Background: Pressure overload–induced pathological cardiac hypertrophy is a common predecessor of heart failure, the latter of which remains a major cardiovascular disease with increasing incidence...

https://www.ahajournals.org/doi/pdf/10.1161/CIRCULATIONAHA.119.043053?download=true

Single-Cell Reconstruction of Progression Trajectory Reveals Intervention Principles in Pathological Cardiac Hypertrophy

Cardiac maturation lays the foundation for postnatal heart development and disease, yet little is known about the contributions of the microenvironment to cardiomyocyte maturation. By integrating single-cell RNA-sequencing data of mouse hearts at multiple postnatal stages, we construct cellular interactomes and regulatory signaling networks. Here we report switching of fibroblast subtypes from a neonatal to adult state and this drives cardiomyocyte maturation. Molecular and functional maturation of neonatal mouse cardiomyocytes and human embryonic stem cell-derived cardiomyocytes are considerably enhanced upon co-culture with corresponding adult cardiac fibroblasts. Further, single-cell analysis of in vivo and in vitro cardiomyocyte maturation trajectories identify highly conserved signaling pathways, pharmacological targeting of which substantially delays cardiomyocyte maturation in postnatal hearts, and markedly enhances cardiomyocyte proliferation and improves cardiac function in infarcted hearts. Together, we identify cardiac fibroblasts as a key constituent in the microenvironment promoting cardiomyocyte maturation, providing insights into how the manipulation of cardiomyocyte maturity may impact on disease development and regeneration.

/pdf/single-cell-analysis-of-murine-fibroblasts-identifies-z7p0gi7vu3.pdf

Single-cell analysis of murine fibroblasts identifies neonatal to adult switching that regulates cardiomyocyte maturation.

Cardiovascular disease is the leading cause of death globally. An advanced understanding of cardiovascular disease mechanisms is required to improve therapeutic strategies and patient risk stratification. State-of-the-art, large-scale, single-cell and single-nucleus transcriptomics facilitate the exploration of the cardiac cellular landscape at an unprecedented level, beyond its descriptive features, and can further our understanding of the mechanisms of disease and guide functional studies. In this Review, we provide an overview of the technical challenges in the experimental design of single-cell and single-nucleus transcriptomics studies, as well as a discussion of the type of inferences that can be made from the data derived from these studies. Furthermore, we describe novel findings derived from transcriptomics studies for each major cardiac cell type in both health and disease, and from development to adulthood. This Review also provides a guide to interpreting the exhaustive list of newly identified cardiac cell types and states, and highlights the consensus and discordances in annotation, indicating an urgent need for standardization. We describe advanced applications such as integration of single-cell data with spatial transcriptomics to map genes and cells on tissue and define cellular microenvironments that regulate homeostasis and disease progression. Finally, we discuss current and future translational and clinical implications of novel transcriptomics approaches, and provide an outlook of how these technologies will change the way we diagnose and treat heart disease. In this Review, Noseda and colleagues provide an overview of the technical challenges in the experimental design of single-cell and single-nucleus transcriptomics studies, and describe novel findings derived from transcriptomics studies for each major cardiac cell type in both health and disease, and from development to adulthood. 

/pdf/single-cell-transcriptomics-for-the-assessment-of-cardiac-r8wseoem.pdf

Single-cell transcriptomics for the assessment of cardiac disease

We performed a comprehensive analysis of the transcriptional changes occurring during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using single cell RNA-seq, we sequenced > 20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation. Differentiated cell populations clustered by time point, with differential expression analysis revealing markers of cardiomyocyte differentiation and maturation changing from D12 to D90. We next performed a complementary cluster-independent sparse regression analysis to identify and rank genes that best assigned cells to differentiation time points. The two highest ranked genes between D12 and D24 (MYH7 and MYH6) resulted in an accuracy of 0.84, and the three highest ranked genes between D24 and D90 (A2M, H19, IGF2) resulted in an accuracy of 0.94, revealing that low dimensional gene features can identify differentiation or maturation stages in differentiating cardiomyocytes. Expression levels of select genes were validated using RNA FISH. Finally, we interrogated differences in cardiac gene expression resulting from two differentiation protocols, experimental replicates, and three hiPSC lines in the WTC-11 background to identify sources of variation across these experimental variables.

/pdf/a-comprehensive-analysis-of-gene-expression-changes-in-a-40vx0pw8dl.pdf

A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes.

https://orbilu.uni.lu/bitstream/10993/44098/1/Trends_in_Biotech_2020.pdf

The Importance of Computational Modeling in Stem Cell Research

Cardiac differentiation from human pluripotent stem cells provides a unique opportunity to study human heart development in vitro and offers a potential cell source for cardiac regeneration. Compared to the large body of studies investigating cardiac maturation and cardiomyocyte subtype-specific induction, molecular events underlying cardiac lineage commitment from pluripotent stem cells at early stage remain poorly characterized. In order to uncover key molecular events and regulators controlling cardiac lineage commitment from a pluripotent state during differentiation, we performed single-cell RNA-Seq sequencing and obtained high-quality data for 6879 cells collected from 6 stages during cardiac differentiation from human embryonic stem cells and identified multiple cell subpopulations with distinct molecular features. Through constructing developmental trajectory of cardiac differentiation and putative ligand-receptor interactions, we revealed crosstalk between cardiac progenitor cells and endoderm cells, which could potentially provide a cellular microenvironment supporting cardiac lineage commitment at day 5. In addition, computational analyses of single-cell RNA-Seq data unveiled ETS1 (ETS Proto-Oncogene 1) activation as an important downstream event induced by crosstalk between cardiac progenitor cells and endoderm cells. Consistent with the findings from single-cell analysis, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) against ETS1 revealed genomic occupancy of ETS1 at cardiac structural genes at day 9 and day 14, whereas ETS1 depletion dramatically compromised cardiac differentiation. Together, our study not only characterized the molecular features of different cell types and identified ETS1 as a crucial factor induced by cell-cell crosstalk contributing to cardiac lineage commitment from a pluripotent state, but may also have important implications for understanding human heart development at early embryonic stage, as well as directed manipulation of cardiac differentiation in regenerative medicine.

/pdf/single-cell-reconstruction-of-differentiation-trajectory-59g7tza29d.pdf

Single-cell reconstruction of differentiation trajectory reveals a critical role of ETS1 in human cardiac lineage commitment.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lin Mao

Author Tools

Chat about Author

Papers

Single-cell reconstruction of differentiation trajectory reveals a critical role of ETS1 in human cardiac lineage commitment.