Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant

Identification of Neural Outgrowth Genes using Genome-Wide RNAi

While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system

A Guide to Competencies, Educational Goals, and Learning Objectives for Teaching Human Embryology in an Undergraduate Medical Education Setting

With the rapidly changing course of medical education and ever-increasing time restrictions on basic biomedical science instruction, most educators have one question in common—what is the most relevant information for the next generation of physicians? The Liaison Committee for Medical Education (LCME) and the Commission on Osteopathic College Accreditation (COCA) support a list of learning objectives for medical students defined by faculty prior to any educational activities, regardless of pedagogy. The question remains—what ensures competency for medical students in a given subject area upon completion of the course? To accomplish the task to ensure competency in human clinical embryology, a 6-month interactive online collaboration was formed. The outcome is a set of competencies in human embryology that should be required of all medical students, with the goals and learning objectives required to achieve these competencies

Use of Emerging 3D Printing and Modeling Technologies in the Health Domain

Three-Dimensional (3D) technologies emerged from the technological advances in manufacturing required to produce physical versions of digital models. The most attractive feature of 3D technologies is that virtual models are easy to mold, and custom-made items can be physically produced. Health domains are areas in which 3D technologies have been applied, and several studies have been conducted assessing the usefulness of such technologies in those domains. In this paper we present the results of a Systematic Literature Review (SLR) on the applications of 3D technologies in the health domain. Discussion from the revision of 33 papers is presented. The main finding of this SLR is that none of the available research papers are focused on computer science related areas (i.e., all papers are published by doctors or researchers in Medicine). Moreover, all the included papers were published in journals specialized in Medicine. Therefore, they do not delve in the computational conclusions of the studies. In this article, we identified significant research gaps (from the computational perspective), as well as new ideas are being proposed on the future of 3D technologies in health.Universidad de Costa Rica/[834-B6-076]/VINV/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ingeniería::Centro de Investigaciones en Tecnologías de Información y Comunicación (CITIC