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Biology subjects

Penna, L.

Publications and source records attributed to Penna, L..

2 recordsLinked to original sources

Automated and closed clinical-grade manufacturing protocol produces potent NK cells against neuroblastoma cells and AML blasts

Natural killer (NK) cells have great potential as allogeneic immune cell therapy due to their natural ability to recognize and kill tumor cells, and due to their apparent safety. This study describes the development of an immunotherapy option tailored for high-risk acute myeloid leukemia (AML) in adults and neuroblastoma in children. A GMP-compliant manufacturing protocol for the local production of functionally potent NK cells is detailed in the study, including a comprehensive description of the quality control strategy and considerations for product batch specifications in early clinical development. The protocol is based on the closed, automated CliniMACS Prodigy(R) platform (Miltenyi Biotec) and a modified Natural Killer Cell Transduction (NKCT) process without transduction and expansion. NK cells are isolated from leukapheresis through CD3 depletion and CD56 enrichment, followed by a 12-hour activation with cytokines (500 IU/ml IL-2, 140 IU/ml IL-15). Three CliniMACS Prodigy(R) NKCT processes were executed, demonstrating the feasibility and consistency of the modified NKCT process. A three-step process without expansion, however, compromised the NK cell yield. T cells were depleted effectively, indicating excellent safety of the product for allogeneic use. Phenotypic and functional characterization of the NK cells before and after cytokine activation revealed a notable increase in the expression of activation markers, particularly CD69, consistent with enhanced functionality. Intriguingly, even following a brief 12-hour activation period, the NK cells exhibited increased killing efficacy against CD33+ AML blasts isolated from patients and against SH-SY5Y neuroblastoma (NBL) target cells in vitro, suggesting a potential therapeutic benefit for AML and NBL patients.

immunology↗

DeepIFC: virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

bioinformatics↗