A Microfluidic System for Studying Ageing and Dynamic Single-Cell Responses in Budding Yeast

Crane, Matthew M.; Clark, Ivan B. N.; Bakker, Elco; Smith, S.; Swain, Peter S.

doi:10.1371/journal.pone.0100042

Cited by 127 publications

(195 citation statements)

References 42 publications

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“…The conventional method for studying yeast aging requires laborious manual separation of daughter cells from mother cells after each division and does not allow tracking of molecular processes over multiple generations during aging (7). Recent advances in microfluidics technology have automated cell separation and enabled continuous single-cell measurements during aging (8)(9)(10)(11)(12)(13)(14). Building on these efforts, we developed a microfluidic aging device.…”

Section: Resultsmentioning

confidence: 99%

“…Supplying media through ∼20-μm-tall main channels readily allowed excess cells to be washed away and prevented clogging. Therefore, a critical feature of our device is its two-layer design, making it extremely robust over the course of our experiments, which takes more than 80 h. This is a unique feature compared with recently published devices that are all single-layer (10,13,14). The device was optimized for using continuous gravity-driven flow during operation, with the three-inlet design also facilitating media switching experiments.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Multigenerational silencing dynamics control cell aging

Jin

O’Laughlin

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

103

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Cellular aging plays an important role in many diseases, such as cancers, metabolic syndromes, and neurodegenerative disorders. There has been steady progress in identifying aging-related factors such as reactive oxygen species and genomic instability, yet an emerging challenge is to reconcile the contributions of these factors with the fact that genetically identical cells can age at significantly different rates. Such complexity requires single-cell analyses designed to unravel the interplay of aging dynamics and cell-to-cell variability. Here we use microfluidic technologies to track the replicative aging of single yeast cells and reveal that the temporal patterns of heterochromatin silencing loss regulate cellular life span. We found that cells show sporadic waves of silencing loss in the heterochromatic ribosomal DNA during the early phases of aging, followed by sustained loss of silencing preceding cell death. Isogenic cells have different lengths of the early intermittent silencing phase that largely determine their final life spans. Combining computational modeling and experimental approaches, we found that the intermittent silencing dynamics is important for longevity and is dependent on the conserved Sir2 deacetylase, whereas either sustained silencing or sustained loss of silencing shortens life span. These findings reveal that the temporal patterns of a key molecular process can directly influence cellular aging, and thus could provide guidance for the design of temporally controlled strategies to extend life span.replicative aging | single-cell analysis | microfluidics | chromatin silencing | computational modeling C ellular aging is generally driven by the accumulation of genetic and cellular damage (1, 2). Although much progress has been made in identifying molecular factors that influence life span, what remains sorely missing is an understanding of how these factors interact and change dynamically during the aging process. This is in part because aging is a complex process wherein isogenic cells have various intrinsic causes of aging and widely different rates of aging. As a result, static population-based approaches could be insufficient to fully reveal sophisticated dynamic changes during aging. Recent developments in single-cell analyses to unravel the interplay of cellular dynamics and variability hold the promise to answer that challenge (3-5). Here we chose the replicative aging of yeast S. cerevisiae as a model and exploited quantitative biology technologies to study the dynamics of molecular processes that control aging at the single-cell level.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Multigenerational silencing dynamics control cell aging

Jin

O’Laughlin

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

103

View full text Add to dashboard Cite

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“…Microfluidic system designs for studying yeast aging have been reported previously (24)(25)(26); however, they did not permit assays of yeast replicative lifespan because they are unable to track the entire lifespan of mother cells due to the low efficiency in removing daughter cells. The designs reported previously can track trapped mother cells for only up to 18 h (24) and for up to eight divisions (25).…”

Section: Resultsmentioning

confidence: 99%

High-throughput analysis of yeast replicative aging using a microfluidic system

Liu

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

150

176

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Saccharomyces cerevisiae has been an important model for studying the molecular mechanisms of aging in eukaryotic cells. However, the laborious and low-throughput methods of current yeast replicative lifespan assays limit their usefulness as a broad genetic screening platform for research on aging. We address this limitation by developing an efficient, high-throughput microfluidic single-cell analysis chip in combination with high-resolution time-lapse microscopy. This innovative design enables, to our knowledge for the first time, the determination of the yeast replicative lifespan in a highthroughput manner. Morphological and phenotypical changes during aging can also be monitored automatically with a much higher throughput than previous microfluidic designs. We demonstrate highly efficient trapping and retention of mother cells, determination of the replicative lifespan, and tracking of yeast cells throughout their entire lifespan. Using the high-resolution and large-scale data generated from the high-throughput yeast aging analysis (HYAA) chips, we investigated particular longevity-related changes in cell morphology and characteristics, including critical cell size, terminal morphology, and protein subcellular localization. In addition, because of the significantly improved retention rate of yeast mother cell, the HYAA-Chip was capable of demonstrating replicative lifespan extension by calorie restriction.microfluidics | high-throughput | replicative aging | calorie restriction | Saccharomyces cerevisiae

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“…3c) [16], anhydrotetracycline steps and pulses to characterize an engineered TCS with negative feedback [107], a-factor [31,32] and phosphate starvation steps and pulses [108], and auxin [109] and KCl [73] square waves in recent studies of natural and synthetic yeast networks. A comparison of devices used in published yeast studies is given in a recent report of a long-term microchemostat for monitoring yeast aging [110]. …”

Section: Microfluidicsmentioning

confidence: 99%

How to train your microbe: methods for dynamically characterizing gene networks

Castillo-Hair

Igoshin

Tabor

2015

Current Opinion in Microbiology

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Gene networks regulate biological processes dynamically. However, researchers have largely relied upon static perturbations, such as growth media variations and gene knockouts, to elucidate gene network structure and function. Thus, much of the regulation on the path from DNA to phenotype remains poorly understood. Recent studies have utilized improved genetic tools, hardware, and computational control strategies to generate precise temporal perturbations outside and inside of live cells. These experiments have, in turn, provided new insights into the organizing principles of biology. Here, we introduce the major classes of dynamical perturbations that can be used to study gene networks, and discuss technologies available for creating them in a wide range of microbial pathways.

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A Microfluidic System for Studying Ageing and Dynamic Single-Cell Responses in Budding Yeast

Cited by 127 publications

References 42 publications

Multigenerational silencing dynamics control cell aging

Multigenerational silencing dynamics control cell aging

High-throughput analysis of yeast replicative aging using a microfluidic system

How to train your microbe: methods for dynamically characterizing gene networks

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