The DOI matches its Crossref record; 193 of the 193 references it cites were found in a registry or an index.
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Open accessAbstract
Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
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
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
Inference and analysis of cell-cell communication using CellChat
Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M. V., & Nie, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-21246-9 · Cited by 8,409
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
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
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Research works printed but not linked: 3
- Cho, C.-S. et al. Seq-Scope: submicrometer-resolution spatial transcriptomics for single cell and subcellular studies. bioRxiv https://doi.org/10.1101/2021.01.25.427807 (2021). · DOI
- Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015). · DOI · Paper page
- Bishop, C. M. Pattern Recognition and Machine Learning. Oxidation Communications Vol. 27 (Springer, 2004).
Cited by
Sorted by citations
Items 601–700 of 999
Li, J.-R., Pan, L.-X., Su, J., Tian, W., Lin, L.-P., Huang, Z.-H., Luo, C., Li, Z.-Z., Ma, L., Guo, P., et al. (2026) · Hepatology Communications · doi:10.1097/hc9.0000000000000925 · Cited by 3
Integrated single‑cell and spatial transcriptomics reveal the colorectal cancer microenvironment
Yang, R., Hu, S., Li, K., Wang, H., Zhou, X., Han, T., & Guan, L. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105311 · Cited by 5
Wang, Z., Luo, Q., Wu, J., Lu, L., Ding, W., Zhao, Y., Yongfeng, Y. U., Qiu, R., Zhu, L., Ouyang, X., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102741 · Cited by 4
Nuanpirom, J., & Charoensawan, V. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag152 · Cited by 3
From cells to niches: Rewiring cardiovascular disease through spatial immunity
Xie, C., Shu, X., Wu, Y., & Tian, Y. (2026) · International Immunopharmacology · doi:10.1016/j.intimp.2026.116731 · Cited by 3
Single‐cell insights into plant growth, adaptation, and evolution
Wang, F., Wang, F., Wu, Y., Pu, L., & Le, L. (2026) · Journal of Integrative Plant Biology · doi:10.1111/jipb.70272 · Cited by 3
T cell exhaustion landscapes and therapeutic modulation in cancer immunity
Bautista, J., & López‐Cortés, A. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1827716 · Cited by 5
Chang, W., Zhang, W., Li, K., & Huang, P. (2026) · iScience · doi:10.1016/j.isci.2026.116349 · Cited by 4
Single-cell transcriptome study in forensic medicine: prospective applications
Yang, Q., Wu, Y., Li, M., Cao, S., Guo, Y., Zhang, L., Chen, X., & Liang, W. (2022) · International Journal of Legal Medicine · doi:10.1007/s00414-022-02889-9 · Cited by 3
Campbell, I., Glinka, M., Shaban, F., Kirkwood, K. J., Nadalin, F., Adams, D. J., Papatheodorou, I., Burger, A., Baldock, R., Arends, M. J., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12123884 · Cited by 3
Dang, K., Zhao, Y., Ye, K., Guo, Y., Wang, W., Ge, Q., & Zhao, X. (2023) · Biotechnology Journal · doi:10.1002/biot.202300294 · Cited by 3
Bioinformatics and Biomedical Computing
Cai, H., Chen, J., Zhang, F., & Wang, J. (2024) · Fundamental Research · doi:10.1016/j.fmre.2024.06.001 · Cited by 3
Yin, Z., Song, Y., & Wang, L. (2024) · Cancer Cell International · doi:10.1186/s12935-024-03574-0 · Cited by 3
Role of epigenetics in paediatric cancer pathogenesis & drug resistance
Leung, J. Y., Chiu, H. Y., & Taneja, R. (2025) · British Journal of Cancer · doi:10.1038/s41416-025-02961-2 · Cited by 3
A comparison of integration methods for single‐cell RNA sequencing data and ATAC sequencing data
Kan, Y., Wang, W., Qi, Y., Zhang, Z., Liang, X., & Jin, S. (2025) · Quantitative Biology · doi:10.1002/qub2.91 · Cited by 4
Lu, B., Liu, Y., Ji, G., Yao, Y., Yang, Z., Zhu, B., Wang, L., Dong, K., Li, Y., Shi, J., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06661-6 · Cited by 3
Advances in Hereditary Colorectal Cancer: How Precision Medicine Is Changing the Game
Lin, S., Zhou, C., Chen, H., Zhou, X., Yang, H., Sun, L., Zhang, L., & Zhang, Y. (2025) · Cancers · doi:10.3390/cancers17213461 · Cited by 4
Qiu, C., Li, Y., Gong, Y., Sherman, W., Tian, D., Lin, W., Pan, Z., Afolabi, B., Thumbigere‐Math, V., Su, K.-J., et al. (2026) · Bone Research · doi:10.1038/s41413-026-00520-w · Cited by 3
Putting genes on the map: Spatial transcriptomics of the maize shoot apical meristem
Zhang, W. (2021) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiab601 · Cited by 3
Cutting edge technologies in chronic inflammation research
Laman, J. D. (2022) · Experimental Dermatology · doi:10.1111/exd.14648 · Cited by 2
Single-cell sequencing: promises and challenges for human genetics
Sreenivasan, V. K. A., Henck, J., & Spielmann, M. (2022) · Medizinische Genetik · doi:10.1515/medgen-2022-2156 · Cited by 3
Physiology and transcriptome of Eucommia ulmoides seeds at different germination stages
Liu, J. B., Qiu, S., Xue, T., & Yuan, Y. (2024) · Plant Signaling & Behavior · doi:10.1080/15592324.2024.2329487 · Cited by 3
Decoding spatial transcriptomics: Current trends and future prospects in data analysis methodologies
Li, R., Chen, X., & Yang, X. (2024) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2024-0332 · Cited by 3
Qiu, J., Peng, S., Qu, R., Wu, L., Xing, L., Zhang, L., & Sun, J. (2024) · Clinical Science · doi:10.1042/cs20241658 · Cited by 3
Insights into Leukemia from Single-Cell Genomics
Sariipek, N. E., & van Galen, P. (2024) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-062024-060443 · Cited by 3
Shining a light on cell biology of the nucleus with single-cell sequencing
van den Berg, J., & Zeller, P. J. (2025) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2025.102468 · Cited by 4
An Overview of Spatial Transcriptomics Methodologies in Traversing the Biological System
John, A., Nallusamy, S., Senthil, N., Nathan, B., Venugopal, A., & Chinnaswamy, H. B. (2025) · Current Bioinformatics · doi:10.2174/0115748936352261241224053340 · Cited by 3
Dimensionality reduction for visualizing spatially resolved profiling data using SpaSNE
Zhou, Y., Tang, C., Xiao, X., Zhan, X., Wang, T., Xiao, G., & Xu, L. (2025) · GigaScience · doi:10.1093/gigascience/giaf002 · Cited by 5
Deciphering cancer stem cells in liver cancers: new tools with improved resolution
Zhou, L., & Ma, S. (2022) · Carcinogenesis · doi:10.1093/carcin/bgac028 · Cited by 3
Update Breast Cancer 2022 Part 1 – Early Stage Breast Cancer
Welslau, M., Müller, V., Lüftner, D., Schütz, F., Stickeler, E., Fasching, P. A., Janni, W., Thomssen, C., Witzel, I., Fehm, T. N., et al. (2022) · Geburtshilfe und Frauenheilkunde · doi:10.1055/a-1811-6106 · Cited by 3
Wang, X., Fan, W., Xu, Z., Zhang, Q., Li, N., Li, R., Wang, G., He, S., Li, W., Liao, D., et al. (2022) · MedComm · doi:10.1002/mco2.198 · Cited by 3
Yang, M., Wang, Y., Liu, X., Huang, H., Lan, L., Ni, M. A., Han, Y., Yang, H., & Mu, F. (2022) · Research Square · doi:10.21203/rs.3.rs-1067780/v1 · Cited by 3
Fisher, N. C., Byrne, R. M., Leslie, H., Wood, C. S., Legrini, A., Cameron, A. J., Ahmaderaghi, B., Corry, S. M., Malla, S. B., Amirkhah, R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.15.488354 · Cited by 3
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2024) · eLife · doi:10.7554/elife.88431.2 · Cited by 3
Teo, A. Y. Y., Gautier, M., Brock, L. P., Tsai, J., de Coucy, A., Laskaratos, A., Regazzi, N., Barraud, Q., Sofroniew, M. V., Anderson, M. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.13.598641 · Cited by 4
Tackling the Complexity of Spatial Transcriptomics Data Interpretation with Large Language ModelsPreprint
Khan, T., Farley, C. M., Wilson, J. J., Chang, C., & Chaussabel, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.28.625773 · Cited by 3
Neuronal Heterogeneity in the Medial Septum and Diagonal Band of Broca: Classes and ContinuaPreprint
Kuhn, F., Mocellin, P., Pupe, S., Wang, L., Lemire, A. L., Sosulina, L., Barnstedt, O., Spruston, N., Cembrowski, M. S., & Remy, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.27.609593 · Cited by 4
He, L., Wang, B., Wiggin, T., Chen, R., Wang, H., Yang, B., Tattikota, S. G., Maziashvili, L., Zhang, T., Revuru, S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.06.710199 · Cited by 3
Zhang, J., Yuan, W., Zhang, T., Liu, X., & Xu, D. (2025) · Discover Oncology · doi:10.1007/s12672-025-03304-6 · Cited by 2
MuST: multiple-modality structure transformation for single-cell spatial transcriptomics
Zang, Z., Li, L., Xu, Y., Duan, C., Shen, Y., Sun, Y., Lei, Z., & Li, S. Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf405 · Cited by 2
Ladd, S., Talkington, A., O’Sullivan, M., Barnes, R. W., Wessel, R. E., Hanson, G. F., & Dolatshahi, S. (2025) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-025-00857-y · Cited by 2
Nguyen, L., Vallejos, C. A., Mill, P., & Megaw, R. D. (2025) · Genes · doi:10.3390/genes16091088 · Cited by 2
Wang, H., Gao, P., Feng, S., & Ma, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf528 · Cited by 2
Locke, R. C., Miller, L. M., Lemmon, E. A., Assi, S. S., Jones, D. L., Bonnevie, E. D., Burdick, J. A., Heo, S. J., & Mauck, R. L. (2025) · Acta Biomaterialia · doi:10.1016/j.actbio.2025.10.007 · Cited by 2
A Single-cell and Spatially Resolved Cell Atlas of Human Esophageal Squamous Cell Carcinoma
Shi, Y., An, K., Qi, Y., Zhang, X., Wang, Y., Zhang, X., Zhou, S., Li, O., Song, Y., Zhou, J., et al. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf095 · Cited by 3
Sarkar, A., Rabinovich, E. P., & Slingluff, C. L. (2025) · Vaccines · doi:10.3390/vaccines13111150 · Cited by 2
Gao, Q., Wang, Z., Liu, C., Zhang, H., Fan, L., Fan, J., Tan, J., Cai, Z., Li, J., Cai, J., et al. (2025) · Life Sciences · doi:10.1016/j.lfs.2025.124107 · Cited by 2
JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics
Guo, Y., Liu, J. S., Cheng, H., & Ma, Y. (2025) · neural information processing systems · doi:10.52202/085713-0867 · Cited by 2
Yang, Y., Chen, T., Xu, Z., Cai, J., Wang, W., Pan, L., Cheng, X., & Wang, A. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1737749 · Cited by 2
Lin, Z., Jariyasakulroj, S., Shu, Y., Chen, J., Chang, Q., Ko, P., Qiu, Y., Chen, F., Ahn, D., Zhao, Z., et al. (2026) · Advanced Science · doi:10.1002/advs.202519816 · Cited by 2
Li, Y., Ma, T., Lu, H., Yan, Y., Xue, Y., Suo, J., Chen, Y., & Peng, Z. (2026) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000001023 · Cited by 2
SPACESEQUEST: A UNIFIED PIPELINE FOR SPATIAL TRANSCRIPTOMICS DATA ANALYSIS
Sun, Y. H., Piya, S., Ouyang, Z., Chen, Y., Gagnon, J., Cao, S., Zhang, H., Song, B., Zhu, J. K., Chandratre, K. R., et al. (2026) · Journal of Advance Research in Pharmacy & Biological Science · doi:10.61841/nn-pbs-12-1-1 · Cited by 2
The Emerging Role of Olfactory Receptors: From Genomics to Precision Medicine
Dubey, N., Rai, S., Tripathi, P., Arya, A., Sahoo, A. K., & Varadwaj, P. K. (2026) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-026-00832-x · Cited by 4
Lai, Z., Zhang, B., Fu, Z., Li, R., Qian, Y., Zhang, Y., Xu, P., & Du, Y. (2026) · Die Naturwissenschaften · doi:10.1007/s00114-026-02093-4 · Cited by 2
Long, L., Gao, Z., Qi, W., Zhao, X., Zhang, F., Yu, Z., Sun, W., Huang, Y., Wang, Z., Yan, S., et al. (2026) · Molecular Cancer · doi:10.1186/s12943-026-02652-3 · Cited by 2
Game of clones: decipher lineage plasticity in hormone-driven cancers
Leonita, A., Cheng, S., Warrick, J., Kim, I. Y., Deng, S., & Mu, P. (2026) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-026-06171-8 · Cited by 2
The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders
Wang, Q., & Dwivedi, Y. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106721 · Cited by 3
Yu, K., Chen, J., Chu, Y.-Y., Nair, S. K., Crupi, E., Hasanov, E., Mei, Y., Han, X., Liu, Y., Liu, Y., et al. (2026) · Cancer Discovery · doi:10.1158/2159-8290.cd-26-0099 · Cited by 4
Fan, J., Xue, Q., Li, J., Liu, Y., Liu, F., Li, Z., Wang, J., Xu, Y., zhang, H., Zhu, Z., et al. (2026) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2026.111376 · Cited by 2
hSNMF: Hybrid Spatially Regularized NMF for Image-Derived Spatial Transcriptomics
Mahmud, M. I., Kochat, V., Satpati, S., Dwarampudi, J. M. R., Anzum, H., Rai, K., & Banerjee, T. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515324 · Cited by 2
Lin, C., Huang, S., Yang, L., Wang, Z. P., Zhou, H., Zhang, Q., & Zhang, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1855906 · Cited by 2
Fu, Z., Li, L., Perry, C. J., Ghali, F., Tan, W. S., Kim, I. Y., & Mu, P. (2026) · Gene · doi:10.1016/j.gene.2026.150293 · Cited by 2
Cardiovascular utility of single cell RNA-Seq
Safabakhsh, S., Feng, W., Miller, C. L., & Laksman, Z. (2022) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000001014 · Cited by 2
Single-cell protein-DNA interactomics and multiomics tools for deciphering genome regulation
Xiong, H., Wang, R., & He, A. (2023) · National Science Open · doi:10.1360/nso/20220057 · Cited by 3
Mao, S., Zeng, L., Yang, Y., Liu, Z., & Zhang, L. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1170942 · Cited by 2
Smit, M. M., Feller, K. J., You, L., & Chien, M. (2023) · STAR Protocols · doi:10.1016/j.xpro.2023.102447 · Cited by 2
Wang, J., Liu, C.-H., Yang, L.-T., Chen, H.-X., Zheng, M.-Q., Wan, Y.-B., Hong, X.-X., Li, S., Han, J., Luo, R.-B., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-41874-z · Cited by 1
Tu, S., Chen, J. Z., Singh, S. R. K., Aydın, A. M., Gökden, N., Tam, N. N. C., Leung, Y., Langford, T., & Ho, S. (2023) · Cancers · doi:10.3390/cancers15235533 · Cited by 2
Toolkit for mapping the clonal landscape of tumor-infiltrating B cells
Serebrovskaya, E. O., Bryushkova, E. A., Lukyanov, D. K., Mushenkova, N. V., Chudakov, D. M., & Turchaninova, M. A. (2024) · Seminars in Immunology · doi:10.1016/j.smim.2024.101864 · Cited by 3
Brennan, P. G., Mota, L., Aridi, T. G., Patel, N., Liang, P., & Ferran, C. J. (2024) · Annals of Vascular Surgery · doi:10.1016/j.avsg.2024.01.031 · Cited by 2
Danino-Levi, M., Goldberg, T., Keter, M., Akselrod, N., Shprach-Buaron, N., Safra, M., Singer, G., & Alon, S. (2024) · RNA · doi:10.1261/rna.079801.123 · Cited by 2
Pita-Juárez, Y. H., Kalavros, N., Karagkouni, D., Ma, Y., Katopodi, X.-L., & Vlachos, I. S. (2024) · Contemporary diabetes · doi:10.1007/978-3-031-55715-6_11 · Cited by 2
Chen, R., Nie, P., Wang, J., & Wang, G. (2024) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1865 · Cited by 3
A single-cell atlas of spatial and temporal gene expression in the mouse cranial neural plate
Brooks, E., Moorman, A. R., Bhattacharya, B., Prudhomme, I. S., Land, M., Alcorn, H. L., Sharma, R., Pe’er, D., & Zallen, J. A. (2025) · eLife · doi:10.7554/elife.102819.3 · Cited by 2
Type 1 innate lymphoid cells: a biomarker and therapeutic candidate in sarcoidosis
Cho, I., & Ji, A. L. (2024) · Journal of Clinical Investigation · doi:10.1172/jci183708 · Cited by 3
Self-citationAdvancements in artificial intelligence-driven spatial transcriptomics: Decoding cellular complexity
Bano, N., Sajid, I., Barh, D., & Raza, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27523-4.00006-8 · Cited by 2
Understanding single-cell multi-omics for high resolution cellular interactome mapping
Joshi, S., Kumari, A., & Joshi, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23736-2.00002-2 · Cited by 2
Cancer vaccine trial evaluations: immunobridging and potential immunological endpoints
Hussain, A., Moxley-Wyles, B., Bryan, M. E., Gordon-Weeks, A. N., Al-Obaidi, I., Sandhu, C., & Lee, L. Y. W. (2024) · Immunotherapy Advances · doi:10.1093/immadv/ltaf016 · Cited by 2
CCR2+ monocytes promote memory CD8+ T-cell differentiation via membrane-bound TGF-β
Sun, L., Zhang, C., Jiao, A., Su, Y., Zhang, T., Wang, Q., Ge, Y., Chen, Y., Yuan, N., Zhang, L., et al. (2025) · Cellular and Molecular Immunology · doi:10.1038/s41423-025-01299-2 · Cited by 2
Xiaoting, W. A. N. G., Zhang, J., Jiang, J., & Wang, Y. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110369 · Cited by 2
Radiation effects research foundation—a view to the future
Gray, J. W., & Feinberg, A. P. (2025) · Carcinogenesis · doi:10.1093/carcin/bgaf061 · Cited by 2
Liu, A., Zhao, Y., Kim, W.-K., Shen, H., Ding, Z., & Deng, H. (2026) · Genome biology · doi:10.1186/s13059-026-04168-x · Cited by 1
Single whole genome sequencing analysis blazes the trail for precision medicine
Napoli, G. C., Chau, C. H., & Figg, W. D. (2022) · Cancer Biology & Therapy · doi:10.1080/15384047.2022.2033058 · Cited by 2
Single Cell Multiomic Approaches to Disentangle T Cell Heterogeneity
Abondio, P., Intinis, C. D., Júnior, J. L. D. S. G. V., & Pace, L. (2022) · Immunology Letters · doi:10.1016/j.imlet.2022.04.008 · Cited by 2
Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants
Chen, Y., Paramo, M. I., Zhang, Y., Li, Y., Shah, S. R., Jin, Y., Zhang, J., Pan, X., & Yu, H. (2023) · Annual Review of Genetics · doi:10.1146/annurev-genet-030723-120717 · Cited by 3
Zhang, B., Ji, Z., & Ji, H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.27.466024 · Cited by 2
Supervised spatial inference of dissociated single-cell data with SageNetPreprint
Heidari, E., Lohoff, T., Tyser, R. C. V., Marioni, J. C., Robinson, M. D., & Ghazanfar, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.14.488419 · Cited by 2
Wan, X., Xiao, J., Tam, S. S. T., Cai, M., Sugimura, R., Wang, Y., Wan, X., Lin, Z., Wu, A. R., & Yang, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.14.532529 · Cited by 2
Yu, N., Jin, Z., Liang, C., Zhang, J., & Yang, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.28.546974 · Cited by 2
Li, J., Shyr, Y., & Liu, Q. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.13.562261 · Cited by 2
C ell 3 D P ositioning by O ptical encoding (C3PO ) and its application to spatial transcriptomicsPreprint
Cotterell, J., Swoger, J., Robert‐Moreno, A., Cardona-Blaya, H., Musy, M., & Sharpe, J. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.12.584578 · Cited by 2
Mapping lineage-resolved scRNA-seq data with spatial transcriptomics using TemSOMapPreprint
Pan, X., Danies-Lopez, A., & Zhang, X. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.31.621331 · Cited by 2
A single-cell atlas of spatial and temporal gene expression in the mouse cranial neural platePreprint
Brooks, E., Moorman, A. R., Bhattacharya, B., Prudhomme, I. S., Land, M., Alcorn, H. L., Sharma, R., Pe’er, D., & Zallen, J. A. (2024) · eLife · doi:10.7554/elife.102819.1 · Cited by 2
GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data AnalysisPreprint
Wang, S., Liu, Y., Zhang, Z., Ma, Q., Song, Q., & Bian, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.01.662625 · Cited by 2
Hameed, S. A., Iglesias-Martinez, L. F., Kölch, W., & Zhernovkov, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.07.663470 · Cited by 2
Prakrithi, P., Grice, L. F., Zhang, F., Hockey, L., Tan, S. X., Tan, X., Xiong, Z., Mulay, O., Causer, A., Newman, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.25.666708 · Cited by 6
Yu, X., Wan, X., Tian, L., Chen, Y., Liu, Y., Yu, T., Yang, C., & Xiao, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.21.671426 · Cited by 3
A dataset of spatially resolved transcriptomics of post-natal cardiac development in mice
Zheng, Y., Sun, T., Pu, Z., Li, L., Wu, X., Yang, Y., Lei, M., Tan, X., Li, T., Ou, X., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-05838-4 · Cited by 2
Li, Y., Zhou, X., & Cao, H. (2022) · Genetics · doi:10.1093/genetics/iyac095 · Cited by 1
Takahashi, H., Kawahara, D., & Kikuchi, Y. (2022) · Cancers · doi:10.3390/cancers14174080 · Cited by 1
Metadata
- Authors
- Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A.
- Affiliations
- Stanford Cancer Institute (US)
- VA Palo Alto Health Care System (US)
- Corresponding author
- Khavari, Paul A.
- Venue
- Nature Reviews Genetics
- Published
- 2021-06-18
- Volume
- 22
- Issue
- 10
- Pages
- 627-644
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC9888017
- OpenAlex
- W3171692328
- Cited by
- 1,059
- References
- 183
- Citations by year
- 2026: 233 · 2025: 203 · 2024: 242 · 2023: 196 · 2022: 157 · 2021: 27
- FWCI
- 16.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (R01CA142635)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR049737, R01AR043799, R01AR076965)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- single-cell RNA sequencing, spatial transcriptomics, data integration, intercellular communication, tissue dynamics, in situ sequencing, cell localization
- MeSH terms
- Animals, Cell Communication, Humans, Software, Sequence Analysis, RNA, Computational Biology, Single-Cell Analysis, Transcriptome
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