Interfacing data science with cell therapy manufacturing: where we are and where we need to be.

Although several cell-based therapies have received FDA approval, and others are showing promising results, scalable, and quality-driven reproducible manufacturing of therapeutic cells at a lower cost remains challenging. Challenges include starting material and patient variability, limited understanding of manufacturing process parameter effects on quality, complex supply chain logistics, and lack of predictive, well-understood product quality attributes. These issues can manifest as increased production costs, longer production times, greater batch-to-batch variability, and lower overall yield of viable, high-quality cells. The lack of data-driven insights and decision-making in cell manufacturing and delivery is an underlying commonality behind all these problems. Data collection and analytics from discovery, preclinical and clinical research, process development, and product manufacturing have not been sufficiently utilized to develop a "systems" understanding and identify actionable controls. Experience from other industries shows that data science and analytics can drive technological innovations and manufacturing optimization, leading to improved consistency, reduced risk, and lower cost. The cell therapy manufacturing industry will benefit from implementing data science tools, such as data-driven modeling, data management and mining, AI, and machine learning. The integration of data-driven predictive capabilities into cell therapy manufacturing, such as predicting product quality and clinical outcomes based on manufacturing data, or ensuring robustness and reliability using data-driven supply-chain modeling could enable more precise and efficient production processes and lead to better patient access and outcomes. In this review, we introduce some of the relevant computational and data science tools and how they are being or can be implemented in the cell therapy manufacturing workflow. We also identify areas where innovative approaches are required to address challenges and opportunities specific to the cell therapy industry. We conclude that interfacing data science throughout a cell therapy product lifecycle, developing data-driven manufacturing workflow, designing better data collection tools and algorithms, using data analytics and AI-based methods to better understand critical quality attributes and critical-process parameters, and training the appropriate workforce will be critical for overcoming current industry and regulatory barriers and accelerating clinical translation.

Choline Dehydrogenase Contributes to Salt Tolerance in Dunaliella through Betaine Synthesis.

In Dunaliella tertiolecta, a microalga renowned for its extraordinary tolerance to high salinity levels up to 4.5 M NaCl, the mechanisms underlying its stress response have largely remained a mystery. In a groundbreaking discovery, this study identifies a choline dehydrogenase enzyme, termed DtCHDH, capable of converting choline to betaine aldehyde. Remarkably, this is the first identification of such an enzyme not just in D. tertiolecta but across the entire Chlorophyta. A 3D model of DtCHDH was constructed, and molecular docking with choline was performed, revealing a potential binding site for the substrate. The enzyme was heterologously expressed in E. coli Rosetta (DE3) and subsequently purified, achieving enzyme activity of 672.2 U/mg. To elucidate the role of DtCHDH in the salt tolerance of D. tertiolecta, RNAi was employed to knock down DtCHDH gene expression. The results indicated that the Ri-12 strain exhibited compromised growth under both high and low salt conditions, along with consistent levels of DtCHDH gene expression and betaine content. Additionally, fatty acid analysis indicated that DtCHDH might also be a FAPs enzyme, catalyzing reactions with decarboxylase activity. This study not only illuminates the role of choline metabolism in D. tertiolecta's adaptation to high salinity but also identifies a novel target for enhancing the NaCl tolerance of microalgae in biotechnological applications.

Three coordination polymers based on 4,4'-bis(2-methylimidazol-1-yl)diphenyl ether: Synthesis, structure and selective fluorescent sensing properties.

Three CPs [Zn2(PDA)2(BMIOPE)2·3H2O]n (1), [Co(Br-BDC)(BMIOPE)]n (2) and [Co(MIP)(BMIOPE)]n (3) were synthesized by solvothermal method based on dual-ligand strategy (H2PDA, Br-H2BDC, BMIOPE and H2MIP are 1,3-phenylenediacetic acid, 5-bromo-isophthalic acid, 4,4'-bis(2-methylimidazol-1-yl)diphenyl ether and 5-methylisophthalic acid, respectively). Complexes 1 and 3 exhibit twofold parallel interwoven sql nets. Complex 2 is 2D layer structure. The luminescence property investigations showed that complexes 1-3 could act as multi-responsive fluorescent sensors to detect UO22+, Cr2O72- and CrO42- and nitrofurantoin (NFT) through fluorescence turn-off process, presenting excellent sensitivity and selectivity. Finally, the possible fluorescent quenching mechanisms of complexes 1-3 toward the above pollutants are also further investigated by employing spectroscopic methods and quantum chemical calculations. The fluorescence lifetime measurements manifest the mechanism of fluorescence quenching is static quenching process.

Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.

BACKGROUND AIMS: Cell therapy is a promising treatment method that uses living cells to address a variety of diseases and conditions, including cardiovascular diseases, neurologic disorders and certain cancers. As interest in cell therapy grows, there is a need to shift to a more efficient, scalable and automated manufacturing process that can produce high-quality products at a lower cost. METHODS: One way to achieve this is using non-invasive imaging and real-time image analysis techniques to monitor and control the manufacturing process. This work presents a machine learning-based image analysis pipeline that includes semantic segmentation and anomaly detection capabilities. RESULTS/CONCLUSIONS: This method can be easily implemented even when given a limited dataset of annotated images, is able to segment cells and debris and can identify anomalies such as contamination or hardware failure.

FGF-induced LHX9 regulates the progression and metastasis of osteosarcoma via FRS2/TGF-ß/ß-catenin pathway.

BACKGROUND: Fibroblast growth factor (FGF) and tumor growth factor-ß (TGFß) have emerged as pivotal regulators during the progression of osteosarcoma (OS). LHX9 is one crucial transcription factor controlled by FGF, however, its function in OS has not been investigated yet. METHODS: The expression of LHX9, FRS2, BMP4, TGF-beta R1, SMAD2, beta-catenin and metastasis-related proteins was measured by real-time quantitative PCR (RT-qPCR) and Western blot. CCK-8 assay and colony formation assay were employed to determine the proliferation of OS cells, while scratch wound healing assay and transwell assay were used to evaluate their migration and invasion, respectively. In vivo tumor growth and metastasis were determined by subcutaneous or intravenous injection of OS cells into nude mice. RESULTS: LHX9 expression was evidently up-regulated in OS tumor tissues and cell lines. Knockdown of LHX9 impaired the proliferation, migration, invasion and metastasis of OS cells. Mechanistically, LHX9 silencing led to the down-regulation of BMP-4, ß-catenin and metastasis-related proteins, which was also observed in beta-catenin knockdown OS cells. By contrast, FRS2 knockdown conduced to the up-regulation of LHX9, BMP4, ß-catenin and TGF-ßR1, while TGF-beta inhibition repressed the expression of LHX9 and metastasis-related proteins. Additionally, let-7c modulates LHX9 and metastasis-related proteins by suppressing TGF-beta R1 expression on transcriptional level. CONCLUSIONS: This study revealed LHX9 was essential for the proliferation, migration, invasion, and metastasis of OS cells via FGF and TGF-ß/ß-catenin signaling pathways.

TGF-ß1 Upregulates the Expression of Triggering Receptor Expressed on Myeloid Cells 1 in Murine Lungs.

Triggering receptor expressed on myeloid cells 1 (TREM-1) increases the expression of TGF-ß family genes, which are known as profibrogenic cytokines in the pathogenesis of pulmonary fibrosis. In this study, we determined whether TGF-ß1 regulated the expression of TREM-1 in a mouse model of pulmonary fibrosis. The expression of TGF-ß1 and TREM-1 was increased on day 7, 14, and 21 after single intratracheal injection of bleomycin (BLM). And there was positive correlation between the expression of TGF-ß1 and TREM-1. TGF-ß1 increased expression of TREM-1 mRNA and protein in a time- and dose-dependent manner in mouse macrophages. The expression of the activator protein 1 (AP-1) was increased in lung tissues from mouse after BLM injection and in mouse macrophages after TGF-ß1 treatment, respectively. TGF-ß1 significantly increased the relative activity of luciferase in the cells transfected with plasmid contenting wild type-promoter of TREM-1. But TGF-ß1 had no effect on the activity of luciferase in the cells transfected with a mutant-TREM1 plasmid carrying mutations in the AP-1 promoter binding site. In conclusion, we found the expression of TREM-1 was increased in lung tissues from mice with pulmonary fibrosis. TGF-ß1 increased the expression of TREM-1 in mouse macrophages partly via the transcription factor AP-1.