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Friday, October 4 • 10:35am - 10:55am
AI in Screening- Label Free Induced Pluripotent Stem Cell Counting With Deep learning For Automation

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 Label Free Induced Pluripotent Stem Cell Counting With Deep learning For Automation
Artificial intelligence (AI) and deep learning can build smarter and faster automated systems while eliminating the number of steps required in the process. We wanted to evaluate how well an AI could predict cell numbers of human induced pluripotent stem cells (iPSCs) from phase or bright field images for the purposes of automating iPSC culture maintenance. The shift to an automated process can result in several advantages over traditional tissue culture techniques. Current state-of-the-art in stem cell culture is 100% manual labor and is rife with operator-to-operator variation limiting the experimental scale, complexity, and precision. We have designed a fully automated method that obviates the need to conduct cell counts, reduces the number of tasks needed to passage iPSCs, increases iPSC culture consistency, enables portability of protocols from one research site to another. AI has gained the most traction in image analysis and pattern recognition. Based on the supposition iPSCs look like heads in a crowd we adapted an image-based deep learning neural network successful in crowd counting. The deep learning was conducted using a training dataset that consisted of paired bright field and Hoechst stained images, where ground truth was determined by nuclear object detection. The output from the neural network transforms an image into a topological density map, added as an image channel where the total map density is equal to the total cell count across the image. This relationship of cell number to density holds true from the entire dish down to a small user-defined region of the dish. Once trained, the AI enables spatial representation of cells across the dish and at a very wide range of accuracy and cell densities encountered with iPSC cultures without the need for cell labeling. The image-based AI algorithm can determine a precise number of iPSCs within the dish with a percent average error of 5.6%. We can visually observe model training across epochs within the neural network while it is learning to create the correct density maps from the phase contrast images. It is capable of ignoring well edges, dust particulate, and intensity effects due to the meniscus, often times superior to fluorescence labeled object detection. It enables us to measure cell density more rapidly and ‘in line’ with the automation greatly reducing the time and number of steps required for automated iPSC culturing. We are now engineering a working proof-of-concept prototype capable of making a go/no go decision for stem cell passaging and split ratios based on bright field image captures.


Stuart Chambers

Sr. Scientist, Amgen
Stuart Chambers is a Senior Scientist in the Genome Analysis Unit at Amgen with a background in stem cells, development, and neuroscience. His group uses induced pluripotent stem cells to model aspects of disease for drug development and works to create, evaluate, and disseminate... Read More →

avatar for Franck Madoux, PhD

Franck Madoux, PhD

Principal Scientist, Amgen
Franck Madoux heads Amgen’s Ultra-High Throughput Screening (UHTS) group where he and his team are involved in the early phases of drug discovery for small molecules via the execution of large scale biochemical, cell-based and high content assays in miniaturized formats and the... Read More →

Friday October 4, 2019 10:35am - 10:55am EDT
Theater Ballroom (5th Fl) - Courtyard Boston Downtown 275 Tremont Street, Boston, MA 02116