Manufacturing of primary CAR-NK cells in an automated system for the treatment of acute myeloid leukemia.

Acute myeloid leukemia (AML) still constitutes a dreadful disease with limited therapeutic options. Chimeric antigen receptor (CAR)-modified T cells struggle to target AML partly due to a lack of true AML-exclusive antigens and heterogeneity of the disease. Natural killer (NK) cells possess a high intrinsic killing capacity against AML and might be well suited for the treatment of this disease. However, the generation of primary CAR-NK cells can be difficult and time consuming. Therefore, robust systems for the generation of high numbers of CAR-NK cells under GMP conditions are required. Here we report on the automated generation of high numbers of primary CD33-targeting CAR-NK cells using the CliniMACS Prodigy® platform. Automated-produced CD33-CAR-NK cells showed similar phenotype and cytotoxicity compared to small-scale-produced CD33-CAR-NK cells in vitro and were able to strongly reduce leukemic burden in an OCI-AML2 NSG-SGM3 xenograft mouse model in vivo following a cross-site shipment of the cell product. This technology might be well suited for the generation of primary CAR-modified NK cells for a broad range of targets and could facilitate clinical transition.

Rapid detection and online analysis of microbial changes through flow cytometry.

Short-read 16 S rRNA gene sequencing is the dominating technology to profile microbial communities in different habitats. Its uncontested taxonomic resolution paved the way for major contributions to the field. Sample measurement and analysis, that is, sequencing, is rather slow-in order of days. Alternatively, flow cytometry can be used to profile the microbiota of various sources within a few minutes per sample. To keep up with high measurement speed, we developed the open source-analyzing tool FlowSoFine. To validate the ability to distinguish microbial profiles, we examined human skin samples of three body sites (N = 3 × 54) with flow cytometry and 16 S rRNA gene amplicon sequencing. Confirmed by sequencing of the very same samples, body site was found to be significantly different by flow cytometry. For a proof-of-principle multidimensional approach, using stool samples of patients (N = 40) with/without inflammatory bowel diseases, we could discriminate the health status by their bacterial patterns. In conclusion, FlowSoFine enables the generation and comparison of cytometric fingerprints of microbial communities from different sources. The implemented interface supports the user through all analytical steps to work out the biological relevant signals from raw measurements to publication ready figures. Furthermore, we present flow cytometry as a valid method for skin microbiota analysis.