A microfluidic platform for lifelong high-resolution and high throughput imaging of subtle aging phenotypes inC. elegans

Abstract

Aging produces a number of changes in the neuronal structure and function throughout a variety of organisms. These aging-induced changes encompass a wide range of phenotypes, from loss of locomotion ability to defective production of synaptic vesicles. C. elegans is one of the primary systems used to elucidate phenotypes associated with aging processes. Conventional aging studies in C. elegans are typically labor-intensive, low-throughput, and incorporate fluorodeoxyuridine (FUdR) as a sterilizing agent to keep the population age-synchronized throughout the assay. However, FUdR exposure induces lifespan extension, and can potentially mask the phenotypes associated with the natural aging process. In addition, studying cellular or subcellular structures requires anesthetics or adhesives to immobilize nematodes while acquiring high-resolution images. In this platform, we are able to maintain a population (∼1000 worms) age-synchronized throughout its lifespan and perform a series of high-resolution microscopy studies in a drug-free environment. The device is composed of two main interconnected sections, one with the purpose of filtering progeny while keeping the parent population intact, and one for trapping nematodes in individual compartments for microscopy. Immobilization is carried out by decreasing the temperature of the device where nematodes are trapped by placing a heat sink on top of the chip. We were able to perform periodic high-resolution microscopy of fluorescently tagged synapses located at the dorsal side of the nematode's tail throughout the worms' lifespan. To characterize the subtle phenotypes that emerge as nematodes age, computer vision was implemented to perform automated unbiased detection of synapses and quantitative analysis of aging-induced synaptic changes.

Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock

“…Quantitative analysis of PVD neurodegeneration morphology is important to understand the root causes of neurodegeneration. Machine learning has proven useful for analysis of biological systems and deep phenotyping [ 9 , 43 – 45 ]. Specifically, deep phenotyping and machine learning have been implemented in several C. elegans studies.…”

Deep learning-enabled phenotyping reveals distinct patterns of neurodegeneration induced by aging and cold-shock

“…Quantitative analysis of PVD neurodegeneration morphology is important to understand the root causes of neurodegeneration. Machine learning has proven useful for analysis of biological systems and deep phenotyping 9,[38][39][40] . In this work, we sought to integrate cutting-edge deep learning approaches to segment beads in PVD fluorescence images from live animals (Figure 1b).…”

WormBot, an open-source robotics platform for survival and behavior analysis in C. elegans

“…Generally, the imaging-based approaches fall into two categories. First, several microfluidic devices have been developed that allow worms to be kept in liquid media and assayed by light microscopy-based systems (Xian et al 2013;Banse et al 2019a;Saberi-Bosari et al 2018). Microfluidic systems have some benefits over traditional solid culture.…”