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While adoptive cell therapies have revolutionized cancer immunotherapy, current autologous chimeric antigen receptor (CAR) T cell manufacturing face challenges in scaling to meet patient demands. CAR T cell production still largely rely on fed-batch, manual, open processes that lack environmental monitoring and control, whereas most perfusion-based, automated, closed-system bioreactors currently suffer from large footprints and working volumes, thus hindering process development and scaling-out. Here, we present a means of conducting anti-CD19 CAR T cell culture-on-a-chip. We show that T cells can be activated, transduced, and expanded to densities exceeding 150 million cells/mL in a two-milliliter perfusion-capable microfluidic bioreactor, thus enabling the production of CAR T cells at clinical dose levels in a small footprint. Key functional attributes such as exhaustion phenotype and cytolytic function were comparable to T cells generated in a gas-permeable well. The process intensification and online analytics offered by the microbioreactor could facilitate high-throughput process optimization studies, as well as enable efficient scale-out of cell therapy manufacturing, while providing insights into the growth and metabolic state of the CAR T cells duringex vivoculture.

International audience; Severe mosquito bite allergy (SMBA) is a manifestation of chronic active Epstein-Barr virus (CAEBV) infection defined by necrotic ulceration of the stings. CAEBV with SMBA has a high mortality rate as most patients eventually develop fulminant and refractory hemophagocytic lymphohistiocytosis (HLH). However, how self-resolving SMBA escalates to systemic lethal HLH remains unclear. Through comprehensive immune profiling of a SMBA patient with CAEBV and her healthy monozygotic twin, we found that both twins were seropositive for EBV but showed high discordance in their circulating natural killer (NK) cells. The patient's EBV-infected NK cells displayed memory-like properties, including low CD16, high CD226 and induction of enhanced IFNγ production by IL-2 or IL-12. Her leukocytes also produced high levels of IL-2 and IL-12 when stimulated with salivary gland extract (SGE) specifically from A. albopictus mosquitoes, connected again with hyperproduction of IFNγ by her NK cells. Strikingly, pharmacological inhibition of STAT3 suppressed the NK memory-associated cytokine axis of IFNγ, IL-2 and IL-12 that is generated by A. albopictus SGE stimulation. Altogether, this study shows that NK memory is promoted during CAEBV with SMBA by repeated cytokine restimulation leading up to lethal HLH, and proposes STAT3 as a therapeutic target to halt its progression.

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimised experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 CFU / mL using metagenomic classification. Machine learning can be coupled with long read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP organisms to 10 CFU / mL.ImportanceThis research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7-14 days, while our approach can significantly reduce this time. By combining advanced long read Nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process.