Biophysical and biomolecular determination of cellular age in humans

Abstract

Ageing research has focused either on assessing organ- and tissue-based changes, such as lung capacity and cardiac function, or on changes at the molecular scale such as gene expression, epigenetic modifications and metabolism. Here, by using a cohort of 32 samples of primary dermal fibroblasts collected from individuals between 2 and 96 years of age, we show that the degradation of functional cellular biophysical features—including cell mechanics, traction strength, morphology and migratory potential—and associated descriptors of cellular heterogeneity predict cellular age with higher accuracy than conventional biomolecular markers. We also demonstrate the use of high-throughput single-cell technologies, together with a deterministic model based on cellular features, to compute the cellular age of apparently healthy males and females, and to explore these relationships in cells from individuals with Werner syndrome and Hutchinson-Gilford progeria syndrome, two rare genetic conditions that result in phenotypes that show aspects of premature ageing. Our findings suggest that the quantification of cellular age may be used to stratify individuals on the basis of cellular phenotypes and serve as a biological proxy of healthspan.

Nrf2 stabilization prevents critical oxidative damage in Down syndrome cells

“…Not surprisingly, other antioxidants targeted to mitochondria, specifically MitoQ (1 μM) and MitoTEMPO (20 μM) also induced Nrf2 deactivation/degradation in DS HF (Supporting Information Figure S5D). Finally, we tested whether an antioxidant intervention (mCAT) would ameliorate the senescent phenotype present in DS HF including increased cell size, a well‐characterized early marker of cellular senescence (Hayflick, 1965; Phillip et al, 2017; Rodier & Campisi, 2011). DS HF displayed significantly reduced proliferation (Figure 4c) and larger average cell area (Figure 4d).…”

Rethinking mortality rates in men and women: do men age faster?

“…In this study, we have investigated mortality data to test whether men age faster than women, as was previously suggested by several authors 2,[19][20][21] The potential weakness of our work is that our analysis of mortality data does not distinguish between intrinsic and extrinsic sources of mortality. There is a great body of literature focusing on partitioning mortality to intrinsic and extrinsic mortality 22,23 .…”

“…We investigated mortality data to test whether men age faster than women, as previously suggested in several studies. 2,[19][20][21] they are ultimately highly constrained models which may sometimes produce incorrect results. We therefore also compared the pace of aging between the sexes using non-parametric approaches.…”

Use of thep-valueas a size-dependent function to address practical differences when analyzing large datasets

“…The last use of the method consists of analyzing whether a single specific feature of the data (variable) can fully characterize the problem at hand. For instance, many different biomolecular and biophysical features of human cells were analyzed [18] to predict cellular age in healthy humans. This is only possible if these features contain enough information about the aging of the patients.…”

“…This is only possible if these features contain enough information about the aging of the patients. To show that, we re-analyzed a large and a small dataset with information of nuclei morphology and cell motility respectively, collected by Philip et al [18]. The information of 2 year-old human cells (the youngest one) was compared with the rest of the ages.…”