Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cabello-Aguilar, S. et al. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 48, e55 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lee, Y. et al. XYZeq: spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv. 7, eabg4755 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cho, C.-S. et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184, 3559–3572 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
Google ScholarÂ
Park, S. et al. Bayesian multiple instance regression for modeling immunogenic neoantigens. Stat. Methods Med. Res. 29, 3032–3047 (2020).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Shao, X. et al. Knowledge-graph-based cell–cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat. Commun. 13, 4429 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cang, Z. et al. Screening cell–cell communication in spatial transcriptomics via collective optimal transport. Nat. Methods 20, 218–228 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cachot, A. et al. Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer. Sci. Adv. 7, eabe3348 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sigurdsson, V. et al. Endothelial induced EMT in breast epithelial cells with stem cell properties. PLoS ONE 6, e23833 (2011).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
Google ScholarÂ
Eden, E., Lipson, D., Yogev, S. & Yakhini, Z. Discovering motifs in ranked lists of DNA sequences. PLoS Comput. Biol. 3, e39 (2007).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tsujimoto, Y. Role of Bcl-2 family proteins in apoptosis: apoptosomes or mitochondria? Genes Cells 3, 697–707 (1998).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hibino, S. et al. Inflammation-induced tumorigenesis and metastasis. Int. J. Mol. Sci. 22, 5421 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lu, T. et al. Netie: inferring the evolution of neoantigen-T cell interactions in tumors. Nat. Methods 19, 1480–1489 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lu, T. et al. Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes. Sci. Immunol. 5, eaaz3199 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lu, T. et al. Deep learning-based prediction of the T cell receptor-antigen binding specificity. Nat. Mach. Intell. 3, 864–875 (2021).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ribot, J. C., Lopes, N. & Silva-Santos, B. γδ T cells in tissue physiology and surveillance. Nat. Rev. Immunol. 21, 221–232 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Wei, Y. et al. Liver homeostasis is maintained by midlobular zone 2 hepatocytes. Science 371, eabb1625 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kimura, M., Moteki, H. & Ogihara, M. Role of hepatocyte growth regulators in liver regeneration. Cells 12, 208 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Botbol, Y., Guerrero-Ros, I. & Macian, F. Key roles of autophagy in regulating T-cell function. Eur. J. Immunol. 46, 1326–1334 (2016).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Li, H. et al. Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics. Brief. Bioinform. 24, bbad359 (2023).ArticleÂ
PubMedÂ
Google ScholarÂ
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Wang, Y. et al. Sprod for de-noising spatially resolved transcriptomics data based on position and image information. Nat. Methods 19, 950–958 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Rong, R. et al. A deep learning approach for histology-based nucleus segmentation and tumor microenvironment characterization. Mod. Pathol. 36, 100196 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wang, K. et al. Comparative analysis of dimension reduction methods for cytometry by time-of-flight data. Nat. Commun. 14, 1836 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Henry, G. H. et al. A cellular anatomy of the normal adult human prostate and prostatic urethra. Cell Rep. 25, 3530–3542 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Zhu, J. et al. BepiTBR: T–B reciprocity enhances B cell epitope prediction. iScience 25, 103764 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ