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Single-nucleus and single-cell transcriptomes compared in matched cortical cell types.

ABSTRACT: Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

SUBMITTER: Bakken TE

PROVIDER: S-EPMC6306246 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

Project description:Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

2018-12-06 | GSE123454 | GEO

Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

Project description:Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

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Single-nucleus transcriptomes reveal evolutionary and functional properties of cell types in the Drosophila accessory gland.

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Comparison of single‑nucleus and single‑cell transcriptomes in hepatocellular carcinoma tissue.

Project description:Single‑nucleus RNA sequencing (snRNA‑seq) is a method used to analyze gene expression in cells for which isolation is complex, such as those in hepatocellular carcinoma (HCC) tissues. It constitutes an alternative to single‑cell RNA sequencing (scRNA‑seq) by analyzing the nucleus rather than the whole cell; however, whether it can completely replace scRNA‑seq in HCC remains to be clarified. In the present study, scRNA‑seq was compared with snRNA‑seq in tumor tissue obtained from patients with HCC, using the 10X Genomics Chromium platform. Seurat was also used to process the data and compare the differences between the two sequencing methods in identifying different cell types. In the present study, the transcriptomes of 14,349 single nuclei and 9,504 single cells were obtained from the aforementioned HCC tissue. A total of 21 discrete cell clusters, including hepatocytes, endothelial cells, fibroblasts, B cells, T cells, natural killer cells and macrophages were identified. Notably, a high number of hepatocytes were detected using snRNA‑seq, while an increased number of immunocytes were identified in the tumor microenvironment using scRNA‑seq. Results of the present study provided a comprehensive image of human HCC at a single‑cell resolution. Moreover, results of the present study further demonstrated that snRNA‑seq may be adequate in replacing scRNA‑seq in certain cases, and snRNA‑seq performs at an improved level in hepatocyte sequencing. Combined use of the two sequencing methods may contribute to the study of intercellular interactions.

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Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation.

Project description:A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

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Identification of cell types from single-cell transcriptomes using a novel clustering method.

Project description:MotivationThe recent advance of single-cell technologies has brought new insights into complex biological phenomena. In particular, genome-wide single-cell measurements such as transcriptome sequencing enable the characterization of cellular composition as well as functional variation in homogenic cell populations. An important step in the single-cell transcriptome analysis is to group cells that belong to the same cell types based on gene expression patterns. The corresponding computational problem is to cluster a noisy high dimensional dataset with substantially fewer objects (cells) than the number of variables (genes).ResultsIn this article, we describe a novel algorithm named shared nearest neighbor (SNN)-Cliq that clusters single-cell transcriptomes. SNN-Cliq utilizes the concept of shared nearest neighbor that shows advantages in handling high-dimensional data. When evaluated on a variety of synthetic and real experimental datasets, SNN-Cliq outperformed the state-of-the-art methods tested. More importantly, the clustering results of SNN-Cliq reflect the cell types or origins with high accuracy.Availability and implementationThe algorithm is implemented in MATLAB and Python. The source code can be downloaded at http://bioinfo.uncc.edu/SNNCliq.

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Comparison of cell type distribution between single-cell and single-nucleus RNA sequencing: enrichment of adherent cell types in single-nucleus RNA sequencing.

Project description:Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) is an effective technique for estimating the cellular composition and transcriptional profiles of individual cells from fresh tissue. Single-nucleus RNA sequencing (snRNA-seq) is necessary to perform this type of analysis in frozen or difficult-to-dissociate tissues, which cannot be subjected to scRNA-seq. This difference in the state of tissues leads to variation in cell-type distributions among each platform. To identify the characteristics of these methods and their differences, scRNA-seq and snRNA-seq were performed in parallel for colon and liver tissues. The two platforms revealed similar diversity but different proportions of cell types in matched tissues. The proportions of epithelial cells in the colon and hepatocytes in the liver were relatively high in snRNA-seq and that of immune cells was relatively high in scRNA-seq. This difference could be explained by variations in the expression scores of adhesion genes due to the disruption of the cytoplasmic contents during scRNA-seq. The enrichment of epithelial cells in the colon resulted in a discrepancy in the differentiation of epithelial cells. This enrichment was also well matched with the images of hematoxylin and eosin staining and the estimated distribution of cell types in bulk RNA sequencing. These results showed that snRNA-seq could be used to analyze tissues that cannot be subjected to scRNA-seq and provides more information in specific cell type analysis.

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Single-cell transcriptomes reveal molecular specializations of neuronal cell types in the developing cerebellum.

Project description:The cerebellum is critical for controlling motor and non-motor functions via cerebellar circuit that is composed of defined cell types, which approximately account for more than half of neurons in mammals. The molecular mechanisms controlling developmental progression and maturation processes of various cerebellar cell types need systematic investigation. Here, we analyzed transcriptome profiles of 21119 single cells of the postnatal mouse cerebellum and identified eight main cell clusters. Functional annotation of differentially expressed genes revealed trajectory hierarchies of granule cells (GCs) at various states and implied roles of mitochondrion and ATPases in the maturation of Purkinje cells (PCs), the sole output cells of the cerebellar cortex. Furthermore, we analyzed gene expression patterns and co-expression networks of 28 ataxia risk genes, and found that most of them are related with biological process of mitochondrion and around half of them are enriched in PCs. Our results also suggested core transcription factors that are correlated with interneuron differentiation and characteristics for the expression of secretory proteins in glia cells, which may participate in neuronal modulation. Thus, this study presents a systematic landscape of cerebellar gene expression in defined cell types and a general gene expression framework for cerebellar development and dysfunction.

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