Massively parallel quantification of phenotypic heterogeneity in single-cell drug responses.
Abstract
[Figure: see text].
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https://hdl.handle.net/10161/23877Published Version (Please cite this version)
10.1126/sciadv.abf9840Publication Info
(2021). Massively parallel quantification of phenotypic heterogeneity in single-cell drug
responses. Sci Adv, 7(38). pp. eabf9840. 10.1126/sciadv.abf9840. Retrieved from https://hdl.handle.net/10161/23877.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Micah Alan Luftig
Professor of Molecular Genetics and Microbiology
The Luftig laboratory studies viruses that cause cancer with an overarching goal of
defining the basic molecular mechanisms underlying pathogenesis and leveraging these
findings for diagnostic value and therapeutic intervention. Our work primarily focuses
on the common herpesvirus, Epstein-Barr virus (EBV). This virus latently infects virtually
all adults worldwide being acquired early in life. In the immune suppressed, EBV promotes
lymphomas in the B cells that it naturally infects. However, EB
Elliott Sorelle
Postdoctoral Associate
Kris Cameron Wood
Associate Professor of Pharmacology and Cancer Biology
Our laboratory uses genomic and pharmacological approaches to understand how tumor
dependencies are shaped by cell intrinsic factors, environmental factors, and drug
treatments during the dynamic process of tumor evolution. To learn more, please visit
our laboratory website: https://woodlabduke.com/.
Benjamin Yellen
Associate Professor in the Department of Mechanical Engineering and Materials Science
Yellen's group is interested in developing highly parallel mechanisms for controlling
the transport and assembly of ensembles of objects ranging from micron-sized colloidal
particles to single cells. As of 2013, Professor Yellen is active in two main areas
of research:1) Development of single cell analysis tools using magnetic circuits.
The goal of this project is to develop an automated single cell analysis platform
that allows for highly flexible and highly paralle
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