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 Single cell transcriptome profiling of the human developing spinal cord reveals a conserved genetic programme with human specific features

2021 ◽  
Author(s):  
Teresa Rayon ◽  
Rory J. Maizels ◽  
Christopher Barrington ◽  
James Briscoe
Keyword(s):  
Gene Expression ◽  
Spinal Cord ◽  
Single Cell ◽  
Motor Neurons ◽  
Neural Tube ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Human Embryos ◽  
Neuronal Populations ◽  
Cell Transcriptome

AbstractThe spinal cord receives input from peripheral sensory neurons and controls motor output by regulating muscle innervating motor neurons. These functions are carried out by neural circuits comprising molecularly and physiologically distinct neuronal subtypes that are generated in a characteristic spatial-temporal arrangement from progenitors in the embryonic neural tube. The systematic mapping of gene expression in mouse embryos has provided insight into the diversity and complexity of cells in the neural tube. For human embryos, however, less information has been available. To address this, we used single cell mRNA sequencing to profile cervical and thoracic regions in four human embryos of Carnegie Stages (CS) CS12, CS14, CS17 and CS19 from Gestational Weeks (W) 4-7. In total we recovered the transcriptomes of 71,219 cells. Analysis of progenitor and neuronal populations from the neural tube, as well as cells of the peripheral nervous system, in dorsal root ganglia adjacent to the neural tube, identified dozens of distinct cell types and facilitated the reconstruction of the differentiation pathways of specific neuronal subtypes. Comparison with existing mouse datasets revealed the overall similarity of mouse and human neural tube development while highlighting specific features that differed between species. These data provide a catalogue of gene expression and cell type identity in the developing neural tube that will support future studies of sensory and motor control systems and can be explored at https://shiny.crick.ac.uk/scviewer/neuraltube/.

scholarly journals A Single-Cell Transcriptome Atlas for Zebrafish Development

2019 ◽  
Author(s):  
Dylan R. Farnsworth ◽  
Lauren Saunders ◽  
Adam C. Miller
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Developmental Time ◽  
Cell Type ◽  
Specific Expression ◽  
Transcriptional Profiles ◽  
Larval Stages ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

ABSTRACTThe ability to define cell types and how they change during organogenesis is central to our understanding of animal development and human disease. Despite the crucial nature of this knowledge, we have yet to fully characterize all distinct cell types and the gene expression differences that generate cell types during development. To address this knowledge gap, we produced an Atlas using single-cell RNA-sequencing methods to investigate gene expression from the pharyngula to early larval stages in developing zebrafish. Our single-cell transcriptome Atlas encompasses transcriptional profiles from 44,102 cells across four days of development using duplicate experiments that confirmed high reproducibility. We annotated 220 identified clusters and highlighted several strategies for interrogating changes in gene expression associated with the development of zebrafish embryos at single-cell resolution. Furthermore, we highlight the power of this analysis to assign new cell-type or developmental stage-specific expression information to many genes, including those that are currently known only by sequence and/or that lack expression information altogether. The resulting Atlas is a resource of biologists to generate hypotheses for genetic (mutant) or functional analysis, to launch an effort to define the diversity of cell-types during zebrafish organogenesis, and to examine the transcriptional profiles that produce each cell type over developmental time.

scholarly journals scAAVengr: Single-cell transcriptome-based quantification of engineered AAVs in non-human primate retina

2020 ◽  
Author(s):  
Bilge E. Öztürk ◽  
Molly E. Johnson ◽  
Michael Kleyman ◽  
Serhan Turunç ◽  
Jing He ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Adeno Associated Virus ◽  
Gene Therapies ◽  
Naturally Occurring ◽  
Cell Transcriptome ◽  
Large Animals ◽  
Single Cell Transcriptome ◽  
Human Primate

AbstractAdeno-associated virus (AAV)-mediated gene therapies are rapidly advancing to the clinic, and AAV engineering has resulted in vectors with increased ability to deliver therapeutic genes. Although the choice of vector is critical, quantitative comparison of AAVs, especially in large animals, remains challenging. Here, we developed an efficient single-cell AAV engineering pipeline (scAAVengr) to quantify efficiency of AAV-mediated gene expression across all cell types. scAAVengr allows for definitive, head-to-head comparison of vectors in the same animal. To demonstrate proof-of-concept for the scAAVengr workflow, we quantified – with cell-type resolution – the abilities of naturally occurring and newly engineered AAVs to mediate gene expression in primate retina following intravitreal injection. A top performing variant, K912, was used to deliver SaCas9 and edit the rhodopsin gene in macaque retina, resulting in editing efficiency similar to infection rates detected by the scAAVengr workflow. These results validate scAAVengr as a powerful method for development of AAV vectors.

scholarly journals Single-Cell RNA-seq Identification of the Cellular Molecular Characteristics of Sporadic Bilateral Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhenyuan Yu ◽  
Wenhao Lu ◽  
Cheng Su ◽  
Yufang Lv ◽  
Yu Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Single Cell ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Types ◽  
Molecular Characteristics ◽  
Left And Right ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Bilateral renal cell carcinoma (RCC) is a rare disease that can be classified as either familial or sporadic. Studying the cellular molecular characteristics of sporadic bilateral RCC is important to provide guidance for clinical treatment. Cellular molecular characteristics can be expressed at the RNA level, especially at the single-cell degree. Single-cell RNA sequencing (scRNA-seq) was performed on bilateral clear cell RCC (ccRCC). A total of 3,575 and 3,568 high-quality single-cell transcriptome data were captured from the left and right tumour tissues, respectively. Gene characteristics were identified by comparing left and right tumours at the scRNA level. The complex cellular environment of bilateral ccRCC was presented by using scRNA-seq. Single-cell transcriptomic analysis revealed high similarity in gene expression among most of the cell types of bilateral RCCs but significant differences in gene expression among different site tumour cells. Additionally, the potential biological function of different tumour cell types was determined by gene ontology (GO) analysis. The transcriptome characteristics of tumour tissues in different locations at the single-cell transcriptome level were revealed through the scRNA-seq of bilateral sporadic ccRCC. This work provides new insights into the diagnosis and treatment of bilateral RCC.

scholarly journals DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243360
Author(s):  
Johan Gustafsson ◽  
Jonathan Robinson ◽  
Juan S. Inda-Díaz ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Log Likelihood ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Average Gene ◽  
Single Cell Transcriptome

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

scholarly journals CellView: Interactive exploration of high dimensional single cell RNA-seq data

2017 ◽  
Author(s):  
Mohan T. Bolisetty ◽  
Michael L. Stitzel ◽  
Paul Robson
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Cell Types ◽  
High Dimensional ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Measure Gene Expression

Advances in high-throughput single cell transcriptomics technologies have revolutionized the study of complex tissues. It is now possible to measure gene expression across thousands of individual cells to define cell types and states. While powerful computational and statistical frameworks are emerging to analyze these complex datasets, a gap exists between this data and a biologist’s insight. The CellView web application fills this gap by providing easy and intuitive exploration of single cell transcriptome data.

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 Characterization of the Zebrafish Cell Landscape at Single-Cell Resolution

2021 ◽  
Vol 9 ◽  
Author(s):  
Mengmeng Jiang ◽  
Yanyu Xiao ◽  
Weigao E ◽  
Lifeng Ma ◽  
Jingjing Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Tissue Repair ◽  
Single Cells ◽  
Genetic Regulation ◽  
Model Organism ◽  
Cell Types ◽  
Biological Research ◽  
Cell Mapping ◽  
Fin Regeneration

Zebrafish have been found to be a premier model organism in biological and regeneration research. However, the comprehensive cell compositions and molecular dynamics during tissue regeneration in zebrafish remain poorly understood. Here, we utilized Microwell-seq to analyze more than 250,000 single cells covering major zebrafish cell types and constructed a systematic zebrafish cell landscape. We revealed single-cell compositions for 18 zebrafish tissue types covering both embryo and adult stages. Single-cell mapping of caudal fin regeneration revealed a unique characteristic of blastema population and key genetic regulation involved in zebrafish tissue repair. Overall, our single-cell datasets demonstrate the utility of zebrafish cell landscape resources in various fields of biological research.

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 In search of a core cellular network in single cell transcriptome data

2021 ◽  
Author(s):  
Ming Yang ◽  
Benjamin R. Harrison ◽  
Daniel E.L. Promislow
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cellular Network ◽  
Cell Types ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractBackgroundAlong with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, remains a central question in biology. Single-cell RNA-sequencing (scRNA-seq) measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current analyses focus primarily on identifying differentially expressed genes across cells. However, patterns of co-expression between genes are probably more indicative of biological processes than are the expression of individual genes. Using single cell transcriptome data from the fly brain, here we focus on gene co-expression to search for a core cellular network.ResultsIn this study, we constructed cell type-specific gene co-expression networks using single cell transcriptome data of brains from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types in fly brains and defined this set as the core cellular network. This core is very small compared with cell type-specific gene co-expression networks and shows dense connectivity. Modules within this core are enriched for basic cellular functions, such as translation and ATP metabolic processes, and gene members of these modules have distinct evolutionary signatures.ConclusionsOverall, we demonstrated that a core cellular network exists in diverse cell types of fly brains and this core exhibits unique topological, structural, functional and evolutionary properties.

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