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 tumor ecosystem: Rewiring an open, systemically integrated network for therapeutic gain

Chiodelli, P., Papait, A., Agoni, L., Quaglia, F., Silini, A. R., & Parolini, O. (2026) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2026.119047 · Cited by 10

An improved hierarchical variational autoencoder for cell–cell communication estimation using single-cell RNA-seq data

Liu, S., Zhang, Y., Peng, J., & Shang, X. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac056 · Cited by 9

Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model

Wu, D., Gaskins, J., Sekula, M. N., & Datta, S. (2023) · Genes · doi:10.3390/genes14071368 · Cited by 8

Creation of a High‐Throughput Microfluidic Platform for Single‐Cell Transcriptome Sequencing of Cell‐Cell Interactions

Qi, J., Zhu, H., Li, Y., Guan, X., He, Y., Ren, G., Guo, Q., Liu, L., Gu, Y., Dong, X., et al. (2023) · Small Methods · doi:10.1002/smtd.202300730 · Cited by 8

Can the Tumor Microenvironment Alter Ion Channels? Unraveling Their Role in Cancer

Gentile, R., Feudi, D., Sallicandro, L., & Biagini, A. (2025) · Cancers · doi:10.3390/cancers17071244 · Cited by 7

Dynamic gene regulatory network inference from single-cell data using optimal transport

Lamoline, F., Haasler, I., Karlsson, J., Gonçalves, J. M., & Aalto, A. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf394 · Cited by 8

How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification

Mircea, M., & Semrau, S. (2021) · Biochemical Society Transactions · doi:10.1042/bst20210135 · Cited by 9

Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication

Shilts, J., & Wright, G. J. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102523-103821 · Cited by 7

Answering open questions in biology using spatial genomics and structured methods

Jena, S. G., Verma, A., & Engelhardt, B. E. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05912-5 · Cited by 7

Integrating multiomics and Single-Cell communication analysis to uncover Ankylosing spondylitis mechanisms

Chen, T., Zhan, X., Zhu, J., Zhou, C., Huang, C., Wu, S., Yao, Y., Zhang, B., Feng, S., Chen, J., et al. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.113276 · Cited by 6

Comparison of single-cell long-read and short-read transcriptome sequencing via cDNA molecule matching: quality evaluation of the MAS-ISO-seq approach

Zajac, N., Zhang, Q., Bratus-Neuenschwander, A., Qi, W., Bolck, H. A., Karakulak, T., Oltra, T. C., Moch, H., Kahraman, A., & Rehrauer, H. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf089 · Cited by 10

SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes

Meng-Lin, K., Ung, C. Y., Zhang, C., Weisskittel, T., Wiśniewski, P., Zhang, Z. M., Tan, S.-H., Yeo, K. S., Zhu, S., Correia, C., et al. (2023) · Biomolecules · doi:10.3390/biom13060895 · Cited by 6

ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor–ligand interactions that specify cell fate

Kyaw, W., Chai, R. C. C., Khoo, W. H., Goldstein, L. D., Croucher, P. I., Murray, J. M., & Phan, T. G. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad765 · Cited by 6

Intercellular signaling reinforces single-cell level phenotypic transitions and facilitates robust re-equilibrium of heterogeneous cancer cell populations

Lopez, D. E. R., Tyson, D. R., & Hong, T. (2025) · Cell Communication and Signaling · doi:10.1186/s12964-025-02405-7 · Cited by 5

Dissecting cellular heterogeneity and intercellular communication in cholangiocarcinoma: implications for individualized therapeutic strategies

Zhou, Z., Zhang, Y., Xu, Z., Tang, X., Chen, X., Guan, J., & Zhang, Z. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2023.1241834 · Cited by 5

CellMag‐CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications

Rupp, B., Cook, C. D., Purcell, E., Pop, M., Radomski, A., Mesyngier, N., Bailey, R. C., & Nagrath, S. (2024) · Advanced Biology · doi:10.1002/adbi.202400066 · Cited by 5

Anti-Ferroptotic Treatment Deteriorates Myocardial Infarction by Inhibiting Angiogenesis and Altering Immune Response

Stairley, R. A., Trouten, A. M., Li, S., Roddy, P. L., DeLeon‐Pennell, K. Y., Lee, K., Sucov, H. M., Liu, C., & Tao, G. (2024) · Antioxidants · doi:10.3390/antiox13070769 · Cited by 5

FastCCC: a permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies

Hou, S., Ma, W., & Zhou, X. (2025) · Nature Communications · doi:10.1038/s41467-025-66272-z · Cited by 6

Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics

Wang, X., Almet, A. A., & Nie, Q. H. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.948508 · Cited by 5

Spatial Chemoproteomics for Mapping the Active Proteome

Swenson, C., Pillai, K. S., Carlos, A. J., & Moellering, R. E. (2023) · Israel Journal of Chemistry · doi:10.1002/ijch.202200104 · Cited by 3

Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity

Huang, W., Hu, Y., Wang, L., Wu, G., Zhang, C., & Shi, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf021 · Cited by 4

Novel ribosome biogenesis-related biomarkers and therapeutic targets identified in psoriasis

Liu, J., Luo, N., Ma, W., Qiao, Z., Zhong, X., Cheng, Z., Hao, P., & Lv, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-03833-8 · Cited by 4

RIDDEN: Data-driven inference of receptor activity from transcriptomic data

Barsi, S., Varga, E., Dimitrov, D., Sáez-Rodríguez, J., Hunyady, L., & Szalai, B. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013188 · Cited by 3

SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets

Seal, S., & Neelon, B. (2026) · GigaScience · doi:10.1093/gigascience/giag006 · Cited by 7

PriorCCI: Interpretable Deep Learning Framework for Identifying Key Ligand–Receptor Interactions Between Specific Cell Types from Single-Cell Transcriptomes

Kim, H., Choi, E., Shim, Y., & Kwon, J. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26157110 · Cited by 3

Gut-larynx axis and its contribution to laryngeal immunity

An, R., Xie, E., Binns, J., Rey, F. E., Kendziorski, C. M., & Thibeault, S. L. (2025) · mSystems · doi:10.1128/msystems.01044-25 · Cited by 3

Mapping TAM–tumor crosstalk in glioma via ligand–receptor multi-omics: mechanisms of immune evasion

Zhang, D., Ma, Y., & Feng, D. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1699915 · Cited by 3

Advancing spatial cellular communication inference with ligand diffusion and transport model

Yu, J., Zhao, J., Ren, T., Sun, D., & Wu, L. (2026) · Communications Biology · doi:10.1038/s42003-025-09413-w · Cited by 3

Tensor-cell2cell v2 unravels coordinated dynamics of protein- and metabolite-mediated cell–cell communication

Armingol, E., Larsen, R. O., Gale, L., Cequeira, M., Baghdassarian, H., & Lewis, N. E. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btaf667 · Cited by 11

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Du, J., Gu, X.-R., Yu, X., Cao, Y.-J., & Hou, J. (2023) · Blood Science · doi:10.1097/bs9.0000000000000172 · 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

A Gene Expression Clustering Method to Extraction of Cell-to-Cell Biological Communication

Wang, H., Sha, Y., Wang, D., & Nazari, H. (2022) · INTELIGENCIA ARTIFICIAL · doi:10.4114/intartif.vol25iss69pp1-12 · Cited by 3

Physical cell-cell interactions regulate transcriptional programmes that control the responses of high grade serous ovarian cancer patients to therapyPreprint

Hameed, S. A., Kölch, W., Brennan, D. J., & Zhernovkov, V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.16.589574 · Cited by 3

In-silico discovery of druggable molecular signatures that drive dengue fever to severe dengue fever highlighting common pathogenesis through single-cell RNA-Seq analysis

Noman, M. A., Latif, A., Ahmed, M. F., Islam, M. S., Pappu, M. A. A., Hossain, M. S., Hossen, M. B., Noor, T., Mollah, M. M. H., Kabir, M. H., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-23000-3 · Cited by 3

Decoding the archipelago: single-cell biomarkers rechart the molecular geography of acute myeloid leukemia

Kang, F., Yuan, H., Samarkhazan, H. S., & Tavakoli, F. (2026) · Cell Communication and Signaling · doi:10.1186/s12964-026-02734-1 · Cited by 4

Defining and identifying cell sub-crosstalk pairs for characterizing cell–cell communication patterns

Zhang, C., Hu, Y., & Gao, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-42883-8 · Cited by 3

TWCOM: an R package for inference of cell–cell communication on spatially resolved transcriptomics data

Wu, D., & Datta, S. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae101 · Cited by 2

collectNET: a web server for integrated inference of cell–cell communication network

Pan, Y., Gao, Z., Cui, X., Li, Z., & Jiang, R. (2024) · Database · doi:10.1093/database/baae098 · Cited by 2

Epigenetic Reprogramming by Decitabine in Retinoblastoma

Gherardini, L. M., Sharma, A., Taranta, M., & Cinti, C. (2025) · Frontiers in Bioscience-Landmark · doi:10.31083/fbl33386 · Cited by 2

Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data

Cesaro, G., Baruzzo, G., Tussardi, G., & Camillo, B. D. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf084 · Cited by 3

Less is more: improving cell-type identification with augmentation-free single-cell RNA-Seq contrastive learning

Alsaggaf, I., Buchan, D. W. A., & Wan, C. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf437 · Cited by 2

Interactions endothélium vasculaire – cellules immunitaires : un point de contrôle clef des lésions digestives radio-induites

Mondini, M., Guipaud, O., François, A., Mathieu, N., Deutsch, É., & Milliat, F. (2023) · Cancer/Radiothérapie · doi:10.1016/j.canrad.2023.06.013 · 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

Direct cell interactions potentially regulate transcriptional programmes that control the responses of high grade serous ovarian cancer patients to therapy

Hameed, S. A., Kölch, W., Brennan, D. J., & Zhernovkov, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-98463-5 · Cited by 3

Identification of the role of MED6 in the development and prognosis of lung adenocarcinoma based on multi-omics profiling

Yang, C., Cheng, D., Wang, S., Wang, B., Li, Y., Wang, G., Wang, X., Shi, C., Tian, Y., Zhu, K., et al. (2025) · Journal of Cancer · doi:10.7150/jca.110981 · Cited by 1

scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding

Jia, C., Wang, H., Zhao, J., Xia, J., & Zheng, C. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013027 · Cited by 1

Delineation of single-cell Altas provides new insights for development of coronary artery lesions in Kawasaki disease: bad and good signaling molecules

Lin, Q., Lv, X., Qiu, Q., Mei, L., Chen, L., Song, S., Liu, W. X., Jiang, X., Huang, M., Shen, L., et al. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1596643 · Cited by 1

Advancing Cardiovascular Medicine: Innovative Therapeutic Pathways with Single-Cell Technologies

Ji, J., Lindberg, E. L., & Reichart, D. (2025) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-025-01100-7 · Cited by 1

Hi-C3: a statistical inference-based model for reconstructing higher-order cell–cell communication networks

Tong, Y., Hong, R., Li, M., Yang, N., Deng, W., Tang, H., Zeng, T., & Liu, R. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf568 · Cited by 1

Transcriptomic integration and functional validation reveal PLAC8, CLCA4, and ADAMDEC1 as key biomarkers linking periodontitis and colorectal cancer

Peng, E., Hu, S., & Liu, S. (2026) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2026.102206 · Cited by 1

scComm: a contrastive learning framework for deciphering cell–cell communications at single-cell resolution

Jin, Z., Tang, Z., Li, X., Zhang, K., Xie, Z., & Zhang, N. (2026) · Genome biology · doi:10.1186/s13059-026-04043-9 · Cited by 2

Benchmarking tools for deciphering cellular crosstalk in spatially-resolved transcriptomics

Ku, L.-T., Bernard, V., Min, J., Yuan, Y., Koay, E. J., Maitra, A., Li, L., & Li, Z. Y. (2026) · Genome biology · doi:10.1186/s13059-026-04063-5 · Cited by 1

Predicting Relevant Microglia‐Associated Cell–Cell Communication Pathways in Alzheimer's Disease: A Role for SPP1

Kotah, J. M., Trombetta-Lima, M., Dragt, E. C., Voulgaroglou, A., Brouwer, N., Holtman, I. R., Kooistra, S. M., & Eggen, B. J. L. (2026) · Glia · doi:10.1002/glia.70210 · Cited by 2

Spp1 secreted by macrophages impairs osteogenic ability of Ctsk + Fmod + periosteal cells in jaw bonePreprint

Yi, Z., Liu, Y., Wang, J., Hu, C., & Man, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.23.541910 · Cited by 1

Multi-view graph learning for deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq dataPreprint

Ji, B., Wang, X., Wang, X., Xu, L., & Peng, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.28.582416 · Cited by 1

CELLeBT: exploring physical B cell-T cell clonal interactions through single-cell multi-omicsPreprint

Bashford-Rogers, R. J. M., Shohan, M. U. S., Tucci, F. A., Antanaviciute, A., Pernes, J. I., Walker, C., Wu, S., Overend, L., Leedham, S. J., & Koohy, H. (2024) · Research Square · doi:10.21203/rs.3.rs-4437721/v1 · Cited by 1

Quantitative profiling of lifespan-dependent cell-cell communication potential reveals dynamic ligand-receptor network shifts across mouse tissues

Wei, B., Troendle, E. P., Simpson, D. A., & Stitt, A. W. (2026) · PLoS ONE · doi:10.1371/journal.pone.0345045

An Instance-Specific Causal Framework for Learning Intercellular Communication Networks that Define Microenvironments Of Individual TumorsPreprint

Chen, X., Chen, L., Kürten, C. H. L., Jabbari, F., Vujanovic, L. N., Ding, Y., Kulkarni, A., Tabib, T., Lafyatis, R., Cooper, G. F., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3925258

Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications

Tan, F., Xuan, Y., Long, L., Yu, Y., Zhang, C., Liang, P., Wang, Y., Chen, M., Wen, J., & Chen, G. (2023) · BMC Genomics · doi:10.1186/s12864-023-09615-8

Decipher: A computational pipeline to extract context-specific mechanistic insights from single-cell profilesPreprint

Basto, E., Wajid, B., Read, J. F., Armitage, J. D., Waithman, J., Small, M., & Bosco, A. (2025) · bioRxiv · doi:10.1101/2024.05.01.591681

Single-cell RNA-Seq reveals the heterogeneity of fibroblasts within the tympanojugular paraganglioma microenvironment

Wang, S., Zhang, B., Lou, Z., Hu, Y., Wang, J., Wang, J., Chen, Z., & Yin, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35478

CellCom: A web server for prediction, visualization, and evaluation of cell-cell communication mediated by ligand-receptor pairs

Wang, J., Jin, A., Pan, H., Zheng, R., Wu, C., & Li, M. (2024) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3698587.3701352

Decipher cell communication with attention: CLARAPreprint

Wang, H., Chung, Y., & Shin, D.-G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.09.642280 · Cited by 1

Airway Spatial Transcriptomics in SmokingPreprint

Morrow, J. D., Elhusseini, Z., Yun, J. H., & Hersh, C. P. (2025) · medRxiv · doi:10.1101/2025.04.01.25325047

Advances in rolling circle amplification-based in situ sequencing for spatial transcriptomics technologies

Liu, Y., Ye, P., Chen, S., Chen, J., Lin, C., & Ke, R. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2025-0014

spRefine Denoises and Imputes Spatial Transcriptomics with a Reference-Free Framework Powered by Genomic Language ModelPreprint

Liu, T., Huang, T., Jin, W., Chu, T., Ying, R., & Zhao, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.22.649977 · Cited by 2

Systemic chemotherapy in metastatic TNBC polarizes effector T cell differentiation

Ju, H.-Y., Yoon, H., Jung, S. P., Park, S. I., Kim, T. K., & Han, M.-R. (2025) · Cancer Cell International · doi:10.1186/s12935-025-04011-6

Cell-CLIP: Multi-algorithm causal discovery of directed cell program interaction networks from single-cell transcriptomicsPreprint

Al-Tal, M. K. F., Liu, X., & Zhou, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.01.691666

Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in spatial transcriptomics

Wang, Z., Li, J., Xie, L., Wang, Y., Wang, T., Chen, J., Shang, X., Li, X., Liu, Z., & Hu, J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag371

Additional file 4 of 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) · Figshare · doi:10.6084/m9.figshare.26730970

Additional file 2 of Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications

Tan, F., Xuan, Y., Long, L., Yu, Y., Zhang, C., Liang, P., Wang, Y., Chen, M., Wen, J., & Chen, G. (2023) · Figshare · doi:10.6084/m9.figshare.24077045

Additional file 2 of Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Liu, Z., Sun, D., & Wang, C. (2022) · Figshare · doi:10.6084/m9.figshare.21351415

Additional file 1 of 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) · Figshare · doi:10.6084/m9.figshare.26730961

Additional file 1 of Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Liu, Z., Sun, D., & Wang, C. (2022) · Figshare · doi:10.6084/m9.figshare.21351412

Additional file 2 of 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) · Figshare · doi:10.6084/m9.figshare.26730964

Additional file 3 of Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications

Tan, F., Xuan, Y., Long, L., Yu, Y., Zhang, C., Liang, P., Wang, Y., Chen, M., Wen, J., & Chen, G. (2023) · Figshare · doi:10.6084/m9.figshare.24077048

Additional file 1 of Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications

Tan, F., Xuan, Y., Long, L., Yu, Y., Zhang, C., Liang, P., Wang, Y., Chen, M., Wen, J., & Chen, G. (2023) · Figshare · doi:10.6084/m9.figshare.24077039

Additional file 3 of 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) · Figshare · doi:10.6084/m9.figshare.26730967

Navigating Tumor Microenvironments with Spatial Transcriptomics for Effective Therapeutics

Krolla, C., Das, T., Tsouris, A., Chakraborty, S., & Andrieux, G. (2026) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-95-3612-2_4

Predictive modeling of molecular activity underlying physical cell-cell interactions

Azad, T., Walker, S., Victora, G. D., & Pritykin, Y. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101301

T cell-Macrophage Interactions Potentially Influence Chemotherapeutic Response in Ovarian Cancer PatientsPreprint

Hameed, S. A., Kolch, W., & Zhernovkov, V. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.02.709041

Systems analysis of intercellular communication in the tumor microenvironment

Massenet-Regad, L. (2023) · theses.fr (ABES) · doi:10.70675/909b8153z00efz42f7za922z6b476829dbf2

Robust identification of cell-cell communication heterogeneity in single cells

Bocci, F., Jia, Y. Y., Atwood, S. X., & Nie, Q. H. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101703

Robust identification of cell-cell communication heterogeneity in single cellsPreprint

Bocci, F., Jia, Y. Y., Atwood, S. X., & Nie, Q. H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.29.721691

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