(D-146) Defining functional subtypes of cellular senescence at single-cell resolution

Introduction:: Introduction. Biological aging is described as the continuous degradation of physiological function that eventually drives age-associated disease and frailty^1,2Cellular senescence is identified as a key driver of adverse aging patterns, linking defective cell-based changes to physiological dysfunctions^3,4.Cellular senescence is a state of stress-induced cell cycle arrest typically accompanied by increased expression of CDK inhibitors—e.g., p16 and p21, increased secretion of proinflammatory factors—SASP, and a characteristic flattening and increased sizes of cells when cultured in vitro. However, current studies are limited, as they treat senescence as binary (i.e., senescent, or not), use few senescence-associated biomarkers to classify senescence, and are unable to discriminate between the heterogeneous and potentially functional subtypes of senescence⁵. To address this, we hypothesized that subtypes of senescent cells with distinct functional behavior exist and can be identified and classified based on morphological phenotypes. These functional subtypes could provide valuable insights to: (1) understand the association between abundance of senescent subtypes and age-associated pathologies, (2) identify novel senescence-based therapeutic interventions, and (3) develop cheap, robust biomarkers of senescence.


Materials and Methods:: Materials and Methods. We induced senescence in primary human dermal fibroblasts (aged 23 and 85) by exposing to senescence-inducing agents that include Bleomycin, Atazanavir, Hydrogen Peroxide, and replication using serial passaging. Following induction, cells were stained for combinations of two senescence-associated biomarkers (p21, p16, Lamin B1, HMGB1, Beta Galactosidase) for time points of 2-, 5-, 8,12-, and 15-days post induction. Cells/Nuclei were segmented using a combination of computational and deep learning approaches, and the dataset was subsequently clustered based on morphological features computed using custom software to identify morphological subtypes of senescence. Morphological subtypes were further validated with an in-house Xception convolutional neural network (CNN).


Results, Conclusions, and Discussions:: Results. Senescent cells exhibit a heterogeneous collection of morphological subtypes, each of which expresses a unique set of senescence-associated protein biomarkers (Figure 1a-b). We found that the abundance of cells within each of the morphological clusters depended on the mode of induction, age of the sample, and time after induction, highlighting senescence as a dynamic ensemble of subtypes (Figure 1c-f). Together, Similarly, senescence associated protein biomarkers expressed unique temporal activity- (turning “on” at different times post induction) and could be imputed from morphological properties alone (Figure 1g). Morphological clusters were supplemented with the CNN scoring platform which provided quantitative assessment of senescent burden at true single-cell resolution (Figure 1h).


Conclusion. In this work, we establish a novel biophysical framework to elicit functional behavior of senescent cells at single cell-resolution. We can comprehensively evaluate the senescence burden of cells, classify their morphological subtype, and interface cellular morphology with senescence-associated protein biomarker behavior. We are currently in the process of ascribing functional readouts to morphological subtypes based on clearance modalities to senotherapies and spatial transcriptomic technologies (ATAC sequencing and MALDI imaging) to identify molecular differences.




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References (Optional): : [1]Phillip, J., et.al. Annual Review of Biomedical Engineering. 2015 [2] López-Otín, C., et. al. Cell. 2013 [3] Van Deursen, J. M., Nature. 2014 [4] Campisi, J., et. al. Nature. 2019 [5] Di Micco, R., et.al. Nature Reviews Molecular Cell Biology. 2020