Biology subjects

Kippner, L. E.

Publications and source records attributed to Kippner, L. E..

2 recordsLinked to original sources

bioRxiv2025

Multi-omics integration at cell type resolution uncovers gene-metabolite mechanisms underlying osteoarthritis heterogeneity

Metabolic dysregulation is an important factor for osteoarthritis pathogenesis, but comprehensive studies of underlying mechanisms and pathways are rare. We analyzed newly generated metabolomics data on bone marrow from 119 osteoarthritis patients, along with single-cell transcriptomics data to reconstruct networks of gene-metabolite associations at cell type resolution. Hubs of these networks - cell type-specific as well as pan-cell type hubs - revealed key molecular factors of osteoarthritis heterogeneity. Systems-level analysis of hubs revealed major roles for glycerophospholipid, glycerolipid and sphingolipid metabolism pathways, along with lipid signaling. We used Machine Learning models of gene-metabolite relationships to discover cell types most relevant to each metabolite. Integrative analysis of disease severity scores along with multi-omics data revealed a shift in specific immune cell subtypes in low versus high grade disease. We conclude that leveraging gene-metabolite covariation in a patient cohort can uncover underlying molecular mechanisms, overcoming the challenges posed by high dimensionality of multi-omics data.

bioinformatics↗

bioRxiv2025

Label-Free In-Line Characterization of Immune Cell Culture using Quantitative Phase Imaging

Cell therapies, including T cell immunotherapies, offer promising treatments for previously untreatable diseases, but their widespread use is hindered by challenges in monitoring therapeutic cells during culture--impacting consistency, potency, and cost. This work demonstrates the use of quantitative phase imaging (QPI), specifically a compact, non-interferometric form called quantitative oblique back illumination microscopy (qOBM), for non-destructive, label-free, in-line assessment of T cell cultures. qOBM enables near real-time feedback on culture growth, contamination, and cell status (viability and activation), comparable to flow cytometry. We further apply this method to characterize genetically modified CAR T cells and explore its potential for advanced T cell phenotyping. Analysis of data from over 50 independent donors shows strong correlation between qOBM metrics and traditional destructive at-line assays. Overall, qOBM provides a powerful tool for continuous, in-line monitoring of therapeutic cell cultures, which can be transformative for improving reproducibility, reducing costs, and advancing the development of cell-based therapies.

immunology↗