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Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.

ABSTRACT: Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain tissue, and yeast cell cycle, revealed novel marker genes that were otherwise undetectable using existing methods. Importantly, CAM requires no a priori information on the number, identity, or composition of the subpopulations present in mixed samples, and does not require the presence of pure subpopulations in sample space. This advantage is significant in that CAM can achieve all of its goals using only a small number of heterogeneous samples, and is more powerful to distinguish between phenotypically similar subpopulations.

SUBMITTER: Wang N

PROVIDER: S-EPMC4703969 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.

Wang Niya N Hoffman Eric P EP Chen Lulu L Chen Li L Zhang Zhen Z Liu Chunyu C Yu Guoqiang G Herrington David M DM Clarke Robert R Wang Yue Y

Scientific reports 20160107

Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a ...[more]

PMID: 26739359

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Project description:The lung mesenchyme gives rise to multiple distinct lineages of cells in the mature respiratory system, including smooth muscle cells of the airway and vasculature. However, a thorough understanding of the specification and mesenchymal cell diversity in the human lung is lacking. We completed single-cell RNA sequencing analysis of fetal human lung tissues. Canonical correlation analysis, clustering, cluster marker gene identification and t-distributed stochastic neighbour embedding representation was performed in Seurat. Cell populations were annotated using ToppFun. Immunohistochemistry and in situ hybridisation were used to validate spatiotemporal gene expression patterns for key marker genes. We identified molecularly distinct populations representing "committed" fetal human lung endothelial cells, pericytes and smooth muscle cells. Early endothelial lineages expressed "classic" endothelial cell markers (platelet endothelial cell adhesion molecule/CD31 and claudin 5), while pericytes expressed platelet-derived growth factor receptor-β, Thy-1 membrane glycoprotein and basement membrane molecules (collagen IV, laminin and proteoglycans). We observed a large population of "nonspecific" human lung mesenchymal progenitor cells characterised by expression of collagen I and multiple elastin fibre genes (ELN, MFAP2 and FBN1). We closely characterised the diversity of mesenchymal lineages defined by α2-smooth muscle actin (ACTA2) expression. Two cell populations, with the highest levels of ACTA2 transcriptional activity, expressed unique sets of markers associated with airway or vascular smooth muscle cells. Spatiotemporal analysis of these marker genes confirmed early and persistent spatial specification of airway (HHIP, MYLK and IGF1) and vascular (NTRK3 and MEF2C) smooth muscle cells in the developing human lung. Our data suggest that specification of distinct airway and vascular smooth muscle cell phenotypes is established early in development and can be identified using the markers we provide.

Project description:BackgroundMathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness.MethodsWe utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types. For each patient type, we define the optimal treatment, identify biologic programs responsible for clinical responses, and predict biomarkers of those programs.FindingsModel states representing older and hyperinflamed patients respond better to immunomodulation than those representing obese and diabetic patients. The disparate clinical responses are driven by distinct biologic programs. Optimal treatment initiation time is determined by neutrophil recruitment, systemic cytokine expression, systemic microthrombosis and the renin-angiotensin system (RAS) in older patients, and by RAS, systemic microthrombosis and trans IL6 signalling for hyperinflamed patients. For older and hyperinflamed patients, IL6 modulating therapy is predicted to be optimal when initiated very early (<4th day of infection) and broad immunosuppression therapy (corticosteroids) is predicted to be optimally initiated later in the disease (7th - 9th day of infection). We show that markers of biologic programs identified by the model correspond to clinically identified markers of disease severity.InterpretationWe demonstrate that modelling of COVID-19 pathobiology can suggest biomarkers that predict optimal response to a given immunomodulatory treatment. Mathematical modelling thus constitutes a novel adjunct to predictive enrichment and may aid in the reduction of heterogeneity in critical care trials.FundingC.V. received a Marie Skłodowska Curie Actions Individual Fellowship (MSCA-IF-GF-2020-101028945). R.K.J.'s research is supported by R01-CA208205, and U01-CA 224348, R35-CA197743 and grants from the National Foundation for Cancer Research, Jane's Trust Foundation, Advanced Medical Research Foundation and Harvard Ludwig Cancer Center. No funder had a role in production or approval of this manuscript.

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Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.

Publications

Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.

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