scholarly journals Combination of single cell sequencing data and GWAS summary statistics reveals genetically-influenced liver cell types for primary biliary cholangitis

2021 ◽  
Author(s):  
Bingyu Xiang ◽  
Chunyu Deng ◽  
Jingjing Li ◽  
Shanshan Li ◽  
Huifang Zhang ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Liver Cell ◽  
Cell Types ◽  
Liver Cells ◽  
Primary Biliary Cholangitis ◽  
Summary Statistics ◽  
Sequencing Data ◽  
Immune Microenvironment ◽  
Single Cell Sequencing

Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with primary biliary cholangitis (PBC). However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. We constructed a powerful computational framework to integrate a large-scale GWAS summary statistics (N = 13,239) with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. We found that 29 genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. Gene-property analysis revealed that four immune cell types including Cst3+ dendritic cell, Chil3+ macrophage, Trbc2+ T cell, and Gzma+ T cell were significantly enriched by PBC-risk genes. By combining GWAS summary statistics with scRNA-seq data, we identified that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals. The ORMDL3 gene showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). Compared with ORMDL3+ cholangiocytes, we identified that ORMDL3- cholangiocytes predispose to play important immune-modulatory roles in the etiology of PBC. To the best of our knowledge, this is the first study to integrate human genetic information with single cell sequencing data for parsing genetics-influenced liver cells and its immune microenvironment for PBC risk.

scholarly journals Single Cell Sequencing Analysis Identifies Genetics-Modulated ORMDL3+ Cholangiocytes Having Higher Metabolic Effects On Primary Biliary Cholangitis

2021 ◽  
Author(s):  
Bingyu Xiang ◽  
Chunyu Deng ◽  
Fei Qiu ◽  
Jingjing Li ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Association Studies ◽  
Liver Cells ◽  
Metabolic Effects ◽  
Primary Biliary Cholangitis ◽  
Genome Wide Association Studies ◽  
Sequencing Analysis ◽  
Summary Statistics ◽  
Genetic Determinants ◽  
Single Cell Sequencing

Abstract Background: Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. Results: We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3+ cholangiocytes have prominently higher metabolism activity score than ORMDL3- cholangiocytes (P = 1.383×10-15). Compared with ORMDL3- cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3+ cholangiocytes (FDR < 0.05), and these significant genes were associated with autoimmune diseases-related functional terms or pathways. The ORMDL3+ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3- cholangiocytes, the VEGF signaling pathway is specific for ORMDL3+ cholangiocytes to interact with other cell populations. Conclusions: To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3+ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC.

scholarly journals Single cell sequencing analysis identifies genetics-modulated ORMDL3+ cholangiocytes having higher metabolic effects on primary biliary cholangitis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Bingyu Xiang ◽  
Chunyu Deng ◽  
Fei Qiu ◽  
Jingjing Li ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Association Studies ◽  
Liver Cells ◽  
Metabolic Effects ◽  
Primary Biliary Cholangitis ◽  
Genome Wide Association Studies ◽  
Sequencing Analysis ◽  
Summary Statistics ◽  
Genetic Determinants ◽  
Single Cell Sequencing

Abstract Background Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. Results We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3+ cholangiocytes have prominently higher metabolism activity score than ORMDL3− cholangiocytes (P = 1.38 × 10–15). Compared with ORMDL3− cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3+ cholangiocytes (FDR < 0.05), and these significant genes were associated with autoimmune diseases-related functional terms or pathways. The ORMDL3+ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3− cholangiocytes, the VEGF signaling pathway is specific for ORMDL3+ cholangiocytes to interact with other cell populations. Conclusions To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3+ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC. Graphical Abstract

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

scholarly journals Integrated analysis of bulk multi omic and single-cell sequencing data confirms the molecular origin of hemodynamic changes in Covid-19 infection explaining coagulopathy and higher geriatric mortality

2020 ◽  
Author(s):  
Shreya Johri ◽  
Deepali Jain ◽  
Ishaan Gupta
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Older Patients ◽  
Cell Types ◽  
Integrated Analysis ◽  
Molecular Evidence ◽  
Sequencing Data ◽  
Phagocytic Cells ◽  
Hemodynamic Changes ◽  
Single Cell Sequencing

AbstractBesides severe respiratory distress, recent reports in Covid-19 patients have found a strong association between platelet counts and patient survival. Along with hemodynamic changes such as prolonged clotting time, high fibrin degradation products and D-dimers, increased levels of monocytes with disturbed morphology have also been identified. In this study, through an integrated analysis of bulk RNA-sequencing data from Covid-19 patients with data from single-cell sequencing studies on lung tissues, we found that most of the cell-types that contributed to the altered gene expression were of hematopoietic origin. We also found that differentially expressed genes in Covid-19 patients formed a significant pool of the expressing genes in phagocytic cells such as Monocytes and platelets. Interestingly, while we observed a general enrichment for Monocytes in Covid-19 patients, we found that the signal for FCGRA3+ Monocytes was depleted. Further, we found evidence that age-associated gene expression changes in Monocytes and platelets, associated with inflammation, mirror gene expression changes in Covid-19 patients suggesting that pro-inflammatory signalling during aging may worsen the infection in older patients. We identified more than 20 genes that change in the same direction between Covid-19 infection and aging cells that may act as potential therapeutic targets. Of particular interest were IL2RG, GNLY and GMZA expressed in platelets, which facilitates cytokine signalling in Monocytes through an interaction with platelets. To understand whether infection can directly manipulate the biology of Monocytes and platelets, we hypothesize that these non-ACE2 expressing cells may be infected by the virus through the phagocytic route. We observed that phagocytic cells such as Monocytes, T-cells, and platelets have a significantly higher expression of genes that are a part of the Covid-19 viral interactome. Hence these cell-types may have an active rather than a reactive role in viral pathogenesis to manifest clinical symptoms such as coagulopathy. Therefore, our results present molecular evidence for pursuing both anti-inflammatory and anticoagulation therapy for better patient management especially in older patients.

scholarly journals Interpretable Autoencoders Trained on Single Cell Sequencing Data Can Transfer Directly to Data from Unseen Tissues

Cells ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 85
Author(s):  
Julie Sparholt Walbech ◽  
Savvas Kinalis ◽  
Ole Winther ◽  
Finn Cilius Nielsen ◽  
Frederik Otzen Bagger
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Biological Pathways ◽  
Training Data ◽  
Sequencing Data ◽  
Data Simulation ◽  
Single Cell Sequencing ◽  
Saliency Maps ◽  
Unseen Data ◽  
Biological Concepts

Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.

scholarly journals PNOC Expressed by B Cells in Cholangiocarcinoma Was Survival Related and LAIR2 Could Be a T Cell Exhaustion Biomarker in Tumor Microenvironment: Characterization of Immune Microenvironment Combining Single-Cell and Bulk Sequencing Technology

2021 ◽  
Vol 12 ◽  
Author(s):  
Zheng Chen ◽  
Mincheng Yu ◽  
Jiuliang Yan ◽  
Lei Guo ◽  
Bo Zhang ◽  
...  
Keyword(s):  
T Cell ◽  
B Cells ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Malignant Cell ◽  
Cell Populations ◽  
Sequencing Data ◽  
Sequencing Technology ◽  
Immune Infiltration ◽  
Single Cell Sequencing

BackgroundCholangiocarcinoma was a highly malignant liver cancer with poor prognosis, and immune infiltration status was considered an important factor in response to immunotherapy. In this investigation, we tried to locate immune infiltration related genes of cholangiocarcinoma through combination of bulk-sequencing and single-cell sequencing technology.MethodsSingle sample gene set enrichment analysis was used to annotate immune infiltration status in datasets of TCGA CHOL, GSE32225, and GSE26566. Differentially expressed genes between high- and low-infiltrated groups in TCGA dataset were yielded and further compressed in other two datasets through backward stepwise regression in R environment. Single-cell sequencing data of GSE138709 was loaded by Seurat software and was used to examined the expression of infiltration-related gene set. Pathway changes in malignant cell populations were analyzed through scTPA web tool.ResultsThere were 43 genes differentially expressed between high- and low-immune infiltrated patients, and after further compression, PNOC and LAIR2 were significantly correlated with high immune infiltration status in cholangiocarcinoma. Through analysis of single-cell sequencing data, PNOC was mainly expressed by infiltrated B cells in tumor microenvironment, while LAIR2 was expressed by Treg cells and partial GZMB+ CD8 T cells, which were survival related and increased in tumor tissues. High B cell infiltration levels were related to better overall survival. Also, malignant cell populations demonstrated functionally different roles in tumor progression.ConclusionPNOC and LAIR2 were biomarkers for immune infiltration evaluation in cholangiocarcinoma. PNOC, expressed by B cells, could predict better survival of patients, while LAIR2 was a potential marker for exhaustive T cell populations, correlating with worse survival of patients.

scholarly journals Learning for single-cell assignment

2020 ◽  
Vol 6 (44) ◽  
pp. eabd0855
Author(s):  
Bin Duan ◽  
Chenyu Zhu ◽  
Guohui Chuai ◽  
Chen Tang ◽  
Xiaohan Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Marker Gene ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Limited Effectiveness ◽  
Benchmark Datasets ◽  
Cell Assignment ◽  
Single Cell Type ◽  
Sequencing Data Analysis

Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability.

scholarly journals Single-cell transcriptomic analysis of mIHC images via antigen mapping

2021 ◽  
Vol 7 (10) ◽  
pp. eabc5464
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Rachael G. Aubin ◽  
Steven Woodhouse ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Patterns ◽  
Cell Types ◽  
Level Of Detail ◽  
Cell Populations ◽  
Sequencing Data ◽  
Spatially Resolved ◽  
Murine Spleen ◽  
Single Cell Rna Sequencing ◽  
Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

scholarly journals Single-cell analysis reveals cell communication triggered by macrophages associated with the reduction and exhaustion of CD8+ T cells in COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lei He ◽  
Quan Zhang ◽  
Yue Zhang ◽  
Yixian Fan ◽  
Fahu Yuan ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Communication ◽  
Cell Types ◽  
Trial Registration ◽  
T Cell Subsets ◽  
Disease Group ◽  
Communication Analysis ◽  
Receptor Interactions

Abstract Background The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has become an ongoing pandemic. Understanding the respiratory immune microenvironment which is composed of multiple cell types, together with cell communication based on ligand–receptor interactions is important for developing vaccines, probing COVID-19 pathogenesis, and improving pandemic control measures. Methods A total of 102 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study. Clinical information, routine laboratory tests, and flow cytometry analysis data with different conditions were collected and assessed for predictive value in COVID-19 patients. Next, we analyzed public single-cell RNA-sequencing (scRNA-seq) data from bronchoalveolar lavage fluid, which offers the closest available view of immune cell heterogeneity as encountered in patients with varying severity of COVID-19. A weighting algorithm was used to calculate ligand–receptor interactions, revealing the communication potentially associated with outcomes across cell types. Finally, serum cytokines including IL6, IL1β, IL10, CXCL10, TNFα, GALECTIN-1, and IGF1 derived from patients were measured. Results Of the 102 COVID-19 patients, 42 cases (41.2%) were categorized as severe. Multivariate logistic regression analysis demonstrated that AST, D-dimer, BUN, and WBC were considered as independent risk factors for the severity of COVID-19. T cell numbers including total T cells, CD4+ and CD8+ T cells in the severe disease group were significantly lower than those in the moderate disease group. The risk model containing the above mentioned inflammatory damage parameters, and the counts of T cells, with AUROCs ranged from 0.78 to 0.87. To investigate the molecular mechanism at the cellular level, we analyzed the published scRNA-seq data and found that macrophages displayed specific functional diversity after SARS-Cov-2 infection, and the metabolic pathway activities in the identified macrophage subtypes were influenced by hypoxia status. Importantly, we described ligand–receptor interactions that are related to COVID-19 serverity involving macrophages and T cell subsets by communication analysis. Conclusions Our study showed that macrophages driving ligand–receptor crosstalk contributed to the reduction and exhaustion of CD8+ T cells. The identified crucial cytokine panel, including IL6, IL1β, IL10, CXCL10, IGF1, and GALECTIN-1, may offer the selective targets to improve the efficacy of COVID-19 therapy. Trial registration: This is a retrospective observational study without a trial registration number.

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