scholarly journals Single cell transcriptomic landscape of diabetic foot ulcers

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Georgios Theocharidis ◽  
Beena E. Thomas ◽  
Debasree Sarkar ◽  
Hope L. Mumme ◽  
William J. R. Pilcher ◽  
...  
Keyword(s):  
Single Cell ◽  
Diabetic Foot ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Macrophage Polarization ◽  
Cell Types ◽  
Peripheral Blood Mononuclear ◽  
Preferential Localization ◽  
M1 Macrophage ◽  
Healing Wounds

AbstractDiabetic foot ulceration (DFU) is a devastating complication of diabetes whose pathogenesis remains incompletely understood. Here, we profile 174,962 single cells from the foot, forearm, and peripheral blood mononuclear cells using single-cell RNA sequencing. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 and increased M1 macrophage polarization in the DFU patients with healing wounds. Further, analysis of spatially separated samples from the same patient and spatial transcriptomics reveal preferential localization of these healing associated fibroblasts toward the wound bed as compared to the wound edge or unwounded skin. Spatial transcriptomics also validates our findings of higher abundance of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

scholarly journals Single Cell Transcriptomic Landscape of Diabetic Foot Ulcers

2021 ◽  
Author(s):  
Georgios Theocharidis ◽  
Beena E. Thomas ◽  
Debasree Sarkar ◽  
William JR Pilcher ◽  
Bhakti Dwivedi ◽  
...  
Keyword(s):  
Wound Healing ◽  
Single Cell ◽  
Single Cells ◽  
Macrophage Polarization ◽  
Cell Types ◽  
Deep Wound ◽  
Preferential Localization ◽  
M1 Macrophage ◽  
Diabetic Wound Healing ◽  
Healing Wounds

AbstractTo understand the diabetic wound healing microenvironment, we profiled 174,962 single cells from foot, forearm, and PBMCs using single-cell RNA sequencing (scRNASeq) approach. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 genes and M1 macrophage polarization in the DFU patients with healing wounds. Further, scRNASeq of spatially separated samples from same patient and spatial transcriptomics (ST) revealed preferential localization of these healing associated fibroblasts toward deep wound/ulcer bed as compared to wound edge or non-wounded skin. ST also validated our findings of higher enrichment of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

scholarly journals Sierra: discovery of differential transcript usage from polyA-captured single-cell RNA-seq data

2019 ◽  
Author(s):  
Ralph Patrick ◽  
David T. Humphreys ◽  
Vaibhao Janbandhu ◽  
Alicia Oshlack ◽  
Joshua W.K. Ho ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Cardiac Fibroblasts ◽  
Single Cells ◽  
Cell Types ◽  
Computational Pipeline ◽  
Rna Seq ◽  
Peripheral Blood Mononuclear ◽  
Mrna Isoform

AbstractHigh-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell-types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3’UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/VCCRI/Sierra.

scholarly journals Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single cell sequencing

2021 ◽  
Author(s):  
Zhibin Li ◽  
chengcheng Sun ◽  
Fei Wang ◽  
Xiran Wang ◽  
Jiacheng Zhu ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Evolutionary Biology ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Genetic Regulatory Networks ◽  
Peripheral Blood Mononuclear

Background: Immune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. Methods: In this study, we compared the single-cell transcriptomes of 77 957 immune cells from 12 species using single-cell RNA-sequencing (scRNA-seq). Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. Results: The results revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular cross-talks and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. Conclusions: This study is the first to provide a comprehensive analysis of the cross-species single-cell atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders

scholarly journals Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhe Cui ◽  
Ya Cui ◽  
Yan Gao ◽  
Tao Jiang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Support Vector ◽  
Cell Type ◽  
Peripheral Blood Mononuclear ◽  
Copy Numbers ◽  
Genome Wide ◽  
Accessible Chromatin

Single-cell Assay Transposase Accessible Chromatin sequencing (scATAC-seq) has been widely used in profiling genome-wide chromatin accessibility in thousands of individual cells. However, compared with single-cell RNA-seq, the peaks of scATAC-seq are much sparser due to the lower copy numbers (diploid in humans) and the inherent missing signals, which makes it more challenging to classify cell type based on specific expressed gene or other canonical markers. Here, we present svmATAC, a support vector machine (SVM)-based method for accurately identifying cell types in scATAC-seq datasets by enhancing peak signal strength and imputing signals through patterns of co-accessibility. We applied svmATAC to several scATAC-seq data from human immune cells, human hematopoietic system cells, and peripheral blood mononuclear cells. The benchmark results showed that svmATAC is free of literature-based markers and robust across datasets in different libraries and platforms. The source code of svmATAC is available at https://github.com/mrcuizhe/svmATAC under the MIT license.

scholarly journals Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue

2018 ◽  
Vol 115 (52) ◽  
pp. E12363-E12369 ◽  
Author(s):  
Fabio Zanini ◽  
Makeda L. Robinson ◽  
Derek Croote ◽  
Malaya Kumar Sahoo ◽  
Ana Maria Sanz ◽  
...  
Keyword(s):  
B Cells ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Dengue Infection ◽  
Cell Types ◽  
Denv Infection ◽  
Molecular Signature ◽  
Severe Dengue ◽  
Peripheral Blood Mononuclear

Dengue virus (DENV) infection can result in severe complications. However, the understanding of the molecular correlates of severity is limited, partly due to difficulties in defining the peripheral blood mononuclear cells (PBMCs) that contain DENV RNA in vivo. Accordingly, there are currently no biomarkers predictive of progression to severe dengue (SD). Bulk transcriptomics data are difficult to interpret because blood consists of multiple cell types that may react differently to infection. Here, we applied virus-inclusive single-cell RNA-seq approach (viscRNA-Seq) to profile transcriptomes of thousands of single PBMCs derived early in the course of disease from six dengue patients and four healthy controls and to characterize distinct leukocyte subtypes that harbor viral RNA (vRNA). Multiple IFN response genes, particularly MX2 in naive B cells and CD163 in CD14+ CD16+ monocytes, were up-regulated in a cell-specific manner before progression to SD. The majority of vRNA-containing cells in the blood of two patients who progressed to SD were naive IgM B cells expressing the CD69 and CXCR4 receptors and various antiviral genes, followed by monocytes. Bystander, non-vRNA–containing B cells also demonstrated immune activation, and IgG1 plasmablasts from two patients exhibited clonal expansions. Lastly, assembly of the DENV genome sequence revealed diversity at unexpected sites. This study presents a multifaceted molecular elucidation of natural dengue infection in humans with implications for any tissue and viral infection and proposes candidate biomarkers for prediction of SD.

A single-cell map for the transcriptomic signatures of peripheral blood mononuclear cells in end-stage renal disease

2019 ◽  
Author(s):  
Ting Luo ◽  
Fengping Zheng ◽  
Kang Wang ◽  
Yong Xu ◽  
Huixuan Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
End Stage Renal Disease ◽  
Mononuclear Cells ◽  
Cell Types ◽  
Cell Type ◽  
Peripheral Blood Mononuclear ◽  
Blood Mononuclear Cells ◽  
Cell Type Specific ◽  
Stage Renal Disease ◽  
End Stage

Abstract Background Immune aberrations in end-stage renal disease (ESRD) are characterized by systemic inflammation and immune deficiency. The mechanistic understanding of this phenomenon remains limited. Methods We generated 12 981 and 9578 single-cell transcriptomes of peripheral blood mononuclear cells (PBMCs) that were pooled from 10 healthy volunteers and 10 patients with ESRD by single-cell RNA sequencing. Unsupervised clustering and annotation analyses were performed to cluster and identify cell types. The analysis of hallmark pathway and regulon activity was performed in the main cell types. Results We identified 14 leukocytic clusters that corresponded to six known PBMC types. The comparison of cells from ESRD patients and healthy individuals revealed multiple changes in biological processes. We noticed an ESRD-related increase in inflammation response, complement cascade and cellular metabolism, as well as a strong decrease in activity related to cell cycle progression in relevant cell types in ESRD. Furthermore, a list of cell type-specific candidate transcription factors (TFs) driving the ESRD-associated transcriptome changes was identified. Conclusions We generated a distinctive, high-resolution map of ESRD-derived PBMCs. These results revealed cell type-specific ESRD-associated pathways and TFs. Notably, the pooled sample analysis limits the generalization of our results. The generation of larger single-cell datasets will complement the current map and drive advances in therapies that manipulate immune cell function in ESRD.

scholarly journals Scalable full-transcript coverage single cell RNA sequencing with Smart-seq3xpress

2021 ◽  
Author(s):  
Michael Hagemann-Jensen ◽  
Christoph Ziegenhain ◽  
Rickard Sandberg
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Peripheral Blood Mononuclear Cells ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Cell Types ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells ◽  
Additional Protein

Plate-based single-cell RNA-sequencing methods with full-transcript coverage typically excel at sensitivity but are more resource and time-consuming. Here, we miniaturized and streamlined the Smart-seq3 protocol for drastically reduced cost and increased throughput. Applying Smart-seq3xpress to 16,349 human peripheral blood mononuclear cells revealed a highly granular atlas complete with both common and rare cell types whose identification previously relied on additional protein measurements or the integration with a reference atlas.

scholarly journals Single cell transcriptomics reveals opioid usage evokes widespread suppression of antiviral gene program

2019 ◽  
Author(s):  
Tanya T. Karagiannis ◽  
John P. Cleary ◽  
Busra Gok ◽  
Nicholas G. Martin ◽  
Elliot C. Nelson ◽  
...  
Keyword(s):  
Immune System ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Ex Vivo ◽  
Cell Types ◽  
Short Exposure ◽  
Opioid Use ◽  
Peripheral Blood Mononuclear ◽  
Antiviral Gene

AbstractChronic opioid usage not only causes addiction behavior through the central nervous system (CNS), but it also modulates the peripheral immune system. However, whether opioid usage positively or negatively impacts the immune system is still controversial. In order to understand the immune modulatory effect of opioids in a systematic and unbiased way, we performed single cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from opioid-dependent individuals and non-dependent controls. We show that chronic opioid usage evokes widespread suppression of interferon-stimulated genes (ISGs) and antiviral gene program in naive monocytes and upon ex vivo stimulation with the pathogen component lipopolysaccharide (LPS) in multiple innate and adaptive immune cell types. Furthermore, scRNA-seq revealed the same phenomenon with in vitro morphine treatment; after just a short exposure to morphine stimulation, we observed the same suppression of antiviral genes in multiple immune cell types. These findings indicate that both acute and chronic opioid exposure may be harmful to our immune system by suppressing the antiviral gene program, our body’s defense response to potential infection. Our results suggest that further characterization of the immune modulatory effects of opioid use is critical to ensure the safety of clinical opioid usage.

scholarly journals Targeting individual cells by barcode in pooled sequence libraries

2017 ◽  
Author(s):  
Navpreet Ranu ◽  
Alexandra-Chloé Villani ◽  
Nir Hacohen ◽  
Paul C. Blainey
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Dna Barcodes ◽  
Rna Seq ◽  
Enrichment Method ◽  
Peripheral Blood Mononuclear ◽  
Efficient Sampling ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

There is rising interest in applying single-cell transcriptome analysis and other single-cell sequencing methods to resolve differences between cells. Pooled processing of thousands of single cells is now routinely practiced by introducing cell-specific DNA barcodes early in cell processing protocols1-5. However, researchers must sequence a large number of cells to sample rare subpopulations6-8, even when fluorescence-activated cell sorting (FACS) is used to pre-enrich rare cell populations. Here, a new molecular enrichment method is used in conjunction with FACS enrichment to enable efficient sampling of rare dendritic cell (DC) populations, including the recently identified AXL+SIGLEC6+ (AS DCs) subset7, within a 10X Genomics single-cell RNA-Seq library. DC populations collectively represent 1-2% of total peripheral blood mononuclear cells (PBMC), with AS DC representing only 1-3% of human blood DCs and 0.01-0.06% of total PBMCs.

scholarly journals Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

2021 ◽  
Author(s):  
Wenkai Han ◽  
Yuqi Cheng ◽  
Jiayang Chen ◽  
Huawen Zhong ◽  
Zhihang Hu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Biological Diversity ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Data Representation ◽  
Superior Performance ◽  
Peripheral Blood Mononuclear ◽  
Technical Noise ◽  
Downstream Analysis

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool to reveal the complex biological diversity and heterogeneity among cell populations. However, the technical noise and bias of the technology still have negative impacts on the downstream analysis. Here, we present a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and the downstream analysis. CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events. In the task, the deep learning model learns to pull together the representations of similar cells while pushing apart distinct cells, without manual labeling. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43,695 single cells from peripheral blood mononuclear cells. Further experiments to process a million-scale single-cell dataset demonstrate the scalability of CLEAR. This scalable method generates effective scRNA-seq data representation while eliminating technical noise, and it will serve as a general computational framework for single-cell data analysis.

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