RNA-Scoop: interactive visualization of transcripts in single-cell transcriptomes

Abstract Recent advances in single-cell RNA sequencing technologies have made detection of transcripts in single cells possible. The level of resolution provided by these technologies can be used to study changes in transcript usage across cell populations and help investigate new biology. Here, we introduce RNA-Scoop, an interactive cell cluster and transcriptome visualization tool to analyze transcript usage across cell categories and clusters. The tool allows users to examine differential transcript expression across clusters and investigate how usage of specific transcript expression mechanisms varies across cell groups.

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A comparison between single cell RNA sequencing and single molecule RNA FISH for rare cell analysis

10.1101/138289 ◽

2017 ◽

Cited By ~ 6

Author(s):

Eduardo Torre ◽

Hannah Dueck ◽

Sydney Shaffer ◽

Janko Gospocic ◽

Rohit Gupte ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Single Molecule ◽

Single Cells ◽

Cycle Phase ◽

Expression Variability ◽

Sequencing Technologies ◽

Rna Fish ◽

Single Cell Rna Sequencing ◽

Sequencing Platforms

AbstractThe development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what “transcriptome coverage” (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of ∼2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.

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Isoform-level gene expression patterns in single-cell RNA-sequencing data

10.1101/036988 ◽

2016 ◽

Author(s):

Trung Nghia Vu ◽

Quin F Wills ◽

Krishna R Kalari ◽

Nifang Niu ◽

Liewei Wang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Pattern Analysis ◽

Single Cells ◽

Expression Patterns ◽

Cell Populations ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Differential Pattern

RNA-sequencing of single-cells enables characterization of transcriptional heterogeneity in seemingly homogenous cell populations. In this study we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of pairs of isoforms from the same gene in single-cell isoform-level expression data. We define six principal patterns of isoform expression relationships and introduce the concept of differential pattern analysis. We applied ISOP for analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in two independent datasets. In the primary dataset we detected and assigned pattern type of 16562 isoform-pairs from 4929 genes. Our results showed that 78% of the isoform pairs displayed a mutually exclusive expression pattern, 14% of the isoform pairs displayed bimodal isoform preference and 8% isoform pairs displayed isoform preference. 26% of the isoform-pair patterns were significant, while remaining isoform-pair patterns can be understood as effects of transcriptional bursting, drop-out and biological heterogeneity. 32% of genes discovered through differential pattern analysis were novel and not detected by differential expression analysis. ISOP provides a novel approach for characterization of isoform-level expression in single-cell populations. Our results reveal a common occurrence of isoform-level preference, commitment and heterogeneity in single-cell populations.

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Insights Into Human Intrahepatic NK Cell Function From Single Cell RNA Sequencing Datasets

Frontiers in Immunology ◽

10.3389/fimmu.2021.649311 ◽

2021 ◽

Vol 12 ◽

Author(s):

Gráinne Jameson ◽

Mark W. Robinson

Keyword(s):

Nk Cells ◽

Single Cell ◽

Rna Sequencing ◽

Human Liver ◽

Immune Cell ◽

Nk Cell ◽

Single Cells ◽

Cell Populations ◽

Cellular Interactions ◽

Single Cell Rna Sequencing

Diverse populations of natural killer (NK) cells have been identified in circulating peripheral blood and a wide variety of different tissues and organs. These tissue-resident NK cell populations are phenotypically distinct from circulating NK cells, however, functional descriptions of their roles within tissues are lacking. Recent advances in single cell RNA sequencing (scRNA-seq) have enabled detailed transcriptional profiling of tissues at the level of single cells and provide the opportunity to explore NK cell diversity within tissues. This review explores potential novel functions of human liver-resident (lr)NK cells identified in human liver scRNA-seq studies. By comparing these datasets we identified up-regulated and down-regulated genes associated with lrNK cells clusters. These genes encode a number of activating and inhibiting receptors, as well as signal transduction molecules, which highlight potential unique pathways that lrNK cells utilize to respond to stimuli within the human liver. This unique receptor repertoire of lrNK cells may confer the ability to regulate a number of immune cell populations, such as circulating monocytes and T cells, while avoiding activation by liver hepatocytes and Kupffer cells. Validating the expression of these receptors on lrNK cells and the proposed cellular interactions within the human liver will expand our understanding of the liver-specific homeostatic roles of this tissue-resident immune cell population.

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Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

Genome Medicine ◽

10.1186/s13073-021-00885-z ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Sunny Z. Wu ◽

Daniel L. Roden ◽

Ghamdan Al-Eryani ◽

Nenad Bartonicek ◽

Kate Harvey ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cells ◽

Cellular Heterogeneity ◽

Tumour Heterogeneity ◽

Fresh Tissue ◽

Human Cancers ◽

Cryopreserved Cell ◽

Single Cell Rna Sequencing

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

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Single-cell transcriptomic analysis of mIHC images via antigen mapping

Science Advances ◽

10.1126/sciadv.abc5464 ◽

2021 ◽

Vol 7 (10) ◽

pp. eabc5464

Author(s):

Kiya W. Govek ◽

Emma C. Troisi ◽

Zhen Miao ◽

Rachael G. Aubin ◽

Steven Woodhouse ◽

...

Keyword(s):

Single Cell ◽

Spatial Patterns ◽

Cell Types ◽

Level Of Detail ◽

Cell Populations ◽

Sequencing Data ◽

Spatially Resolved ◽

Murine Spleen ◽

Single Cell Rna Sequencing ◽

Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

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Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

Nature Communications ◽

10.1038/s41467-021-23238-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jeremy A. Lombardo ◽

Marzieh Aliaghaei ◽

Quy H. Nguyen ◽

Kai Kessenbrock ◽

Jered B. Haun

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Cell Analysis ◽

Processing Technologies ◽

Microfluidic Platform ◽

Tissue Processing ◽

Single Cell Rna Sequencing ◽

Culture Models ◽

Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

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Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

10.1101/2019.12.31.890269 ◽

2019 ◽

Author(s):

Imad Abugessaisa ◽

Shuhei Noguchi ◽

Melissa Cardon ◽

Akira Hasegawa ◽

Kazuhide Watanabe ◽

...

Keyword(s):

Quality Assessment ◽

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Assessment Method ◽

Poor Quality ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Gene Coverage ◽

The Impact

AbstractAnalysis and interpretation of single-cell RNA-sequencing (scRNA-seq) experiments are compromised by the presence of poor quality cells. For meaningful analyses, such poor quality cells should be excluded to avoid biases and large variation. However, no clear guidelines exist. We introduce SkewC, a novel quality-assessment method to identify poor quality single-cells in scRNA-seq experiments. The method is based on the assessment of gene coverage for each single cell and its skewness as a quality measure. To validate the method, we investigated the impact of poor quality cells on downstream analyses and compared biological differences between typical and poor quality cells. Moreover, we measured the ratio of intergenic expression, suggesting genomic contamination, and foreign organism contamination of single-cell samples. SkewC is tested in 37,993 single-cells generated by 15 scRNA-seq protocols. We envision SkewC as an indispensable QC method to be incorporated into scRNA-seq experiment to preclude the possibility of scRNA-seq data misinterpretation.

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Assessing the measurement transfer function of single-cell RNA sequencing

10.1101/045450 ◽

2016 ◽

Author(s):

Hannah R. Dueck ◽

Rizi Ai ◽

Adrian Camarena ◽

Bo Ding ◽

Reymundo Dominguez ◽

...

Keyword(s):

Transfer Function ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Transfer Functions ◽

Single Cells ◽

Biological Information ◽

Gene Detection ◽

Single Cell Rna Sequencing ◽

Scale Control

AbstractRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

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Super-cells untangle large and complex single-cell transcriptome networks

10.1101/2021.06.07.447430 ◽

2021 ◽

Author(s):

Mariia Bilous ◽

Loc Tran ◽

Chiara Cianciaruso ◽

Santiago J Carmona ◽

Mikael J Pittet ◽

...

Keyword(s):

Single Cell ◽

Coarse Graining ◽

Cell Populations ◽

Cell Type ◽

Large Numbers ◽

Single Cell Rna Sequencing ◽

Gene Correlation ◽

Cell Transcriptome ◽

Single Cell Transcriptome

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

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Modeling cellular crosstalk and organotypic vasculature development with human iPSC-derived endothelial cells and cardiomyocytes

10.1101/2020.05.04.075846 ◽

2020 ◽

Author(s):

Emmi Helle ◽

Minna Ampuja ◽

Alexandra Dainis ◽

Laura Antola ◽

Elina Temmes ◽

...

Keyword(s):

Endothelial Cells ◽

Shear Stress ◽

Endothelial Cell ◽

Single Cell ◽

Rna Sequencing ◽

Tissue Growth ◽

Cell Phenotype ◽

Cell Populations ◽

Pump System ◽

Single Cell Rna Sequencing

AbstractRationaleCell-cell interactions are crucial for the development and function of the organs. Endothelial cells act as essential regulators of tissue growth and regeneration. In the heart, endothelial cells engage in delicate bidirectional communication with cardiomyocytes. The mechanisms and mediators of this crosstalk are still poorly known. Furthermore, endothelial cells in vivo are exposed to blood flow and their phenotype is greatly affected by shear stress.ObjectiveWe aimed to elucidate how cardiomyocytes regulate the development of organotypic phenotype in endothelial cells. In addition, the effects of flow-induced shear stress on endothelial cell phenotype were studied.Methods and resultsHuman induced pluripotent stem cell (hiPSC) -derived cardiomyocytes and endothelial cells were grown either as a monoculture or as a coculture. hiPS-endothelial cells were exposed to flow using the Ibidi-pump system. Single-cell RNA sequencing was performed to define cell populations and to uncover the effects on their transcriptomic phenotypes. The hiPS-cardiomyocyte differentiation resulted in two distinct populations; atrial and ventricular. Coculture had a more pronounced effect on hiPS-endothelial cells compared to hiPS-cardiomyocytes. Coculture increased hiPS-endothelial cell expression of transcripts related to vascular development and maturation, cardiac development, and the expression of cardiac endothelial cell -specific genes. Exposure to flow significantly reprogrammed the hiPS-endothelial cell transcriptome, and surprisingly, promoted the appearance of both venous and arterial clusters.ConclusionsSingle-cell RNA sequencing revealed distinct atrial and ventricular cell populations in hiPS-cardiomyocytes, and arterial and venous-like cell populations in flow exposed hiPS-endothelial cells. hiPS-endothelial cells acquired cardiac endothelial cell identity in coculture. Our study demonstrated that hiPS-cardiomoycytes and hiPS-endothelial cells readily adapt to coculture and flow in a consistent and relevant manner, indicating that the methods used represent improved physiological cell culturing conditions that potentially are more relevant in disease modelling. In addition, novel cardiomyocyte-endothelial cell crosstalk mediators were revealed.

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