scholarly journals Single-cell gene expression of the bovine blastocyst

Reproduction ◽  
2017 ◽  
Vol 154 (5) ◽  
pp. 627-644 ◽  
Author(s):  
Verónica M Negrón-Pérez ◽  
Yanping Zhang ◽  
Peter J Hansen
Keyword(s):  
Gene Expression ◽  
Cell Lineage ◽  
Transcript Abundance ◽  
Cell Types ◽  
Cell Type ◽  
Clade B ◽  
Cell Gene Expression ◽  
Lineage Markers ◽  
Cell Gene ◽  
Bovine Blastocyst

The first two differentiation events in the embryo result in three cell types – epiblast, trophectoderm (TE) and hypoblast. The purpose here was to identify molecular markers for each cell type in the bovine and evaluate the differences in gene expression among individual cells of each lineage. The cDNA from 67 individual cells of dissociated blastocysts was used to determine transcript abundance for 93 genes implicated as cell lineage markers in other species or potentially involved in developmental processes. Clustering analysis indicated that the cells belonged to two major populations (clades A and B) with two subpopulations of clade A and four of clade B. Use of lineage-specific markers from other species indicated that the two subpopulations of clade A represented epiblast and hypoblast respectively while the four subpopulations of clade B were TE. Among the genes upregulated in epiblast were AJAP1, DNMT3A, FGF4, H2AFZ, KDM2B, NANOG, POU5F1, SAV1 and SLIT2. Genes overexpressed in hypoblast included ALPL, FGFR2, FN1, GATA6, GJA1, HDAC1, MBNL3, PDGFRA and SOX17, while genes overexpressed in all four TE populations were ACTA2, CDX2, CYP11A1, GATA2, GATA3, IFNT, KRT8, RAC1 and SFN. The subpopulations of TE varied among each other for multiple genes including the prototypical TE marker IFNT. New markers for each cell type in the bovine blastocyst were identified. Results also indicate heterogeneity in gene expression among TE cells. Further studies are needed to confirm whether subpopulations of TE cells represent different stages in the development of a committed TE phenotype.

scholarly journals Feature extraction approach in single-cell gene expression profiling for cell-type marker identification

2019 ◽  
Author(s):  
Nigatu A. Adossa ◽  
Leif Schauser ◽  
Vivi G. Gregersen ◽  
Laura L. Elo
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Type I ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Gene Expression ◽  
Cell Type Specific ◽  
Cell Gene ◽  
Marker Identification

AbstractBackgroundRecent advances in single-cell gene expression profiling technology have revolutionized the understanding of molecular processes underlying developmental cell and tissue differentiation, enabling the discovery of novel cell-types and molecular markers that characterize developmental trajectories. Common approaches for identifying marker genes are based on pairwise statistical testing for differential gene expression between cell-types in heterogeneous cell populations, which is challenging due to unequal sample sizes and variance between groups resulting in little statistical power and inflated type I errors.ResultsWe developed an alternative feature extraction method, Marker gene Identification for Cell-type Identity (MICTI) that encodes the cell-type specific expression information to each gene in every single-cell. This approach identifies features (genes) that are cell-type specific for a given cell-type in heterogeneous cell population. To validate this approach, we used (i) simulated single cell RNA-seq data, (ii) human pancreatic islet single-cell RNA-seq data and (iii) a simulated mixture of human single-cell RNA-seq data related to immune cells, particularly B cells, CD4+ memory cells, CD8+ memory cells, dendritic cells, fibroblast cells, and lymphoblast cells. For all cases, we were able to identify established cell-type-specific markers.ConclusionsOur approach represents a highly efficient and fast method as an alternative to differential expression analysis for molecular marker identification in heterogeneous single-cell RNA-seq data.

scholarly journals Inference of Cell Type Composition from Human Brain Transcriptomic Datasets Illuminates the Effects of Age, Manner of Death, Dissection, and Psychiatric Diagnosis

2016 ◽  
Author(s):  
Megan Hastings Hagenauer ◽  
Anton Schulmann ◽  
Jun Z. Li ◽  
Marquis P. Vawter ◽  
David M. Walsh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Blood Cell ◽  
Cell Types ◽  
Post Mortem ◽  
Cell Type ◽  
Cell Content ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPsychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type composition for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-packageBrainInABlender(validated and publicly-released:https://github.com/hagenaue/BrainInABlender). Using this method, we found that the principal components of variation in the datasets strongly correlated with the neuron to glia ratio of the samples.This variability was not simply due to dissection – the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.

scholarly journals Putative cell type discovery from single-cell gene expression data

2020 ◽  
Vol 17 (6) ◽  
pp. 621-628 ◽  
Author(s):  
Zhichao Miao ◽  
Pablo Moreno ◽  
Ni Huang ◽  
Irene Papatheodorou ◽  
Alvis Brazma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Expression Data ◽  
Cell Type ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression

2017 ◽  
Vol 101 (5) ◽  
pp. 686-699 ◽  
Author(s):  
Diego Calderon ◽  
Anand Bhaskar ◽  
David A. Knowles ◽  
David Golan ◽  
Towfique Raj ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Complex Traits ◽  
Cell Types ◽  
Cell Gene Expression ◽  
Cell Gene

The GATA-1 Cofactor FOG-1 Recruits NuRD to Promote Normal Erythroid and Megakaryocyte Development and Maintain Lineage Fidelity by Restricting Mast Cell Gene Expression.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1373-1373
Author(s):  
Gregory D Gregory ◽  
Yuhuan Wang ◽  
Wei Hong ◽  
Annarita Miccio ◽  
Alexey Bersenev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mast Cell ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Cell Lineage ◽  
Common Mechanism ◽  
Erythroid Cells ◽  
Nucleosome Remodeling ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract Tissue-specific nuclear factors can establish gene expression patterns in one cell lineage and suppress that of another. GATA-1 and its cofactor FOG-1 (Zfpm1) regulate erythroid and megakaryocyte development by activating and repressing gene transcription. We previously showed that a conserved motif within the N-terminus of FOG-1 binds the Nucleosome Remodeling and Deacetylase (NuRD) co-repressor complex. Here we report that mice bearing FOG-1 point mutations that disrupt the NuRD interaction display mild anemia with splenomegaly and macrothrombocytopenia, a phenotype reminiscent of that observed in animals bearing germline mutations that disrupt the GATA-1/FOG-1 interaction. Microarray studies revealed relatively few changes in gene expression pattern sin mutant erythroid cells and megakaryocytes. Among the most prominent findings was a marked increase in the levels of Gata2, which is normally silenced in mature erythroid cells. Strikingly, mutant erythroid cells also displayed activation of several genes of the mast cell lineage where FOG-1 is normally extinguished. Furthermore, mutant megakaryocytes misexpressed the same set of mast cell genes, suggesting that NuRD binding by FOG-1 is required to suppress mast cell fate throughout the erythro-megakaryocytic ontogeny. In agreement, prospectively isolated megakaryocytic-erythroid progenitors (MEP) not only exhibited elevated Gata2 and mast cell gene expression, but maintained a multilineage capacity, generating both mast cells and other myeloid lineage cells in culture. Upregulation of mast cell-specific genes is likely the combined consequence of the failure of mutant FOG-1 to function as a repressor and the high levels of GATA-2. Together, these results underscore the importance of the FOG-1-NuRD interaction as an effector of GATA-1 activity. In particular, recruitment of NuRD to GATA-1/FOG-1 regulated genes is required to optimize erythroid and megakaryocytic maturation and restrict a mast cell program in those lineages. More generally, recruitment of NuRD by lineage-specific transcription factors may be a common mechanism to narrow and focus gene expression during tissue maturation.

scholarly journals Foxi1 inactivation rescues loss of principal cell fate selection in Hes1-deficient kidneys but does not ensure maintenance of principal cell gene expression

2019 ◽  
Author(s):  
Malini Mukherjee ◽  
Jennifer DeRiso ◽  
Madhusudhana Janga ◽  
Eric Fogarty ◽  
Kameswaran Surendran
Keyword(s):  
Gene Expression ◽  
Notch Signaling ◽  
Cell Fate ◽  
Collecting Duct ◽  
Cell Types ◽  
Principal Cell ◽  
Intercalated Cell ◽  
Collecting Ducts ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractThe distal nephron and collecting duct segments of the mammalian kidney consist of intercalated cell types intermingled among principal cell types. Notch signaling ensures that a sufficient number of cells select a principal instead of an intercalated cell fate. However, the precise mechanisms by which Notch signaling patterns the distal nephron and collecting duct cell fates is unknown. Here we observed that Hes1, a direct target of Notch signaling pathway, is required within the mouse developing collecting ducts for repression of Foxi1 expression, an essential intercalated cell specific transcription factor. Interestingly, inactivation of Foxi1 in Hes1-deficient collecting ducts rescues the deficiency in principal cell fate selection, overall urine concentrating deficiency, and reduces the occurrence of hydronephrosis. However, Foxi1 inactivation does not rescue the reduction in expression of all principal cell genes in the Hes1-deficient kidney collecting duct cells that select the principal cell fate. Additionally, suppression of Notch/Hes1 signaling in mature principal cells reduces principal cell gene expression without activating Foxi1. We conclude that Hes1 is a Notch signaling target that is essential for normal patterning of the collecting ducts with intermingled cell types by repressing Foxi1, and for maintenance of principal cell gene expression independent of repressing Foxi1.

scholarly journals Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

2020 ◽  
Author(s):  
Nan Papili Gao ◽  
Olivier Gandrillon ◽  
András Páldi ◽  
Ulysse Herbach ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Cell Differentiation ◽  
Single Cell ◽  
Burst Size ◽  
Mechanistic Model ◽  
Cell Lineage ◽  
Transcriptional Analysis ◽  
Cell Gene Expression ◽  
Branching Point ◽  
Cell Gene

ABSTRACTWe employed our previously-described single-cell gene expression analysis CALISTA (Clustering And Lineage Inference in Single-Cell Transcriptional Analysis) to evaluate transcriptional uncertainty at the single-cell level using a stochastic mechanistic model of gene expression. We reconstructed a transcriptional uncertainty landscape during cell differentiation by visualizing single-cell transcriptional uncertainty surface over a two dimensional representation of the single-cell gene expression data. The reconstruction of transcriptional uncertainty landscapes for ten publicly available single-cell gene expression datasets from cell differentiation processes with linear, single or multi-branching cell lineage, reveals universal features in the cell differentiation trajectory that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceeding the increase in the cell transcriptional uncertainty. Finally, we provided biological interpretations of the universal rise-then-fall profile of the transcriptional uncertainty landscape, including a link with the Waddington’s epigenetic landscape, that is generalizable to every cell differentiation system.

scholarly journals In situelectro-sequencing in three-dimensional tissues

2021 ◽  
Author(s):  
Qiang Li ◽  
Zuwan Lin ◽  
Ren Liu ◽  
Xin Tang ◽  
Jiahao Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

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