Advances in quantitative biology methods for studying replicative aging in Saccharomyces cerevisiae - PubMed (original) (raw)
Advances in quantitative biology methods for studying replicative aging in Saccharomyces cerevisiae
Richard O'Laughlin et al. Transl Med Aging. 2020.
Abstract
Aging is a complex, yet pervasive phenomenon in biology. As human cells steadily succumb to the deteriorating effects of aging, so too comes a host of age-related ailments such as neurodegenerative disorders, cardiovascular disease and cancer. Therefore, elucidation of the molecular networks that drive aging is of paramount importance to human health. Progress toward this goal has been aided by studies from simple model organisms such as Saccharomyces cerevisiae. While work in budding yeast has already revealed much about the basic biology of aging as well as a number of evolutionarily conserved pathways involved in this process, recent technological advances are poised to greatly expand our knowledge of aging in this simple eukaryote. Here, we review the latest developments in microfluidics, single-cell analysis and high-throughput technologies for studying single-cell replicative aging in S. cerevisiae. We detail the challenges each of these methods addresses as well as the unique insights into aging that each has provided. We conclude with a discussion of potential future applications of these techniques as well as the importance of single-cell dynamics and quantitative biology approaches for understanding cell aging.
Conflict of interest statement
Conflict of interest The authors declare that they have no conflict of interest to disclose.
Figures
Fig. 1.. Microfluidic devices for single-cell replicative aging in yeast.
(A) The cell trap designs for currently published yeast aging microfluidic devices [,–39]. (B) Example of loading in a device relying on contact-based mechanical trapping, where hydrostatic pressure is used to raise the height of the micropad traps (left). Releasing pressure by allowing fluid flow to the waste port lowers the height of the micropads once more and traps cells underneath them [23,33]. (C) Devices that use hydrodynamic trapping to guide cells to the outside of the jails [37]. Cells can produce a daughter cell through the openings in the jail (left). The cell that enters the jail is geometrically confined, and is analyzed as the mother cell for RLS experiments [37,39]. (D) Loading of microchannel traps [38] using hydrodynamic trapping. Cells are guided into the traps by the flow and can produce buds through a small opening at the bottom of the trap or toward the entrance (left). Loading of a cell into the trap increases the hydraulic resistance of this path for fluid flow, facilitating the loading of adjacent traps (right). (Figure adapted from refs. , –39).
Fig. 2.. Quantitative analysis of single-cell dynamics during replicative aging enabled from microfluidics experiments.
(A) Microfluidics experiments allow interfacing of yeast RLS measurements with time-lapse fluorescence microscopy. This allows dynamic and quantitative single-cell data about a number of biomolecular processes to be obtained. Mathematical modeling is a powerful too that can be utilized for such data. (B) Divergent aging trajectories uncovered by Jin et al. [52]. Cells in a population progress along two different aging paths marked by four possible phenotypic states, the dynamics of which can be described using a non-Markovian mathematical model [52]. (C) Measurement of chromatin silencing in single cells at the HML and HMR mating-type loci via a genomically integrated fluorescent reporter by Schlissel et al. [67] revealed strong silencing of this region throughout a cell’s lifespan. (D) Pulses of rDNA silencing dynamics in single cells dying with the elongated daughter phenotype were uncovered by Li et al. [38] through a GFP reporter inserted into the NTS1 region of the rDNA. (E) Liu et al. [84] found a reduction of gene expression noise during aging as determined by the declining Fano factor (variance divided by the mean) from single-cell measurements of P _GAL1_-YFP expression. In the cartoon figure, black circles represent single-cell values at various ages and the yellow line represents the mean Fano factor of all cells at that age [84]. As done by Liu et al. [84], this cartoon plot omits P _GAL1_-YFP measurements from the final four divisions of each cell, where Liu et al. [84] found that gene expression noise sharply increased. (F) Stochastic modeling of chromatin transitions between open and closed conformations at different rates or probabilities (p ij and p ji) at heterochromatin regions [38] and of local chromatin remodeling [84] has provided biological insight for follow-up experiments. (Figure adapted from refs. 38, 52, 67, 84). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3.. New high-throughput technologies for systems-level insights into aging.
(A) Mother cells can be labeled on their surface with biotin and magnetic beads, which are not passed to their daughters. This renders mother cells able to be drawn toward magnetic surfaces [49,50]. (B) Example of MAD layout [88]. (C) Operation of these devices [87,88] allows RLS values to be highly correlated with time. Removing devices from the magnet allows release of mother cells. (D) Collecting large numbers of mother cells allows omics analysis to be done [87,88]. (Figure adapted from refs. 87, 88).
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