A Bayesian approach to optimizing stem cell cryopreservation protocols

Cryopreservation is beset with the challenge of protocol alignment across a wide range of cell types and process variables. By taking a cross-sectional assessment of previously published cryopreservation data (sample means and standard errors) as preliminary meta-data, a decision tree learning analysis (DTLA) was performed to develop an understanding of target survival based on different approaches. Briefly, using a DTLA approach, a clear direction on the decision process for selection of methods was developed with key choices being the cooling rate, plunge temperature on the one hand and biomaterial choice, use of composites (sugars and proteins), loading procedure and cell location in 3D scaffold. Since machine learning and generalized approaches were employed, these metadata could be used to develop posterior probabilities via Naïve Bayes Classification (NBC) for combinatorial approaches that were not initially captured in the metadata. These results showed that newer protocol choices could lead to improved cell survival consistent with physical reports. In conclusion, this article proposes the use of DTLA models and NBC for the improvement of modern cryopreservation techniques through an integrative approach. Keywords: 3D cryopreservation, decision-tree learning (DTL), sugars, mouse embryonic stem cells, meta-data, Naïve Bayes Classifier (NBC)