Single-cell meta-analysis of cigarette smoking lung atlas

Mapping Intimacies ◽

10.1101/2021.12.09.472029 ◽

2021 ◽

Author(s):

Jun Nakayama ◽

Yusuke Yamamoto

Keyword(s):

Gene Expression ◽

Cigarette Smoking ◽

Single Cell ◽

Cell Population ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Fundamental Properties ◽

Patient State ◽

Cell Gene Expression ◽

Public Datasets

Single-cell RNA-seq (scRNA-seq) technologies have been broadly utilized to reveal the molecular mechanisms of respiratory diseases and physiology at single-cell resolution. Here, we constructed a cigarette smoking lung atlas by integrating data from 8 public datasets, including 104 lung scRNA-seq samples with patient state information. The cigarette smoking lung atlas generated by this single-cell meta-analysis (scMeta-analysis) revealed early carcinogenesis events and defined the alterations of single-cell gene expression, cell population, fundamental properties of biological pathways, and cell-cell interactions induced by cigarette smoking. In addition, we developed two novel scMeta-analysis methods incorporating clinical metadata: VARIED (Visualized Algorithms of Relationships In Expressional Diversity) and AGED (Aging-related Gene Expressional Differences). VARIED analysis revealed the expressional diversity associated with smoking carcinogenesis in each cell population. AGED analysis revealed differences in gene expression related to both aging and smoking states. Our scMeta-analysis provided new insights into the effects of smoking and into cellular diversity in the human lung at single-cell resolution.

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Relationships between type 2 diabetes, cell dysfunction, and redox signaling: A meta‐analysis of single‐cell gene expression of human pancreatic α‐and β‐cells

Journal of Diabetes ◽

10.1111/1753-0407.13236 ◽

2021 ◽

Author(s):

Emily Sara Marques ◽

Emily Formato ◽

Wenle Liang ◽

Emily Leonard ◽

Alicia R. Timme‐Laragy

Keyword(s):

Gene Expression ◽

Type 2 Diabetes ◽

Single Cell ◽

Meta Analysis ◽

Redox Signaling ◽

Β Cells ◽

Cell Dysfunction ◽

Cell Gene Expression ◽

Cell Gene

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Single cell gene expression analysis of circulating tumor cells in ovarian cancer reveals CTCs co-expressing stem cell and mesenchymal markers

Geburtshilfe und Frauenheilkunde ◽

10.1055/s-0036-1592665 ◽

2016 ◽

Vol 76 (10) ◽

Cited By ~ 1

Author(s):

C Blassl ◽

JD Kuhlmann ◽

A Webers ◽

P Wimberger ◽

T Fehm ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Stem Cell ◽

Single Cell ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Expression Analysis ◽

Gene Expression Analysis ◽

Cell Gene Expression ◽

Cell Gene

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Faculty Opinions recommendation of Molecular heterogeneity of developing retinal ganglion and amacrine cells revealed through single cell gene expression profiling.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1082928.535886 ◽

2007 ◽

Author(s):

Alain Ghysen

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Amacrine Cells ◽

Molecular Heterogeneity ◽

Retinal Ganglion ◽

Cell Gene Expression ◽

Cell Gene

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Faculty Opinions recommendation of Single-cell gene expression of the bovine blastocyst.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.728770917.793543100 ◽

2018 ◽

Author(s):

Anthony Michael Carter

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Gene Expression ◽

Cell Gene ◽

Bovine Blastocyst

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Faculty Opinions recommendation of Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732647286.793544281 ◽

2018 ◽

Author(s):

Matthias von Herrath ◽

Gustaf Christoffersson

Keyword(s):

Gene Expression ◽

T Cell ◽

Single Cell ◽

Regulatory T Cell ◽

Cell Gene Expression ◽

T Cell Phenotypes ◽

Cell Phenotypes ◽

Cell Gene

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How to build regulatory networks from single-cell gene expression data

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics ◽

10.1145/3388440.3414213 ◽

2020 ◽

Author(s):

Aditya Pratapa ◽

Amogh P. Jalihal ◽

Jeffrey N. Law ◽

Aditya Bharadwaj ◽

T. M. Murali

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Regulatory Networks ◽

Expression Data ◽

Cell Gene Expression ◽

Cell Gene

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GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

Genome Biology ◽

10.1186/s13059-016-1010-4 ◽

2016 ◽

Vol 17 (1) ◽

Cited By ~ 126

Author(s):

Lan Jiang ◽

Huidong Chen ◽

Luca Pinello ◽

Guo-Cheng Yuan

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Gini Index ◽

Cell Types ◽

Expression Data ◽

Cell Gene Expression ◽

Cell Gene

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Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

Developmental Cell ◽

10.1016/j.devcel.2010.02.012 ◽

2010 ◽

Vol 18 (4) ◽

pp. 675-685 ◽

Cited By ~ 568

Author(s):

Guoji Guo ◽

Mikael Huss ◽

Guo Qing Tong ◽

Chaoyang Wang ◽

Li Li Sun ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Fate ◽

Expression Analysis ◽

Gene Expression Analysis ◽

Cell Fate Decisions ◽

Cell Gene Expression ◽

Cell Gene

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MO262THE SINGLE-CELL TRANSCRIPTOMICS OF HUMAN IGA NEPHROPATHY

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab104.0020 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Yong Zhong ◽

Xiangcheng Xiao

Keyword(s):

Gene Expression ◽

Single Cell ◽

Signaling Pathways ◽

Iga Nephropathy ◽

Molecular Mechanisms ◽

Mesangial Cells ◽

Therapeutic Targets ◽

Cell Types ◽

Receptor Crosstalk ◽

Overt Proteinuria

Abstract Background and Aims The exact molecular mechanisms underlying IgA nephropathy (IgAN) remains incompletely defined. Therefore, it is necessary to further elucidate the mechanism of IgA nephropathy and find novel therapeutic targets. Method Single-cell RNA sequencing (scRNA-seq) was applied to kidney biopsies from 4 IgAN and 1 control subjects to define the transcriptomic landscape at the single-cell resolution. Unsupervised clustering analysis of kidney specimens was used to identify distinct cell clusters. Differentially expressed genes and potential signaling pathways involved in IgAN were also identified. Results Our analysis identified 14 cell subsets in kidney biopsies from IgAN patients, and analyzed changing gene expression in distinct renal cell types. We found increased mesangial expression of several novel genes including MALAT1, GADD45B, SOX4 and EDIL3, which were related to proliferation and matrix accumulation and have not been reported in IgAN previously. The overexpressed genes in tubule cells of IgAN were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling and NOD-like receptor signaling. Moreover, the receptor-ligand crosstalk analysis revealed potential interactions between mesangial cells and other cells in IgAN. Specifically, IgAN with overt proteinuria displayed elevated genes participating in several signaling pathways which may be involved in pathogenesis of progression of IgAN. Conclusion The comprehensive analysis of kidney biopsy specimen demonstrated different gene expression profile, potential pathologic ligand-receptor crosstalk, signaling pathways in human IgAN. These results offer new insight into pathogenesis and identify new therapeutic targets for patients with IgA nephropathy.

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