Transcriptome States Reflect Imaging of Aging States

We developed a morphological biomarker that detects multiple discrete sub-populations (or “age states”) at any given chronological age in a population of nematodes (C. elegans). Age-matched animals in different age states have distinct transcriptome profiles, one markedly older than the other as determined by comparison with other aging study microarray datasets. Here we characterized the frequencies of three healthy adult states and the transitions between them across the lifespan. Jaccard similarity showed expression profiles were more similar within states than between states. Chronologically identical individuals showed less similarity than morphologically identical individuals isolated on different days. We used short-lived and long-lived strains to confirm the general applicability of the state classifier and to monitor state progression. This exploration revealed healthy and unhealthy states, the former being favored in long-lived strains and the latter showing delayed onset. Short-lived strains rapidly transitioned through the putative healthy state. We expected state exit factors to be up regulated later in a given state. Several small heat shock protein encoding genes demonstrated oscillation within states. Oscillatory expression of sHSPs within states and proteasome components between states, supports an extension of a proteostasis collapse model to include periodic collapses and rebuilding phases. Six groups representing chronologically early and late stages for three aging states (N=3-6 per state, total N=24 microarray samples)