Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView

Jan 1, 2025·
T. kudo
,
A. m. meireles
,
R. moncada
,
Y. chen
,
P. wu
Joshua Gould
Joshua Gould
,
X. hu
,
O. kornfeld
,
R. jesudason
,
C. foo
,
B. hockendorf
,
H. c. bravo
,
J. p. town
,
R. wei
,
A. rios
,
V. chandrasekar
,
M. heinlein
,
A. s. chuong
,
S. cai
,
C. s. lu
,
P. coelho
,
M. mis
,
C. celen
,
N. kljavin
,
J. jiang
,
D. richmond
,
P. thakore
,
E. benito gutierrez
,
K. geiger schuller
Jose Sergio Hleap Lozano
Jose Sergio Hleap Lozano
,
N. kayagaki
,
F. de sousa e melo
,
L. mcginnis
Dr. Bo Li
Dr. Bo Li
,
A. singh
,
L. garraway
,
O. rozenblatt rosen
,
A. regev
,
E. lubeck
· 0 min read
Abstract
Optical pooled screening (OPS) is a scalable method for linking image-based phenotypes with cellular perturbations. However, it has thus far been restricted to relatively low-plex phenotypic readouts in cancer cell lines in culture due to limitations associated with in situ sequencing of perturbation barcodes. Here, we develop PerturbView, an OPS technology that leverages in vitro transcription to amplify barcodes before in situ sequencing, enabling screens with highly multiplexed phenotypic readouts across diverse systems, including primary cells and tissues. We demonstrate PerturbView in induced pluripotent stem cell-derived neurons, primary immune cells and tumor tissue sections from animal models. In a screen of immune signaling pathways in primary bone marrow-derived macrophages, PerturbView uncovered both known and novel regulators of NF-κB signaling. Furthermore, we combine PerturbView with spatial transcriptomics in tissue sections from a mouse xenograft model, paving the way to in situ screens with rich optical and transcriptomic phenotypes. PerturbView broadens the scope of OPS to a wide range of models and applications.
Type
Publication
Nature Biotechnology
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Joshua Gould
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Senior Research Software Engineer
Joshua Gould is a senior research software engineer in Li Lab.
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Jose Sergio Hleap Lozano
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Consultant
Jose Sergio Hleap Lozano is a consultant in Li Lab.
Dr. Bo Li
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Principal Scientist II
Dr. Bo Li is a Principal Scientist II at AI for Biology and Translation (AIBT), Genentech, Inc. His research focuses on three major topics: Science, Technology and Computational Methods. For Science, his team works on lung cancer and especially small cell lung cancer. For Technology, his team evaluates and adopts cutting-edge high-throughput data generation technologies, such as Cellanome and SBX sequencing. For Computational Methods, his team develops novel computational and deep learning tools for enabling insight discovery from high-throughput multi-modal data. Before joining in Genentech, he was an Assistant Professor of Medicine at Harvard Medical School and the director of Bioinformatics and Computational Biology at Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital. He received his Ph.D. in computer science from UW-Madison and completed two postdoctoral trainings with Dr. Lior Pachter at UC Berkeley and Dr. Aviv Regev at Broad Institute. He is best known for developing RSEM, an impactful RNA-seq transcript quantification software. RSEM is cited 22,602 times (Google Scholar) and adopted by several big consortia such as TCGA, ENCODE, GTEx and TOPMed.
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