scholarly journals Single-cell RNA-sequencing reveals pre-meiotic X-chromosome dosage compensation in Drosophila testis

PLoS Genetics ◽  
2021 ◽  
Vol 17 (8) ◽  
pp. e1009728
Author(s):  
Evan Witt ◽  
Zhantao Shao ◽  
Chun Hu ◽  
Henry M. Krause ◽  
Li Zhao
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
X Chromosome ◽  
Dosage Compensation ◽  
Expression Patterns ◽  
Single Cell Rna Sequencing ◽  
Germline Expression ◽  
Drosophila Testis ◽  
Male Specific ◽  
Cell Expression

Dosage compensation equalizes X-linked expression between XY males and XX females. In male fruit flies, expression levels of the X-chromosome are increased approximately two-fold to compensate for their single X chromosome. In testis, dosage compensation is thought to cease during meiosis; however, the timing and degree of the resulting transcriptional suppression is difficult to separate from global meiotic downregulation of each chromosome. To address this, we analyzed testis single-cell RNA-sequencing (scRNA-seq) data from two Drosophila melanogaster strains. We found evidence that the X chromosome is equally transcriptionally active as autosomes in somatic and pre-meiotic cells, and less transcriptionally active than autosomes in meiotic and post-meiotic cells. In cells experiencing dosage compensation, close proximity to MSL (male-specific lethal) chromatin entry sites (CES) correlates with increased X chromosome transcription. We found low or undetectable levels of germline expression of most msl genes, mle, roX1 and roX2 via scRNA-seq and RNA-FISH, and no evidence of germline nuclear roX1/2 localization. Our results suggest that, although dosage compensation occurs in somatic and pre-meiotic germ cells in Drosophila testis, there might be non-canonical factors involved in the dosage compensation mechanism. The single-cell expression patterns and enrichment statistics of detected genes can be explored interactively in our database: https://zhao.labapps.rockefeller.edu/gene-expr/.

scholarly journals Single-cell RNA-sequencing reveals pre-meiotic X-chromosome dosage compensation in Drosophila testis

2021 ◽  
Author(s):  
Evan Witt ◽  
Zhantao Shao ◽  
Chun Hu ◽  
Henry M. Krause ◽  
Li Zhao
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
X Chromosome ◽  
Dosage Compensation ◽  
Expression Patterns ◽  
Single Cell Rna Sequencing ◽  
Germline Expression ◽  
Drosophila Testis ◽  
Male Specific ◽  
Cell Expression

AbstractDosage compensation (DC) is a mechanism by which X chromosome transcription is equalized in the somatic cells of both males and females. In male fruit flies, expression levels of the X-chromosome are increased two-fold to compensate for their single X chromosome. In testis, dosage compensation is thought to cease during meiosis, however, the timing and degree of the resulting transcriptional suppression is difficult to separate from global meiotic downregulation of each chromosome. To address this, we analyzed testis single-cell RNA-sequencing (scRNA-seq) data from two Drosophila melanogaster strains. We found evidence that the X chromosome is equally transcriptionally active as autosomes in somatic and pre-meiotic cells, and less transcriptionally active than autosomes in meiotic and post-meiotic cells. In cells experiencing dosage compensation, close proximity to MSL (male-specific lethal) chromatin entry sites (CES) correlates with increased X chromosome transcription. We found low or undetectable level of germline expression of most msl genes, mle, roX1 and roX2 via sequencing or RNA-FISH, and no evidence of germline nuclear roX1/2 localization. Our results suggest that, although DC occurs in somatic and premeiotic germ cells in Drosophila testis, there might be non-canonical factors involved in the dosage compensation. The single-cell expression patterns and enrichment statistics of detected genes can be explored interactively in our database: https://zhao.labapps.rockefeller.edu/gene-expr/.

scholarly journals Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102357
Author(s):  
Brenda Morsey ◽  
Meng Niu ◽  
Shetty Ravi Dyavar ◽  
Courtney V. Fletcher ◽  
Benjamin G. Lamberty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Related Gene ◽  
Single Cell Rna Sequencing ◽  
Disease Related Gene

scholarly journals Single-Cell RNA Sequencing Unveils Unique Transcriptomic Signatures of Organ-Specific Endothelial Cells

Circulation ◽  
2020 ◽  
Vol 142 (19) ◽  
pp. 1848-1862 ◽  
Author(s):  
David T. Paik ◽  
Lei Tian ◽  
Ian M. Williams ◽  
Siyeon Rhee ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Receptor Expression ◽  
Transcriptional Networks ◽  
Sequencing Data ◽  
Tissue Specific ◽  
Functional Relationships ◽  
Single Cell Rna Sequencing

Background: Endothelial cells (ECs) display considerable functional heterogeneity depending on the vessel and tissue in which they are located. Whereas these functional differences are presumably imprinted in the transcriptome, the pathways and networks that sustain EC heterogeneity have not been fully delineated. Methods: To investigate the transcriptomic basis of EC specificity, we analyzed single-cell RNA sequencing data from tissue-specific mouse ECs generated by the Tabula Muris consortium. We used a number of bioinformatics tools to uncover markers and sources of EC heterogeneity from single-cell RNA sequencing data. Results: We found a strong correlation between tissue-specific EC transcriptomic measurements generated by either single-cell RNA sequencing or bulk RNA sequencing, thus validating the approach. Using a graph-based clustering algorithm, we found that certain tissue-specific ECs cluster strongly by tissue (eg, liver, brain), whereas others (ie, adipose, heart) have considerable transcriptomic overlap with ECs from other tissues. We identified novel markers of tissue-specific ECs and signaling pathways that may be involved in maintaining their identity. Sex was a considerable source of heterogeneity in the endothelial transcriptome and we discovered Lars2 to be a gene that is highly enriched in ECs from male mice. We found that markers of heart and lung ECs in mice were conserved in human fetal heart and lung ECs. We identified potential angiocrine interactions between tissue-specific ECs and other cell types by analyzing ligand and receptor expression patterns. Conclusions: We used single-cell RNA sequencing data generated by the Tabula Muris consortium to uncover transcriptional networks that maintain tissue-specific EC identity and to identify novel angiocrine and functional relationships between tissue-specific ECs.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Combined transient ablation and single cell RNA sequencing reveals the development of medullary thymic epithelial cells

2020 ◽  
Author(s):  
Kristen L. Wells ◽  
Corey N. Miller ◽  
Andreas R. Gschwind ◽  
Wu Wei ◽  
Jonah D. Phipps ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Critical Role ◽  
Lineage Tracing ◽  
Thymic Epithelial Cells ◽  
Single Cell Rna Sequencing ◽  
Medullary Thymic Epithelial Cells

AbstractMedullary thymic epithelial cells (mTECs) play a critical role in central immune tolerance by mediating negative selection of autoreactive T cells through the collective expression of the peripheral self-antigen compartment, including tissue-specific antigens (TSAs). Recent work has shown that gene expression patterns within the mTEC compartment are remarkably heterogenous and include multiple differentiated cell states. To further define mTEC development and medullary epithelial lineage relationships, we combined lineage tracing and recovery from transient in vivo mTEC ablation with single cell RNA-sequencing. The combination of bioinformatic and experimental approaches revealed a non-stem transit-amplifying population of cycling mTECs that preceded Aire expression. Based on our findings, we propose a branching model of mTEC development wherein a heterogeneous pool of transit-amplifying cells gives rise to Aire- and Ccl21a-expressing mTEC subsets. We further use experimental techniques to show that within the Aire-expressing developmental branch, TSA expression peaked as Aire expression decreased, implying Aire expression must be established before TSA expression can occur. Collectively, these data provide a higher order roadmap of mTEC development and demonstrate the power of combinatorial approaches leveraging both in vivo models and high-dimensional datasets.

scholarly journals Aging-associated alterations in the mammary gland revealed by single-cell RNA sequencing

2019 ◽  
Author(s):  
Carman Man-Chung Li ◽  
Hana Shapiro ◽  
Christina Tsiobikas ◽  
Laura Selfors ◽  
Huidong Chen ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Numerous Cell ◽  
Luminal Cells ◽  
Increased Susceptibility ◽  
Mammary Tissues

AbstractAging of the mammary gland is closely associated with increased susceptibility to diseases such as cancer, but there have been limited systematic studies of aging-induced alterations within this organ. We performed high-throughput single-cell RNA-sequencing (scRNA-seq) profiling of mammary tissues from young and old nulliparous mice, including both epithelial and stromal cell types. Our analysis identified altered proportions and distinct gene expression patterns in numerous cell populations as a consequence of the aging process, independent of parity and lactation. In addition, we detected a subset of luminal cells that express both hormone-sensing and alveolar markers and decrease in relative abundance with age. These data provide a high-resolution landscape of aging mammary tissues, with potential implications for normal tissue functions and cancer predisposition.

scholarly journals Transcriptional kinetics of X-chromosome upregulation

2019 ◽  
Author(s):  
Anton J. M. Larsson ◽  
Christos Coucoravas ◽  
Rickard Sandberg ◽  
Björn Reinius
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
X Chromosome ◽  
Single Cell Rna Sequencing ◽  
Allele Specific ◽  
Mouse Cells ◽  
Kinetics Of

Ohno’s hypothesis postulates that X-chromosome upregulation rectifies X-dose imbalance relative to autosomal genes, present in two active copies per cell. Here we dissected X-upregulation into kinetics of transcription, inferred from allele-specific single-cell RNA-sequencing (scRNAseq) data from somatic mouse cells. We confirmed increased X-chromosome expression, and remarkably found that the X-chromosome achieved upregulation by elevated burst frequencies. This provides mechanistic insights into X-chromosome upregulation.

scholarly journals Probabilistic index models for testing differential expression in single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Effect Size ◽  
Expression Patterns ◽  
Simulation Studies ◽  
Sequencing Data ◽  
Rank Tests ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies profile gene expression patterns in individual cells. It is often of interest to test for differential expression (DE) between conditions, e.g. treatment vs control or between cell types. Simulation studies have shown that non-parametric tests, such as the Wilcoxon-rank sum test, can robustly detect significant DE, with better performance than many parametric tools specifically developed for scRNA-seq data analysis. However, these rank tests cannot be used for complex experimental designs involving multiple groups, multiple factors and confounding variables. Further, rank based tests do not provide an interpretable measure of the effect size. We propose a semi-parametric approach based on probabilistic index models (PIM) that form a flexible class of models that generalize classical rank tests. Our method does not rely on strong distributional assumptions and it allows accounting for confounding factors. Moreover, it allows for the estimation of the effect size in terms of a probabilistic index. Real data analysis demonstrate that PIM is capable of identifying biologically meaningful DE. Our simulation studies also show that DE tests succeed well in controlling the false discovery rate at its nominal level, while maintaining good sensitivity as compared to competing methods.

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