scholarly journals Distinct Gene Expression Profiles in Egg and Synergid Cells of Rice as Revealed by Cell Type-Specific Microarrays

2010 ◽  
Vol 155 (2) ◽  
pp. 881-891 ◽  
Author(s):  
Takayuki Ohnishi ◽  
Hideki Takanashi ◽  
Mirai Mogi ◽  
Hirokazu Takahashi ◽  
Shunsuke Kikuchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Type ◽  
Distinct Gene ◽  
Cell Type Specific

scholarly journals Digital sorting of complex tissues for cell type-specific gene expression profiles

2013 ◽  
Vol 14 (1) ◽  
pp. 89 ◽  
Author(s):  
Yi Zhong ◽  
Ying-Wooi Wan ◽  
Kaifang Pang ◽  
Lionel ML Chow ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific

scholarly journals SC2disease: a manually curated database of single-cell transcriptome for human diseases

2020 ◽  
Vol 49 (D1) ◽  
pp. D1413-D1419 ◽  
Author(s):  
Tianyi Zhao ◽  
Shuxuan Lyu ◽  
Guilin Lu ◽  
Liran Juan ◽  
Xi Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cellular Level ◽  
Human Diseases ◽  
Cell Type ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract SC2disease (http://easybioai.com/sc2disease/) is a manually curated database that aims to provide a comprehensive and accurate resource of gene expression profiles in various cell types for different diseases. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering cellular heterogeneity of different tissues for different diseases has become feasible by profiling transcriptomes across cell types at the cellular level. In particular, comparing gene expression profiles between different cell types and identifying cell-type-specific genes in various diseases offers new possibilities to address biological and medical questions. However, systematic, hierarchical and vast databases of gene expression profiles in human diseases at the cellular level are lacking. Thus, we reviewed the literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples, and developed the SC2disease database to summarize all the data by different diseases, tissues and cell types. SC2disease documents 946 481 entries, corresponding to 341 cell types, 29 tissues and 25 diseases. Each entry in the SC2disease database contains comparisons of differentially expressed genes between different cell types, tissues and disease-related health status. Furthermore, we reanalyzed gene expression matrix by unified pipeline to improve the comparability between different studies. For each disease, we also compare cell-type-specific genes with the corresponding genes of lead single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) to implicate cell type specificity of the traits.

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

2020 ◽  
Author(s):  
Johan Gustafsson ◽  
Felix Held ◽  
Jonathan Robinson ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific ◽  
Technical Factors

Abstract Background Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. Results We evaluated different normalization methods, quantified the magnitude of variation introduced by different sources, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We applied methods such as random forest regression to a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is of the same magnitude as the biological variation across cell types. Tissue of origin and cell subtype are less important but still substantial factors, while the difference between individuals is relatively small. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample.Conclusions Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

scholarly journals Ovarian Steroids, Mitogen-Activated Protein Kinases, and/or Aspartic Proteinases Cooperate to Control Endometrial Remodeling by Regulating Gene Expression in the Stroma and Glands

Endocrinology ◽  
2010 ◽  
Vol 151 (9) ◽  
pp. 4515-4526 ◽  
Author(s):  
Héloïse P. Gaide Chevronnay ◽  
Pascale Lemoine ◽  
Pierre J. Courtoy ◽  
Etienne Marbaix ◽  
Patrick Henriet
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tissue Remodeling ◽  
Explant Culture ◽  
Cell Type ◽  
Aspartic Proteinases ◽  
Expression Of Genes ◽  
Mitogen Activated Protein ◽  
Cell Type Specific

Explants from nonmenstrual endometria cultured in the absence of ovarian hormones undergo tissue breakdown. Addition of estradiol and progesterone (EP) prevents proteolysis. Explants include stromal and epithelial compartments which play different but complementary roles in endometrial physiology, including tissue remodeling and hormonal response. In order to characterize the cell type-specific contribution to regulation of tissue breakdown, we characterized the transcriptomes of microdissected stromal and glandular areas from endometrial explants cultured with or without EP. The datasets were also compared to other published endometrial transcriptomes. Finally, the contribution of proteolysis, hypoxia, and MAPKs to the regulation of selected genes was further investigated in explant culture. This analysis identified distinct gene expression profiles in stroma and glands, with differential response to EP, but functional clustering underlined convergence in biological processes, further indicating that endometrial remodeling requires cooperation between the two compartments through expression of cell type-specific genes. Only partial overlaps were observed between lists of genes involved in different occurrences of endometrial breakdown, pointing to a limited number of potentially crucial regulators but also to the requirement for additional mechanisms controlling tissue remodeling. We identified a group of genes differentially regulated by EP in stroma and glands among which some were sensitive to MAPKs and/or aspartic proteinases and were not induced by hypoxia. In conclusion, MAPKs and/or aspartic proteinases likely act in concert with EP to locally and specifically control differential expression of genes between degrading and preserved areas of the human endometrium.

TEL/AML1 Expression in a Non-B Cell Line Induces a Set of Differentially Expressed Genes, but Points to Cell Type Specific Effects of the Fusion Protein in Leukemic Blasts of TEL/AML1 Positive Patients.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2221-2221
Author(s):  
Kerstin Hasse ◽  
Renate Kirschner-Schwabe ◽  
Claudio Lottaz ◽  
Jutta Proba ◽  
Ute Ungetuem ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Cell Line ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Leukemic Cells ◽  
Cell Type ◽  
Specific Effects ◽  
Diagnostic Signature ◽  
Cell Type Specific

Abstract Acute lymphoblastic leukemia (ALL) is the predominant malignancy in childhood (30%). The majority of ALL (75%) exhibit characteristic chromosomal aberrations with prognostic significance that are used to classify the heterogenic disease into subgroups. Among these, the translocation t(12;21) resulting in the fusion of the TEL and AML1 genes is the most frequent chromosomal rearrangement in childhood ALL (20 – 25%). Both genes code for transcription factors essential for normal hematopoiesis. Whereas AML1 functions mostly as an activator of expression, TEL/AML1 acts as a repressor in a dominant manner. To date, detailed knowledge on the effect of TEL/AML1 in the process of malignant transformation is widely lacking. To address the consequence of TEL/AML1 expression, we established stable TEL/AML1 expressing cell lines of B lymphoblastoid and non-B cell origin using an episomal vector system and performed microarray-based gene expression profiling. To this end, we obtained gene expression profiles of a TEL/AML1 expressing non-B cell clone and the corresponding empty vector control. We first set out to gain a measure for the comparability of gene expression profiles in leukemic cells of TEL/AML1 positive ALL patients and the TEL/AML1 expressing non-B cell line. We therefore used published gene expression data of initial and relapsed ALL to calculate diagnostic gene expression signatures that faithfully predict genetic and immunological ALL subtypes in patient samples. We then used these signatures to classify a test set of patient samples and the TEL/AML1 expressing non-B cell into these subtypes. All patient samples were predicted correctly, however, the TEL/AML1 non-B cell line was not classified as TEL/AML1 positive, indicating that the diagnostic signature of TEL/AML1 positive leukemic cells is not induced by TEL/AML1 in the non-B cell line. Nevertheless, we identified several genes differentially expressed in the non-B cell line as compared to the empty vector control. The majority of genes are down-regulated, in agreement with the repressor function of the fusion protein. Among these genes are several transcription factors and genes playing a role in hematopoietic development and immune response. Furthermore, we found several genes with a role in neuronal development and disease. Interestingly, TEL knock out mice die during embryonal development due to apoptosis of mesenchymal and neuronal cells, supporting a function of TEL in the nervous system. Thus, a set of genes is regulated by TEL/AML1 in a non-B cell line, compatible with the proposed functions of TEL and AML1. However, the diagnostic signature of leukemic blasts of TEL/AML1 positive ALL patients was not induced in these cells supporting the view that besides common changes in gene expression cell type specific effects of TEL/AML1 exist and contribute to leukemogenesis.

scholarly journals DEBay: a computational tool for deconvolution of quantitative PCR data for estimation of cell type-specifc gene expression in a mixed population

2020 ◽  
Author(s):  
Vimalathithan Devaraj ◽  
Biplab Bose
Keyword(s):  
Gene Expression ◽  
Quantitative Pcr ◽  
Target Gene ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mixed Population ◽  
Specific Gene ◽  
Cell Type ◽  
Computational Tool ◽  
Cell Type Specific

AbstractThe expression of a gene is commonly estimated by quantitative PCR (qPCR) using RNA isolated from a large number of pooled cells. Such pooled samples often have subpopulations of cells with different levels of expression of the target gene. Estimation of gene expression from an ensemble of cells obscures the pattern of expression in different subpopulations. Physical separation of various subpopulations is a demanding task. We have developed a computational tool, Deconvolution of Ensemble through Bayes-approach (DEBay), to estimate cell type-specific gene expression from qPCR data of a mixed population. DEBay estimates Normalized Gene Expression Coefficient (NGEC), which is a relative measure of the expression of the target gene in each cell type in a population. NGEC has a direct algebraic correspondence with the normalized fold change in gene expression measured by qPCR. DEBay can deconvolute both time-dependent and -independent gene expression profiles. It uses the Bayesian method of model selection and parameter estimation. We have evaluated DEBay using synthetic and real experimental data. DEBay is implemented in Python. A GUI of DEBay and its source code are available for download at SourceForge (https://sourceforge.net/projects/debay).

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

2020 ◽  
Author(s):  
Johan Gustafsson ◽  
Felix Held ◽  
Jonathan Robinson ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific ◽  
Technical Factors

Abstract Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of applying cell type profiles derived from blood on mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

Sign in / Sign up

Export Citation Format

Share Document