Dynamic plasma extracellular vesicle long RNA profiling changes during the peri-operative period

2020 ◽  
Author(s):  
qing hua ◽  
wenhao xu ◽  
xuefang shen ◽  
xi tian ◽  
Peng Wang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Surgical Stress ◽  
Distant Metastases ◽  
Cell Types ◽  
R Package ◽  
Extracellular Vesicle ◽  
Differentially Expressed ◽  
Dynamic Changes ◽  
Rna Profiling ◽  
Time Points

Abstract Background: Surgery remains the most important treatment strategy for solid tumors, such as colorectal cancer (CRC); However, a number of studies have suggested that surgical stress contributes to tumor recurrence or distant metastases. Extracellular vesicles (EVs), which contain a rich variety of RNAs with specialized functions and clinical applications, have been shown to be an indicator for diagnosis and prognosis of cancers. The effect of surgical stress on the landscape and characteristics of EV long RNA (exLR) in human blood, however, remains largely unknown.Methods: We present an optimized strategy for exLR sequencing (exLR-seq) the plasma from three patients with CRC at 4 time points (before surgery [T0], after extubation [T1], 1 day after surgery [T2], and 3 days after surgery [T4]). The “Limma” R package was used to evaluate the dynamic changes of mRNAs and long non-coding (lnc)RNAs from EVs. We also constructed a protein–protein interaction (PPI) network of hub genes and predicted biological processes, cellular components, and molecular functions of gene ontology (GO) functional analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Results: We observed a sufficient number of exLRs, including 12,924 mRNAs and 2196 lncRNAs. Both mRNAs and lncRNAs underwent dynamic changes during the peri-operative period. Compared with T0, there were 110 mRNAs differentially expressed after extubation, 60 differentially expressed genes（DEGs）1 day after surgery, and 50 DEGs 3 days after surgery. A total of 11 genes changed at all 3 time points and were related to regulation of the membrane potential, receptor complex, and passive transmembrane transporter activity. In addition, 22 lncRNAs were differentially expressed after extubation (T1). Nineteen lncRNAs were differentially expressed between T0 and T2, and 38 lncRNAs were differentially expressed between T0 and T3. In addition, we found that only 3 lncRNAs changed at 3 time points. Interestingly, blood exLRs reflected the tissue origins and relative fractions of different immune cell types. EVs from CD8+ T，CD4+ memory T, and NK cells decreased after surgery and the absolute quality of EVs from immune cells decreased as well. Conclusion: In summary, this study demonstrated abundant exLRs in human plasma and the dynamic changes of these exLRs and exLRs originating from CD8+ T and CD4+ memory T cells were reduced during the peri-operative period.

scholarly journals USP8 Status Affects the Immune Landscape of Corticotroph Pituitary Adenomas

2021 ◽  
Author(s):  
Dahlia Greidinger ◽  
Ronit Mor-Cohen ◽  
Roni Zemet ◽  
Nitzan Maixner ◽  
Amit Tirosh
Keyword(s):  
Pituitary Adenomas ◽  
Immune Cell ◽  
Cell Types ◽  
Differentially Expressed ◽  
Toll Like Receptor ◽  
M1 Macrophages ◽  
Mutation Status ◽  
Specific Protease ◽  
Signature Recognition ◽  
Ubiquitin Specific Protease

Abstract Purpose Activating somatic mutations in ubiquitin-specific protease-8 (USP8), encoding a deubiquitinating protein, are found in approximately 30% of corticotroph-derived pituitary adenomas (CPA). USP8 has immunomodulating properties that were demonstrated in non-tumoral diseases. Our study aims to assess the influence of USP8 mutation status on the immune tumor microenvironment (iTME) of CPAs. Methods We analyzed 20 PCAs by RNA sequencing. In six of them, USP8 mutations were detected. We assessed the immune landscape of tumors by quantifying 22 immune cell types based on the CIBERSORT transcriptome signature-recognition algorithms. Also, we performed a pathway analysis for genes that were differentially expressed between groups using the Wikipathways 2019 and Reactome 2016 databases and using the EnrichR platform results. Results CPA with activating USP8 mutations were associated with "cold" iTME compared with wild type USP8 CPA. This "cold" iTME was reflected by lower fractions of B cells, CD4, regulatory and gamma/delta T cells, natural killer cells, M0 and M1 macrophages, dendritic cells and eosinophils (p < 0.05 for all comparisons). Pathways altered by the presence of USP8 mutation, based on the most differentially expressed genes (3,061 genes) included Microglia Pathogen Phagocytosis and multiple toll-like receptor signaling pathways (p < 0.0001). Conclusion USP8 status affects the immune landscape of corticotroph pituitary adenomas, with USP8 mutation associated with "cold" iTME.

scholarly journals Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19

2020 ◽  
Author(s):  
Sudhir Ghandikota ◽  
Mihika Sharma ◽  
Anil G. Jegga
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Genome Wide Association Study ◽  
Immune Cell ◽  
Gene Clusters ◽  
Cell Types ◽  
Host Protein ◽  
Differentially Expressed ◽  
Protein Interaction Data ◽  
Neuropsychiatric Diseases

ABSTRACTKnowledge about the molecular mechanisms driving COVID-19 pathophysiology and outcomes is still limited. To learn more about COVID-19 pathophysiology we performed secondary analyses of transcriptomic data from two in vitro (Calu-3 and Vero E6 cells) and one in vivo (Ad5-hACE2-sensitized mice) models of SARS-CoV-2 infection. We found 1467 conserved differentially expressed host genes (differentially expressed in at least two of the three model system transcriptomes compared) in SARS-CoV-2 infection. To find potential genetic factors associated with COVID-19, we analyzed these conserved differentially expressed genes using known human genotype-phenotype associations. Genome-wide association study enrichment analysis showed evidence of enrichment for GWA loci associated with platelet functions, blood pressure, body mass index, respiratory functions, and neurodegenerative and neuropsychiatric diseases, among others. Since human protein complexes are known to be directly related to viral infection, we combined and analyzed the conserved transcriptomic signature with SARS-CoV-2-host protein-protein interaction data and found more than 150 gene clusters. Of these, 29 clusters (with 5 or more genes in each cluster) had at least one gene encoding protein that interacts with SARS-CoV-2 proteome. These clusters were enriched for different cell types in lung including epithelial, endothelial, and immune cell types suggesting their pathophysiological relevancy to COVID-19. Finally, pathway analysis on the conserved differentially expressed genes and gene clusters showed alterations in several pathways and biological processes that could enable in understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.

scholarly journals Multiomics profiling of primary lung cancers and distant metastases reveals immunosuppression as a common characteristic of tumor cells with metastatic plasticity

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Won-Chul Lee ◽  
Alexandre Reuben ◽  
Xin Hu ◽  
Nicholas McGranahan ◽  
Runzhe Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Copy Number ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Distant Metastases ◽  
Cell Types ◽  
Copy Number Aberration ◽  
Primary Tumors ◽  
Deconvolution Analysis ◽  
Transcriptomic Data

Abstract Background Metastasis is the primary cause of cancer mortality accounting for 90% of cancer deaths. Our understanding of the molecular mechanisms driving metastasis is rudimentary. Results We perform whole exome sequencing (WES), RNA sequencing, methylation microarray, and immunohistochemistry (IHC) on 8 pairs of non-small cell lung cancer (NSCLC) primary tumors and matched distant metastases. Furthermore, we analyze published WES data from 35 primary NSCLC and metastasis pairs, and transcriptomic data from 4 autopsy cases with metastatic NSCLC and one metastatic lung cancer mouse model. The majority of somatic mutations are shared between primary tumors and paired distant metastases although mutational signatures suggest different mutagenesis processes in play before and after metastatic spread. Subclonal analysis reveals evidence of monoclonal seeding in 41 of 42 patients. Pathway analysis of transcriptomic data reveals that downregulated pathways in metastases are mainly immune-related. Further deconvolution analysis reveals significantly lower infiltration of various immune cell types in metastases with the exception of CD4+ T cells and M2 macrophages. These results are in line with lower densities of immune cells and higher CD4/CD8 ratios in metastases shown by IHC. Analysis of transcriptomic data from autopsy cases and animal models confirms that immunosuppression is also present in extracranial metastases. Significantly higher somatic copy number aberration and allelic imbalance burdens are identified in metastases. Conclusions Metastasis is a molecularly late event, and immunosuppression driven by different molecular events, including somatic copy number aberration, may be a common characteristic of tumors with metastatic plasticity.

scholarly journals Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology

2019 ◽  
Vol 35 (14) ◽  
pp. i436-i445 ◽  
Author(s):  
Gregor Sturm ◽  
Francesca Finotello ◽  
Florent Petitprez ◽  
Jitao David Zhang ◽  
Jan Baumbach ◽  
...  
Keyword(s):  
Single Cell ◽  
Computational Methods ◽  
Immune Cell ◽  
Comprehensive Evaluation ◽  
Cell Types ◽  
R Package ◽  
Supplementary Information ◽  
Rna Seq ◽  
Cell Type ◽  
Real World Datasets

Abstract Motivation The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. Results We developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of ∼11 000 cells from the TME to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and ∼1800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. Availability and implementation A snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

2020 ◽  
Author(s):  
Dustin J. Sokolowski ◽  
Mariela Faykoo-Martinez ◽  
Lauren Erdman ◽  
Huayun Hou ◽  
Cadia Chan ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
R Package ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kidney Regeneration ◽  
Cell Type ◽  
User Friendly

AbstractRNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.HighlightsscMappR integrates scRNA-seq and bulk RNA-seq to re-calibrate bulk differentially expressed genes (DEGs).scMappR correctly identified immune-cell expressed DEGs from a bulk RNA-seq analysis of mouse kidney regeneration.scMappR is deployed as a user-friendly R package available at CRAN.

scholarly journals MicroRNA Expression Differences in Blood-Derived CD19+ B Cells of Methotrexate Treated Rheumatoid Arthritis Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Fatima Heinicke ◽  
Xiangfu Zhong ◽  
Siri T. Flåm ◽  
Johannes Breidenbach ◽  
Magnus Leithaug ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
B Cells ◽  
B Cell ◽  
Mirna Target ◽  
Immune Cell ◽  
Cell Types ◽  
Differentially Expressed ◽  
Newly Diagnosed ◽  
Wide Range ◽  
Differentially Expressed Mirnas

Rheumatoid arthritis (RA) is a complex disease with a wide range of underlying susceptibility factors. Recently, dysregulation of microRNAs (miRNAs) in RA have been reported in several immune cell types from blood. However, B cells have not been studied in detail yet. Given the autoimmune nature of RA with the presence of autoantibodies, CD19+ B cells are a key cell type in RA pathogenesis and alterations in CD19+ B cell subpopulations have been observed in patient blood. Therefore, we aimed to reveal the global miRNA repertoire and to analyze miRNA expression profile differences in homogenous RA patient phenotypes in blood-derived CD19+ B cells. Small RNA sequencing was performed on CD19+ B cells of newly diagnosed untreated RA patients (n=10), successfully methotrexate (MTX) treated RA patients in remission (MTX treated RA patients, n=18) and healthy controls (n=9). The majority of miRNAs was detected across all phenotypes. However, significant expression differences between MTX treated RA patients and controls were observed for 27 miRNAs, while no significant differences were seen between the newly diagnosed patients and controls. Several of the differentially expressed miRNAs were previously found to be dysregulated in RA including miR-223-3p, miR-486-3p and miR-23a-3p. MiRNA target enrichment analysis, using the differentially expressed miRNAs and miRNA-target interactions from miRTarBase as input, revealed enriched target genes known to play important roles in B cell activation, differentiation and B cell receptor signaling, such as STAT3, PRDM1 and PTEN. Interestingly, many of those genes showed a high degree of correlated expression in CD19+ B cells in contrast to other immune cell types. Our results suggest important regulatory functions of miRNAs in blood-derived CD19+ B cells of MTX treated RA patients and motivate for future studies investigating the interactive mechanisms between miRNA and gene targets, as well as the possible predictive power of miRNAs for RA treatment response.

scholarly journals Single-Cell Profiles of Age-Related Osteoarthritis Uncover Underlying Heterogeneity Associated With Disease Progression

2022 ◽  
Vol 8 ◽  
Author(s):  
Wenzhou Liu ◽  
Yanbo Chen ◽  
Gang Zeng ◽  
Shuting Yang ◽  
Tao Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Synovial Tissue ◽  
Immune Cell ◽  
Cell Types ◽  
Degenerative Joint Disease ◽  
Differentially Expressed ◽  
Joint Disease ◽  
Marker Genes ◽  
Age Related ◽  
Synovial Tissues

Objective: Osteoarthritis (OA) is the most common chronic degenerative joint disease, which represents the leading cause of age-related disability. Here, this study aimed to depict the intercellular heterogeneity of OA synovial tissues.Methods: Single-cell RNA sequencing (scRNA-seq) data were preprocessed and quality controlled by the Seurat package. Cell cluster was presented and cell types were annotated based on the mRNA expression of corresponding marker genes by the SingleR package. Cell-cell communication was assessed among different cell types. After integrating the GSE55235 and GSE55457 datasets, differentially expressed genes were identified between OA and normal synovial tissues. Then, differentially expressed marker genes were overlapped and their biological functions were analyzed.Results: Totally, five immune cell subpopulations were annotated in OA synovial tissues including macrophages, dendritic cells, T cells, monocytes and B cells. Pseudo-time analysis revealed the underlying evolution process in the inflammatory microenvironment of OA synovial tissue. There was close crosstalk between five cell types according to the ligand-receptor network. The genetic heterogeneity was investigated between OA and normal synovial tissues. Furthermore, functional annotation analysis showed the intercellular heterogeneity across immune cells in OA synovial tissues.Conclusion: This study offered insights into the heterogeneity of OA, which provided in-depth understanding of the transcriptomic diversities within synovial tissue. This transcriptional heterogeneity may improve our understanding on OA pathogenesis and provide potential molecular therapeutic targets for OA.

scholarly journals Whole-Tissue Deconvolution and scRNAseq Analysis Identify Altered Endometrial Cellular Compositions and Functionality Associated With Endometriosis

2022 ◽  
Vol 12 ◽  
Author(s):  
Daniel G. Bunis ◽  
Wanxin Wang ◽  
Júlia Vallvé-Juanico ◽  
Sahar Houshdaran ◽  
Sushmita Sen ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Dynamic Changes ◽  
Inflammatory Phenotype ◽  
Full Complement ◽  
Bulk Tissue ◽  
Statistical Metrics

The uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single-cell RNAseq, we comprehensively characterized immune and nonimmune cell types in the endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single-cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue.

scholarly journals Deconvolution of bulk blood eQTL effects into immune cell subpopulations

2020 ◽  
Author(s):  
Raul Aguirre-Gamboa ◽  
Niek de Klein ◽  
Jennifer di Tommaso ◽  
Annique Claringbould ◽  
Monique van der Wijst ◽  
...  
Keyword(s):  
Whole Blood ◽  
Complex Disease ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
R Package ◽  
Population Based ◽  
Cell Type ◽  
Type Interaction ◽  
Cell Subpopulations

Abstract Background Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, the current methods are labor-intensive and expensive. Here we introduce a new method, Decon2, a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL).Results The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell we can predict the proportions of 34 circulating cell types for 3,194 samples from a population-based cohort. Next we identified 16,362 whole blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with those of eQTL(≥ 96%-100%) and chromatin mark QTL (≥87%-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect.Conclusions Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs, which is useful in pinpointing the most relevant cell type for a certain complex disease. Decon2 is available as an R package and Java application. (https://github.com/molgenis/systemsgenetics/tree/master/Decon2), and as a web tool (www.molgenis.org/deconvolution).

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