Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.

Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react in real-time to key control parameter variations, adapt to different patient profiles, and optimize the process. The aim of the current work is to develop and calibrate smart sensors for their deployment in a real bioreactor platform, with adaptive control and monitoring for diverse patient/donor cell profiles. A set of contrasting smart sensors has been implemented and tested on automated cell expansion batch runs, which incorporate advanced data-driven machine learning and statistical techniques to detect variations and disturbances of the key system features. Furthermore, a 'consensus' approach is applied to the six smart sensor alerts as a confidence factor which helps the human operator identify significant events that require attention. Initial results show that the smart sensors can effectively model and track the data generated by the Aglaris FACER bioreactor, anticipate events within a 30 min time window, and mitigate perturbations in order to optimize the key performance indicators of cell quantity and quality. In quantitative terms for event detection, the consensus for sensors across batch runs demonstrated good stability: the AI-based smart sensors (Fuzzy and Weighted Aggregation) gave 88% and 86% consensus, respectively, whereas the statistically based (Stability Detector and Bollinger) gave 25% and 42% consensus, respectively, the average consensus for all six being 65%. The different results reflect the different theoretical approaches. Finally, the consensus of batch runs across sensors gave even higher stability, ranging from 57% to 98% with an average consensus of 80%.

Efficient cell reprogramming using bioengineered surfaces.

A novel method for cell reprogramming is been developed by immobilizing nucleic acid transfer vectors containing free amino groups, like lentiviral particles, onto pentafluorophenyl methacrylate (PFM)-modified surfaces obtained by PFM grafting affter Ar plasma treatment. This technique is able to reprogram murine somatic cells into pluripotent cells at high efficiencies. We call these modified surfaces cell reprogramming surfaces, or CRS.

The effect of self-assembling peptide nanofiber scaffolds on mouse embryonic fibroblast implantation and proliferation.

Development of new materials for tissue engineering can be facilitated by the capacity to efficiently monitor in vivo the survival, proliferation and differentiation behaviour of cells implanted in different target tissues. We present here the application of a previously developed platform that allows to monitor in real time the survival and proliferative behaviour of implanted cells in two anatomical sites: subcutaneous and intramuscular. Basically, the system is based on the use of a non-invasive bioluminescence imaging (BLI) technique to detect luciferase expressing C57BL/6 cells, mouse embryonic fibroblasts, seeded in two sets of scaffolds: 1, a RAD16-I self-assembling peptide nanofiber matrix and 2, a composite consisted of the same RAD16-I nanofibers contained into a microporous biorubber scaffold. Interestingly, our results indicated considerable differences in the behaviour of implanted cells in each scaffold type. We observed that the self-assembling peptide scaffold alone foster cell survival and promotes cell proliferation where the composite scaffold not. Since self-assembling peptide scaffolds presents value stiffness proximal to the implanted tissues it is suggestive to think that harder materials will provide a physical constriction for cells to proliferate as well as mechanical discontinuity. We therefore propose that it is important to close match the implantation environment with the cell/material constructs in order to obtain the best response of the cells, illustrating the convenience of this strategy for the development of new tissue engineering platforms.