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Abstract Background Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput, traditional image-based analysis is mostly limited to error-prone qualitative or semi-quantitative assessment of phenotypes, particularly for complex subcellular morphologies. The PVD neuron in Caenorhabditis elegans , which is responsible for harsh touch and thermosensation, undergoes structural degeneration as nematodes age characterized by the appearance of dendritic protrusions. Analysis of these neurodegenerative patterns is labor-intensive and limited to qualitative assessment. Results In this work, we apply deep learning to perform quantitative image-based analysis of complex neurodegeneration patterns exhibited by the PVD neuron in C. elegans . We apply a convolutional neural network algorithm (Mask R-CNN) to identify neurodegenerative subcellular protrusions that appear after cold-shock or as a result of aging. A multiparametric phenotypic profile captures the unique morphological changes induced by each perturbation. We identify that acute cold-shock-induced neurodegeneration is reversible and depends on rearing temperature and, importantly, that aging and cold-shock induce distinct neuronal beading patterns. Conclusion The results of this work indicate that implementing deep learning for challenging image segmentation of PVD neurodegeneration enables quantitatively tracking subtle morphological changes in an unbiased manner. This analysis revealed that distinct patterns of morphological alteration are induced by aging and cold-shock, suggesting different mechanisms at play. This approach can be used to identify the molecular components involved in orchestrating neurodegeneration and to characterize the effect of other stressors on PVD degeneration. 

The ability to rapidly and accurately evaluate bioactive compounds immobilized on porous particles is crucial in the discovery of drugs, diagnostic reagents, ligands, and catalysts. Existing options for solid phase screening of bioactive compounds, while highly effective and well established, can be cost-prohibitive for proof-of-concept and early stage work, limiting its applicability and flexibility in new research areas. Here, we present a low-cost microfluidics-based platform enabling automated screening of small porous beads from solid-phase peptide libraries with high sensitivity and specificity, to identify leads with high binding affinity for a biological target. The integration of unbiased computer assisted image processing and analysis tools, provided the platform with the flexibility of sorting through beads with distinct fluorescence patterns. The customized design of the microfluidic device helped with handling beads with different diameters (~100–300 µm). As a microfluidic device, this portable novel platform can be integrated with a variety of analytical instruments to perform screening. In this study, the system utilizes fluorescence microscopy and unsupervised image analysis, and can operate at a sorting speed of up to 125 beads/hr (~3.5 times faster than a trained operator) providing >90% yield and >90% bead sorting accuracy. Notably, the device has proven successful in screening a model solid-phase peptide library by showing the ability to select beads carrying peptides binding a target protein (human IgG).

 

Photo‐affinity adsorbents (i.e., translucent matrices functionalized with ligands featuring light‐controlled biorecognition) represent a futuristic technology for purifying labile biologics. In this study, a framework for prototyping photo‐affinity adsorbents comprising azobenzene‐cyclized peptides (ACPs) conjugated to translucent porous beads (ChemMatrix) is presented. This approach combines computational and experimental tools for designing ACPs and investigating their light‐controlled isomerization kinetics and protein biorecognition. First, a modular design for tailoring ACP's conformation, facilitating sequencing, and streamlining the in silico modeling of cis/trans isomers and their differential protein binding is introduced. Then, a spectroscopic system for measuring the photo‐isomerization kinetics of ACPs on ChemMatrix beads is reported; using this device, it is demonstrated that the isomerization at different light intensities is correlated to the cyclization geometry, specifically the energy difference of trans versus cis isomers as calculated in silico. Also, a microfluidic device for sorting ACP‐ChemMatrix beads to select and validate photo‐affinity ligands using Vascular Cell Adhesion Molecule 1 (VCAM‐1) as target protein and cyclo_AZOB[GVHAKQHRN‐K*]‐G‐ChemMatrix as model photo‐affinity adsorbent is presented. The proposed ACPs exhibit rapid and defined light‐controlled isomerization and biorecognition. Controlling the adsorption and release of VCAM‐1 using light demonstrates the potential of photo‐affinity adsorbents for targets whose biochemical liability poses challenges to its purification.