Morphological hallmarks of dopaminergic neurodegeneration are associated with altered neuron function in Caenorhabditis elegans.

Caenorhabditis elegans (C. elegans) is an excellent model system to study neurodegenerative diseases, such as Parkinson's disease, as it enables analysis of both neuron morphology and function in live animals. Multiple structural changes in neurons, such as cephalic dendrite morphological abnormalities, have been considered hallmarks of neurodegeneration in this model, but their relevance to changes in neuron function are not entirely clear. We sought to test whether hallmark morphological changes associated with chemically induced dopaminergic neuron degeneration, such as dendrite blebbing, breakage, and loss, are indicative of neuronal malfunction and result in changes in behavior. We adapted an established dopaminergic neuronal function assay by measuring paralysis in the presence of exogenous dopamine, which revealed clear differences between cat-2 dopamine deficient mutants, wildtype worms, and dat-1 dopamine abundant mutants. Next, we integrated an automated image processing algorithm and a microfluidic device to segregate worm populations by their cephalic dendrite morphologies. We show that nematodes with dopaminergic dendrite degeneration markers, such as blebbing or breakage, paralyze at higher rates in a dopamine solution, providing evidence that dopaminergic neurodegeneration morphologies are correlated with functional neuronal outputs.

A primordial DNA store and compute engine.

Any modern information system is expected to feature a set of primordial features and functions: a substrate stably carrying data; the ability to repeatedly write, read, erase, reload and compute on specific data from that substrate; and the overall ability to execute such functions in a seamless and programmable manner. For nascent molecular information technologies, proof-of-principle realization of this set of primordial capabilities would advance the vision for their continued development. Here we present a DNA-based store and compute engine that captures these primordial capabilities. This system comprises multiple image files encoded into DNA and adsorbed onto ~50-µm-diameter, highly porous, hierarchically branched, colloidal substrate particles comprised of naturally abundant cellulose acetate. Their surface areas are over 200 cm2 mg-1 with binding capacities of over 1012 DNA oligos mg-1, 10 TB mg-1 or 104 TB cm-3. This 'dendricolloid' stably holds DNA files better than bare DNA with an extrapolated ability to be repeatedly lyophilized and rehydrated over 170 times compared with 60 times, respectively. Accelerated ageing studies project half-lives of ~6,000 and 2 million years at 4 °C and -18 °C, respectively. The data can also be erased and replaced, and non-destructive file access is achieved through transcribing from distinct synthetic promoters. The resultant RNA molecules can be directly read via nanopore sequencing and can also be enzymatically computed to solve simplified 3 × 3 chess and sudoku problems. Our study establishes a feasible route for utilizing the high information density and parallel computational advantages of nucleic acids.

An unbiased, automated platform for scoring dopaminergic neurodegeneration in C. elegans.

Caenorhabditis elegans ( C. elegans ) has served as a simple model organism to study dopaminergic neurodegeneration, as it enables quantitative analysis of cellular and sub-cellular morphologies in live animals. These isogenic nematodes have a rapid life cycle and transparent body, making high-throughput imaging and evaluation of fluorescently tagged neurons possible. However, the current state-of-the-art method for quantifying dopaminergic degeneration requires researchers to manually examine images and score dendrites into groups of varying levels of neurodegeneration severity, which is time consuming, subject to bias, and limited in data sensitivity. We aim to overcome the pitfalls of manual neuron scoring by developing an automated, unbiased image processing algorithm to quantify dopaminergic neurodegeneration in C. elegans . The algorithm can be used on images acquired with different microscopy setups and only requires two inputs: a maximum projection image of the four cephalic neurons in the C. elegans head and the pixel size of the user’s camera. We validate the platform by detecting and quantifying neurodegeneration in nematodes exposed to rotenone, cold shock, and 6-hydroxydopamine using 63x epifluorescence, 63x confocal, and 40x epifluorescence microscopy, respectively. Analysis of tubby mutant worms with altered fat storage showed that, contrary to our hypothesis, increased adiposity did not sensitize to stressor-induced neurodegeneration. We further verify the accuracy of the algorithm by comparing code-generated, categorical degeneration results with manually scored dendrites of the same experiments. The platform, which detects 19 different metrics of neurodegeneration, can provide comparative insight into how each exposure affects dopaminergic neurodegeneration patterns.

Morphological hallmarks of dopaminergic neurodegeneration are associated with altered neuron function in Caenorhabditis elegans.

Caenorhabditis elegans (C. elegans) is an excellent model system to study neurodegenerative diseases, such as Parkinson's disease, as it enables analysis of both neuron morphology and function in live animals. Multiple structural changes in neurons, such as cephalic dendrite morphological abnormalities, have been considered hallmarks of neurodegeneration in this model, but their relevance to changes in neuron function are not entirely clear. We sought to test whether hallmark morphological changes associated with chemically induced dopaminergic neuron degeneration, such as dendrite blebbing, breakage, and loss, are indicative of neuronal malfunction and result in changes in behavior. We adapted an established dopaminergic neuronal function assay by measuring paralysis in the presence of exogenous dopamine, which revealed clear differences between cat-2 dopamine deficient mutants, wildtype worms, and dat-1 dopamine abundant mutants. Next, we integrated an automated image processing algorithm and a microfluidic device to segregate worm populations by their cephalic dendrite morphologies. We show that nematodes with dopaminergic dendrite degeneration markers, such as blebbing or breakage, paralyze at higher rates in a dopamine solution, providing evidence that dopaminergic neurodegeneration morphologies are correlated with functional neuronal outputs.

An unbiased, automated platform for scoring dopaminergic neurodegeneration in C. elegans.

Caenorhabditis elegans (C. elegans) has served as a simple model organism to study dopaminergic neurodegeneration, as it enables quantitative analysis of cellular and sub-cellular morphologies in live animals. These isogenic nematodes have a rapid life cycle and transparent body, making high-throughput imaging and evaluation of fluorescently tagged neurons possible. However, the current state-of-the-art method for quantifying dopaminergic degeneration requires researchers to manually examine images and score dendrites into groups of varying levels of neurodegeneration severity, which is time consuming, subject to bias, and limited in data sensitivity. We aim to overcome the pitfalls of manual neuron scoring by developing an automated, unbiased image processing algorithm to quantify dopaminergic neurodegeneration in C. elegans. The algorithm can be used on images acquired with different microscopy setups and only requires two inputs: a maximum projection image of the four cephalic neurons in the C. elegans head and the pixel size of the user's camera. We validate the platform by detecting and quantifying neurodegeneration in nematodes exposed to rotenone, cold shock, and 6-hydroxydopamine using 63x epifluorescence, 63x confocal, and 40x epifluorescence microscopy, respectively. Analysis of tubby mutant worms with altered fat storage showed that, contrary to our hypothesis, increased adiposity did not sensitize to stressor-induced neurodegeneration. We further verify the accuracy of the algorithm by comparing code-generated, categorical degeneration results with manually scored dendrites of the same experiments. The platform, which detects 20 different metrics of neurodegeneration, can provide comparative insight into how each exposure affects dopaminergic neurodegeneration patterns.

A bioinspired, passive microfluidic lobe filtration system.

Size-based microfluidic filtration systems can be affected by clogging, which prevents their use in high-throughput and continuous applications. To address these concerns, we have developed two microfluidic lobe filters bioinspired by the filtration mechanism of two species of manta ray. These chips enable filtration of particles around 10-30 µm with precise control and high throughput by using two arrays of equally spaced filter lobes. For each filter design, we investigated multiple inlet flow rates and particle sizes to identify successful operational parameters. Filtration efficiency increases with fluid flow rate, suggesting that particle inertial effects play a key role in lobe filter separation. Microparticle filtration efficiencies up to 99% were obtainable with inlet flow rates of 20 mL min-1. Each filter design successfully increased microparticle concentrations by a factor of two or greater at different inlet flow rates ranging from 6-16 mL min-1. At higher inlet flow rates, ANSYS Fluent simulations of each device revealed a complex velocity profile that contains three local maxima and two inflection points. Ultimately, we show that distances from the lobe array to the closest local maxima and inflection point of the velocity profile can be used to successfully estimate lobe filtration efficiency at each operational flow rate.