scholarly journals Single Cell, Single Nucleus and Spatial RNA Sequencing of the Human Liver Identifies Hepatic Stellate Cell and Cholangiocyte Heterogeneity

2021 ◽  
Author(s):  
Tallulah S Andrews ◽  
Jawairia Atif ◽  
Jeff C Liu ◽  
Catia T Perciani ◽  
Xue-Zhong Ma ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Human Liver ◽  
Stellate Cell ◽  
Parenchymal Cell ◽  
Cell Types ◽  
Cell Populations ◽  
Healthy Human ◽  
Single Nucleus

The critical functions of the human liver are coordinated through the interactions of hepatic parenchymal and non-parenchymal cells. Recent advances in single cell transcriptional approaches have enabled an examination of the human liver with unprecedented resolution. However, dissociation related cell perturbation can limit the ability to fully capture the human liver's parenchymal cell fraction, which limits the ability to comprehensively profile this organ. Here, we report the transcriptional landscape of 73,295 cells from the human liver using matched single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq). The addition of snRNA-seq enabled the characterization of interzonal hepatocytes at single-cell resolution, revealed the presence of rare subtypes of hepatic stellate cells previously only seen in disease, and detection of cholangiocyte progenitors that had only been observed during in vitro differentiation experiments. However, T and B lymphocytes and NK cells were only distinguishable using scRNA-seq, highlighting the importance of applying both technologies to obtain a complete map of tissue-resident cell-types. We validated the distinct spatial distribution of the hepatocyte, cholangiocyte and stellate cell populations by an independent spatial transcriptomics dataset and immunohistochemistry. Our study provides a systematic comparison of the transcriptomes captured by scRNA-seq and snRNA-seq and delivers a high-resolution map of the parenchymal cell populations in the healthy human liver.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

scholarly journals CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

2021 ◽  
Author(s):  
Zhengyu Ouyang ◽  
Nathanael Bourgeois ◽  
Eugenia Lyashenko ◽  
Paige Cundiff ◽  
Patrick F Cullen ◽  
...  
Keyword(s):  
Single Cell ◽  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Model Systems ◽  
Rna Seq ◽  
Cell Type ◽  
Fine Grained ◽  
Single Nucleus ◽  
Induced Pluripotent

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

scholarly journals Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing

2017 ◽  
Author(s):  
Trygve E. Bakken ◽  
Rebecca D. Hodge ◽  
Jeremy M. Miller ◽  
Zizhen Yao ◽  
Thuc N. Nguyen ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transcriptional Profiling ◽  
Neuronal Cell ◽  
Cell Types ◽  
Matched Pair ◽  
Rna Seq ◽  
Nuclear Rna ◽  
Single Nucleus

AbstractTranscriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (∼11,000 genes) than nuclei (∼7,000 genes), but the majority of genes have similar detection across cells and nuclei. 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.

scholarly journals Cell atlas of the regenerating human liver after portal vein embolization

2021 ◽  
Author(s):  
Agnieska Brazovskaja ◽  
Tomás Gomes ◽  
Christiane Körner ◽  
Zhisong He ◽  
Theresa Schaffer ◽  
...  
Keyword(s):  
Portal Vein ◽  
Portal Vein Embolization ◽  
Human Liver ◽  
Portal Blood Flow ◽  
Cell Types ◽  
Cellular Adhesion ◽  
Healthy Human ◽  
Tissue Responses ◽  
Single Nucleus ◽  
Homeostatic Mechanisms

The liver has the remarkable capacity to regenerate. In the clinic, this capacity can be induced by portal vein embolization (PVE), which redirects portal blood flow resulting in liver hypertrophy in locations with increased blood supply, and atrophy of embolized segments. Here we apply single-cell and single-nucleus transcriptomics on healthy, hypertrophied, and atrophied patient-derived liver samples to explore cell states in the liver during regeneration. We first establish an atlas of cell subtypes from the healthy human liver using fresh and frozen tissues, and then compare post-PVE samples with their reference counterparts. We find that PVE alters portal-central zonation of hepatocytes and endothelial cells. Embolization upregulates expression programs associated with development, cellular adhesion and inflammation across cell types. Analysis of interlineage crosstalk revealed key roles for immune cells in modulating regenerating tissue responses. Altogether, our data provides a rich resource for understanding homeostatic mechanisms arising during human liver regeneration and degeneration.

scholarly journals Single-cell RNA sequencing of human liver reveals hepatic stellate cell heterogeneity

JHEP Reports ◽  
2021 ◽  
pp. 100278
Author(s):  
L. Payen Valéry ◽  
Lavergne Arnaud ◽  
Alevra Sarika Niki ◽  
Colonval Megan ◽  
Karim Latifa ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Hepatic Stellate Cell ◽  
Human Liver ◽  
Stellate Cell ◽  
Cell Heterogeneity ◽  
Single Cell Rna Sequencing

scholarly journals Single‐Cell, Single‐Nucleus, and Spatial RNA Sequencing of the Human Liver Identifies Cholangiocyte and Mesenchymal Heterogeneity

2021 ◽  
Author(s):  
Tallulah S. Andrews ◽  
Jawairia Atif ◽  
Jeff C. Liu ◽  
Catia T. Perciani ◽  
Xue‐Zhong Ma ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Human Liver ◽  
Single Nucleus

scholarly journals Single-cell transcriptomic profiling of healthy and fibrotic adult zebrafish liver reveals conserved cell identities and stellate cell activation phenotypes with human liver

2021 ◽  
Author(s):  
Joshua K Morrison ◽  
Charles DeRossi ◽  
Isaac L Alter ◽  
Shikha Nayar ◽  
Mamta Giri ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Single Cell ◽  
Human Liver ◽  
Cell Activation ◽  
Stellate Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Adult Zebrafish ◽  
Transcriptional Profiles ◽  
Zebrafish Liver

Liver fibrosis is the excessive accumulation of extracellular matrix that can progress to cirrhosis and failure if untreated. The mechanisms of fibrogenesis are multi-faceted and remain elusive with no approved antifibrotic treatments available. Here we use single-cell RNA sequencing (scRNA-seq) of the adult zebrafish liver to study the molecular and cellular dynamics of the liver at a single-cell level and demonstrate the value of the adult zebrafish as a model for studying liver fibrosis. scRNA-seq reveals transcriptionally unique populations of hepatic cell types that comprise the zebrafish liver. Joint clustering with human liver scRNA-seq data demonstrates high conservation of transcriptional profiles and human marker genes in zebrafish cell types. Human and zebrafish hepatic stellate cells (HSCs), the driver cell in liver fibrosis, specifically show conservation of transcriptional profiles and we uncover Colec11 as a novel, conserved marker for zebrafish HSCs. To demonstrate the power of scRNA-seq to study liver fibrosis, we performed scRNA-seq on our zebrafish model of a pediatric liver disease with characteristic early, progressive liver fibrosis caused by mutation in mannose phosphate isomerase (MPI). Comparison of differentially expressed genes from human and zebrafish MPI mutant HSC datasets demonstrated similar activation of fibrosis signaling pathways and upstream regulators. CellPhoneDB analysis revealed important receptor-ligand interactions within normal and fibrotic states. This study establishes the first scRNA-seq atlas of the adult zebrafish liver, highlights the high degree of similarity to the human liver, and strengthens its value as a model to study liver fibrosis.

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

2019 ◽  
Author(s):  
Alan Selewa ◽  
Ryan Dohn ◽  
Heather Eckart ◽  
Stephanie Lozano ◽  
Bingqing Xie ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Time Course ◽  
Cell Types ◽  
Heart Tissue ◽  
Intact Cells ◽  
Tissue Samples ◽  
Single Nucleus ◽  
Induced Pluripotent

ABSTRACTA 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. We compared the detected cell types from primary tissue with iPSC-derived cardiomyocytes profiled with DroNc-seq. 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.

scholarly journals Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects

2019 ◽  
Author(s):  
Elisabetta Mereu ◽  
Atefeh Lafzi ◽  
Catia Moutinho ◽  
Christoph Ziegenhain ◽  
Davis J. MacCarthy ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Reference Sample ◽  
State Level ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Benchmark Datasets ◽  
Single Nucleus ◽  
Guidelines And Standards ◽  
Multi Center Study

AbstractSingle-cell RNA sequencing (scRNA-seq) is the leading technique for charting the molecular properties of individual cells. The latest methods are scalable to thousands of cells, enabling in-depth characterization of sample composition without prior knowledge. However, there are important differences between scRNA-seq techniques, and it remains unclear which are the most suitable protocols for drawing cell atlases of tissues, organs and organisms. We have generated benchmark datasets to systematically evaluate techniques in terms of their power to comprehensively describe cell types and states. We performed a multi-center study comparing 13 commonly used single-cell and single-nucleus RNA-seq protocols using a highly heterogeneous reference sample resource. Comparative and integrative analysis at cell type and state level revealed marked differences in protocol performance, highlighting a series of key features for cell atlas projects. These should be considered when defining guidelines and standards for international consortia, such as the Human Cell Atlas project.

scholarly journals Advantages of Single-Nucleus over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis

2018 ◽  
Vol 30 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Haojia Wu ◽  
Yuhei Kirita ◽  
Erinn L. Donnelly ◽  
Benjamin D. Humphreys
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Stress Responses ◽  
Cell Types ◽  
Gene Detection ◽  
Stress Response Genes ◽  
Adult Kidney ◽  
Single Cell Rna Sequencing ◽  
Transcriptional Stress ◽  
Single Nucleus

BackgroundA challenge for single-cell genomic studies in kidney and other solid tissues is generating a high-quality single-cell suspension that contains rare or difficult-to-dissociate cell types and is free of both RNA degradation and artifactual transcriptional stress responses.MethodsWe compared single-cell RNA sequencing (scRNA-seq) using the DropSeq platform with single-nucleus RNA sequencing (snRNA-seq) using sNuc-DropSeq, DroNc-seq, and 10X Chromium platforms on adult mouse kidney. We validated snRNA-seq on fibrotic kidney from mice 14 days after unilateral ureteral obstruction (UUO) surgery.ResultsA total of 11,391 transcriptomes were generated in the comparison phase. We identified ten clusters in the scRNA-seq dataset, but glomerular cell types were absent, and one cluster consisted primarily of artifactual dissociation–induced stress response genes. By contrast, snRNA-seq from all three platforms captured a diversity of kidney cell types that were not represented in the scRNA-seq dataset, including glomerular podocytes, mesangial cells, and endothelial cells. No stress response genes were detected. Our snRNA-seq protocol yielded 20-fold more podocytes compared with published scRNA-seq datasets (2.4% versus 0.12%, respectively). Unexpectedly, single-cell and single-nucleus platforms had equivalent gene detection sensitivity. For validation, analysis of frozen day 14 UUO kidney revealed rare juxtaglomerular cells, novel activated proximal tubule and fibroblast cell states, and previously unidentified tubulointerstitial signaling pathways.ConclusionssnRNA-seq achieves comparable gene detection to scRNA-seq in adult kidney, and it also has substantial advantages, including reduced dissociation bias, compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and successful performance on inflamed fibrotic kidney.

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