Abstract
High Content Analysis (HCA) has become a cornerstone of cellular analysis within the drug discovery industry. To expand the capabilities of HCA, we have applied the same analysis methods, validated in numerous mammalian cell models, to microbiology methodology. Image acquisition and analysis of various microbial samples, ranging from pure cultures to culture mixtures containing up to three different bacterial species, were quantified and identified using various machine learning processes. These HCA techniques allow for faster cell enumeration than standard agar-plating methods, identification of “viable but not plate culturable” microbe phenotype, classification of antibiotic treatment effects, and identification of individual microbial strains in mixed cultures. These methods greatly expand the utility of HCA methods and automate tedious and low-throughput standard microbiological methods.
Highlights
High Content Analysis (HCA) methods are widely deployed in the drug discovery realm and are utilized for phenotypic drug discovery, targeted drug discovery, and target identification and characterization[1]
Our initial efforts were focused on the practicality of using an HCA instrument to image microbes
Direct microscopic observation is common in microbiology, utilizing a multi-well plate as the sample holder vs. a glass slide and cover slip presented the simple challenge of getting the cells in a flat, planar location to obtain in-focus images
Summary
High Content Analysis (HCA) methods are widely deployed in the drug discovery realm and are utilized for phenotypic drug discovery, targeted drug discovery, and target identification and characterization[1]. Enabled by the commercialization of various HCA instruments beginning in the late 1990’s, these automated microscopic platforms have greatly expanded the ability to perform cell-based screening by reducing labor time and variability of manual microscopic analysis. The ability of HCA instruments to multiplex data types and associate numerous features to individual cells can generate large datasets, which allows for feature analysis on a massive scale utilizing advanced machine learning techniques[2]. Linking high-content imaging, single-cell analysis, and machine learning, researchers within drug discovery accelerate the process and quickly extract results from big data. We demonstrate here the power of HCA to progress beyond the traditional methods of colony
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
