Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency.

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T-cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high-potency MSC lines and metabolic engineering.

Label-free in vitro assays predict the potency of anti-disialoganglioside chimeric antigen receptor T-cell products.

BACKGROUND AIMS: Chimeric antigen receptor (CAR) T cells have demonstrated remarkable efficacy against hematological malignancies; however, they have not experienced the same success against solid tumors such as glioblastoma (GBM). There is a growing need for high-throughput functional screening platforms to measure CAR T-cell potency against solid tumor cells. METHODS: We used real-time, label-free cellular impedance sensing to evaluate the potency of anti-disialoganglioside (GD2) targeting CAR T-cell products against GD2+ patient-derived GBM stem cells over a period of 2 days and 7 days in vitro. We compared CAR T products using two different modes of gene transfer: retroviral transduction and virus-free CRISPR-editing. Endpoint flow cytometry, cytokine analysis and metabolomics data were acquired and integrated to create a predictive model of CAR T-cell potency. RESULTS: Results indicated faster cytolysis by virus-free CRISPR-edited CAR T cells compared with retrovirally transduced CAR T cells, accompanied by increased inflammatory cytokine release, CD8+ CAR T-cell presence in co-culture conditions and CAR T-cell infiltration into three-dimensional GBM spheroids. Computational modeling identified increased tumor necrosis factor α concentrations with decreased glutamine, lactate and formate as being most predictive of short-term (2 days) and long-term (7 days) CAR T cell potency against GBM stem cells. CONCLUSIONS: These studies establish impedance sensing as a high-throughput, label-free assay for preclinical potency testing of CAR T cells against solid tumors.

Functional assessment of homozygous ALDH18A1 variants reveals alterations in amino acid and antioxidant metabolism.

Mono- and bi-allelic variants in ALDH18A1 cause a spectrum of human disorders associated with cutaneous and neurological findings that overlap with both cutis laxa and spastic paraplegia. ALDH18A1 encodes the bifunctional enzyme pyrroline-5-carboxylate synthetase (P5CS) that plays a role in the de novo biosynthesis of proline and ornithine. Here we characterize a previously unreported homozygous ALDH18A1 variant (p.Thr331Pro) in four affected probands from two unrelated families, and demonstrate broad-based alterations in amino acid and antioxidant metabolism. These four patients exhibit variable developmental delay, neurological deficits and loose skin. Functional characterization of the p.Thr331Pro variant demonstrated a lack of any impact on the steady-state level of the P5CS monomer or mitochondrial localization of the enzyme, but reduced incorporation of the monomer into P5CS oligomers. Using an unlabeled NMR-based metabolomics approach in patient fibroblasts and ALDH18A1-null human embryonic kidney cells expressing the variant P5CS, we identified reduced abundance of glutamate and several metabolites derived from glutamate, including proline and glutathione. Biosynthesis of the polyamine putrescine, derived from ornithine, was also decreased in patient fibroblasts, highlighting the functional consequence on another metabolic pathway involved in antioxidant responses in the cell. RNA sequencing of patient fibroblasts revealed transcript abundance changes in several metabolic and extracellular matrix-related genes, adding further insight into pathogenic processes associated with impaired P5CS function. Together these findings shed new light on amino acid and antioxidant pathways associated with ALDH18A1-related disorders, and underscore the value of metabolomic and transcriptomic profiling to discover new pathways that impact disease pathogenesis.

Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.

Large-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T-cell process, we developed an artificial intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters and critical quality attributes from heterogeneous, high-dimensional, time-dependent multiomics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, design-of-experiment-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from early in-culture media assessment that were highly predictive of the end-product CD4/CD8 ratio and total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+). Our results demonstrate a broadly applicable platform tool to predict end-product quality and composition from early time point in-process measurements during therapeutic cell manufacturing.