ArticleNature communications2020
Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 1 of them a synthesis that pooled it.
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Who cites it
92 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on the culture methods and applications of 3D tumoroids for cancer research and personalized medicine.Cellular oncology (Dordrecht, Netherlands) · 2025Pooled it
- Quantifying distribution shifts in single-cell data with scXMatch.Bioinformatics (Oxford, England) · 2026Article
- Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.Genome research · 2026Article
- GEM-GPT Enables Personalized Cell Type-Resolved Therapeutic Design for Systems Pharmacology.bioRxiv : the preprint server for biology · 2026Article
- DECANT: decoupling mechanism from context in single-cell drug perturbation representation.Bioinformatics (Oxford, England) · 2026Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- Active learning-guided mechanistic modeling reveals context-specific regulators of CXCL9 expression in pancreatic cancer cells.Molecular systems biology · 2026Article
- Pre-Existing Heterogeneity Predicts Rare Proteostasis-Stress Programs Across Diverse Perturbations.Biology · 2026Article
- Multiplexed Transcriptomics for Screening Drug Combinations and Defining the Mechanism of Action of HCC Therapeutics at Single-Cell Resolution.Cell proliferation · 2026Article
- Contrastive Dimension Reduction: A Systematic Review.Wiley interdisciplinary reviews. Computational statistics · 2026Article
- Article
- ExPO: an exposure-conditioned neural operator for L1000 signature prediction.Journal of cheminformatics · 2026Article
- PVAED: prior-guided variational autoencoders with diffusion denoising for interpretable single-cell representation learning.Briefings in bioinformatics · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Insights from pooled CRISPRi single-cell screens in K562 cells reveal gene functions, regulatory networks, and highlight opportunities and limitations.BMC genomics · 2026Article
- Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE.bioRxiv : the preprint server for biology · 2026Article
- The GENEVA platform models tumor mosaicism to reveal variations of responses to KRAS inhibitors and identify improved drug combinations.Nature cancer · 2026Article
- Pertpy: an end-to-end framework for perturbation analysis.Nature methods · 2026Article
- Benchmarking algorithms for generalizable single-cell perturbation response prediction.Nature methods · 2026Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from short-term transcriptional responses to treatment.
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Registered trials
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.