Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy – Nature

  • Savarese, G. & Lund, L. H. Global Public Health Burden of Heart Failure. Card. Fail. Rev. 03, 7 (2017).

    Article 

    Google Scholar 

  • Oktay, A. A. et al. Diabetes, Cardiomyopathy, and Heart Failure. Endotext (MDText.com, Inc., 2000).

  • Liu, Y. et al. RNA-Seq identifies novel myocardial gene expression signatures of heart failure. Genomics 105, 83–89 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Chen, C. Y. et al. Suppression of detyrosinated microtubules improves cardiomyocyte function in human heart failure. Nat. Med. 24, 1225–1233 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Maron, B. J. et al. Contemporary definitions and classification of the cardiomyopathies: An American Heart Association Scientific Statement from the Council on Clinical Cardiology, Heart Failure and Transplantation Committee; Quality of Care and Outcomes Research and Functional Genomics and Translational Biology Interdisciplinary Working Groups; and Council on Epidemiology and Prevention. Circulation 113, 1807–1816 (2006).

    PubMed 
    Article 

    Google Scholar 

  • Sweet, M. E. et al. Transcriptome analysis of human heart failure reveals dysregulated cell adhesion in dilated cardiomyopathy and activated immune pathways in ischemic heart failure. BMC Genomics 19, 812 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Tucker, N. R. et al. Transcriptional and Cellular Diversity of the Human Heart. Circulation 142, 466–482 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Litviňuková, M. et al. Cells of the adult human heart. Nature 588, 466–472 (2020).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Wang, L. et al. Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function. Nat. Cell Biol. 22, 108–119 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Ashburner, M. et al. Gene ontology: Tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Carbon, S. et al. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    CAS 
    Article 

    Google Scholar 

  • Kalucka, J. et al. Single-Cell Transcriptome Atlas of Murine Endothelial Cells. Cell 180, 764–779.e20 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Crinier, A. et al. High-Dimensional Single-Cell Analysis Identifies Organ-Specific Signatures and Conserved NK Cell Subsets in Humans and Mice. Immunity 49, 971–986.e5 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Bajpai, G. et al. The human heart contains distinct macrophage subsets with divergent origins and functions. Nat. Med. 24, 1234–1245 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Tallquist, M. D. & Molkentin, J. D. Redefining the identity of cardiac fibroblasts. Nat. Rev. Cardiol. 14, 484–491 (2017).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Cucoranu, I. et al. NAD(P)H oxidase 4 mediates transforming growth factor-β1-induced differentiation of cardiac fibroblasts into myofibroblasts. Circ. Res. 97, 900–907 (2005).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Tillmanns, J. et al. Fibroblast activation protein alpha expression identifies activated fibroblasts after myocardial infarction. J. Mol. Cell. Cardiol. 87, 194–203 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Shinde, A. V. & Frangogiannis, N. G. Mechanisms of Fibroblast Activation in the Remodeling Myocardium. Curr. Pathobiol. Rep. 5, 145–152 (2017).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • McLellan, M. A. et al. High-Resolution Transcriptomic Profiling of the Heart During Chronic Stress Reveals Cellular Drivers of Cardiac Fibrosis and Hypertrophy. Circulation 142, 1448–1463 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Forte, E. et al. Dynamic Interstitial Cell Response during Myocardial Infarction Predicts Resilience to Rupture in Genetically Diverse Mice. Cell Rep. 30, 3149–3163 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Frolova, E. G. et al. Thrombospondin‐4 regulates fibrosis and remodeling of the myocardium in response to pressure overload. FASEB J. 26, 2363–2373 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Liu, X. et al. Long non-coding and coding RNA profiling using strand-specific RNA-seq in human hypertrophic cardiomyopathy. Sci. Data 6, 90 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Bengtsson, E. et al. The Leucine-rich Repeat Protein PRELP Binds Perlecan and Collagens and May Function as a Basement Membrane Anchor. J. Biol. Chem. 277, 15061–15068 (2002).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Li, L. et al. The role of JAZF1 on lipid metabolism and related genes in vitro. Metabolism. 60, 523–530 (2011).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Guang-feng, M. et al. JAZF1 can regulate the expression of lipid metabolic genes and inhibit lipid accumulation in adipocytes. Biochem. Biophys. Res. Commun. 445, 673–680 (2014).

    Article 
    CAS 

    Google Scholar 

  • Yuan, L. et al. Transcription factor TIP27 regulates glucose homeostasis and insulin sensitivity in a PI3-kinase/Akt-dependent manner in mice. Int. J. Obes. 39, 949–958 (2015).

    CAS 
    Article 

    Google Scholar 

  • Koch, M. et al. A Novel Marker of Tissue Junctions, Collagen XXII. J. Biol. Chem. 279, 22514 (2004).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Watanabe, T. et al. A Human Skin Model Recapitulates Systemic Sclerosis Dermal Fibrosis and Identifies COL22A1 as a TGFβ Early Response Gene that Mediates Fibroblast to Myofibroblast Transition. Genes (Basel). 10, 75 (2019).

    CAS 
    PubMed Central 
    Article 

    Google Scholar 

  • Ma, Y. et al. Cardiomyocyte d-dopachrome tautomerase protects against heart failure. JCI Insight 4, e128900 (2019).

    PubMed Central 
    Article 

    Google Scholar 

  • Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).

    Article 

    Google Scholar 

  • Fleming, S. J., Marioni, J. C. & Babadi, M. CellBender remove-background: A deep generative model for unsupervised removal of background noise from scRNA-seq datasets. Preprint at bioRxiv https://doi.org/10.1101/791699 (2019).

  • Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. Cell Syst. 8, 281–291.e9 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Lun, A. T. L. & Marioni, J. C. Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data. Biostatistics 18, 451–464 (2017).

    MathSciNet 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Chen, Y., Lun, A. T. L. & Smyth, G. K. From reads to genes to pathways: Differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research 5, 1438 (2016).

    PubMed 
    PubMed Central 

    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, 550 (2014).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Büttner, M., Ostner, J., Müller, C. L., Theis, F. J. & Schubert, B. scCODA is a Bayesian model for compositional single-cell data analysis. Nat. Commun. 12, 1–10 (2021).

    Article 
    CAS 

    Google Scholar 

  • Brill, B., Amir, A. & Heller, R. Testing for differential abundance in compositional counts data, with application to microbiome studies. Preprint at arXiv https://arxiv.org/abs/1904.08937 (2019).

  • Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498–D503 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2016).

  • Yu, G. & He, Q. Y. ReactomePA: An R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12, 477–479 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    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).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Van den Berge, K. et al. Trajectory-based differential expression analysis for single-cell sequencing data. Nat. Commun. 11, 1201 (2020).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://arxiv.org/abs/1303.3997 (2013).

  • Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2017).

  • McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 97, 676–682 (2012).

    Article 
    CAS 

    Google Scholar 

  • Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 1–15 (2018).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

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