258
00:36:58,331 --> 00:36:59,533
WHAT I THINK IS MOST CONVINCING

1259
00:36:59,533 --> 00:37:01,268
IS THE IDEA OF THIS

1260
00:37:01,268 --> 00:37:01,601
NEIGHBORHOOD.

1261
00:37:01,601 --> 00:37:03,370
SO THESE ARE KIND OF THE

1262
00:37:03,370 --> 00:37:04,037
NEIGHBORHOODS.

1263
00:37:04,037 --> 00:37:07,307
I HATE THIS LITERALLY, SO IN

1264
00:37:07,307 --> 00:37:08,608
THIS NEIGHBORHOOD MAYBE IT'S A

1265
00:37:08,608 --> 00:37:10,443
BLUE NEIGHBORHOOD WHERE THERE'S

1266
00:37:10,443 --> 00:37:12,979
EPITHELIAL CELLS SURROUNDED BY

1267
00:37:12,979 --> 00:37:13,280
MACROPHAGES.

1268
00:37:13,280 --> 00:37:14,281
THIS IS KIND OF THE SWITCH IN

1269
00:37:14,281 --> 00:37:15,148
YOUR BRAIN THAT YOU HAVE TO

1270
00:37:15,148 --> 00:37:18,218
THINK ABOUT IN TERMS OF KIND OF

1271
00:37:18,218 --> 00:37:19,553
WHAT SINGLE CELL DATA TELLS US

1272
00:37:19,553 --> 00:37:21,521
AND WHAT SPATIAL DATA TELLS US,

1273
00:37:21,521 --> 00:37:22,923
BECAUSE GOING BACK TO THIS,

1274
00:37:22,923 --> 00:37:24,591
RIGHT, GOING BACK TO THIS -- OH,

1275
00:37:24,591 --> 00:37:26,693
I WENT THE WRONG WAY.

1276
00:37:26,693 --> 00:37:28,962
WE KNOW WHAT THEIR CELL

1277
00:37:28,962 --> 00:37:29,696
COMPOSITION IS.

1278
00:37:29,696 --> 00:37:30,931
WE KNOW.

1279
00:37:30,931 --> 00:37:34,334
THEY COULD BE ECADHERENT CELLS

1280
00:37:34,334 --> 00:37:36,169
BUT WHO DO THEY TALK TO AND WHY

1281
00:37:36,169 --> 00:37:37,704
DO THEY LOOK DIFFERENT?

1282
00:37:37,704 --> 00:37:39,239
REMEMBERING THE IDEA THAT U.S.

1283
00:37:39,239 --> 00:37:40,574
BLACK WOMEN LOOK VERY DIFFERENT

1284
00:37:40,574 --> 00:37:41,541
THAN CARIBBEAN BLACK IT'S

1285
00:37:41,541 --> 00:37:43,176
ACTUALLY THE CELL NEIGHBORHOOD

1286
00:37:43,176 --> 00:37:44,277
COMPOSITION THAT WHO THOSE CELLS

1287
00:37:44,277 --> 00:37:46,446
ARE AROUND, ARE THEIR

1288
00:37:46,446 --> 00:37:47,681
MACROPHAGES AROUND THEM, ARE

1289
00:37:47,681 --> 00:37:49,416
THERE DON'T EAT ME MACROPHAGES

1290
00:37:49,416 --> 00:37:50,851
THAT ARE PROTECTING THE TUMOR

1291
00:37:50,851 --> 00:37:51,051
CELLS?

1292
00:37:51,051 --> 00:37:52,485
THAT'S REALLY WHERE THE OUTCOMES

1293
00:37:52,485 --> 00:37:52,919
ARE CHANGE.

1294
00:37:52,919 --> 00:37:54,921
SO YOU CAN SEE HERE THEY HAVE

1295
00:37:54,921 --> 00:37:55,989
DIFFERENT COMPOSITIONS OF WHAT

1296
00:37:55,989 --> 00:37:57,724
THOSE NEIGHBORHOODS ARE.

1297
00:37:57,724 --> 00:37:58,225
NOO ACTUALITY, THERE ARE

1298
00:37:58,225 --> 00:37:59,659
NEIGHBORHOODS THAT EXIST IN ONE

1299
00:37:59,659 --> 00:38:01,695
AND NOT THE OTHER.

1300
00:38:01,695 --> 00:38:03,730
SO HOW CAN WE DO BETTER?

1301
00:38:03,730 --> 00:38:04,931
AGAIN, BACK TO THE IDEA THAT I

1302
00:38:04,931 --> 00:38:06,566
SAID THE LAST PART OF THE TALK

1303
00:38:06,566 --> 00:38:08,335
IS GOING TO BE HOW DIFFICULT

1304
00:38:08,335 --> 00:38:09,636
COMPUTATIONAL ANALYSIS IS ON

1305
00:38:09,636 --> 00:38:09,970
THIS.

1306
00:38:09,970 --> 00:38:12,272
BUT I SAID DON'T FEAR, WE'LL GET

1307
00:38:12,272 --> 00:38:12,806
THERE.

1308
00:38:12,806 --> 00:38:15,008
THE IDEA AND EVEN FOR ME MY LAB

1309
00:38:15,008 --> 00:38:16,343
DOES MACHINE LEARNING AND AI AND

1310
00:38:16,343 --> 00:38:17,277
I NEVER WOULD HAVE THOUGHT THAT

1311
00:38:17,277 --> 00:38:18,945
TO BE THE CASE.

1312
00:38:18,945 --> 00:38:20,280
BUT CAN WE DO BETTER?

1313
00:38:20,280 --> 00:38:23,550
I THINK WE CAN AGO NOS CLI LEARN

1314
00:38:23,550 --> 00:38:26,286
FROM -- AGO NOSSICLY LEARN FROM

1315
00:38:26,286 --> 00:38:26,519
THIS.

1316
00:38:26,519 --> 00:38:28,288
I'M VERY SIMPLISTIC IN HOW I

1317
00:38:28,288 --> 00:38:29,222
THINK ABOUT MACHINE LEARNING AND

1318
00:38:29,222 --> 00:38:29,756
A I.

1319
00:38:29,756 --> 00:38:31,658
I SAID TO ONE OF MY COMPUTER

1320
00:38:31,658 --> 00:38:32,525
SCIENTISTS ABOUT IMPROVING

1321
00:38:32,525 --> 00:38:33,627
BACKGROUND AND SIGNAL TO NOISE

1322
00:38:33,627 --> 00:38:35,061
IN ONE OF OUR PICTURES AND SAID

1323
00:38:35,061 --> 00:38:36,363
LISTEN I CAN OPEN UP MY PHONE

1324
00:38:36,363 --> 00:38:37,764
WITH MY FACE, IT JUST NEEDED

1325
00:38:37,764 --> 00:38:39,733
MORE AND MORE IMAGES TO LEARN

1326
00:38:39,733 --> 00:38:39,933
THAT.

1327
00:38:39,933 --> 00:38:40,834
WE WEREN'T THERE YET.

1328
00:38:40,834 --> 00:38:42,502
BUT IMAGINE WHAT WE COULD DO

1329
00:38:42,502 --> 00:38:44,170
WITH SPATIAL DATA THAT WE

1330
00:38:44,170 --> 00:38:46,273
COULDN'T DO WITH SINGLE CELL OR

1331
00:38:46,273 --> 00:38:47,741
BULK DATA BECAUSE WHAT WE'RE

1332
00:38:47,741 --> 00:38:50,277
VERY GOOD WITH AI AND ML IS

1333
00:38:50,277 --> 00:38:51,912
LANGUAGE MODELS AND SPATIAL

1334
00:38:51,912 --> 00:38:52,312
RECOGNITION.

1335
00:38:52,312 --> 00:38:54,881
WE TIED A SMALL APPROACH WITH

1336
00:38:54,881 --> 00:38:55,315
THIS.

1337
00:38:55,315 --> 00:38:57,284
WE TOOK MACHINE LEARNING MODEL

1338
00:38:57,284 --> 00:39:01,221
BUILT IN BULK RNA SEQ DATD FROM

1339
00:39:01,221 --> 00:39:02,622
TCGA, FOR ANYONE THAT'S IN

1340
00:39:02,622 --> 00:39:03,957
TUMORS YOU'LL KNOW WHY WE HAD TO

1341
00:39:03,957 --> 00:39:05,358
DO THAT BECAUSE MACHINE LEARNING

1342
00:39:05,358 --> 00:39:06,993
NEEDS LOTS AND LOTS OF DATA'S

1343
00:39:06,993 --> 00:39:09,029
INPUT AND WE TOOK ONE FEATURE TO

1344
00:39:09,029 --> 00:39:10,530
KIND OF LEARN THIS MODEL WHICH

1345
00:39:10,530 --> 00:39:13,833
IS A VERY BASIC CANCER BIOLOGY

1346
00:39:13,833 --> 00:39:15,602
PHENOTYPE WHICH IS THAT THE IDEA

1347
00:39:15,602 --> 00:39:18,038
OF CANCER STEMNESS, SO YOU HAVE

1348
00:39:18,038 --> 00:39:21,441
CANCER, YOU GO THROUGH THERAPY,

1349
00:39:21,441 --> 00:39:23,510
AND THAT TUMORS THAT RECUR

1350
00:39:23,510 --> 00:39:25,779
REVERT AND DEDIFFERENTIATE TO

1351
00:39:25,779 --> 00:39:27,314
MORE STEMNESS PHENOTYPE.

1352
00:39:27,314 --> 00:39:30,250
SO MY COLLABORATOR DR. MALTA IN

1353
00:39:30,250 --> 00:39:32,552
BRAZIL, IT'S A CELL PAPER, SHE

1354
00:39:32,552 --> 00:39:34,354
LEARNED THIS AGAINST CLINICAL

1355
00:39:34,354 --> 00:39:35,789
OUTCOME DATA AND IT PERFORMED

1356
00:39:35,789 --> 00:39:40,393
MUCH BETTER THAN METHYLATION RNA

1357
00:39:40,393 --> 00:39:41,394
SEQ AXOMS ALONE.

1358
00:39:41,394 --> 00:39:42,896
ME BEING NAIVE TO MACHINE

1359
00:39:42,896 --> 00:39:44,331
LEARNING I SAID WELL COULDN'T WE

1360
00:39:44,331 --> 00:39:45,532
USE SINGLE CELL DATA?

1361
00:39:45,532 --> 00:39:47,300
BECAUSE SINGLE CELL DATA HAS --

1362
00:39:47,300 --> 00:39:48,835
YOU COULD USE EVERY CELL LIKE

1363
00:39:48,835 --> 00:39:50,103
YOU USED EVERY PATIENT AND COULD

1364
00:39:50,103 --> 00:39:51,404
WE LEARN THE MODEL AT THAT WAY?

1365
00:39:51,404 --> 00:39:53,206
YOU CAN SEE WE DOWNLOAD THE

1366
00:39:53,206 --> 00:39:55,608
ENTIRE EVERY SINGLE CELL BREAST

1367
00:39:55,608 --> 00:39:56,810
CANCER DATASET AVAILABLE TO US

1368
00:39:56,810 --> 00:39:59,479
AND WE CALCULATED 9 MODEL.

1369
00:39:59,479 --> 00:40:01,781
IN THIS MODEL THE IDEA IS STILL

1370
00:40:01,781 --> 00:40:03,316
THE SAME WHERE YOUR HIGH STEM

1371
00:40:03,316 --> 00:40:05,185
YOU'RE RED, WHERE YOU'RE BLUE

1372
00:40:05,185 --> 00:40:06,853
YOU'RE LOW STEM SO YOU SHOULD BE

1373
00:40:06,853 --> 00:40:08,722
MORE QUOTE UNQUOTE NORMAL LIKE

1374
00:40:08,722 --> 00:40:08,922
CELLS.

1375
00:40:08,922 --> 00:40:10,490
YOU CAN SEE THAT OVERLAPS VERY

1376
00:40:10,490 --> 00:40:12,192
HIGHLY WITH THE CLINICAL SUBTYPE

1377
00:40:12,192 --> 00:40:13,727
SO AGNOSTIC TO WHAT YOUR CELL

1378
00:40:13,727 --> 00:40:14,828
TYPE IS, RIGHT?

1379
00:40:14,828 --> 00:40:16,029
DON'T LOOK AT THIS PICTURE, LOOK

1380
00:40:16,029 --> 00:40:17,130
AT THIS PICTURE.

1381
00:40:17,130 --> 00:40:17,998
THERE'S STEMNESS IN CERTAIN

1382
00:40:17,998 --> 00:40:19,299
TYPES OF CELLS AND NOT OTHER

1383
00:40:19,299 --> 00:40:20,734
TYPES OF CELLS, RIGHT?

1384
00:40:20,734 --> 00:40:22,369
SOLT CELLS WE DON'T EXPECT

1385
00:40:22,369 --> 00:40:24,004
STEMNESS IN THEY'RE BLUE.

1386
00:40:24,004 --> 00:40:26,940
THE CELLS WE EXPECT STEMNESS IN

1387
00:40:26,940 --> 00:40:28,742
LIKE CANCER EPITHELIAL CELLS

1388
00:40:28,742 --> 00:40:30,143
THEY'RE SUPER SUPER RED AND THAT

1389
00:40:30,143 --> 00:40:31,878
ACTUALLY OVERLAPS, REMEMBER WE

1390
00:40:31,878 --> 00:40:34,080
DOWNLOADED THESE DATASETS,

1391
00:40:34,080 --> 00:40:34,748
THEY'RE DIFFERENT CLINICAL SUB

1392
00:40:34,748 --> 00:40:34,981
TYPES.

1393
00:40:34,981 --> 00:40:36,850
WHEN WE LOOK INTO THIS STUDY SET

1394
00:40:36,850 --> 00:40:38,451
WHICH IS MERGED EVEN IN THE

1395
00:40:38,451 --> 00:40:39,886
CLINICAL SUBTYPES FOR NO ONE

1396
00:40:39,886 --> 00:40:41,955
THAT KNOWS BREAST CANCER TRIPLE

1397
00:40:41,955 --> 00:40:42,789
NEGATIVE BREAST CANCER WHICH IS

1398
00:40:42,789 --> 00:40:44,691
IN THE GREEN IS THE MOST

1399
00:40:44,691 --> 00:40:45,992
AGGRESSIVE FORM AND YOU CAN SEE

1400
00:40:45,992 --> 00:40:47,861
THAT THE GREEN OVERLAPS VERY

1401
00:40:47,861 --> 00:40:50,597
HIGHLY WITH STEMNESS.

1402
00:40:50,597 --> 00:40:51,898
SO ARE WE GOOD AT THIS?

1403
00:40:51,898 --> 00:40:53,566
I WOULD SAY YES, WE'RE PRETTY

1404
00:40:53,566 --> 00:40:56,436
GOOD AT IT IN SINGLE CELL, WE

1405
00:40:56,436 --> 00:40:58,838
CAN WE CAN GET DOWN TO WHICH

1406
00:40:58,838 --> 00:41:00,673
CELL TYPES, THIS CAN BE ANY

1407
00:41:00,673 --> 00:41:02,509
MODEL, WE'RE JUST USING STEM

1408
00:41:02,509 --> 00:41:04,644
RIGHT NOW, AND A VERY CLEVER

1409
00:41:04,644 --> 00:41:06,179
COMPUTATIONAL BIOLOGIST IN MY

1410
00:41:06,179 --> 00:41:07,480
LAB WHO WAS LIKE WELL SPATIAL

1411
00:41:07,480 --> 00:41:09,349
HAS MORE DATA, CAN WE APPLY IT

1412
00:41:09,349 --> 00:41:09,949
TO SPATIAL?

1413
00:41:09,949 --> 00:41:16,623
SO WE USE VISIUM, XE MIUM,

1414
00:41:16,623 --> 00:41:19,459
COSMICS, CODEX, WHATEVER, YOU

1415
00:41:19,459 --> 00:41:22,662
NAME T WE CAN USE, AND WE CALL

1416
00:41:22,662 --> 00:41:24,431
THESE CLUSTERS.

1417
00:41:24,431 --> 00:41:25,632
IMAGINE THAT IS SINGLE CELL

1418
00:41:25,632 --> 00:41:28,234
PROJECTION, THIS IS PATHOLOGY

1419
00:41:28,234 --> 00:41:28,601
CLASSIFICATION.

1420
00:41:28,601 --> 00:41:30,537
PATHOLOGIST IS MISSING ALL THE

1421
00:41:30,537 --> 00:41:31,337
STEMNESS.

1422
00:41:31,337 --> 00:41:33,173
YOU'RE CAPTURING SOME OF THE

1423
00:41:33,173 --> 00:41:34,808
STEPHNESS WITH EPITHELIAL CELLS

1424
00:41:34,808 --> 00:41:39,312
BUT NOT ALL AND SAME IN XENIUM

1425
00:41:39,312 --> 00:41:42,682
AND MERSCOPE, YOU HAVE A TUMOR,

1426
00:41:42,682 --> 00:41:46,286
THEY'RE OVERLAPPING, I SEE A CD4

1427
00:41:46,286 --> 00:41:47,954
SO I'LL CALL IT IMMUNE CELL.

1428
00:41:47,954 --> 00:41:48,888
IT'S TELLING YOU WHAT PART OF

1429
00:41:48,888 --> 00:41:51,324
THE TUMOR IS THE MOST AGGRESSIVE

1430
00:41:51,324 --> 00:41:51,524
PART.

1431
00:41:51,524 --> 00:41:52,859
THIS IS A BEAUTIFUL

1432
00:41:52,859 --> 00:41:54,727
REPRESENTATION, THIS IS JUST

1433
00:41:54,727 --> 00:41:55,495
CODEX DATA.

1434
00:41:55,495 --> 00:41:57,931
SO IF YOU DON'T THINK YOU CAN DO

1435
00:41:57,931 --> 00:41:59,232
MULTI-OMIC APPROACHES OR DEEP

1436
00:41:59,232 --> 00:41:59,999
LEARNING WITH PROTEIN DATA

1437
00:41:59,999 --> 00:42:01,568
YOU'RE WRONG IF YOU HAVE ENOUGH

1438
00:42:01,568 --> 00:42:02,569
MARKERS YOU CAN.

1439
00:42:02,569 --> 00:42:04,204
SO THIS IS ACTUALLY NORMAL

1440
00:42:04,204 --> 00:42:06,139
TISSUE ALL UP IN HERE.

1441
00:42:06,139 --> 00:42:07,607
IF YOU LOOK, THERE'S NO

1442
00:42:07,607 --> 00:42:08,675
STEMNESS, IT'S ALL BLUE.

1443
00:42:08,675 --> 00:42:10,477
BUT WHAT YOU SEE HERE IS THAT IN

1444
00:42:10,477 --> 00:42:12,212
THE TUMOR, AT THE TUMOR

1445
00:42:12,212 --> 00:42:13,613
INTERFACE, WE GET REALLY HIGH

1446
00:42:13,613 --> 00:42:15,048
STEM, AND THEN IN CERTAIN

1447
00:42:15,048 --> 00:42:16,349
POCKETS THAT ARE KIND OF

1448
00:42:16,349 --> 00:42:17,884
DRIPPING OUT WE GET CERTAIN STEM

1449
00:42:17,884 --> 00:42:18,151
TOO.

1450
00:42:18,151 --> 00:42:20,286
BUT THESE ARE ALL INTERMIXED

1451
00:42:20,286 --> 00:42:22,589
CELL ANNOTATIONS, RIGHT?

1452
00:42:22,589 --> 00:42:24,557
THEY'RE NOT THAT AN EPITHELIAL

1453
00:42:24,557 --> 00:42:26,759
CELL IS AN EPITHELIAL CELL BY

1454
00:42:26,759 --> 00:42:26,960
IT'S.

1455
00:42:26,960 --> 00:42:28,628
DOES IT WORK BACK TO THOSE

1456
00:42:28,628 --> 00:42:29,696
BEAUTIFUL BIOPSY SNS SO

1457
00:42:29,696 --> 00:42:31,898
REMEMBERING I HAD ALL THESE CELL

1458
00:42:31,898 --> 00:42:33,433
ANNOTATIONS, I SHOWED YOU COOL

1459
00:42:33,433 --> 00:42:34,067
NEIGHBORHOODS, THERE'S DIFFERENT

1460
00:42:34,067 --> 00:42:35,502
PARTS OF IT, WHICH YOU CAN KIND

1461
00:42:35,502 --> 00:42:36,469
OF SEE HERE.

1462
00:42:36,469 --> 00:42:37,804
BUT REMEMBER CARIBBEAN BLACK

1463
00:42:37,804 --> 00:42:38,905
WOMEN ARE VERY DIFFERENT THAN

1464
00:42:38,905 --> 00:42:41,741
U.S. BLACK IN THEIR OUTCOME, AND

1465
00:42:41,741 --> 00:42:43,042
REMEMBERING THEY HAD KIND OF

1466
00:42:43,042 --> 00:42:44,577
MORE EPITHELIAL CELLS ON THIS

1467
00:42:44,577 --> 00:42:45,778
SIDE VERSUS THIS SIDE.

1468
00:42:45,778 --> 00:42:47,180
BUT LOOK AT THE STEMNESS.

1469
00:42:47,180 --> 00:42:48,848
THERE'S NOT AS MANY POCKETS OF

1470
00:42:48,848 --> 00:42:49,048
RED.

1471
00:42:49,048 --> 00:42:51,151
WHILE THERE'S NOT A LOT OF RED,

1472
00:42:51,151 --> 00:42:53,219
THERE IS SOME RED HERE AND HERE,

1473
00:42:53,219 --> 00:42:55,188
THERE'S LOTS OF RED HOT SPOTS IN

1474
00:42:55,188 --> 00:42:56,189
THE BIOPSY.

1475
00:42:56,189 --> 00:42:58,491
THOSE ALL OVERLAP WITH CANCER

1476
00:42:58,491 --> 00:42:59,559
EPITHELIAL CELLS BUT AGAIN THERE

1477
00:42:59,559 --> 00:43:01,427
MUST BE SOME OTHER DRIVER THAT'S

1478
00:43:01,427 --> 00:43:03,530
BEYOND OUR CELL ANNOTATION.

1479
00:43:03,530 --> 00:43:05,064
AND HOW DO WE SEE TREATMENTS

1480
00:43:05,064 --> 00:43:06,132
REALLY BEING ALTERED?

1481
00:43:06,132 --> 00:43:07,667
I DIDN'T SEE THIS IN THE SINGLE

1482
00:43:07,667 --> 00:43:09,169
CELL DATA, AND YOU'RE GOING TO

1483
00:43:09,169 --> 00:43:11,004
LEAVE AND WANT TO BE A SPATIAL

1484
00:43:11,004 --> 00:43:12,405
OMICS PERSON LIKE ME AND I

1485
00:43:12,405 --> 00:43:14,707
DIDN'T WANT TO BE, IT'S VERY

1486
00:43:14,707 --> 00:43:15,909
EXPENSIVE, BUT LOOK AT THIS

1487
00:43:15,909 --> 00:43:16,276
DATA.

1488
00:43:16,276 --> 00:43:18,478
THIS DATA IS PART OF THE

1489
00:43:18,478 --> 00:43:20,413
8 MILLION CELL TYPE, IT'S TRIPLE

1490
00:43:20,413 --> 00:43:21,481
NEGATIVE BREAST CANCER.

1491
00:43:21,481 --> 00:43:24,450
THEY DON'T USUALLY EXHIBIT

1492
00:43:24,450 --> 00:43:25,985
PARP1, WE KIND OF IGNORE THEM.

1493
00:43:25,985 --> 00:43:29,689
BUT THE PARP1 CELLS ARE

1494
00:43:29,689 --> 00:43:31,457
LITERALLY BEING PROTECTED BY THE

1495
00:43:31,457 --> 00:43:33,760
MACROPHAGES, THE DON'T EAT ME

1496
00:43:33,760 --> 00:43:34,861
MACROPHAGES, FOR US THAT'S

1497
00:43:34,861 --> 00:43:36,095
ACTUALLY IMMUNOTHERAPY THINGS WE

1498
00:43:36,095 --> 00:43:38,131
CAN SEE IN THE DATA IFLTS THAT

1499
00:43:38,131 --> 00:43:40,200
ACTUALLY WILL -- ITSELF THAT

1500
00:43:40,200 --> 00:43:41,634
ACTUALLY WILL LINK TO

1501
00:43:41,634 --> 00:43:41,935
TREATMENTS.

1502
00:43:41,935 --> 00:43:43,036
THOSE AREN'T NEW TREATMENTS, WE

1503
00:43:43,036 --> 00:43:44,771
DON'T NEED TO DEVELOP NEW DRUGS,

1504
00:43:44,771 --> 00:43:46,206
WE JUST MIGHT BE GIVING THEM THE

1505
00:43:46,206 --> 00:43:46,873
WRONG DRUGS.

1506
00:43:46,873 --> 00:43:48,308
SO I WON'T SPEND A LOT OF TIME

1507
00:43:48,308 --> 00:43:49,976
ON THIS, BUT I THINK THIS IS ONE

1508
00:43:49,976 --> 00:43:51,711
WAY AND YOU CAN LOOK UP A PAPER

1509
00:43:51,711 --> 00:43:53,379
WHERE IT'S KIND OF OUR CALL TO

1510
00:43:53,379 --> 00:43:55,982
ARMS TO KIND OF INCREASE

1511
00:43:55,982 --> 00:43:57,383
ACCESSIBILITY BECAUSE WE KNOW

1512
00:43:57,383 --> 00:43:59,686
THAT HNE CAN BE DONE EVERYWHERE.

1513
00:43:59,686 --> 00:44:01,321
LIKELY WE WON'T BE ABLE TO DO

1514
00:44:01,321 --> 00:44:02,755
THESE CRAZY SPATIAL OMICS STUFF

1515
00:44:02,755 --> 00:44:04,857
THERE BUT HOPEFULLY COST OF

1516
00:44:04,857 --> 00:44:06,259
ASSAYS, TRAINING AND COMPUTATION

1517
00:44:06,259 --> 00:44:08,661
CAN BE GIVEN BACK TO THEM.

1518
00:44:08,661 --> 00:44:09,562
AND WHERE ARE WE?

1519
00:44:09,562 --> 00:44:11,664
SO WHERE ARE WE AND WHERE DO WE

1520
00:44:11,664 --> 00:44:13,399
THINK WE'RE HEADED?

1521
00:44:13,399 --> 00:44:14,634
I'M REALLY HAPPY TO BE HERE

1522
00:44:14,634 --> 00:44:15,969
BECAUSE I THINK YOU GUYS ARE

1523
00:44:15,969 --> 00:44:17,136
ALREADY ON THE PLATFORM TO

1524
00:44:17,136 --> 00:44:18,771
THINKING ABOUT THIS.

1525
00:44:18,771 --> 00:44:21,174
SO I MOVED TO ST. JUDE FOR THIS

1526
00:44:21,174 --> 00:44:21,574
REASON.

1527
00:44:21,574 --> 00:44:22,842
WE ALREADY HAVE AWFUL THESE

1528
00:44:22,842 --> 00:44:24,210
SPATIAL OMICS TECHNOLOGIES UP

1529
00:44:24,210 --> 00:44:26,312
AND RUNNING, I'M HAPPY TO HELP

1530
00:44:26,312 --> 00:44:27,780
COLLABORATE OR GET YOU GUYS OFF

1531
00:44:27,780 --> 00:44:28,982
THE GROUND BECAUSE IT'S GOING TO

1532
00:44:28,982 --> 00:44:30,850
NEED A LOT OF PEOPLE TO DO THIS

1533
00:44:30,850 --> 00:44:32,051
ALL AT ONCE.

1534
00:44:32,051 --> 00:44:33,486
WE'RE ALREADY DOING IT.

1535
00:44:33,486 --> 00:44:35,221
SO WE'RE DOING IT LIVE THROUGH

1536
00:44:35,221 --> 00:44:38,057
CLINICAL TRIALS, DOING TMA'S

1537
00:44:38,057 --> 00:44:38,858
ACROSS 43 MARKERS.

1538
00:44:38,858 --> 00:44:40,126
WE REALLY THINK IT'S APPLICABLE

1539
00:44:40,126 --> 00:44:42,095
TO OTHER ORGANISMS AND MORE

1540
00:44:42,095 --> 00:44:44,631
TISSUE TYPES ESPECIALLY HE

1541
00:44:44,631 --> 00:44:47,467
GENOMICS SO I'M REALLY -- EPI

1542
00:44:47,467 --> 00:44:49,535
GENOMICS SO I'M REALLY WATCHING

1543
00:44:49,535 --> 00:44:51,037
THE FIELD CLOSELY.

1544
00:44:51,037 --> 00:44:53,139
THIS IS A CALL OUT TO THE FIELD,

1545
00:44:53,139 --> 00:44:54,474
HOPEFULLY TO HELP YOU OUT AS WE

1546
00:44:54,474 --> 00:44:55,241
MOVE LOONG.

1547
00:44:55,241 --> 00:44:56,876
THERE'S BEEN A BIG CALL THE IN

1548
00:44:56,876 --> 00:44:58,278
FIELD FOR ESTABLISHING METRICS

1549
00:44:58,278 --> 00:44:59,445
SO LOOK OUT FOR THIS.

1550
00:44:59,445 --> 00:45:00,780
WE'RE TRYING TO FINISH THIS OFF

1551
00:45:00,780 --> 00:45:03,750
BUT WE ESSENTIALLY DID CALLED

1552
00:45:03,750 --> 00:45:05,518
SPATIAL TOUCHSTONE WHERE WE

1553
00:45:05,518 --> 00:45:07,920
DOWNLOADED EVERY PUBLIC DATASET

1554
00:45:07,920 --> 00:45:11,758
THAT'S BEEN IMAGED SPATIAL

1555
00:45:11,758 --> 00:45:12,625
TRANSCRIPTOMICS, WE OURSELVES

1556
00:45:12,625 --> 00:45:14,594
HAVE PROFILED MORE CELLS THAN

1557
00:45:14,594 --> 00:45:17,297
THEY HAVE, SO WE'VE CONDUCTED,

1558
00:45:17,297 --> 00:45:19,499
WE HAVE OVER 8 MILLION CELLS IN

1559
00:45:19,499 --> 00:45:21,934
OUR REPOSITORY AND THE ENTIRE

1560
00:45:21,934 --> 00:45:24,871
REPOSITORY ONLINE SO FAR

1561
00:45:24,871 --> 00:45:25,405
6.4 MILLION.

1562
00:45:25,405 --> 00:45:27,907
WE'VE DONE THE SAME TISSUE

1563
00:45:27,907 --> 00:45:29,776
SECTION ACROSS THREE DIFFERENT

1564
00:45:29,776 --> 00:45:32,178
SITES, ACROSS TWO DIFFERENT

1565
00:45:32,178 --> 00:45:34,981
PLATFORMS, AND WE HAVE

1566
00:45:34,981 --> 00:45:35,948
2.5 BILLION TRANSCRIPTS.

1567
00:45:35,948 --> 00:45:37,583
THIS IS FRESH OFF THE PRESS.

1568
00:45:37,583 --> 00:45:39,752
THIS IS THE SAME PROSTATE TUMOR

1569
00:45:39,752 --> 00:45:41,621
DONE ACROSS THREE SITES ACROSS

1570
00:45:41,621 --> 00:45:43,923
TWO PLATFORMS ACROSS DIFFERENT

1571
00:45:43,923 --> 00:45:45,325
CELL SEGMENTATION MARKS.

1572
00:45:45,325 --> 00:45:46,526
WE'RE VERY GOOD AT IT.

1573
00:45:46,526 --> 00:45:47,727
OBVIOUSLY THIS GETS TO THE IDEA

1574
00:45:47,727 --> 00:45:49,062
WHEN PEOPLE SAY WHEN YOU CUT

1575
00:45:49,062 --> 00:45:51,331
THROUGH THINGS WITH THE TUMOR

1576
00:45:51,331 --> 00:45:52,332
HETEROGENEITY, I THINK WE'LL GET

1577
00:45:52,332 --> 00:45:54,067
THERE, I THINK THE MORE PEOPLE

1578
00:45:54,067 --> 00:45:56,602
DEPOSIT DATA AND EVEN WITH THAT

1579
00:45:56,602 --> 00:45:58,471
QUESTION WE STILL SEE

1580
00:45:58,471 --> 00:45:58,838
REPRODUCIBILITY.

1581
00:45:58,838 --> 00:46:00,540
SO WHAT YOU CAN SEE ON THE

1582
00:46:00,540 --> 00:46:01,941
LEFT-HAND SIDE IS OUR DATA.

1583
00:46:01,941 --> 00:46:03,242
SO OUR DATA IS MORE CONSISTENT

1584
00:46:03,242 --> 00:46:05,278
IN TERMS OF HOW MANY TRANSCRIPTS

1585
00:46:05,278 --> 00:46:07,347
PER CELL WE GET ACROSS ALL OF

1586
00:46:07,347 --> 00:46:08,314
THEM BUT MOST IMPORTANTLY I

1587
00:46:08,314 --> 00:46:10,750
THINK IS THIS METRIC, IS THIS IS

1588
00:46:10,750 --> 00:46:12,352
THE PUBLIC DATASET WHICH IS THE

1589
00:46:12,352 --> 00:46:13,653
PINK WHICH IS KIND OF ALL OVER

1590
00:46:13,653 --> 00:46:15,188
THE MAP FOR SIGNAL TO NOISE AND

1591
00:46:15,188 --> 00:46:17,323
IF I'M HONEST WE HAD CRAPPY DATA

1592
00:46:17,323 --> 00:46:18,691
TOO, LIKE IF YOU LOOK AT THIS,

1593
00:46:18,691 --> 00:46:20,660
I'M NOT GOING TO TELL WHAT THE

1594
00:46:20,660 --> 00:46:21,961
INSTITUTE IS BUT THEY GUESSED

1595
00:46:21,961 --> 00:46:23,830
WHEN THEY SAW THEIRS BUT EVEN

1596
00:46:23,830 --> 00:46:25,298
ACROSS YOU ARE ADATD SET WE DO

1597
00:46:25,298 --> 00:46:27,233
VERY WELL WITH SIGNAL TO NOISE

1598
00:46:27,233 --> 00:46:27,800
RATIOS.

1599
00:46:27,800 --> 00:46:28,868
WHY AM I TELLING YOU THIS?

1600
00:46:28,868 --> 00:46:30,603
BECAUSE THIS ENTIRE REPOSITORY

1601
00:46:30,603 --> 00:46:31,804
WILL BE AVAILABLE WHERE WE'VE

1602
00:46:31,804 --> 00:46:33,439
DONE THREE THINGS FOR THE FIELD.

1603
00:46:33,439 --> 00:46:35,775
ONE IS TO DO SPATIAL TOUCHSTONE

1604
00:46:35,775 --> 00:46:38,711
PROTOCOLS, IT'S PROTOCOLS.IO,

1605
00:46:38,711 --> 00:46:41,347
IT'S RIT RESIDUALLY EVEN MY

1606
00:46:41,347 --> 00:46:44,951
SINGLE NEW PATHO SEQ, FFPE IS ON

1607
00:46:44,951 --> 00:46:47,120
IT, HOW TO STAIN EVERYTHING TO

1608
00:46:47,120 --> 00:46:49,655
MAKE REPRODUCIBLE DATA, WE KNOW

1609
00:46:49,655 --> 00:46:52,725
OUR DATASET WAS MUCH MORE

1610
00:46:52,725 --> 00:46:53,059
REPRODUCIBLE.

1611
00:46:53,059 --> 00:46:54,494
WE'VE DEPOSITED ALL OUR CURATION

1612
00:46:54,494 --> 00:46:56,162
AND MINING OF IT SO YOU CAN TAKE

1613
00:46:56,162 --> 00:46:58,131
ANYTHING THAT GOT DERIVED OFF A

1614
00:46:58,131 --> 00:47:00,533
MACHINE HERE AND EITHER RUN IT

1615
00:47:00,533 --> 00:47:01,968
LOCALLY, WE'VE PACKAGED THIS OUT

1616
00:47:01,968 --> 00:47:02,902
AS OUR OWN SOFTWARE.

1617
00:47:02,902 --> 00:47:04,437
THE NICE PARTED ABOUT OUR

1618
00:47:04,437 --> 00:47:06,105
SOFTWARE IS YOU CAN CALCULATE

1619
00:47:06,105 --> 00:47:07,707
ALL OUR METRICS AND ALSO TAKE

1620
00:47:07,707 --> 00:47:09,509
ALL YOUR SINGLE CELL DATA WITH

1621
00:47:09,509 --> 00:47:11,344
ALL YOUR ANNOTATIONS AND PUT IT

1622
00:47:11,344 --> 00:47:12,979
DIRECTLY ON YOUR IMAGING BASE

1623
00:47:12,979 --> 00:47:14,414
DATA THROUGH THE PORTAL WHICH I

1624
00:47:14,414 --> 00:47:15,615
THINK MOST PEOPLE IN THIS ROOM

1625
00:47:15,615 --> 00:47:19,285
WILL WANT TO USE TAKE EVERYTHING

1626
00:47:19,285 --> 00:47:20,520
THAT COMES OFF THE MACHINE, PUT

1627
00:47:20,520 --> 00:47:24,157
IT IN OUR PORTAL, I JUST TO WANT

1628
00:47:24,157 --> 00:47:25,825
SEE WHERE MY EXAMPLE IS, SO THIS

1629
00:47:25,825 --> 00:47:26,759
IS YOUR RED LINE.

1630
00:47:26,759 --> 00:47:27,760
ARE YOU ON THE RED LINE?

1631
00:47:27,760 --> 00:47:29,629
IS IT A GOOD RED?

1632
00:47:29,629 --> 00:47:30,863
IF YOU'RE BREAST, WHAT DOES

1633
00:47:30,863 --> 00:47:31,597
BREAST LIKE?

1634
00:47:31,597 --> 00:47:33,699
IF WE PROFILE PROSTATE AND YOU

1635
00:47:33,699 --> 00:47:35,001
DID PROSTATE, HOW MANY

1636
00:47:35,001 --> 00:47:36,102
TRANSCRIPTS OR CELLS SHOULD I

1637
00:47:36,102 --> 00:47:36,869
THINK ABOUT?

1638
00:47:36,869 --> 00:47:38,271
SO YOU'RE ABLE TO KIND OF DO

1639
00:47:38,271 --> 00:47:38,604
THAT.

1640
00:47:38,604 --> 00:47:41,874
SO THAT'S COMING OUT SOON.

1641
00:47:41,874 --> 00:47:43,976
LET BY GESTALT, SO IF YOU HAVE

1642
00:47:43,976 --> 00:47:46,579
ANY QUESTIONS ABOUT SPATIAL,

1643
00:47:46,579 --> 00:47:49,449
THIS IS KIND OF A HOME-GROWN

1644
00:47:49,449 --> 00:47:51,851
VERY FRUSTRATED CENTER FOR

1645
00:47:51,851 --> 00:47:53,753
SPATIAL PEOPLE REALLY STARTING.

1646
00:47:53,753 --> 00:47:56,322
THIS IS WHAT I LOOKED LIKE

1647
00:47:56,322 --> 00:47:57,690
BEFORE SPATIAL.

1648
00:47:57,690 --> 00:47:58,524
I HAD NO NECK.

1649
00:47:58,524 --> 00:48:01,427
I GREW A NECK LATER.

1650
00:48:01,427 --> 00:48:03,863
BUT YEAH, I GREW A NECK BECAUSE

1651
00:48:03,863 --> 00:48:05,631
THERE'S BOTTLENECKS, SEE?

1652
00:48:05,631 --> 00:48:07,533
BUT REALLY, I ENCOURAGE ALL OF

1653
00:48:07,533 --> 00:48:10,770
YOU TO COME, LTS A MONTHLY -- I

1654
00:48:10,770 --> 00:48:11,971
THINK -- PLEASE COME BECAUSE WE

1655
00:48:11,971 --> 00:48:13,506
NEED MORE PEOPLE TO ORGANIZE

1656
00:48:13,506 --> 00:48:14,907
BECAUSE ACTUALLY THIS ISN'T EVEN

1657
00:48:14,907 --> 00:48:16,342
OUR DAY JOB, BUT THE IDEA IS

1658
00:48:16,342 --> 00:48:17,910
REALLY TO THINK ABOUT HOW TO

1659
00:48:17,910 --> 00:48:19,212
HAVE MORE COLLECTIVE EFFORTS

1660
00:48:19,212 --> 00:48:20,813
BECAUSE THE AMOUNT OF DATA, IF

1661
00:48:20,813 --> 00:48:22,048
YOU'RE OVERWHELMED WITH THE IDEA

1662
00:48:22,048 --> 00:48:23,649
OF DOING SPATIAL, THESE ARE THE

1663
00:48:23,649 --> 00:48:24,917
PLACES YOU WANT TO GO TO.

1664
00:48:24,917 --> 00:48:26,853
YOU CAN ASK QUESTIONS.

1665
00:48:26,853 --> 00:48:28,387
THINK ABOUT BROADENING YOUR

1666
00:48:28,387 --> 00:48:28,821
TEAMS.

1667
00:48:28,821 --> 00:48:30,156
I HAVEN'T SPENT AS MUCH TIME IN

1668
00:48:30,156 --> 00:48:32,091
A ROOM WITH A PATHOLOGIST AS I

1669
00:48:32,091 --> 00:48:33,893
HAVE FOR THE LAST THREE YEARS.

1670
00:48:33,893 --> 00:48:36,395
AND REALLY TO ANY TRAINEES, IF

1671
00:48:36,395 --> 00:48:38,564
YOU'RE DOING ANY KIND OF Ph.D.

1672
00:48:38,564 --> 00:48:40,299
OR POST-DOCKING, I WOULD THINK

1673
00:48:40,299 --> 00:48:41,801
ABOUT COMPUTATIONAL TRAINING

1674
00:48:41,801 --> 00:48:42,869
ESPECIALLY IF YOU'RE BRINGING

1675
00:48:42,869 --> 00:48:43,603
THIS IN-HOUSE.

1676
00:48:43,603 --> 00:48:46,472
SO I'M JUST GOING TO END WITH

1677
00:48:46,472 --> 00:48:47,673
ACKNOWLEDGMENTS, TO MY TEAM THAT

1678
00:48:47,673 --> 00:48:50,877
WAS AT CEDARS, TO HARRY FU WHO

1679
00:48:50,877 --> 00:48:53,112
IS IN OHIO FOR THE ALZHEIMER'S,

1680
00:48:53,112 --> 00:48:54,213
FOR THE BEAUTIFUL EAST ONE

1681
00:48:54,213 --> 00:48:59,185
HYBRID STUFF IS WITH DR. MARION

1682
00:48:59,185 --> 00:49:00,553
HOT[READING NAIMEDZ]

1683
00:49:00,553 --> 00:49:02,889
, AND ACROSS MANY INSTITUTES AND

1684
00:49:02,889 --> 00:49:06,158
FUNDING FROM AWS TISM SPEAKS --

1685
00:49:06,158 --> 00:49:06,726
FROM AUTISM SPEAKS.

1686
00:49:06,726 --> 00:49:09,662
THIS IS THE LARGE CONSORTIUM,

1687
00:49:09,662 --> 00:49:11,597
ALL 32 SITES, LED BY DR. GEORGE,

1688
00:49:11,597 --> 00:49:13,065
THIS IS ACTUALLY US IN AFRICA

1689
00:49:13,065 --> 00:49:14,133
BECAUSE YOU HAVE TO GO THERE AND

1690
00:49:14,133 --> 00:49:15,234
COLLECT SAMPLES, THIS IS THE

1691
00:49:15,234 --> 00:49:20,406
TEAM THERE THAT PROCESSED 1K3

1692
00:49:20,406 --> 00:49:22,842
DR. MARLETTA 1K3 DR. MALTIFF FOR

1693
00:49:22,842 --> 00:49:25,011
THE MACHINE LEARNING MODELS AND

1694
00:49:25,011 --> 00:49:26,612
THEN SPATIAL TOUCHSTONE, THIS IS

1695
00:49:26,612 --> 00:49:28,381
THE LARGE TEAM, IT'S BEEN A

1696
00:49:28,381 --> 00:49:31,250
GLOBAL EFFORT, IT'S BEEN

1697
00:49:31,250 --> 00:49:33,019
GOLIATH, THIS IS THE FUNDING

1698
00:49:33,019 --> 00:49:34,754
SOURCE, SEEING THAT I'M ALTHOUGH

1699
00:49:34,754 --> 00:49:34,954
NIH.

1700
00:49:34,954 --> 00:49:35,922
NOBODY FUNDS THIS WORK.

1701
00:49:35,922 --> 00:49:37,924
IT WAS REALLY MY DEEP SEEDED

1702
00:49:37,924 --> 00:49:39,892
ANNOYANCE WITH THE FIELD AND

1703
00:49:39,892 --> 00:49:40,993
MARKETING, WHEN COMPANIES TELL

1704
00:49:40,993 --> 00:49:43,296
YOU THINGS WORK AND THEY DON'T.

1705
00:49:43,296 --> 00:49:45,598
SO HOPEFULLY YOU'LL USE T

1706
00:49:45,598 --> 00:49:47,233
HOPEFULLY YOU'LL HELP US IN THE

1707
00:49:47,233 --> 00:49:49,635
CAUSE, BECAUSE MY PIGGY BANK IS

1708
00:49:49,635 --> 00:49:50,503
BROKEN NOW.

1709
00:49:50,503 --> 00:49:51,804
SO THANK YOU AND I'M HAPPY TO

1710
00:49:51,804 --> 00:49:57,944
TAKE QUESTIONS.

1711
00:49:57,944 --> 00:50:08,120
[APPLAUSE]

1712
00:50:10,623 --> 00:50:14,126
>> THANK YOU SO MUCH FOR
THE

1713
00:50:14,126 --> 00:50:15,161
WONDERFUL TALK.

1714
00:50:15,161 --> 00:50:18,230
I WONDER IF YOU COULD COMMENTED

1715
00:50:18,230 --> 00:50:20,733
A LITTLE BIT ON SORT OF YOUR

1716
00:50:20,733 --> 00:50:23,002
EXPERIENCE WITH THE BIOPSY

1717
00:50:23,002 --> 00:50:25,304
SAMPLES, AND I EVEN SAW THE

1718
00:50:25,304 --> 00:50:26,973
TMA'S NOW THAT YOU'RE USING.

1719
00:50:26,973 --> 00:50:28,941
YOU KNOW, WE TALKED THIS MORNING

1720
00:50:28,941 --> 00:50:31,243
ABOUT TUMOR HETEROGENEITY AND

1721
00:50:31,243 --> 00:50:32,545
HERE YOU'RE ALWAYS GOING TO BE

1722
00:50:32,545 --> 00:50:34,981
LIMITED WHERE THE NEEDLE GETS

1723
00:50:34,981 --> 00:50:35,281
STUCK.

1724
00:50:35,281 --> 00:50:40,753
SO HOW DO YOU SORTED OF ENVISIOF

1725
00:50:40,753 --> 00:50:42,955
ENVISION GETTING A BIG ENOUGH

1726
00:50:42,955 --> 00:50:44,724
SAMPLE FROM A BIOPSY SAMPLE TO

1727
00:50:44,724 --> 00:50:45,891
ANSWER SOME OF THE BIG DISCOVERY

1728
00:50:45,891 --> 00:50:46,626
QUESTIONS YOU'RE ASKING.

1729
00:50:46,626 --> 00:50:48,861
>> SO I WOULD SAY, I
DIDN'T

1730
00:50:48,861 --> 00:50:51,097
SPEND A LOT OF TIME ON T BUT I

1731
00:50:51,097 --> 00:50:52,531
THINK FIELDS OF VIEW MATTERS.

1732
00:50:52,531 --> 00:50:54,567
SO I KNOW EVERYBODY WANTS TO GO

1733
00:50:54,567 --> 00:50:55,668
SUB MOLECULAR, RIGHT?

1734
00:50:55,668 --> 00:50:57,770
WE WANT TO DO THINGS LIKE MAR

1735
00:50:57,770 --> 00:51:00,873
FISH AND XENIUM AND COSMICS, BUT

1736
00:51:00,873 --> 00:51:01,907
I THINK YOU'RE RIGHT, YOU HAVE

1737
00:51:01,907 --> 00:51:04,110
TO START WITH THE IDEA OF LARGE

1738
00:51:04,110 --> 00:51:05,311
PROFILINGS ESPECIALLY IF YOU

1739
00:51:05,311 --> 00:51:07,213
THINK ABOUT TUMOR HETEROGENEITY.

1740
00:51:07,213 --> 00:51:08,914
I WOULD SAY THE GOOD NEWS ABOUT

1741
00:51:08,914 --> 00:51:11,984
SINGLE CELLS, MAYBE YOU PUSHED

1742
00:51:11,984 --> 00:51:12,752
UP 3 MILLION CELLS SO YOU WOULD

1743
00:51:12,752 --> 00:51:14,420
FEEL LIKE YOU GOT A BETTER

1744
00:51:14,420 --> 00:51:15,187
REPRESENTATION OF THE TUMOR.

1745
00:51:15,187 --> 00:51:16,555
I WOULD ARGUE THAT'S PROBABLY

1746
00:51:16,555 --> 00:51:21,527
NOT THE CASE GIVEN THAT YOU ONLY

1747
00:51:21,527 --> 00:51:22,828
TOOK LIKE ONE PERCENT OF THE

1748
00:51:22,828 --> 00:51:23,496
TUMOR, RIGHT?

1749
00:51:23,496 --> 00:51:25,364
SO YOU'RE NOT GOING TO GET THERE

1750
00:51:25,364 --> 00:51:26,799
WITH SINGLE CELL EITHER, EVEN IF

1751
00:51:26,799 --> 00:51:29,502
YOU HAVE A 5-POUND TUMOR, YOU'RE

1752
00:51:29,502 --> 00:51:30,736
TAKING 10,000 CELLS TO REPRESENT

1753
00:51:30,736 --> 00:51:30,903
IT.

1754
00:51:30,903 --> 00:51:39,245
I WOULD SAY APPROACHES LIKE G HE

1755
00:51:39,245 --> 00:51:39,478
OMICS.

1756
00:51:39,478 --> 00:51:41,881
WE WERE ASKING QUESTIONS LIKE

1757
00:51:41,881 --> 00:51:44,850
DOES INNER MASS OF A STROMAL

1758
00:51:44,850 --> 00:51:46,519
VERSUS EPITHELIAL LOOK DIFFERENT

1759
00:51:46,519 --> 00:51:49,321
ON THE NORMAL, WHAT'S THE

1760
00:51:49,321 --> 00:51:52,558
CONTRIBUTION OF NORMAL --

1761
00:51:52,558 --> 00:51:54,593
SOMETHING LIKE G HE OMIC SRKSZ

1762
00:51:54,593 --> 00:51:57,263
WOULD GIVE YOU BULK RNA SEQ DATA

1763
00:51:57,263 --> 00:51:58,431
BUT YOU COULD DO COOL STUFF

1764
00:51:58,431 --> 00:51:59,532
WHERE YOU TAKE ALL THE SINGLE

1765
00:51:59,532 --> 00:52:00,833
CELL DATA FROM EVERYBODY ELSE'S

1766
00:52:00,833 --> 00:52:02,802
DATA SET AND YOU DECOMPOSE THE

1767
00:52:02,802 --> 00:52:03,469
CELLS, RIGHT?

1768
00:52:03,469 --> 00:52:05,538
SO EVEN IN THAT BULK DATA YOU

1769
00:52:05,538 --> 00:52:07,406
COULD FIND OUT ALL THE IMMUNE

1770
00:52:07,406 --> 00:52:09,375
CELLS THAT ARE EXPRESSING CD4 SO

1771
00:52:09,375 --> 00:52:10,609
IT'S A BULK SIGNAL THAT GIVES

1772
00:52:10,609 --> 00:52:12,445
YOU KIND OF SINGLY LEVEL DATA.

1773
00:52:12,445 --> 00:52:14,013
SO I WOULD START THERE.

1774
00:52:14,013 --> 00:52:16,182
I WOULD SAY THAT EVEN IF YOU

1775
00:52:16,182 --> 00:52:18,017
TOOK KIND OF THE PATHOLOGY

1776
00:52:18,017 --> 00:52:19,118
APPROACHES YOU'RE STILL IN A

1777
00:52:19,118 --> 00:52:24,190
BETTER PLACE, RIGHT?

1778
00:52:24,190 --> 00:52:25,691
SO OUR THING WITH THE BIOPSIES

1779
00:52:25,691 --> 00:52:27,660
IS THAT WE'RE GETTING 8 MILLION

1780
00:52:27,660 --> 00:52:28,227
CELLS, RIGHT?

1781
00:52:28,227 --> 00:52:31,297
SO THE ASMALLEST TMA I'M GETTING

1782
00:52:31,297 --> 00:52:31,931
70,000 CELLS.

1783
00:52:31,931 --> 00:52:36,569
THAT'S LIKE THE BIGGEST, THAT'S

1784
00:52:36,569 --> 00:52:39,638
SEVEN 10X EXPERIMENTS, RIGHT?

1785
00:52:39,638 --> 00:52:40,606
SEVEN 10X EXPERIMENTS.

1786
00:52:40,606 --> 00:52:41,807
SO EVEN WHEN THE PRICING IS

1787
00:52:41,807 --> 00:52:43,342
SHOCKING TO YOU, THE AMOUNT OF

1788
00:52:43,342 --> 00:52:45,010
DATA YOU'RE GETTING OFF THE

1789
00:52:45,010 --> 00:52:46,612
SLIDE IS JUST THAT YOU -- LOOK

1790
00:52:46,612 --> 00:52:48,514
AT THAT 8 MILLION CELL, I WOULD

1791
00:52:48,514 --> 00:52:49,915
NEVER HAVE GOTTEN THERE IF I WAS

1792
00:52:49,915 --> 00:52:51,350
TAKING A HUMAN CELL ATLAS

1793
00:52:51,350 --> 00:52:52,985
APPROACH OF JUST SINGLE CELLING

1794
00:52:52,985 --> 00:52:53,419
IT OUT.

1795
00:52:53,419 --> 00:52:55,287
SO GRANTED YOU'RE MUSHING UP A

1796
00:52:55,287 --> 00:52:57,823
TUMOR BUT YOU'VE GOT 10 PERCENT,

1797
00:52:57,823 --> 00:52:59,358
ONE PERCENT, TWO PERCENT OF THE

1798
00:52:59,358 --> 00:53:01,093
TUMOR, YOU'RE NOT GETTING A FULL

1799
00:53:01,093 --> 00:53:03,929
VIEWPOINT AND THE RARE CELL

1800
00:53:03,929 --> 00:53:06,132
TYPES, YOU TALK ABOUT TUMOR

1801
00:53:06,132 --> 00:53:06,899
MICROENVIRONMENT, I WOULD RATHER

1802
00:53:06,899 --> 00:53:10,069
HAVE ONE-THIRD OF THE TUMOR

1803
00:53:10,069 --> 00:53:12,905
PROFILED AND KNOW THAT A

1804
00:53:12,905 --> 00:53:14,807
MACROPHAGE -- WE SEE PRY BRO

1805
00:53:14,807 --> 00:53:16,275
BLASTS LITERALLY PROTECTING THE

1806
00:53:16,275 --> 00:53:16,509
TUMOR.

1807
00:53:16,509 --> 00:53:18,477
THAT'S WHEN T CELLS CAN GET IN,

1808
00:53:18,477 --> 00:53:18,778
RIGHT?

1809
00:53:18,778 --> 00:53:20,246
THAT'S WHEN KWR50UR CAR T, YOU

1810
00:53:20,246 --> 00:53:22,948
WANT TO TALK ABOUT RESPONDERS,

1811
00:53:22,948 --> 00:53:24,383
NONRESPONDERS, THAT TOTAL QULAIJ

1812
00:53:24,383 --> 00:53:26,685
HAS EVERYTHING TO DO WITH WHAT

1813
00:53:26,685 --> 00:53:28,854
THERAPY GOT IN.

1814
00:53:28,854 --> 00:53:31,590
-- THAT TOTAL LINEAGE HAS

1815
00:53:31,590 --> 00:53:32,925
EVERYTHING TO DO WITH WHAT

1816
00:53:32,925 --> 00:53:38,430
THERAPY GOT IN.

1817
00:53:38,430 --> 00:53:40,466
>> VERY NICE TALK.

1818
00:53:40,466 --> 00:53:42,468
SO IN THIS SORT OF BALANCE

1819
00:53:42,468 --> 00:53:44,637
BETWEEN SORT OF BROAD SURVEY TO

1820
00:53:44,637 --> 00:53:45,838
THE MORE TARGETED AND MAYBE

1821
00:53:45,838 --> 00:53:47,740
BEING ABLE TO RUN MORE SAMPLES

1822
00:53:47,740 --> 00:53:49,041
FOR THE SAKE OF COST, WHERE DO

1823
00:53:49,041 --> 00:53:51,110
YOU MAKE THAT CHOICE?

1824
00:53:51,110 --> 00:53:52,978
IN YOUR EXPERIENCE, WHEN DO YOU

1825
00:53:52,978 --> 00:53:54,346
FEEL LIKE YOU FIGURED IT OUT AND

1826
00:53:54,346 --> 00:53:59,618
YOU CAN NOW DESIGN THAT LOWER

1827
00:53:59,618 --> 00:53:59,952
PANEL?

1828
00:53:59,952 --> 00:54:02,621
>> SO NO ONE IS IN THIS --
NO

1829
00:54:02,621 --> 00:54:04,023
ONE IN THIS ROOM IS GOING TO

1830
00:54:04,023 --> 00:54:05,558
WANT TO HEAR THIS BUT I THINK

1831
00:54:05,558 --> 00:54:07,092
IT'S MY JOB ESPECIALLY RUNNING

1832
00:54:07,092 --> 00:54:08,928
THE LARGE EQUIPMENT DIENLD OF

1833
00:54:08,928 --> 00:54:10,396
FIRMT EVERYBODY THAT WANTS TO DO

1834
00:54:10,396 --> 00:54:11,063
A PANEL.

1835
00:54:11,063 --> 00:54:12,464
I THINK YOU NEED TO HAVE BEEN IN

1836
00:54:12,464 --> 00:54:13,599
THE FIELD A WHILE.

1837
00:54:13,599 --> 00:54:17,169
EVEN IF IT'S GEOMICS, LOW

1838
00:54:17,169 --> 00:54:20,573
HANGING FRUIT OF WHERE YOU SEE

1839
00:54:20,573 --> 00:54:21,440
THE SPATIAL CONTEXT.

1840
00:54:21,440 --> 00:54:23,609
SO I HAVE THIS VERY CLEAR

1841
00:54:23,609 --> 00:54:24,610
EXAMPLE WHERE SOMEBODY WAS

1842
00:54:24,610 --> 00:54:30,182
TRYING TO LOOK AT DOWN'S

1843
00:54:30,182 --> 00:54:32,618
SYNDROME THIGH MUSES TUMORS --

1844
00:54:32,618 --> 00:54:33,819
THYMUS TUMORS.

1845
00:54:33,819 --> 00:54:35,988
DOWN'S SYNDROME, IT'S A WHOLE

1846
00:54:35,988 --> 00:54:38,624
CHROMOSOME CHANGE, THEY SAW ALL

1847
00:54:38,624 --> 00:54:39,925
THIS COOL STUFF IN THEIR SINGLE

1848
00:54:39,925 --> 00:54:40,926
CELL DATA SO THEY WANTED TO

1849
00:54:40,926 --> 00:54:42,895
COMPARE ONE REGION BACK TO THIS

1850
00:54:42,895 --> 00:54:44,563
OTHER REGION BARKS TO THIS

1851
00:54:44,563 --> 00:54:45,631
QUESTION OF TUMOR

1852
00:54:45,631 --> 00:54:46,065
MICROENVIRONMENT.

1853
00:54:46,065 --> 00:54:47,600
IT HAD EVERYTHING TO DO WITH WAS

1854
00:54:47,600 --> 00:54:49,468
IT PROXIMAL TO THE THYMUS, DID

1855
00:54:49,468 --> 00:54:51,237
THEY GET NORMAL CELLS OUT CIALG

1856
00:54:51,237 --> 00:54:51,570
RIGHT?

1857
00:54:51,570 --> 00:54:53,105
IT WASN'T -- RIGHT?

1858
00:54:53,105 --> 00:54:54,974
IT WASN'T THAT WE CLEANED UP THE

1859
00:54:54,974 --> 00:54:56,141
NORMAL, BECAUSE WE SHUNTED HAVE

1860
00:54:56,141 --> 00:54:58,010
DONE THAT 2349 1234E8, IT WAS

1861
00:54:58,010 --> 00:54:59,311
THE INFILTRATION AT THE BORDER

1862
00:54:59,311 --> 00:55:00,079
OF THOSE CELLS.

1863
00:55:00,079 --> 00:55:02,548
IT WAS FUNNY THEY WANTED US TO

1864
00:55:02,548 --> 00:55:04,250
GROUP IT OUT BUT IT WASN'T THAT

1865
00:55:04,250 --> 00:55:06,318
DOWN'S SYNDROME WAS LEADING IT,

1866
00:55:06,318 --> 00:55:08,220
IT WAS THAT THE TUMOR BORDER

1867
00:55:08,220 --> 00:55:09,421
THAT WAS LEADING ALL OF THE

1868
00:55:09,421 --> 00:55:09,688
ANALYSIS.

1869
00:55:09,688 --> 00:55:14,026
SO I WOULD SAY, PLAY WITH SOME

1870
00:55:14,026 --> 00:55:20,099
LEVEL OF VIZIUM OFFER GEOMICS.

1871
00:55:20,099 --> 00:55:21,700
RIGHT NOW TO GET A PAPER YOU

1872
00:55:21,700 --> 00:55:23,469
DON'T NEED AN NF30 BUT I WOULD

1873
00:55:23,469 --> 00:55:25,571
USE IT WHERE YOU HAVE THE WHOLE

1874
00:55:25,571 --> 00:55:25,905
TRANSCRIPTOME.

1875
00:55:25,905 --> 00:55:28,641
I WOULDN'T GO AT XENIUM UNLESS

1876
00:55:28,641 --> 00:55:30,376
YOU HAVE A LOT OF DATA TO MAKE A

1877
00:55:30,376 --> 00:55:31,043
CUSTOM PANEL.

1878
00:55:31,043 --> 00:55:31,877
YOU COULD PROBE.

1879
00:55:31,877 --> 00:55:33,746
YOU DON'T EVEN HAVE TO SPEND

1880
00:55:33,746 --> 00:55:33,946
MONEY.

1881
00:55:33,946 --> 00:55:35,281
WE HAVE SO MUCH SINGLE CELL DATD

1882
00:55:35,281 --> 00:55:37,182
THAT NOBODY HAS INTERPOLATED TO

1883
00:55:37,182 --> 00:55:38,484
THE LEVEL THEY NEED.

1884
00:55:38,484 --> 00:55:39,785
LOCK AT THAT SINGLE CELL BREAST

1885
00:55:39,785 --> 00:55:41,086
CANCER DATASET I SHOWED YOU.

1886
00:55:41,086 --> 00:55:43,188
I WOULD TAKE MARKERS NOW FROM

1887
00:55:43,188 --> 00:55:43,522
THAT.

1888
00:55:43,522 --> 00:55:47,026
I GENERATED ZERO DATA FOR THAT

1889
00:55:47,026 --> 00:55:47,459
PAPER.

1890
00:55:47,459 --> 00:55:48,694
BUT REALLY HAVING A BASELINE,

1891
00:55:48,694 --> 00:55:50,195
THAT DOESN'T MEAN ALWAYS RELYING

1892
00:55:50,195 --> 00:55:51,096
ON COMPUTATIONAL PEOPLE.

1893
00:55:51,096 --> 00:55:54,366
I WOULD SAY EVEN MORE MY LAB I

1894
00:55:54,366 --> 00:55:56,869
RELY -- EVEN FOR MY LAB I RELY

1895
00:55:56,869 --> 00:55:59,838
HEAVILY ON THE BIOLOGISTS, I

1896
00:55:59,838 --> 00:56:00,839
DON'T WANT TO KEEP GOING INTO

1897
00:56:00,839 --> 00:56:01,373
THE WEEDS.

1898
00:56:01,373 --> 00:56:03,676
I WOULD SAY SOME LEVEL OF A

1899
00:56:03,676 --> 00:56:04,877
WHOLE TRANSCRIPTOME AND WHEN YOU

1900
00:56:04,877 --> 00:56:07,179
HAVE THAT CONFIDENTLY I WOULD DO

1901
00:56:07,179 --> 00:56:10,349
COSMICS OR XENIUM AND NOW I'M

1902
00:56:10,349 --> 00:56:14,253
DOING A LOT OF ACOOYA.

1903
00:56:14,253 --> 00:56:16,522
I DIDN'T HAVE TIME FOR THAT, I'M

1904
00:56:16,522 --> 00:56:19,158
HAPPY TO COME BACK, AND REALLY

1905
00:56:19,158 --> 00:56:20,125
INTRODUCING HOW MUCH DO YOU NEED

1906
00:56:20,125 --> 00:56:21,093
AND HOW MUCH CORRELATION IS

1907
00:56:21,093 --> 00:56:21,293
THERE.

1908
00:56:21,293 --> 00:56:24,163
>> YES, MY NAME IS AMY,
I'M

1909
00:56:24,163 --> 00:56:26,265
LEADING THE SPATIAL IMAGING

1910
00:56:26,265 --> 00:56:29,435
TECHNOLOGY RESOURCE AND WE HAVE

1911
00:56:29,435 --> 00:56:33,839
COS MECHANICS GEOMICS THAT WE

1912
00:56:33,839 --> 00:56:36,241
RUN AND HAVE CODEX IN THE NEW

1913
00:56:36,241 --> 00:56:37,443
SYSTEM AND THESE ARE JUST

1914
00:56:37,443 --> 00:56:38,978
BEAUTIFUL DATA AND YOU ARE

1915
00:56:38,978 --> 00:56:41,213
SHOWING AN EXAMPLE OF THE

1916
00:56:41,213 --> 00:56:42,548
DIFFERENCE BETWEEN U.S. BLACK

1917
00:56:42,548 --> 00:56:44,950
AND CARIBBEAN BLACK IN ONE

1918
00:56:44,950 --> 00:56:47,119
EXAMPLE HIGHLIGHTING THE

1919
00:56:47,119 --> 00:56:48,153
NEIGHBORHOODS.

1920
00:56:48,153 --> 00:56:49,755
BUT WHAT DO YOU THINK, HOW MANY

1921
00:56:49,755 --> 00:56:51,390
SAMPLES DO YOU NEED JUST TO

1922
00:56:51,390 --> 00:56:53,592
REALLY GET TO WHAT IS DRIVING

1923
00:56:53,592 --> 00:56:55,861
THE DIFFERENCES OR WHAT CAN BE

1924
00:56:55,861 --> 00:56:58,630
ONE MECHANISM DRIVING THE

1925
00:56:58,630 --> 00:57:00,566
DIFFERENCES BECAUSE IF YOU RUN

1926
00:57:00,566 --> 00:57:03,736
TEN BIOPSIES FROM OTHER SAMPLES,

1927
00:57:03,736 --> 00:57:06,238
WE SEE DIFFERENCES, RIGHT,

1928
00:57:06,238 --> 00:57:08,874
DEPENDING ON WHERE WE HIT AND

1929
00:57:08,874 --> 00:57:11,076
OBVIOUSLY WHERE THE SAMPLES COME

1930
00:57:11,076 --> 00:57:12,311
FROM, THE PATIENTS THEY COME

1931
00:57:12,311 --> 00:57:12,511
FROM.

1932
00:57:12,511 --> 00:57:14,380
>> YEAH, THAT'S KIND OF
WHY I

1933
00:57:14,380 --> 00:57:16,348
STARTED TO SHOW IT, BUT WE'RE

1934
00:57:16,348 --> 00:57:18,984
PRETTY MUCH DOING HIGH LEVEL

1935
00:57:18,984 --> 00:57:21,153
KIND OF TISSUE ANNOTATION, SO

1936
00:57:21,153 --> 00:57:22,354
BACK TO YOUR QUESTION.

1937
00:57:22,354 --> 00:57:26,558
AND THEN WE GO AFTER TMA'S.

1938
00:57:26,558 --> 00:57:29,395
SO OUR LAST WAS 600 IT WAS A

1939
00:57:29,395 --> 00:57:31,830
CLINICAL TRIAL FROM MSK WE DID

1940
00:57:31,830 --> 00:57:32,898
600 PATIENTS.

1941
00:57:32,898 --> 00:57:33,999
PATIENTS MUST LOOK LIKE

1942
00:57:33,999 --> 00:57:35,734
THEMSELVES EVEN WHEN A

1943
00:57:35,734 --> 00:57:36,835
RECURRENCE HAPPENS BUT WHEN WE

1944
00:57:36,835 --> 00:57:38,604
HAVE THAT MUCH DATA THERE'S NO

1945
00:57:38,604 --> 00:57:40,105
LEVEL OF -- LIKE THE ONLY THING

1946
00:57:40,105 --> 00:57:42,508
CLOSE TO THAT WOULD BE TCGA

1947
00:57:42,508 --> 00:57:44,243
LEVEL DATA AND THAT TOOK THEM 20

1948
00:57:44,243 --> 00:57:44,710
YEARS TO DO.

1949
00:57:44,710 --> 00:57:46,345
I WOULD SAY YOU WOULD HAVE THAT,

1950
00:57:46,345 --> 00:57:48,213
IT'S A COLLECTION MAKING THE

1951
00:57:48,213 --> 00:57:50,716
TMA'S BUT EVEN IN THESE SMALL

1952
00:57:50,716 --> 00:57:53,685
CORES WE GET 70,000 OR 100,000

1953
00:57:53,685 --> 00:57:55,954
CELLS, SO IN TERMS OF NUMBERS, I

1954
00:57:55,954 --> 00:57:57,389
MEAN, DON'T QUOTE ME ON THIS,

1955
00:57:57,389 --> 00:57:58,590
BECAUSE LIKE PEOPLE ASK ME ALL

1956
00:57:58,590 --> 00:58:00,125
THE TIME FOR LETTERS OF SUPPORT

1957
00:58:00,125 --> 00:58:00,993
ARE DATA.

1958
00:58:00,993 --> 00:58:03,295
WE'RE NOT THERE COMPUTATIONALLY

1959
00:58:03,295 --> 00:58:03,462
YET.

1960
00:58:03,462 --> 00:58:05,364
I WOULD SAY WE'VE DONE IT WITH

1961
00:58:05,364 --> 00:58:09,868
LIKE ENDS OF 4 FOR VISIUM OR

1962
00:58:09,868 --> 00:58:11,437
GENOMICS, THEN WE'VE PROFILED

1963
00:58:11,437 --> 00:58:15,107
AND GOTTEN OUR PROBE SET N OF 4

1964
00:58:15,107 --> 00:58:17,843
OR '6EVERYTHING HAS TO HAPPEN IN

1965
00:58:17,843 --> 00:58:20,145
END OF TWO ON A 10X SYSTEM AND

1966
00:58:20,145 --> 00:58:22,147
GONE TO PROBE SETS AT THAT

1967
00:58:22,147 --> 00:58:22,414
POINT.

1968
00:58:22,414 --> 00:58:23,849
PROBE SETS WITH SPATIAL

1969
00:58:23,849 --> 00:58:26,852
TOUCHSTONE IS NOT A SMALL FEAT,

1970
00:58:26,852 --> 00:58:28,053
BUT EVEN I THINK ONCE YOU HAVE

1971
00:58:28,053 --> 00:58:29,688
KIND OF A SET TISSUE TYPE,

1972
00:58:29,688 --> 00:58:31,090
YOU'LL BE BETTER AT IT.

1973
00:58:31,090 --> 00:58:32,724
SO LIKE ONCE YOU'VE KIND OF

1974
00:58:32,724 --> 00:58:33,892
INVESTED IN PROSTATE CANCER AND

1975
00:58:33,892 --> 00:58:35,260
YOU KNOW THATLY WITH, PROSTATE

1976
00:58:35,260 --> 00:58:36,795
CANCER IS REALLY LOW TRANSCRIPT,

1977
00:58:36,795 --> 00:58:38,864
SO WHEN WE FIRST STARTED DOING,

1978
00:58:38,864 --> 00:58:41,934
WE'RE LIKE THIS IS AWFUL.

1979
00:58:41,934 --> 00:58:43,368
BUT HUMAN BRAIN IS LIKE, THERE'S

1980
00:58:43,368 --> 00:58:45,003
A REASON WHY EVERY SINGLE

1981
00:58:45,003 --> 00:58:46,772
COMPANY SHOWS A MOUSE BRAIN,

1982
00:58:46,772 --> 00:58:47,106
RIGHT?

1983
00:58:47,106 --> 00:58:48,674
BECAUSE A GENE, YOU SAW THAT,

1984
00:58:48,674 --> 00:58:49,942
EVERY GENE IS LITERALLY

1985
00:58:49,942 --> 00:58:51,076
EXPRESSED IN THE BRAIRCH AT SOME

1986
00:58:51,076 --> 00:58:52,244
POINT IN TIME.

1987
00:58:52,244 --> 00:58:54,012
-- BRAIN AT SOME POINT IN TIME.

1988
00:58:54,012 --> 00:58:55,781
SO I WOULD SAY THAT IS A FEW IN

1989
00:58:55,781 --> 00:58:57,282
THE SEQUENCING REALM AND THEN

1990
00:58:57,282 --> 00:59:00,452
MAYBE IN THE TENS TO 20s IN

1991
00:59:00,452 --> 00:59:01,386
THE PROBE LAND.

1992
00:59:01,386 --> 00:59:02,888
BUT DON'T QUOTE ME ON THAT.

1993
00:59:02,888 --> 00:59:04,523
THAT'S A LOT OF MONEY TOO WHEN I

1994
00:59:04,523 --> 00:59:06,191
SAY THAT, LIKE EACH SLIDE IN THE

1995
00:59:06,191 --> 00:59:09,161
PROBE LAND IS $4,000.

1996
00:59:09,161 --> 00:59:09,394
YEAH.

1997
00:59:09,394 --> 00:59:11,096
THAT'S WHY I SAY THAT.

1998
00:59:11,096 --> 00:59:12,831
I WOULD LIKE HUNDREDS BUT I'M

1999
00:59:12,831 --> 00:59:13,832
BEING PRACTICAL TO THE EEM IN

2000
00:59:13,832 --> 00:59:14,233
THE ROOM.

2001
00:59:14,233 --> 00:59:16,335
>> AND -- TO THE PEOPLE IN
THE

2002
00:59:16,335 --> 00:59:16,535
ROOM.

2003
00:59:16,535 --> 00:59:18,837
>> CAN YOU COMMENT ON THE
TME'S?

2004
00:59:18,837 --> 00:59:22,040
FOR EXAMPLE YOU SHOWED THE VERY

2005
00:59:22,040 --> 00:59:22,941
LOCALIZED INTERACTION BETWEEN

2006
00:59:22,941 --> 00:59:25,210
THE MACROPHAGE PS AND THE

2007
00:59:25,210 --> 00:59:25,511
FIBROBLASTS.

2008
00:59:25,511 --> 00:59:28,413
WHAT IF YOU ARE MISSING THAT?

2009
00:59:28,413 --> 00:59:30,382
AND I DON'T KNOW HOW MANY

2010
00:59:30,382 --> 00:59:31,683
CORES -- INTLO WELL, THAT'S WHY

2011
00:59:31,683 --> 00:59:33,018
YOU DO 600, RIGHT?

2012
00:59:33,018 --> 00:59:34,987
THAT WAS FOUR SLIDES WHEN WE DID

2013
00:59:34,987 --> 00:59:35,187
600.

2014
00:59:35,187 --> 00:59:36,622
SO IT'S AFFORDABLE.

2015
00:59:36,622 --> 00:59:38,991
SO IF I'M MISSING TBHUN A

2016
00:59:38,991 --> 00:59:40,492
PATIENT, I'LL LEARN ACTUALLY

2017
00:59:40,492 --> 00:59:43,295
WHEN YOU TALK ABOUT TUMOR

2018
00:59:43,295 --> 00:59:44,029
HETEROGENEITY THAT'S HOW YOU

2019
00:59:44,029 --> 00:59:45,264
WOULD DO IT BECAUSE YOU HAVE

2020
00:59:45,264 --> 00:59:45,831
DIFFERENT PUNCHES.

2021
00:59:45,831 --> 00:59:47,466
THAT'S WHEN YOU PULL THE

2022
00:59:47,466 --> 00:59:49,735
PATHOLOGISTS IN THE ROOM, I'M

2023
00:59:49,735 --> 00:59:52,371
LUCKY AT ST. JUDE THAT WE HAVE

2024
00:59:52,371 --> 00:59:55,774
FOUR IN HOUSE PATHOLOGISTS,

2025
00:59:55,774 --> 00:59:57,543
THEY'RE REALLY GOOD AT

2026
00:59:57,543 --> 00:59:59,178
GETTING -- VET PATHOLOGISTS,

2027
00:59:59,178 --> 01:00:00,279
THEY'RE REALLY GOOD AT LOOKING

2028
01:00:00,279 --> 01:00:02,014
AT HUMAN STUFF BECAUSE THEY'RE

2029
01:00:02,014 --> 01:00:04,082
LOOKING AT MOUSE AND

2030
01:00:04,082 --> 01:00:06,418
INTERPRETATION AND PDX MODEL SO

2031
01:00:06,418 --> 01:00:08,687
IN THAT SENSE I SPEND A LOFT

2032
01:00:08,687 --> 01:00:10,656
TIME IN THE ROOM WITH THE

2033
01:00:10,656 --> 01:00:10,989
PATHOLOGISTS.

2034
01:00:10,989 --> 01:00:13,292
THEY SPENT YEARS AND YEARS OF

2035
01:00:13,292 --> 01:00:15,260
THEIR FELLOWSHIP BEING MINI

2036
01:00:15,260 --> 01:00:15,761
COMPUTERS.

2037
01:00:15,761 --> 01:00:18,030
SO WHEN YOU LOOK AT THE TUMORS

2038
01:00:18,030 --> 01:00:19,531
AND BACK TO YOUR QUESTION WHAT

2039
01:00:19,531 --> 01:00:20,866
PART OF THE TUMOR DO YOU TAKE?

2040
01:00:20,866 --> 01:00:22,167
WE DON'T REALLY KNOW.

2041
01:00:22,167 --> 01:00:23,702
WE'RE BIOLOGISTS.

2042
01:00:23,702 --> 01:00:25,304
PATHOLOGISTS CAN TELL YOU, THEY

2043
01:00:25,304 --> 01:00:27,573
HAVE AT LEAST A GEOGRAPHICAL

2044
01:00:27,573 --> 01:00:29,374
LOCATION THAT MIGHT BE THE SAME,

2045
01:00:29,374 --> 01:00:29,841
RIGHT?

2046
01:00:29,841 --> 01:00:31,376
DOESN'T HAVE MOLECULAR PROFILING

2047
01:00:31,376 --> 01:00:33,245
SO WHEN YOU MAKE THAT TMA IT'S

2048
01:00:33,245 --> 01:00:35,214
NOT JUST DON'T GO PUNCH THE

2049
01:00:35,214 --> 01:00:37,616
TUMOR, LIKE THAT WAS SIX

2050
01:00:37,616 --> 01:00:38,717
PATHOLOGISTS, BUT THAT'S FOUR

2051
01:00:38,717 --> 01:00:41,119
YEARS OLD THAT ACOYA DATA,

2052
01:00:41,119 --> 01:00:41,320
RIGHT?

2053
01:00:41,320 --> 01:00:42,854
YOU HAVE TO SIT WITH THE

2054
01:00:42,854 --> 01:00:44,156
PATHOLOGISTS AND GET THEM TO SAY

2055
01:00:44,156 --> 01:00:45,691
THIS IS WHAT I THINK IT IS AND

2056
01:00:45,691 --> 01:00:47,559
MAKE THE TMA AND MAYBE IT WILL

2057
01:00:47,559 --> 01:00:49,661
TAKE LONGER BUT THEN LOOK AT HOW

2058
01:00:49,661 --> 01:00:50,963
MUCH DATA YOU GET, RIGHT?

2059
01:00:50,963 --> 01:00:52,931
SO IF THERE'S A VARIANCE, LIKELY

2060
01:00:52,931 --> 01:00:54,800
IT'S THAT PATIENT AND MAYBE IT'S

2061
01:00:54,800 --> 01:00:56,335
THEY'RE GOING TO BE A

2062
01:00:56,335 --> 01:00:57,069
NONRESPONDER BECAUSE THEY DON'T

2063
01:00:57,069 --> 01:00:59,071
HAVE THE RIGHT IMMUNE

2064
01:00:59,071 --> 01:00:59,605
BACKGROUND.

2065
01:00:59,605 --> 01:01:01,039
BUT AT LEAST THAT LAYER HELPS

2066
01:01:01,039 --> 01:01:03,442
YOU WITH THE CYTOARCHITECTURE

2067
01:01:03,442 --> 01:01:04,743
THAT THEY'RE SEEING THE SAME

2068
01:01:04,743 --> 01:01:04,943
THING.

2069
01:01:04,943 --> 01:01:06,411
THEN YOU HAVE TO START WITH A

2070
01:01:06,411 --> 01:01:07,012
HYPOTHESIS, RIGHT?

2071
01:01:07,012 --> 01:01:09,147
SO IF YOU THINK THERE'S A

2072
01:01:09,147 --> 01:01:11,250
REGIONALIZATION OF CALFS, THEN

2073
01:01:11,250 --> 01:01:13,285
MARK CALFS, DO A SERIAL SECTION,

2074
01:01:13,285 --> 01:01:15,354
GET THEM TO STAIN AND PUNCH FROM

2075
01:01:15,354 --> 01:01:17,389
THERE, RIGHT 1234 SO I WOULD SAY

2076
01:01:17,389 --> 01:01:19,558
THERE ARE WAYS AND APPROACHES

2077
01:01:19,558 --> 01:01:21,426
THAT, BUT THERE HAS TO BE

2078
01:01:21,426 --> 01:01:22,628
LEARNED EXPERIENCE, THIS IS TO

2079
01:01:22,628 --> 01:01:23,962
THE SCIENTISTS IN THE ROOM.

2080
01:01:23,962 --> 01:01:25,797
DON'T THINK YOU CAN JUST GO INTO

2081
01:01:25,797 --> 01:01:26,565
THE BIO BANK AND PULL.

2082
01:01:26,565 --> 01:01:27,232
YOU CAN.

2083
01:01:27,232 --> 01:01:30,168
BUT I GUARANTEE YOU'LL HAVE MUCH

2084
01:01:30,168 --> 01:01:31,837
BETTER QUALITY OF DATA AND NOT

2085
01:01:31,837 --> 01:01:33,338
MAKE A MISTAKE AND HAVE LIKE SIX

2086
01:01:33,338 --> 01:01:34,873
YEARS OF DATA YOU CAN LOOK AT,

2087
01:01:34,873 --> 01:01:35,073
RIGHT?

2088
01:01:35,073 --> 01:01:36,875
I WOULD SAY THAT IT'S WORTH THE

2089
01:01:36,875 --> 01:01:39,945
ADDED TIME IN THE BEGINNING.

2090
01:01:39,945 --> 01:01:40,245
YES?

2091
01:01:40,245 --> 01:01:41,013
>> WONDERFUL TALK.

2092
01:01:41,013 --> 01:01:42,914
I MEAN, THIS IS KIND OF LIKE MY

2093
01:01:42,914 --> 01:01:44,116
LOVE LANGUAGE THAT YOU'RE

2094
01:01:44,116 --> 01:01:44,883
TALKING ABOUT.

2095
01:01:44,883 --> 01:01:46,451
>> DON'T TELL MY HUSBAND.

2096
01:01:46,451 --> 01:01:48,487
>> SO WHAT ARE YOUR
THOUGHTS

2097
01:01:48,487 --> 01:01:50,055
ABOUT UTILIZING YOUR APPROACHES,

2098
01:01:50,055 --> 01:01:52,457
WHETHER IT BE SPATIAL

2099
01:01:52,457 --> 01:01:54,726
PROTEOMICS, TRANSCRIPTOMICS OR

2100
01:01:54,726 --> 01:01:56,595
IDEALLY THE COMBINATION OF BOTH

2101
01:01:56,595 --> 01:02:01,433
TO REALLY LOOK AT THINGS SUCH AS

2102
01:02:01,433 --> 01:02:03,502
GRAFT VERSUS HOST, TRANSPLANT

2103
01:02:03,502 --> 01:02:04,703
REJECTION, I THINK THAT WOULD BE

2104
01:02:04,703 --> 01:02:05,937
ACTUALLY KIND OF INTERESTING.

2105
01:02:05,937 --> 01:02:06,672
HAVE YOU EVER THOUGHT ABOUT

2106
01:02:06,672 --> 01:02:07,372
DOING ANYTHING?

2107
01:02:07,372 --> 01:02:09,074
>> OH, WE'RE HACKING THAT
NOW,

2108
01:02:09,074 --> 01:02:10,342
SO DON'T TELL ANY OF THE

2109
01:02:10,342 --> 01:02:10,776
COMPANIES.

2110
01:02:10,776 --> 01:02:12,611
OH, YEAH, WE DO IT ALL THE TIME

2111
01:02:12,611 --> 01:02:12,778
NOW.

2112
01:02:12,778 --> 01:02:13,378
>> OKAY.

2113
01:02:13,378 --> 01:02:16,882
>> SO THE BEAUTY OF OLD
CLASSIC

2114
01:02:16,882 --> 01:02:18,317
VEZIUM IS FOR THIS, SO YOU DON'T

2115
01:02:18,317 --> 01:02:20,252
REALLY HAVE TO HACK IT, YOU CAN

2116
01:02:20,252 --> 01:02:22,587
PUT IN, LIKE WE DID IT IN COVID,

2117
01:02:22,587 --> 01:02:25,624
WE PUT COVID PROBES IN 1K3 THEN

2118
01:02:25,624 --> 01:02:28,527
JUST ALIGN CDNA TO WHATEVER AND

2119
01:02:28,527 --> 01:02:32,464
WITH VEZM YOU JUST PUT PROBES IF

2120
01:02:32,464 --> 01:02:34,266
THE INSTALL IS LOW, YOU HAVE ANY

2121
01:02:34,266 --> 01:02:36,868
SPECIES AND YOU ALIGN IT WHETHER

2122
01:02:36,868 --> 01:02:38,003
THAT'S VIRUS OR WHATEVER.

2123
01:02:38,003 --> 01:02:39,504
SO WE CAN DO THAT.

2124
01:02:39,504 --> 01:02:42,808
I FIGURED OUT HOW TO DO IT IN

2125
01:02:42,808 --> 01:02:43,442
XENIUM I THINK.

2126
01:02:43,442 --> 01:02:45,210
BUT LIKE YOU CAN GET THEM TO

2127
01:02:45,210 --> 01:02:46,945
MAKE CUSTOM PANELS AND YOU CAN

2128
01:02:46,945 --> 01:02:48,146
DO WHATEVER SPECIES YOU WANT

2129
01:02:48,146 --> 01:02:49,348
INTO IT, RIGHT?

2130
01:02:49,348 --> 01:02:51,316
SO I THINK IT'S IN ONE BEAUTIFUL

2131
01:02:51,316 --> 01:02:52,884
THING EVEN WITH BACTERIAS YOU

2132
01:02:52,884 --> 01:02:55,854
CAN SEE IT IN THE HNE SO IT

2133
01:02:55,854 --> 01:02:57,289
DOESN'T EVEN HAVE TO COST YOU.

2134
01:02:57,289 --> 01:02:58,790
MAYBE YOU DON'T EVEN CARE B YOU

2135
01:02:58,790 --> 01:03:00,525
JUST WANT TO IDENTIFY AN IMAGE

2136
01:03:00,525 --> 01:03:03,061
REGISTER WHERE THE HOST IS, AND

2137
01:03:03,061 --> 01:03:06,365
WE SEE BEAUTIFUL THINGS I KNOW

2138
01:03:06,365 --> 01:03:09,434
NOTHING ABOUT INFECTION,

2139
01:03:09,434 --> 01:03:10,168
PLEURIFIC BEAUTIFUL THINGS.

2140
01:03:10,168 --> 01:03:11,737
THE IDEA THAT THERE'S AN INITIAL

2141
01:03:11,737 --> 01:03:13,004
T CELL RESPONSE, IT'S ACTUALLY

2142
01:03:13,004 --> 01:03:15,574
NOT THERE, IT'S NOT LOCALIZED TO

2143
01:03:15,574 --> 01:03:17,309
THE BACTERIA RKTS IT'S KIND OF

2144
01:03:17,309 --> 01:03:18,276
LOCALIZED HERE, YOU HAVE

2145
01:03:18,276 --> 01:03:19,177
EVERYTHING HERE AND SOMETHING

2146
01:03:19,177 --> 01:03:20,445
HAPPENS IN RESPONSE IN THAT PART

2147
01:03:20,445 --> 01:03:21,713
OF THE TISSUE.

2148
01:03:21,713 --> 01:03:22,881
SO WE ARE SEEING THAT AND I

2149
01:03:22,881 --> 01:03:23,982
THINK THIS IS THE WAY FORWARD

2150
01:03:23,982 --> 01:03:25,951
FOR SURE.

2151
01:03:25,951 --> 01:03:32,224
>> THANKS.

2152
01:03:32,224 --> 01:03:33,125
>> HI.

2153
01:03:33,125 --> 01:03:33,925
THANK YOU.

2154
01:03:33,925 --> 01:03:35,694
GREAT TALK.

2155
01:03:35,694 --> 01:03:39,865
I HAVE JUST TWO QUESTIONS FOR

2156
01:03:39,865 --> 01:03:41,299
THE FUTURE.

2157
01:03:41,299 --> 01:03:42,934
THE FIRST ONE SOMETHING THAT WE

2158
01:03:42,934 --> 01:03:47,439
ARE STRUGGLING RIGHT NOW, AND IN

2159
01:03:47,439 --> 01:03:49,040
THIS WHOLE SPATIAL STUDIES,

2160
01:03:49,040 --> 01:03:50,742
YOU'RE SHOWING ALL THESE NICE

2161
01:03:50,742 --> 01:03:52,010
CORRELATION THAT IT SEEMED TO

2162
01:03:52,010 --> 01:03:55,313
MAKE SENSE WHEN YOU SHOW ONE

2163
01:03:55,313 --> 01:03:57,249
TISSUE VERSUS THIS TISSUE AND WE

2164
01:03:57,249 --> 01:03:59,017
CAN SEE OH YEAH THESE CELLS ARE

2165
01:03:59,017 --> 01:04:00,352
HERE, THESE OTHER CELLS ARE

2166
01:04:00,352 --> 01:04:00,552
HERE.

2167
01:04:00,552 --> 01:04:02,788
BUT WHEN YOU START TO LOOK AT

2168
01:04:02,788 --> 01:04:05,924
600 PATIENTS OR 600 DATA, THEN

2169
01:04:05,924 --> 01:04:08,126
YOU HAVE THESE NICHES MEASURE AT

2170
01:04:08,126 --> 01:04:09,594
DIFFERENT LEVELS HERE AND

2171
01:04:09,594 --> 01:04:12,631
DIFFERENT LEVELS HERE, AND I AM

2172
01:04:12,631 --> 01:04:14,733
CURRENTLY MISSING, BECAUSE I AM

2173
01:04:14,733 --> 01:04:17,803
NOT A COMPUTATIONAL BIOLOGY OR A

2174
01:04:17,803 --> 01:04:19,004
STATISTICIAN, THE TOOLS THAT

2175
01:04:19,004 --> 01:04:23,308
WILL ALLOW YOU TO ACTUALLY

2176
01:04:23,308 --> 01:04:27,112
QUANTIFY THOSE THINGS AND

2177
01:04:27,112 --> 01:04:29,748
ESTABLISH THESE NICHES ARE TRULY

2178
01:04:29,748 --> 01:04:31,483
DIFFERENT IN THESE SET OF

2179
01:04:31,483 --> 01:04:32,918
PATIENTS VERSUS THESE OTHER SET

2180
01:04:32,918 --> 01:04:33,752
OF PATIENTS.

2181
01:04:33,752 --> 01:04:35,153
I DON'T KNOW IF THAT'S CLEAR FOR

2182
01:04:35,153 --> 01:04:35,320
YOU.

2183
01:04:35,320 --> 01:04:37,289
>> SO I WOULD SAY, AGAIN,
LEARN

2184
01:04:37,289 --> 01:04:39,057
FROM MY MISTAKE.

2185
01:04:39,057 --> 01:04:41,259
THEY'RE NOT MISTAKES ACTUALLY

2186
01:04:41,259 --> 01:04:48,333
BECAUSE I USE BIOSTAT --

2187
01:04:48,333 --> 01:04:50,635
BIOSTATISTICIAN BUT THE LAB

2188
01:04:50,635 --> 01:04:52,270
BIOLOGIST IS GOING TO LOOK AT IT

2189
01:04:52,270 --> 01:04:56,541
THE SAME WAY AS AN IMAGING BASED

2190
01:04:56,541 --> 01:05:01,179
TRAINED PERSON 6789 SMFTD MOST

2191
01:05:01,179 --> 01:05:02,781
BEAUTIFUL AL G ALGORITHMS ARE CG

2192
01:05:02,781 --> 01:05:03,882
FROM SATELLITE PEOPLE BECAUSE

2193
01:05:03,882 --> 01:05:05,851
THE TOOLS I USE NOW, THERE'S A

2194
01:05:05,851 --> 01:05:07,486
WHOLE ARGUMENT ABOUT CELL

2195
01:05:07,486 --> 01:05:08,787
AUGMENTATION BLAH BLAH BLAH I

2196
01:05:08,787 --> 01:05:10,121
DON'T EVEN DO THAT ANYMORE

2197
01:05:10,121 --> 01:05:11,756
BECAUSE I JUST HIRE PEOPLE THAT

2198
01:05:11,756 --> 01:05:12,891
LITERALLY THEY DON'T EVEN KNOW

2199
01:05:12,891 --> 01:05:14,626
WHAT A CELL IS, THEY DON'T EVEN

2200
01:05:14,626 --> 01:05:16,394
KNOW WHAT A MOLECULAR BARCODE

2201
01:05:16,394 --> 01:05:18,530
IS, BUT THEY WILL DRAINTD SETS

2202
01:05:18,530 --> 01:05:20,432
TO TELL UH-OH, WELL, THINK ABOUT

2203
01:05:20,432 --> 01:05:22,300
T THEY'RE JUST PIXELS, THEY'RE

2204
01:05:22,300 --> 01:05:24,603
LIKE OH YEAH YEAH THEY'RE JUST

2205
01:05:24,603 --> 01:05:25,570
PIXELS, THEY HAVE DIFFERENT

2206
01:05:25,570 --> 01:05:27,105
COLORS, YOU CAN PUT WHATEVER

2207
01:05:27,105 --> 01:05:28,306
CHANNEL YOU WANT BUT OVER TIME

2208
01:05:28,306 --> 01:05:29,741
THEY CAN NORMALIZE THAT DATA

2209
01:05:29,741 --> 01:05:31,376
BECAUSE THEY'VE NORMALIZED IT

2210
01:05:31,376 --> 01:05:36,214
ACROSS THE ENTIRE SLIDE.

2211
01:05:36,214 --> 01:05:37,849
THEY'RE AGNOSTIC TO THE IDEA OF

2212
01:05:37,849 --> 01:05:39,718
PATIENT ONE VERSUS PATIENT TWO

2213
01:05:39,718 --> 01:05:41,453
VERSUS PATIENT THREE.

2214
01:05:41,453 --> 01:05:42,787
SO THINK ABOUT IT IN TERMS OF

2215
01:05:42,787 --> 01:05:43,321
YOUR PHONE.

2216
01:05:43,321 --> 01:05:46,591
SO YOUR PHONE CAN AUTO FOCUS AND

2217
01:05:46,591 --> 01:05:48,894
AUTO REGULATE HIGH LIGHT, LOW

2218
01:05:48,894 --> 01:05:49,227
LIGHT.

2219
01:05:49,227 --> 01:05:50,862
THAT'S THE LEVEL OF COMPUTATION

2220
01:05:50,862 --> 01:05:51,563
THEY'RE DOING.

2221
01:05:51,563 --> 01:05:53,698
THEY'RE NOT DOING IT DID CD4

2222
01:05:53,698 --> 01:05:55,800
KIND OF COME UP?

2223
01:05:55,800 --> 01:05:56,001
RIGHT?

2224
01:05:56,001 --> 01:05:56,902
I THINK THAT'S THE PROBLEM WITH

2225
01:05:56,902 --> 01:05:58,470
SOME OF THE SOFTWARES COMING OFF

2226
01:05:58,470 --> 01:06:00,605
THE MACHINES IS THEY'RE BEING

2227
01:06:00,605 --> 01:06:05,410
LEARNED BY GENOMISISTS, HOW MANY

2228
01:06:05,410 --> 01:06:07,178
TRANSCRIPTS WERE IN THAT CELL.

2229
01:06:07,178 --> 01:06:09,247
EVERY COMPANY HATES TALKING TO

2230
01:06:09,247 --> 01:06:10,682
ME BECAUSE I KEEP ASKING WHAT

2231
01:06:10,682 --> 01:06:11,683
ABOUT THE NEBULA?

2232
01:06:11,683 --> 01:06:13,351
I DON'T SHOW YOU THAT DATA BUT

2233
01:06:13,351 --> 01:06:14,753
WHEN YOU TAKE THE TISSUE WHEN

2234
01:06:14,753 --> 01:06:16,187
YOU RUN THESE MACHINES THERE ARE

2235
01:06:16,187 --> 01:06:17,489
PROBES IN THE BLACK PART THAT

2236
01:06:17,489 --> 01:06:20,325
HAS NO TISSUE SO FOR ME IF

2237
01:06:20,325 --> 01:06:21,459
THERE'S 64 PROBES THERE THEN HOW

2238
01:06:21,459 --> 01:06:23,428
CAN I RELY ON THE COUNT IN MY

2239
01:06:23,428 --> 01:06:23,728
CELL?

2240
01:06:23,728 --> 01:06:24,896
THEY'RE LIKE OH JUST DON'T LOOK

2241
01:06:24,896 --> 01:06:26,131
AT THAT.

2242
01:06:26,131 --> 01:06:27,666
LITERALLY WHAT I'VE BEEN TOLD.

2243
01:06:27,666 --> 01:06:27,866
RIGHT?

2244
01:06:27,866 --> 01:06:29,467
SO I WOULD SAY THE APPROACHES

2245
01:06:29,467 --> 01:06:32,370
ARE COMING, WE HOPE EVEN WITH

2246
01:06:32,370 --> 01:06:33,371
GESTALT PEOPLE DEPOSIT DATA

2247
01:06:33,371 --> 01:06:35,006
BECAUSE THOSE APPROACHES NEED US

2248
01:06:35,006 --> 01:06:37,175
TO LEARN ON EVERY KIND OF TISSUE

2249
01:06:37,175 --> 01:06:37,842
TYPE, RIGHT?

2250
01:06:37,842 --> 01:06:39,611
SO I WOULD SAY START LOOKING

2251
01:06:39,611 --> 01:06:40,912
INTO THOSE KIND OF THINGS WHERE

2252
01:06:40,912 --> 01:06:43,081
YOU START TO SEE IMAGING BASE

2253
01:06:43,081 --> 01:06:45,650
#-D APPROACHES TOO -- BASED

2254
01:06:45,650 --> 01:06:47,752
APPROACHES TOO, IGNORING THE

2255
01:06:47,752 --> 01:06:48,954
BARCODES AND I THINK WHEN WE

2256
01:06:48,954 --> 01:06:51,356
START FOCUSING ON THAT AND NOT

2257
01:06:51,356 --> 01:06:52,991
GENOMIC TOOLS INTO SPATIAL, THE

2258
01:06:52,991 --> 01:06:54,659
FIELD WILL EVOLVE VERY QUICKLY

2259
01:06:54,659 --> 01:06:55,293
AND IT IS.

2260
01:06:55,293 --> 01:06:56,728
A LOT OF THOSE PEOPLE ARE LIKE

2261
01:06:56,728 --> 01:06:57,696
OH, I CAN DO THAT.

2262
01:06:57,696 --> 01:06:59,331
I JUST CAME FROM AN AI MEETING

2263
01:06:59,331 --> 01:07:00,765
ABOUT THAT WHERE ONCE YOU PLANT

2264
01:07:00,765 --> 01:07:02,867
IT IN THEIR HEAD THAT TISSUE IS

2265
01:07:02,867 --> 01:07:03,868
SPATIAL, THEY'RE LIKE OH, I

2266
01:07:03,868 --> 01:07:05,270
COULD DO THAT.

2267
01:07:05,270 --> 01:07:05,837
SO IT'S COMING.

2268
01:07:05,837 --> 01:07:06,905
>> OKAY.

2269
01:07:06,905 --> 01:07:08,640
AND VERY SHORT, BECAUSE I DON'T

2270
01:07:08,640 --> 01:07:10,108
WANT TO DISTRACT, BUT WHAT IS

2271
01:07:10,108 --> 01:07:14,613
YOUR THOUGHT ABOUT REUSING

2272
01:07:14,613 --> 01:07:15,280
MATERIAL?

2273
01:07:15,280 --> 01:07:19,017
BECAUSE I WORK IN A PLACE WHERE

2274
01:07:19,017 --> 01:07:21,553
BIOPSIES ARE ONE MILLIMETER, TWO

2275
01:07:21,553 --> 01:07:26,558
MILLIMETER, VERY TINY, SMALL,

2276
01:07:26,558 --> 01:07:27,659
AND WE'VE BEEN SPER

2277
01:07:27,659 --> 01:07:29,294
CONVENIENTING AND DOING THINGS

2278
01:07:29,294 --> 01:07:30,395
THAT -- WE'VE BEEN INTERVENING

2279
01:07:30,395 --> 01:07:31,596
AND DOING THINGS THE COMPANY

2280
01:07:31,596 --> 01:07:32,931
DOESN'T USUALLY TELL YOU TO DO.

2281
01:07:32,931 --> 01:07:34,866
>> I WOULD SAY LOOK OUT
FOR THIS

2282
01:07:34,866 --> 01:07:36,167
ONE RIGHT HERE.

2283
01:07:36,167 --> 01:07:37,769
SPATIAL TOUCHTONE PROTOCOLS.

2284
01:07:37,769 --> 01:07:38,336
>> OKAY.

2285
01:07:38,336 --> 01:07:39,704
>> WE'VE TRIED TO INCLUDE

2286
01:07:39,704 --> 01:07:40,639
EVERYTHING BUT OBVIOUSLY WE'RE

2287
01:07:40,639 --> 01:07:42,073
TRYING TO GET IT PUBLISHED RIGHT

2288
01:07:42,073 --> 01:07:43,675
NOW BUT ON THE PROTOCOLS I

2289
01:07:43,675 --> 01:07:44,876
IMAGINE WE'LL INCLUDE MORE BUT

2290
01:07:44,876 --> 01:07:46,111
WE'VE DONE EVERYTHING.

2291
01:07:46,111 --> 01:07:49,047
WE'VE DONE DMAISH SWRO SO YOU

2292
01:07:49,047 --> 01:07:49,247
USE --

2293
01:07:49,247 --> 01:07:53,752
>> VEZIUM, WE'VE DONE THE

2294
01:07:53,752 --> 01:07:55,620
COOLEST ONE, OH, MAYBE I EVEN

2295
01:07:55,620 --> 01:07:57,355
HAVE IT THERE, THE ACTUAL

2296
01:07:57,355 --> 01:07:58,690
CRAPPY, THAT GUY RIGHT THERE.

2297
01:07:58,690 --> 01:08:00,325
SEE THE CRAPPY ONE IN THE

2298
01:08:00,325 --> 01:08:00,759
MIDDLE?

2299
01:08:00,759 --> 01:08:05,430
THAT ACTUALLY GOT RUN ON FULL

2300
01:08:05,430 --> 01:08:08,500
XENIUM RNA PANEL INTO COSMIC'S

2301
01:08:08,500 --> 01:08:08,867
PROTEIN.

2302
01:08:08,867 --> 01:08:09,968
SO LIKE WE'RE THERE.

2303
01:08:09,968 --> 01:08:12,404
LIKE WE CAN DO IT.

2304
01:08:12,404 --> 01:08:15,073
I WOULD SAY THE AMAZING PART

2305
01:08:15,073 --> 01:08:17,175
ABOUT THE ACOYA SYSTEM AND I

2306
01:08:17,175 --> 01:08:18,710
BRAG ABOUT THIS A LOT, IT'S HARD

2307
01:08:18,710 --> 01:08:20,011
TO GET A FUNCTIONING PANEL.

2308
01:08:20,011 --> 01:08:21,646
IF YOU HAVE A FUNCTIONING PANEL,

2309
01:08:21,646 --> 01:08:23,515
THAT THING CAN SIT THERE FOR SIX

2310
01:08:23,515 --> 01:08:23,748
MONTHS.

2311
01:08:23,748 --> 01:08:25,150
YOU CAN TAKE ALL YOUR TIME TO

2312
01:08:25,150 --> 01:08:26,151
ANALYZE YOUR DATA AND YOU WANT

2313
01:08:26,151 --> 01:08:27,652
TO ADD ANOTHER ANTIBODY YOU JUST

2314
01:08:27,652 --> 01:08:29,954
GO BACK, TAKE THE FLOW CELL OFF,

2315
01:08:29,954 --> 01:08:31,723
IT HAS TO HAVE A NEW BARCODE BUT

2316
01:08:31,723 --> 01:08:32,490
THEN YOU CAN STAIN.

2317
01:08:32,490 --> 01:08:33,792
WE'RE DOING A LOT OF THAT NOW.

2318
01:08:33,792 --> 01:08:36,428
SO LIKE, YOU KNOW, THE HNE LIKE

2319
01:08:36,428 --> 01:08:41,132
AFTER XENIUM, THE H -- THE HNE

2320
01:08:41,132 --> 01:08:42,734
AFTER COSMIC SS.

2321
01:08:42,734 --> 01:08:44,202
I WOULD SAY IT'S VERY DIFFICULT

2322
01:08:44,202 --> 01:08:46,438
FOR CORE TO DO, IF CORE SAYS NO

2323
01:08:46,438 --> 01:08:47,906
TO YOU DON'T BE UPSET, THEY

2324
01:08:47,906 --> 01:08:52,711
DON'T WANT TO RUIN YOUR $3,000

2325
01:08:52,711 --> 01:08:55,113
CELL, BUT FLOW SET I'VE TAUGHT

2326
01:08:55,113 --> 01:08:58,750
THEM ON ZOOM OF THE YO YOU CAN T

2327
01:08:58,750 --> 01:09:00,485
TOWF TAKE RAZOR BLADES AND YOU

2328
01:09:00,485 --> 01:09:02,120
HAVE TO DO THINGS CORES DON'T

2329
01:09:02,120 --> 01:09:03,555
WANT TO DO, BUT I WOULD SAY THE

2330
01:09:03,555 --> 01:09:04,556
FIELD IS THERE.

2331
01:09:04,556 --> 01:09:06,391
ALL THE PROTEIN ONES CAN BE

2332
01:09:06,391 --> 01:09:06,591
HELD.

2333
01:09:06,591 --> 01:09:06,991
>> ALL RIGHT.

2334
01:09:06,991 --> 01:09:16,991
THANK YOU.