Hypoxia-Induced Cardiopulmonary Remodeling and Recovery: Critical Roles of the Proximal Pulmonary Artery, Macrophages, and Exercise.

Hypoxemia impairs cardiopulmonary function. We investigated pulmonary artery remodeling in mice exposed to chronic hypoxia for up to five weeks and quantified associated changes in cardiac and lung function, without or with subsequent normoxic recovery in the absence or presence of exercise or pharmacological intervention. Hypoxia-induced stiffening of the proximal pulmonary artery stemmed primarily from remodeling of the adventitial collagen, which resulted in part from altered inter-cellular signaling associated with phenotypic changes in the mural smooth muscle cells and macrophages.

Predicting lung aging using scRNA-Seq data.

Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time.

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data.

Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots.

Activated sputum eosinophils associated with exacerbations in children on mepolizumab.

Background: MUPPITS-2 was a randomized, placebo-controlled clinical trial that demonstrated mepolizumab (anti-IL-5) reduced exacerbations and blood and airway eosinophils in urban children with severe eosinophilic asthma. Despite this reduction in eosinophilia, exacerbation risk persisted in certain patients treated with mepolizumab. This raises the possibility that subpopulations of airway eosinophils exist that contribute to breakthrough exacerbations.

Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies.

Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis.

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases.

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data.

Peripheral Blood Single-Cell Sequencing Uncovers Common and Specific Immune Aberrations in Fibrotic Lung Diseases.

Rationale And Objectives: The extent and commonality of peripheral blood immune aberrations in fibrotic interstitial lung diseases are not well characterized. In this study, we aimed to identify common and distinct immune aberrations in patients with idiopathic pulmonary fibrosis (IPF) and fibrotic hypersensitivity pneumonitis (FHP) using cutting-edge single-cell profiling technologies.

Methods: Single-cell RNA sequencing was performed on patients and healthy controls' peripheral blood and bronchoalveolar lavage samples using 10X Genomics 5' gene expression and V(D)J profiling.

A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data.

Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER uses a machine learning approach to remove the systematic difference between ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile.

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.

Background: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis.

Differences in Mortality Among Patients With Asthma and COPD Hospitalized With COVID-19.

Background: It remains unclear whether patients with asthma and/or chronic obstructive pulmonary disease (COPD) are at increased risk for severe coronavirus disease 2019 (COVID-19).

Objective: Compare in-hospital COVID-19 outcomes among patients with asthma, COPD, and no airway disease.

Methods: A retrospective cohort study was conducted on 8,395 patients admitted with COVID-19 between March 2020 and April 2021.

A Multi-omic Analysis of the Human Lung Reveals Distinct Cell Specific Aging and Senescence Molecular Programs.

Age is a major risk factor for lung disease. To understand the mechanisms underlying this association, we characterized the changing cellular, genomic, transcriptional, and epigenetic landscape of lung aging using bulk and single-cell RNAseq (scRNAseq) data. Our analysis revealed age-associated gene networks that reflected hallmarks of aging, including mitochondrial dysfunction, inflammation, and cellular senescence.

Construction of prediction model of lymph node metastasis of early cervical cancer based on machine learning algorithm and its application: experience of 204 cases in a single center.

Objectives: The prediction model of para-aortic lymph node metastasis (LNM) in patients with early cervical cancer was constructed based on the logistic regression (LR) and random forest (RF) algorithms in the machine learning algorithm. The prediction efficiencies of the two models were compared.

Methods: The clinical data of 204 patients with early cervical cancer in the First Affiliated Hospital of Guangxi Medical University were retrospectively collected.

Integrated Analysis of Tracheobronchial Fluid from Before and After Cardiopulmonary Bypass Reveals Activation of the Integrated Stress Response and Altered Pulmonary Microvascular Permeability.

: We aim to comprehensively describe the transcriptional activity and signaling of pulmonary parenchymal and immune cells before and after cardiopulmonary bypass (CPB) by using a multi-omic approach coupled with functional cellular assays. We hypothesize that key signaling pathways from specific cells within the lung alter pulmonary endothelial cell function resulting in worsening or improving disease. : We collected serial tracheobronchial lavage samples from intubated patients less than 2-years-old undergoing surgery with CPB.

A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy.

Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource.

Metagenomic sequencing of the bronchoalveolar lavage extracellular virome and cellular transcriptome of sarcoidosis patients does not detect rubella virus.

Background:   Sarcoidosis is a multisystem granulomatous inflammatory disease of unclear etiology that involves the lung, skin and other organs, with an unknown antigenic trigger. Recently, evidence has been found in both immune deficient and immune competent patients for rubella virus in cutaneous granulomas. These granulomatous lesions share overlapping features with cutaneous sarcoidosis, raising the question of rubella virus in sarcoidosis.

Emerging insights in sarcoidosis: moving forward through reverse translational research.

Sarcoidosis is a chronic granulomatous disease of unknown etiology that primarily affects the lungs. The development of stage IV or fibrotic lung disease accounts for a significant proportion of the morbidity and mortality attributable to sarcoidosis. Further investigation into the active mechanisms of disease pathogenesis and fibrogenesis might illuminate fundamental mediators of injury and repair while providing new opportunities for clinical intervention.

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19.

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab.

Lung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis.

Fibrotic hypersensitivity pneumonitis (fHP) is an interstitial lung disease caused by sensitization to an inhaled allergen. To identify the molecular determinants associated with progression of fibrosis. Nine fHP explant lungs and six unused donor lungs (as controls) were systematically sampled (4 samples/lung).

Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants.

Motivation: Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations.

Transcriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis.

Background: Sarcoidosis is a multisystem granulomatous disease of unknown origin with a variable and often unpredictable course and pattern of organ involvement. In this study we sought to identify specific bronchoalveolar lavage (BAL) cell gene expression patterns indicative of distinct disease phenotypic traits.

Methods: RNA sequencing by Ion Torrent Proton was performed on BAL cells obtained from 215 well-characterised patients with pulmonary sarcoidosis enrolled in the multicentre Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study.