scholarly journals The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors

2021 ◽  
Author(s):  
◽  
Stephen R Quake
Keyword(s):  
Single Cell ◽  
Molecular Characterization ◽  
Rna Splicing ◽  
Detailed Comparison ◽  
Cell Types ◽  
Proliferative Potential ◽  
Tissue Specific ◽  
Multiple Organs ◽  
Cell Transcriptome

In recent years there has been tremendous progress towards deep molecular characterization of cell types using single cell transcriptome sequencing. Here we report a single cell transcriptomic atlas comprising nearly 500,000 cells from 24 different human tissues and organs. In several instances multiple organs were analyzed from the same donor. Analyzing organs from the same individual controls for genetic background, age, environment, and epigenetic effects, and enables a detailed comparison of cell types that are shared between tissues. This resource provides a rich molecular characterization of more than 400 cell types, their distribution across tissues, and detailed information about tissue specific variation in gene expression. We have used the fact that multiple tissues came from the same donor to study the clonal distribution of T cells between tissues, to understand the tissue specific mutation rate in B cells, and to analyze the cell cycle state and proliferative potential of shared cell types across tissues. Finally, we have also used this data to characterize cell type specific RNA splicing and how such splicing varies across tissues within an individual.

scholarly journals RNA splicing programs define tissue compartments and cell types at single cell resolution

2021 ◽  
Author(s):  
Julia Eve Olivieri ◽  
Roozbeh Dehghannasiri ◽  
Peter Wang ◽  
SoRi Jang ◽  
Antoine de Morree ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
High Throughput ◽  
Rna Splicing ◽  
Single Cells ◽  
Cell Types ◽  
Mouse Lemur ◽  
Cell Type ◽  
Multiple Organs ◽  
Single Cell Pcr

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach that is agnostic to transcript annotation, to detect cell-type-specific regulated splicing in > 110K carefully annotated single cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type specifically spliced. These results are validated with RNA FISH, single cell PCR, and in high throughput with Smart-seq2. Regulated splicing is found in ubiquitously expressed genes such as actin light chain subunit MYL6 and ribosomal protein RPS24, which has an epithelial-specific microexon. 13% of the statistically most variable splice sites in cell-type specifically regulated genes are also most variable in mouse lemur or mouse. SpliZ analysis further reveals 170 genes with regulated splicing during sperm development using, 10 of which are conserved in mouse and mouse lemur. The statistical properties of the SpliZ allow model-based identification of subpopulations within otherwise indistinguishable cells based on gene expression, illustrated by subpopulations of classical monocytes with stereotyped splicing, including an un-annotated exon, in SAT1, a Diamine acetyltransferase. Together, this unsupervised and annotation-free analysis of differential splicing in ultra high throughput droplet-based sequencing of human cells across multiple organs establishes splicing is regulated cell-type-specifically independent of gene expression.

scholarly journals Characterization of transcript enrichment and detection bias in single-nuclei RNA-seq for mapping of distinct human adipocyte lineages

2021 ◽  
Author(s):  
Anushka Gupta ◽  
Farnaz Shamsi ◽  
Nicolas Altemos ◽  
Gabriel F. Dorlhiac ◽  
Aaron M. Cypess ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Biological Tissues ◽  
Brown Adipocyte ◽  
Bioinformatic Tools ◽  
Accurate Comparison ◽  
Cell Transcriptome

ABSTRACTSingle-cell RNA-sequencing (scRNA-seq) enables molecular characterization of complex biological tissues at high resolution. The requirement of single-cell extraction, however, makes it challenging for profiling tissues such as adipose tissue where collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nuclei extraction is often much more efficient and therefore single-nuclei RNA-sequencing (snRNA-seq) presents an alternative to scRNA-seq. However, nuclear transcripts represent only a fraction of the transcriptome in a single cell, with snRNA-seq marked with inherent transcript enrichment and detection biases. Therefore, snRNA-seq may be inadequate for mapping important transcriptional signatures in adipose tissue. In this study, we compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. We demonstrate that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, we also explore how and why the nuclear transcriptome is biased and limited, and how it can be advantageous. We robustly characterize the enrichment of nuclear-localized transcripts and adipogenic regulatory lncRNAs in snRNA-seq, while also providing a detailed understanding for the preferential detection of long genes upon using this technique. To remove such technical detection biases, we propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, we demonstrate successful integration of scRNA-seq and snRNA-seq datasets with existing bioinformatic tools. Overall, our results illustrate the applicability of snRNA-seq for characterization of cellular diversity in the adipose tissue.

scholarly journals Single-cell transcriptomic characterization of 20 organs and tissues from individual mice creates a Tabula Muris

2017 ◽  
Author(s):  
Nicholas Schaum ◽  
Jim Karkanias ◽  
Norma F Neff ◽  
Andrew P. May ◽  
Stephen R. Quake ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Biology ◽  
Model Organism ◽  
High Sensitivity ◽  
Cell Types ◽  
Cell Transcriptome ◽  
Full Length Transcript ◽  
Low Coverage

The Tabula Muris ConsortiumWe have created a compendium of single cell transcriptome data from the model organism Mus musculus comprising more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, revealing gene expression in poorly characterized cell populations and allowing for direct and controlled comparison of gene expression in cell types shared between tissues, such as T-lymphocytes and endothelial cells from distinct anatomical locations. Two distinct technical approaches were used for most tissues: one approach, microfluidic droplet-based 3’-end counting, enabled the survey of thousands of cells at relatively low coverage, while the other, FACS-based full length transcript analysis, enabled characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.

scholarly journals RNA splicing programs define tissue compartments and cell types at single cell resolution

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Julia Eve Olivieri ◽  
Roozbeh Dehghannasiri ◽  
Peter L Wang ◽  
SoRi Jang ◽  
Antoine de Morree ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Splicing ◽  
Cell Types ◽  
Cell Type ◽  
Multiple Organs ◽  
Single Cell Pcr ◽  
Cell Type Specific ◽  
Human Spermatogenesis ◽  
Tissue Compartments ◽  
Classical Monocytes

The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24. These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1. Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically.

scholarly journals Adult tissue-resident stem cells—fact or fiction?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Deepa Bhartiya
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Adult Stem Cells ◽  
Cell Types ◽  
Specific Cell ◽  
Tissue Specific ◽  
Cardiac Tissues ◽  
Adult Tissues ◽  
Resident Stem Cells ◽  
Abundant Cytoplasm

AbstractLife-long tissue homeostasis of adult tissues is supposedly maintained by the resident stem cells. These stem cells are quiescent in nature and rarely divide to self-renew and give rise to tissue-specific “progenitors” (lineage-restricted and tissue-committed) which divide rapidly and differentiate into tissue-specific cell types. However, it has proved difficult to isolate these quiescent stem cells as a physical entity. Recent single-cell RNAseq studies on several adult tissues including ovary, prostate, and cardiac tissues have not been able to detect stem cells. Thus, it has been postulated that adult cells dedifferentiate to stem-like state to ensure regeneration and can be defined as cells capable to replace lost cells through mitosis. This idea challenges basic paradigm of development biology regarding plasticity that a cell enters point of no return once it initiates differentiation. The underlying reason for this dilemma is that we are putting stem cells and somatic cells together while processing for various studies. Stem cells and adult mature cell types are distinct entities; stem cells are quiescent, small in size, and with minimal organelles whereas the mature cells are metabolically active and have multiple organelles lying in abundant cytoplasm. As a result, they do not pellet down together when centrifuged at 100–350g. At this speed, mature cells get collected but stem cells remain buoyant and can be pelleted by centrifuging at 1000g. Thus, inability to detect stem cells in recently published single-cell RNAseq studies is because the stem cells were unknowingly discarded while processing and were never subjected to RNAseq. This needs to be kept in mind before proposing to redefine adult stem cells.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

scholarly journals Single Cell Atlas of Human Dura Reveals Cellular Meningeal Landscape and Insights into Meningioma Immune Response

2021 ◽  
Author(s):  
Anthony Z Wang ◽  
Jay Bowman-Kirigin ◽  
Rupen Desai ◽  
Pujan Patel ◽  
Bhuvic Patel ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Copy Number Variant ◽  
Cell Types ◽  
Cell Receptor ◽  
Immune Microenvironment ◽  
Clinical Models ◽  
Insight Into

Recent investigation of the meninges, specifically the dura layer, has highlighted its importance in CNS immune surveillance beyond a purely structural role. However, most of our understanding of the meninges stems from the use of pre-clinical models rather than human samples. In this study, we use single cell RNA-sequencing to perform the first characterization of both non-tumor-associated human dura and meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, through T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. We also identify a functionally heterogeneous population of non-immune cell types and report copy-number variant heterogeneity within our meningioma samples. Our comprehensive investigation of both the immune and non-immune cell landscapes of human dura and meningioma at a single cell resolution provide new insight into previously uncharacterized roles of human dura.

scholarly journals SARS-CoV-2 Neurotropism and Single Cell Responses in Brain Organoids Containing Innately Developing Microglia

2021 ◽  
Author(s):  
Samudyata ◽  
Ana Osorio Oliveira ◽  
Susmita Malwade ◽  
Nuno Rufino de Sousa ◽  
Sravan K Goparaju ◽  
...  
Keyword(s):  
Single Cell ◽  
Neuronal Cell Death ◽  
Neuronal Cell ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Cellular Responses ◽  
Cellular Respiration ◽  
Neuropsychiatric Manifestations ◽  
Altered Blood ◽  
Cell Transcriptome

Neuropsychiatric manifestations are common in both acute and post-acute phase of SARS-CoV-2 infection, but the mechanism of these effects is unknown. Here, we derive human brain organoids with innately developing microglia to investigate the cellular responses to SARS-CoV-2 infection on a single cell level. We find evidence of limited tropism to SARS-CoV-2 for all major cell types and observe extensive neuronal cell death that also include non-infected cells. Single cell transcriptome profiling reveals distinct responses in microglia and astrocytes that share features with cellular states observed in neurodegenerative diseases, includes upregulation of genes with relevance for synaptic stripping, and suggests altered blood brain barrier integrity. Across all cell types, we observe a global translational shut-down as well as altered carbohydrate metabolism and cellular respiration. Together, our findings provide insights into cellular responses of the resident brain immune cells to SARS-CoV-2 and pinpoint mechanisms that may be of relevance for the neuropathological changes observed in COVID-19 patients.

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

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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