Selected Grantee Publications
Deep Learning Is Widely Applicable to Phenotyping Embryonic Development and Disease
Naert et al., Development. 2021.
https://pubmed.ncbi.nlm.nih.gov/34739029/
Genome editing simplifies the generation of new animal models for congenital disorders. The authors illustrate how deep learning (U-Net) automates segmentation tasks in various imaging modalities. They demonstrate this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). They provide a library of pre-trained networks and detailed instructions for applying deep learning to datasets and demonstrate the versatility, precision, and scalability of deep neural network phenotyping on embryonic disease models. Supported by ORIP (P40OD010997, R24OD030008), NICHD, NIDDK, and NIMH.
Fluorescence-Based Sorting of Caenorhabditis elegans via Acoustofluidics
Zhang et al., Lab on a Chip. 2020.
The authors present an integrated acoustofluidic chip capable of identifying worms of interest based on expression of a fluorescent protein in a continuous flow and then separate them in a high-throughput manner. Utilizing planar fiber optics, their acoustofluidic device requires no temporary immobilization of worms for interrogation/detection, thereby improving the throughput. The device can sort worms of different developmental stages (L3 and L4 stage worms) at high throughput and accuracy. In their acoustofluidic chip, the time to complete the detection and sorting of one worm is only 50 ms, which outperforms nearly all existing microfluidics-based worm sorting devices. Supported by ORIP (R43OD024963), NIEHS, and NIDDK.