An Introduction to the Analysis of Single-Cell RNA-Sequencing Data

AlJanahi, Aisha A.; Danielsen, Mark; Dunbar, Cynthia E.

doi:10.1016/j.omtm.2018.07.003

Cited by 100 publications

(69 citation statements)

References 89 publications

(126 reference statements)

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“…Cells with high percentages of mitochondrial reads are generally excluded from analysis (35) . In our data the fraction of mitochondrial reads was low, with no significant change in proportion, except in spleen where mitochondrial reads increase by 72h in 4 out of 5 donors (Figure 2e,f).…”

Section: Resultsmentioning

confidence: 99%

Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservation

Madissoon

Wilbrey-Clark

Miragaia

et al. 2019

Preprint

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BackgroundThe Human Cell Atlas is a large international collaborative effort to map all cell types of the human body. Single cell RNA sequencing can generate high quality data for the delivery of such an atlas. However, delays between fresh sample collection and processing may lead to poor data and difficulties in experimental design. Despite this, there has not yet been a systematic assessment of the effect of cold storage time on the quality of scRNAseq ResultsThis study assessed the effect of cold storage on fresh healthy spleen, oesophagus and lung from ≥5 donors over 72 hours. We collected 240,000 high quality single cell transcriptomes with detailed cell type annotations and whole genome sequences of donors, enabling future eQTL studies. Our data provide a valuable resource for the study of these three organs and will allow cross-organ comparison of cell types.We see little effect of cold ischaemic time on cell viability, yield, total number of reads per cell and other quality control metrics in any of the tissues within the first 24 hours. However, we observed higher percentage of mitochondrial reads, indicative of cellular stress, and

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Section: Resultsmentioning

confidence: 99%

Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservation

Madissoon

Wilbrey-Clark

Miragaia

et al. 2019

Preprint

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“…We next sought to assess the quality of our sequenced CM libraries. One commonly used metric for assessing scRNA-seq quality is the percentage of reads originating from mitochondrial transcripts 26,27 . In addition to being a potential read out of cellular stress, a high mitochondrial read percentage is a useful indicator of potential membrane rupture and cell death.…”

Section: Generation Of High Quality Scrna-seq Libraries From Sorted Cmsmentioning

confidence: 99%

Large particle fluorescence-activated cell sorting enables high quality single cell RNA-sequencing and functional analysis of adult cardiomyocytes

Kannan¹,

Miyamoto²,

Lin³

et al. 2019

Preprint

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Rationale: Single cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to profile the transcriptome at single cell resolution, enabling comprehensive analysis of cellular trajectories and heterogeneity during development and disease. However, the use of scRNA-seq remains limited in cardiac pathology owing to technical difficulties associated with the isolation of single adult cardiomyocytes (CMs). Objective:We investigated the capability of large-particle fluorescence-activated cell sorting (LP-FACS) for isolation of viable single adult CMs. Methods and Results:We found that LP-FACS readily outperforms conventional FACS for isolation of struturally competent CMs, including large CMs. Additionally, LP-FACS enables isolation of fluorescent CMs from mosaic models. Importantly, the sorted CMs allow generation of high-quality scRNA-seq libraries. Unlike CMs isolated via previously utilized fluidic or manual methods, LP-FACisolated CMs generate libraries exhibiting normal levels of mitochondrial transcripts. Moreover, LP-FACS isolated CMs remain functionally competent and can be studied for contractile properties. Conclusions:Our study enables high quality dissection of adult CM biology at single-cell resolution.To date, however, scRNA-seq of adult CMs has been limited owing to technical difficulties in the isolation of single CMs. Specialized techniques are required for dissociation of the heart without damaging cells, as CMs are highly sensitive to pH, buffer ionic concentrations, and mechanical disruption 7 . Moreover, adult CMs are large (approximately 125x25µm 8 ), rod-shaped cells whose structure precludes isolation by either fluorescence-activated cell sorting (FACS) or commercial single cell microfluidic platforms (such as the Fluidigm C1 or Chromium 10X) 2,3 . Previous studies that have attempted to isolate CMs using conventional FACS equipment have resulted in frequent clogging of the instrument 9 or generation and/of fragmented myocytes 10 . Moreover, scRNA-seq libraries generated from CMs following FACS 10 or isolation on the Fluidigm C1 4 have been dominated by mitochondrial reads, potentially suggesting that the cells were damaged prior to library generation. While one group has previously reported on isolation of rod-shaped CMs by FACS 11,12 , this approach only worked with fixed cells, and required significant instrument and sort parameter customization that may preclude its use in other labs. One approach that has been successful in generating high quality scRNA-seq libraries from single CMs has been single cell picking by micropipette 13 . However, this approach is technically challenging, low throughput, and time intensive, which may in turn lead to significant transcriptomic changes during isolation. In lieu of whole cell scRNA-seq, others have utilized single nuclear RNA-seq (snRNA-seq), which overcomes the challenges of isolating large CMs [14][15][16] . While this approach can broadly capture cellular heterogeneity, snRNA-seq inherently detects fewer molecules and may skew gene expressi...

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“…After the basic processing of the raw data has been performed, the data will consist of a matrix of integers n gc giving the number of captured mRNA molecules for each gene g in each cell c. The key assumption of our probabilistic model is that, in a scRNA-seq experiment, each mRNA molecule in a given cell c has a probability p c to be captured and sequenced. This capture probability p c , which varies from cell to cell, has been estimated to be in the range of 10 to 15% [40] and up to 30% with the most recent protocols [41]. Under this assumption, the probability of the observed UMI counts n gc in cell c given the transcription quotients α gc is still given by a product of Poisson distributions (see Supplementary Methods)…”

Section: A Probabilistic Model For a Scrna-seq Experimentsmentioning

confidence: 99%

Bayesian inference of the gene expression states of single cells from scRNA-seq data

Breda

Zavolan

Nimwegen

2019

Preprint

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In spite of a large investment in the development of methodologies for analysis of singlecell RNA-seq data, there is still little agreement on how to best normalize such data, i.e. how to quantify gene expression states of single cells from such data. Starting from a few basic requirements such as that inferred expression states should correct for both intrinsic biological fluctuations and measurement noise, and that changes in expression state should be measured in terms of fold-changes rather than changes in absolute levels, we here derive a unique Bayesian procedure for normalizing single-cell RNA-seq data from first principles. Our implementation of this normalization procedure, called Sanity (SAmpling Noise corrected Inference of Transcription activitY), estimates log expression values and associated errors bars directly from raw UMI counts without any tunable parameters.Comparison of Sanity with other recent normalization methods on a selection of scRNAseq datasets shows that Sanity outperforms other methods on basic downstream processing tasks such as clustering cells into subtypes and identification of differentially expressed genes. More importantly, we show that all other normalization methods present severely distorted pictures of the data. By failing to account for biological and technical Poisson noise, many methods systematically predict the lowest expressed genes to be most variable in expression, whereas in reality these genes provide least evidence of true biological variability. In addition, by confounding noise removal with lower-dimensional representation of the data, many methods introduce strong spurious correlations of expression levels with the total UMI count of each cell as well as spurious co-expression of genes.

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An Introduction to the Analysis of Single-Cell RNA-Sequencing Data

Cited by 100 publications

References 89 publications

Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservation

Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservation

Large particle fluorescence-activated cell sorting enables high quality single cell RNA-sequencing and functional analysis of adult cardiomyocytes

Bayesian inference of the gene expression states of single cells from scRNA-seq data

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