Impact of the Human Cell Atlas on medicine

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

Oncology meets immunology: the cancer-immunity cycle.

Chen, D. S., & Mellman, I. (2013) · Immunity · doi:10.1016/j.immuni.2013.07.012 · Cited by 6,929

A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease

Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.018 · Cited by 5,493

SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes

HCA Lung Biological Network, Sungnak, W., Huang, N., Bécavin, C., Berg, M., Queen, R., Litvinukova, M., Talavera-López, C., Maatz, H., Reichart, D., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0868-6 · Cited by 2,501

Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection

Zou, X., Chen, K., Zou, J., Han, P., Hao, J., & Han, Z. (2020) · Frontiers of Medicine · doi:10.1007/s11684-020-0754-0 · Cited by 2,287

SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues

Ziegler, C. G. K., Allon, S. J., Nyquist, S. K., Mbano, I. M., Miao, V. N., Tzouanas, C. N., Cao, Y., Yousif, A. S., Bals, J., Hauser, B. M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.035 · Cited by 2,284

The human cell atlas

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

Complex cell-state changes revealed by single cell RNA sequencing of 76,149 microglia throughout the mouse lifespan and in the injured brain

Hammond, T. R., Dufort, C., Dissing-Olesen, L., Giera, S., Young, A., Wysoker, A., Walker, A. J., Segel, M., Nemesh, J., Saunders, A., et al. (2018) · doi:10.1101/406140 · Cited by 2,225

Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma

Sade-Feldman, M., Yizhak, K., Bjorgaard, S. L., Ray, J. P., de Boer, C. G., Jenkins, R. W., Lieb, D. J., Chen, J. H., Frederick, D. T., Barzily-Rokni, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.038 · Cited by 2,053

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863

Large-scale foundation model on single-cell transcriptomics

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628

Profiling 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 335

Automatic cell-type harmonization and integration across Human Cell Atlas datasets

Xu, C., Prete, M., Webb, S., Jardine, L., Stewart, B. J., Hoo, R., He, P., Meyer, K. B., & Teichmann, S. A. (2023) · Cell · doi:10.1016/j.cell.2023.11.026 · Cited by 299

Regulation of phospholipid distribution in the lipid bilayer by flippases and scramblases

Sakuragi, T., & Nagata, S. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00604-z · Cited by 278

Single-cell sequencing to multi-omics: technologies and applications

Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 222

The Human Cell Atlas from a cell census to a unified foundation model

Rood, J., Wynne, S., Robson, L., Hupalowska, A., Randell, J., Teichmann, S. A., & Regev, A. (2024) · Nature · doi:10.1038/s41586-024-08338-4 · Cited by 173

Universal Cell Embeddings: A Foundation Model for Cell BiologyPreprint

Rosen, Y., Roohani, Y., Agrawal, A., Samotorcan, L., Quake, S. R., & Leskovec, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.28.568918 · Cited by 165

A cell atlas foundation model for scalable search of similar human cells

Heimberg, G., Kuo, T. C.-Y., DePianto, D. J., Salem, O., Heigl, T., Diamant, N., Scalia, G., Biancalani, T., Turley, S. J., Rock, J. R., et al. (2024) · Nature · doi:10.1038/s41586-024-08411-y · Cited by 187

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas

Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139

Single-cell genomics meets human genetics

Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00599-5 · Cited by 124

Towards multimodal foundation models in molecular cell biology

Cui, H., Tejada-Lapuerta, A., Brbić, M., Sáez-Rodríguez, J., Cristea, S., Goodarzi, H., Lotfollahi, M., Theis, F. J., & Wang, B. (2025) · Nature · doi:10.1038/s41586-025-08710-y · Cited by 144

Large Scale Foundation Model on Single-cell Transcriptomics

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L., & Zhang, X. (2023) · doi:10.1101/2023.05.29.542705 · Cited by 639

Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping

Netskar, H., Pfefferle, A., Goodridge, J. P., Sohlberg, E., Dufva, O., Teichmann, S. A., Brownlie, D., Michaëlsson, J., Marquardt, N., Clancy, T., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01884-z · Cited by 118

Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0

Zeng, D., Fang, Y., Qiu, W., Luo, P., Wang, S., Shen, R., Gu, W., Huang, X., Mao, Q., Wang, G., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100910 · Cited by 111

Single-cell and spatially resolved interactomics of tooth-associated keratinocytes in periodontitis

Easter, Q. T., Matuck, B. F., Stark, G. B., Worth, C. L., Predeus, A. V., Fremin, B. J., Huynh, K., Ranganathan, V. N., Ren, Z. F., Pereira, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49037-y · Cited by 97

Integration mapping of cardiac fibroblast single-cell transcriptomes elucidates cellular principles of fibrosis in diverse pathologies

Patrick, R., Janbandhu, V. C., Tallapragada, V. J., Tan, S. S. M., McKinna, E. E., Contreras, O., Ghazanfar, S., Humphreys, D. T., Murray, N. J., Tran, T. H. Y., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk8501 · Cited by 90

A single-cell and spatial genomics atlas of human skin fibroblasts reveals shared disease-related fibroblast subtypes across tissues

Steele, L., Olabi, B., Roberts, K., Mazin, P., Koplev, S., Tudor, C. L., Rumney, B. M., Admane, C., Jiang, T., Correa‐Gallegos, D., et al. (2025) · Nature Immunology · doi:10.1038/s41590-025-02267-8 · Cited by 95

Contextual AI models for single-cell protein biology

Li, M. M., Huang, Y., Sumathipala, M. G., Liang, M. Q., Valdeolivas, A., Ananthakrishnan, A. N., Liao, K. P., Marbach, D., & Žitnik, M. (2024) · Nature Methods · doi:10.1038/s41592-024-02341-3 · Cited by 80

Sequential immunotherapy: towards cures for autoimmunity

Ramírez‐Valle, F., Maranville, J., Roy, S., & Plenge, R. M. (2024) · Nature Reviews Drug Discovery · doi:10.1038/s41573-024-00959-8 · Cited by 81

Encompassing view of spatial and single-cell RNA sequencing renews the role of the microvasculature in human atherosclerosis

Bleckwehl, T., Babler, A., Tebbens, M., Maryam, S., Nyberg, M., Bosteen, M. H., Halder, M., Shaw, I., Fleig, S. V., Pyke, C., et al. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00582-1 · Cited by 80

Transitioning single-cell genomics into the clinic

Lim, J. W. Y., Chin, V. T., Fairfax, K. A., Moutinho, C., Suan, D., Ji, H. P., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00613-w · Cited by 74

Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients

Radtke, A. J., Postovalova, E., Varlamova, A., Bagaev, A., Sorokina, M. A., Kudryashova, O. M., Meerson, M., Polyakova, M., Galkin, I., Svekolkin, V., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.02.001 · Cited by 76

Exploring the untapped potential of single‐cell and spatial omics in plant biology

Nobori, T. (2025) · New Phytologist · doi:10.1111/nph.70220 · Cited by 83

Optimal transport for single-cell and spatial omics

Bunne, C., Schiebinger, G., Krause, A., Regev, A., & Cuturi, M. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00334-2 · Cited by 71

Asian diversity in human immune cells

Kock, K. H., Tan, L. M., Han, K. Y., Ando, Y., Jevapatarakul, D., Chatterjee, A., Lin, Q. X. X., Buyamin, E. V., Sonthalia, R., Rajagopalan, D., et al. (2025) · Cell · doi:10.1016/j.cell.2025.02.017 · Cited by 85

Transcriptome-wide analysis of differential expression in perturbation atlases

Nadig, A., Replogle, J. M., Pogson, A. N., Murthy, M., McCarroll, S. A., Weissman, J. S., Robinson, E. B., & O’Connor, L. J. (2025) · Nature Genetics · doi:10.1038/s41588-025-02169-3 · Cited by 70

Advances and applications in single-cell and spatial genomics

Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 70

Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet

Hoffman, G. E., Lee, D., Bendl, J. M., Prashant, N. M., Hong, A., Casey, C., Alvia, M., Shao, Z., Argyriou, S., Therrien, K., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-75680-8 · Cited by 75

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

Considerations for building and using integrated single-cell atlases

Hrovatin, K., Sikkema, L., Shitov, V. A., Heimberg, G., Shulman, M., Oliver, A. J., Mueller, M. F., Ibarra, I. L., Wang, H., Ramírez-Suástegui, C., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02532-y · Cited by 68

Integrating single-cell RNA-seq and bulk RNA-seq to construct prognostic signatures to explore the role of glutamine metabolism in breast cancer

Pei, S., Zhang, P., Chen, H., Zhao, S., Dai, Y., Yang, L., Kang, Y., Zheng, M., Xia, Y., & Xie, H. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1135297 · Cited by 63

Active learning framework leveraging transcriptomics identifies modulators of disease phenotypes

DeMeo, B., Nesbitt, C., Miller, S. A., Burkhardt, D. B., Lipchina, I., Fu, D., Holderrieth, P., Kim, D., Kolchenko, S., Szałata, A., et al. (2025) · Science · doi:10.1126/science.adi8577 · Cited by 68

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Maden, S. K., Kwon, S. H., Huuki-Myers, L. A., Collado‐Torres, L., Hicks, S. C., & Maynard, K. R. (2023) · Genome biology · doi:10.1186/s13059-023-03123-4 · Cited by 58

Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

Pekayvaz, K., Losert, C., Knottenberg, V., Gold, C., van Blokland, I. V., Oelen, R., Groot, H. E., Benjamins, J. W., Brambs, S., Kaiser, R. W. J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02953-4 · Cited by 63

Cell-type deconvolution methods for spatial transcriptomics

Gaspard-Boulinc, L., Gortana, L., Walter, T., Barillot, E., & Cavalli, F. M. G. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00845-y · Cited by 57

Mitochondrial genetics through the lens of single-cell multi-omics

Nitsch, L., Lareau, C. A., & Ludwig, L. S. (2024) · Nature Genetics · doi:10.1038/s41588-024-01794-8 · Cited by 54

Mapping cells through time and space with moscotPreprint

Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Bastidas-Ponce, A., Tarquis-Medina, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.11.540374 · Cited by 49

Microtechnologies for single-cell and spatial multi-omics

Deng, Y., Bai, Z., & Fan, R. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00084-y · Cited by 45

The emerging importance of lymphatics in health and disease: an NIH workshop report

Mehrara, B. J., Radtke, A. J., Randolph, G. J., Wachter, B. T., Greenwel, P., Rovira, I. I., Galis, Z. S., & Muratoglu, S. C. (2023) · Journal of Clinical Investigation · doi:10.1172/jci171582 · Cited by 48

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

Flores, R. O. R., Lanzer, J. D., Dimitrov, D., Velten, B., & Sáez-Rodríguez, J. (2023) · eLife · doi:10.7554/elife.93161 · Cited by 43

Region-specific transcriptomic responses to obesity and diabetes in macaque hypothalamus

Lei, Y., Liang, X., Sun, Y., Yao, T. S., Gong, H., Chen, Z., Gao, Y., Wang, H., Wang, R., Huang, Y., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.01.003 · Cited by 42

Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states

Southard, K. M., Ardy, R. C., Tang, A., O'Sullivan, D., Metzner, E., Guruvayurappan, K., & Norman, T. M. (2025) · Nature Genetics · doi:10.1038/s41588-025-02284-1 · Cited by 43

Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages

Heimberg, G., Kuo, T., DePianto, D., Heigl, T., Diamant, N., Salem, O., Scalia, G., Biancalani, T., Turley, S., Rock, J., et al. (2023) · doi:10.1101/2023.07.18.549537 · Cited by 40

Clinical trials in-a-dish for cardiovascular medicine

Wu, X., Swanson, K., Yıldırım, Z., Liu, W., Liao, R., & Wu, J. C. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae519 · Cited by 42

Bioinformatics: Advancing biomedical discovery and innovation in the era of big data and artificial intelligence

Liu, Y., Chen, Y., & Han, L. (2023) · The Innovation Medicine · doi:10.59717/j.xinn-med.2023.100012 · Cited by 36

The Biology and Biochemistry of Kynurenic Acid, a Potential Nutraceutical with Multiple Biological Effects

Alves, L. D. F., Moore, J. B., & Kell, D. B. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25169082 · Cited by 38

Inference of single cell profiles from histology stains with the Single Cell omics from Histology Analysis Framework (SCHAF)Preprint

Comiter, C. S., Chen, X., Vaishnav, E. D., Kobayashi-Kirschvink, K. J., Ciampricotti, M., Zhang, K., Murray, J., Monticolo, F., Qi, J., Tanaka, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.21.533680 · Cited by 34

scBaseCount: an AI agent-curated, uniformly processed, and autonomously updated single cell data repositoryPreprint

Youngblut, N. D., Carpenter, C., Nayebnazar, A., Adduri, A. K., Shah, R., Ricci-Tam, C., Prashar, J., Ilango, R., Teyssier, N., Konermann, S., et al. (2025) · bioRxiv · doi:10.1101/2025.02.27.640494 · Cited by 18

Machine learning integrative approaches to advance computational immunology

Curion, F., & Theis, F. J. (2024) · Genome Medicine · doi:10.1186/s13073-024-01350-3 · Cited by 36

Towards systems immunology of critical illness at scale: from single cell ‘omics to digital twins

Yoram Vodovotz, B. (2023) · Trends in Immunology · doi:10.1016/j.it.2023.03.004 · Cited by 28

What is a cell type, really? The quest to categorize life’s myriad forms

Dance, A. (2024) · Nature · doi:10.1038/d41586-024-03073-2 · Cited by 27

Overcoming barriers to single-cell RNA sequencing adoption in low- and middle-income countries

Serebour, T. B., Cribbs, A. P., Baldwin, M. J., Masimirembwa, C. M., Chikwambi, Z., Kerasidou, A., & Snelling, S. J. B. (2024) · European Journal of Human Genetics · doi:10.1038/s41431-024-01564-4 · Cited by 26

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations

Gondal, M. N., Shah, S. U. R., Chinnaiyan, A. M., & Cieslik, M. P. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2024.1417428 · Cited by 23

Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes

Martin-Rufino, J. D., Caulier, A., Lee, S., Castano, N., King, E., Joubran, S. R., Jones, M. K., Goldman, S. R., Arora, U. P., Wahlster, L., et al. (2025) · Science · doi:10.1126/science.ads7951 · Cited by 30

Critical Challenges of Intravenous Nanomaterials Crossing the Blood‐Brain Barrier: from Blood to Brain

Luo, W., Chen, C., Guo, X., Guo, X., Zheng, J., Liu, J., Fan, X., Luo, M., Yu, Z., Li, H., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202409522 · Cited by 27

The commitment of the human cell atlas to humanity

Amit, I., Ardlie, K., Arzuaga, F., Awandare, G. A., Bader, G. D., Bernier, A., Carninci, P., Donnelly, S., Eils, R., Forrest, A. R. R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54306-x · Cited by 26

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

Estimating the impact of single-cell RNA sequencing of human tissues on drug target validationPreprint

Dann, E., Teeple, E. A., Elmentaite, R., Meyer, K. B., Gaglia, G., Nestle, F. O., Savova, V., de Rinaldis, E., & Teichmann, S. A. (2024) · medRxiv · doi:10.1101/2024.04.04.24305313 · Cited by 22

Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE

Missarova, A., Dann, E., Dann, E., Satija, R., & Marioni, J. C. (2024) · Genome biology · doi:10.1186/s13059-024-03334-3 · Cited by 32

Rat Models of Hormone Receptor-Positive Breast Cancer

Nicotra, R., Lutz, C., Messal, H. A., & Jonkers, J. (2024) · Journal of Mammary Gland Biology and Neoplasia · doi:10.1007/s10911-024-09566-0 · Cited by 27

Adapting systems biology to address the complexity of human disease in the single-cell era

Fischer, D. S., Villanueva, M. A., Winter, P. S., & Shalek, A. K. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00821-6 · Cited by 30

From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems

Wu, R., Veličković, M., & Burnum-Johnson, K. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103174 · Cited by 21

Artificial intelligence in tuberculosis: a new ally in disease control

McClean, M., Panciu, T. C., Lange, C. G., Duarte, R., & Theis, F. J. (2024) · Breathe · doi:10.1183/20734735.0056-2024 · Cited by 21

Single-cell transcriptomics identifies chondrocyte differentiation dynamics in vivo and in vitro

Lawrence, J. E. G., Woods Jon, S., Roberts, K., Sumanaweera, D., Balogh, P., Li, T., Predeus, A. V., He, P., Polański, K., Prigmore, E., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.06.031 · Cited by 19

Weakly supervised learning uncovers phenotypic signatures in single-cell dataPreprint

Litinetskaya, A., Hediyeh-zadeh, S., Moinfar, A. A., Lotfollahi, M., & Theis, F. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.29.605625 · Cited by 19

Universal cell embedding provides a foundation model for cell biology

Rosen, Y., Roohani, Y., Agrawal, A., Samotorcan, L., Quake, S. R., & Leskovec, J. (2026) · Nature · doi:10.1038/s41586-026-10689-z · Cited by 233

Discovery and design of G protein-coupled receptor targeting antibodies

Peterson, S. M., Hutchings, C. J., Hu, C. F., Mathur, M., Salameh, J. W., Axelrod, F. T., & Sato, A. K. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2193389 · Cited by 19

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Curion, F., Rich‐Griffin, C., Agarwal, D., Ouologuem, S., Rue-Albrecht, K., May, L., Garcia, G. E. L., Heumos, L., Thomas, T. R., Lason, W., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03322-7 · Cited by 26

Congenital Athymia: Unmet Needs and Practical Guidance

Howley, E., Davies, E. G., & Kreins, A. Y. (2023) · Therapeutics and Clinical Risk Management · doi:10.2147/tcrm.s379673 · Cited by 20

150 risk variants for diverticular disease of intestine prioritize cell types and enable polygenic prediction of disease susceptibility

Wu, Y., Goleva, S. B., Breidenbach, L. B., Kim, M., MacGregor, S., Gandal, M. J., Davis, L. K., & Wray, N. R. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100326 · Cited by 18

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Carraro, C., Montgomery, J. V., Klimmt, J., Paquet, D., Schultze, J. L., & Beyer, M. (2024) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2024.1414886 · Cited by 15

Control of Cellular Differentiation Trajectories for Cancer Reversion

Gong, J., Lee, C., Kim, H., Kim, J., Jeon, J., Park, S., & Cho, K. (2024) · Advanced Science · doi:10.1002/advs.202402132 · Cited by 17

Disentangling cellular heterogeneity into interpretable biological factors through structured latent representationsPreprint

Moinfar, A. A., & Theis, F. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.06.622266 · Cited by 20

Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity

McDermott, M., Mehta, R., Torres, E. T. R., & MacLean, A. L. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-025-00512-2 · Cited by 15

Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas

Klötzer, K. A., Abedini, A., Shen, L., Balzer, M. S., Liang, X., Levinsohn, J. L., Ha, E., Dumoulin, B., Hogan, J., Quinn, G. Z., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02285-0 · Cited by 16

Single‐Cell and Spatial Multiomics: Applications for Diseases

Li, W., Chen, C., Zhu, X., & Zhang, C. (2025) · MedComm · doi:10.1002/mco2.70553 · Cited by 16

Efficient profiling of total RNA in single cells with STORM-seqPreprint

Johnson, B. K., Majewski, M. F., Jang, H. J., Siwicki, R. A., Wegener, M., Semwal, A., Morrison, J., Gallik, K., Hagemann-Jensen, M., Foy, K. K., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.484332 · Cited by 18

Construction of a prognostic model based on disulfidptosis-related genes and identification of CCNA2 as a novel biomarker for hepatocellular carcinoma

Wang, T., Li, W., Wu, Y., You, L., Zheng, C., Zhang, J., Qu, L., & Sun, X. (2024) · Biology Direct · doi:10.1186/s13062-024-00569-9 · Cited by 15

Insights, opportunities, and challenges provided by large cell atlases

Hemberg, M., Marini, F., Ghazanfar, S., Al-Ajami, A., Abassi, N., Anchang, B., Benayoun, B. A., Cao, Y., Chen, K., Cuesta-Astroz, Y., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03771-8 · Cited by 16

Sensitive cluster-free differential expression testing.Preprint

Missarova, A. M., Rosen, L. U., Dann, E., Satija, R., & Marioni, J. C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.08.531744 · Cited by 15

Scaling Dense Representations for Single Cell with Transcriptome-Scale ContextPreprint

Ho, N., Ellington, C. N., Hou, J., Addagudi, S., Mo, S., Tao, T., Li, D., Zhuang, Y., Wang, H., Cheng, X., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.28.625303 · Cited by 17

Metabolomics across scales: from single cells to population studies

Alexandrov, T., & Zamboni, N. (2026) · Nature · doi:10.1038/s41586-026-10277-1 · Cited by 14

Single-cell sequencing in diabetic retinopathy: progress and prospects

Yang, T., Zhang, N., & Yang, N. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-024-06066-x · Cited by 15

Single-Cell Advances in Investigating and Understanding Chronic Kidney Disease and Diabetic Kidney Disease

Bhayana, S., Schytz, P. A., Olesen, E. T. B., Soh, K., & Das, V. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.07.007 · Cited by 14

Integrating Spatially‐Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities

Guo, B., Ling, W., Kwon, S. H., Panwar, P., Ghazanfar, S., Martinowich, K., & Hicks, S. C. (2025) · Small Methods · doi:10.1002/smtd.202401194 · Cited by 15

Initiative of clinical single‐cell biomedicine in clinical and translational medicine

Liu, F., Liu, X., Powell, C. A., Chen, C., Zeng, Y., Fang, H., & Wang, X. (2023) · Clinical and Translational Medicine · doi:10.1002/ctm2.1173 · Cited by 13

CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model AbilitiesPreprint

Choi, H., Park, J., Kim, S., Kim, J., Lee, D., Bae, S., Shin, H., & Lee, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.08.593094 · Cited by 13

Cancer genomics and bioinformatics in Latin American countries: applications, challenges, and perspectives

Torres-Narvaez, E. S., González, D., Artunduaga-Alvarado, J. A., & Ortega‐Recalde, O. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1584178 · Cited by 16

Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq

Carraro, C., Bonaguro, L., Srinivasa, R., van Uelft, M., Isakzai, V., Schulte-Schrepping, J., Gambhir, P., Elmzzahi, T., Montgomery, J. V., Hayer, H., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100598 · Cited by 14

The Swedish initiative for the study of Primary sclerosing cholangitis (SUPRIM)

Cornillet, M., Villard, C., Rorsman, F., Molinaro, A., Nilsson, E., Kechagias, S., von Seth, E., & Bergquist, A. M. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102526 · Cited by 12

The COVID-19 thrombus: distinguishing pathological, mechanistic, and phenotypic features and management

Becker, R. C., Tantry, U. S., Khan, M. H., & Gurbel, P. A. (2024) · Journal of Thrombosis and Thrombolysis · doi:10.1007/s11239-024-03028-4 · Cited by 14

Application of Single‐Cell Genomics to Animal Models of Periodontitis and Peri‐Implantitis

Hasuike, A., Easter, Q. T., Clark, D. L., & Byrd, K. M. (2024) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.14093 · Cited by 12

Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics

Yang, X., Huang, M., Chen, H., Dai, J., Chen, J., Chen, K., Zhou, J., Li, A., & Li, P. (2025) · Bioactive Materials · doi:10.1016/j.bioactmat.2025.07.047 · Cited by 13

Fast, flexible analysis of differences in cellular composition with crumblr

Hoffman, G. E., & Roussos, P. (2026) · Nature Communications · doi:10.1038/s41467-026-75681-7 · Cited by 15

Identification of drug candidates targeting monocyte reprogramming in people living with HIV

Knoll, R., Bonaguro, L., dos Santos, J. C., Warnat‐Herresthal, S., Cleophas, M. C. P., Blümel, E. P., Reusch, N., Horne, A., Herbert, M., Nuesch-Germano, M., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1275136 · Cited by 12

Cell fate simulation reveals cancer cell features in the tumor microenvironment

Sato, S., Rancourt, A., & Satoh, M. S. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.107697 · Cited by 11

Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mappingPreprint

Netskar, H., Pfefferle, A., Goodridge, J. P., Sohlberg, E., Dufva, O., Teichmann, S. A., Clancy, T., Horowitz, A., & Malmberg, K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.26.564050 · Cited by 12

Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions

Salem, H., Draz, M. M., Elkhalik, W. A., & Elbaz, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-11254-w · Cited by 11

Mapping Cell Atlases at the Single‐Cell Level

Ye, F., Wang, J., Li, J., Mei, Y., & Guo, G. (2023) · Advanced Science · doi:10.1002/advs.202305449 · Cited by 11

Adversarial training improves model interpretability in single-cell RNA-seq analysis

Sadria, M., Layton, A. T., & Bader, G. D. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad166 · Cited by 10

Cell Atlases as Roadmaps in Health and Disease

Regev, A. (2020) · The Keio Journal of Medicine · doi:10.2302/kjm.69-002-abst

The Human Cell Atlas from a cell census to a unified foundation model

Rood, J., Wynne, S., Robson, L., Hupalowska, A., Randell, J., Teichmann, S. A., & Regev, A. (2024) · Nature · doi:10.1038/s41586-024-08338-4 · Cited by 173

Human cross-tissue cell atlases: unprecedented resources towards systematic understanding of physiology and diseases

Zhang, P., & Li, S. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01201-w · Cited by 13

Exploring cell-type associations of rare disease phenotypes using non-diseased cell atlases

Novoa, J., Pazos, F., & Chagoyen, M. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag426 · Cited by 1

Mining single-cell data for cell type–disease associations

Chen, K., Farley, K. O., & Lassmann, T. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae180