Algorithms for a Commons Cell Atlas

Booeshaghi, A. Sina; Galvez-Merchán, Ángel; Pachter, Lior

doi:10.1101/2024.03.23.586413

Cited by 3 publications

References 46 publications

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85 million cells — and counting — at your fingertips

Perkel

2024

Nature

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When it comes to single-cell gene-expression data, biologists face an embarrassment of riches. There are thousands of data sets to choose from. Unfortunately, those data sets have not all been processed in the same way; they might use different names for similar or identical cells or tissues; and they are scattered across the Internet -or available only on request.Using any one data set is relatively straightforward. But collecting, curating and integrating the data to draw conclusions across experiments, is -in the words of bioinformatician Timothy Triche Jr at the Van Andel Institute in Grand Rapids, Michigan -"a huge pain in the butt".In one 2023 study 1 , for instance, computational biologist Christina Theodoris at Gladstone Institutes in San Francisco, California, described a deep-learning model called Geneformer. Building on some 30 million single-cell transcriptomic data sets that Theodoris manually aggregated in 2021, Geneformer allows researchers to predict the impact of gene perturbations in cell types or genes it has never seen. But because the data were scattered across 18 public databases and multiple independent laboratories, she says, "it took me two months to collect all that data and process it". "That's approximately 11-and-a-half million more cells than we would typically run."The CZ CELLxGENE tool helps researchers to visualize gene-expression data.

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85 million cells — and counting — at your fingertips

Perkel

2024

Nature

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Considerations for building and using integrated single-cell atlases

Hrovatin,

Sikkema,

Shitov

et al. 2024

Nat Methods

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A Perturbation Cell Atlas of Human Induced Pluripotent Stem Cells

Nourreddine,

Doctor,

Dailamy

et al. 2024

Preprint

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Towards comprehensively investigating the genotype-phenotype relationships governing the human pluripotent stem cell state, we generated an expressed genome-scale CRISPRi Perturbation Cell Atlas in KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes. Using the transcriptional phenotypes, we created a minimum distortion embedding map of the pluripotent state, demonstrating rich recapitulation of protein complexes, such as strong co-clustering of MRPL, BAF, SAGA, and Ragulator family members. Additionally, we uncovered transcriptional regulators that are uncoupled from cell fitness, discovering potential novel pluripotency (JOSD1, RNF7) and metabolic factors (ZBTB41). We validated these findings via phenotypic, protein-interaction, and metabolic tracing assays. Finally, we propose a contrastive human-cell engineering framework (CHEF), a machine learning architecture that learns from perturbation cell atlases to predict perturbation recipes that achieve desired transcriptional states. Taken together, our study presents a comprehensive resource for interrogating the regulatory networks governing pluripotency.

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Algorithms for a Commons Cell Atlas

Cited by 3 publications

References 46 publications

85 million cells — and counting — at your fingertips

85 million cells — and counting — at your fingertips

Considerations for building and using integrated single-cell atlases

A Perturbation Cell Atlas of Human Induced Pluripotent Stem Cells

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