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 Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

2018 ◽  
Author(s):  
Xuran Wang ◽  
Jihwan Park ◽  
Katalin Susztak ◽  
Nancy R. Zhang ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

scholarly journals Gene length and detection bias in single cell RNA sequencing protocols

2017 ◽  
Author(s):  
Belinda Phipson ◽  
Luke Zappia ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Full Length ◽  
Specific Gene ◽  
Gene Length ◽  
Length Bias ◽  
Single Cell Rna Sequencing ◽  
Full Length Transcript ◽  
Gene Length Bias

AbstractSingle cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include Unique Molecular Identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene and PCR duplicates resulting in copies of the UMI are not included in expression estimates. Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets differing in terms of capture technology, library preparation, cell types and species. We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, and have a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. We briefly discuss the role of these genes in the context of differential expression testing and GO analysis. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal underlying biology influencing expression of mESCs.

scholarly journals RNA-seq library preparation from single pancreatic acinar cells

2016 ◽  
Author(s):  
Damian Wollny ◽  
Sheng Zhao ◽  
Ana Martin-Villalba
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Acinar Cells ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Pancreatic Acinar Cells ◽  
Library Preparation ◽  
Cell Type ◽  
Promising Tool ◽  
Single Cell Rna Sequencing

Single cell RNA sequencing technology has emerged as a promising tool to uncover previously neglected cellular heterogeneity. Multiple methods and protocols have been developed to apply single cell sequencing to different cell types from various organs. However, library preparation for RNA sequencing remains challenging for cell types with high RNAse content due to rapid degradation of endogenous RNA molecules upon cell lysis. To this end, we developed a protocol based on the SMART-seq2 technology for single cell RNA sequencing of pancreatic acinar cells, the cell type with one of the highest ribonuclease concentration measured to date. This protocol reliably produces high quality libraries from single acinar cells reaching a total of 5x106 reads / cell and ∼ 80% transcript mapping rate with no detectable 3´end bias. Thus, our protocol makes single cell transcriptomics accessible to cell type with very high RNAse content.

scholarly journals Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis

2019 ◽  
Author(s):  
Xiangjie Li ◽  
Yafei Lyu ◽  
Jihwan Park ◽  
Jingxiao Zhang ◽  
Dwight Stambolian ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Batch Effects ◽  
Cluster Assignment ◽  
Cell Clustering ◽  
Deep Embedding ◽  
Single Cell Rna Sequencing ◽  
Number Of Cells

Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells imposes computational challenges. We present an unsupervised deep embedding algorithm for single-cell clustering (DESC) that iteratively learns cluster-specific gene expression signatures and cluster assignment. DESC significantly improves clustering accuracy across various datasets and is capable of removing complex batch effects while maintaining true biological variations.

scholarly journals Kidney pathological changes alter cell clustering features in single-cell RNA sequencing

2021 ◽  
Author(s):  
Lijun Ma ◽  
Mariana Murea ◽  
Young A Choi ◽  
Ashok K. Hemal ◽  
Alexei V. Mikhailov ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cell Types ◽  
Specific Gene ◽  
Glomerular Injury ◽  
Cell Clustering ◽  
Single Cell Rna Sequencing

The kidney is composed of multiple cell types, each with specific physiological functions. Single-cell RNA sequencing (scRNA-Seq) is useful for classifying cell-specific gene expression profiles in kidney tissue. Because viable cells are critical in scRNA-Seq analyses, we report an optimized cell dissociation process and the necessity for histological screening of human kidney sections prior to performing scRNA-Seq. We show that glomerular injury can result in loss of select cell types during the cell clustering process. Subsequent fluorescence microscopy confirmed reductions in cell-specific markers among the injured cells seen on kidney sections and these changes need to be considered when interpreting results of scRNA-Seq.

scholarly journals Subclones of Bone Marrow CD34+ Cells in Acute Myeloid Leukemia at Diagnosis Confer Responses of Patients to Induction Chemotherapy

2021 ◽  
Author(s):  
Ruinan Jia ◽  
Min Ji ◽  
Guosheng Li ◽  
Yuan Xia ◽  
Shouhui Guo ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Induction Chemotherapy ◽  
Myeloid Leukemia ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Newly Diagnosed ◽  
Hematopoietic Stem ◽  
Hematopoietic Malignancy

Abstract BackgroundAcute myeloid leukemia (AML) is a fatal hematopoietic malignancy which has a prognosis that varies with genetic heterogeneity of its hematopoietic stem/progenitor cells(HSCPs). Intensive induction chemotherapy with cytarabine and anthracycline has been the standard of care for newly diagnosed AML, but about 30% of patients have no response to this regimen. The resistance mechanisms require deeper understanding. Methods In our study, using 10× Genomics, a high-throughput single cell RNA sequencing (scRNA-seq) platform, we analyzed the cellular heterogeneity of bone marrow CD34+ cells from newly diagnosed AML patients who were then divided into sensitive and resistant groups according to their responses to induction chemotherapy with cytarabine and anthracycline. ScRNA-seq data for healthy controls were obtained from the GEO database. We verified our findings by the TCGA database, GEO datasets and the multiparameter flow cytometry.ResultsFrom the integrative analysis of 60,402 cells, we established a landscape for single-cell CD34+ cells in AML patients and identified the hematopoietic stem/progenitor cell types based on the lineage signature genes. Interestingly, through recognizing the malignant-like clusters and comparing “resistant group” with “sensitive group”, we found a cell population with specific gene signatures (CRIP1highLGALS1highS100Ashigh) showing features of granulocyte-monocyte progenitors (GMP) was associated with poor prognosis of AML. And two cell populations of AML CD34+ cells marked by CD52+ or CD74+DAP12+ were related to good response of patients to induction therapy, showing characteristics of hematopoietic stem cell (HSC). ConclusionOur study indicates the heterogeneity of AML CD34+ cells confers for outcomes of AML and provides possible biomarkers to predict the response of AML patients to induction chemotherapy.

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 Gene length and detection bias in single cell RNA sequencing protocols

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 595 ◽  
Author(s):  
Belinda Phipson ◽  
Luke Zappia ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Full Length ◽  
Specific Gene ◽  
Gene Length ◽  
Length Bias ◽  
Single Cell Rna Sequencing ◽  
Full Length Transcript ◽  
Gene Length Bias

Background: Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates. Methods: Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. Results: We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. Conclusions: We find that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.

scholarly journals A molecular cell atlas of the human lung from single cell RNA sequencing

2019 ◽  
Author(s):  
Kyle J. Travaglini ◽  
Ahmad N. Nabhan ◽  
Lolita Penland ◽  
Rahul Sinha ◽  
Astrid Gillich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Human Lung ◽  
Expression Profiles ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Trafficking ◽  
Single Cell Rna Sequencing

AbstractAlthough single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet- and plate-based single cell RNA sequencing applied to ∼75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution.

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