74 research outputs found
Evolution and Impact of High Content Imaging
Abstract/outline: The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990′s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:• Evolution and impact of high content imaging: An academic perspective• Evolution and impact of high content imaging: An industry perspective• Evolution of high content image analysis• Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications• The role of data integration and multiomics• The role and evolution of image data repositories and sharing standards• Future perspective of high content imaging hardware and softwar
Image informatics approaches to advance cancer drug discovery
High content image-based screening assays utilise cell based models to extract and quantify morphological
phenotypes induced by small molecules. The rich datasets produced can be used to
identify lead compounds in drug discovery efforts, infer compound mechanism of action, or aid
biological understanding with the use of tool compounds. Here I present my work developing and
applying high-content image based screens of small molecules across a panel of eight genetically
and morphologically distinct breast cancer cell lines.
I implemented machine learning models to predict compound mechanism of action from morphological
data and assessed how well these models transfer to unseen cell lines, comparing the
use of numeric morphological features extracted using computer vision techniques against more
modern convolutional neural networks acting on raw image data.
The application of cell line panels have been widely used in pharmacogenomics in order to compare
the sensitivity between genetically distinct cell lines to drug treatments and identify molecular
biomarkers that predict response. I applied dimensional reduction techniques and distance metrics
to develop a measure of differential morphological response between cell lines to small molecule
treatment, which controls for the inherent morphological differences between untreated cell lines.
These methods were then applied to a screen of 13,000 lead-like small molecules across the eight
cell lines to identify compounds which produced distinct phenotypic responses between cell lines.
Putative hits from a subset of approved compounds were then validated in a three-dimensional
tumour spheroid assay to determine the functional effect of these compounds in more complex
models, as well as proteomics to determine the responsible pathways.
Using data generated from the compound screen, I carried out work towards integrating knowledge
of chemical structures with morphological data to infer mechanistic information of the unannotated
compounds, and assess structure activity relationships from cell-based imaging data
A novel high-content phenotypic screen to identify inhibitors of mitochondrial DNA maintenance in trypanosomes
Kinetoplastid parasites cause diverse neglected diseases in humans and livestock, with an urgent need for new treatments. The survival of kinetoplastids depends on their uniquely structured mitochondrial genome (kDNA), the eponymous kinetoplast. Here, we report the development of a high-content screen for pharmacologically induced kDNA loss, based on specific staining of parasites and automated image analysis. As proof of concept, we screened a diverse set of ∼14,000 small molecules and exemplify a validated hit as a novel kDNA-targeting compound
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