203-OR: ADA Presidents’ Select Abstract: Landscape of Liver Cell Heterogeneity and Reprogramming during NASH Pathogenesis Revealed by Single Cell RNA Sequencing

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 203-OR
Author(s):  
XUELIAN XIONG
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Liver Cell ◽  
Cell Heterogeneity ◽  
Single Cell Rna Sequencing ◽  
Select Abstract

scholarly journals MBRS-46. CHARTING NEOPLASTIC AND IMMUNE CELL HETEROGENEITY IN HUMAN AND GEM MODELS OF MEDULLOBLASTOMA USING scRNAseq

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii406-iii406
Author(s):  
Andrew Donson ◽  
Kent Riemondy ◽  
Sujatha Venkataraman ◽  
Ahmed Gilani ◽  
Bridget Sanford ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Genetically Engineered ◽  
Cellular Heterogeneity ◽  
Cell Heterogeneity ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing ◽  
Transcript Profiles

Abstract We explored cellular heterogeneity in medulloblastoma using single-cell RNA sequencing (scRNAseq), immunohistochemistry and deconvolution of bulk transcriptomic data. Over 45,000 cells from 31 patients from all main subgroups of medulloblastoma (2 WNT, 10 SHH, 9 GP3, 11 GP4 and 1 GP3/4) were clustered using Harmony alignment to identify conserved subpopulations. Each subgroup contained subpopulations exhibiting mitotic, undifferentiated and neuronal differentiated transcript profiles, corroborating other recent medulloblastoma scRNAseq studies. The magnitude of our present study builds on the findings of existing studies, providing further characterization of conserved neoplastic subpopulations, including identification of a photoreceptor-differentiated subpopulation that was predominantly, but not exclusively, found in GP3 medulloblastoma. Deconvolution of MAGIC transcriptomic cohort data showed that neoplastic subpopulations are associated with major and minor subgroup subdivisions, for example, photoreceptor subpopulation cells are more abundant in GP3-alpha. In both GP3 and GP4, higher proportions of undifferentiated subpopulations is associated with shorter survival and conversely, differentiated subpopulation is associated with longer survival. This scRNAseq dataset also afforded unique insights into the immune landscape of medulloblastoma, and revealed an M2-polarized myeloid subpopulation that was restricted to SHH medulloblastoma. Additionally, we performed scRNAseq on 16,000 cells from genetically engineered mouse (GEM) models of GP3 and SHH medulloblastoma. These models showed a level of fidelity with corresponding human subgroup-specific neoplastic and immune subpopulations. Collectively, our findings advance our understanding of the neoplastic and immune landscape of the main medulloblastoma subgroups in both humans and GEM models.

scholarly journals Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Haruka Ozaki ◽  
Tetsutaro Hayashi ◽  
Mana Umeda ◽  
Itoshi Nikaido
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Read Coverage ◽  
Cell Heterogeneity ◽  
Single Cell Rna Sequencing

scholarly journals Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

2015 ◽  
Vol 33 (2) ◽  
pp. 155-160 ◽  
Author(s):  
Florian Buettner ◽  
Kedar N Natarajan ◽  
F Paolo Casale ◽  
Valentina Proserpio ◽  
Antonio Scialdone ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Computational Analysis ◽  
Cell Heterogeneity ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

2019 ◽  
Author(s):  
Haruka Ozaki ◽  
Tetsutaro Hayashi ◽  
Mana Umeda ◽  
Itoshi Nikaido
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Heterogeneity ◽  
Specific Cell ◽  
Read Coverage ◽  
Cell Heterogeneity ◽  
Sequencing Data ◽  
Rna Transcription ◽  
Link Type ◽  
Single Cell Rna Sequencing

AbstractBackgroundRead coverage of RNA sequencing data reflects gene expression and RNA processing events. Single-cell RNA sequencing (scRNA-seq) methods, particularly “full-length” ones, provide read coverage of many individual cells and have the potential to reveal cellular heterogeneity in RNA transcription and processing. However, visualization tools suited to highlighting cell-to-cell heterogeneity in read coverage are still lacking.ResultsHere, we have developed Millefy, a tool for visualizing read coverage of scRNA-seq data in genomic contexts. Millefy is designed to show read coverage of all individual cells at once in genomic contexts and to highlight cell-to-cell heterogeneity in read coverage. By visualizing read coverage of all cells as a heat map and dynamically reordering cells based on diffusion maps, Millefy facilitates discovery of “local” region-specific, cell-to-cell heterogeneity in read coverage, including variability of transcribed regions.ConclusionsMillefy simplifies the examination of cellular heterogeneity in RNA transcription and processing events using scRNA-seq data. Millefy is available as an R package (https://github.com/yuifu/millefy) and a Docker image to help use Millefy on the Jupyter notebook (https://hub.docker.com/r/yuifu/datascience-notebook-millefy).

Landscape of Cell Heterogeneity and Evolutionary Trajectory in Ulcerative Colitis-associated Colon Cancer Revealed by Single-cell Rna Sequencing

2020 ◽  
Author(s):  
Quan Wang ◽  
Zhu Wang ◽  
Zhen Zhang ◽  
Wei Zhang ◽  
Mengmeng Zhang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Colon Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Trajectory ◽  
The Cancer Genome Atlas ◽  
Cell Heterogeneity ◽  
Evolutionary Trajectory ◽  
Cell Clusters ◽  
Single Cell Rna Sequencing

Abstract Background: Patients with colitis-associated cancer (CAC), a particular kind of colorectal cancer that develops from inflammatory bowel diseases (IBDs), have an earlier morbidity and a poorer prognosis. However, in CAC, single cell transcriptome analysis of the microenvironment composition and characteristics has yet to be performed. To understand the intra-tumor heterogeneity in CAC and to reveal a potential evolutionary trajectory from ulcerative colitis (UC) to CAC at the single cell level. Methods: Fresh samples of tumor- and adjacent tissue, from a CAC patient with pT3N1M0, were examined by single cell RNA sequencing (scRNA-seq). Data from The Cancer Genome Atlas (TCGA) and The Human Protein Atlas were used to confirm the different expression levels in normal and tumor tissues and to determine their relationships with prognosis. Results: Ultimately, 4,777 single-cell transcriptomes (1220 genes per cell) were studied, which composed of 2,250 (47%) and 2,527(53%) originated from tumor- and non-malignant tissue, respectively. And we defined the composition of cancer-associated stromal cells and identified six cell clusters included myeloid, T and B cells, fibroblasts, endothelial and epithelial cells. Likewise, the notable pathways and transcription factors (TFs) involved of these cell clusters were analyzed and described. Moreover, we graphed the precise cellular composition and developmental trajectory from UC to UC-associated colon cancer, and predicted that CD74, CLCA1 and DPEP1 had a potential role in the disease progression. Conclusions: scRNA-seq technology could reveal intratumor cell heterogeneity in ulcerative colitis-associated colon cancer, and might provide a promising direction to seek the novel potential therapeutic targets in the evolution from IBD to CAC.

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