Automated, high-throughput, motility analysis in Caenorhabditis elegans and parasitic nematodes: Applications in the search for new anthelmintics

The scale of the damage worldwide to human health, animal health and agricultural crops resulting from parasitic nematodes, together with the paucity of treatments and the threat of developing resistance to the limited set of widely-deployed chemical tools, underlines the urgent need to develop novel drugs and chemicals to control nematode parasites. Robust chemical screens which can be automated are a key part of that discovery process. Hitherto, the successful automation of nematode behaviours has been a bottleneck in the chemical discovery process. As the measurement of nematode motility can provide a direct scalar readout of the activity of the neuromuscular system and an indirect measure of the health of the animal, this omission is acute. Motility offers a useful assay for high-throughput, phenotypic drug/chemical screening and several recent developments have helped realise, at least in part, the potential of nematode-based drug screening. Here we review the challenges encountered in automating nematode motility and some important developments in the application of machine vision, statistical imaging and tracking approaches which enable the automated characterisation of nematode movement. Such developments facilitate automated screening for new drugs and chemicals aimed at controlling human and animal nematode parasites (anthelmintics) and plant nematode parasites (nematicides)

Orally active antischistosomal early leads identified from the open access malaria box.

BACKGROUND: Worldwide hundreds of millions of schistosomiasis patients rely on treatment with a single drug, praziquantel. Therapeutic limitations and the threat of praziquantel resistance underline the need to discover and develop next generation drugs. METHODOLOGY: We studied the antischistosomal properties of the Medicines for Malaria Venture (MMV) malaria box containing 200 diverse drug-like and 200 probe-like compounds with confirmed in vitro activity against Plasmodium falciparum. Compounds were tested against schistosomula and adult Schistosoma mansoni in vitro. Based on in vitro performance, available pharmacokinetic profiles and toxicity data, selected compounds were investigated in vivo. PRINCIPAL FINDINGS: Promising antischistosomal activity (IC50: 1.4-9.5 µM) was observed for 34 compounds against schistosomula. Three compounds presented IC50 values between 0.8 and 1.3 µM against adult S. mansoni. Two promising early leads were identified, namely a N,N'-diarylurea and a 2,3-dianilinoquinoxaline. Treatment of S. mansoni infected mice with a single oral 400 mg/kg dose of these drugs resulted in significant worm burden reductions of 52.5% and 40.8%, respectively. CONCLUSIONS/SIGNIFICANCE: The two candidates identified by investigating the MMV malaria box are characterized by good pharmacokinetic profiles, low cytotoxic potential and easy chemistry and therefore offer an excellent starting point for antischistosomal drug discovery and development

Automated microscopy for high-content RNAi screening

Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs

Emergent patterns of cellular phenotypes in health and disease

The cellular framework that constitutes the building blocks of every living organism undergoes significant changes and transformations throughout its live time. In humans, many processes that involve these cellular changes can greatly influence the healthspan and survival of individuals, two of such processes include: aging and cancer. The two related, yet independent processes both arise due to the deterioration of ‘naïve’ cellular function, and the deficiency—later inability, of cells to properly regulate its physiology. Published studies have demonstrated a bi-phasic relationship between cancer and aging. With the incidences of cancer increasing with increasing age, followed by a plateau point and subsequent decrease; with cancer-type dependent shifts in this plateau point with age. There are a multitude of factors that affect the initiation and rate of progression of these cellular changes, and they stem from both intrinsic factors—such as the individuals’ underlying molecular and phenotypic profiles (i.e. genetics and protein expressions)—and extrinsic factors, such as lifestyle and environmental influences. To gain better understanding of these two naturally occurring processes, I took a piece-wise approach and asked two overarching questions. In regards to aging I asked how does the biochemical and biophysical features of cells construct the phenotypic portrait of human aging, and cane it be used to determine the biological age of individuals? Likewise, in regards to cancer: how does the cells’ physical properties associate with cancer progression and metastasis, and can it predict metastatic state based on the features of individual cells? In the first part of this study, I focus on human aging. Many studies have shown that there are marked changes in the cells’ molecular profiles and phenotypic behaviors with increasing age. To better understand this I procured a cohort of primary dermal fibroblasts and measured various aspects of the cellular biochemical framework (cell secretions, DNA damage response and DNA organization, cytoskeletal content and organization, and ATP content), as well as cellular biophysical features (morphology, motility, wound closure, traction strength, and cytoplasmic rheological properties). With this comprehensive approach, I was able to quantify age-dependent changes in various cellular features, and use these features to further predict biological age with a high degree of certainty. Knowing the biological age of an individual is important, since it is now apparent from the literature that the biological age is a better predictor of human healthspan and longevity than their corresponding chronological age. Secondly, according to the American Cancer Society, two out of every five persons in the US will develop cancer during his/her lifetime, with ninety percent of cancer-related deaths resulting from metastases, i.e. the migration of cancer cells from the primary tumor to distal sites in other organs. Since the completion of the Human Genome Project, researchers have focused on trying to understand the genetic basis of metastasis in an effort to better predict disease progression and uncover new therapeutic targets. However, possibly due to the inherent heterogeneity of cancer, no genetic signatures that clearly delineate cells from the primary tumors versus cells from metastatic sites have been found. Recent estimates suggest that millions of cells are shed from a primary tumor site each day, yet, progression to metastatic disease often take years, suggesting that metastasis is a highly inefficient process. From a biophysical perspective, I reasoned that in order to successfully overcome the difficult multi-step metastatic cascade—invasion and migration through the dense, tortuous stromal matrix, intravasation, survival of shear forces of blood flow, successful re-attachment to blood vessel walls, colonization at distal sites, and reactivation following dormancy—metastatic cells may share precise sets of physical properties. And these key physical properties (which can be thought of as the ensemble effects of it’s genetic, epigenetic and proteomic profiles, etc.) may contribute to the progression and diminished response to therapeutics exhibited by metastatic cells. Using a cohort of 13 clinically annotated PDAC (Pancreatic ductal adenocarcinoma) patient samples, cells were subjected to a phenotyping platform that I have co-developed—htCP (high-throughput cell phenotyping). This study revealed that using biophysical features described by the variations in the cellular morphological features, I was able to discover a phenotypic signature for metastasis, demonstrated in pancreatic and breast cancers, for both 2D and 3D environments

SegNema: Nematode segmentation strategy in digital microscopy images using deep learning and shape models

Proyecto de Graduación (Maestría en Computación con énfasis en Ciencias de la Computación) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Computación, 2019.Nematodes are the most numerous multicellular animals on Earth and their study has a direct impact in the improvement and development of agricultural activities. This document introduces SegNema, a strategy for the segmentation of nematodes in microscopy images where deep learning is used for classification of pixels as nematode or background, and a shape model is used to associate landmarks that describe the position of the nematode in the image. To train the segmentation model, a set of 2939 manually labeled uncompressed images of size 1024 ⇥ 768 pixels obtained from 13 di↵erent sequences of microscopy images is used. The landmarks that describe the position of the nematodes in these training images are used to adjust a model capable of representing shapes corresponding to a nematode. The disparity between the shapes of the regions classified as nematode in the segmentation stage and their possible truncated representation with the shape model is used to rule out possible erroneous classifications. The validation of this model was performed on 321 images of the microscopy sequences that were not used in the training stage. In each image used for training and validation, there is information on the position of landmarks where a single nematode is delimited although more nematodes may be present.Los nematodos son los animales pluricelulares más numerosos en la Tierra y su estudio tiene un impacto en el desarrollo de actividades agrícolas. En este documento se introduce SegNema, una estrategia para la segmentación de nematodos en imágenes de microscopia donde se utiliza aprendizaje profundo para clasificación de píxeles como nematodo o fondo, y modelos de forma para asociar hitos que describen la posición del nematodo en la imagen. Para entrenar el modelo de segmentación se usan 2939 imágenes sin comprimir etiquetadas manualmente de tamaño 1024 ⇥ 768 p´ıxeles obtenidas de 13 secuencias de imágenes de microscopia. Por otro lado, los hitos que describen la posición de los nematodos en estas imágenes de entrenamiento son utilizados para ajustar un modelo capaz de representar formas correspondientes a nematodo. La disparidad entre formas de las regiones clasificadas como nematodo en la etapa de segmentación y su posible representación truncada con el modelo de forma es usado para descartar posibles clasificaciones err´oneas. Para la validación de este modelo se usan 321 imágenes de las secuencias de microscopia que no son utilizadas en la etapa de entrenamiento. En cada imagen usada para entrenamiento y validación existe la información de la posici´on de hitos donde se delimita un único nematodo aunque otros nematodos pueden estar presentes

Computing Interpretable Representations of Cell Morphodynamics

Shape changes (morphodynamics) are one of the principal ways cells interact with their environments and perform key intrinsic behaviours like division. These dynamics arise from a myriad of complex signalling pathways that often organise with emergent simplicity to carry out critical functions including predation, collaboration and migration. A powerful method for analysis can therefore be to quantify this emergent structure, bypassing the low-level complexity. Enormous image datasets are now available to mine. However, it can be difficult to uncover interpretable representations of the global organisation of these heterogeneous dynamic processes. Here, such representations were developed for interpreting morphodynamics in two key areas: mode of action (MoA) comparison for drug discovery (developed using the economically devastating Asian soybean rust crop pathogen) and 3D migration of immune system T cells through extracellular matrices (ECMs). For MoA comparison, population development over a 2D space of shapes (morphospace) was described using two models with condition-dependent parameters: a top-down model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. A variety of landscapes were discovered, describing phenotype transitions during growth, and possible perturbations in the tip growth machinery that cause this variation were identified. For interpreting T cell migration, a new 3D shape descriptor that incorporates key polarisation information was developed, revealing low-dimensionality of shape, and the distinct morphodynamics of run-and-stop modes that emerge at minute timescales were mapped. Periodically oscillating morphodynamics that include retrograde deformation flows were found to underlie active translocation (run mode). Overall, it was found that highly interpretable representations could be uncovered while still leveraging the enormous discovery power of deep learning algorithms. The results show that whole-cell morphodynamics can be a convenient and powerful place to search for structure, with potentially life-saving applications in medicine and biocide discovery as well as immunotherapeutics.Open Acces

A TALE of Two Nucleases: Using TALENs to Edit the Genome of C. elegans

Genetic engineering is an emerging technology that offers the potential to prevent, treat, or cure genetic diseases. The technology can permanently alter the genome, providing an alternative therapy to drugs and surgery. Specifically, gene therapy is a promising treatment option for many incurable genetic diseases, such as cystic fibrosis and muscular cell dystrophy. Our project gives rise to a better understanding of TALENs and its uses in the genetic engineering field. TALENs, transcription activator-like effector nucleases, are a genetic engineering technology that can be used for targeted gene modification. They are engineered proteins that can bind to specific sequences of DNA and induce a double-stranded break. The DNA sequence that the TALENs bind to is determined by the user; therefore the TALENs can be engineered to target specific DNA sequences that cause genetic diseases. We used TALENs within the model organism, nematode C. elegans, to explore their potential for use in gene therapy. By utilizing TALENs to introduce a lin-31 mutant into the genome of C. elegans we aim to advance the understanding of TALENs as a genetic engineering tool and contribute to the research on the docking site of LIN-31 in the Ras/MAPK signaling pathway. Our group was successful in creating the DNA that encodes for these TALENs proteins, providing a foundation for future student researchers to continue on the project