An automated platform to monitor long-term behavior and healthspan in Caenorhabditis elegans under precise environmental control

Le, Kim; Zhan, Min; Cho, Yong-Min; Wan, Jason; Patel, Dhaval; Lu, Hang

doi:10.1038/s42003-020-1013-2

Cited by 48 publications

(48 citation statements)

References 36 publications

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“…Thus, it is possibly reducing attrition rates for the downstream phases of the drug development process. We also observed that various behavioral features declined during the aging process, as like other model organisms 11,40 . This observation points that our platform and pipeline can establish the areas in which Daphnia can complement existing models through improvements in sensitivity, cost, or efficiency for the aging research and phenotypic screening for the anti-aging drug discovery.…”

Section: Discussionsupporting

confidence: 74%

"Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

Cho

Yampolsky

et al. 2021

Preprint

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We present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi transparent freshwater organism, sensitive to drugs with complex behavior and physiology - the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioural features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using long term dose response profile of metformin and short term assay of such well-studied substances as caffeine and alcohol.

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Section: Discussionsupporting

confidence: 74%

"Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

Cho

Yampolsky

et al. 2021

Preprint

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“…Next we tested how well the model can be used to track motion in a realistic biological discovery context: examining behavioral decline in the aging process. Behavior and movement are common methods to gauge the health and physiological age of an individual (Golden et al 2008; Hahm et al 2015; Zhang et al 2016; Churgin et al 2017; Le et al 2020). For these experiments, it can be challenging to accurately measure how the movement of individuals changes throughout their entire lifespan due to complex environmental conditions and the large-scale of the data.…”

Section: Resultsmentioning

confidence: 99%

“…See prior work for more details. (Le et al 2020) Training Faster R-CNN network For the egg detection and aging model, we used TensorFlow GPU (v 1.14) to train the model. For the developmental tracking model, we used TensorFlow CPU (v 1.14).…”

Section: Elegans On-chip Culturementioning

confidence: 99%

Deep learning for robust and flexible tracking in behavioral studies forC. elegans

Bates

2021

Preprint

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Robust and accurate behavioral tracking is essential for ethological studies. Common methods for tracking and extracting behavior rely on user adjusted heuristics that can significantly vary across different individuals, environments, and experimental conditions. As a result, they are difficult to implement in large-scale behavioral studies with complex, heterogenous environmental conditions. Recently developed deep-learning methods for object recognition such as Faster R-CNN have advantages in their speed, accuracy, and robustness. Here, we show that Faster R-CNN can be employed for identification and detection of Caenorhabditis elegans in a variety of life stages in complex environments. We applied the algorithm to track animal speeds during development, fecundity rates and spatial distribution in reproductive adults, and behavioral decline in aging populations. By doing so, we demonstrate the flexibility, speed, and scalability of Faster R-CNN across a variety of experimental conditions, illustrating its generalized use for future large-scale behavioral studies.

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“…In conclusion, we here present a tool that is capable of predicting C. elegans healthspan, based on myofilament features, as exemplified by our analysis of an insulin/IGF1-like signaling mutant. In its current form, this tool can be used downstream of the many high-throughput screens for genes, treatments and compounds that modulate healthspan in C. elegans (Bulterijs and Braeckman, 2020; Le et al, 2020; Luyten et al, 2016; Sayed et al, 2021). It objectively probes for the effect of such treatments on sarcopenia, an age-related pathology conserved from worms to humans.…”

Section: Discussionmentioning

confidence: 99%

Prediction of biological age by morphological staging of sarcopenia inCaenorhabditis elegans

Dhondt

Verschuuren

Zecic

et al. 2021

Preprint

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Sarcopenia encompasses a progressive decline in allover muscle quantity and quality. Given its close association with aging, it may represent a valuable healthspan marker. Given the strong commonalities with human muscle structure and the facile visualization possibilities, C. elegans represents an attractive model for studying the relationship between sarcopenia and healthspan. However, classical assessment relies on visual scoring of muscle architecture, which is subjective and inaccurate. To resolve this, we have developed an automated image analysis pipeline for the detailed quantification and classification of muscle integrity in confocal microscopy images from a cohort of aging myosin::GFP reporter strains. We then extracted a variety of morphological descriptors and found a subset to scale linearly with age. This allowed us to establish a general linear model that predicts biological age from a morphological muscle signature. To validate the model, we evaluated muscle architecture in long-lived worms that are known to experience delayed sarcopenia by targeted RNAi-mediated knockdown of the daf-2 gene. We conclude that quantitative microscopy allows for staging sarcopenia in C. elegans and will be of use for systematic screening for pharmacological or genetic modulators that mitigate age-related muscle frailty and thus improve healthspan in C. elegans.

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An automated platform to monitor long-term behavior and healthspan in Caenorhabditis elegans under precise environmental control

Cited by 48 publications

References 36 publications

"Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

"Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

Deep learning for robust and flexible tracking in behavioral studies forC. elegans

Prediction of biological age by morphological staging of sarcopenia inCaenorhabditis elegans

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