Evidence map›Paper›PMID 41292737›Full record

ArticlebioRxiv : the preprint server for biology2025

Scalable imaging-based profiling of CRISPR perturbations with protein barcodes.

Krishna Choudhary, Michael T McManus

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Krishna ChoudharyUCSF Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, United States of America.ORCID 0000-0002-7966-1527
Michael T McManusChan Zuckerberg Biohub San Francisco, San Francisco, California, 94158, United States of America.ORCID 0000-0003-3013-6569

Funding

Illuminating Druggable Dark MatterU24DK116214 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JAN, LILY Y, MCMANUS, MICHAEL T · 2017 to 2022
$12.9M
Bay Area Cancer Target Discovery and DevelopmentU01CA272546 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Sourav Bandyopadhyay, Trever G Bivona · 2022 to 2026
$4.8M
NCI NIH HHS U01 CA272546NIDDK NIH HHS U24 DK116214
6 · The paper itself

Abstract

Imaging-based CRISPR screens enable high-content functional genomics by capturing phenotypic changes in cells after genetic perturbation. Protein barcodes provide cost-effective, easy-to-implement, and imaging-compatible barcoding for pooled perturbations, yet their scalability has been constrained by the need for arrayed cloning, lentiviral recombination between barcodes and guides, and difficulties in decoding barcodes with high confidence. Here, we introduce poolVis and cellPool, an integrated experimental and computational platform designed to address these limitations. poolVis uses Cre-lox-mediated reconfiguration to position barcode-sgRNA pairs in proximity during viral integration, which greatly reduces barcode shuffling during pooled cloning and delivery. cellPool leverages a scalable computational workflow and the unique aspects of protein barcodes to produce unpooled image galleries from multi-terabyte scale datasets. Applying this platform to single- and double-CRISPRi profiling of cell-cycle genes and chromokinesins in the MCF10A cells uncovered established and previously unrecognized phenotypes, including nuclear morphology changes and reciprocal sign epistasis in DNA damage.

Indexed as

CRISPR interferencegenetic interaction mappingimaging functional genomicsmitotic gene networkoptical barcoding

Identifiers

PMID41292737
PMCPMC12642500

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.