Evaluation of commercially available point-of-care ultrasound for automated optic nerve sheath measurement.

BACKGROUND: Measurement of the optic nerve sheath diameter (ONSD) via ultrasonography has been proposed as a non-invasive metric of intracranial pressure that may be employed during in-field patient triage. However, first responders are not typically trained to conduct sonographic exams and/or do not have access to an expensive ultrasound device. Therefore, for successful deployment of ONSD measurement in-field, we believe that first responders must have access to low-cost, portable ultrasound and be assisted by artificial intelligence (AI) systems that can automatically interpret the optic nerve sheath ultrasound scan. We examine the suitability of five commercially available, low-cost, portable ultrasound devices that can be combined with future artificial intelligence algorithms to reduce the training required for and cost of in-field optic nerve sheath diameter measurement. This paper is focused on the quality of the images generated by these low-cost probes. We report results of a clinician preference survey and compare with a lab analysis of three quantitative image quality metrics across devices. We also examine the suitability of the devices in a hypothetical far-forward deployment using operators unskilled in ultrasound, with the assumption of a future onboard AI video interpreter. RESULTS: We find statistically significant differences in clinician ranking of the devices in the following categories: "Image Quality", "Ease of Acquisition", "Software", and "Overall ONSD". We show differences in signal-to-noise ratio, generalized contrast-to-noise ratio, point-spread function across the devices. These differences in image quality result in a statistically significant difference in manual ONSD measurement. Finally, we show that sufficiently wide transducers can capture the optic nerve sheath during blind (no visible B-mode) scans performed by operators unskilled in sonography. CONCLUSIONS: Ultrasound of the optic nerve sheath has the potential to be a convenient, non-invasive, point-of-injury or triage measure for elevated intracranial pressure in cases of traumatic brain injury. When transducer width is sufficient, briefly trained operators may obtain video sequences of the optic nerve sheath without guidance. This data suggest that unskilled operators are able to achieve the images needed for AI interpretation. However, we also show that image quality differences between ultrasound probes may influence manual ONSD measurements.

Population Selection and Sequencing of Caenorhabditis elegans Wild Isolates Identifies a Region on Chromosome III Affecting Starvation Resistance.

To understand the genetic basis of complex traits, it is important to be able to efficiently phenotype many genetically distinct individuals. In the nematode Caenorhabditis elegans, individuals have been isolated from diverse populations around the globe and whole-genome sequenced. As a result, hundreds of wild strains with known genome sequences can be used for genome-wide association studies (GWAS). However, phenotypic analysis of these strains can be laborious, particularly for quantitative traits requiring multiple measurements per strain. Starvation resistance is likely a fitness-proximal trait for nematodes, and it is related to metabolic disease risk in humans. However, natural variation in C. elegans starvation resistance has not been systematically characterized, and precise measurement of the trait is time-intensive. Here, we developed a population-selection-and-sequencing-based approach to phenotype starvation resistance in a pool of 96 wild strains. We used restriction site-associated DNA sequencing (RAD-seq) to infer the frequency of each strain among survivors in a mixed culture over time during starvation. We used manual starvation survival assays to validate the trait data, confirming that strains that increased in frequency over time are starvation-resistant relative to strains that decreased in frequency. Further, we found that variation in starvation resistance is significantly associated with variation at a region on chromosome III. Using a near-isogenic line (NIL), we showed the importance of this genomic interval for starvation resistance. This study demonstrates the feasibility of using population selection and sequencing in an animal model for phenotypic analysis of quantitative traits, documents natural variation of starvation resistance in C. elegans, and identifies a genomic region that contributes to such variation.

daf-16/FoxO promotes gluconeogenesis and trehalose synthesis during starvation to support survival.

daf-16/FoxO is required to survive starvation in Caenorhabditis elegans, but how daf-16IFoxO promotes starvation resistance is unclear. We show that daf-16/FoxO restructures carbohydrate metabolism by driving carbon flux through the glyoxylate shunt and gluconeogenesis and into synthesis of trehalose, a disaccharide of glucose. Trehalose is a well-known stress protectant, capable of preserving membrane organization and protein structure during abiotic stress. Metabolomic, genetic, and pharmacological analyses confirm increased trehalose synthesis and further show that trehalose not only supports survival as a stress protectant but also serves as a glycolytic input. Furthermore, we provide evidence that metabolic cycling between trehalose and glucose is necessary for this dual function of trehalose. This work demonstrates that daf-16/FoxO promotes starvation resistance by shifting carbon metabolism to drive trehalose synthesis, which in turn supports survival by providing an energy source and acting as a stress protectant.

dbl-1/TGF-ß and daf-12/NHR Signaling Mediate Cell-Nonautonomous Effects of daf-16/FOXO on Starvation-Induced Developmental Arrest.

Nutrient availability has profound influence on development. In the nematode C. elegans, nutrient availability governs post-embryonic development. L1-stage larvae remain in a state of developmental arrest after hatching until they feed. This "L1 arrest" (or "L1 diapause") is associated with increased stress resistance, supporting starvation survival. Loss of the transcription factor daf-16/FOXO, an effector of insulin/IGF signaling, results in arrest-defective and starvation-sensitive phenotypes. We show that daf-16/FOXO regulates L1 arrest cell-nonautonomously, suggesting that insulin/IGF signaling regulates at least one additional signaling pathway. We used mRNA-seq to identify candidate signaling molecules affected by daf-16/FOXO during L1 arrest. dbl-1/TGF-ß, a ligand for the Sma/Mab pathway, daf-12/NHR and daf-36/oxygenase, an upstream component of the daf-12 steroid hormone signaling pathway, were up-regulated during L1 arrest in a daf-16/FOXO mutant. Using genetic epistasis analysis, we show that dbl-1/TGF-ß and daf-12/NHR steroid hormone signaling pathways are required for the daf-16/FOXO arrest-defective phenotype, suggesting that daf-16/FOXO represses dbl-1/TGF-ß, daf-12/NHR and daf-36/oxygenase. The dbl-1/TGF-ß and daf-12/NHR pathways have not previously been shown to affect L1 development, but we found that disruption of these pathways delayed L1 development in fed larvae, consistent with these pathways promoting development in starved daf-16/FOXO mutants. Though the dbl-1/TGF-ß and daf-12/NHR pathways are epistatic to daf-16/FOXO for the arrest-defective phenotype, disruption of these pathways does not suppress starvation sensitivity of daf-16/FOXO mutants. This observation uncouples starvation survival from developmental arrest, indicating that DAF-16/FOXO targets distinct effectors for each phenotype and revealing that inappropriate development during starvation does not cause the early demise of daf-16/FOXO mutants. Overall, this study shows that daf-16/FOXO promotes developmental arrest cell-nonautonomously by repressing pathways that promote larval development.

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture.

Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r(2) = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.

WormSizer: high-throughput analysis of nematode size and shape.

The fundamental phenotypes of growth rate, size and morphology are the result of complex interactions between genotype and environment. We developed a high-throughput software application, WormSizer, which computes size and shape of nematodes from brightfield images. Existing methods for estimating volume either coarsely model the nematode as a cylinder or assume the worm shape or opacity is invariant. Our estimate is more robust to changes in morphology or optical density as it only assumes radial symmetry. This open source software is written as a plugin for the well-known image-processing framework Fiji/ImageJ. It may therefore be extended easily. We evaluated the technical performance of this framework, and we used it to analyze growth and shape of several canonical Caenorhabditis elegans mutants in a developmental time series. We confirm quantitatively that a Dumpy (Dpy) mutant is short and fat and that a Long (Lon) mutant is long and thin. We show that daf-2 insulin-like receptor mutants are larger than wild-type upon hatching but grow slow, and WormSizer can distinguish dauer larvae from normal larvae. We also show that a Small (Sma) mutant is actually smaller than wild-type at all stages of larval development. WormSizer works with Uncoordinated (Unc) and Roller (Rol) mutants as well, indicating that it can be used with mutants despite behavioral phenotypes. We used our complete data set to perform a power analysis, giving users a sense of how many images are needed to detect different effect sizes. Our analysis confirms and extends on existing phenotypic characterization of well-characterized mutants, demonstrating the utility and robustness of WormSizer.

A microfluidic device and computational platform for high-throughput live imaging of gene expression.

To fully describe gene expression dynamics requires the ability to quantitatively capture expression in individual cells over time. Automated systems for acquiring and analyzing real-time images are needed to obtain unbiased data across many samples and conditions. We developed a microfluidics device, the RootArray, in which 64 Arabidopsis thaliana seedlings can be grown and their roots imaged by confocal microscopy over several days without manual intervention. To achieve high throughput, we decoupled acquisition from analysis. In the acquisition phase, we obtain images at low resolution and segment to identify regions of interest. Coordinates are communicated to the microscope to record the regions of interest at high resolution. In the analysis phase, we reconstruct three-dimensional objects from stitched high-resolution images and extract quantitative measurements from a virtual medial section of the root. We tracked hundreds of roots to capture detailed expression patterns of 12 transgenic reporter lines under different conditions.