scholarly journals Single-Cell RNA Sequencing With Combined Use of Bulk RNA Sequencing to Reveal Cell Heterogeneity and Molecular Changes at Acute Stage of Ischemic Stroke in Mouse Cortex Penumbra Area

2021 ◽  
Vol 9 ◽  
Author(s):  
Kang Guo ◽  
Jianing Luo ◽  
Dayun Feng ◽  
Lin Wu ◽  
Xin Wang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Single Cell ◽  
Rna Sequencing ◽  
Therapeutic Targets ◽  
Cell Types ◽  
Acute Stage ◽  
Gene Markers ◽  
Molecular Changes ◽  
Early Stages ◽  
The Impact

Stroke has been the leading cause of adult morbidity and mortality over the past several years. After an ischemic stroke attack, many dormant or reversibly injured brain cells exist in the penumbra area. However, the pathological processes and unique cell information in the penumbra area of an acute ischemic stroke remain elusive. We applied unbiased single cell sequencing in combination with bulk RNA-seq analysis to investigate the heterogeneity of each cell type in the early stages of ischemic stroke and to detect early possible therapeutic targets to help cell survival. We used these analyses to study the mouse brain penumbra during this phase. Our results reveal the impact of ischemic stroke on specific genes and pathways of different cell types and the alterations of cell differentiation trajectories, suggesting potential pathological mechanisms and therapeutic targets. In addition to classical gene markers, single-cell genomics demonstrates unique information on subclusters of several cell types and metabolism changes in an ischemic stroke. These findings suggest that Gadd45b in microglia, Cyr61 in astrocytes, and Sgk3 in oligodendrocytes may play a subcluster-specific role in cell death or survival in the early stages of ischemic stroke. Moreover, RNA-scope multiplex in situ hybridization and immunofluorescence staining were applied to selected target gene markers to validate and confirm the existence of these cell subtypes and molecular changes during acute stage of ischemic stroke.

scholarly journals Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

2021 ◽  
Author(s):  
Saptarshi Bej ◽  
Anne-Marie Galow ◽  
Robert David ◽  
Markus Wolfien ◽  
Olaf Wolkenhauer
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Classification Problem ◽  
Use Case ◽  
Cell Capture ◽  
Sequencing Data ◽  
Rare Cells ◽  
The Impact

AbstractThe research landscape of single-cell and single-nuclei RNA sequencing is evolving rapidly, and one area that is enabled by this technology, is the detection of rare cells. An automated, unbiased and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it will usually be necessary to generate other datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare cell subpopulations constitute an imbalanced classification problem.We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class.We demonstrate the effectiveness of the method for two independent use cases, each consisting of two published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8,635). This use case was designed to take a larger imbalance ratio (∼1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio (∼1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single cell capture procedures and the impact of “less” rare-cell types. For validation purposes, all datasets have also been analyzed in a traditional manner using common data analysis approaches, such as the Seurat3 workflow.Our algorithm identifies rare-cell populations with a high accuracy and low false positive detection rate. A striking benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis is publicly available at FairdomHub (https://fairdomhub.org/assays/1368) and can easily be transferred to train other customized approaches.

Single-Cell RNA Sequencing of Mouse Left Ventricle Reveals Cellular Diversity and Intercommunication

2021 ◽  
Author(s):  
Lan Wu ◽  
Yan-Fei Li ◽  
Jun-wei Shen ◽  
Qian Zhu ◽  
Jing Jiang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Left Ventricle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Left Ventricular ◽  
Cardiovascular Research ◽  
Gene Markers ◽  
Single Cell Rna Sequencing

Previous studies have revealed the diversity of the whole cardiac cellulome but not refined the left ventricle, which was essential for finding therapeutic targets. Here, we characterized single-cell transcriptional profiles of the mouse left ventricular cellular landscape using single-cell RNA sequencing (10×Genomics). Detailed t-Distributed Stochastic Neighbor Embedding (tSNE) analysis revealed the cell types of left ventricle with gene markers. Left ventricular cellulome contained cardiomyocytes highly expressed Trdn, endothelial cells highly expressed Pcdh17, fibroblast highly expressed Lama2 and macrophages highly expressed Hpgds, also proved by in situ hybridization. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis (ListHits>2, p<0.05) were employed with the DAVID database to investigate subtypes of each cell type with the underlying functions of differentially expressed genes (DEGs). Endothelial cells included five subtypes, fibroblasts comprised of seven subtypes and macrophages contained eleven subtypes. The key representative DEGs (p<0.001) were Gja4 and Gja5 in cluster 3 of endothelial cells, Aqp2 and Thbs4 in cluster 2 of fibroblasts, as well as Clec4e and Trem-1 in in cluster 3 of marcophages perhaps involved in the occur of atherosclerosis, heart failure and acute myocardial infarction proved by literature review. We also revealed extensive networks of intercellular communication in left ventricle. We suggested possible therapeutic targets for cardiovascular disease and autocrine and paracrine signaling underpins left ventricular homeostasis. This study provided new insights into the structure and function of the mammalian left ventricular cellulome and offers an important resource that will stimulate studies in cardiovascular research.

scholarly journals Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Saptarshi Bej ◽  
Anne-Marie Galow ◽  
Robert David ◽  
Markus Wolfien ◽  
Olaf Wolkenhauer
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Brain Atlas ◽  
Use Case ◽  
Cell Capture ◽  
Sequencing Data ◽  
Rare Cells ◽  
The Impact

Abstract Background The research landscape of single-cell and single-nuclei RNA-sequencing is evolving rapidly. In particular, the area for the detection of rare cells was highly facilitated by this technology. However, an automated, unbiased, and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it is usually necessary to generate further specific datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare-cell subpopulations constitute an imbalanced classification problem. We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class. Results We demonstrate the effectiveness of our method for three independent use cases, each consisting of already published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8635). This use case was designed to take a larger imbalance ratio (~1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio (~1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single-cell capture procedures and the impact of “less” rare-cell types. The third dataset refers to the murine data of the Allen Brain Atlas, including more than 1 million cells. For validation purposes only, all datasets have also been analyzed traditionally using common data analysis approaches, such as the Seurat workflow. Conclusions In comparison to baseline testing without oversampling, our approach identifies rare-cells with a robust precision-recall balance, including a high accuracy and low false positive detection rate. A practical benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis in R and Python is publicly available at FairdomHub, as well as GitHub, and can easily be transferred to identify other rare-cell types.

MAPK: A Key Player in the Development and Progression of Stroke

2020 ◽  
Vol 19 (4) ◽  
pp. 248-256
Author(s):  
Yangmin Zheng ◽  
Ziping Han ◽  
Haiping Zhao ◽  
Yumin Luo
Keyword(s):  
Ischemic Stroke ◽  
Signaling Pathway ◽  
Complex Disease ◽  
Therapeutic Targets ◽  
Cell Types ◽  
Mapk Signaling ◽  
Mapk Signaling Pathway ◽  
Effective Prevention ◽  
Prevention And Treatment ◽  
Different Cell Types

Conclusion: Stroke is a complex disease caused by genetic and environmental factors, and its etiological mechanism has not been fully clarified yet, which brings great challenges to its effective prevention and treatment. MAPK signaling pathway regulates gene expression of eukaryotic cells and basic cellular processes such as cell proliferation, differentiation, migration, metabolism and apoptosis, which are considered as therapeutic targets for many diseases. Up to now, mounting evidence has shown that MAPK signaling pathway is involved in the pathogenesis and development of ischemic stroke. However, the upstream kinase and downstream kinase of MAPK signaling pathway are complex and the influencing factors are numerous, the exact role of MAPK signaling pathway in the pathogenesis of ischemic stroke has not been fully elucidated. MAPK signaling molecules in different cell types in the brain respond variously after stroke injury, therefore, the present review article is committed to summarizing the pathological process of different cell types participating in stroke, discussed the mechanism of MAPK participating in stroke. We further elucidated that MAPK signaling pathway molecules can be used as therapeutic targets for stroke, thus promoting the prevention and treatment of stroke.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia

2021 ◽  
Author(s):  
Ugomma C. Eze ◽  
Aparna Bhaduri ◽  
Maximilian Haeussler ◽  
Tomasz J. Nowakowski ◽  
Arnold R. Kriegstein
Keyword(s):  
Human Brain ◽  
Brain Development ◽  
Single Cell ◽  
Radial Glia ◽  
Cortical Development ◽  
Cell Types ◽  
Human Cortex ◽  
Early Stages ◽  
Human Brain Development ◽  
Neuroepithelial Cells

AbstractThe human cortex comprises diverse cell types that emerge from an initially uniform neuroepithelium that gives rise to radial glia, the neural stem cells of the cortex. To characterize the earliest stages of human brain development, we performed single-cell RNA-sequencing across regions of the developing human brain, including the telencephalon, diencephalon, midbrain, hindbrain and cerebellum. We identify nine progenitor populations physically proximal to the telencephalon, suggesting more heterogeneity than previously described, including a highly prevalent mesenchymal-like population that disappears once neurogenesis begins. Comparison of human and mouse progenitor populations at corresponding stages identifies two progenitor clusters that are enriched in the early stages of human cortical development. We also find that organoid systems display low fidelity to neuroepithelial and early radial glia cell types, but improve as neurogenesis progresses. Overall, we provide a comprehensive molecular and spatial atlas of early stages of human brain and cortical development.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

scholarly journals Molecular characteristics and spatial distribution of adult human corneal cell subtypes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ann J. Ligocki ◽  
Wen Fury ◽  
Christian Gutierrez ◽  
Christina Adler ◽  
Tao Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cross Sections ◽  
Cell Types ◽  
Marker Genes ◽  
Molecular Characteristics ◽  
Transcriptional Level ◽  
Human Cornea ◽  
Adult Human ◽  
And Migration

AbstractBulk RNA sequencing of a tissue captures the gene expression profile from all cell types combined. Single-cell RNA sequencing identifies discrete cell-signatures based on transcriptomic identities. Six adult human corneas were processed for single-cell RNAseq and 16 cell clusters were bioinformatically identified. Based on their transcriptomic signatures and RNAscope results using representative cluster marker genes on human cornea cross-sections, these clusters were confirmed to be stromal keratocytes, endothelium, several subtypes of corneal epithelium, conjunctival epithelium, and supportive cells in the limbal stem cell niche. The complexity of the epithelial cell layer was captured by eight distinct corneal clusters and three conjunctival clusters. These were further characterized by enriched biological pathways and molecular characteristics which revealed novel groupings related to development, function, and location within the epithelial layer. Moreover, epithelial subtypes were found to reflect their initial generation in the limbal region, differentiation, and migration through to mature epithelial cells. The single-cell map of the human cornea deepens the knowledge of the cellular subsets of the cornea on a whole genome transcriptional level. This information can be applied to better understand normal corneal biology, serve as a reference to understand corneal disease pathology, and provide potential insights into therapeutic approaches.

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