scholarly journals Endovascular Biopsy of Vertebrobasilar Aneurysm in Patient With Polyarteritis Nodosa

2021 ◽  
Vol 12 ◽  
Author(s):  
Kazim H. Narsinh ◽  
Kamileh Narsinh ◽  
David B. McCoy ◽  
Zhengda Sun ◽  
Cathra Halabi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Polyarteritis Nodosa ◽  
Intracranial Aneurysms ◽  
Gene Set Enrichment Analysis ◽  
Positive Staining ◽  
Disease Pathogenesis ◽  
Unruptured Intracranial Aneurysms ◽  
Single Cell Rna Sequencing

Background and Purpose: The management of unruptured intracranial aneurysms remains controversial. The decisions to treat are heavily informed by estimated risk of bleeding. However, these estimates are imprecise, and better methods for stratifying the risk or tailoring treatment strategy are badly needed. Here, we demonstrate an initial proof-of-principle concept for endovascular biopsy to identify the key molecular pathways and gene expression changes associated with aneurysm formation. We couple this technique with single cell RNA sequencing (scRNAseq) to develop a roadmap of the pathogenic changes of a dolichoectatic vertebrobasilar aneurysm in a patient with polyarteritis nodosa.Methods: Endovascular biopsy and fluorescence activated cell sorting was used to isolate the viable endothelial cells (ECs) using the established techniques. A single cell RNA sequencing (scRNAseq) was then performed on 24 aneurysmal ECs and 23 patient-matched non-aneurysmal ECs. An integrated panel of bioinformatic tools was applied to determine the differential gene expression, enriched signaling pathways, and cell subpopulations hypothesized to drive disease pathogenesis.Results: We identify a subset of 7 (29%) aneurysm-specific ECs with a distinct gene expression signature not found in the patient-matched control ECs. A gene set enrichment analysis identified these ECs to have increased the expression of genes regulating the leukocyte-endothelial cell adhesion, major histocompatibility complex (MHC) class I, T cell receptor recycling, tumor necrosis factor alpha (TNFα) response, and interferon gamma signaling. A histopathologic analysis of a different intracranial aneurysm that was later resected yielded a diagnosis of polyarteritis nodosa and positive staining for TNFα.Conclusions: We demonstrate feasibility of applying scRNAseq to the endovascular biopsy samples and identify a subpopulation of ECs associated with cerebral aneurysm in polyarteritis nodosa. Endovascular biopsy may be a safe method for deriving insight into the disease pathogenesis and tailoring the personalized treatment approaches to intracranial aneurysms.

Fuzzy C-Means based LSED analysis model of single-cell RNA sequencing data for gene expression difference data

2021 ◽  
pp. 1-10
Author(s):  
Min Wu ◽  
Junhua Xu ◽  
Shanshan Zhu ◽  
Jinzhi Lei ◽  
Jie Gao
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Set Enrichment Analysis ◽  
Parameter Analysis ◽  
Analysis Model ◽  
Sequencing Data ◽  
Process Data ◽  
Fuzzy C Means ◽  
Single Cell Rna Sequencing

Analysis of single-cell RNA sequencing (scRNA-seq) data is often complicate due to the sparsity and high data dimensionality. In this work, we proposed Fuzzy C-means based linear stable-exponential distribution (LSED) model for analyzing scRNA-seq count data of chronic myeloid leukemia (CML) patients. We propose pipelines stages for analysis in which noisy and inconsistent data form sequencing is removed during data preprocessing, this process data then form s the cluster of gene feature using fuzzy c-means (FCM) clustering, relevant features are extracted during feature extraction approach. These extracted features are then fed into LSED model in order to difference feature data of gene expression. Finally we evaluate the performance for proposed analysis model based on parameter estimation, distribution comparison and parameter analysis. From the result analysis it was observed that proposed analysis model parameter reflect change in condition of patient more effectively as well as this model fits difference data of gene expression in more better way in comparison to Cauchy and stable distribution. Additional, the results of Gene-set enrichment analysis specify the affinity of proposed model can replicate the distinct enhancement of BCR-ABL+ stem cell as well as BCR-ABL- stem cells. Significantly, Proposed FCM based LSED analysis model studies CML from the perspective of statistical models, which present a new sight for CML scientific research.

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 of the mammalian pineal gland identifies two pinealocyte subtypes and cell type-specific daily patterns of gene expression

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0205883 ◽  
Author(s):  
Joseph C. Mays ◽  
Michael C. Kelly ◽  
Steven L. Coon ◽  
Lynne Holtzclaw ◽  
Martin F. Rath ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pineal Gland ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Mammalian Pineal Gland ◽  
Daily Patterns

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

scholarly journals Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽  
2020 ◽  
Vol 142 (14) ◽  
pp. 1374-1388
Author(s):  
Yanming Li ◽  
Pingping Ren ◽  
Ashley Dawson ◽  
Hernan G. Vasquez ◽  
Waleed Ageedi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Aortic Wall ◽  
Genome Wide Association ◽  
Aortic Tissue ◽  
Sequencing Data ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

Abstract A10: Using single-cell RNA sequencing to assess the impact of pancreatic oncogenic Kras on macrophage gene expression in vitro

2019 ◽  
Author(s):  
Katelyn Donahue ◽  
Yaqing Zhang ◽  
Veerin Sirihorachai ◽  
Stephanie The ◽  
Arvind Rao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cell Rna Sequencing ◽  
The Impact

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.

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