Microfluidic devices for imaging and manipulation of C. elegans

Clark, A. Clay; Huayta, Javier; Arulalan, Karthik Suresh; San-Miguel, Adriana

doi:10.1016/b978-0-12-823990-2.00013-1

Cited by 3 publications

(2 citation statements)

References 109 publications

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“…Levamisole alone will not effectively immobilize the worms, and in our experience, there will be zero movements in 5 of the 25 videos collected for in vivo live microscopy analysis when only anesthetic levamisole was used, thus increasing the overall effort of microscopy analysis. A microfluidic device or polystyrene beads, or a higher concentration of agar pads are alternative ways of immobilizing worms ( Kim et al., 2013 ; Clark et al., 2021 ). They can be used in conjunction with levamisole.…”

Section: Step-by-step Methods Detailsmentioning

confidence: 99%

Protocol for determining the average speed and frequency of kinesin and dynein-driven intraflagellar transport (IFT) in C. elegans

et al. 2022

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Section: Step-by-step Methods Detailsmentioning

confidence: 99%

Protocol for determining the average speed and frequency of kinesin and dynein-driven intraflagellar transport (IFT) in C. elegans

et al. 2022

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“…Later, the complex object parametric analyzer and sorter (COPAS) was developed for efficient and high-throughput worm sorting, utilizing fluorescent signals and other optical features [ 21 , 22 ]. However, the widespread use of COPAS is limited due to its bulkiness, high cost, and operational complexity [ 23 ]. In recent years, the advancements of lab-on-a-chip (microfluidics) have made it a promising platform for facilitating various studies, such as microinjection and drug screening [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7

et al. 2023

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The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union (mAP@0.5) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an mAP@0.5 of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms.

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Distinct bacterial population dynamics and disease dissemination after biofilm dispersal and disassembly

Deng

Hua

et al. 2023

The ISME Journal

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Microbial communities that form surface-attached biofilms must release and disperse their constituent cells into the environment to colonize fresh sites for continued survival of their species. For pathogens, biofilm dispersal is crucial for microbial transmission from environmental reservoirs to hosts, cross-host transmission, and dissemination of infections across tissues within the host. However, research on biofilm dispersal and its consequences in colonization of fresh sites remain poorly understood. Bacterial cells can depart from biofilms via stimuli-induced dispersal or disassembly due to direct degradation of the biofilm matrix, but the complex heterogeneity of bacterial populations released from biofilms rendered their study difficult. Using a novel 3D-bacterial “biofilm-dispersal-then-recolonization” (BDR) microfluidic model, we demonstrated that Pseudomonas aeruginosa biofilms undergo distinct spatiotemporal dynamics during chemical-induced dispersal (CID) and enzymatic disassembly (EDA), with contrasting consequences in recolonization and disease dissemination. Active CID required bacteria to employ bdlA dispersal gene and flagella to depart from biofilms as single cells at consistent velocities but could not recolonize fresh surfaces. This prevented the disseminated bacteria cells from infecting lung spheroids and Caenorhabditis elegans in on-chip coculture experiments. In contrast, EDA by degradation of a major biofilm exopolysaccharide (Psl) released immotile aggregates at high initial velocities, enabling the bacteria to recolonize fresh surfaces and cause infections in the hosts efficiently. Hence, biofilm dispersal is more complex than previously thought, where bacterial populations adopting distinct behavior after biofilm departure may be the key to survival of bacterial species and dissemination of diseases.

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Microfluidic devices for imaging and manipulation of C. elegans

Cited by 3 publications

References 109 publications

Protocol for determining the average speed and frequency of kinesin and dynein-driven intraflagellar transport (IFT) in C. elegans

Protocol for determining the average speed and frequency of kinesin and dynein-driven intraflagellar transport (IFT) in C. elegans

Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7

Distinct bacterial population dynamics and disease dissemination after biofilm dispersal and disassembly

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