Rodrigues, M. A. | Search bioRxiv

bioRxiv2024

Enhancing cryopreservation of human iPSCs: Bottom-up vs Conventional freezing geometry

Induced pluripotent stem cells (iPSCs) hold large potential on regenerative medicine due to their pluripotency and unlimited self-renewal capacity without the ethical issues of embryonic stem cells. To provide quality-controlled iPSCs for clinical therapies, it is essential to develop safe cryopreservation protocols for long-term storage, preferable amenable for scale-up and automation. We have compared the impact of two different freezing geometries (bottom-up and conventional radial freezing) on the viability and differentiation capability of human iPSCs. Our results demonstrate that the bottom-up freezing under optimized conditions significantly increases iPSCs viability, up to 9% for the cell membrane integrity and up to 21% for the cell metabolic state, compared to conventional freezing. The improvement achieved for bottom-up versus conventional freezing was maintained after scale-up from cryogenic vials to 30 mL bags, highlighting the methods potential for clinical applications. These findings show that bottom-up freezing can offer a more controlled and scalable cryopreservation strategy for iPSCs, promoting their future use in regenerative medicine.

cell biology↗

bioRxiv2021

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25 - 5.0 {micro}g/mL) and/or carbendazim (0.8 - 1.6 {micro}g/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for mononucleates, binucleates, mononucleates with MN and binucleates with MN, respectively. Successful classifications of trinucleates (90%) and tetranucleates (88%) in addition to other or unscorable phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful ([≤] 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent dose regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

pharmacology and toxicology↗