2018 SLAS Advanced 3D Human Models and High-Content Analysis Conference

This session will focus on applications of high throughput microscopy and multiparametric analysis (otherwise known as High Content Screening, HCS) in both basic and applied research. The development of platforms for creating 3D cellular systems will be presented. Automated imaging of 3D cellular systems is challenging as the objects are rare and the volume of data acquired is large. Advances for imaging these challenging objects will be presented. In summary, the session on HCS will present the cutting edge of screening with difficult, but physiologically highly relevant cellular systems.

Primary Pig Islets for Type 2 Diabetes Screening
Marc Bickle
The world is experiencing an epidemic of diabetes type 2, due to changes in life style and life expectancy. There is currently no effective drug treatment against type 2 diabetes although several drugs improve the condition and are used for controlling disease progression. Here, we took advantage of the development of a SNAP tagged version of insulin that is correctly processed into secretory granules to develop a screening assay for modifying insulin secretion and secretory granule metabolism. We started the development with the rat insuloma cell line Ins1 in a 2D cell culture system. Due to disappointing pharmacological results with this experimental system, a transgenic pig model was developed. An assay was developed using isolated pancreatic islets from transgenic pigs and a pilot screen executed.

Supervising the Unsupervised: Maximizing Biological Impact in High Content Cellular Imaging 
David Brierley
The exciting challenge of high content imaging data is the sheer number of options to recognize and retrieve meaningful content; while some turn to the ever-growing algorithmic tool-shed of machine learning, others utilize a priori knowledge of the biology at hand to arrive at the answer. With a balance between these two paramount, we implemented a hybrid workflow to drive compound data analysis within a high content multiparametric phenotypic screen focussed on enhancing macrophage phagocytosis. Allowing biological subject matter expertise to guide data-driven decisions, and vice-versa, we used a combination of knowledge-based, supervised, and unsupervised methods to both deconvolute healthy human-derived macrophages into distinct subtype-specific populations, and COPD patient-derived macrophages into patient-specific subpopulations. At this level of granularity, we could discern previously masked effects of compounds on healthy and diseased cells, both in their physical properties and population makeup. Such differences proved to be key when both understanding cell based phenotypic changes and exploring disease-specific eitology. Avoiding “black box” algorithms, instead favouring those which could be interrogated by biological and data scientists alike, led to faster and more relevant analysis cycles, and helped cement a “marriage” between statistical significance and human disease relevant biology.

High-Content Analysis of Autophagy Dynamics in Skeletal Muscle Using Living Zebrafish
Guillaume Jacquot
Autophagy is an intracellular catabolic process that promotes the recycling of organelles and cytoplasmatic components, acting as regulator of homeostasis and cellular metabolism. Several pathways either can positively or negatively regulate different steps of autophagosome formation and maturation, making autophagy a highly dynamic process. Due to its critical role in cellular quality control and metabolism, autophagy modulation has raised interest as possible therapeutic target for various human conditions ranging from age-related diseases, such as neurodegeneration and muscle frailty, to immunity and cancer. Despite the great potential, no molecules specifically targeting autophagy have been used for human interventions so far. In addition, most of the known autophagy modulators have been identified by in vitro screening, which is not sufficiently predictive and informative for in vivo implementation. Here, we describe a zebrafish transgenic autophagy reporter which expresses ZsGreen-tagged map1lc3 protein specifically in skeletal muscle: Tg(actc1b:map1lc3-ZsGreen;cryaa:TdTomato), hereafter termed actc1b:lc3-ZsGreen. Treatment of transgenic zebrafish larvae with spermidine clearly induced autophagic flux quantified by confocal microscopy. To further automate the analysis of autophagy dynamics in vivo, we developed a method for high-content image acquisition and analysis of actc1b:lc3-ZsGreen larvae.  The fully automated acquisition finds the location of three fish larvea per well of a 96-well plate using low-magnification objective and acquire subsequently a high-magnification Z-Stack of the Zebrafish tail. The acquired volume is then analyzed to provide quantitative analysis of the autophagic flux.