Genomic Stability QC
Detecting Genomic Aberrations in iPSC Clones Using Label-free Brightfield Imaging and AI-based Analysis
Controlling the genetic integrity of iPSC clones during expansion and maintenance is crucial, as it (1) determines the cells' potential to differentiate into the desired functional target cell types and (2) ensures viability of the resulting cultures. In this application note, we demonstrate the concept of a morphology-based quality control that is able to predict whether a genetic aberration has occurred.
“My group is focused on using the latest technologies to advance the field of stem cell research. We will continue to use AI-based image analysis to support our automated workflows.”
–Sebastian Diecke, Group Leader at MDC
Sebastian Diecke leads the pluripotent stem cell group at Max-Delbrück-Centrum (MDC) Berlin, Germany. His research centers on advanced methodologies, like fully automated cell culture. With this work, he is laying the foundations for a future in which novel cures are based on therapeutic cell products, reliably produced at an industrial scale.
Sebastian's group supported this proof-of-concept study with planning and and execution of the cell culture experiments.
We cultured 8 clones from one iPSC line and characterized them for genomic aberrations for reference using [method]. Three clones (A1, A2, A3) were genetically normal while three others (B1, B2, B3) had an aberration on chromosome 20. The remaining two clones X and Y were also characterized for reference but their genetic status was not revealed to the analysis team until after they had made their prediction. The cells were cultured for 24h and then imaged with the VAIDR microscope using only the brightfield channel at 40x magnification. For each clone, several replicate wells were prepared in 6-well plates and each well was imaged at 49 locations.
VAIDR offers a powerful, customizable workflow to create classification methods for cell images with very little effort. In situations where the aim is to distinguish several key conditions and then map test samples to one of these controls, this workflow is ideal. Importantly, no knowledge about the morphological differences between conditions is required to use this workflow.
We used the built-in VAIDR classification workflow to train a classification method that distinguished genetically normal clones from clones with an aberration on chromosome 20. For the training, only clones A1, A2, A3, B1, B2 and B3 were used. This left the remaining clones X and Y to be used as a completely independent test for challenging the method.
Results of cross-validation during training. For training the AI model, each clone was left out once, and the model was trained on all remaining clones. Then, the left-out clone was scored with the model. The score plots and ROC on the left show, that the training went perfectly: Each clone was correctly mapped as being genetically normal or having an aberration on chromosome 20. After this positive validation result, the final model was trained using all clones at once. This type of cross-validation is performed automatically by the VAIDR classification workflow for each new classification method, and a validation report containing performance statistics and plots is generated automatically.
After successful method development and validation, we applied the method to all clones, in particular to the ones whose genetic status was still blinded to the analysis team at that time point. Each clone received a classification score, where a high score corresponds to the detection of a genetic aberration on chromosome 20 and a low score indicates a genetically normal clone.
Analysis results of scoring images from all clones on with VAIDR classification method trained on genetically normal clones A1, A2 and A3 vs the clones with an aberration on Chromosome 20 (B1, B2, B3). The clones X and Y were not used in the training but their scoring results are clear: X is predicted to be normal, while Y is predicted to have a genomic aberration. After performing the analysis, the experimental team revealed to the analysis team that this prediction is correct.
Conclusion: The VAIDR classification method correctly predicted the genomic status.
VAIDR is ideal for quickly establishing sophisticated analyses using label-free images of live cells. This opens up countless possibilities in the field of stem cell technology and cell therapy manufacturing. For a free trial, contact us at info@vaidr.de
“We have successfully supported many projects using iPSCs, both in drug discovery as well as cell therapy applications. If you have an application in mind, sign up for a free trial now!”
–Bruno Chilian, CSO at TRI