Biology subjects

McDonough, J. E.

Publications and source records attributed to McDonough, J. E..

3 recordsLinked to original sources

bioRxiv2023

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. Cell type deconvolution revealed age-associated changes in the cellular composition of the lung: decreased alveolar epithelial cells and increased fibroblasts and endothelial cells. In the alveolar microenvironment, aging is characterized by decreased AT2B cells and reduced surfactant production, a finding that was validated by scRNAseq and IHC. We showed that a previously reported senescence signature, SenMayo, captures cells expressing canonical senescence markers. SenMayo signature also identified cell-type specific senescence-associated co-expression modules that have distinct molecular functions, including ECM regulation, cell signaling, and damage response pathways. Analysis of somatic mutations showed that burden was highest in lymphocytes and endothelial cells and was associated with high expression of senescence signature. Finally, aging and senescence gene expression modules were associated with differentially methylated regions, with inflammatory markers such as IL1B, IL6R, and TNF being significantly regulated with age. Our findings provide new insights into the mechanisms underlying lung aging and may have implications for the development of interventions to prevent or treat age-related lung diseases.

genomics↗

bioRxiv2022

Spatiotemporal coordination of stem cell behavior following alveolar injury

Tissue repair requires a highly coordinated cellular response to injury. In the lung, alveolar type 2 (AT2) cells act as stem cells and can replace both themselves and alveolar type 1 cells (AT1); however, the complex orchestration of AT2 stem cell activity following lung injury is poorly understood owing to the inability of tracking individual stem cells and their dynamic behavior over time. Here, we apply live time lapse imaging to ex vivo mouse precision cut lung slice (PCLS) culture and in vivo mouse lung to track individual GFP-labeled AT2 cells following induction of alveolar injury by bleomycin. We observe highly dynamic movement of AT2 cells, including migration within and between alveoli. To map the dynamic evolution of AT2 cell behavior, we introduce Live Cell Encoder (LCE-PHATE), a novel method for converting static snapshots from time lapse imaging into single points representative of entire, dynamic cellular trajectories. Applying LCE-PHATE, we observe the emergence of at least three distinct morphokinetic AT2 cell states associated with AT2 stem cell injury response. Finally, small molecule-based inhibition of Rho-associated protein kinase (ROCK) pathway significantly reduced motility of AT2 stem cells following injury and reduced expression of Krt8, a marker of intermediate progenitor cells. Together, our results uncover motility of alveolar stem cells as a new injury response mechanism in the lung and reveal properties of stem cell motility at high cellular resolution.

cell biology↗

bioRxiv2022

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

Single-cell RNA sequencing (scRNA-seq) enables assessment of transcriptome-wide changes at single-cell resolution. However, dominant subject effect in scRNA-seq datasets with multiple subjects severely confounds cell-type-specific differential expression (DE) analysis. We developed iDESC to separate subject effect from disease effect with consideration of dropouts to identify DE genes. iDESC was shown to have well-controlled type I error and high power compared to existing methods and obtained the best consistency between datasets and disease relevance in two scRNA-seq datasets from same disease, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

bioinformatics↗