scholarly journals A Statistical Framework to Identify Cell Types Whose Genetically Regulated Proportions are Associated with Complex Diseases

2021 ◽  
Author(s):  
Wei Liu ◽  
Wenxuan Deng ◽  
Ming Chen ◽  
Zihan Dong ◽  
Biqing Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Statistical Power ◽  
Predictive Biomarkers ◽  
Complex Diseases ◽  
Cell Types ◽  
Chronic Obstructive ◽  
Cell Type ◽  
Obstructive Pulmonary Disease ◽  
Statistical Framework ◽  
Transcriptomics Data

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. Here, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed substantial statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

scholarly journals A Statistical Framework to Identify Cell Types Whose Genetically Regulated Proportions are Associated with Complex Diseases

2021 ◽  
Author(s):  
Hongyu Zhao ◽  
Wei Liu ◽  
Wenxuan Deng ◽  
Ming Chen ◽  
Zihan Dong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Statistical Power ◽  
Predictive Biomarkers ◽  
Complex Diseases ◽  
Cell Types ◽  
Chronic Obstructive ◽  
Cell Type ◽  
Obstructive Pulmonary Disease ◽  
Statistical Framework ◽  
Transcriptomics Data

Abstract Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. Here, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed substantial statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblast in the lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

scholarly journals Detection of allele-specific expression in spatial transcriptomics with spASE

2021 ◽  
Author(s):  
Luli S. Zou ◽  
Tongtong Zhao ◽  
Dylan M. Cable ◽  
Evan Murray ◽  
Martin J. Aryee ◽  
...  
Keyword(s):  
Statistical Power ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Preferential Expression ◽  
Mouse Hippocampus ◽  
Transcriptomics Data ◽  
Allele Specific ◽  
Cell Type Specific

AbstractAllele-specific expression (ASE), or the preferential expression of one allele, can be observed in transcriptomics data from early development throughout the lifespan. However, the prevalence of spatial and cell type-specific ASE variation remains unclear. Spatial transcriptomics technologies permit the study of spatial ASE patterns genome-wide at near-single-cell resolution. However, the data are highly sparse, and confounding between cell type and spatial location present further statistical challenges. Here, we introduce spASE (https://github.com/lulizou/spase), a computational framework for detecting spatial patterns in ASE within and across cell types from spatial transcriptomics data. To tackle the challenge presented by the low signal to noise ratio due to the sparsity of the data, we implement a spatial smoothing approach that greatly improves statistical power. We generated Slide-seqV2 data from the mouse hippocampus and detected ASE in X-chromosome genes, both within and across cell type, validating our ability to recover known ASE patterns. We demonstrate that our method can also identify cell type-specific effects, which we find can explain the majority of the spatial signal for autosomal genes. The findings facilitated by our method provide new insight into the uncharacterized landscape of spatial and cell type-specific ASE in the mouse hippocampus.

scholarly journals Single-cell transcriptomics reveal cell type-specific molecular changes and altered intercellular communications in chronic obstructive pulmonary disease

2021 ◽  
Author(s):  
Qiqing Huang ◽  
Jingshen Wang ◽  
Shaoran Shen ◽  
Yuanyuan Wang ◽  
Yan Chen ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Single Cell ◽  
Pulmonary Disease ◽  
Alveolar Epithelium ◽  
Cell Types ◽  
Chronic Obstructive ◽  
Cell Type ◽  
Obstructive Pulmonary Disease ◽  
Cell Type Specific ◽  
Intercellular Communications

AbstractChronic obstructive pulmonary disease (COPD) is a common and heterogeneous respiratory disease, the molecular complexity of which remains poorly understood, as well as the mechanisms by which aging and smoking facilitate COPD development. Here, using single-cell RNA sequencing of more than 65,000 cells from COPD and age-stratified control lung tissues of donors with different smoking histories, we identified monocytes, club cells, and macrophages as the most disease-, aging-, and smoking-relevant cell types, respectively. Notably, we found these highly cell-type specific changes under different conditions converged on cellular dysfunction of the alveolar epithelium. Deeper investigations revealed that the alveolar epithelium damage could be attributed to the abnormally activated monocytes in COPD lungs, which could be amplified via exhaustion of club cell stemness as ages. Moreover, the enhanced intercellular communications in COPD lungs as well as the pro-inflammatory interaction between macrophages and endothelial cells indued by smoking could facilitate signaling between monocyte and the alveolar epithelium. Our findings complement the existing model of COPD pathogenesis by emphasizing the contributions of the previously less appreciated cell types, highlighting their candidacy as potential therapeutic targets for COPD.

scholarly journals Signaling Control of Mucociliary Epithelia: Stem Cells, Cell Fates, and the Plasticity of Cell Identity in Development and Disease

2021 ◽  
pp. 1-18
Author(s):  
Peter Walentek
Keyword(s):  
Stem Cells ◽  
Mucociliary Clearance ◽  
Cell Types ◽  
Secretory Cells ◽  
Chronic Obstructive ◽  
Cell Identity ◽  
Obstructive Pulmonary Disease ◽  
Congenital Diseases ◽  
Inhaled Particles ◽  
Mucociliary Epithelia

Mucociliary epithelia are composed of multiciliated, secretory, and stem cells and line various organs in vertebrates such as the respiratory tract. By means of mucociliary clearance, those epithelia provide a first line of defense against inhaled particles and pathogens. Mucociliary clearance relies on the correct composition of cell types, that is, the proper balance of ciliated and secretory cells. A failure to generate and to maintain correct cell type composition and function results in impaired clearance and high risk to infections, such as in congenital diseases (e.g., ciliopathies) as well as in acquired diseases, including asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF). While it remains incompletely resolved how precisely cell types are specified and maintained in development and disease, many studies have revealed important mechanisms regarding the signaling control in mucociliary cell types in various species. Those studies not only provided insights into the signaling contribution to organ development and regeneration but also highlighted the remarkable plasticity of cell identity encountered in mucociliary maintenance, including frequent trans-differentiation events during homeostasis and specifically in disease. This review will summarize major findings and provide perspectives regarding the future of mucociliary research and the treatment of chronic airway diseases associated with tissue remodeling.

scholarly journals Jointly leveraging spatial transcriptomics and deep learning models for pathology image annotation improves cell type identification over either approach alone.

2021 ◽  
Author(s):  
Asif Zubair ◽  
Richard H. Chapple ◽  
Sivaraman Natarajan ◽  
William C. Wright ◽  
Min Pan ◽  
...  
Keyword(s):  
Immune Cell ◽  
Image Annotation ◽  
Cell Types ◽  
Tissue Cell ◽  
Cell Type ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Diagnostic Applications ◽  
The Many ◽  
Level Performance

The disorganization of cell types within tissues underlies many human diseases and has been studied for over a century using the conventional tools of pathology, including tissue-marking dyes such as the H&E stain. Recently, spatial transcriptomics technologies were developed that can measure spatially resolved gene expression directly in pathology-stained tissues sections, revealing cell types and their dysfunction in unprecedented detail. In parallel, artificial intelligence (AI) has approached pathologist-level performance in computationally annotating H&E images of tissue sections. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and AI-based pathology has performed less impressively outside their training datasets. Here, we describe a methodology that can computationally integrate AI-annotated pathology images with spatial transcriptomics data to markedly improve inferences of tissue cell type composition made over either class of data alone. We show that this methodology can identify regions of clinically relevant tumor immune cell infiltration, which is predictive of response to immunotherapy and was missed by an initial pathologist's manual annotation. Thus, combining spatial transcriptomics and AI-based image annotation has the potential to exceed pathologist-level performance in clinical diagnostic applications and to improve the many applications of spatial transcriptomics that rely on accurate cell type annotations.

scholarly journals Roles of Mesenchymal Cells in the Lung: From Lung Development to Chronic Obstructive Pulmonary Disease

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3467
Author(s):  
Amel Nasri ◽  
Florent Foisset ◽  
Engi Ahmed ◽  
Zakaria Lahmar ◽  
Isabelle Vachier ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Lung Development ◽  
Lung Diseases ◽  
Mesenchymal Cells ◽  
Chronic Obstructive ◽  
Multilineage Differentiation ◽  
Cell Type ◽  
Obstructive Pulmonary Disease ◽  
Lung Regeneration

Mesenchymal cells are an essential cell type because of their role in tissue support, their multilineage differentiation capacities and their potential clinical applications. They play a crucial role during lung development by interacting with airway epithelium, and also during lung regeneration and remodeling after injury. However, much less is known about their function in lung disease. In this review, we discuss the origins of mesenchymal cells during lung development, their crosstalk with the epithelium, and their role in lung diseases, particularly in chronic obstructive pulmonary disease.

scholarly journals SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

2020 ◽  
Author(s):  
Xiaoyu Lu ◽  
Szu-Wei Tu ◽  
Wennan Chang ◽  
Changlin Wan ◽  
Jiashi Wang ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Cell Types ◽  
Small Sample ◽  
Training Data ◽  
Mouse Tissue ◽  
Marker Genes ◽  
Specific Cell ◽  
Cell Type ◽  
Data Set ◽  
Transcriptomics Data

Abstract Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

scholarly journals Unsupervised discovery of phenotype-specific multi-omics networks

2019 ◽  
Vol 35 (21) ◽  
pp. 4336-4343 ◽  
Author(s):  
W Jenny Shi ◽  
Yonghua Zhuang ◽  
Pamela H Russell ◽  
Brian D Hobbs ◽  
Margaret M Parker ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Wide Spectrum ◽  
Supplementary Information ◽  
Chronic Obstructive ◽  
Phenotypic Traits ◽  
Omics Data ◽  
Data Types ◽  
Obstructive Pulmonary Disease ◽  
Quantitative Phenotype ◽  
Treatment Plans

Abstract Motivation Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. Results We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA–mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA–mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. Availability and implementation The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations

2019 ◽  
Vol 51 (3) ◽  
pp. 494-505 ◽  
Author(s):  
Phuwanat Sakornsakolpat ◽  
◽  
Dmitry Prokopenko ◽  
Maxime Lamontagne ◽  
Nicola F. Reeve ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Cell Type ◽  
Obstructive Pulmonary Disease ◽  
Genetic Landscape

scholarly journals Emerging concepts in smooth muscle contributions to airway structure and function: implications for health and disease

2016 ◽  
Vol 311 (6) ◽  
pp. L1113-L1140 ◽  
Author(s):  
Y. S. Prakash
Keyword(s):  
Smooth Muscle ◽  
Cell Types ◽  
Structure And Function ◽  
Normal Lung ◽  
Future Research ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Health And Disease ◽  
Growth And Aging ◽  
And Function

Airway structure and function are key aspects of normal lung development, growth, and aging, as well as of lung responses to the environment and the pathophysiology of important diseases such as asthma, chronic obstructive pulmonary disease, and fibrosis. In this regard, the contributions of airway smooth muscle (ASM) are both functional, in the context of airway contractility and relaxation, as well as synthetic, involving production and modulation of extracellular components, modulation of the local immune environment, cellular contribution to airway structure, and, finally, interactions with other airway cell types such as epithelium, fibroblasts, and nerves. These ASM contributions are now found to be critical in airway hyperresponsiveness and remodeling that occur in lung diseases. This review emphasizes established and recent discoveries that underline the central role of ASM and sets the stage for future research toward understanding how ASM plays a central role by being both upstream and downstream in the many interactive processes that determine airway structure and function in health and disease.

Sign in / Sign up

Export Citation Format

Share Document