Multicohort Analysis Identifies Monocyte Gene Signatures to Accurately Monitor Subset-Specific Changes in Human Diseases

Monocytes are crucial regulators of inflammation, and are characterized by three distinct subsets in humans, of which classical and non-classical are the most abundant. Different subsets carry out different functions and have been previously associated with multiple inflammatory conditions. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and poor reproducibility. Publicly available transcriptome profiles provide an alternative source of data characterized by high statistical power and real-world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in cell levels. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures maintain their accuracy regardless of disease state in an independent cohort profiled by RNA-sequencing and are specific to their respective subset when compared to other immune cells from both myeloid and lymphoid lineages profiled across 6160 transcriptome profiles. Consequently, we show that these signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

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Multicohort Analysis of Publicly-available Monocyte Expression Data Identifies Gene Signatures to Accurately Monitor Subset-specific Changes in Human Diseases

10.1101/2020.12.21.423397 ◽

2020 ◽

Author(s):

Francesco Vallania ◽

Liron Zisman ◽

Claudia Macaubas ◽

Shu-Chen Hung ◽

Narendiran Rajasekaran ◽

...

Keyword(s):

Statistical Power ◽

Expression Profiles ◽

Human Monocyte ◽

Cell Types ◽

Specific Cell ◽

Monocyte Subset ◽

Monocyte Subsets ◽

Alternative Source ◽

Tissue Samples ◽

Cellular Markers

Monocytes and monocyte-derived cells play important roles in the regulation of inflammation, both as precursors as well as effector cells. Monocytes are heterogeneous and characterized by three distinct subsets in humans. Classical and non-classical monocytes represent the most abundant subsets, each carrying out distinct biological functions. Consequently, altered frequencies of different subsets have been associated with inflammatory conditions, such as infections and autoimmune disorders including lupus, rheumatoid arthritis, inflammatory bowel disease, and, more recently, COVID-19. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and, consequently, poor reproducibility. Public transcriptomes provide an alternative source of data characterized by high statistical power and real world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in the levels of specific cell types.Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures, although derived using only datasets profiling healthy individuals, maintain their accuracy independent of the disease state in an independent cohort profiled by RNA-sequencing (AUC = 1.0). Furthermore, we demonstrate that our signatures are specific to monocyte subsets compared to other immune cells such as B, T, dendritic cells (DCs) and natural killer (NK) cells (AUC = 0.87~0.88, p<2.2e-16). This increased specificity results in estimated monocyte subset levels that are strongly correlated with cytometry-based quantification of cellular subsets (r = 0.69, p = 6.7e-4). Consequently, we show that these monocyte subset-specific signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

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High-Content Immunophenotyping and Hierarchical Clustering Reveal Sources of Heterogeneity and New Surface Markers of Human Blood Monocyte Subsets

Thrombosis and Haemostasis ◽

10.1055/s-0039-1700871 ◽

2019 ◽

Vol 120 (01) ◽

pp. 141-155 ◽

Cited By ~ 3

Author(s):

Jedrzej Hoffmann ◽

Karel Fišer ◽

Christoph Liebetrau ◽

Nora Staubach ◽

David Kost ◽

...

Keyword(s):

Linear Models ◽

Transcriptional Profiling ◽

Hierarchical Cluster ◽

Human Monocyte ◽

Blood Monocyte ◽

Monocyte Subset ◽

Surface Markers ◽

Monocyte Subsets ◽

Inflammatory Conditions ◽

Human Blood Monocyte

Abstract Objective Blood monocyte subsets are emerging as biomarkers of cardiovascular inflammation. However, our understanding of human monocyte heterogeneity and their immunophenotypic features under healthy and inflammatory conditions is still evolving. Rationale In this study, we sought to investigate the immunophenome of circulating human monocyte subsets. Methods Multiplexed, high-throughput flow cytometry screening arrays and computational data analysis were used to analyze the expression and hierarchical relationships of 242 specific surface markers on circulating classical (CD14++CD16−), intermediate (CD14++CD16+), and nonclassical (CD14+CD16++) monocytes in healthy adults. Results Using generalized linear models and hierarchical cluster analysis, we selected and clustered epitopes that most reliably differentiate between monocyte subsets. We validated existing transcriptional profiling data and revealed potential new surface markers that uniquely define the classical (e.g., BLTR1, CD35, CD38, CD49e, CD89, CD96), intermediate (e.g., CD39, CD275, CD305, CDw328), and nonclassical (e.g., CD29, CD132) subsets. In addition, our analysis revealed phenotypic cell clusters, identified by dendritic markers CMRF-44 and CMRF-56, independent of the traditional monocyte classification. Conclusion These results reveal an advancement of the clinically applicable multiplexed screening arrays that may facilitate monocyte subset characterization and cytometry-based biomarker selection in various inflammatory disorders.

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Comparison of gene expression profiles between human and mouse monocyte subsets

Blood ◽

10.1182/blood-2009-07-235028 ◽

2010 ◽

Vol 115 (3) ◽

pp. e10-e19 ◽

Cited By ~ 424

Author(s):

Molly A. Ingersoll ◽

Rainer Spanbroek ◽

Claudio Lottaz ◽

Emmanuel L. Gautier ◽

Marion Frankenberger ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Surface Protein ◽

Human Monocyte ◽

Peroxisome Proliferator ◽

Cell Surface Protein ◽

Monocyte Subsets ◽

Peroxisome Proliferator Activated Receptor ◽

Human And Mouse

Abstract Blood of both humans and mice contains 2 main monocyte subsets. Here, we investigated the extent of their similarity using a microarray approach. Approximately 270 genes in humans and 550 genes in mice were differentially expressed between subsets by 2-fold or more. More than 130 of these gene expression differences were conserved between mouse and human monocyte subsets. We confirmed numerous of these differences at the cell surface protein level. Despite overall conservation, some molecules were conversely expressed between the 2 species' subsets, including CD36, CD9, and TREM-1. Other differences included a prominent peroxisome proliferator-activated receptor γ (PPARγ) signature in mouse monocytes, which is absent in humans, and strikingly opposed patterns of receptors involved in uptake of apoptotic cells and other phagocytic cargo between human and mouse monocyte subsets. Thus, whereas human and mouse monocyte subsets are far more broadly conserved than currently recognized, important differences between the species deserve consideration when models of human disease are studied in mice.

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Monocyte Subsets Are Differentially Lost from the Circulation during Acute Inflammation Induced by Human Experimental Endotoxemia

Journal of Innate Immunity ◽

10.1159/000475665 ◽

2017 ◽

Vol 9 (5) ◽

pp. 464-474 ◽

Cited By ~ 17

Author(s):

Tamar Tak ◽

Roger van Groenendael ◽

Peter Pickkers ◽

Leo Koenderman

Keyword(s):

Acute Inflammation ◽

Human Monocyte ◽

Monocyte Subset ◽

Monocyte Subsets ◽

Human Endotoxemia ◽

Cd38 Expression ◽

Inflammatory Conditions ◽

Endotoxemia Model ◽

Differentiation Status ◽

Classical Monocytes

Three human monocyte subsets are recognized with different functions in the immune system: CD14++/CD16- classical monocytes (CM), CD14++/CD16+ intermediate monocytes (IM) and CD14+/CD16++ non-classical monocytes (NCM). Increased IM and NCM percentages have been reported under inflammatory conditions, yet little is known about monocyte subsets at the onset of inflammation. The human endotoxemia model is uniquely capable of studying the first phases of acute inflammation induced by intravenous injection of 2 ng/kg bodyweight lipopolysaccharide (LPS) into healthy volunteers. After that, monocyte subset counts, activation/differentiation status and chemokine levels were studied over 24 h. The numbers of all subsets were decreased by >95% after LPS injection. CM numbers recovered first (3- 6 h), followed by IM (6-8 h) and NCM numbers (8-24 h). Similarly, increased monocyte counts were observed first in CM (8 h), followed by IM and NCM (24 h). Monocytes did not display a clear activated phenotype (minor increase in CD11b and CD38 expression). Plasma levels of CCL2, CCL4 and CX3CL1 closely resembled the cell numbers of CM, IM and NCM, respectively. Our study provides critical insights into the earliest stages of acute inflammation and emphasizes the necessity to stain for different monocyte subsets when studying the role of monocytes in disease, as neither function nor kinetics of the subsets overlap.

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Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽

10.1186/s12885-021-08173-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ping Yan ◽

Zuotian Huang ◽

Tong Mou ◽

Yunhai Luo ◽

Yanyao Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Malignant Tumors ◽

Expression Profiles ◽

Clinical Information ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

High Rate ◽

Competing Endogenous Rna ◽

Tissue Samples ◽

Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

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Overexpression of β-Arrestins inhibits proliferation and motility in triple negative breast cancer cells

Scientific Reports ◽

10.1038/s41598-021-80974-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Saber Yari Bostanabad ◽

Senem Noyan ◽

Bala Gur Dedeoglu ◽

Hakan Gurdal

Keyword(s):

Breast Cancer ◽

Cell Cycle ◽

Cancer Cells ◽

Cancer Cell ◽

Breast Cancer Cells ◽

Cell Function ◽

Expression Profiles ◽

Tissue Samples ◽

Expression Levels ◽

Cell Cycle Genes

Abstractβ-Arrestins (βArrs) are intracellular signal regulating proteins. Their expression level varies in some cancers and they have a significant impact on cancer cell function. In general, the significance of βArrs in cancer research comes from studies examining GPCR signalling. Given the diversity of different GPCR signals in cancer cell regulation, contradictory results are inevitable regarding the role of βArrs. Our approach examines the direct influence of βArrs on cellular function and gene expression profiles by changing their expression levels in breast cancer cells, MDA-MB-231 and MDA-MB-468. Reducing expression of βArr1 or βArr2 tended to increase cell proliferation and invasion whereas increasing their expression levels inhibited them. The overexpression of βArrs caused cell cycle S-phase arrest and differential expression of cell cycle genes, CDC45, BUB1, CCNB1, CCNB2, CDKN2C and reduced HER3, IGF-1R, and Snail. Regarding to the clinical relevance of our results, low expression levels of βArr1 were inversely correlated with CDC45, BUB1, CCNB1, and CCNB2 genes compared to normal tissue samples while positively correlated with poorer prognosis in breast tumours. These results indicate that βArr1 and βArr2 are significantly involved in cell cycle and anticancer signalling pathways through their influence on cell cycle genes and HER3, IGF-1R, and Snail in TNBC cells.

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Robust partial reference-free cell composition estimation from tissue expression

Bioinformatics ◽

10.1093/bioinformatics/btaa184 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3431-3438

Author(s):

Ziyi Li ◽

Zhenxing Guo ◽

Ying Cheng ◽

Peng Jin ◽

Hao Wu

Keyword(s):

Expression Profiles ◽

Gene Expression Profiles ◽

Real Data ◽

Estimation Procedure ◽

Free Cell ◽

Biological Information ◽

Supplementary Information ◽

Tissue Samples ◽

Cell Composition ◽

Heterogeneous Tissues

Abstract Motivation In the analysis of high-throughput omics data from tissue samples, estimating and accounting for cell composition have been recognized as important steps. High cost, intensive labor requirements and technical limitations hinder the cell composition quantification using cell-sorting or single-cell technologies. Computational methods for cell composition estimation are available, but they are either limited by the availability of a reference panel or suffer from low accuracy. Results We introduce TOols for the Analysis of heterogeneouS Tissues TOAST/-P and TOAST/+P, two partial reference-free algorithms for estimating cell composition of heterogeneous tissues based on their gene expression profiles. TOAST/-P and TOAST/+P incorporate additional biological information, including cell-type-specific markers and prior knowledge of compositions, in the estimation procedure. Extensive simulation studies and real data analyses demonstrate that the proposed methods provide more accurate and robust cell composition estimation than existing methods. Availability and implementation The proposed methods TOAST/-P and TOAST/+P are implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens

Biomedicines ◽

10.3390/biomedicines8050114 ◽

2020 ◽

Vol 8 (5) ◽

pp. 114

Author(s):

Maxim Sorokin ◽

Kirill Ignatev ◽

Elena Poddubskaya ◽

Uliana Vladimirova ◽

Nurshat Gaifullin ◽

...

Keyword(s):

Breast Cancer ◽

Lung Cancer ◽

Rna Sequencing ◽

Expression Profiles ◽

Correlation Study ◽

Cancer Tissue ◽

Transcriptional Level ◽

Tissue Samples ◽

Protein Levels ◽

Formalin Fixed Paraffin Embedded

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman’s rho 0.65–0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.

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