Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100$\times$ increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.Comment: 6 pages, 5 figures, submitted to IEEE HPEC 2020 proceeding

An image analysis toolbox for high-throughput C. elegans assays

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available through the open-source CellProfiler project and enables objective scoring of whole-worm high-throughput image-based assays of C. elegans for the study of diverse biological pathways that are relevant to human disease.National Institutes of Health (U.S.) (U54 EB005149

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data set. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery

QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents

Crowdsourcing the creation of image segmentation algorithms for connectomics

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This “deep learning” approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI

UNC-45a promotes myosin folding and stress fiber assembly

Contractile actomyosin bundles, stress fibers, are crucial for adhesion, morphogenesis, and mechanosensing in nonmuscle cells. However, the mechanisms by which nonmuscle myosin II (NM-II) is recruited to those structures and assembled into functional bipolar filaments have remained elusive. We report that UNC-45a is a dynamic component of actin stress fibers and functions as a myosin chaperone in vivo. UNC-45a knockout cells display severe defects in stress fiber assembly and consequent abnormalities in cell morphogenesis, polarity, and migration. Experiments combining structured-illumination microscopy, gradient centrifugation, and proteasome inhibition approaches revealed that a large fraction of NM-II and myosin-1c molecules fail to fold in the absence of UNC-45a. The remaining properly folded NM-II molecules display defects in forming functional bipolar filaments. The C-terminal UNC-45/Cro1/She4p domain of UNC-45a is critical for NM-II folding, whereas the N-terminal tetratricopeptide repeat domain contributes to the assembly of functional stress fibers. Thus, UNC-45a promotes generation of contractile actomyosin bundles through synchronized NM-II folding and filament-assembly activities.Peer reviewe

661W photoreceptor cell line as a cell model for studying retinal ciliopathies

Copyright © 2019 Wheway, Nazlamova, Turner and Cross. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. The retina contains several ciliated cell types, including the retinal pigment epithelium (RPE) and photoreceptor cells. The photoreceptor cilium is one of the most highly modified sensory cilia in the human body. The outer segment of the photoreceptor is a highly elaborate primary cilium, containing stacks or folds of membrane where the photopigment molecules are located. Perhaps unsurprisingly, defects in cilia often lead to retinal phenotypes, either as part of syndromic conditions involving other organs, or in isolation in the so-called retinal ciliopathies. The study of retinal ciliopathies has been limited by a lack of retinal cell lines. RPE1 retinal pigment epithelial cell line is commonly used in such studies, but the existence of a photoreceptor cell line has largely been neglected in the retinal ciliopathy field. 661W cone photoreceptor cells, derived from mouse, have been widely used as a model for studying macular degeneration, but not described as a model for studying retinal ciliopathies such as retinitis pigmentosa. Here, we characterize the 661W cell line as a model for studying retinal ciliopathies. We fully characterize the expression profile of these cells, using whole transcriptome RNA sequencing, and provide this data on Gene Expression Omnibus for the advantage of the scientific community. We show that these cells express the majority of markers of cone cell origin. Using immunostaining and confocal microscopy, alongside scanning electron microscopy, we show that these cells grow long primary cilia, reminiscent of photoreceptor outer segments, and localize many cilium proteins to the axoneme, membrane and transition zone. We show that siRNA knockdown of cilia genes Ift88 results in loss of cilia, and that this can be assayed by high-throughput screening. We present evidence that the 661W cell line is a useful cell model for studying retinal ciliopathies

APETALA2 control of barley internode elongation

Many plants dramatically elongate their stems during flowering, yet how this response is coordinated with the reproductive phase is unclear. We demonstrate that microRNA (miRNA) control of APETALA2 (AP2) is required for rapid, complete elongation of stem internodes in barley, especially of the final 'peduncle' internode directly underneath the inflorescence. Disrupted miR172 targeting of AP2 in the Zeo1.b barley mutant caused lower mitotic activity, delayed growth dynamics and premature lignification in the peduncle leading to fewer and shorter cells. Stage- and tissue-specific comparative transcriptomics between Zeo1.b and its parent cultivar showed reduced expression of proliferation-associated genes, ectopic expression of maturation-related genes and persistent, elevated expression of genes associated with jasmonate and stress responses. We further show that applying methyl jasmonate (MeJA) phenocopied the stem elongation of Zeo1.b, and that Zeo1.b itself was hypersensitive to inhibition by MeJA but less responsive to promotion by gibberellin. Taken together, we propose that miR172-mediated restriction of AP2 may modulate the jasmonate pathway to facilitate gibberellin-promoted stem growth during flowering