PDMS filter structures for size-dependent larval sorting and on-chip egg extraction of<i>C. elegans</i>

Atakan, Hüseyin Baris; Ayhan, Furkan; Gijs, Martin A. M.

doi:10.1039/c9lc00949c

“…Likely due to the challenging nature of the task, several popular software packages for worm behavior quantification do not include methods to track egg-laying [ 18 , 19 , 46 ]. Other egg-counting methods rely on specialized cytometry to count eggs as they are flushed from liquid culture [ 47 , 48 ]. Another factor that makes counting eggs difficult is the burst-like timing of egg-laying events [ 49 – 51 ].…”

Section: Resultsmentioning

confidence: 99%

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

Bates

¹

,

Le

²

,

Lu

³

2022

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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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“…Likely due to the challenging nature of the task, several popular software packages for worm behavior quantification do not include methods to track egg-laying (N. Wählby et al 2012;Javer et al 2018). Other egg-counting methods rely on specialized cytometry to count eggs as they are flushed from liquid culture (Sofela et al 2018;Atakan et al 2020). Another factor that makes counting eggs difficult is the burst-like timing of egg-laying events (Waggoner et al 1998;Schafer 2005;Collins et al 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Likely due to the challenging nature of the task, several popular software packages for worm behavior quantification do not include methods to track egg-laying (Swierczek et al 2011;Wählby et al 2012;Javer et al 2018). Other egg-counting methods rely on specialized cytometry to count eggs as they are flushed from liquid culture (Sofela et al 2018;Atakan et al 2020).…”

Section: Resultsmentioning

confidence: 99%

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

Lu

¹

,

Bates

²

,

K

³

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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“…PDMS nanostructures can be used in climbing robots, 57 microfluidics 58 and dry dermal patch 11 devices. Here, an adhesion test was conducted to stimulate potential application as dry adhesive.…”

Section: Adhesive Strengthmentioning

confidence: 99%

Patterning of worm‐like soft polydimethylsiloxane structures using a TiO₂ nanotubular array

Harito

¹

,

Lledo

²

,

Bavykin

³

et al. 2020

J of Applied Polymer Sci

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Patterned polydimethylsiloxane (PDMS) is an important structure for soft lithography. Various materials have been deployed as mold for patterning PDMS. Anodized nanotubular array has been sought after as cost‐effective alternative for textured silicon. An array of TiO2 nanotubes with characteristic diameter ≈140 nm and the length of ≈1.5 microns, created by anodic oxidation of a titanium substrate, was used here as a template for soft PDMS molding. The optimal molding process was developed by a combination of silanization, use of solvent, application of a vacuum, and hydraulic pressing. The silanization was confirmed by Fourier transform infrared spectroscopy and contact angle measurements while the PDMS structure was examined by scanning electron microscope and energy dispersive X‐ray spectroscopy. Hydraulic pressing significantly improved the infiltration of PDMS into the pores of nanotubular array resulting in formation of PDMS nanobumps after separation of the polymer from the template. Complete infiltration of PDMS precursor into the cavity of nanotubes was observed on the hydraulic‐pressed sample without toluene impurities. The hydraulic‐pressed samples exhibited higher adhesion strength than nonpressed ones. The adhesive strength was measured by a simple experimental arrangement, in which the PDMS layer was stuck on a vertical glass surface followed by pulling it downwards.

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PDMS filter structures for size-dependent larval sorting and on-chip egg extraction ofC. elegans

Abstract: We present a microfluidic device for rapid larvae sorting and on-chip egg extraction of C. elegans at high purity and high throughput.

Cited by 15 publications

References 45 publications

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

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

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

Patterning of worm‐like soft polydimethylsiloxane structures using a TiO₂ nanotubular array

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PDMS filter structures for size-dependent larval sorting and on-chip egg extraction ofC. elegans

Abstract: We present a microfluidic device for rapid larvae sorting and on-chip egg extraction of C. elegans at high purity and high throughput.

Cited by 15 publications

References 45 publications

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

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

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

Patterning of worm‐like soft polydimethylsiloxane structures using a TiO2 nanotubular array

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Product

Resources

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Patterning of worm‐like soft polydimethylsiloxane structures using a TiO₂ nanotubular array