Periodic Reporting for period 4 - SYSAGING (A platform for rapidly mapping the molecular and systemic dynamics of aging)
Reporting period: 2024-07-01 to 2025-06-30
Over the last century, preventative approaches have been dramatically more effective than therapeutics at reducing the societal burden of disease. Historic killers such as smallpox, polio, and tuberculosis were beaten not by new therapies, but by vaccines and sanitation improvements that eliminated the context in which infectious diseases flourished. For today’s biggest killers, cardiovascular disease, cancer, and diabetes, we have only the most basic preventative measures. Our best advice—eat well and exercise—would be familiar to doctors in ancient Greece. To continue last century's progress in improving human health, we need to develop better preventative strategies against age-related diseases. We need to identify and disrupt the physiologic processes that, throughout life, increase the risk of disease.
Yet, age-related diseases are difficult to prevent because they have many causes, arising from a complex interaction between many genes, diet, and environment. It is increasingly clear that most age-related diseases do not arise from a linear chain of events involving a small number of molecules. Instead, age-related diseases emerge from interactions among large networks of genes, influenced by environmental factors, whose cumulative contribution throughout life determines the risk of falling ill. To study the complex interaction between many causal factors that contribute to disease risk, we need new methods capable of measuring high-dimensional dynamics of physiologic change during aging.
In this project, we have developed a set of measurement techniques combining transcriptomic profiling, in vivo biosensors, and new imaging technology. We collected across multiple spatial scales—molecules, cells, organs, individuals, and populations—developing 1) a set of transcriptomic and functional genetic approaches to study how gene regulation varies between individuals and is altered by age, 2) a new imaging approach to simultaneously study age-associated changes in behavior and lifespan, and 3) an atlas of inter-individual variation in aging and how it is altered by lifespan-extending interventions.
Combining molecular genetics with theoretic approaches, this project has helped us build better quantitative models of how complex diseases emerge from slow molecular-level changes, and in particular how multi-scale interactions drive differences in lifespan that arise stochastically within isogenic populations.
Yet, age-related diseases are difficult to prevent because they have many causes, arising from a complex interaction between many genes, diet, and environment. It is increasingly clear that most age-related diseases do not arise from a linear chain of events involving a small number of molecules. Instead, age-related diseases emerge from interactions among large networks of genes, influenced by environmental factors, whose cumulative contribution throughout life determines the risk of falling ill. To study the complex interaction between many causal factors that contribute to disease risk, we need new methods capable of measuring high-dimensional dynamics of physiologic change during aging.
In this project, we have developed a set of measurement techniques combining transcriptomic profiling, in vivo biosensors, and new imaging technology. We collected across multiple spatial scales—molecules, cells, organs, individuals, and populations—developing 1) a set of transcriptomic and functional genetic approaches to study how gene regulation varies between individuals and is altered by age, 2) a new imaging approach to simultaneously study age-associated changes in behavior and lifespan, and 3) an atlas of inter-individual variation in aging and how it is altered by lifespan-extending interventions.
Combining molecular genetics with theoretic approaches, this project has helped us build better quantitative models of how complex diseases emerge from slow molecular-level changes, and in particular how multi-scale interactions drive differences in lifespan that arise stochastically within isogenic populations.
This project involved the development of several interacting technologies that we then applied to study the biology of aging. First, we have developed an optimized fast, cost-effective single-individual RNA-sequencing approach that allows us to perform population-scale individually-resolved transcriptomics experiments. We have applied this method in a series of collaborative projects leading to publication, as well as in our own research on age-associate changes in gene regulation. Using our single-individual sequencing protocol, we found it was possible to build gene co-expression networks from large cohorts of age-matched individuals, which in turn highlighted the crucial role of germline/soma coordination of transcriptome in driving inter-individual variation gene-expression. We call our approach “Asynch-seq”, as it takes advantage of the naturally asynchronous nature of aging—individuals varying in their aging rate and therefore experiencing aging in an asynchronous fashion—to avoid the laborious task of performing long longitudinal timeseries. Using asynch-seq, we were able to screen for the mechanisms responsible for generating the gene-regulatory heterogeneity that arises in aging isogenic populations. We discovered these mechanisms serve as sources of inter-individual variation in aging, providing a new mechanistic entry point into the age-old question: “why do some individuals live longer than others?”, which we describe in our publication Eder et al. Cell 2024.
Over the course of this project, we also developed our imaging platform “the lifespan machine” to capture the joint distribution of health-span and lifespan across large populations. Like humans, C. elegans cease moving vigorously in old age, long before death, and we find that a close study of the statistics describing the relative timing of these can clarify a long-standing debate about the causal relationship between behavioral metrics of health-span and lifespan, which we describe in our publication Oswal et al PloS Computational Biology 2022.
Finally, our work on lifespan-interventions led to an unexpected success in developing the auxin-inducible degron (AID) system for modulating the aging process in vivo, leading to the publication Vicencio et al 2025.
Over the course of this project, we also developed our imaging platform “the lifespan machine” to capture the joint distribution of health-span and lifespan across large populations. Like humans, C. elegans cease moving vigorously in old age, long before death, and we find that a close study of the statistics describing the relative timing of these can clarify a long-standing debate about the causal relationship between behavioral metrics of health-span and lifespan, which we describe in our publication Oswal et al PloS Computational Biology 2022.
Finally, our work on lifespan-interventions led to an unexpected success in developing the auxin-inducible degron (AID) system for modulating the aging process in vivo, leading to the publication Vicencio et al 2025.
Our Asynch-seq method, described in Eder et al 2024 was an unexpected breakthrough that let us progress beyond the state of the art. We discovered that it is possible to rapidly map organism-scale networks in aging using population-asynchrony, which is the natural differences that arise between aging individuals in the absence of environmental or genetic heterogeneity. This result was unexpected, as originally, we had proposed a more conventional and laborious longitudinal experimental design. Our Asynch-seq method then rapidly led to our discovery of a set of mechanisms that act as intrinsic sources or transducers of inter-individual variation in lifespan—a crucial new entry point into understanding why some individuals age better than others.