Biology subjects

Hirsh, S.

Publications and source records attributed to Hirsh, S..

2 recordsLinked to original sources

bioRxiv2025

AlveolEye: Rapid and precise lung morphometry guided by computer vision

Rigorous and reproducible evaluation of lung tissue under different conditions is necessary to interpret development, injury, and pharmacologic interventions. Common histological measurements in the distal lung include mean linear intercept (MLI) as a metric of alveolarization and airspace volume density (ASVD) as a metric of airspaces relative to tissue. Historically, these have been performed manually in a time-intensive process, with reproducible trends, but a high degree of variability between individuals. To improve the reproducibility and throughput of lung morphometry, we developed AlveolEye, an open source, semi-automated, computer vision-assisted tool that rapidly and reproducibly calculates MLI and ASVD from images of standard hematoxylin and eosin (H&E) stained tissue sections. AlveolEye-assisted MLI calculation closely aligns with manually-derived measurements for corresponding images, with preservation of trends in measurements between non-injured controls and neonatal mice subjected to two different injury models. Analyzing human tissue of varying ages suggests that the approach developed in AlveolEye is generalizable across species. Notably, AlveolEye markedly reduced the average variation across individual analyzers, with the greatest improvement in precision among individuals with the least experience in performing lung morphometry. The design of AlveolEye is intentionally semi-automated, preserving the investigators ability to assess and adjust parameters based on sample characteristics. AlveolEye facilitates efficient lung morphological measurements on larger sample sizes, allowing for greater statistical power for preclinical studies, and improves precision across individual observers, allowing for improved rigor in experimental design and execution.

developmental biology↗

bioRxiv2023

Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis

The human lung is structurally complex, with a diversity of specialized epithelial, stromal and immune cells playing specific functional roles in anatomically distinct locations, and large-scale changes in the structure and cellular makeup of this distal lung is a hallmark of pulmonary fibrosis (PF) and other progressive chronic lung diseases. Single-cell transcriptomic studies have revealed numerous disease-emergent/enriched cell types/states in PF lungs, but the spatial contexts wherein these cells contribute to disease pathogenesis has remained uncertain. Using sub-cellular resolution image-based spatial transcriptomics, we analyzed the gene expression of more than 1 million cells from 19 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell-types, established the cellular and molecular basis of classical PF histopathologic disease features, and identified a diversity of distinct molecularly-defined spatial niches in control and PF lungs. Using machine-learning and trajectory analysis methods to segment and rank airspaces on a gradient from normal to most severely remodeled, we identified a sequence of compositional and molecular changes that associate with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially-resolved characterization of the cellular and molecular programs of PF and control lungs, provide new insights into the heterogeneous pathobiology of PF, and establish analytical approaches which should be broadly applicable to other imaging-based spatial transcriptomic studies.

genomics↗