Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages
Open accessAbstract
Single-cell RNA-seq (scRNA-seq) studies have profiled over 100 million human cells across diseases, developmental stages, and perturbations to date. A singular view of this vast and growing expression landscape could help reveal novel associations between cell states and diseases, discover cell states in unexpected tissue contexts, and relate in vivo cells to in vitro models. However, these require a common, scalable representation of cell profiles from across the body, a general measure of their similarity, and an efficient way to query these data. Here, we present SCimilarity, a metric learning framework to learn and search a unified and interpretable representation that annotates cell types and instantaneously queries for a cell state across tens of millions of profiles. We demonstrate SCimilarity on a 22.7 million cell corpus assembled across 399 published scRNA-seq studies, showing accurate integration, annotation and querying. We experimentally validated SCimilarity by querying across tissues for a macrophage subset originally identified in interstitial lung disease, and showing that cells with similar profiles are found in other fibrotic diseases, tissues, and a 3D hydrogel system, which we then repurposed to yield this cell state in vitro . SCimilarity serves as a foundational model for single cell gene expression data and enables researchers to query for similar cellular states across the entire human body, providing a powerful tool for generating novel biological insights from the growing Human Cell Atlas.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [26]
Cites
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267 · see citing works
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288 · see citing works
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works
Self-citationBiopython: freely available python tools for computational molecular biology and bioinformatics
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080 · see citing works
Macrophages in Tissue Repair, Regeneration, and Fibrosis
Wynn, T. A., & Vannella, K. M. (2016) · Immunity · doi:10.1016/j.immuni.2016.02.015 · Cited by 4,481 · see citing works
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · see citing works
Martínez, F. O., Gordon, S., Locati, M., & Mantovani, A. (2006) · The Journal of Immunology · doi:10.4049/jimmunol.177.10.7303 · Cited by 2,336 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citationReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization
Yu, G., & He, Q. (2015) · Molecular BioSystems · doi:10.1039/c5mb00663e · Cited by 1,941 · see citing works
Malkov, Y. A., & Yashunin, D. A. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889473 · Cited by 1,904 · see citing works
Resolving the fibrotic niche of human liver cirrhosis at single-cell level
Ramachandran, P., Dobie, R., Wilson-Kanamori, J. R., Dora, E. F., Henderson, B. E. P., Luu, N. T., Portman, J. R., Matchett, K. P., Brice, M., Marwick, J. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1631-3 · Cited by 1,784 · see citing works
A molecular cell atlas of the human lung from single-cell RNA sequencing
Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., Chang, S., Conley, S. D., Mori, Y., Seita, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2922-4 · Cited by 1,657 · see citing works
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
Adams, T. S., Schupp, J. C., Poli, S., Ayaub, E. A., Neumark, N., Ahangari, F., Chu, S. G., Raby, B. A., DeIuliis, G., Januszyk, M., et al. (2020) · Science Advances · doi:10.1126/sciadv.aba1983 · Cited by 1,420 · see citing works
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,383 · see citing works
Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner
Jaitin, D. A., Adlung, L., Thaiss, C. A., Weiner, A., Li, B., Descamps, H., Lundgren, P., Bleriot, C., Liu, Z., Deczkowska, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.05.054 · Cited by 1,377 · see citing works
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works
Cross-tissue immune cell analysis reveals tissue-specific features in humans
Conde, C. D., Xu, C., Jarvis, L. B., Rainbow, D. B., Wells, S. B., Gomes, T., Howlett, S. K., Suchánek, O., Polański, K., King, H. W., et al. (2022) · Science · doi:10.1126/science.abl5197 · Cited by 1,165 · see citing works
Sampling Matters in Deep Embedding Learning
Wu, C.-Y., Manmatha, R., Smola, A. J., & Krähenbühl, P. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.309 · Cited by 880 · see citing works
Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary fibrosis
Morse, C., Tabib, T., Sembrat, J., Buschur, K. L., Bittar, H. E. T., Valenzi, E., Jiang, Y., Kass, D. J., Gibson, K. F., Chen, W., et al. (2019) · European Respiratory Journal · doi:10.1183/13993003.02441-2018 · Cited by 832 · see citing works
Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease
Mulder, K., Patel, A. A., Kong, W. T., Piot, C., Halitzki, E., Dunsmore, G., Khalilnezhad, S., Irac, S. E., Dubuisson, A., Chevrier, M., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.07.007 · Cited by 678 · see citing works
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Baldi, P., & Sadowski, P. (2013) · neural information processing systems · Cited by 532 · see citing works
SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis
Wendisch, D., Dietrich, O., Mari, T., von Stillfried, S., Ibarra, I. L., Mittermaier, M., Mache, C., Chua, R. L., Knoll, R., Timm, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.033 · Cited by 528 · see citing works
Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes
Heaton, H., Talman, A. M., Knights, A., Imaz, M., Gaffney, D. J., Durbin, R., Hemberg, M., & Lawniczak, M. K. N. (2020) · Nature Methods · doi:10.1038/s41592-020-0820-1 · Cited by 506 · see citing works
Human Disease Ontology 2018 update: classification, content and workflow expansion
Schriml, L. M., Mitraka, E., Munro, J. B., Tauber, B., Schor, M., Nickle, L., Felix, V., Jeng, L. J. B., Bearer, C. F., Lichenstein, R., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1032 · Cited by 471 · see citing works
Progenitor identification and SARS-CoV-2 infection in human distal lung organoids
Salahudeen, A. A., Choi, S. S., Rustagi, A., Zhu, J., van Unen, V., de la O, S. M., Flynn, R. A., Margalef-Català, M., Santos, A. J. M., Ju, J., et al. (2020) · Nature · doi:10.1038/s41586-020-3014-1 · Cited by 416 · see citing works
Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function
Eraslan, G., Drokhlyansky, E., Anand, S. K., Fiškin, E., Subramanian, A., Slyper, M., Wang, J., Wittenberghe, N. V., Rouhana, J. M., Waldman, J., et al. (2022) · Science · doi:10.1126/science.abl4290 · Cited by 396 · see citing works
Self-citationThe Cell Ontology 2016: enhanced content, modularization, and ontology interoperability
Diehl, A. D., Meehan, T. F., Bradford, Y. M., Brush, M., Dahdul, W., Dougall, D. S., He, Y. O., Osumi-Sutherland, D., Ruttenberg, A., Sarntivijai, S., et al. (2016) · Journal of Biomedical Semantics · doi:10.1186/s13326-016-0088-7 · Cited by 332 · see citing works
Lin, W., Noel, P., Borazanci, E. H., Lee, J., Amini, A., Han, I. W., Heo, J. S., Jameson, G., Fraser, C. R., Steinbach, M. A., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00776-9 · Cited by 328 · see citing works
Cano-Gamez, K., Soskic, B., Roumeliotis, T. I., So, E., Smyth, D. J., Baldrighi, M., Willé, D., Nakic, N., Esparza-Gordillo, J., Larminie, C. G. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15543-y · Cited by 274 · see citing works
Chemotherapy Induces Senescence-Like Resilient Cells Capable of Initiating AML Recurrence
Duy, C., Li, M., Teater, M., Meydan, C., Garrett-Bakelman, F. E., Lee, T. C., Chin, C. R., Durmaz, C., Kawabata, K. C., Dhimolea, E., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1375 · Cited by 253 · see citing works
Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
Li, B., Gould, J., Yang, Y., Sarkizova, S., Tabaka, M., Ashenberg, O., Rosen, Y., Slyper, M., Kowalczyk, M. S., Villani, A., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0905-x · Cited by 235 · see citing works
Self-citationImpact of the Human Cell Atlas on medicine
Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A., & Regev, A. (2022) · Nature Medicine · doi:10.1038/s41591-022-02104-7 · Cited by 222 · see citing works
Self-citationGremse, M., Chang, A., Schomburg, D., Grote, A., Scheer, M. P., Ebeling, C., & Schomburg, D. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq968 · Cited by 173 · see citing works
Supervised autoencoders: Improving generalization performance with unsupervised regularizers
Le, L., Patterson, A., & White, M. (2018) · neural information processing systems · Cited by 173 · see citing works
Heimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.001 · Cited by 156 · see citing works
Self-citationBeers, M. F., & Moodley, Y. P. (2017) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2016-0426ps · Cited by 149 · see citing works
Pancreatic cancer is marked by complement-high blood monocytes and tumor-associated macrophages
Kemp, S. B., Steele, N. G., Carpenter, E. S., Donahue, K. L., Bushnell, G. G., Morris, A. H., The, S., Orbach, S. M., Sirihorachai, V. R., Nwosu, Z. C., et al. (2021) · Life Science Alliance · doi:10.26508/lsa.202000935 · Cited by 129 · see citing works
Rosen, Y., Brbić, M., Roohani, Y., Swanson, K., Li, Z., & Leskovec, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02191-z · Cited by 125 · see citing works
Osteopontin Links Myeloid Activation and Disease Progression in Systemic Sclerosis
Gao, X., Jia, G., Guttman, A., DePianto, D. J., Morshead, K. B., Sun, K.-H., Ramamoorthi, N., Heiden, J. A. V., Modrusan, Z. D., Wolters, P. J., et al. (2020) · Cell Reports Medicine · doi:10.1016/j.xcrm.2020.100140 · Cited by 110 · see citing works
Self-citationDing, J., & Regev, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22851-4 · Cited by 103 · see citing works
Self-citationIs the Macrophage Phenotype Determinant for Fibrosis Development?
Lis-López, L., Bauset, C., Seco-Cervera, M., & Cosín‐Roger, J. (2021) · Biomedicines · doi:10.3390/biomedicines9121747 · Cited by 97 · see citing works
Batch alignment of single-cell transcriptomics data using deep metric learning
Yu, X., Xu, X., Zhang, J., & Li, X. (2023) · Nature Communications · doi:10.1038/s41467-023-36635-5 · Cited by 81 · see citing works
Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale
Yang, M., Yang, Y., Xie, C., Ni, M., Liu, J., Yang, H., Mu, F., & Wang, J. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00518-z · Cited by 64 · see citing works
Single cell derived mRNA signals across human kidney tumors
Young, M. D., Mitchell, T. J., Custers, L., Margaritis, T., Morales-Rodriguez, F., Kwakwa, K., Khabirova, E., Kildisiute, G., Oliver, T. R. W., de Krijger, R. R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23949-5 · Cited by 60 · see citing works
Karagiannis, T. T., Cleary, J. P., Gok, B., Henderson, A. J., Martin, N. G., Yajima, M., Nelson, E. C., & Cheng, C. S. (2020) · Nature Communications · doi:10.1038/s41467-020-16159-y · Cited by 59 · see citing works
Ayaub, E., Poli, S., Ng, J., Adams, T. S., Schupp, J. C., Quesada-Arias, L. D., Poli, F., Cosme, C., Robertson, M. J., Manzano, J. M. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.04.425268 · Cited by 46 · see citing works
Mirizio, E., Tabib, T., Wang, X., Chen, W., Liu, C., Lafyatis, R., Jacobe, H. T., & Torok, K. S. (2020) · Arthritis Research & Therapy · doi:10.1186/s13075-020-02343-4 · Cited by 40 · see citing works
Integration of millions of transcriptomes using batch-aware triplet neural networks
Simon, L. M., Wang, Y., & Zhao, Z. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00361-8 · Cited by 37 · see citing works
Insights from Transcriptomics: CD163+ Profibrotic Lung Macrophages in COVID-19
Bhattacharya, M. (2022) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2022-0107tr · Cited by 23 · see citing works
Inflammatory Cytokine Signaling during Development of Pancreatic and Prostate Cancers
Liou, G. (2017) · Journal of Immunology Research · doi:10.1155/2017/7979637 · Cited by 19 · see citing works
Xu, Y., Zeng, X., Zhang, M., Wang, B., Guo, X., Wei, S., Cai, S., Luo, Q., Li, H., Li, X., et al. (2022) · Protein & Cell · doi:10.1007/s13238-021-00900-4 · Cited by 14 · see citing works
Shen, H., Shen, X., Hu, J., Liu, J., Zhang, C., Wu, D., Feng, M., Yang, M., Li, Y., Yang, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.31.478596 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Program, C. C. S., Abdulla, S., Aevermann, B., Assis, P., Badajoz, S., Bell, S. M., Bezzi, E., Cakir, B., Chaffer, J., Chambers, S., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1142 · Cited by 399 · see citing works
How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393 · see citing works
Self-citationProfiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 328 · see citing works
Transformers in single-cell omics: a review and new perspectives
Szałata, A., Hrovatin, K., Becker, S., Tejada-Lapuerta, A., Cui, H., Wang, B., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02353-z · Cited by 176 · see citing works
Program, C. S.-C. B., Abdulla, S., Aevermann, B., Assis, P., Badajoz, S., Bell, S. M., Bezzi, E., Cakir, B., Chaffer, J., Chambers, S., et al. (2023) · doi:10.1101/2023.10.30.563174 · Cited by 142 · see citing works
Spatial transcriptomic characterization of pathologic niches in IPF
Mayr, C. H., Santacruz, D., Jarosch, S., Bleck, M., Dalton, J., McNabola, A., Lempp, C., Neubert, L., Rath, B., Kamp, J. C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adl5473 · Cited by 119 · see citing works
Zero-shot evaluation reveals limitations of single-cell foundation models
Kedzierska, K. Z., Crawford, L., Amini, A. P., & Lu, A. X. (2025) · Genome Biology · doi:10.1186/s13059-025-03574-x · Cited by 109 · see citing works
Ge, S., Sun, S., Xu, H., Cheng, Q. S., & Ren, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf136 · Cited by 89 · see citing works
Nicheformer: A Foundation Model for Single-Cell and Spatial OmicsPreprint
Schaar, A. C., Tejada-Lapuerta, A., Palla, G., Gutgesell, R. M., Halle, L., Minaeva, M., Vornholz, L., Dony, L., Drummer, F., Bahrami, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.15.589472 · Cited by 66 · see citing works
Evaluating the Utilities of Foundation Models in Single-cell Data Analysis
Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2023) · doi:10.1101/2023.09.08.555192 · Cited by 62 · see citing works
Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis
Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2026) · Advanced Science · doi:10.1002/advs.202514490 · Cited by 76 · see citing works
Assessing the limits of zero-shot foundation models in single-cell biology
Kedzierska, K. Z., Crawford, L., Amini, A. P., & Lu, A. X. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.561085 · Cited by 61 · see citing works
“These include scBERT [4], Geneformer [5], scGPT [6], tGPT [7], scFoundation [8], SCimilarity [9], GET [10], GeneCompass [11], and GENEPT [12] which all present themselves as general models applicable to diverse analyses.”
BackgroundSchäfer, P. S. L., Dimitrov, D., Villablanca, E. J., & Sáez-Rodríguez, J. (2024) · Nature Immunology · doi:10.1038/s41590-024-01768-2 · Cited by 66 · see citing works
scTab: Scaling cross-tissue single-cell annotation models
Fischer, F., Fischer, D. S., Mukhin, R. S., Isaev, A. V., Biederstedt, E., Villani, A., & Theis, F. J. (2024) · Nature Communications · doi:10.1038/s41467-024-51059-5 · Cited by 67 · see citing works
A Deep Dive into Single-Cell RNA Sequencing Foundation Models
Boiarsky, R., Singh, N., Buendia, A., Getz, G., & Sontag, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.19.563100 · Cited by 43 · see citing works
Delineating the effective use of self-supervised learning in single-cell genomics
Richter, T., Bahrami, M., Xia, Y., Fischer, D. S., & Theis, F. J. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00934-3 · Cited by 36 · see citing works
Systems Human Immunology and AI: Immune Setpoint and Immune Health
Lei, Y., & Tsang, J. S. (2025) · Annual Review of Immunology · doi:10.1146/annurev-immunol-090122-042631 · Cited by 25 · see citing works
Limitations of cell embedding metrics assessed using drifting islands
Wang, H., Leskovec, J., & Regev, A. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02702-z · Cited by 24 · see citing works
Self-citationIdentification of markers for the isolation of neuron-specific extracellular vesiclesPreprint
Ter‐Ovanesyan, D., Whiteman, S. T., Gilboa, T., Kowal, E. J. K., Trieu, W., Iyer, S., Budnik, B., Babila, C. M., Heimberg, G., Burgess, M. W., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.03.587267 · Cited by 16 · see citing works
Self-citationSynthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms
Zhang, Z., Melzer, M. E., Arun, K. M., Sun, H., Eriksson, C., Fabian, I., Shaashua, S., Kiani, K., Oren, Y., & Goyal, Y. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100592 · Cited by 15 · see citing works
Cheng, J., Pan, X., Fang, Y., Yang, K., Xue, Y., Yan, Q., & Yuan, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae525 · Cited by 19 · see citing works
Liu, T., Brouwer, E. D., Verma, A., Missarova, A. M., Kuo, T. C.-Y., Diamant, N., Wang, H., Hao, M., Biancalani, T., Bravo, H. C., et al. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101570 · Cited by 8 · see citing works
Self-citationHe, F., Fei, R., Gao, M., Su, L., Zhang, X., & Xu, D. H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.27.577455 · Cited by 7 · see citing works
Scaling cross-tissue single-cell annotation modelsPreprint
Fischer, F., Fischer, D. S., Biederstedt, E., Villani, A., & Theis, F. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.07.561331 · Cited by 7 · see citing works
Evaluating the Utilities of Large Language Models in Single-cell Data AnalysisPreprint
Zhao, H., Liu, T., Li, K., Wang, Y., & Li, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3376641/v1 · Cited by 7 · see citing works
Shirazi, S. P., Negretti, N. M., Jetter, C. S., Sharkey, A. L., Garg, S., Kapp, M., Wilkins, D. J., Fortier, G., Mallapragada, S., Banovich, N. E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.27.609955 · Cited by 7 · see citing works
Scoring gene importance by interpreting single-cell foundation models
Gold, M. P., Reyes, M., Diamant, N., Kuo, T. C.-Y., Hajiramezanali, E., Newburger, J. W., Son, M. B. F., Lee, P. Y., Scalia, G., BenTaieb, A., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03112-5 · Cited by 5 · see citing works
Self-citationscCompass: An integrated cross-species scRNA-seq database for AI-readyPreprint
Wang, P., Liu, W., Wang, J., Liu, Y., Li, P., Xu, P., Cui, W., Zhang, R., Long, Q., Hu, Z., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.12.623138 · Cited by 5 · see citing works
A cellular and spatial atlas of TP53 -associated tissue remodeling in lung adenocarcinomaPreprint
Zhao, W., Kepecs, B., Mahadevan, N. R., Segerstolpe, Å., Weirather, J. L., Besson, N. R., Giotti, B., Soong, B. Y., Li, C., Vigneau, S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.28.546977 · Cited by 4 · see citing works
Self-citationZhang, H., Jiang, Z., Zhang, S., Tu, L., & Carlson, D. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111724 · Cited by 3 · see citing works
Zhang, Z., Melzer, M. E., Kiani, K., & Goyal, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.04.552078 · Cited by 3 · see citing works
Identification of cell types, states and programs by learning gene set representationsPreprint
Hediyeh-zadeh, S., Whitfield, H. J., Kharbanda, M., Curion, F., Bhuva, D. D., Theis, F. J., & Davis, M. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.08.556842 · Cited by 3 · see citing works
Zemp, F. J., Breckenridge, Z. M., Song, H., Gill, G. S., Liu, H., Suh, Y., Guignard, L., Pyczek, J., John, C. M., Mah, L. K., et al. (2025) · medRxiv · doi:10.1101/2025.02.26.24319604 · Cited by 3 · see citing works
GenoHoption: Bridging Gene Network Graphs and Single-Cell Foundation Models
Cheng, J., Li, J., Yang, K., Shen, H., & Yuan, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822153 · Cited by 3 · see citing works
Eshghi, S. T., Gubatan, J. M. B., Mazrooei, P., Quintanilla, L., Nguyen, A., Au‐Yeung, A., Holman, D., Takahashi, C., Schiffman, C., O’Gorman, W. E., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1705328 · Cited by 1 · see citing works
Spatial Transcriptomic Characterization of Novel Pathologic Niches in IPFPreprint
Mayr, C. H., Santacruz, D., Jarosch, S., Lempp, C., Neubert, L., Rath, B., Kamp, J. C., Jonigk, D. D., Kühnel, M., Schlueter, H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.13.571464 · Cited by 1 · see citing works
Eshghi, S. T., Gubatan, J. M. B., Mazrooei, P., Quintanilla, L., Nguyen, A., Au‐Yeung, A., Holman, D., Takahashi, C., Schiffman, C., O’Gorman, W. E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.02.601267 · Cited by 3 · see citing works
Pedoman Peri-operatif Renal Assessment
Sinaga, U. M. (2009)
Bakhtiari, M., Elkjaer, M. L., Can, A. O., Theis, F. J., Oubounyt, M., & Baumbach, J. (2026) · BioData Mining · doi:10.1186/s13040-026-00582-w
CytoVerse: Single-Cell AI Foundation Models in the BrowserPreprint
Currie, R., Gonzalez-Ferrer, J., Mostajo-Radji, M. A., & Haussler, D. H. (2026) · SSRN Electronic Journal · doi:10.64898/2026.01.29.702554
Transcriptomics data generation with deep generative models
Lacan, A. (2025) · theses.fr (ABES) · doi:10.70675/4bbb4d4fzed5ez4f41z86f4zf6f892f9e3e8
- Authors
- Heimberg, G., Kuo, T., DePianto, D., Heigl, T., Diamant, N., Salem, O., Scalia, G., Biancalani, T., Turley, S., Rock, J., Bravo, H. C., Kaminker, J., Heiden, J. A. V., & Regev, A.
- Affiliations
- Genentech (US)
- Roche (Canada) (CA)
- Corresponding author
- Heimberg, Graham
- Heiden, Jason A. Vander
- Regev, Aviv
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2023-07-19
- OpenAlex
- W4384821687
- Cited by
- 40
- References
- 57
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 17 · 2023: 9
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- metric learning, single-cell RNA sequencing, Human Cell Atlas, cell type annotation, interstitial lung disease, fibrotic diseases, 3D hydrogels, in vitro disease model, foundation models, data integration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review