scholarly journals Robust transcriptional profiling and identification of differentially expressed genes with low input RNA sequencing of adult hippocampal neural stem and progenitor populations

2021 ◽  
Author(s):  
Jiyeon Kim Denninger ◽  
Logan A Walker ◽  
Xi Chen ◽  
Altan M Turkoglu ◽  
Alexander Pan ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Transcriptional Profiling ◽  
Population Level ◽  
Similar Efficacy ◽  
Differentially Expressed ◽  
Mammalian Brain ◽  
Single Cell Sequencing

Multipotent neural stem cells (NSCs) are found in several isolated niches of the adult mammalian brain where they have unique potential to assist in tissue repair. Modern transcriptomics offer high-throughput methods for identifying disease or injury associated gene expression signatures in endogenous adult NSCs, but they require adaptation to accommodate the rarity of NSCs. Bulk RNA sequencing (RNAseq) of NSCs requires pooling several mice, which impedes application to labor-intensive injury models. Alternatively, single cell RNAseq can profile hundreds to thousands of cells from a single mouse and is increasingly used to study NSCs. The consequences of the low RNA input from a single NSC on downstream identification of differentially expressed genes (DEGs) remains largely unexplored. Here, to clarify the role that low RNA input plays in NSC DEG identification, we directly compared DEGs in an oxidative stress model of cultured NSCs by bulk and single cell sequencing. While both methods yielded DEGs that were replicable, single cell sequencing DEGs derived from genes with higher relative transcript counts compared to all detected genes and exhibited smaller fold changes than DEGs identified by bulk RNAseq. The loss of high fold-change DEGs in the single cell platform presents an important limitation for identifying disease-relevant genes. To facilitate identification of such genes, we determined an RNA-input threshold that enables transcriptional profiling of NSCs comparable to standard bulk sequencing and used it to establish a workflow for in vivo profiling of endogenous NSCs. We then applied this workflow to identify DEGs after lateral fluid percussion injury, a labor-intensive animal model of traumatic brain injury. Our work suggests that single cell RNA sequencing may underestimate the diversity of pathologic DEGs but population level transcriptomic analysis can be adapted to capture more of these DEGs with similar efficacy and diversity as standard bulk sequencing. Together, our data and workflow will be useful for investigators interested in understanding and manipulating adult hippocampal NSC responses to various stimuli.

scholarly journals scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bobby Ranjan ◽  
Florian Schmidt ◽  
Wenjie Sun ◽  
Jinyu Park ◽  
Mohammad Amin Honardoost ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. Results We present scConsensus, an $${\mathbf {R}}$$ R framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. Conclusions scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is implemented in $${\mathbf {R}}$$ R and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

scholarly journals Ileal transcriptomic analysis in paediatric Crohn’s disease reveals IL17- and NOD-signalling expression signatures in treatment-naïve patients and identifies epithelial cells driving differentially expressed genes

2020 ◽  
Author(s):  
James J Ashton ◽  
Konstantinos Boukas ◽  
James Davies ◽  
Imogen S Stafford ◽  
Andres F Vallejo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Single Cell ◽  
Differentially Expressed Genes ◽  
Gene Signature ◽  
Oncostatin M ◽  
Differentially Expressed ◽  
Single Cell Sequencing ◽  
Treatment Naïve ◽  
Ileal Tissue

Abstract Background/Aims Crohn’s disease (CD) arises through host-environment interaction. Abnormal gene expression results from disturbed pathway activation or response to bacteria. We aimed to determine activated pathways and driving cell types in paediatric CD. Methods - We employed contemporary targeted autoimmune RNA sequencing, in parallel to single-cell sequencing, to ileal tissue derived from paediatric CD and controls. Weighted-gene-co-expression-network-analysis (WGCNA) was performed and differentially expressed genes (DEGs) were determined. We integrated clinical data to determine co-expression modules associated with outcomes. Results - Twenty-seven treatment-naive CD (TN-CD), 26 established-CD patients and 17 controls were included. WGCNA revealed a 31-gene signature characterising TN-CD patients, but not established-CD, or controls. The CSF3R gene is a hub within this module and is key in neutrophil expansion and differentiation. Antimicrobial genes including S100A12 and the calprotectin subunit S100A9 were significantly upregulated in TN CD compared to controls (p=2.61x10 -15 and p=9.13x10 -14, respectively) and established-CD (both p=0.0055). Gene-enrichment analysis confirmed upregulation of the IL17-, NOD- and Oncostatin-M-signalling pathways in TN-CD patients, identified in both WGCNA and DEG analyses. An upregulated gene-signature was enriched for transcripts promoting Th17-cell differentiation and correlated with prolonged time to relapse (correlation-coefficient-0.36, p=0.07).Single-cell sequencing of TN-CD patients identified specialised epithelial cells driving differential expression of S100A9. Cell groups, determined by single-cell gene-expression, demonstrated enrichment of IL17-signalling in monocytes and epithelial cells. Conclusion - Ileal tissue from treatment naïve paediatric patients is significantly upregulated for genes driving IL17-, NOD- and Oncostatin-M-signalling. This signal is driven by a distinct subset of epithelial cells expressing antimicrobial gene transcripts.

scholarly journals Comprehensive Integration of Single-Cell Transcriptional Profiling Reveals the Heterogeneities of Non-cardiomyocytes in Healthy and Ischemic Hearts

2020 ◽  
Vol 7 ◽  
Author(s):  
Lingfang Zhuang ◽  
Lin Lu ◽  
Ruiyan Zhang ◽  
Kang Chen ◽  
Xiaoxiang Yan
Keyword(s):  
Myocardial Infarction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Developmental Trajectories ◽  
Transcriptional Profiling ◽  
Sequencing Data ◽  
Signature Genes

Advances in single-cell RNA sequencing (scRNA-seq) technology have recently shed light on the molecular mechanisms of the spatial and temporal changes of thousands of cells simultaneously under homeostatic and ischemic conditions. The aim of this study is to investigate whether it is possible to integrate multiple similar scRNA-seq datasets for a more comprehensive understanding of diseases. In this study, we integrated three representative scRNA-seq datasets of 27,349 non-cardiomyocytes isolated at 3 and 7 days after myocardial infarction or sham surgery. In total, seven lineages, including macrophages, fibroblasts, endothelia, and lymphocytes, were identified in this analysis with distinct dynamic and functional properties in healthy and nonhealthy hearts. Myofibroblasts and endothelia were recognized as the central hubs of cellular communication via ligand-receptor interactions. Additionally, we showed that macrophages from different origins exhibited divergent transcriptional signatures, pathways, developmental trajectories, and transcriptional regulons. It was found that myofibroblasts predominantly expand at 7 days after myocardial infarction with pro-reparative characteristics. We identified signature genes of myofibroblasts, such as Postn, Cthrc1, and Ddah1, among which Ddah1 was exclusively expressed on activated fibroblasts and exhibited concordant upregulation in bulk RNA sequencing data and in vivo and in vitro experiments. Collectively, this compendium of scRNA-seq data provides a valuable entry point for understanding the transcriptional and dynamic changes of non-cardiomyocytes in healthy and nonhealthy hearts by integrating multiple datasets.

scholarly journals Identification of a differentiation stall in epithelial mesenchymal transition in histone H3–mutant diffuse midline glioma

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Lauren M Sanders ◽  
Allison Cheney ◽  
Lucas Seninge ◽  
Anouk van den Bout ◽  
Marissa Chen ◽  
...  
Keyword(s):  
Brain Development ◽  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Epithelial Mesenchymal Transition ◽  
Histone H3 ◽  
Differentially Expressed ◽  
Cell Phenotype ◽  
Epigenetic Mark ◽  
Mesenchymal Transition

Abstract Background Diffuse midline gliomas with histone H3 K27M (H3K27M) mutations occur in early childhood and are marked by an invasive phenotype and global decrease in H3K27me3, an epigenetic mark that regulates differentiation and development. H3K27M mutation timing and effect on early embryonic brain development are not fully characterized. Results We analyzed multiple publicly available RNA sequencing datasets to identify differentially expressed genes between H3K27M and non-K27M pediatric gliomas. We found that genes involved in the epithelial-mesenchymal transition (EMT) were significantly overrepresented among differentially expressed genes. Overall, the expression of pre-EMT genes was increased in the H3K27M tumors as compared to non-K27M tumors, while the expression of post-EMT genes was decreased. We hypothesized that H3K27M may contribute to gliomagenesis by stalling an EMT required for early brain development, and evaluated this hypothesis by using another publicly available dataset of single-cell and bulk RNA sequencing data from developing cerebral organoids. This analysis revealed similarities between H3K27M tumors and pre-EMT normal brain cells. Finally, a previously published single-cell RNA sequencing dataset of H3K27M and non-K27M gliomas revealed subgroups of cells at different stages of EMT. In particular, H3.1K27M tumors resemble a later EMT stage compared to H3.3K27M tumors. Conclusions Our data analyses indicate that this mutation may be associated with a differentiation stall evident from the failure to proceed through the EMT-like developmental processes, and that H3K27M cells preferentially exist in a pre-EMT cell phenotype. This study demonstrates how novel biological insights could be derived from combined analysis of several previously published datasets, highlighting the importance of making genomic data available to the community in a timely manner.

scholarly journals Phlpp1 is induced by estrogen in osteoclasts and its loss in Ctsk-expressing cells does not protect against ovariectomy-induced bone loss

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251732
Author(s):  
Marcelline K. Hanson ◽  
Ismael Y. Karkache ◽  
David H. H. Molstad ◽  
Andrew A. Norton ◽  
Kim C. Mansky ◽  
...  
Keyword(s):  
Bone Loss ◽  
Bone Mass ◽  
Differentially Expressed Genes ◽  
Transcriptional Profiling ◽  
Differentially Expressed ◽  
Specific Expression ◽  
Estrogen Response ◽  
Chemical Inhibition

Prior studies demonstrated that deletion of the protein phosphatase Phlpp1 in Ctsk-Cre expressing cells enhances bone mass, characterized by diminished osteoclast activity and increased coupling to bone formation. Due to non-specific expression of Ctsk-Cre, the definitive mechanism for this observation was unclear. To further define the role of bone resorbing osteoclasts, we performed ovariectomy (Ovx) and Sham surgeries on Phlpp1 cKOCtsk and WT mice. Micro-CT analyses confirmed enhanced bone mass of Phlpp1 cKOCtsk Sham females. In contrast, Ovx induced bone loss in both groups, with no difference between Phlpp1 cKOCtsk and WT mice. Histomorphometry demonstrated that Ovx mice lacked differences in osteoclasts per bone surface, suggesting that estradiol (E2) is required for Phlpp1 deficiency to have an effect. We performed high throughput unbiased transcriptional profiling of Phlpp1 cKOCtsk osteoclasts and identified 290 differentially expressed genes. By cross-referencing these differentially expressed genes with all estrogen response element (ERE) containing genes, we identified IGFBP4 as potential estrogen-dependent target of Phlpp1. E2 induced PHLPP1 expression, but reduced IGFBP4 levels. Moreover, genetic deletion or chemical inhibition of Phlpp1 was correlated with IGFBP4 levels. We then assessed IGFBP4 expression by osteoclasts in vivo within intact 12-week-old females. Modest IGFBP4 immunohistochemical staining of TRAP+ osteoclasts within WT females was observed. In contrast, TRAP+ bone lining cells within intact Phlpp1 cKOCtsk females robustly expressed IGFBP4, but levels were diminished within TRAP+ bone lining cells following Ovx. These results demonstrate that effects of Phlpp1 conditional deficiency are lost following Ovx, potentially due to estrogen-dependent regulation of IGFBP4.

Sign in / Sign up

Export Citation Format

Share Document