Divergent Aging of Isogenic Yeast Cells Revealed through Single-Cell Phenotypic Dynamics

Similarities and differences in the gene expression signatures of physiological age versus future lifespan

Across all taxa of life, individuals within a species exhibit variable lifespans. Differences in genotype or environment are not sufficient to explain this variance, as even isogenic Caenorhabditis elegans nematodes reared under uniform conditions show significant variability in lifespan. To investigate this phenomenon, we used lifespan-predictive biomarkers to isolate, at mid-adulthood, prospectively long- and short-lived individuals from an otherwise identical population. We selected two biomarkers which correlated positively with lifespan, lin-4p::GFP and mir-243p::GFP, and two which correlated negatively, mir-240/786p::GFP and autofluorescence. The gene-expression signature of long versus short future lifespan was strikingly similar across all four biomarkers tested. Since these biomarkers are expressed in different tissues, these results suggest a shared connection to a global health state correlated with future lifespan. To further investigate this underlying state, we compared the transcriptional signature of long versus short future lifespan to that of chronologically young versus old individuals. By comparison to a high-resolution time series of the average aging transcriptome, we determined that subpopulations predicted to be long- or short-lived by biomarker expression had significantly different transcriptional ages despite their shared chronological age. We found that this difference in apparent transcriptional age accounted for the majority of differentially expressed genes associated with future lifespan. Interestingly, we also identified several genes whose expression consistently separated samples by biomarker expression independent of apparent transcriptional age. These results suggest that the commonalities in the long-lived versus short-lived state reported across different biomarkers of aging extends beyond simply transcriptionally young versus transcriptionally old.

Deciphering the topological landscape of glioma using a network theory framework

Glioma stem cells have been recognized as key players in glioma recurrence and therapeutic resistance, presenting a promising target for novel treatments. However, the limited understanding of the role glioma stem cells play in the glioma hierarchy has drawn controversy and hindered research translation into therapies. Despite significant advances in our understanding of gene regulatory networks, the dynamics of these networks and their implications for glioma remain elusive. This study employs a systemic theoretical perspective to integrate experimental knowledge into a core endogenous network model for glioma, thereby elucidating its energy landscape through network dynamics computation. The model identifies two stable states corresponding to astrocytic-like and oligodendrocytic-like tumor cells, connected by a transition state with the feature of high stemness, which serves as one of the energy barriers between astrocytic-like and oligodendrocytic-like states, indicating the instability of glioma stem cells in vivo. We also obtained various stable states further supporting glioma's multicellular origins and uncovered a group of transition states that could potentially induce tumor heterogeneity and therapeutic resistance. This research proposes that the transition states linking both glioma stable states are central to glioma heterogeneity and therapy resistance. Our approach may contribute to the advancement of glioma therapy by offering a novel perspective on the complex landscape of glioma biology.

Study of impacts of two types of cellular aging on the yeast bud morphogenesis

Understanding the mechanisms of the cellular aging processes is crucial for attempting to extend organismal lifespan and for studying age-related degenerative diseases. Yeast cells divide through budding, providing a classical biological model for studying cellular aging. With their powerful genetics, relatively short cell cycle, and well-established signaling pathways also found in animals, yeast cells offer valuable insights into the aging process. Recent experiments suggested the existence of two aging modes in yeast characterized by nucleolar and mitochondrial declines, respectively. By analyzing experimental data, this study shows that cells evolving into those two aging modes behave differently when they are young. While buds grow linearly in both modes, cells that consistently generate spherical buds throughout their lifespan demonstrate greater efficacy in controlling bud size and growth rate at young ages. A three-dimensional multiscale chemical-mechanical model was developed and used to suggest and test hypothesized impacts of aging on bud morphogenesis. Experimentally calibrated model simulations showed that during the early stage of budding, tubular bud shape in one aging mode could be generated by locally inserting new materials at the bud tip, a process guided by the polarized Cdc42 signal. Furthermore, the aspect ratio of the tubular bud could be stabilized during the late stage as observed in experiments in this work. The model simulation results suggest that the localization of new cell surface material insertion, regulated by chemical signal polarization, could be weakened due to cellular aging in yeast and other cell types, leading to the change and stabilization of the bud aspect ratio.

Enhanced cellular longevity arising from environmental fluctuations

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.

Study of Impacts of Two Types of Cellular Aging on the Yeast Bud Morphogenesis

Understanding the mechanisms of cellular aging processes is crucial for attempting to extend organismal lifespan and for studying age-related degenerative diseases. Yeast cells divide through budding, providing a classical biological model for studying cellular aging. With their powerful genetics, relatively short lifespan and well-established signaling pathways also found in animals, yeast cells offer valuable insights into the aging process. Recent experiments suggested the existence of two aging modes in yeast characterized by nucleolar and mitochondrial declines, respectively. In this study, by analyzing experimental data it was shown that cells evolving into those two aging modes behave differently when they are young. While buds grow linearly in both modes, cells that consistently generate spherical buds throughout their lifespan demonstrate greater efficacy in controlling bud size and growth rate at young ages. A three-dimensional chemical-mechanical model was developed and used to suggest and test hypothesized mechanisms of bud morphogenesis during aging. Experimentally calibrated simulations showed that tubular bud shape in one aging mode could be generated by locally inserting new materials at the bud tip guided by the polarized Cdc42 signal during the early stage of budding. Furthermore, the aspect ratio of the tubular bud could be stabilized during the late stage, as observed in experiments, through a reduction on the new cell surface material insertion or an expansion of the polarization site. Thus model simulations suggest the maintenance of new cell surface material insertion or chemical signal polarization could be weakened due to cellular aging in yeast and other cell types.

Role of Mitochondrial DNA in Yeast Replicative Aging

Despite the diverse manifestations of aging across different species, some common aging features and underlying mechanisms are shared. In particular, mitochondria appear to be among the most vulnerable systems in both metazoa and fungi. In this review, we discuss how mitochondrial dysfunction is related to replicative aging in the simplest eukaryotic model, the baker's yeast Saccharomyces cerevisiae. We discuss a chain of events that starts from asymmetric distribution of mitochondria between mother and daughter cells. With age, yeast mother cells start to experience a decrease in mitochondrial transmembrane potential and, consequently, a decrease in mitochondrial protein import efficiency. This induces mitochondrial protein precursors in the cytoplasm, the loss of mitochondrial DNA (mtDNA), and at the later stages - cell death. Interestingly, yeast strains without mtDNA can have either increased or decreased lifespan compared to the parental strains with mtDNA. The direction of the effect depends on their ability to activate compensatory mechanisms preventing or mitigating negative consequences of mitochondrial dysfunction. The central role of mitochondria in yeast aging and death indicates that it is one of the most complex and, therefore, deregulation-prone systems in eukaryotic cells.

A Microfluidic Platform for Screening Gene Expression Dynamics across Yeast Strain Libraries

The relative ease of genetic manipulation in S. cerevisiae is one of its greatest strengths as a model eukaryotic organism. Researchers have leveraged this quality of the budding yeast to study the effects of a variety of genetic perturbations, such as deletion or overexpression, in a high-throughput manner. This has been accomplished by producing a number of strain libraries that can contain hundreds or even thousands of distinct yeast strains with unique genetic alterations. While these strategies have led to enormous increases in our understanding of the functions and roles that genes play within cells, the techniques used to screen genetically modified libraries of yeast strains typically rely on plate or sequencing-based assays that make it difficult to analyze gene expression changes over time. Microfluidic devices, combined with fluorescence microscopy, can allow gene expression dynamics of different strains to be captured in a continuous culture environment; however, these approaches often have significantly lower throughput compared to traditional techniques. To address these limitations, we have developed a microfluidic platform that uses an array pinning robot to allow for up to 48 different yeast strains to be transferred onto a single device. Here, we detail a validated methodology for constructing and setting up this microfluidic device, starting with the photolithography steps for constructing the wafer, then the soft lithography steps for making polydimethylsiloxane (PDMS) microfluidic devices, and finally the robotic arraying of strains onto the device for experiments. We have applied this device for dynamic screens of a protein aggregation library; however, this methodology has the potential to enable complex and dynamic screens of yeast libraries for a wide range of applications. Key features • Major steps of this protocol require access to specialized equipment (i.e., microfabrication tools typically found in a cleanroom facility and an array pinning robot). • Construction of microfluidic devices with multiple different feature heights using photolithography and soft lithography with PDMS. • Robotic spotting of up to 48 different yeast strains onto microfluidic devices.

Spontaneous Emergence of Multicellular Heritability

The major transitions in evolution include events and processes that result in the emergence of new levels of biological individuality. For collectives to undergo Darwinian evolution, their traits must be heritable, but the emergence of higher-level heritability is poorly understood and has long been considered a stumbling block for nascent evolutionary transitions. Using analytical models, synthetic biology, and biologically-informed simulations, we explored the emergence of trait heritability during the evolution of multicellularity. Prior work on the evolution of multicellularity has asserted that substantial collective-level trait heritability either emerges only late in the transition or requires some evolutionary change subsequent to the formation of clonal multicellular groups. In a prior analytical model, we showed that collective-level heritability not only exists but is usually more heritable than the underlying cell-level trait upon which it is based, as soon as multicellular groups form. Here, we show that key assumptions and predictions of that model are borne out in a real engineered biological system, with important implications for the emergence of collective-level heritability.

Fabrication of Microfluidic Devices for Continuously Monitoring Yeast Aging

For several decades, aging in Saccharomyces cerevisiae has been studied in hopes of understanding its causes and identifying conserved pathways that also drive aging in multicellular eukaryotes. While the short lifespan and unicellular nature of budding yeast has allowed its aging process to be observed by dissecting mother cells away from daughter cells under a microscope, this technique does not allow continuous, high-resolution, and high-throughput studies to be performed. Here, we present a protocol for constructing microfluidic devices for studying yeast aging that are free from these limitations. Our approach uses multilayer photolithography and soft lithography with polydimethylsiloxane (PDMS) to construct microfluidic devices with distinct single-cell trapping regions as well as channels for supplying media and removing recently born daughter cells. By doing so, aging yeast cells can be imaged at scale for the entirety of their lifespans, and the dynamics of molecular processes within single cells can be simultaneously tracked using fluorescence microscopy. Key features This protocol requires access to a photolithography lab in a cleanroom facility. Photolithography process for patterning photoresist on silicon wafers with multiple different feature heights. Soft lithography process for making PDMS microfluidic devices from silicon wafer templates.

Dietary change without caloric restriction maintains a youthful profile in ageing yeast

Caloric restriction increases lifespan and improves ageing health, but it is unknown whether these outcomes can be separated or achieved through less severe interventions. Here, we show that an unrestricted galactose diet in early life minimises change during replicative ageing in budding yeast, irrespective of diet later in life. Average mother cell division rate is comparable between glucose and galactose diets, and lifespan is shorter on galactose, but markers of senescence and the progressive dysregulation of gene expression observed on glucose are minimal on galactose, showing that these are not intrinsic aspects of replicative ageing but rather associated processes. Respiration on galactose is critical for minimising hallmarks of ageing, and forced respiration during ageing on glucose by overexpression of the mitochondrial biogenesis factor Hap4 also has the same effect though only in a fraction of cells. This fraction maintains Hap4 activity to advanced age with low senescence and a youthful gene expression profile, whereas other cells in the same population lose Hap4 activity, undergo dramatic dysregulation of gene expression and accumulate fragments of chromosome XII (ChrXIIr), which are tightly associated with senescence. Our findings support the existence of two separable ageing trajectories in yeast. We propose that a complete shift to the healthy ageing mode can be achieved in wild-type cells through dietary change in early life without caloric restriction.

Enhanced cellular longevity arising from environmental fluctuations

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions, but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity - the creation of a stable fixed point in the "healthy" state of the cell and the dynamic stabilization of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.

Engineering longevity-design of a synthetic gene oscillator to slow cellular aging

Synthetic biology enables the design of gene networks to confer specific biological functions, yet it remains a challenge to rationally engineer a biological trait as complex as longevity. A naturally occurring toggle switch underlies fate decisions toward either nucleolar or mitochondrial decline during the aging of yeast cells. We rewired this endogenous toggle to engineer an autonomous genetic clock that generates sustained oscillations between the nucleolar and mitochondrial aging processes in individual cells. These oscillations increased cellular life span through the delay of the commitment to aging that resulted from either the loss of chromatin silencing or the depletion of heme. Our results establish a connection between gene network architecture and cellular longevity that could lead to rationally designed gene circuits that slow aging.

Age-dependent aggregation of ribosomal RNA-binding proteins links deterioration in chromatin stability with challenges to proteostasis

Chromatin instability and protein homeostasis (proteostasis) stress are two well-established hallmarks of aging, which have been considered largely independent of each other. Using microfluidics and single-cell imaging approaches, we observed that, during the replicative aging of Saccharomyces cerevisiae, a challenge to proteostasis occurs specifically in the fraction of cells with decreased stability within the ribosomal DNA (rDNA). A screen of 170 yeast RNA-binding proteins identified ribosomal RNA (rRNA)-binding proteins as the most enriched group that aggregate upon a decrease in rDNA stability induced by inhibition of a conserved lysine deacetylase Sir2. Further, loss of rDNA stability induces age-dependent aggregation of rRNA-binding proteins through aberrant overproduction of rRNAs. These aggregates contribute to age-induced proteostasis decline and limit cellular lifespan. Our findings reveal a mechanism underlying the interconnection between chromatin instability and proteostasis stress and highlight the importance of cell-to-cell variability in aging processes.

DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis

Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-cell trajectories have prevented large-scale investigations to assess the dynamics of entry into replicative senescence in yeast. Here, we have developed DetecDiv, a microfluidic-based image acquisition platform combined with deep learning-based software for high-throughput single-cell division tracking. We show that DetecDiv can automatically reconstruct cellular replicative lifespans with high accuracy and performs similarly with various imaging platforms and geometries of microfluidic traps. In addition, this methodology provides comprehensive temporal cellular metrics using time-series classification and image semantic segmentation. Last, we show that this method can be further applied to automatically quantify the dynamics of cellular adaptation and real-time cell survival upon exposure to environmental stress. Hence, this methodology provides an all-in-one toolbox for high-throughput phenotyping for cell cycle, stress response, and replicative lifespan assays.

A complex systems approach to aging biology

Having made substantial progress understanding molecules, cells, genes and pathways, aging biology research is now moving toward integration of these parts, attempting to understand how their joint dynamics may contribute to aging. Such a shift of perspective requires the adoption of a formal complex systems framework, a transition being facilitated by large-scale data collection and new analytical tools. Here, we provide a theoretical framework to orient researchers around key concepts for this transition, notably emergence, interaction networks and resilience. Drawing on evolutionary theory, network theory and principles of homeostasis, we propose that organismal function is accomplished by the integration of regulatory mechanisms at multiple hierarchical scales, and that the disruption of this ensemble causes the phenotypic and functional manifestations of aging. We present key examples at scales ranging from sub-organismal biology to clinical geriatrics, outlining how this approach can potentially enrich our understanding of aging.

Counteracting aged DNA methylation states to combat ageing and age-related diseases

DNA methylation (DNAm) overwrites information about multiple extrinsic factors on the genome. Age is one of these factors. Age causes characteristic DNAm changes that are thought to be not only major drivers of normal ageing but also precursors to diseases, cancer being one of these. Although there is still much to learn about the relationship between ageing, age-related diseases and DNAm, we now know how to interpret some of the effects caused by age in the form of changes in methylation marks at specific loci. In fact, these changes form the basis of the so called "epigenetic clocks", which translate the genomic methylation profile into an "epigenetic age". Epigenetic age does not only estimate chronological age but can also predict the risk of chronic diseases and mortality. Epigenetic age is believed to be one of the most accurate metrics of biological age. Initial evidence has recently been gathered pointing to the possibility that the rate of epigenetic ageing can be slowed down or even reversed. In this review, we discuss some of the most relevant advances in this field. Expected outcome is that this approach can provide insights into how to preserve health and reduce the impact of ageing diseases in humans.

rDNA array length is a major determinant of replicative lifespan in budding yeast

The complex processes and interactions that regulate aging and determine lifespan are not fully defined for any organism. Here, taking advantage of recent technological advances in studying aging in budding yeast, we discovered a previously unappreciated relationship between the number of copies of the ribosomal RNA gene present in its chromosomal array and replicative lifespan (RLS). Specifically, the chromosomal ribosomal DNA (rDNA) copy number (rDNA CN) positively correlated with RLS and this interaction explained over 70% of variability in RLS among a series of wild-type strains. In strains with low rDNA CN, SIR2 expression was attenuated and extrachromosomal rDNA circle (ERC) accumulation was increased, leading to shorter lifespan. Suppressing ERC formation by deletion of FOB1 eliminated the relationship between rDNA CN and RLS. These data suggest that previously identified rDNA CN regulatory mechanisms limit lifespan. Importantly, the RLSs of reported lifespan-enhancing mutations were significantly impacted by rDNA CN, suggesting that changes in rDNA CN might explain the magnitude of some of those reported effects. We propose that because rDNA CN is modulated by environmental, genetic, and stochastic factors, considering rDNA CN is a prerequisite for accurate interpretation of lifespan data.

Modeling aging and its impact on cellular function and organismal behavior

Aging is a complex phenomenon of functional decay in a biological organism. Although the effects of aging are readily recognizable in a wide range of organisms, the cause(s) of aging are ill defined and poorly understood. Experimental methods on model organisms have driven significant insight into aging as a process, but have not provided a complete model of aging. Computational biology offers a unique opportunity to resolve this gap in our knowledge by generating extensive and testable models that can help us understand the fundamental nature of aging, identify the presence and characteristics of unaccounted aging factor(s), demonstrate the mechanics of particular factor(s) in driving aging, and understand the secondary effects of aging on biological function. In this review, we will address each of the above roles for computational biology in aging research. Concurrently, we will explore the different applications of computational biology to aging in single-celled versus multicellular organisms. Given the long history of computational biogerontological research on lower eukaryotes, we emphasize the key future goals of gradually integrating prior models into a holistic map of aging and translating successful models to higher-complexity organisms.

A new mechanistic insight into fate decisions during yeast cell aging process

Despite massive technological advances in mammalian models in recent years, studies in yeast still have the power to inform on the basic mechanisms of aging. Illustrating this, in Nan Hao's recent article published in the journal Science, he and his lab use microfluidics and fluorescent imaging technology to analyze the dynamics and interactions of aging mechanisms within yeast cells. They focused in on the Sir2 gene and the heme activator protein and, through the manipulation of these two molecular aging pathways, were able to determine that yeast cells can undergo one of three modes of aging, with one of them having a significantly longer lifespan than the others. These findings provide unexpected insights into mechanisms of aging, apparently as regulated fate-decision process, and open up avenues for future research.

Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.

A ride through the epigenetic landscape: aging reversal by reprogramming

Aging has become one of the fastest-growing research topics in biology. However, exactly how the aging process occurs remains unknown. Epigenetics plays a significant role, and several epigenetic interventions can modulate lifespan. This review will explore the interplay between epigenetics and aging, and how epigenetic reprogramming can be harnessed for age reversal. In vivo partial reprogramming holds great promise as a possible therapy, but several limitations remain. Rejuvenation by reprogramming is a young but rapidly expanding subfield in the biology of aging.

Protein interaction potential landscapes for yeast replicative aging

We proposed a novel interaction potential landscape approach to map the systems-level profile changes of gene networks during replicative aging in Saccharomyces cerevisiae. This approach enabled us to apply quasi-potentials, the negative logarithm of the probabilities, to calibrate the elevation of the interaction landscapes with young cells as a reference state. Our approach detected opposite landscape changes based on protein abundances from transcript levels, especially for intra-essential gene interactions. We showed that essential proteins play different roles from hub proteins on the age-dependent interaction potential landscapes. We verified that hub proteins tend to avoid other hub proteins, but essential proteins prefer to interact with other essential proteins. Overall, we showed that the interaction potential landscape is promising for inferring network profile change during aging and that the essential hub proteins may play an important role in the uncoupling between protein and transcript levels during replicative aging.

Slipstreaming Mother Machine: A Microfluidic Device for Single-Cell Dynamic Imaging of Yeast

The yeast Saccharomyces cerevisiae is one of the most basic model organisms for studies of aging and other phenomena such as division strategies. These organisms have been typically studied with the use of microfluidic devices to keep cells trapped while under a flow of fresh media. However, all of the existing devices trap cells mechanically, subjecting them to pressures that may affect cell physiology. There is evidence mechanical pressure affects growth rate and the movement of intracellular components, so it is quite possible that it affects other physiological aspects such as aging. To allow studies with the lowest influence of mechanical pressure, we designed and fabricated a device that takes advantage of the slipstreaming effect. In slipstreaming, moving fluids that encounter a barrier flow around it forming a pressure gradient behind it. We trap mother cells in this region and force daughter cells to be in the negative pressure gradient region so that they are taken away by the flow. Additionally, this device can be fabricated using low resolution lithography techniques, which makes it less expensive than devices that require photolithography masks with resolution under 5 µm. With this device, it is possible to measure some of the most interesting aspects of yeast dynamics such as growth rates and Replicative Life Span. This device should allow future studies to eliminate pressure bias as well as extending the range of labs that can do these types of measurements.

Age-dependent decline in stress response capacity revealed by proteins dynamics analysis

The aging process is regarded as the progressive loss of physiological integrity, leading to impaired biological functions and the increased vulnerability to death. Among various biological functions, stress response capacity enables cells to alter gene expression patterns and survive when facing internal and external stresses. Here, we explored changes in stress response capacity during the replicative aging of Saccharomyces cerevisiae. To this end, we used a high-throughput microfluidic device to deliver intermittent pulses of osmotic stress and tracked the dynamic changes in the production of downstream stress-responsive proteins, in a large number of individual aging cells. Cells showed a gradual decline in stress response capacity of these osmotic-related downstream proteins during the aging process after the first 5 generations. Among the downstream stress-responsive genes and unrelated genes tested, the residual level of response capacity of Trehalose-6-Phosphate Synthase (TPS2) showed the best correlation with the cell remaining lifespan. By monitor dynamics of the upstream transcription factors and mRNA of Tps2, it was suggested that the decline in downstream stress response capacity was caused by the decline of translational rate of these proteins during aging.

A programmable fate decision landscape underlies single-cell aging in yeast

Chromatin instability and mitochondrial decline are conserved processes that contribute to cellular aging. Although both processes have been explored individually in the context of their distinct signaling pathways, the mechanism that determines which process dominates during aging of individual cells is unknown. We show that interactions between the chromatin silencing and mitochondrial pathways lead to an epigenetic landscape of yeast replicative aging with multiple equilibrium states that represent different types of terminal states of aging. The structure of the landscape drives single-cell differentiation toward one of these states during aging, whereby the fate is determined quite early and is insensitive to intracellular noise. Guided by a quantitative model of the aging landscape, we genetically engineered a long-lived equilibrium state characterized by an extended life span.

Searching for the Mechanisms of Mammalian Cellular Aging Through Underlying Gene Regulatory Networks

Aging attracts the attention throughout the history of humankind. However, it is still challenging to understand how the internal driving forces, for example, the fundamental building blocks of life, such as genes and proteins, as well as the environments work together to determine longevity in mammals. In this study, we built a gene regulatory network for mammalian cellular aging based on the experimental literature and quantify its underlying driving force for the dynamics as potential and flux landscape. We found three steady-state attractors: a fast-aging state attractor, slow-aging state attractor, and intermediate state attractor. The system can switch from one state attractor to another driven by the intrinsic or external forces through the genetics and the environment. We identified the dominant path from the slow-aging state directly to the fast-aging state. We also identified the dominant path from slow-aging to fast-aging through an intermediate state. We quantified the evolving landscape for revealing the dynamic characteristics of aging through certain regulation changes in time. We also predicted the key genes and regulations for fast-aging and slow-aging through the analysis of the stability for landscape basins. We also found the oscillation dynamics between fast-aging and slow-aging and showed that more energy is required to sustain such oscillations. We found that the flux is the dynamic cause and the entropy production rate the thermodynamic origin of the phase transitions or the bifurcations between the three-state phase and oscillation phase. The landscape quantification provides a global and physical approach to explore the underlying mechanisms of cellular aging in mammals.

Proteostatic stress as a nodal hallmark of replicative aging

Aging is characterized by the progressive decline of physiology at the cell, tissue and organism level, leading to an increased risk of mortality. Proteotoxic stress, mitochondrial dysfunction and genomic instability are considered major universal drivers of cell aging, and accumulating evidence establishes clear biunivocal relationships among these key hallmarks. In this regard, the finite lifespan of the budding yeast, together with the extensive armamentarium of available analytical tools, has made this single cell eukaryote a key model to study aging at molecular and cellular levels. Here we review the current data that link proteostasis to cell cycle progression in the budding yeast, focusing on senescence as an inherent phenotype displayed by aged cells. Recent advances in high-throughput systems to study yeast mother cells while they replicate are providing crucial information on aging-related processes and their temporal interdependencies at a systems level. In our view, the available data point to the existence of multiple feedback mechanisms among the major causal factors of aging, which would converge into the loss of proteostasis as a nodal driver of cell senescence and death.

Loss of vacuolar acidity results in iron-sulfur cluster defects and divergent homeostatic responses during aging in Saccharomyces cerevisiae

The loss of vacuolar/lysosomal acidity is an early event during aging that has been linked to mitochondrial dysfunction. However, it is unclear how loss of vacuolar acidity results in age-related dysfunction. Through unbiased genetic screens, we determined that increased iron uptake can suppress the mitochondrial respiratory deficiency phenotype of yeast vma mutants, which have lost vacuolar acidity due to genetic disruption of the vacuolar ATPase proton pump. Yeast vma mutants exhibited nuclear localization of Aft1, which turns on the iron regulon in response to iron-sulfur cluster (ISC) deficiency. This led us to find that loss of vacuolar acidity with age in wild-type yeast causes ISC defects and a DNA damage response. Using microfluidics to investigate aging at the single-cell level, we observe grossly divergent trajectories of iron homeostasis within an isogenic and environmentally homogeneous population. One subpopulation of cells fails to mount the expected compensatory iron regulon gene expression program, and suffers progressively severe ISC deficiency with little to no activation of the iron regulon. In contrast, other cells show robust iron regulon activity with limited ISC deficiency, which allows extended passage and survival through a period of genomic instability during aging. These divergent trajectories suggest that iron regulation and ISC homeostasis represent a possible target for aging interventions.

Microfluidic platforms for the dynamic characterisation of synthetic circuitry

Generating novel functionality from well characterised synthetic parts and modules lies at the heart of synthetic biology. Ideally, circuitry is rationally designed in silico with quantitatively predictive models to predetermined design specifications. Synthetic circuits are intrinsically stochastic, often dynamically modulated and set in a dynamic fluctuating environment within a living cell. To build more complex circuits and to gain insight into context effects, intrinsic noise and transient performance, characterisation techniques that resolve both heterogeneity and dynamics are required. Here we review recent advances in both in vitro and in vivo microfluidic technologies that are suitable for the characterisation of synthetic circuitry, modules and parts.

Trajectories of Aging: How Systems Biology in Yeast Can Illuminate Mechanisms of Personalized Aging

All organisms age, but the extent to which all organisms age the same way remains a fundamental unanswered question in biology. Across species, it is now clear that at least some aspects of aging are highly conserved and are perhaps universal, but other mechanisms of aging are private to individual species or sets of closely related species. Within the same species, however, it has generally been assumed that the molecular mechanisms of aging are largely invariant from one individual to the next. With the development of new tools for studying aging at the individual cell level in budding yeast, recent data has called this assumption into question. There is emerging evidence that individual yeast mother cells may undergo fundamentally different trajectories of aging. Individual trajectories of aging are difficult to study by traditional population level assays, but through the application of systems biology approaches combined with novel microfluidic technologies, it is now possible to observe and study these phenomena in real time. Understanding the spectrum of mechanisms that determine how different individuals age is a necessary step toward the goal of personalized geroscience, where healthy longevity is optimized for each individual.

Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp

Phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to define four Cryptococcus spp. capsule morphotypes, namely Regular, Spiky, Bald, and Phantom. These morphotypes were persistently observed in varying proportions among yeast isolates. To assess the distribution of such morphotypes we implemented an automated pipeline capable of (1) identifying potentially cell-associated objects in the SEM-derived images; (2) computing object-level features; and (3) classifying these objects into their corresponding classes. The machine learning approach used a Random Forest (RF) classifier whose overall accuracy reached 85% on the test dataset, with per-class specificity above 90%, and sensitivity between 66 and 94%. Additionally, the RF model indicates that structural and texture features, e.g., object area, eccentricity, and contrast, are most relevant for classification. The RF results agree with the observed variation in these features, consistently also with visual inspection of SEM images. Finally, our work introduces morphological variants of Cryptococcus spp. capsule. These can be promptly identified and characterized using computational models so that future work may unveil morphological associations with yeast virulence.

DNA damage checkpoint activation impairs chromatin homeostasis and promotes mitotic catastrophe during aging

Genome instability is a hallmark of aging and contributes to age-related disorders such as cancer and Alzheimer's disease. The accumulation of DNA damage during aging has been linked to altered cell cycle dynamics and the failure of cell cycle checkpoints. Here, we use single cell imaging to study the consequences of increased genomic instability during aging in budding yeast and identify striking age-associated genome missegregation events. This breakdown in mitotic fidelity results from the age-related activation of the DNA damage checkpoint and the resulting degradation of histone proteins. Disrupting the ability of cells to degrade histones in response to DNA damage increases replicative lifespan and reduces genomic missegregations. We present several lines of evidence supporting a model of antagonistic pleiotropy in the DNA damage response where histone degradation, and limited histone transcription are beneficial to respond rapidly to damage but reduce lifespan and genomic stability in the long term.

Advances in quantitative biology methods for studying replicative aging in Saccharomyces cerevisiae

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.

An inexpensive microscopy system for microfluidic studies in budding yeast

Recently, microfluidic technologies have been developed to allow higher throughput collection of yeast replicative lifespan data. Adoption of these devices has been limited, in part, due to the high cost of the motorized microscopy instrumentation from mainline manufacturers. Inspired by recent development of open source microscopy hardware and software, we developed minimal-cost hardware attachments to provide long-term focus stabilization for lower-cost microscopes and open source software to manage concurrent time-lapse image acquisition from multiple microscopes. We hope that these tools will help spur the wider adoption of microfluidic technologies for the study of aging in yeast.