Author

Tsukasa Shibue

Broad InstituteORCID 0000-0002-3665-6154Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Cancer Biology· 1 Genomics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:differential expression· 2 Organism:human· 2 Research Focus:benchmarking· 1 Assay:CRISPR screen· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Assay:imaging· 1

Frequent Co-Authors

Francisca VázqueziD· 2 shared Abigail Chase· 1 shared Alina SimerziniD· 1 shared Andrew W. NaviaiD· 1 shared Anisah Adeagbo· 1 shared Anne Elizabeth CarpenteriD· 1 shared Berta Escude Velasco· 1 shared Brenton R. PaolellaiD· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersCancer Biology

Francisca VázquezORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Cancer BiologybioRxivPreprintAug 25, 2026

Dissecting context-dependent cancer vulnerabilities using Perturb-seq

Samuel Maffa, Isabella Boyle, Lie Ward…·Joshua M. Dempster

The paper uses Perturb-seq to analyze gene knockouts in cancer cell lines, identifying context-specific gene dependencies and transcriptional responses.

DOI 10.64898/2026.08.24.746802

humanscRNA-seq

GenomicsbioRxivPreprintJul 4, 2026

Direct comparison of CRISPR knockout and interference with Perturb-seq

Laura M. Drepanos, Berta Escude Velasco, Abigail Chase…·John G. Doench

This study compares CRISPR knockout and interference using Perturb-seq, finding consistent transcriptional signatures and tradeoffs in off-target activity and cellular response artifacts.

DOI 10.64898/2026.07.04.736492

CRISPR screenexperimental assay

BioinformaticsMolecular Biology of the CellPeer-reviewedFeb 3, 2021

Predicting cell health phenotypes using image-based morphology profiling

Gregory P. Way, Maria Kost‐Alimova, Tsukasa Shibue…·Shantanu Singh

The paper develops a method to predict cell health phenotypes from Cell Painting images using machine learning, validated across multiple perturbations and assays.

145 citationstop 1%

humanimaging