scholarly journals CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Rebekka Wegmann ◽  
Marilisa Neri ◽  
Sven Schuierer ◽  
Bilada Bilican ◽  
Huyen Hartkopf ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Populations ◽  
Rna Seq ◽  
Specific Detection

scholarly journals Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3126
Author(s):  
Dominik Saul ◽  
Robyn Laura Kosinsky
Keyword(s):  
Single Cell ◽  
Myelogenous Leukemia ◽  
Ductal Adenocarcinoma ◽  
Cell Populations ◽  
Rna Seq ◽  
Healthy Control ◽  
Transcriptional Changes ◽  
The Relationship ◽  
Senescence Associated Genes

The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.

scholarly journals Full-Length Transcriptome: A Reliable Alternative for Single-Cell RNA-Seq Analysis in the Spleen of Teleost Without Reference Genome

2021 ◽  
Vol 12 ◽  
Author(s):  
Lixing Huang ◽  
Ying Qiao ◽  
Wei Xu ◽  
Linfeng Gong ◽  
Rongchao He ◽  
...  
Keyword(s):  
Data Analysis ◽  
B Cells ◽  
Single Cell ◽  
Reference Genome ◽  
Immune Cell ◽  
Cell Types ◽  
Full Length ◽  
Cell Populations ◽  
Epinephelus Coioides ◽  
Rna Seq

Fish is considered as a supreme model for clarifying the evolution and regulatory mechanism of vertebrate immunity. However, the knowledge of distinct immune cell populations in fish is still limited, and further development of techniques advancing the identification of fish immune cell populations and their functions are required. Single cell RNA-seq (scRNA-seq) has provided a new approach for effective in-depth identification and characterization of cell subpopulations. Current approaches for scRNA-seq data analysis usually rely on comparison with a reference genome and hence are not suited for samples without any reference genome, which is currently very common in fish research. Here, we present an alternative, i.e. scRNA-seq data analysis with a full-length transcriptome as a reference, and evaluate this approach on samples from Epinephelus coioides-a teleost without any published genome. We show that it reconstructs well most of the present transcripts in the scRNA-seq data achieving a sensitivity equivalent to approaches relying on genome alignments of related species. Based on cell heterogeneity and known markers, we characterized four cell types: T cells, B cells, monocytes/macrophages (Mo/MΦ) and NCC (non-specific cytotoxic cells). Further analysis indicated the presence of two subsets of Mo/MΦ including M1 and M2 type, as well as four subsets in B cells, i.e. mature B cells, immature B cells, pre B cells and early-pre B cells. Our research will provide new clues for understanding biological characteristics, development and function of immune cell populations of teleost. Furthermore, our approach provides a reliable alternative for scRNA-seq data analysis in teleost for which no reference genome is currently available.

scholarly journals Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Coral Fustero-Torre ◽  
María José Jiménez-Santos ◽  
Santiago García-Martín ◽  
Carlos Carretero-Puche ◽  
Luis García-Jimeno ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Tumour Cell ◽  
Enrichment Score ◽  
Cell Populations ◽  
Drug Selection ◽  
Malignant Cells ◽  
Rna Seq ◽  
Computational Methodology ◽  
Cell Subpopulations

AbstractWe present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell.

scholarly journals CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data

2019 ◽  
Author(s):  
Rebekka Wegmann ◽  
Marilisa Neri ◽  
Sven Schuierer ◽  
Bilada Bilican ◽  
Huyen Hartkopf ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Populations ◽  
Rna Seq ◽  
Differentiation Protocol ◽  
Layer V ◽  
Retzius Cells ◽  
Rare Cells ◽  
Cell Cycle Status

AbstractComprehensive benchmarking of computational methods for single-cell RNA sequencing (scRNA-seq) analysis is scarce. Using a modular workflow and a large dataset with known cell composition, we benchmarked feature selection and clustering methodologies for scRNA-seq data. Results highlighted a methodology gap for rare cell population identification for which we developed CellSIUS (Cell Subtype Identification from Upregulated gene Sets). CellSIUS outperformed existing approaches, enabled the identification of rare cell populations and, in contrast to other methods, simultaneously revealed transcriptomic signatures indicative of the rare cells’ function. We exemplified the use of our workflow and CellSIUS for the characterization of a human pluripotent cell 3D spheroid differentiation protocol recapitulating deep-layer corticogenesis in vitro. Results revealed lineage bifurcation between Cajal-Retzius cells and layer V/VI neurons as well as rare cell populations that differ by migratory, metabolic, or cell cycle status, including a choroid plexus neuroepithelial subgroup, revealing previously unrecognized complexity in human stem cell-derived cellular populations.

scholarly journals HGG-26. SINGLE-CELL RNA-SEQ OF PEDIATRIC HIGH-GRADE GLIOMAS IDENTIFIES COMMON ONCOGENIC PROCESSES AMONG DISTINCT TUMOR HISTOLOGIES

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i22-i22
Author(s):  
John DeSisto ◽  
Andrew Donson ◽  
Rui Fu ◽  
Bridget Sanford ◽  
Kent Riemondy ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Treatment Modalities ◽  
Patient Specific ◽  
Cell Populations ◽  
High Grade ◽  
Methylation Data ◽  
Rna Seq

Abstract Background Pediatric high-grade glioma (PHGG) is a deadly childhood brain tumor that responds poorly to treatment. PHGG comprises two major subtypes: cortical tumors with wild-type H3K27 and diffuse midline gliomas (DMG) that occur in the midline and have characteristic H3K27M mutations. Cortical PHGG is heterogeneous with multiple molecular subtypes. In order to identify underlying commonalities in cortical PHGG that might lead to better treatment modalities, we performed molecular profiling, including single-cell RNA-Seq (scRNA-Seq), on PHGG samples from Children’s Hospital Colorado. Methods Nineteen cortical PHGG tumor samples, one DMG and one normal margin sample obtained at biopsy were disaggregated to isolate viable cells. Fifteen were glioblastomas (GBM), including five with epithelioid and/or giant cell features and five radiation-induced glioblastomas (RIG). There were also four non-GBM PHGG. We performed scRNA-Seq using 10X Genomics v.3 library preparation to enable capture of infiltrating immune cells. We also performed bulk RNA-Seq and DNA methylation profiling. Results After eliminating patient-specific and cell-cycle effects, RIG, epithelioid GBM, and other GBM each formed identifiable subgroups in bulk RNA-Seq and scRNA-Seq datasets. In the scRNA-Seq data, clusters with cells from multiple tumor samples included a PDGFRA-positive population expressing oligodendrocyte progenitor markers, astrocytic, mesenchymal and stemlike populations, macrophage/monocyte immune cells, and a smaller T-cell population. Analyses of DNA methylation data showed PDGFRA and CDK4 amplification and CDKN2A deletion are common alterations among PHGG. Inferred copy number variation analysis of the single-cell data confirmed that individual tumors include populations that both include and lack the molecular alterations identified in the methylation data. RNA velocity studies to define tumor cells of origin and further analyses of the immune cell populations are underway. Conclusions Single-cell analysis of PHGG confirms a large degree of tumor heterogeneity but also shows that PHGG have stemlike, mesenchymal and immune cell populations with common characteristics.

scholarly journals Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Juber Herrera-Uribe ◽  
Jayne E. Wiarda ◽  
Sathesh K. Sivasankaran ◽  
Lance Daharsh ◽  
Haibo Liu ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Peripheral Blood ◽  
Cell Types ◽  
Cell Populations ◽  
Rna Seq ◽  
Cell Type ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

scholarly journals ILoReg enables high-resolution cell population identification from single-cell RNA-seq data

2020 ◽  
Author(s):  
Johannes Smolander ◽  
Sini Junttila ◽  
Mikko S Venäläinen ◽  
Laura L Elo
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Single Cell ◽  
Cell Population ◽  
R Package ◽  
High Dimensionality ◽  
Cell Populations ◽  
Rna Seq ◽  
Extraction Step ◽  
Resolution Cell

AbstractSingle-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. We introduce ILoReg (https://github.com/elolab/iloreg), an R package implementing a new cell population identification method that achieves high differentiation resolution through a probabilistic feature extraction step that is applied before clustering and visualization.

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