scholarly journals Single-embryo and single-blastomere immunoblotting reports protein expression heterogeneity in early-stage preimplantation embryos

2018 ◽  
Author(s):  
Elisabet Rosàs-Canyelles ◽  
Andrew J. Modzelewski ◽  
Lin He ◽  
Amy E. Herr
Keyword(s):  
Protein Expression ◽  
Single Cell ◽  
High Specificity ◽  
Preimplantation Development ◽  
Cellular Heterogeneity ◽  
Preimplantation Embryos ◽  
Rna Seq ◽  
Cell Fate Decisions ◽  
Single Blastomere ◽  
Β Tubulin

AbstractUnderstanding how a zygote develops from a single cell into a multicellular organism has benefitted from single-cell tools, including RNA sequencing (RNA-Seq) and immunofluorescence (IF). However, scrutinizing inter- and intra-embryonic phenotypic variation is hindered by two fundamental limitations; the loose correlation between transcription and translation and the cross-reactivity of immunoreagents. To address these challenges, we describe a high-specificity microfluidic immunoblot optimized to quantify protein expression from all stages of mouse preimplantation development. Despite limited availability of isoform-specific immunoreagents, the immunoblot resolves inter-embryonic heterogeneity of embryo-specific isoforms (i.e., DICER-1). We observed significantly higher DICER-1 isoform expression in oocytes when compared to two-cell embryos, and further find that protein expression levels follow the same trend as mRNA for both the full-length and truncated DICER-1 isoforms. At the morula stage, we assayed both whole and disaggregated embryos for loading controls (β-tubulin, GAPDH) and markers that regulate cell fate decisions (CDX-2, SOX-2). In disaggregated morula, we found that cell volume showed positive, linear correlation with expression of β-tubulin and SOX-2. In dissociated two-cell and four-cell blastomeres, we detect significant inter-blastomeric variation in GADD45a expression, corroborating suspected cellular heterogeneity even in the earliest multicellular stage of preimplantation embryos. As RNA-Seq and other transcript-centric approaches continue to further probe preimplantation development, the demand for companion protein-based techniques rises. The reported microfluidic immunoblot serves as an essential tool for understanding mammalian development by providing high-specificity and direct measurements of protein targets at single-embryo and single-blastomere resolution.

scholarly journals Cell-to-cell heterogeneity in p38-mediated cross-inhibition of JNK causes stochastic cell death

2017 ◽  
Author(s):  
Haruko Miura ◽  
Yohei Kondo ◽  
Michiyuki Matsuda ◽  
Kazuhiro Aoki
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Stress Responses ◽  
Imaging System ◽  
Single Cells ◽  
High Specificity ◽  
Cellular Heterogeneity ◽  
Cell Fate Decisions ◽  
Specificity And Sensitivity ◽  
Cross Inhibition

AbstractThe stress activated protein kinases (SAPKs), c-Jun N-terminal kinase (JNK) and p38, are important players in cell fate decisions in response to environmental stress signals. Crosstalk signaling between JNK and p38 is emerging as an important regulatory mechanism in the inflammatory and stress responses. However, it is still unknown how this crosstalk affects signaling dynamics, cell-to-cell variation, and cellular responses at the single-cell level. To address these questions, we established a multiplexed live-cell imaging system based on kinase translocation reporters to simultaneously monitor JNK and p38 activities with high specificity and sensitivity at single-cell resolution. Various stresses, such as anisomycin, osmotic stress, and UV irradiation, and pro-inflammatory cytokines activated JNK and p38 with various dynamics. In all cases, however, p38 suppressed JNK activity in a cross-inhibitory manner. We demonstrate that p38 antagonizes JNK through both transcriptional and post-translational mechanisms. This cross-inhibition of JNK appears to generate cellular heterogeneity in JNK activity after stress exposure. Our data indicate that this heterogeneity in JNK activity plays a role in fractional killing in response to UV stress. Our highly sensitive multiplexed imaging system enables detailed investigation into the p38-JNK interplay in single cells.One Sentence SummaryCross-inhibition by p38 generates cell-to-cell variability in JNK activity.

scholarly journals CCPE: Cell Cycle Pseudotime Estimation for Single Cell RNA-seq Data

2021 ◽  
Author(s):  
Jiajia Liu ◽  
Mengyuan Yang ◽  
Weiling Zhao ◽  
Xiaobo Zhou
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Rapid Development ◽  
Estimation Method ◽  
Accurate Prediction ◽  
Cellular Heterogeneity ◽  
Marker Genes ◽  
Biological Processes ◽  
Rna Seq ◽  
Cell Fate Decisions

AbstractThe rapid development of single-cell RNA-sequencing (scRNA-seq) technologies makes it possible to characterize cellular heterogeneity by detecting and quantifying transcriptional changes at the single-cell level. Pseudotime analysis enables to characterize the continuous progression of various biological processes, such as cell cycle. Cell cycle plays an important regulatory role in cell fate decisions and differentiation and is also often regarded as a confounder in scRNA-seq data analysis when analyzing the role of other factors on transcriptional regulation. Therefore, accurate prediction of cell cycle pseudotime and identify cell stages are important steps for characterizing the development-related biological processes, identifying important regulatory molecules and promoting the analysis of transcriptional heterogeneity. Here, we develop CCPE, a novel cell cycle pseudotime estimation method to characterize cell cycle timing and determine cell cycle phases from single-cell RNA-seq data. CCPE uses a discriminative helix to characterize the circular process and estimates pseudotime in the cell cycle. We evaluated the model performance based on a variety of simulated and real scRNA-seq datasets. Our results indicate that CCPE is an effective method for cell cycle estimation and competitive in various downstream analyses compared with other existing methods. CCPE successfully identified cell cycle marker genes and is robust to dropout events in scRNA-seq data. CCPE also has excellent performance on small datasets with fewer genes or cells. Accurate prediction of the cell cycle in CCPE effectively contributes to cell cycle effect removal across cell types or conditions.

scholarly journals scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

2019 ◽  
Author(s):  
Congting Ye ◽  
Qian Zhou ◽  
Xiaohui Wu ◽  
Chen Yu ◽  
Guoli Ji ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Rna Seq ◽  
Computational Tool ◽  
Cell Level ◽  
Wilcoxon Rank Sum Test ◽  
Transcriptional Regulatory ◽  
Cell Groups

Abstract Motivation Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Resolving Cell Fate Decisions during Somatic Cell Reprogramming by Single-Cell RNA-Seq

2019 ◽  
Vol 73 (4) ◽  
pp. 815-829.e7 ◽  
Author(s):  
Lin Guo ◽  
Lihui Lin ◽  
Xiaoshan Wang ◽  
Mingwei Gao ◽  
Shangtao Cao ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Reprogramming ◽  
Rna Seq ◽  
Cell Fate Decisions ◽  
Somatic Cell Reprogramming

scholarly journals scDoc: correcting drop-out events in single-cell RNA-seq data

2020 ◽  
Vol 36 (15) ◽  
pp. 4233-4239
Author(s):  
Di Ran ◽  
Shanshan Zhang ◽  
Nicholas Lytal ◽  
Lingling An
Keyword(s):  
Single Cell ◽  
Simulated Data ◽  
Drop Out ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Cell Subpopulation ◽  
Rna Seq ◽  
Imputation Methods ◽  
Similarity Estimation ◽  
Differential Expression Detection

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of ‘drop-out’ events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. Results scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data. Availability and implementation R code is available at https://github.com/anlingUA/scDoc. Supplementary information Supplementary data are available at Bioinformatics online.

Single Cell RNA seq for Analysis of Cell Fate Decisions

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. SCI-20-SCI-20
Author(s):  
H. Leighton Grimes ◽  
Singh Harinder ◽  
Andre Olsson ◽  
Nathan Salomonis ◽  
Bruce J. Aronow ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Clustering Analysis ◽  
Conflicts Of Interest ◽  
Regulatory Gene ◽  
Rna Seq ◽  
Regulatory State ◽  
Cell Fate Decisions ◽  
General Utility ◽  
Developmental Hierarchy

Abstract Single-cell RNA-Seq has the potential to become a dominant approach in probing diverse and complex developmental compartments. Its unbiased and comprehensive nature could enable developmental ordering of cellular and regulatory gene hierarchies without prior knowledge. To test general utility we performed single-cell RNA-seq of murine hematopoietic progenitors focusing on the myeloid developmental hierarchy. Using novel unsupervised clustering analysis, ICDS, we correctly ordered known hierarchical states as well as revealed rare intermediates. Regulatory state analysis suggested that the transcription factors Gfi1 and Irf8 function antagonistically to control homeostatic neutrophil and macrophage production, respectively. This prediction was validated by complementary genetic and genomic experiments in granulocyte-macrophage progenitors. Using knock-in reporters for Gfi1 and Irf8 and clonogenic analyses coupled with single-cell RNA-seq we distinguished regulatory states of bi-potential progenitors from their lineage specifying or committed progeny. Thus single-cell RNA-Seq is a powerful developmental tool to characterize hierarchical and rare cellular states along with the regulators that control their dynamics. Disclosures No relevant conflicts of interest to declare.

scholarly journals Integrated profiling of single cell epigenomic and transcriptomic landscape of Parkinson’s disease mouse brain

2020 ◽  
Author(s):  
Jixing Zhong ◽  
Gen Tang ◽  
Jiacheng Zhu ◽  
Xin Qiu ◽  
Weiying Wu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Cell ◽  
Early Stage ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Distinct Cell ◽  
Single Nucleus

AbstractParkinson’s disease (PD) is a neurodegenerative disease leading to the impairment of execution of movement. PD pathogenesis has been largely investigated, but either restricted in bulk level or at certain cell types, which failed to capture cellular heterogeneity and intrinsic interplays among distinct cell types. To overcome this, we applied single-nucleus RNA-seq and single cell ATAC-seq on cerebellum, midbrain and striatum of PD mouse and matched control. With 74,493 cells in total, we comprehensively depicted the dysfunctions under PD pathology covering proteostasis, neuroinflammation, calcium homeostasis and extracellular neurotransmitter homeostasis. Besides, by multi-omics approach, we identified putative biomarkers for early stage of PD, based on the relationships between transcriptomic and epigenetic profiles. We located certain cell types that primarily contribute to PD early pathology, narrowing the gap between genotypes and phenotypes. Taken together, our study provides a valuable resource to dissect the molecular mechanism of PD pathogenesis at single cell level, which could facilitate the development of novel methods regarding diagnosis, monitoring and practical therapies against PD at early stage.

scholarly journals CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets

2020 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zijian Ni ◽  
Michael Collins ◽  
Mark E. Burkard ◽  
Christina Kendziorski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Individual Gene ◽  
Genome Wide ◽  
Progression Of Disease ◽  
Cell Subpopulations ◽  
Available Information

AbstractBackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets.ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

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