Summary Although many genetic factors and lifestyle interventions are known to affect the mean lifespan of animal populations, the physiological variation displayed by individuals across their lifespans remains largely uncharacterized. Here, we use a custom culture apparatus to continuously monitor five aspects of aging physiology across hundreds of isolated Caenorhabditis elegans individuals kept in a constant environment from hatching until death. Aggregating these measurements into an overall estimate of senescence, we find two chief differences between longer- and shorter-lived individuals. First, though long- and short-lived individuals are physiologically equivalent in early adulthood, longer-lived individuals experience a lower rate of physiological decline throughout life. Second, and counter-intuitively, long-lived individuals have a disproportionately extended “twilight” period of low physiological function. While longer-lived individuals experience more overall days of good health, their proportion of good to bad health, and thus their average quality of life, is systematically lower than that of shorter-lived individuals. We conclude that within a homogeneous population reared under constant conditions, the period of early-life good health is comparatively uniform and the most plastic period in the aging process is end-of-life senescence.

We have miniaturized standard culture techniques to rear arrays of isolated, individual C. elegans throughout their lives on solid gel media. The resulting apparatus is compatible with brightfield and fluorescent microscopy, enabling longitudinal studies of morphology and fluorescent transgene expression. Our culture system exploits a novel crosslinking reaction between a polyethylene glycol hydrogel and a silicone elastomer to constrain animals to individual “corrals” on the gel surface. These devices are simple to construct on the benchtop with commercially available reagents, and, unlike microfluidic isolation methods, does not rely on micropatterned materials. We demonstrate that this new culture method has negligible effects on the physiology of C. elegans compared to standard culture on agar plates. In addition, RNAi techniques are effective in this system. Finally, the hydrogel–silicone binding chemistry that we developed also allows traditional microfluidic devices to be covalently attached to gel substrates instead of glass.

Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g., serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape these transformations, we modeled feed-forward networks of 7–22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity (STDP) rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS) in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

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Age-related physiological changes are most notable and best-studied late in life, while the nature of aging in early- or middle-aged individuals has not been explored as thoroughly. In C. elegans, studies of movement vs. age generally delineate three distinct phases: sustained, youthful movement; a discrete onset of rapidly progressing impairment; and gross immobility. We investigated whether this first period of early-life adult movement is simply a sustained “healthy” level of high function followed by a discrete “movement catastrophe” — or whether there are early-life changes in movement that precede future physiological declines. To determine how movement varies during early adult life, we followed isolated individuals throughout life with a previously unachieved combination of duration and temporal resolution. By tracking individuals across the first six days of adulthood, we observed declines in movement starting as early as the first two days of adult life, as well as high interindividual variability in total daily movement. These findings suggest that movement is a highly dynamic behavior early in life, and that factors driving movement decline may begin acting as early as the first day of adulthood. Using simulation studies based on acquired data, we suggest that too infrequent sampling in common movement assays limits observation of early-adult changes in motility, and we propose feasible alternate strategies and a framework for designing assays with increased sensitivity for early movement declines.

Age-related physiological changes are most notable and best-studied late in life, while the nature of aging in early- or middle-aged individuals has not been explored as thoroughly. In C. elegans, many studies of movement vs. age generally focus on three distinct phases: sustained, youthful movement; onset of rapidly progressing impairment; and gross immobility. We investigated whether this first period of early-life adult movement is a sustained “healthy” level of high function followed by a discrete “movement catastrophe”—or whether there are early-life changes in movement that precede future physiological declines. To determine how movement varies during early adult life, we followed isolated individuals throughout life with a previously unachieved combination of duration and temporal resolution. By tracking individuals across the first six days of adulthood, we observed declines in movement starting as early as the first two days of adult life, as well as high interindividual variability in total daily movement. These findings suggest that movement is a highly dynamic behavior early in life, and that factors driving movement decline may begin acting as early as the first day of adulthood. Using simulation studies based on acquired data, we suggest that too-infrequent sampling in common movement assays limits observation of early-adult changes in motility, and we propose feasible strategies and a framework for designing assays with increased sensitivity for early movement declines.

Modern aging biology aims to understand how health and morbidity evolve over an individual’s life. To conceptualize health and “healthspan” in different ways that may be useful for biological or sociological study, several common analyses quantify lifetime health using statistics that combine measures of health and lifespan. While such efforts appear reasonable, we argue that such statistics confuse what types of processes contribute to health and lifespan; and, in fact, they risk circular reasoning by treating as an assumption what is in fact a very specific hypothesis: that lifespan and healthspan are tightly correlated. While this hypothesis is not impossible a priori, it remains a matter of active debate. Such hypotheses must not be unwittingly smuggled into the literature as hidden assumptions of specific analytic techniques. Consequently, we caution against generally quantifying lifetime health by any metric that combines both healthspan and argue that investigators should not mix longevity statistics with health measurements.