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      Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing

      review-article
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      Biology
      MDPI
      scRNA-seq, scATAC-seq, mitochondria, heteroplasmy, heterogeneity

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          Abstract

          Simple Summary

          Mitochondria, the powerhouse of the cell, exist in the range of 100 s–1000 s of copies in almost every cell in the body, each with their own mitochondrial DNA, called mtDNA. When the healthy operation of a significant proportion of these mitochondria is disrupted, it can lead to dysfunction and by extension disease. One source of dysfunction arises due to mutations in the mtDNA, resulting in individual cells harbouring multiple versions of mtDNA—a “standard” wild type and a variant—a state called heteroplasmy. Heteroplasmy is a state that can arise either through inheritance or by mutations that occur through life, resulting in a new mitochondrial allele within a cell. The proportion of mitochondria that have a wild type and that have a variant allele differs between individuals, tissues within an individual, and even cells within a tissue. Historically, heteroplasmy has mainly been studied with bulk sequencing technologies, which miss variation within a tissue. The cellular variation in heteroplasmy throughout the body and its implications for pathology is not fully understood. In this review article we outline recent developments in scRNA-seq and scATAC-seq techniques which allow researchers to discover the extent of this cellular variation and further uncover the role heteroplasmy plays in disease at the cellular level.

          Abstract

          Next-generation sequencing technologies have revolutionised the study of biological systems by enabling the examination of a broad range of tissues. Its application to single-cell genomics has generated a dynamic and evolving field with a vast amount of research highlighting heterogeneity in transcriptional, genetic and epigenomic state between cells. However, compared to these aspects of cellular heterogeneity, relatively little has been gleaned from single-cell datasets regarding cellular mitochondrial heterogeneity. Single-cell sequencing techniques can provide coverage of the mitochondrial genome which allows researchers to probe heteroplasmies at the level of the single cell, and observe interactions with cellular function. In this review, we give an overview of two popular single-cell modalities—single-cell RNA sequencing and single-cell ATAC sequencing—whose throughput and widespread usage offers researchers the chance to probe heteroplasmy combined with cell state in detailed resolution across thousands of cells. After summarising these technologies in the context of mitochondrial research, we give an overview of recent methods which have used these approaches for discovering mitochondrial heterogeneity. We conclude by highlighting current limitations of these approaches and open problems for future consideration.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Massively parallel digital transcriptional profiling of single cells

            Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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              Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

              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.
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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Biology (Basel)
                Biology (Basel)
                biology
                Biology
                MDPI
                2079-7737
                05 June 2021
                June 2021
                : 10
                : 6
                : 503
                Affiliations
                Department of Mathematics, Imperial College London, Huxley Building, South Kensington Campus, London SW7 2AZ, UK; a.marshall19@ 123456imperial.ac.uk
                Author notes
                Article
                biology-10-00503
                10.3390/biology10060503
                8230039
                33540832
                ee48b65b-d6bc-418e-80ee-5ffa17328858
                © 2021 by the authors.

                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 ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 06 May 2021
                : 28 May 2021
                Categories
                Review

                scrna-seq,scatac-seq,mitochondria,heteroplasmy,heterogeneity

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