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Cited by 399Single-cell and spatial transcriptomicsScientific Computing and Data ManagementBioinformatics and Genomic Networks

CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data

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CZI Cell Science Program, Abdulla, Shibla, Aevermann, Brian, Assis, Pedro, Badajoz, Seve, Bell, Sidney M, Bezzi, Emanuele, Cakir, Batuhan +46 more·2025·Nucleic Acids Research
DOI 10.1093/nar/gkae1142PMC PMC11701654
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Abstract

Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, the sheer number of datasets, data models and accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable single-cell data. Available via a free-to-use online data portal, CZ CELLxGENE hosts a growing corpus of community-contributed data of over 93 million unique cells. Curated, standardized and associated with consistent cell-level metadata, this collection of single-cell transcriptomic data is the largest of its kind and growing rapidly via community contributions. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to explore individual datasets, perform cross-corpus analysis, and run meta-analyses of tens of millions of cells across studies and tissues at the resolution of single cells.

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  • The exponential increase in throughput of measurement technologies has led to collection of large and growing reference datasets within and across different cell and tissue systems, with data doubling every 6 months for the past several years.

    “Experimentally, the exponential increase in the throughput of measurement technologies has led to the collection of large and growing reference datasets within and across different cell and tissue systems,23–25 with data doubling every 6 months for the past several years,26”

    From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [25]

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  1. [1]Lane N. The unseen world: reflections on Leeuwenhoek (1677) ‘Concerning little animals’. Phil. Trans. R. Soc. B. 2015; 370: 20140344. 25750239 PMC4360124
  2. [3]HuBMAP Consortium The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature. 2019; 574: 187– 192. 31597973 PMC6800388
  3. [4]Li H., Janssens J., De Waegeneer M., Kolluru S.S., Davie K., Gardeux V., Saelens W., David F.P.A., Brbić M., Spanier K. et al. Fly Cell Atlas: a single-nucleus transcriptomic atlas of the adult fruit fly. Science. 2022; 375: eabk2432. 35239393 PMC8944923
  4. [5]The Tabula Sapiens Consortium Jones R.C., Karkanias J., Krasnow M.A., Pisco A.O., Quake S.R., Salzman J., Yosef N., Bulthaup B., Brown P. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science. 2022; 376: eabl4896. 35549404 PMC9812260
  5. [6]Du Y., Ouyang W., Kitzmiller J.A., Guo M., Zhao S., Whitsett J.A., Xu Y. Lung Gene Expression Analysis Web Portal Version 3: Lung-at-a-Glance. Am. J. Respir. Cell Mol. Biol. 2021; 64: 146– 149. 33385216 PMC7781007
  6. [8]Tarhan L., Bistline J., Chang J., Galloway B., Hanna E., Weitz E. Single Cell Portal: an interactive home for single-cell genomics data. 2023; bioRxiv doi: 17 July 2023, preprint: not peer reviewed 10.1101/2023.07.13.548886. · DOI
  7. [9]Puntambekar S., Hesselberth J.R., Riemondy K.A., Fu R. Cell-level metadata are indispensable for documenting single-cell sequencing datasets. PLoS Biol. 2021; 19: e3001077. 33945522 PMC8121533
  8. [10]Megill C., Martin B., Weaver C., Bell S., Prins L., Badajoz S., McCandless B., Pisco A.O., Kinsella M., Griffin F. et al. cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices. 2021; bioRxiv doi: 06 April 2021, preprint: not peer reviewed 10.1101/2021.04.05.438318. · DOI
  9. [11]Hawrylycz M., Martone M.E., Ascoli G.A., Bjaalie J.G., Dong H.-W., Ghosh S.S., Gillis J., Hertzano R., Haynor D.R., Hof P.R. et al. A guide to the BRAIN Initiative Cell Census Network data ecosystem. PLoS Biol. 2023; 21: e3002133. 37390046 PMC10313015
  10. [12]Nayak R., Hasija Y. A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines. Genomics. 2021; 113: 606– 619. 33485955
  11. [14]Stuart T., Butler A., Hoffman P., Hafemeister C., Papalexi E., Mauck W.M., Hao Y., Stoeckius M., Smibert P., Satija R. Comprehensive Integration of Single-Cell Data. Cell. 2019; 177: 1888– 1902. 31178118 PMC6687398
  12. [15]Argelaguet R., Cuomo A.S.E., Stegle O., Marioni J.C. Computational principles and challenges in single-cell data integration. Nat. Biotechnol. 2021; 39: 1202– 1215. 33941931
  13. [16]Rozenblatt-Rosen O., Regev A., Oberdoerffer P., Nawy T., Hupalowska A., Rood J.E., Ashenberg O., Cerami E., Coffey R.J., Demir E. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell. 2020; 181: 236– 249. 32302568 PMC7376497
  14. [17]Sikkema L., Ramírez-Suástegui C., Strobl D.C., Gillett T.E., Zappia L., Madissoon E., Markov N.S., Zaragosi L.-E., Ji Y., Ansari M. et al. An integrated cell atlas of the lung in health and disease. Nat. Med. 2023; 29: 1563– 1577. 37291214 PMC10287567
  15. [19]Muus C., Luecken M.D., Eraslan G., Sikkema L., Waghray A., Heimberg G., Kobayashi Y., Vaishnav E.D., Subramanian A., Smillie C. et al. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics. Nat. Med. 2021; 27: 546– 559. 33654293 PMC9469728
  16. [21]Read D.F., Booth G.T., Daza R.M., Jackson D.L., Gladden R.G., Srivatsan S.R., Ewing B., Franks J.M., Spurrell C.H., Gomes A.R. et al. Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart. Commun. Biol. 2024; 7: 1052. 39187646 PMC11347658
  17. [23]Wang S., Pisco A.O., McGeever A., Brbic M., Zitnik M., Darmanis S., Leskovec J., Karkanias J., Altman R.B. Leveraging the Cell Ontology to classify unseen cell types. Nat. Commun. 2021; 12: 5556. 34548483 PMC8455606
  18. [24]Malladi V.S., Erickson D.T., Podduturi N.R., Rowe L.D., Chan E.T., Davidson J.M., Hitz B.C., Ho M., Lee B.T., Miyasato S. et al. Ontology application and use at the ENCODE DCC. Database (Oxford). 2015; 2015: bav010. 25776021 PMC4360730
  19. [25]Jin K., Bardes E.E., Mitelpunkt A., Wang J.Y., Bhatnagar S., Sengupta S., Krummel D.P., Rothenberg M.E., Aronow B.J. An interactive single cell web portal identifies gene and cell networks in COVID-19 host responses. iScience. 2021; 24: 103115. 34522848 PMC8428985
  20. [26]Ruiz-Moreno C., Salas S.M., Samuelsson E., Brandner S., Kranendonk M.E.G., Nilsson M., Stunnenberg H.G. Harmonized single-cell landscape, intercellular crosstalk and tumor architecture of glioblastoma. 2022; bioRxiv doi: 27 August 2022, preprint: not peer reviewed 10.1101/2022.08.27.505439. · DOI
  21. [27]Hrovatin K., Bastidas-Ponce A., Bakhti M., Zappia L., Büttner M., Sallino C., Sterr M., Böttcher A., Migliorini A., Lickert H. et al. Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas. 2023; bioRxiv doi: 25 April 2023, preprint: not peer reviewed 10.1101/2022.12.22.521557. PMC10513934 37697055 · DOI
  22. [28]De Boer I.H., Alpers C.E., Azeloglu E.U., Balis U.G.J., Barasch J.M., Barisoni L., Blank K.N., Bomback A.S., Brown K., Dagher P.C. et al. Rationale and design of the Kidney Precision Medicine Project. Kidney Int. 2021; 99: 498– 510. 33637194 PMC8330551
  23. [29]Virshup I., Rybakov S., Theis F.J., Angerer P., Wolf F.A. anndata: annotated data. 2021; bioRxiv doi: 19 December 2021, preprint: not peer reviewed 10.1101/2021.12.16.473007. · DOI
  24. [30]Cakir B., Prete M., Huang N., van Dongen S., Pir P., Kiselev V.Y. Comparison of visualization tools for single-cell RNAseq data. NAR Genom. Bioinform. 2020; 2: lqaa052. 32766548 PMC7391988
  25. [31]Osumi-Sutherland D. Cell Ontology in an age of data-driven cell classification. BMC Bioinform. 2017; 18: 558. PMC5763290 29322914
  26. [33]Haendel M.A., Balhoff J.P., Bastian F.B., Blackburn D.C., Blake J.A., Bradford Y., Comte A., Dahdul W.M., Dececchi T.A., Druzinsky R.E. et al. Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon. J. Biomed. Sem. 2014; 5: 21. PMC4089931 25009735
  27. [34]Cao J., Spielmann M., Qiu X., Huang X., Ibrahim D.M., Hill A.J., Zhang F., Mundlos S., Christiansen L., Steemers F.J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019; 566: 496– 502. 30787437 PMC6434952
  28. [35]Qiu C., Martin B.K., Welsh I.C., Daza R.M., Le T.-M., Huang X., Nichols E.K., Taylor M.L., Fulton O., O’Day D.R. et al. A single-cell time-lapse of mouse prenatal development from gastrula to birth. Nature. 2024; 626: 1084– 1093. 38355799 PMC10901739
  29. [36]Barbarino J.M., McGregor T.L., Altman R.B., Klein T.E. PharmGKB summary: very important pharmacogene information for MT-RNR1. Pharmacogenetics and Genomics. 2016; 26: 558– 567. 27654872 PMC5083147
  30. [37]Jain S., Pei L., Spraggins J.M., Angelo M., Carson J.P., Gehlenborg N., Ginty F., Gonçalves J.P., Hagood J.S., Hickey J.W. et al. Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nat. Cell Biol. 2023; 25: 1089– 1100. 37468756 PMC10681365
  31. [38]Lähnemann D., Köster J., Szczurek E., McCarthy D.J., Hicks S.C., Robinson M.D., Vallejos C.A., Campbell K.R., Beerenwinkel N., Mahfouz A. et al. Eleven grand challenges in single-cell data science. Genome Biol. 2020; 21: 31. 32033589 PMC7007675
  32. [39]Virshup I., Bredikhin D., Heumos L., Palla G., Sturm G., Gayoso A., Kats I., Koutrouli M. Scverse Community Scverse Community Angerer P. et al. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nat. Biotechnol. 2023; 41: 604– 606. 37037904
  33. [41]Amezquita R.A., Lun A.T.L., Becht E., Carey V.J., Carpp L.N., Geistlinger L., Marini F., Rue-Albrecht K., Risso D., Soneson C. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods. 2020; 17: 137– 145. 31792435 PMC7358058
  34. [42]Gentleman R.C., Carey V.J., Bates D.M., Bolstad B., Dettling M., Dudoit S., Ellis B., Gautier L., Ge Y., Gentry J. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004; 5: R80. 15461798 PMC545600
  35. [43]Hao Y., Stuart T., Kowalski M., Choudhary S., Hoffman P., Hartman A., Srivastava A., Molla G., Madad S., Fernandez-Granda C. et al. Dictionary learning for integrative, multimodal, and scalable single-cell analysis. Nat Biotechnol. 2024; 42: 293– 304. 37231261 PMC10928517
  36. [50]Richard M.K. On-line algorithms versus off-line algorithms: how much is it worth to know the future. Proceedings of the IFIP 12th World Computer Congress on Algorithms, Software, Architecture. 1992; 1: Netherlands North-Holland Publishing Co 416– 429.
  37. [51]Marouf M., Machart P., Bansal V., Kilian C., Magruder D.S., Krebs C.F., Bonn S. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 2020; 11: 166. 31919373 PMC6952370
  38. [52]Cao Y., Yang P., Yang J.Y.H. A benchmark study of simulation methods for single-cell RNA sequencing data. Nat. Commun. 2021; 12: 6911. 34824223 PMC8617278
  39. [53]Adossa N., Khan S., Rytkönen K.T., Elo L.L. Computational strategies for single-cell multi-omics integration. Comput. Struct. Biotechnol. J. 2021; 19: 2588– 2596. 34025945 PMC8114078
  40. [54]Clarke Z.A., Andrews T.S., Atif J., Pouyabahar D., Innes B.T., MacParland S.A., Bader G.D. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat. Protoc. 2021; 16: 2749– 2764. 34031612
  41. [55]Alquicira-Hernandez J., Sathe A., Ji H.P., Nguyen Q., Powell J.E. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 2019; 20: 264. 31829268 PMC6907144
  42. [56]Thompson M., Matsumoto M., Ma T., Senabouth A., Palpant N.J., Powell J.E., Nguyen Q. scGPS: determining cell states and global fate potential of subpopulations. Front. Genet. 2021; 12: 666771. 34349778 PMC8326972
  43. [57]Sun G., Li Z., Rong D., Zhang H., Shi X., Yang W., Zheng W., Sun G., Wu F., Cao H. et al. Single-cell RNA sequencing in cancer: applications, advances, and emerging challenges. Mol. Ther. Oncolytics. 2021; 21: 183– 206. 34027052 PMC8131398
  44. [58]Van De Sande B., Lee J.S., Mutasa-Gottgens E., Naughton B., Bacon W., Manning J., Wang Y., Pollard J., Mendez M., Hill J. et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat. Rev. Drug Discov. 2023; 22: 496– 520. 37117846 PMC10141847
  45. [59]Lunke S., Bouffler S.E., Patel C.V., Sandaradura S.A., Wilson M., Pinner J., Hunter M.F., Barnett C.P., Wallis M., Kamien B. et al. Integrated multi-omics for rapid rare disease diagnosis on a national scale. Nat. Med. 2023; 29: 1681– 1691. 37291213 PMC10353936
  46. [60]Paszke A., Gross S., Massa F., Lerer A., Bradbury J., Chanan G., Killeen T., Lin Z., Gimelshein N., Antiga L. et al. PyTorch: an imperative style, high-performance deep learning library. 2019; arXiv doi: 03 December 2019. preprint: not peer reviewed. 10.48550/ARXIV.1912.01703. · DOI · Paper page
  47. [63]Castro J.P., Shindyapina A.V., Barbieri A., Ying K., Strelkova O.S., Paulo J.A., Tyshkovskiy A., Meinl R., Kerepesi C., Petrashen A.P. et al. Integrative analyses uncover mechanisms by which aging drives B cell lymphoma. 2023; bioRxiv doi: 11 July 2023, preprint: not peer reviewed 10.1101/2021.02.23.432500. · DOI
  48. [64]Schmitt A., Sakthivelu V., Ndoci K., Wani G.A., Touet M., Pintelon I., Kisis I., Ibruli O., Weber J., Maresch R. et al. Functional synapses between small cell lung cancer and glutamatergic neurons. 2023; bioRxiv doi: 20 January 2023. preprint: not peer reviewed 10.1101/2023.01.19.524045. · DOI
  49. [65]Charytonowicz D., Brody R., Sebra R. Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve. Nat. Commun. 2023; 14: 1350. 36906603 PMC10008582
  50. [66]Fischer F., Fischer D.S., Mukhin R., Isaev A., Biederstedt E., Villani A.-C., Theis F.J. scTab: scaling cross-tissue single-cell annotation models. Nat. Commun. 2024; 15: 6611. 39098889 PMC11298532
  51. [67]Little P., Liu S., Zhabotynsky V., Li Y., Lin D.-Y., Sun W. A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data. Nat. Commun. 2023; 14: 3030. 37231002 PMC10212972
  52. [70]Palla G., Fischer D.S., Regev A., Theis F.J. Spatial components of molecular tissue biology. Nat. Biotechnol. 2022; 40: 308– 318. 35132261

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Authors
Program, C. C. S., Abdulla, S., Aevermann, B., Assis, P., Badajoz, S., Bell, S. M., Bezzi, E., Cakir, B., Chaffer, J., Chambers, S., Cherry, J. M., Chi, T., Chien, J., Dorman, L., Garcia-Nieto, P., Gloria, N., Hastie, M., Hegeman, D., Hilton, J., Huang, T., Infeld, A., Istrate, A.-M., Jelic, I., Katsuya, K., Kim, Y. J., Liang, K., Lin, M., Lombardo, M., Marshall, B., Martin, B., McDade, F., Megill, C., Patel, N., Predeus, A., Raymor, B., Robatmili, B., Rogers, D., Rutherford, E., Sadgat, D., Shin, A., Small, C., Smith, T., Sridharan, P., Tarashansky, A., Tavares, N., Thomas, H., Tolopko, A., Urisko, M., Yan, J., Yeretssian, G., Zamanian, J., Mani, A., Cool, J., & Carr, A.
Affiliations
  • Wellcome Sanger Institute (GB)
  • Chan Zuckerberg Initiative (United States) (US)
  • Wellcome Sanger Institute, Wellcome Genome Campus , Hinxton CB10 1SA ,
  • Chan Zuckerberg Biohub San Francisco (US)
  • Chan Zuckerberg Initiative , 1180 Main Street, Redwood City, CA 94063 ,
  • Stanford Medicine (US)
Corresponding author
  • Mani, Arathi
  • Cool, Jonah
  • Carr, Ambrose
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2024-11-28
Volume
53
Issue
D1
Pages
D886-D900
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkae1142
PMC
PMC11701654
OpenAlex
W4404793799
Cited by
399
References
14
Citations by year
2026: 219 · 2025: 163 · 2024: 16 · 2023: 1
FWCI
54.18
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
single-cell transcriptomics, meta-analysis, large language models, data reusability
MeSH terms
Machine Learning, Humans, Software, Computational Biology, Gene Expression Profiling, Single-Cell Analysis, Transcriptome

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