Automated lifespan determination across Caenorhabditis strains and species reveals assay-specific effects of chemical interventions

Stephen A. Banse, Mark Lucanic, Christine A. Sedore, Anna L. Coleman-Hulbert, W. Todd Plummer, Esteban Chen, Jason L. Kish, David Hall, Brian Onken, Michael P. Presley, E. Grace Jones, Benjamin W. Blue, Theo Garrett, Mark Abbott, Jian Xue, Suzhen Guo, Erik Johnson, Anna C. Foulger, Manish Chamoli, Ron FalkowskiIlija Melentijevic, Girish Harinath, Phu Huynh, Shobhna Patel, Daniel Edgar, Cody M. Jarrett, Max Guo, Pankaj Kapahi, Gordon J. Lithgow, Monica Driscoll, Patrick C. Phillips

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The goal of the Caenorhabditis Intervention Testing Program is to identify robust and reproducible pro-longevity interventions that are efficacious across genetically diverse cohorts in the Caenorhabditis genus. The project design features multiple experimental replicates collected by three different laboratories. Our initial effort employed fully manual survival assays. With an interest in increasing throughput, we explored automation with flatbed scanner-based Automated Lifespan Machines (ALMs). We used ALMs to measure survivorship of 22 Caenorhabditis strains spanning three species. Additionally, we tested five chemicals that we previously found extended lifespan in manual assays. Overall, we found similar sources of variation among trials for the ALM and our previous manual assays, verifying reproducibility of outcome. Survival assessment was generally consistent between the manual and the ALM assays, although we did observe radically contrasting results for certain compound interventions. We found that particular lifespan outcome differences could be attributed to protocol elements such as enhanced light exposure of specific compounds in the ALM, underscoring that differences in technical details can influence outcomes and therefore interpretation. Overall, we demonstrate that the ALMs effectively reproduce a large, conventionally scored dataset from a diverse test set, independently validating ALMs as a robust and reproducible approach toward aging-intervention screening.

Original languageEnglish (US)
Pages (from-to)945-960
Number of pages16
Issue number6
StatePublished - Dec 1 2019

All Science Journal Classification (ASJC) codes

  • Geriatrics and Gerontology
  • Aging


  • Automation
  • CITP
  • Caenorhabditis elegans
  • Lifespan
  • Lifespan machine
  • Thioflavin T


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