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      Review of Single-Cell RNA Sequencing in the Heart

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          Abstract

          Single-cell RNA sequencing (scRNA-seq) technology is a powerful, rapidly developing tool for characterizing individual cells and elucidating biological mechanisms at the cellular level. Cardiovascular disease is one of the major causes of death worldwide and its precise pathology remains unclear. scRNA-seq has provided many novel insights into both healthy and pathological hearts. In this review, we summarize the various scRNA-seq platforms and describe the molecular mechanisms of cardiovascular development and disease revealed by scRNA-seq analysis. We then describe the latest technological advances in scRNA-seq. Finally, we discuss how to translate basic research into clinical medicine using scRNA-seq technology.

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          Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

          Evan Z. Macosko, Anindita Basu, Rahul Satija … (2015)
          Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
          Article 0 comments Cited 1733 times – based on 0 reviews     Review now
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            RNA velocity of single cells

            Gioele La Manno, Ruslan Soldatov, Amit Zeisel … (2018)
            RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.
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              Reversed graph embedding resolves complex single-cell trajectories

              Xiaojie Qiu, Qi Mao, Ying Tang … (2017)
              Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. Applied to two studies of blood development, Monocle 2 revealed that mutations in key lineage transcription factors diverts cells to alternative fates.
              Article 0 comments Cited 1290 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Int J Mol Sci
                Journal ID (iso-abbrev): Int J Mol Sci
                Journal ID (publisher-id): ijms
                Title: International Journal of Molecular Sciences
                Publisher: MDPI
                ISSN (Electronic): 1422-0067
                Publication date (Electronic): 06 November 2020
                Publication date Collection: November 2020
                Volume: 21
                Issue: 21
                Electronic Location Identifier: 8345
                Affiliations
                Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan; shintayamada-tky@ 123456umin.ac.jp
                Author notes
                Author information
                https://orcid.org/0000-0002-3240-358X
                https://orcid.org/0000-0002-2957-2016
                Article
                Publisher ID: ijms-21-08345
                DOI: 10.3390/ijms21218345
                PMC ID: 7664385
                PubMed ID: 33172208
                SO-VID: f2da9d91-a445-444a-8d21-3f7b9fbc94c1
                Copyright © © 2020 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 29 September 2020
                Date accepted : 05 November 2020
                Categories
                Subject: Review

                ScienceOpen disciplines: Molecular biology
                Keywords: single-cell rna sequencing,heart,cardiomyocyte,cardiovascular development,cardiovascular disease
                Data availability:
                ScienceOpen disciplines: Molecular biology
                Keywords: single-cell rna sequencing, heart, cardiomyocyte, cardiovascular development, cardiovascular disease

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