Mapping cellular interactions from spatially resolved transcriptomics data

Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).Article 
CAS 
PubMed 

Google Scholar 
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).Article 
CAS 
PubMed 

Google Scholar 
Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A. & Forrest, A. R. R. Predicting cell-to-cell communication networks using NATMI. Nat. Commun. 11, 5011 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cabello-Aguilar, S. et al. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 48, e55 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Armingol, E., Baghdassarian, H. M. & Lewis, N. E. The diversification of methods for studying cell–cell interactions and communication. Nat. Rev. Genet. 25, 381–400 (2024).Article 
CAS 
PubMed 

Google Scholar 
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).Article 
CAS 
PubMed 

Google Scholar 
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, Y. et al. XYZeq: spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv. 7, eabg4755 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cho, C.-S. et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184, 3559–3572 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiong, D., Zhang, Z., Wang, T. & Wang, X. A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences. Comput. Struct. Biotechnol. J. 19, 3255–3268 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kim, Y., Wang, T., Xiong, D., Wang, X. & Park, S. Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences. BMC Bioinform. 23, 469 (2022).Article 

Google Scholar 
Park, S. et al. Bayesian multiple instance regression for modeling immunogenic neoantigens. Stat. Methods Med. Res. 29, 3032–3047 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2021).Article 
CAS 
PubMed 

Google Scholar 
Shao, X. et al. Knowledge-graph-based cell–cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat. Commun. 13, 4429 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cang, Z. et al. Screening cell–cell communication in spatial transcriptomics via collective optimal transport. Nat. Methods 20, 218–228 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, Z., Wang, T., Liu, P. & Huang, Y. SpatialDM for rapid identification of spatially co-expressed ligand–receptor and revealing cell–cell communication patterns. Nat. Commun. 14, 3995 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cachot, A. et al. Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer. Sci. Adv. 7, eabe3348 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Davari, K. et al. Development of a CD8 co-receptor independent T-cell receptor specific for tumor-associated antigen MAGE-A4 for next generation T-cell-based immunotherapy. J. Immunother. Cancer 9, e002035 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ghosh, D., Jiang, W., Mukhopadhyay, D. & Mellins, E. D. New insights into B cells as antigen presenting cells. Curr. Opin. Immunol. 70, 129–137 (2021).Article 
CAS 
PubMed 

Google Scholar 
Cai, J. et al. Tumor-associated macrophages derived TGF-β‒induced epithelial-to-mesenchymal transition in colorectal cancer cells through Smad2,3-4/Snail signaling pathway. Cancer Res. Treat. 51, 252–266 (2019).Article 
CAS 
PubMed 

Google Scholar 
Sun, D. et al. M2-polarized tumor-associated macrophages promote epithelial-mesenchymal transition via activation of the AKT3/PRAS40 signaling pathway in intrahepatic cholangiocarcinoma. J. Cell. Biochem. 121, 2828–2838 (2020).Article 
CAS 
PubMed 

Google Scholar 
Zhang, W. et al. Interaction with neutrophils promotes gastric cancer cell migration and invasion by inducing epithelial-mesenchymal transition. Oncol. Rep. 38, 2959–2966 (2017).Article 
CAS 
PubMed 

Google Scholar 
Qu, J. et al. Mast cells induce epithelial-to-mesenchymal transition and migration in non-small cell lung cancer through IL-8/Wnt/β-catenin pathway. J. Cancer 10, 5567 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Wu, X. et al. IL-6 secreted by cancer-associated fibroblasts promotes epithelial-mesenchymal transition and metastasis of gastric cancer via JAK2/STAT3 signaling pathway. Oncotarget 8, 20741–20750 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L. et al. Cancer-associated fibroblasts enhance metastatic potential of lung cancer cells through IL-6/STAT3 signaling pathway. Oncotarget 8, 76116–76128 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Yu, Y. et al. Cancer-associated fibroblasts induce epithelial-mesenchymal transition of breast cancer cells through paracrine TGF-β signalling. Br. J. Cancer 110, 724–732 (2014).Article 
CAS 
PubMed 

Google Scholar 
Labelle, M., Begum, S. & Hynes, R. O. Direct signaling between platelets and cancer cells induces an epithelial-mesenchymal-like transition and promotes metastasis. Cancer Cell 20, 576–590 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sigurdsson, V. et al. Endothelial induced EMT in breast epithelial cells with stem cell properties. PLoS ONE 6, e23833 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinform. 10, 48 (2009).Article 

Google Scholar 
Eden, E., Lipson, D., Yogev, S. & Yakhini, Z. Discovering motifs in ranked lists of DNA sequences. PLoS Comput. Biol. 3, e39 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Tirino, V. et al. TGF-β1 exposure induces epithelial to mesenchymal transition both in CSCs and non-CSCs of the A549 cell line, leading to an increase of migration ability in the CD133 + A549 cell fraction. Cell Death Dis. 4, e620 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ping, Q. et al. TGF-β1 dominates stromal fibroblast-mediated EMT via the FAP/VCAN axis in bladder cancer cells. J. Transl. Med. 21, 475 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yadav, A., Kumar, B., Datta, J., Teknos, T. N. & Kumar, P. IL-6 promotes head and neck tumor metastasis by inducing epithelial-mesenchymal transition via the JAK–STAT3–SNAIL signaling pathway. Mol. Cancer Res. 9, 1658–1667 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ebbing, E. A. et al. Stromal-derived interleukin 6 drives epithelial-to-mesenchymal transition and therapy resistance in esophageal adenocarcinoma. Proc. Natl Acad. Sci. USA 116, 2237–2242 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Deng, S. et al. Ectopic JAK-STAT activation enables the transition to a stem-like and multilineage state conferring AR-targeted therapy resistance. Nat. Cancer 3, 1071–1087 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chu, T., Wang, Z., Pe’er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat. Cancer 3, 505–517 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tsujimoto, Y. Role of Bcl-2 family proteins in apoptosis: apoptosomes or mitochondria? Genes Cells 3, 697–707 (1998).Article 
CAS 
PubMed 

Google Scholar 
Wang, Y. et al. GATA-3 controls the maintenance and proliferation of T cells downstream of TCR and cytokine signaling. Nat. Immunol. 14, 714–722 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Merlo, L. M. F., Peng, W. & Mandik-Nayak, L. Impact of IDO1 and IDO2 on the B cell immune response. Front. Immunol. 13, 886225 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).Article 
CAS 
PubMed 

Google Scholar 
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hibino, S. et al. Inflammation-induced tumorigenesis and metastasis. Int. J. Mol. Sci. 22, 5421 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lu, T. et al. Netie: inferring the evolution of neoantigen-T cell interactions in tumors. Nat. Methods 19, 1480–1489 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lu, T. et al. Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes. Sci. Immunol. 5, eaaz3199 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lu, T. et al. Deep learning-based prediction of the T cell receptor-antigen binding specificity. Nat. Mach. Intell. 3, 864–875 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, Y. et al. Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer. Cancer Cell 39, 1578–1593 (2021).Article 
CAS 
PubMed 

Google Scholar 
Ren, H., Li, W., Liu, X. & Zhao, N. γδ T cells: the potential role in liver disease and implications for cancer immunotherapy. J. Leukoc. Biol. 112, 1663–1668 (2022).Article 
CAS 
PubMed 

Google Scholar 
Hou, W. & Wu, X. Diverse functions of γδ T cells in the progression of hepatitis B virus and hepatitis C virus infection. Front. Immunol. 11, 619872 (2020).Article 
CAS 
PubMed 

Google Scholar 
Wang, X. et al. Host-derived lipids orchestrate pulmonary γδ T cell response to provide early protection against influenza virus infection. Nat. Commun. 12, 1914 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ribot, J. C., Lopes, N. & Silva-Santos, B. γδ T cells in tissue physiology and surveillance. Nat. Rev. Immunol. 21, 221–232 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wei, Y. et al. Liver homeostasis is maintained by midlobular zone 2 hepatocytes. Science 371, eabb1625 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kimura, M., Moteki, H. & Ogihara, M. Role of hepatocyte growth regulators in liver regeneration. Cells 12, 208 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Botbol, Y., Guerrero-Ros, I. & Macian, F. Key roles of autophagy in regulating T-cell function. Eur. J. Immunol. 46, 1326–1334 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kumar, A. V., Mills, J. & Lapierre, L. R. Selective autophagy receptor p62/SQSTM1, a pivotal player in stress and aging. Front. Cell Dev. Biol. 10, 793328 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics. Brief. Bioinform. 24, bbad359 (2023).Article 
PubMed 

Google Scholar 
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wang, Y. et al. Sprod for de-noising spatially resolved transcriptomics data based on position and image information. Nat. Methods 19, 950–958 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rong, R. et al. A deep learning approach for histology-based nucleus segmentation and tumor microenvironment characterization. Mod. Pathol. 36, 100196 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, S., Yang, D. M., Rong, R., Zhan, X. & Xiao, G. Pathology image analysis using segmentation deep learning algorithms. Am. J. Pathol. 189, 1686–1698 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, K. et al. Comparative analysis of dimension reduction methods for cytometry by time-of-flight data. Nat. Commun. 14, 1836 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Google Scholar 
McInnes, L., Healy, J., Saul, N. & Grossberger, L. UMAP: uniform manifold approximation and projection. JOSS https://doi.org/10.21105/joss.00861 (2018).Gogola, S. et al. Epithelial-to-mesenchymal transition-related markers in prostate cancer: from bench to bedside. Cancers 15, 2309 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Henry, G. H. et al. A cellular anatomy of the normal adult human prostate and prostatic urethra. Cell Rep. 25, 3530–3542 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Song, H. et al. Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states. Nat. Commun. 13, 141 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun, D. et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res. 49, D1420–D1430 (2021).Article 
CAS 
PubMed 

Google Scholar 
Chang, W. Y. Single cell RNA sequencing data of ADT treated prostate cancer patients. Zenodo. https://doi.org/10.5281/zenodo.8270765 (2023).Zhang, Z., Xiong, D., Wang, X., Liu, H. & Wang, T. Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics. Nat. Methods 18, 92–99 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhu, J. et al. BepiTBR: T–B reciprocity enhances B cell epitope prediction. iScience 25, 103764 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Hot Topics

Related Articles