Amouzgar, M. | Search bioRxiv

bioRxiv2023

Autophagy counters inflammation-driven glycolytic impairment in aging hematopoietic stem cells

Aging of the hematopoietic system promotes various blood, immune and systemic disorders and is largely driven by hematopoietic stem cell (HSC) dysfunction (1). Autophagy is central for the benefits associated with activation of longevity signaling programs (2), and for HSC function and response to nutrient stress (3,4). With age, a subset of HSCs increases autophagy flux and preserves some regenerative capacity, while the rest fail to engage autophagy and become metabolically overactivated and dysfunctional (4). However, the signals that promote autophagy in old HSCs and the mechanisms responsible for the increased regenerative potential of autophagy-activated old HSCs remain unknown. Here, we demonstrate that autophagy activation is an adaptive survival response to chronic inflammation in the aging bone marrow (BM) niche (5). We find that inflammation impairs glucose metabolism and suppresses glycolysis in aged HSCs through Socs3-mediated impairment of AKT/FoxO-dependent signaling. In this context, we show that inflammation-mediated autophagy engagement preserves functional quiescence by enabling metabolic adaptation to glycolytic impairment. Moreover, we demonstrate that transient autophagy induction via a short-term fasting/refeeding paradigm normalizes glucose uptake and glycolytic flux and significantly improves old HSC regenerative potential. Our results identify inflammation-driven glucose hypometabolism as a key driver of HSC dysfunction with age and establish autophagy as a targetable node to reset old HSC glycolytic and regenerative capacity. One-Sentence SummaryAutophagy compensates for chronic inflammation-induced metabolic deregulation in old HSCs, and its transient modulation can reset old HSC glycolytic and regenerative capacity.

cell biology↗

bioRxiv2022

Supervised dimensionality reduction for exploration of single-cell data by Hybrid Subset Selection - Linear Discriminant Analysis

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction enables visualization of data by representing cells in two-dimensional plots that capture the structure and heterogeneity of the original dataset. Visualizations contribute to human understanding of data and are useful for guiding both quantitative and qualitative analysis of cellular relationships. Existing algorithms are typically unsupervised, utilizing only measured features to generate manifolds, disregarding known biological labels such as cell type or experimental timepoint. Here, we repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling users to tailor visualizations to separate specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this flexible, computationally-efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes, such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality reduction algorithms and illustrate its utility and versatility for exploration of single-cell mass cytometry, transcriptomics and chromatin accessibility data.

bioinformatics↗