scholarly journals Inference of immune cell composition on the expression profiles of mouse tissue

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Ziyi Chen ◽  
Anfei Huang ◽  
Jiya Sun ◽  
Taijiao Jiang ◽  
F. Xiao-Feng Qin ◽  
...  
Keyword(s):  
Immune Cell ◽  
Expression Profiles ◽  
Leukemia Cell ◽  
Cell Types ◽  
Mouse Tissue ◽  
Lymphoid Tissues ◽  
Cell Composition ◽  
Mouse Tissues ◽  
Single Positive ◽  
Public Datasets

Abstract Mice are some of the widely used experimental animal models for studying human diseases. Defining the compositions of immune cell populations in various tissues from experimental mouse models is critical to understanding the involvement of immune responses in various physiological and patho-physiological conditions. However, non-lymphoid tissues are normally composed of vast and diverse cellular components, which make it difficult to quantify the relative proportions of immune cell types. Here we report the development of a computational algorithm, ImmuCC, to infer the relative compositions of 25 immune cell types in mouse tissues using microarray-based mRNA expression data. The ImmuCC algorithm showed good performance and robustness in many simulated datasets. Remarkable concordances were observed when ImmuCC was used on three public datasets, one including enriched immune cells, one with normal single positive T cells, and one with leukemia cell samples. To validate the performance of ImmuCC objectively, thorough cross-comparison of ImmuCC predicted compositions and flow cytometry results was done with in-house generated datasets collected from four distinct mouse lymphoid tissues and three different types of tumor tissues. The good correlation and biologically meaningful results demonstrate the broad utility of ImmuCC for assessing immune cell composition in diverse mouse tissues under various conditions.

scholarly journals Erratum: Corrigendum: Inference of immune cell composition on the expression profiles of mouse tissue

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Ziyi Chen ◽  
Anfei Huang ◽  
Jiya Sun ◽  
Taijiao Jiang ◽  
F. Xiao-Feng Qin ◽  
...  
Keyword(s):  
Immune Cell ◽  
Expression Profiles ◽  
Mouse Tissue ◽  
Cell Composition

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 Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Julien Racle ◽  
Kaat de Jonge ◽  
Petra Baumgaertner ◽  
Daniel E Speiser ◽  
David Gfeller
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Response To Therapy ◽  
Expression Data ◽  
Cell Type ◽  
Tumor Gene Expression ◽  
Tumor Gene

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

scholarly journals Breakdown of multiple sclerosis genetics to identify an integrated disease network and potential variant mechanisms

2019 ◽  
Vol 51 (11) ◽  
pp. 562-577
Author(s):  
C. Joy Shepard ◽  
Sara G. Cline ◽  
David Hinds ◽  
Seyedehameneh Jahanbakhsh ◽  
Jeremy W. Prokop
Keyword(s):  
Multiple Sclerosis ◽  
Immune Cell ◽  
Expression Profiles ◽  
Missense Variant ◽  
Cell Types ◽  
Tissue Expression ◽  
Specific Expression ◽  
Genomic Databases ◽  
Genetic Traits ◽  
Immune System Dysfunction

Genetics of multiple sclerosis (MS) are highly polygenic with few insights into mechanistic associations with pathology. In this study, we assessed MS genetics through linkage disequilibrium and missense variant interpretation to yield a MS gene network. This network of 96 genes was taken through pathway analysis, tissue expression profiles, single cell expression segregation, expression quantitative trait loci (eQTLs), genome annotations, transcription factor (TF) binding profiles, structural genome looping, and overlap with additional associated genetic traits. This work revealed immune system dysfunction, nerve cell myelination, energetic control, transcriptional regulation, and variants that overlap multiple autoimmune disorders. Tissue-specific expression and eQTLs of MS genes implicate multiple immune cell types including macrophages, neutrophils, and T cells, while the genes in neural cell types enrich for oligodendrocyte and myelin sheath biology. There are eQTLs in linkage with lead MS variants in 25 genes including the multitissue eQTL, rs9271640, for HLA-DRB1/ DRB5. Using multiple functional genomic databases, we identified noncoding variants that disrupt TF binding for GABPA, CTCF, EGR1, YY1, SPI1, CLOCK, ARNTL, BACH1, and GFI1. Overall, this paper suggests multiple genetic mechanisms for MS associated variants while highlighting the importance of a systems biology and network approach when elucidating intersections of the immune and nervous system.

The gene coding for secreted phosphoprotein1/osteopontin is the most overexpressed gene in cholangiocarcinoma—detected by oligonucleotide arrays and LightCycler analysis

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 10036-10036
Author(s):  
H. G. Hass ◽  
J. Jobst ◽  
O. Nehls ◽  
A. Frilling ◽  
J. T. Hartmann ◽  
...  
Keyword(s):  
Cell Function ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Phenotype ◽  
Oligonucleotide Arrays ◽  
Surface Receptors ◽  
Malignant Liver ◽  
And Control

10036 Background: Cholangiocarcinomas (CCC) are the second most common primary hepatic malignancy with a still poor prognosis and arise from biliary epithelia or cholangiocytes. Until now, less is known about the molecular pathways leeding to CCC. Methods: Oligonucleotide arrays were used to analyze gene expression profiles of 8 intrahepatic CCCs. After isolation of tRNA and transcription into cDNA, biotin-labelled cRNA probes were hybridized to GeneArrays (Affymetrix U 133A) containing probes of more than 22.000 genes/ESTs. For two-dimensional cluster analysis we used special software programs (Genexplore, GeneSpring). Dysregulated genes were determined by presence in more than 70% and a 2-fold change in relation to the corresponding non-malignant liver tissue. Lightcycler analysis were performed to validate the expression datas of dysregulated genes. Results: A total of 694 dysregulated genes (330 up-/364 down-regulated, compared with corresponding non-malignant tissue) were detected. As the gene with the highest and most consistent upregulation we were able to identify osteopontin (OPN) with an average 5-fold overexpression in all CCC tissues. OPN is an acidic phosphoprotein that is secreted by osteoblasts, macrophages and many other cell types and binds to a variety of cell surface receptors (integrins/CD44). OPN is multifunctional, with activities in cell migration, regulation of bone metabolism, immune cell function and control of tumor cell phenotype. Elevated OPN levels were seen in different tumors but until now no data exist about the expression in CCCs. As one possible interaction in human carcinogenesis, OPN has recently been shown to be a novel substrate for some MMPs, which play an importand role in tumor invasion and metastasis. Conclusions: This is the first report about an overexpression of OPN in CCC and our data indicate an important role in cholangiocarcinogenesis. Further studies are needed to illucidate the moleculargenetic mechanisms of OPN interactions in CCC. No significant financial relationships to disclose.

scholarly journals Single-cell RNA sequencing reveals micro-evolution of the stickleback immune system

2021 ◽  
Author(s):  
Lauren E Fuess ◽  
Daniel I Bolnick
Keyword(s):  
Immune System ◽  
Single Cell ◽  
Immune Cells ◽  
Gasterosteus Aculeatus ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Model Systems ◽  
System Structure ◽  
Ecological Processes

Pathogenic infection is an important driver of many ecological processes. Furthermore, variability in immune function is an important driver of differential infection outcomes. New evidence would suggest that immune variation extends to broad cellular structure of immune systems. However, variability at such broad levels is traditionally difficult to detect in non-model systems. Here we leverage single cell transcriptomic approaches to document signatures of microevolution of immune system structure in a natural system, the three-spined stickleback (Gasterosteus aculeatus). We sampled nine adult fish from three populations with variability in resistance to a cestode parasite, Schistocephalus solidus, to create the first comprehensive immune cell atlas for G. aculeatus. Eight major immune cell types, corresponding to major vertebrate immune cells, were identified. We were also able to document significant variation in both abundance and expression profiles of the individual immune cell types, among the three populations of fish. This variability may contribute to observed variability in parasite susceptibility. Finally, we demonstrate that identified cell type markers can be used to reinterpret traditional transcriptomic data. Combined our study demonstrates the power of single cell sequencing to not only document evolutionary phenomena (i.e. microevolution of immune cells), but also increase the power of traditional transcriptomic datasets.

scholarly journals Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells

2019 ◽  
Vol 12 (S8) ◽  
Author(s):  
Yen-Jung Chiu ◽  
Yi-Hsuan Hsieh ◽  
Yen-Hua Huang
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Immune Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Deconvolution Method ◽  
Cell Composition ◽  
Reference Gene Expression ◽  
Cell Fractions

Abstract Background To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. Methods Our deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. Results In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. Conclusions We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

scholarly journals Cross-tissue immune cell analysis reveals tissue-specific adaptations and clonal architecture across the human body

2021 ◽  
Author(s):  
Conde C Domínguez ◽  
T Gomes ◽  
LB Jarvis ◽  
C Xu ◽  
SK Howlett ◽  
...  
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Immune Cell ◽  
Γδ T Cells ◽  
Cell Types ◽  
Cell Receptor ◽  
Lymphoid Tissues ◽  
Cell Type ◽  
Variable Regions ◽  
Tissue Specific

AbstractDespite their crucial role in health and disease, our knowledge of immune cells within human tissues, in contrast to those circulating in the blood, remains limited. Here, we surveyed the immune compartment of lymphoid and non-lymphoid tissues of six adult donors by single-cell RNA sequencing, including alpha beta T-cell receptor (αβ TCR), gamma delta (γδ) TCR and B-cell receptor (BCR) variable regions. To aid systematic cell type identification we developed CellTypist, a tool for automated and accurate cell type annotation. Using this approach combined with manual curation, we determined the tissue distribution of finely phenotyped immune cell types and cell states. This revealed tissue-specific features within cell subsets, such as a subtype of activated dendritic cells in the airways (expressing CSF2RA, GPR157, CRLF2), ITGAD-expressing γδ T cells in spleen and liver, and ITGAX+ splenic memory B cells. Single cell paired chain TCR analysis revealed cell type-specific biases in VDJ usage, and BCR analysis revealed characteristic patterns of somatic hypermutation and isotype usage in plasma and memory B cell subsets. In summary, our multi-tissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis and antigen receptor sequencing.

scholarly journals Systematic Study of the Immune Components after Ischemic Stroke Using CyTOF Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yaning Li ◽  
Yan Wang ◽  
Yang Yao ◽  
Brian B. Griffiths ◽  
Liangshu Feng ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Immune Cells ◽  
Immune Cell ◽  
Deep Understanding ◽  
Cell Types ◽  
Lymphoid Tissues ◽  
Peripheral Tissues ◽  
Ischemic Brain ◽  
Peripheral Organs ◽  
The Brain

Stroke induces a robust inflammatory response. However, it still lacks a systematic view of the various immune cell types due to the limited numbers of fluorophore used in the traditional FACS technique. In our current study, we utilized the novel technique mass cytometry (CyTOF) to analyze multiple immune cell types. We detected these immune cells from the ischemic brain, peripheral blood, spleen, and bone marrow at different time courses after stroke. Our data showed (1) dynamic changes in the immune cell numbers in the ischemic brain and peripheral organs. (2) The expression levels of cell surface markers indicate the inflammation response status after stroke. Interestingly, CD62L, a key adhesion molecule, regulates the migration of leukocytes from blood vessels into secondary lymphoid tissues and peripheral tissues. (3) A strong leukocyte network across the brain and peripheral immune organs was identified using the R program at day 1 after ischemia, suggesting that the peripheral immune cells dramatically migrated into the ischemic areas after stroke. This study provides a systematic, wide view of the immune components in the brain and peripheral organs for a deep understanding of the immune response after ischemic stroke.

scholarly journals ImmuCellDB: An Indicative Database of Immune Cell Composition From Different Tissues and Disease Conditions in Mouse and Human

2021 ◽  
Vol 12 ◽  
Author(s):  
Ziyi Chen ◽  
Han Na ◽  
Aiping Wu
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Computational Tools ◽  
Cell Composition ◽  
Cell Abundance ◽  
Different Tissues

Immune cell composition is highly divergent across different tissues and diseases. A comprehensive resource of tissue immune cells across different conditions in mouse and human will thus provide great understanding of the immune microenvironment of many diseases. Recently, computational methods for estimating immune cell abundance from tissue transcriptome data have been developed and are now widely used. Using these computational tools, large-scale estimation of immune cell composition across tissues and conditions should be possible using gene expression data collected from public databases. In total, 266 tissue types and 706 disease types in humans, as well as 143 tissue types and 61 disease types, and 206 genotypes in mouse had been included in a database we have named ImmuCellDB (http://wap-lab.org:3200/ImmuCellDB/). In ImmuCellDB, users can search and browse immune cell proportions based on tissues, disease or genotype in mouse or humans. Additionally, the variation and correlation of immune cell abundance and gene expression level between different conditions can be compared and viewed in this database. We believe that ImmuCellDB provides not only an indicative view of tissue-dependent or disease-dependent immune cell profiles, but also represents an easy way to pre-determine immune cell abundance and gene expression profiles for specific situations.

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