ArticleCell genomics2024
Multiplex single-cell chemical genomics reveals the kinase dependence of the response to targeted therapy.
Article in Cell genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed, 16 citations in OpenAlex.
- Single-cell mapping of homocysteine-induced perturbations in avian embryogenesis using LMO and BD rhapsody transcriptomics.iScience · 2026Article
- PHAROS: turning single-cell perturbation models into target-directed drug-combination screens.bioRxiv : the preprint server for biology · 2026Article
- Multiplexed single-cell transcriptomics reveals diverse phenotypic outcomes for pathogenic SHP2 variants.Science advances · 2026Article
- Uncovering causal relationships in single-cell omic studies with causarray.Briefings in bioinformatics · 2026Article
- Mapping kinase-dependent tumor immune adaptation with multiplexed single-cell CRISPR screens.bioRxiv : the preprint server for biology · 2026Article
- An expanded role for single-cell chemical genomics profiling in drug discovery.The Biochemical journal · 2026Review
- Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Multi-step genomics on single cells and live cultures in sub-nanoliter capsules.bioRxiv : the preprint server for biology · 2025Article
- Exploring the untapped potential of single-cell and spatial omics in plant biology.The New phytologist · 2025Review
- A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase.bioRxiv : the preprint server for biology · 2025Article
- Revealing gene function with statistical inference at single-cell resolution.Nature reviews. Genetics · 2024Review
- Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells.Genome biology · 2024Article
- Discovering mechanisms of human genetic variation and controlling cell states at scale.Trends in genetics : TIG · 2024Review
- Building and analyzing metacells in single-cell genomics data.Molecular systems biology · 2024Review
- Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
Chemical genetic screens are a powerful tool for exploring how cancer cells' response to drugs is shaped by their mutations, yet they lack a molecular view of the contribution of individual genes to the response to exposure. Here, we present sci-Plex-Gene-by-Environment (sci-Plex-GxE), a platform for combined single-cell genetic and chemical screening at scale. We highlight the advantages of large-scale, unbiased screening by defining the contribution of each of 522 human kinases to the response of glioblastoma to different drugs designed to abrogate signaling from the receptor tyrosine kinase pathway. In total, we probed 14,121 gene-by-environment combinations across 1,052,205 single-cell transcriptomes. We identify an expression signature characteristic of compensatory adaptive signaling regulated in a MEK/MAPK-dependent manner. Further analyses aimed at preventing adaptation revealed promising combination therapies, including dual MEK and CDC7/CDK9 or nuclear factor κB (NF-κB) inhibitors, as potent means of preventing transcriptional adaptation of glioblastoma to targeted therapy.
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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.