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      Single-cell RNA sequencing in cardiovascular development, disease and medicine

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          Abstract

          Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade, as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics. Single-cell RNA sequencing (scRNA-seq) technologies have helped to identify rare cell populations and allowed the comparison of healthy and diseased tissues at single-cell resolution. This Review discusses the available scRNA-seq tools and summarizes the scRNA-seq findings that have contributed to our understanding of cardiovascular development and disease.

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          Most cited references73

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          Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses

          To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single cell analysis of the tumor, non-involved lung and blood cells together with multiplex tissue imaging to assess spatial cell distribution, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. Comparing single tumor cells with adjacent normal tissue and blood from patients with lung adenocarcinoma charts early changes in tumor immunity and provides insights to guide immunotherapy design.
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            Quantitative single-cell RNA-seq with unique molecular identifiers.

            Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.
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              Is Open Access

              A Single-Cell Transcriptome Atlas of the Human Pancreas

              Summary To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.
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                Author and article information

                Journal
                Nature Reviews Cardiology
                Nat Rev Cardiol
                Springer Science and Business Media LLC
                1759-5002
                1759-5010
                March 30 2020
                Article
                10.1038/s41569-020-0359-y
                7528042
                32231331
                586c6d60-17f0-4c2b-b8fd-4d7c904430ee
                © 2020

                http://www.springer.com/tdm

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