Bioinformatics strategies to interrogate the hallmarks of aging in single-cells

Abstract

Understanding the underlying biological mechanisms of aging is critical to elucidate how aging impacts health and disease. Multiple approaches have been used to pursue this endeavor. Unfortunately, traditional bulk measurements cannot provide a comprehensive description of aging from a more precise cellular perspective. In contrast, single-cell analysis, with the capability of characterizing the cell type identity and cell status through protein abundance and function, can provide deeper insights into aging-related cellular characteristics among cells.One of the key age-related processes is senescence. When a cell undergoes this process, it experiences a stable and long-term period of declining proliferative capacity without losing its viability or metabolic activity. While senescence is necessary for tissue homeostasis, the accumulation of senescent cells in aged tissues is associated with a wide range of age-related diseases. The relationship between senescence and aging is complex, with senescence driving aging and age-related damage leading to senescence. Other key age-related processes are autophagy and mitophagy, which are critical cellular in maintaining cellular homeostasis and function. Autophagy is responsible for eliminating unwanted and damaged cellular components, while mitophagy specifically targets damaged mitochondria. Dysfunctional autophagy and mitophagy have been implicated in several age-related diseases and contribute to the aging process. The decline in autophagy during aging leads to the accumulation of damaged proteins and organelles, while impaired mitophagy results in the accumulation of damaged mitochondria. Both changes compromise cellular function and lead to the development of age-related diseases. This work introduces innovative single-cell methodologies for analyzing single-cell data and characterizing senescence, autophagy, and mitophagy using mass cytometry. Specifically, it proposes a clustering algorithm, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden’s algorithm (CosTaL), that can handle large-scale mass cytometry datasets, making it a valuable tool for investigating intracellular protein targets. Additionally, a mass cytometry-based analysis framework is developed for identifying putative senescent cells from multiple types of cells found in skeletal muscle mononuclear cells. This approach enables highly reproducible characterization, with the inclusion of multiple senescent-associated proteins. Furthermore, a mass cytometry-based approach to characterizing mitophagy that does not require the introduction of exogenous mitophagy reporter proteins in vivo is introduced. This new methodology will provide the opportunity to study mitophagy in cell types that are not suitable for transfection. Overall, this work enables the characterization of senescence, autophagy, and mitophagy at the single-cell level. This allows for the integration and collective consideration of multiple hallmarks of aging, which can help to elucidate the biological changes that occur during aging with novel insights at the single-cell resolution.