Targeting immune–fibroblast cell communication in heart failure – Nature
Medzhitov, R. Origin and physiological roles of inflammation. Nature 454, 428–435 (2008).
Google Scholar
Frangogiannis, N. G. Cardiac fibrosis. Cardiovasc. Res. 117, 1450–1488 (2021).
Google Scholar
Rurik, J. G. et al. CAR T cells produced in vivo to treat cardiac injury. Science 375, 91–96 (2022).
Google Scholar
Aghajanian, H. et al. Targeting cardiac fibrosis with engineered T cells. Nature 573, 430–433 (2019).
Google Scholar
Gutstein, D. E. & Fuster, V. Pathophysiology and clinical significance of atherosclerotic plaque rupture. Cardiovasc. Res. 41, 323–333 (1999).
Google Scholar
Thygesen, K. et al. Third universal definition of myocardial infarction. Circulation 126, 2020–2035 (2012).
Google Scholar
Fu, X. et al. Specialized fibroblast differentiated states underlie scar formation in the infarcted mouse heart. J. Clin. Invest. 128, 2127–2143 (2018).
Google Scholar
Kanisicak, O. et al. Genetic lineage tracing defines myofibroblast origin and function in the injured heart. Nat. Commun. 7, 12260 (2016).
Google Scholar
Ivey, M. J. et al. Resident fibroblast expansion during cardiac growth and remodeling. J. Mol. Cell. Cardiol. 114, 161–174 (2018).
Google Scholar
Forte, E. et al. Dynamic interstitial cell response during myocardial infarction predicts resilience to rupture in genetically diverse mice. Cell Rep. https://doi.org/10.1016/j.celrep.2020.02.008 (2020).
Martini, E. et al. Single-cell sequencing of mouse heart immune infiltrate in pressure overload–driven heart failure reveals extent of immune activation. Circulation 140, 2089–2107 (2019).
Google Scholar
Zaman, R. et al. Selective loss of resident macrophage-derived insulin-like growth factor-1 abolishes adaptive cardiac growth to stress. Immunity 54, 2057–2071.e6 (2021).
Google Scholar
Dick, S. A. et al. Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. Nat. Immunol. 20, 29 (2019).
Google Scholar
Matsa, E., Burridge, P. W. & Wu, J. C. Human stem cells for modeling heart disease and for drug discovery. Sci. Transl. Med. 6, 239ps6 (2014).
Google Scholar
Herum, K. M. et al. Cardiac fibroblast sub-types in vitro reflect pathological cardiac remodeling in vivo. Matrix Biol. Plus 15, 100113 (2022).
Google Scholar
Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).
Amrute, J. M. et al. Cell specific peripheral immune responses predict survival in critical COVID-19 patients. Nat. Commun. 13, 882 (2022).
Google Scholar
Tucker, N. R. et al. Transcriptional and cellular diversity of the human heart. Circulation 142, 466–482 (2020).
Google Scholar
Chaffin, M. et al. Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. Nature https://doi.org/10.1038/S41586-022-04817-8 (2022).
Google Scholar
Koenig, A. L. et al. Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. Nat. Cardiovasc. Res. 1, 263–280 (2022).
Google Scholar
Litviňuková, M. et al. Cells of the adult human heart. Nature 588, 466–472 (2020).
Google Scholar
Reichart, D. et al. Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies. Science 377, eabo1984 (2022).
Google Scholar
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods https://doi.org/10.1038/nmeth.4380 (2017).
Google Scholar
Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 184, 5053–5069.e23 (2021).
Google Scholar
Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Nature https://doi.org/10.1038/s41586-022-05060-x (2022).
Google Scholar
Buechler, M. B. et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579 (2021).
Google Scholar
RC, W. et al. Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis. Nat. Med. 25, 1280–1289 (2019).
Google Scholar
Alexanian, M. et al. A transcriptional switch governs fibroblast activation in heart disease. Nature 595, 438–443 (2021).
Google Scholar
Amrute, J. M. et al. Defining cardiac functional recovery in end-stage heart failure at single-cell resolution. Nat. Cardiovasc. Res. 2, 399–416 (2023).
Google Scholar
Alexanian, M. et al. Chromatin remodelling drives immune–fibroblast crosstalk in heart failure pathogenesis. Nature https://doi.org/10.1038/s41586-024-08085-6 (2024).
Ridker, P. M. et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N. Engl. J. Med. 377, 1119–1131 (2017).
Google Scholar
Tanevski, J., Flores, R. O. R., Gabor, A., Schapiro, D. & Saez-Rodriguez, J. Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biol. 23, 1–31 (2022).
Google Scholar
Lavine, K. J., Long, F., Choi, K., Smith, C. & Ornitz, D. M. Hedgehog signaling to distinct cell types differentially regulates coronary artery and vein development. Development 135, 3161–3171 (2008).
Google Scholar
Kadyrov, F. F. et al. Hypoxia sensing in resident cardiac macrophages regulates monocyte fate specification following ischemic heart injury. Preprint at bioRxiv https://doi.org/10.1101/2022.08.04.502542 (2023).
Koenig, A. L. et al. Genetic mapping of monocyte fate decisions following myocardial infarction. Preprint at bioRxiv https://doi.org/10.1101/2023.12.24.573263 (2023).
Strunk, M. et al. Toward quantitative multisite preclinical imaging studies in acute myocardial infarction: evaluation of the immune-fibrosis axis. J. Nucl. Med. 65, 287–293 (2024).
Google Scholar
Hall, C., Gehmlich, K., Denning, C. & Pavlovic, D. Complex relationship between cardiac fibroblasts and cardiomyocytes in health and disease. J. Am. Heart Assoc. 10, 1–15 (2021).
Google Scholar
Xin, L. et al. Fibroblast activation protein-α as a target in the bench-to-bedside diagnosis and treatment of tumors: a narrative review. Front. Oncol. 11, 3187 (2021).
Google Scholar
Khalil, H. et al. Fibroblast-specific TGF-β–Smad2/3 signaling underlies cardiac fibrosis. J. Clin. Invest. 127, 3770–3783 (2017).
Google Scholar
Stratton, M. S. et al. Dynamic chromatin targeting of BRD4 stimulates cardiac fibroblast activation. Circ. Res. 125, 662 (2019).
Google Scholar
Bujak, M. & Frangogiannis, N. G. The role of interleukin-1 in the pathogenesis of heart disease. Arch. Immunol. Ther. Exp. (Warsz.) 57, 165 (2009).
Google Scholar
Bajpai, G. et al. Tissue resident CCR2− and CCR2+ cardiac macrophages differentially orchestrate monocyte recruitment and fate specification following myocardial injury. Circ. Res. 124, 263–278 (2019).
Google Scholar
Bajpai, G. et al. The human heart contains distinct macrophage subsets with divergent origins and functions. Nat. Med. 24, 1234–1245 (2018).
Google Scholar
Ikeuchi, M. et al. Inhibition of TGF-β signaling exacerbates early cardiac dysfunction but prevents late remodeling after infarction. Cardiovasc. Res. 64, 526–535 (2004).
Google Scholar
Dewald, O. et al. CCL2/monocyte chemoattractant protein-1 regulates inflammatory responses critical to healing myocardial infarcts. Circ. Res. 96, 881–889 (2005).
Google Scholar
Mulè, M. P., Martins, A. J. & Tsang, J. S. Normalizing and denoising protein expression data from droplet-based single cell profiling. Nat. Commun. 13, 2099 (2022).
Google Scholar
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 1–15 (2019).
Google Scholar
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
Google Scholar
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).
Google Scholar
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Google Scholar
Schubert, M. et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat. Commun. 9, 20 (2018).
Google Scholar
