The landscape of cell–cell communication through single-cell transcriptomics

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

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834

CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449

Simultaneous epitope and transcriptome measurement in single cells

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424

Single-cell reconstruction of the early maternal–fetal interface in humans

Vento-Tormo, R., Efremova, M., Botting, R. A., Turco, M. Y., Vento-Tormo, M., Meyer, K. B., Park, J.-E., Stephenson, E., Polański, K., Goncalves, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0698-6 · Cited by 2,358

Single-cell chromatin accessibility reveals principles of regulatory variation

Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308

NicheNet: Modeling intercellular communication by linking ligands to target genes

Browaeys, R., Saelens, W., & Saeys, Y. (2020) · Nature Methods · doi:10.1038/s41592-019-0667-5 · Cited by 2,192

Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH

Eng, C.-H. L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., et al. (2019) · Nature · doi:10.1038/s41586-019-1049-y · Cited by 1,754

A comparison of single-cell trajectory inference methods

Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670

The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth

de Visser, K. E., & Joyce, J. A. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.02.016 · Cited by 4,059

CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics

Jin, S., Plikus, M. V., & Nie, Q. (2025) · Nature Protocols · doi:10.1038/s41596-024-01045-4 · Cited by 1,448

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

Dimitrov, D., Türei, D., Garrido-Rodriguez, M., Burmedi, P. L., Nagai, J. S., Boys, C., Ramirez Flores, R. O., Kim, H., Szalai, B., Costa, I. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30755-0 · Cited by 664

Molecularly defined and spatially resolved cell atlas of the whole mouse brain

Zhang, M., Pan, X., Jung, W., Halpern, A. R., Eichhorn, S. W., Lei, Z., Cohen, L., Smith, K. A., Tasic, B., Yao, Z., et al. (2023) · Nature · doi:10.1038/s41586-023-06808-9 · Cited by 531

Screening cell–cell communication in spatial transcriptomics via collective optimal transport

(2023) · doi:10.1038/s41592-022-01728-4 · Cited by 484

Spatial components of molecular tissue biology

Palla, G., Fischer, D. S., Regev, A., & Theis, F. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01182-1 · Cited by 318

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication

Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 253

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Shao, X., Li, C., Yang, H., Lu, X., Liao, J., Qian, J., Wang, K., Cheng, J., Yang, P., Chen, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32111-8 · Cited by 205

The diversification of methods for studying cell–cell interactions and communication

Armingol, E., Baghdassarian, H., & Lewis, N. E. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-023-00685-8 · Cited by 229

Statistical and machine learning methods for spatially resolved transcriptomics data analysis

Zeng, Z., Li, Y., Li, Y., & Luo, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02653-7 · Cited by 186

Single-cell atlases: shared and tissue-specific cell types across human organs

Elmentaite, R., Conde, C. D., Yang, L., & Teichmann, S. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00449-w · Cited by 174

Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

Hu, T., Allam, M., Cai, S., Henderson, W. E., Yueh, B., Alici-Garipcan, A., Ievlev, A. V., Afkarian, M., Beyaz, S., & Coskun, A. F. (2023) · Nature Communications · doi:10.1038/s41467-023-43917-5 · Cited by 178

Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Liu, Z., Sun, D., & Wang, C. (2022) · Genome biology · doi:10.1186/s13059-022-02783-y · Cited by 170

Comparative analysis of cell–cell communication at single-cell resolution

Wilk, A. J., Shalek, A. K., Holmes, S., & Blish, C. A. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01782-z · Cited by 146

MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics dataPreprint

Browaeys, R., Gilis, J., Sang-aram, C., Bleser, P. D., Hoste, L., Tavernier, S. J., Lambrechts, D., Seurinck, R., & Saeys, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.13.544751 · Cited by 136

Computational solutions for spatial transcriptomics

Kleino, I., Frolovaitė, P., Suomi, T., & Elo, L. L. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.043 · Cited by 120

SpatialDM for rapid identification of spatially co-expressed ligand–receptor and revealing cell–cell communication patterns

Li, Z., Wang, T., Liu, P., & Huang, Y. (2023) · Nature Communications · doi:10.1038/s41467-023-39608-w · Cited by 130

Context-aware deconvolution of cell–cell communication with Tensor-cell2cell

Armingol, E., Baghdassarian, H., Martino, C., Pérez-López, A., Aamodt, C., Knight, R. D., & Lewis, N. E. (2022) · Nature Communications · doi:10.1038/s41467-022-31369-2 · Cited by 117

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

Raza, A., Salehi, H., Bashir, S., Tabassum, J., Jamla, M., Charagh, S., Barmukh, R., Mir, R. A., Bhat, B. A., Javed, M. A., et al. (2024) · Plant Cell Reports · doi:10.1007/s00299-024-03153-7 · Cited by 90

Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network

Yang, W., Wang, P., Xu, S., Wang, T., Luo, M., Cai, Y., Xu, C., Xue, G., Que, J., Ding, Q., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51329-2 · Cited by 85

Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Atta, L., & Fan, J. (2021) · Nature Communications · doi:10.1038/s41467-021-25557-9 · Cited by 77

ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methodsPreprint

Luo, J., Deng, M., Zhang, X., & Sun, X. (2023) · Genome Research · doi:10.1101/gr.278001.123 · Cited by 81

Spatial transcriptomics delineates molecular features and cellular plasticity in lung adenocarcinoma progression

Wang, Y., Liu, B., Min, Q., Yang, X., Yan, S., Ma, Y., Li, S., Fan, J., Wang, Y., Dong, B., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00591-7 · Cited by 82

Identify, quantify and characterize cellular communication from single-cell RNA sequencing data with scSeqComm

Baruzzo, G., Cesaro, G., & Camillo, B. D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac036 · Cited by 63

Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry

Zhang, Q., Jiang, S., Schroeder, A. R., Hu, J., Li, K., Zhang, B., Dai, D., Lee, E. B., Xiao, R., & Li, M. (2023) · Nature Communications · doi:10.1038/s41467-023-39895-3 · Cited by 57

Macrophages as key regulators of liver health and disease

Papachristoforou, E., & Ramachandran, P. (2022) · International review of cytology · doi:10.1016/bs.ircmb.2022.04.006 · Cited by 56

Alpha 7 nicotinic acetylcholine receptors signaling boosts cell-cell interactions in macrophages effecting anti-inflammatory and organ protection

Nakamura, Y., Matsumoto, H., Wu, C.-H., Fukaya, D., Uni, R., Hirakawa, Y., Katagiri, M., Yamada, S., Ko, T., Nomura, S., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05051-2 · Cited by 49

scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell–cell interactions

Li, H., Zhang, Z., Squires, M., Chen, X. X., & Zhang, X. (2025) · Nature Methods · doi:10.1038/s41592-025-02651-0 · Cited by 68

CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics

Bafna, M., Li, H., & Zhang, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad269 · Cited by 45

Multiscale proteomic modeling reveals protein networks driving Alzheimer’s disease pathogenesis

Wang, E., Yu, K., Cao, J., Wang, M., Katsel, P., Song, W., Wang, Z., Li, Y., Wang, X., Wang, Q., et al. (2025) · Cell · doi:10.1016/j.cell.2025.08.038 · Cited by 45

Crosstalk between glioblastoma and tumor microenvironment drives proneural–mesenchymal transition through ligand-receptor interactions

Lai, Y., Lu, X., Liao, Y., Ouyang, P., Wang, H., Zhang, X., Huang, G., Qi, S., & Li, Y. (2023) · Genes & Diseases · doi:10.1016/j.gendis.2023.05.025 · Cited by 41

Pathogenesis, therapeutic strategies and biomarker development based on “omics” analysis related to microglia in Alzheimer’s disease

Gao, C., Shen, X., Tan, Y., & Chen, S. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02580-1 · Cited by 48

Astronaut omics and the impact of space on the human body at scale

Rutter, L. A., Cope, H., MacKay, M. J., Herranz, R., Das, S., Ponomarev, S. A., Costes, S. V., Paul, A. M., Barker, R. J., Taylor, D. M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47237-0 · Cited by 38

Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics

Almet, A. A., Tsai, Y., Watanabe, M., & Nie, Q. H. (2024) · Nature Methods · doi:10.1038/s41592-024-02380-w · Cited by 40

A Roadmap for a Consensus Human Skin Cell Atlas and Single-Cell Data Standardization

Almet, A. A., Yuan, H., Annusver, K., Ramos, R., Liu, Y., Wiedemann, J., Sorkin, D. H., Xu, N., Sonkoly, E., Haniffa, M. A., et al. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.03.1679 · Cited by 33

Altered glia-neuron communication in Alzheimer’s Disease affects WNT, p53, and NFkB Signaling determined by snRNA-seq

Soelter, T. M., Howton, T. C., Clark, A. D., Oza, V. H., & Lasseigne, B. N. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01686-8 · Cited by 34

LIANA+: an all-in-one cell-cell communication frameworkPreprint

Dimitrov, D., Schäfer, P. S. L., Farr, E., Rodríguez-Mier, P., Lobentanzer, S., Dugourd, A. J. F., Tanevski, J., Flores, R. O. R., & Sáez-Rodríguez, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.19.553863 · Cited by 34

Learning cell communication from spatial graphs of cellsPreprint

Fischer, D. S., Schaar, A. C., & Theis, F. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.11.451750 · Cited by 32

LRLoop: a method to predict feedback loops in cell–cell communication

Xin, Y., Lyu, P., Jiang, J., Zhou, F., Wang, J., Blackshaw, S., & Qian, J. X. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac447 · Cited by 33

Single‐cell RNA sequencing in exploring the pathogenesis of diabetic retinopathy

Zhang, X. Y., Zhang, F., & Xu, X. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.1751 · Cited by 30

Reversible Chemistry for Cancer Therapy and Diagnosis

Xu, H., Hong, H., Kim, C., Lee, Y., Li, Y., Zhang, Y. S., Makvandi, P., Song, G., Zhang, H., Kang, H., et al. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00549 · Cited by 33

A systematic evaluation of the computational tools for ligand-receptor-based cell–cell interaction inference

Wang, S., Zheng, H., Choi, J. S., Lee, J. K., Li, X., & Hu, H. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac019 · Cited by 31

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Chu, L.-X., Wang, W.-J., Gu, X.-P., Wu, P., Gao, C., Zhang, Q., Wu, J., Jiang, D.-W., Huang, J.-Q., Ying, X.-W., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00537-4 · Cited by 29

Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research

Feng, S., Li, J., Tian, J., Lu, S., & Zhao, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032271 · Cited by 29

Deep Learning in Single-cell Analysis

Molho, D., Ding, J., Tang, W., Li, Z., Wen, H., Wang, Y., Venegas, J., Jin, W., Liu, R., Su, R., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641284 · Cited by 38

Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data

Jin, S., & Ramos, R. (2022) · Biochemical Society Transactions · doi:10.1042/bst20210863 · Cited by 29

Immunotherapy of cancer in single-cell RNA sequencing era: A precision medicine perspective

Erfanian, N., Derakhshani, A., Nasseri, S., Fereidouni, M., Baradaran, B., Tabrizi, N. J., Brunetti, O., Bernardini, R., Silvestris, N., & Safarpour, H. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.112558 · Cited by 28

Crosstalk of T cells within the ovarian cancer microenvironment

Chap, B. S., Rayroux, N., Grimm, A. J., Ghisoni, E., & Laniti, D. D. (2024) · Trends in cancer · doi:10.1016/j.trecan.2024.09.001 · Cited by 33

The Applications of Single-Cell RNA Sequencing in Atherosclerotic Disease

Slenders, L., Tessels, D. E., van der Laan, S. W., Pasterkamp, G., & Mokrý, M. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.826103 · Cited by 30

CellCommuNet: an atlas of cell–cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states

Ma, Q., Li, Q., Xiao, Z., & Pan, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad906 · Cited by 26

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

Baghdassarian, H., Dimitrov, D., Armingol, E., Sáez-Rodríguez, J., & Lewis, N. E. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100758 · Cited by 22

Spatiotemporal Omics-Refining the landscape of precision medicine

Zhang, J., Yin, J., Heng, Y., Xie, K., Chen, A., Amit, I., Bian, X.-W., & Xu, X. (2022) · Life Medicine · doi:10.1093/lifemedi/lnac053 · Cited by 27

Single-cell transcriptomics in tissue engineering and regenerative medicine

Ruta, A., Krishnan, K., & Elisseeff, J. H. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00132-7 · Cited by 25

scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

Yang, Y., Li, G., Zhong, Y., Xu, Q., Lin, Y.-T., Roman-Vicharra, C., Chapkin, R. S., & Cai, J. J. (2023) · Cell Systems · doi:10.1016/j.cels.2023.01.004 · Cited by 26

Deciphering intercellular signaling complexes by interaction-guided chemical proteomics

Zheng, J., Zheng, Z., Fu, C., Weng, Y., He, A., Ye, X., Gao, W., & Tian, R. (2023) · Nature Communications · doi:10.1038/s41467-023-39881-9 · Cited by 27

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology

Arulraj, T., Wang, H., Ippolito, A., Zhang, S., Fertig, E. J., & Popel, A. S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae131 · Cited by 20

Implementation and validation of single-cell genomics experiments in neuroscience

Colonna, M., Konopka, G., Liddelow, S. A., Nowakowski, T. J., Awatramani, R. B., Bateup, H. S., Cadwell, C. R., Caglayan, E., Chen, J. L., Gillis, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01814-0 · Cited by 22

Single-Cell RNA-Seq Analysis Reveals the Acquisition of Cancer Stem Cell Traits and Increase of Cell–Cell Signaling during EMT Progression

Bocci, F., Zhou, P., & Nie, Q. H. (2021) · Cancers · doi:10.3390/cancers13225726 · Cited by 20

Profiling immune cell tissue niches in the spatial -omics era

Lee, C. Y. C., McCaffrey, J., McGovern, D., & Clatworthy, M. R. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2024.11.001 · Cited by 21

Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets

Hao, M., Zou, X., & Jin, S. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.751158 · Cited by 19

Deciphering Cell-Cell Communication in Abdominal Aortic Aneurysm From Single-Cell RNA Transcriptomic Data

Yang, H. M., DeRoo, E., Zhou, T., & Liu, B. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.831789 · Cited by 17

Dysregulated ligand–receptor interactions from single-cell transcriptomics

Liu, Q., Hsu, C., Li, J., & Shyr, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac294 · Cited by 20

A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data

Xie, Z., Li, X., & Mora, A. (2023) · Biomolecules · doi:10.3390/biom13081211 · Cited by 20

CCPlotR: an R package for the visualization of cell–cell interactions

Ennis, S., Broin, P. Ó., & Szegezdi, É. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad130 · Cited by 18

New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics

Heller, G., Fuereder, T., Grandits, A. M., & Wieser, R. (2023) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.32604/or.2023.044774 · Cited by 19

exFINDER: identify external communication signals using single-cell transcriptomics data

He, C., Zhou, P., & Nie, Q. H. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad262 · Cited by 17

Resource allocation in mammalian systems

Baghdassarian, H. M., & Lewis, N. E. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108305 · Cited by 17

CytoSignal detects locations and dynamics of ligand–receptor signaling at cellular resolution from spatial transcriptomic data

Liu, J., Manabe, H., Qian, W., Orikasa, S., Ghaemmaghami, J., Wang, Y., Gu, Y., Chu, A. K. Y., Gadhvi, G. T., Song, Y., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02624-9 · Cited by 20

Deep Learning Applications in Single-Cell Omics Data AnalysisPreprint

Erfanian, N., Heydari, A. A., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Nasseri, S., Safarpour, H., & Sahebkar, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.26.470166 · Cited by 17

Intercellular communication in the tumour microecosystem: Mediators and therapeutic approaches for hepatocellular carcinoma

Wang, W., Jin, W., & Li, X. (2022) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2022.166528 · Cited by 16

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Li, R., Chen, X., & Yang, X. (2024) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1839 · Cited by 15

Predicting cell–cell communication by combining heterogeneous ensemble deep learning and weighted geometric mean

Peng, L., Liu, L., Huang, L., Bai, Z., Chen, M., & Chen, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112839 · Cited by 18

Nanotechnology Meets the Tumor Microenvironment: Unlocking New Horizons in Cancer Therapy

Yadav, P., Yadav, A. K., & Bhatia, D. D. (2025) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.5c01120 · Cited by 16

CITEdb: a manually curated database of cell–cell interactions in human

Shan, N., Lü, Y., Guo, H., Li, D., Jiang, J., Yan, L., Gao, J., Ren, Y., Zhao, X., & Hou, L. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac654 · Cited by 16

Single-cell pair-wise relationships untangled by composite embedding model

Subedi, S., & Park, Y. (2023) · iScience · doi:10.1016/j.isci.2023.106025 · Cited by 15

scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies

Baran, Y., & Doğan, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106634 · Cited by 14

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

Pandey, A. C., Bezney, J., DeAscanis, D., Kirsch, E., Ahmed, F., Crinklaw, A., Choudhary, K. S., Mandala, T., Deason, J., Hamidi, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59880-2 · Cited by 16

EasyCellType: marker-based cell-type annotation by automatically querying multiple databases

Li, R., Zhang, J. J., & Li, Z. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad029 · Cited by 12

Computational cell–cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment

Pong, A., Mah, C. K., Yeo, G. W.-M., & Lewis, N. E. (2023) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2023.103048 · Cited by 13

Dissecting intercellular and intracellular signaling networks with barcoded genetic tools

Herholt, A., Sahoo, V. K., Popović, L., Wehr, M. C., & Rossner, M. J. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.09.002 · Cited by 14

PEG-fibrinogen hydrogel microspheres as a scaffold for therapeutic delivery of immune cells

Cohen, N., Vagima, Y., Mouhadeb, O., Toister, E., Gutman, H., Lazar, S., Jayson, A., Artzy‐Schnirman, A., Sznitman, J., Ordentlich, A., et al. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.905557 · Cited by 13

Technical Advances and Applications of Spatial Transcriptomics

Liang, G., Yin, H. Z., & Ding, F. (2023) · GEN Biotechnology · doi:10.1089/genbio.2023.0032 · Cited by 15

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

Nie, W., Yu, Y., Wang, X., Wang, R., & Li, S. C. (2024) · Advanced Science · doi:10.1002/advs.202403572 · Cited by 13

Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data

Zhang, Z., Wang, Z., Sun, Y., Shen, J., Peng, Q., Li, T., & Zhou, P. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-025-00624-9 · Cited by 16

Deciphering colorectal cancer progression features and prognostic signature by single-cell RNA sequencing pseudotime trajectory analysis

Bazyari, M. J., Saadat, Z., Firouzjaei, A. A., & Aghaee‐Bakhtiari, S. H. (2023) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2023.101491 · Cited by 12

scHyper: reconstructing cell–cell communication through hypergraph neural networks

Li, W., Wang, H., Zhao, J., Xia, J., & Sun, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae436 · Cited by 13

Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data

Hou, J., Zhao, W., & Nie, Q. H. (2025) · Nature Communications · doi:10.1038/s41467-025-61149-7 · Cited by 13

Charting spatial ligand-target activity using Renoir

Rao, N., Kumar, T., Kazemi, D., Hou, S., Khakpoor, A., Eapen, M. M., Pai, R., Qiao, L., Mishra, A., Ginhoux, F., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72388-7 · Cited by 12

Modeling and inference of spatial intercellular communications and multilayer signaling regulations using stMLnetPreprint

Cheng, J., Yan, L., Nie, Q. H., & Sun, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.27.497696 · Cited by 11

SpatialDM: Rapid identification of spatially co-expressed ligand-receptor reveals cell-cell communication patternsPreprint

Zhuoxuan, L. I., Wang, T., Liu, P., & Huang, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.19.504616 · Cited by 11

Intercellular Tunneling Nanotubes as Natural Biophotonic Conveyors

Gong, Z., Wu, T., Zhao, Y., Guo, J., Zhang, Y., Li, B., & Li, Y. (2024) · ACS Nano · doi:10.1021/acsnano.4c12681 · Cited by 10

SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics

Bai, Y., Guo, X., Liu, K., Zheng, B., Wei, Y., Wang, Y., Zhang, W. H., Luo, Q., Yin, J., Wu, L. L., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03697-1 · Cited by 9

Single-Cell RNA Sequencing Analysis of Gene Regulatory Network Changes in the Development of Lung Adenocarcinoma

Yu, D., Zhang, S., Liu, Z., Xu, L., Chen, L., & Xie, L. (2023) · Biomolecules · doi:10.3390/biom13040671 · Cited by 9

The genomic physics of tumor–microenvironment crosstalk

Sang, M., Li, F., Dong, A., Gragnoli, C., Griffin, C. H., & Wu, R. (2023) · Physics Reports · doi:10.1016/j.physrep.2023.07.006 · Cited by 9

Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation

Wei, M., Shi, X., Tang, W., Lv, Q., Wu, Y., & Xu, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-85569-z · Cited by 9

Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis

Zhang, Z., Sun, Y., Peng, Q., Li, T., & Zhou, P. (2025) · Entropy · doi:10.3390/e27050453 · Cited by 18

Mining cell–cell signaling in single-cell transcriptomics atlases

Deng, M., Wang, Y., & Yan, Y. (2022) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2022.102101 · Cited by 10

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors

Chen, X., Chen, L., Kürten, C. H. L., Jabbari, F., Vujanovic, L. N., Ding, Y., Lu, B., Lu, K., Kulkarni, A., Tabib, T., et al. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010761 · Cited by 14

Detecting global and local hierarchical structures in cell-cell communication using CrossChat

Wang, X., Almet, A. A., & Nie, Q. H. (2024) · Nature Communications · doi:10.1038/s41467-024-54821-x · Cited by 9

SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell–cell communication

Liu, J., Ma, L., Ju, F., Zhao, C., & Yu, L. (2025) · BMC Biology · doi:10.1186/s12915-025-02141-x · Cited by 8

New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data

Shao, X., Lu, X., Liao, J., Chen, H., & Fan, X. (2020) · Protein & Cell · doi:10.1007/s13238-020-00727-5 · Cited by 164

CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics

Jin, S., Plikus, M. V., & Nie, Q. (2025) · Nature Protocols · doi:10.1038/s41596-024-01045-4 · Cited by 1,448

Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data

Jin, S., & Ramos, R. (2022) · Biochemical Society Transactions · doi:10.1042/bst20210863 · Cited by 29

Cell-Cell Communication Inference and Analysis: Biological Mechanisms, Computational Approaches, and Future Opportunities

Cheng, X., Huang, H., Su, Y., Nie, Q. H., Zou, X., & Jin, S. (2026) · CSIAM transactions on life sciences. · doi:10.4208/csiam-ls.so-2025-0022 · Cited by 1

Inference of Ligand–Receptor Pairs from Single-Cell Transcriptomics Data

Efremova, M., & Vento‐Tormo, R. (2021) · Methods in molecular biology · doi:10.1007/7651_2020_343 · Cited by 2