GEO series
D-SPIN constructs regulatory network models from scRNA-seq revealing organizing principles of perturbation response
GSE327727
Homo sapiens
Expression profiling by high throughput sequencing; Other
194 samples
Submitted 2026/05/11
Platform GPL24676Platform GPL20301Platform GPL15520
Summary
Gene regulatory networks modulate the expression of the genome in response to signals and environmental conditions. Reconstructions of such networks can reveal the control principles cells use to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that infers mechanistically interpretable and generative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of perturbation conditions. The models explain how perturbations modulate cell state proportions by reconfiguring underlying regulatory interactions. Using large Perturb-seq and drug-response datasets, D-SPIN models reveal key regulators of cell-fate decisions and the coordination of distant cellular pathways in response to gene knockdowns and drug treatments, elucidate how combinations of immunomodulatory drugs induce combinatorial cell states through additive recruitment of gene expression programs, and simulate shifts in immune cell population structures across unobserved drug dosage combinations. D-SPIN provides a computational framework for revealing principles of cellular information processing and physiological control.
This dataset
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Direct links to NCBI, no account and no request form: the whole study as GSE327727_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 194 samples. Raw sequencing reads are also available from ENA.
Also filed as BioProject PRJNA1449626 and SRA study SRP690278. Searching any of these in the dataset finder brings you back here.
Study design
40 conditions, each sampled once — no replicated groups
Read from the first 40 of 194 sample titles: 40 distinct titles with little repetition. Check it against the sample list below before relying on it.
Samples in this study
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- GSM9664244 scRNA-seq human PBMC MULT-6-1 library
- GSM9664245 scRNA-seq human PBMC MULT-6-2 library
- GSM9664246 scRNA-seq human PBMC MULT-6-3 library
- GSM9664247 scRNA-seq human PBMC MULT-6-4 library
- GSM9664248 scRNA-seq human PBMC MULT-6-5 library
- GSM9664249 scRNA-seq human PBMC MULT-6-6 library
- GSM9664250 scRNA-seq human PBMC MULT-6-7 library
- GSM9664251 MULTI-seq hashtag MULT-6-1 TAGS library
- GSM9664252 MULTI-seq hashtag MULT-6-2 TAGS library
- GSM9664253 MULTI-seq hashtag MULT-6-3 TAGS library
- GSM9664254 MULTI-seq hashtag MULT-6-4 TAGS library
- GSM9664255 MULTI-seq hashtag MULT-6-5 TAGS library
- GSM9664256 MULTI-seq hashtag MULT-6-6 TAGS library
- GSM9664257 MULTI-seq hashtag MULT-6-7 TAGS library
- GSM9664258 scRNA-seq human PBMC MULT-7-1 library
- GSM9664259 scRNA-seq human PBMC MULT-7-2 library
- GSM9664260 scRNA-seq human PBMC MULT-7-3 library
- GSM9664261 scRNA-seq human PBMC MULT-7-4 library
- GSM9664262 scRNA-seq human PBMC MULT-7-5 library
- GSM9664263 scRNA-seq human PBMC MULT-7-6 library
- GSM9664264 scRNA-seq human PBMC MULT-7-7 library
- GSM9664265 scRNA-seq human PBMC MULT-7-8 library
- GSM9664266 MULTI-seq hashtag MULT-7-1 TAGS library
- GSM9664267 MULTI-seq hashtag MULT-7-2 TAGS library
- GSM9664268 MULTI-seq hashtag MULT-7-3 TAGS library
- GSM9664269 MULTI-seq hashtag MULT-7-4 TAGS library
- GSM9664270 MULTI-seq hashtag MULT-7-5 TAGS library
- GSM9664271 MULTI-seq hashtag MULT-7-6 TAGS library
- GSM9664272 MULTI-seq hashtag MULT-7-7 TAGS library
- GSM9664273 MULTI-seq hashtag MULT-7-8 TAGS library
- GSM9664274 scRNA-seq human PBMC MULT-TC-1 library
- GSM9664275 scRNA-seq human PBMC MULT-TC-2 library
- GSM9664276 scRNA-seq human PBMC MULT-TC-3 library
- GSM9664277 scRNA-seq human PBMC MULT-TC-4 library
- GSM9664278 scRNA-seq human PBMC MULT-TC-5 library
- GSM9664279 scRNA-seq human PBMC MULT-TC-6 library
- GSM9664280 MULTI-seq hashtag MULT-TC-1 TAGS library
- GSM9664281 MULTI-seq hashtag MULT-TC-2 TAGS library
- GSM9664282 MULTI-seq hashtag MULT-TC-3 TAGS library
- GSM9664283 MULTI-seq hashtag MULT-TC-4 TAGS library
+ 154 more — browse all 194 samples with per-sample file links →
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