scholarly journals Single-cell Transcriptomics Identify Extensive Heterogeneity in the Cellular Composition of Mouse Achilles Tendons

2019 ◽  
Author(s):  
Andrea J De Micheli ◽  
Jacob B Swanson ◽  
Nathaniel P Disser ◽  
Leandro M Martinez ◽  
Nicholas R Walker ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Tissue Homeostasis ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Cellular Composition ◽  
Matrix Assembly ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
Additional Support

AbstractTendon is a connective tissue that transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, but little is known about the diversity of cells that populate tendon. Our objective was to explore the heterogeneity of cells in mouse Achilles tendons using single-cell RNA sequencing. We assembled a transcriptomic atlas and identified 11 distinct cell types in tendons, including 3 previously undescribed populations of fibroblasts. Using trajectory inference analysis, we provide additional support for the notion that pericytes are progenitor cells for the fibroblasts that compose adult tendons. We also modeled cell-interactions and identified ligand-receptor pairs involved in tendon homeostasis. Our findings highlight notable heterogeneity between and within tendon cell populations, which may contribute to our understanding of tendon extracellular matrix assembly and maintenance, and inform the design of therapies to treat tendinopathies.

scholarly journals Single-cell transcriptomic analysis identifies extensive heterogeneity in the cellular composition of mouse Achilles tendons

2020 ◽  
Vol 319 (5) ◽  
pp. C885-C894 ◽  
Author(s):  
Andrea J. De Micheli ◽  
Jacob B. Swanson ◽  
Nathaniel P. Disser ◽  
Leandro M. Martinez ◽  
Nicholas R. Walker ◽  
...  
Keyword(s):  
Single Cell ◽  
Progenitor Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Matrix Assembly ◽  
Potential Source ◽  
Additional Support ◽  
Signaling Interactions ◽  
Heterogeneity Of Cell Populations

Tendon is a dense connective tissue that stores and transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, yet little is known about the diversity of cell types that populate tendon. To address this, we determined the heterogeneity of cell populations within mouse Achilles tendons using single-cell RNA sequencing. In assembling a transcriptomic atlas of Achilles tendons, we identified 11 distinct types of cells, including three previously undescribed populations of tendon fibroblasts. Prior studies have indicated that pericytes, which are found in the vasculature of tendons, could serve as a potential source of progenitor cells for adult tendon fibroblasts. Using trajectory inference analysis, we provide additional support for the notion that pericytes are likely to be at least one of the progenitor cell populations for the fibroblasts that compose adult tendons. We also modeled cell-cell interactions and identified previously undescribed ligand-receptor signaling interactions involved in tendon homeostasis. Our novel and interactive tendon atlas highlights previously underappreciated heterogeneity between and within tendon cell populations. The atlas also serves as a resource to further the understanding of tendon extracellular matrix assembly and maintenance and in the design of therapies for tendinopathies.

DDRE-36. INVESTIGATING MEDULLOBLASTOMA HETEROGENEITY AND PREDICTING COMPOUND RESPONSE

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi82-vi82
Author(s):  
Luz Ruiz ◽  
Nagi Ayad
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Casein Kinase ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Surgical Removal ◽  
Cerebellar Tumor ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
New Treatments

Abstract Medulloblastoma is the most common malignant brain tumor found in children. It is a cerebellar tumor that affects motor and cognitive processes such as coordination and movement. The standard of care is surgical removal, radiation, and chemotherapy. These treatments can be very damaging to the developing child, in that they can impair vision and walking, among other body functions. Due to this, new treatments are necessary. Treatment plans for children with medulloblastoma need to be tailored to the specific subtype that they have. Genetic studies have revealed that there are four subtypes of pediatric medulloblastoma: Group 3, Group 4, SHH, and WNT. Beyond these bulk-resolution subtypes, we hypothesize intratumor heterogeneity as a barrier to new effective treatments. I have mined single-cell RNA sequencing data to investigate cellular heterogeneity and predict compound response. I analyzed Medulloblastoma patient tumor data along with data obtained from a 10X Genomics Chromium single-cell RNA sequencing experiment performed in the laboratory from a Tg (Neurod-Smoothened*A1) mouse. We hypothesize that distinct cell populations within medulloblastoma should show different predicted compounds that would target them. We have ranked compound predictions to investigate whether compounds may selectively target any of these populations using transcriptional response signatures derived from the LINCS L1000 perturbagen-response dataset. We also hypothesize that Medulloblastoma tumors have distinct subtypes of cells that are preferentially sensitive to BET bromodomain, casein kinase, and ATM/ATR inhibitors. Our analysis identified ten transcriptionally distinct cell types across these medulloblastoma tumors as well as compounds predicted to target them in each transcriptional subtype. Furthermore, we identified bromodomain and casein kinase inhibitors as a potential combination therapy due to their predicted synergy at targeting all cell populations within medulloblastoma. Our studies show the importance of considering cellular heterogeneity when identifying new treatments for medulloblastoma and other brain cancers.

Molecular and Cellular Dynamics of Aortic Aneurysms Revealed by Single-Cell Transcriptomics

2021 ◽  
Author(s):  
Yanming Li ◽  
Scott A. LeMaire ◽  
Ying H. Shen
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cellular Level ◽  
Aortic Aneurysms ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Sequencing Analysis ◽  
Molecular Features ◽  
Single Cell Rna Sequencing

The aorta is highly heterogeneous, containing many different types of cells that perform sophisticated functions to maintain aortic homeostasis. Recently, single-cell RNA sequencing studies have provided substantial new insight into the heterogeneity of vascular cell types, the comprehensive molecular features of each cell type, and the phenotypic interrelationship between these cell populations. This new information has significantly improved our understanding of aortic biology and aneurysms at the molecular and cellular level. Here, we summarize these findings, with a focus on what single-cell RNA sequencing analysis has revealed about cellular heterogeneity, cellular transitions, communications among cell populations, and critical transcription factors in the vascular wall. We also review the information learned from single-cell RNA sequencing that has contributed to our understanding of the pathogenesis of vascular disease, such as the identification of cell types in which aneurysm-related genes and genetic variants function. Finally, we discuss the challenges and future directions of single-cell RNA sequencing applications in studies of aortic biology and diseases.

scholarly journals Single-cell transcriptomic analysis of mIHC images via antigen mapping

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.

scholarly journals Single-cell transcriptome analysis reveals cellular heterogeneity in the ascending aortas of normal and high-fat diet-fed mice

2021 ◽  
Vol 53 (9) ◽  
pp. 1379-1389
Author(s):  
Hao Kan ◽  
Ka Zhang ◽  
Aiqin Mao ◽  
Li Geng ◽  
Mengru Gao ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
High Fat Diet ◽  
Cell Types ◽  
Ascending Aorta ◽  
Normal Diet ◽  
Cellular Heterogeneity ◽  
Cellular Composition ◽  
High Fat ◽  
Aortic Diseases

AbstractThe aorta contains numerous cell types that contribute to vascular inflammation and thus the progression of aortic diseases. However, the heterogeneity and cellular composition of the ascending aorta in the setting of a high-fat diet (HFD) have not been fully assessed. We performed single-cell RNA sequencing on ascending aortas from mice fed a normal diet and mice fed a HFD. Unsupervised cluster analysis of the transcriptional profiles from 24,001 aortic cells identified 27 clusters representing 10 cell types: endothelial cells (ECs), fibroblasts, vascular smooth muscle cells (SMCs), immune cells (B cells, T cells, macrophages, and dendritic cells), mesothelial cells, pericytes, and neural cells. After HFD intake, subpopulations of endothelial cells with lipid transport and angiogenesis capacity and extensive expression of contractile genes were defined. In the HFD group, three major SMC subpopulations showed increased expression of extracellular matrix-degradation genes, and a synthetic SMC subcluster was proportionally increased. This increase was accompanied by upregulation of proinflammatory genes. Under HFD conditions, aortic-resident macrophage numbers were increased, and blood-derived macrophages showed the strongest expression of proinflammatory cytokines. Our study elucidates the nature and range of the cellular composition of the ascending aorta and increases understanding of the development and progression of aortic inflammatory disease.

scholarly journals Cellular heterogeneity and lineage restriction during mouse digit tip regeneration at single cell resolution

2019 ◽  
Author(s):  
Gemma L. Johnson ◽  
Erick J. Masias ◽  
Jessica A. Lehoczky
Keyword(s):  
Single Cell ◽  
Limb Regeneration ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Genetic Lineage ◽  
Epimorphic Regeneration ◽  
Related Cell ◽  
Lower Vertebrates ◽  
Single Cell Rna Sequencing

ABSTRACTInnate regeneration following digit tip amputation is one of the few examples of epimorphic regeneration in mammals. Digit tip regeneration is mediated by the blastema, the same structure invoked during limb regeneration in some lower vertebrates. By genetic lineage analyses in mice, the digit tip blastema has been defined as a population of heterogeneous, lineage restricted progenitor cells. These previous studies, however, do not comprehensively evaluate blastema heterogeneity or address lineage restriction of closely related cell types. In this report we present single cell RNA sequencing of over 38,000 cells from mouse digit tip blastemas and unamputated control digit tips and generate an atlas of the cell types participating in digit tip regeneration. We define the differentiation trajectories of vascular, monocytic, and fibroblastic lineages over regeneration, and while our data confirm broad lineage restriction of progenitors, our analysis reveals an early blastema fibroblast population expressing a novel regeneration-specific gene, Mest.

scholarly journals Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1751 ◽  
Author(s):  
Rishikesh Kumar Gupta ◽  
Jacek Kuznicki
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Future Directions ◽  
Strong Basis ◽  
Bodily Fluids ◽  
Single Cell Rna Sequencing

The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer’s disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.

scholarly journals Integrated profiling of single cell epigenomic and transcriptomic landscape of Parkinson’s disease mouse brain

2020 ◽  
Author(s):  
Jixing Zhong ◽  
Gen Tang ◽  
Jiacheng Zhu ◽  
Xin Qiu ◽  
Weiying Wu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Cell ◽  
Early Stage ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Distinct Cell ◽  
Single Nucleus

AbstractParkinson’s disease (PD) is a neurodegenerative disease leading to the impairment of execution of movement. PD pathogenesis has been largely investigated, but either restricted in bulk level or at certain cell types, which failed to capture cellular heterogeneity and intrinsic interplays among distinct cell types. To overcome this, we applied single-nucleus RNA-seq and single cell ATAC-seq on cerebellum, midbrain and striatum of PD mouse and matched control. With 74,493 cells in total, we comprehensively depicted the dysfunctions under PD pathology covering proteostasis, neuroinflammation, calcium homeostasis and extracellular neurotransmitter homeostasis. Besides, by multi-omics approach, we identified putative biomarkers for early stage of PD, based on the relationships between transcriptomic and epigenetic profiles. We located certain cell types that primarily contribute to PD early pathology, narrowing the gap between genotypes and phenotypes. Taken together, our study provides a valuable resource to dissect the molecular mechanism of PD pathogenesis at single cell level, which could facilitate the development of novel methods regarding diagnosis, monitoring and practical therapies against PD at early stage.

scholarly journals An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1306 ◽  
Author(s):  
Clarence K. Mah ◽  
Alexander T. Wenzel ◽  
Edwin F. Juarez ◽  
Thorin Tabor ◽  
Michael M. Reich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Clustering Analysis ◽  
Cell Types ◽  
Cell Populations ◽  
Popular Method ◽  
Transcriptomic Data ◽  
Processing Data ◽  
Single Cell Rna Sequencing ◽  
Analysis Workflow

Single-cell RNA sequencing (scRNA-seq) has emerged as a popular method to profile gene expression at the resolution of individual cells. While there have been methods and software specifically developed to analyze scRNA-seq data, they are most accessible to users who program. We have created a scRNA-seq clustering analysis GenePattern Notebook that provides an interactive, easy-to-use interface for data analysis and exploration of scRNA-Seq data, without the need to write or view any code. The notebook provides a standard scRNA-seq analysis workflow for pre-processing data, identification of sub-populations of cells by clustering, and exploration of biomarkers to characterize heterogeneous cell populations and delineate cell types.

scholarly journals The single-cell epigenetic regulatory landscape in mammalian perinatal testis development

2021 ◽  
Author(s):  
Jinyue Liao ◽  
Hoi Ching Suen ◽  
Shitao Rao ◽  
Alfred Chun Shui Luk ◽  
Ruoyu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Germ Cells ◽  
Somatic Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Cell Type

AbstractSpermatogenesis depends on an orchestrated series of developing events in germ cells and full maturation of the somatic microenvironment. To date, the majority of efforts to study cellular heterogeneity in testis has been focused on single-cell gene expression rather than the chromatin landscape shaping gene expression. To advance our understanding of the regulatory programs underlying testicular cell types, we analyzed single-cell chromatin accessibility profiles in more than 25,000 cells from mouse developing testis. We showed that scATAC-Seq allowed us to deconvolve distinct cell populations and identify cis-regulatory elements (CREs) underlying cell type specification. We identified sets of transcription factors associated with cell type-specific accessibility, revealing novel regulators of cell fate specification and maintenance. Pseudotime reconstruction revealed detailed regulatory dynamics coordinating the sequential developmental progressions of germ cells and somatic cells. This high-resolution data also revealed putative stem cells within the Sertoli and Leydig cell populations. Further, we defined candidate target cell types and genes of several GWAS signals, including those associated with testosterone levels and coronary artery disease. Collectively, our data provide a blueprint of the ‘regulon’ of the mouse male germline and supporting somatic cells.

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