A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences

By performing RNA-seq on cells FACS sorted by their proliferation rate, this study identifies a gene expression signature capable of predicting proliferation rates in diverse eukaryotic cell types and species. This signature, applied to scRNAseq data from C.elegans, reveals lineage-specific differences in proliferation during development. In contrast to the universality of the proliferation signature, mitochondria and metabolism related genes show a high degree of cell-type specificity; mouse pluripotent stem cells (mESCs) and differentiated cells (fibroblasts) exhibit opposite relations between mitochondria state and proliferation. Furthermore, we identified a slow proliferating subpopulation of mESCs with higher expression of pluripotency genes. Finally, we show that fast and slow proliferating subpopulations are differentially sensitive to mitochondria inhibitory drugs in different cell types.

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Uncovering the cell type specificity of blood sample derived gene signatures using RNA expression data

10.1101/684159 ◽

2019 ◽

Author(s):

Mikhail Pomaznoy ◽

Brendan Ha ◽

Bjoern Peters

Keyword(s):

Blood Cell ◽

Web Application ◽

Cell Lineage ◽

Complex Mixture ◽

Cell Types ◽

Cell Type Specificity ◽

Signature Genes ◽

Expression Of Genes ◽

Biological Interpretation ◽

Different Cell Types

AbstractAnalysis of transcriptomic data derived from blood samples is complicated by the complex mixture of cell types such samples contain. Transcriptomic signatures derived from such samples are often driven by a particular cell lineage within the mixture. Identifying this most contributing lineage can help to provide a biological interpretation of the signature. We created a web application CellTypeScore which quantifies and visually represents the expression level of signature genes in common blood cell types. This is done by constructing an interactive stacked bar plot with the bars representing expression of genes across blood cell types. Summed scores serve as a measure of how highly the combined signature is expressed in different cell types. An online version of the application can be found at https://tools.dice-database.org/celltypescore/.

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Single-cell transcriptome analysis of the zebrafish embryonic trunk

10.1101/2021.05.07.443129 ◽

2021 ◽

Author(s):

Sanjeeva S Metikala ◽

Satish Casie Chetty ◽

Saulius Sumanas

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Cell Lineage ◽

Cell Types ◽

Rna Seq ◽

Cell Lineages ◽

Distinct Cell ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Different Cell Types

During embryonic development, cells differentiate into a variety of distinct cell types and subtypes with diverse transcriptional profiles. To date, transcriptomic signatures of different cell lineages that arise during development have been only partially characterized. Here we used single-cell RNA-seq to perform transcriptomic analysis of over 20,000 cells disaggregated from the trunk region of zebrafish embryos at the 30 hpf stage. Transcriptional signatures of 27 different cell types and subtypes were identified and annotated during this analysis. This dataset will be a useful resource for many researchers in the fields of developmental and cellular biology and facilitate the understanding of molecular mechanisms that regulate cell lineage choices during development.

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Single-cell transcriptome analysis of the zebrafish embryonic trunk

PLoS ONE ◽

10.1371/journal.pone.0254024 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254024

Author(s):

Sanjeeva Metikala ◽

Satish Casie Chetty ◽

Saulius Sumanas

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Cell Lineage ◽

Cell Types ◽

Rna Seq ◽

Cell Lineages ◽

Distinct Cell ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Different Cell Types

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Leveraging the cell lineage to predict cell type specificity of regulatory variation from bulk genomics

Genetics ◽

10.1093/genetics/iyab016 ◽

2021 ◽

Author(s):

Gal Yankovitz ◽

Ofir Cohn ◽

Eran Bacharach ◽

Naama Peshes-Yaloz ◽

Yael Steuerman ◽

...

Keyword(s):

Cell Lineage ◽

Cell Types ◽

Genetic Effects ◽

Transcriptome Data ◽

Cell Type ◽

Cell Type Specificity ◽

Dynamic Changes ◽

Lineage Tree ◽

Different Cell Types ◽

Heterogeneous Tissues

Abstract Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered.

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The Developmental Capacity of Nuclei taken from Intestinal Epithelium Cells of Feeding Tadpoles

Development ◽

10.1242/dev.10.4.622 ◽

1962 ◽

Vol 10 (4) ◽

pp. 622-640 ◽

Cited By ~ 2

Author(s):

J. B. Gurdon

Keyword(s):

Genetic Information ◽

Cellular Differentiation ◽

Cell Types ◽

Nuclear Transplantation ◽

Differentiated Cells ◽

Developmental Capacity ◽

Nuclear Changes ◽

Different Cell Types ◽

Epithelium Cells ◽

Saline Medium

An important problem in embryology is whether the differentiation of cells depends upon a stable restriction of the genetic information contained in their nuclei. The technique of nuclear transplantation has shown to what extent the nuclei of differentiating cells can promote the formation of different cell types (e.g. King & Briggs, 1956; Gurdon, 1960c). Yet no experiments have so far been published on the transplantation of nuclei from fully differentiated normal cells. This is partly because it is difficult to obtain meaningful results from such experiments. The small amount of cytoplasm in differentiated cells renders their nuclei susceptible to damage through exposure to the saline medium, and this makes it difficult to assess the significance of the abnormalities resulting from their transplantation. It is, however, very desirable to know the developmental capacity of such nuclei, since any nuclear changes which are necessarily involved in cellular differentiation must have already taken place in cells of this kind.

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LSD induces increased signalling entropy in rats' prefrontal cortex

10.1101/2021.06.23.449556 ◽

2021 ◽

Author(s):

Aurora Savino ◽

Charles D Nichols

Keyword(s):

Prefrontal Cortex ◽

Molecular Level ◽

Transcriptional Regulators ◽

Cell Types ◽

Rna Seq ◽

Psychedelic Drugs ◽

Underlying Mechanisms ◽

Brain Entropy ◽

New Compounds ◽

Different Cell Types

Psychedelic drugs are gaining attention from the scientific community as potential new compounds for the treatment of psychiatric diseases such as mood and substance use disorders. The 5-HT2A receptor has been identified as the main molecular target, and early studies pointed to an effect on the expression of neuroplasticity genes. Analysing RNA-seq data from the prefrontal cortex of rats chronically treated with lysergic acid diethylamide (LSD), we describe the psychedelic-induced rewiring of gene co-expression networks, which become less centralized but more complex, with an overall increase in signalling entropy, typical of highly plastic systems. Intriguingly, signalling entropy mirrors, at the molecular level, the increased brain entropy reported through neuroimaging studies in human, suggesting the underlying mechanisms of higher-order phenomena. Moreover, from the analysis of network topology we identify potential transcriptional regulators and imply different cell types in psychedelics' activity.

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Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer

BioMed Research International ◽

10.1155/2019/6763596 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Anna Pačínková ◽

Vlad Popovici

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Colon Cancer ◽

Microsatellite Instability ◽

Gene Expression Signature ◽

Data Sets ◽

Rna Seq ◽

Cancer Data ◽

Expression Signature ◽

Endometrial Cancers

The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.

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scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

Nucleic Acids Research ◽

10.1093/nar/gkab795 ◽

2021 ◽

Author(s):

Sheng Zhu ◽

Qiwei Lian ◽

Wenbin Ye ◽

Wei Qin ◽

Zhe Wu ◽

...

Keyword(s):

Single Cell ◽

Alternative Polyadenylation ◽

Cell Types ◽

Single Cell Level ◽

Cell Heterogeneity ◽

Rna Seq ◽

Cell Level ◽

Eukaryotic Gene ◽

User Friendly ◽

Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

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Computational approaches towards reducing contamination in single-cell RNA-seq data

10.1101/2020.07.15.205062 ◽

2020 ◽

Author(s):

Siamak Yousefi ◽

Hao Chen ◽

Jesse F. Ingels ◽

Melinda S. McCarty ◽

Arthur G. Centeno ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Real Life ◽

Cell Types ◽

Cell Capture ◽

Rna Seq ◽

Sequence Analyses ◽

Cell Functions ◽

Biological Interpretation ◽

Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

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Single-cell RNA-Seq Reveals Transcriptional Landscape and Intra-tumor Heterogenicity in Gallbladder Cancer Liver Metastasis Microenvironment

10.21203/rs.3.rs-117830/v1 ◽

2020 ◽

Author(s):

Wenhua You ◽

Xiangyu Li ◽

Peng Wang ◽

Bowen Sha ◽

Yuan Liang ◽

...

Keyword(s):

T Cells ◽

Liver Metastasis ◽

Gallbladder Cancer ◽

Single Cell ◽

Molecular Mechanisms ◽

Cell Types ◽

Malignant Cells ◽

Different Cell Types ◽

High Degree

Abstract Background: Gallbladder cancer (GBC) is a highly aggressive biliary epithelial malignancy. Tumor invasion and metastasis contributed to the high mortality of GBC patients. However, molecular mechanisms involved in GBC metastases are still little known. Methods: We performed single-cell RNA sequencing on GBC liver metastasis tissue and analyzed the data based on different cell types.Results: In this study, 8 cell types, including T cells, B cells, malignant cells, fibroblasts, endothelial cells, macrophages, dendritic cells, and mast cells were identified. Malignant cells displayed a high degree of intra-tumor heterogenicity and neutrophils could promote GBC progression in vitro. Besides, cytotoxic CD8+ T cells became exhausted and CD4+ Tregs presented immunosuppressive characteristics. Macrophages played an important role in the tumor microenvironment. We identified three distinct macrophage subsets and emerged M2 polarization. We also found that cancer-associated fibroblasts exhibited heterogeneity and promoted GBC metastasis. Conclusions: In conclusion, our work provided a landscape view at the single-cell level and may clear the way for the therapy of GBC metastases.

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