Kusch, N. | Search bioRxiv

bioRxiv2026

A Cellular Cytotoxicity Assay using Ready-to-Thaw Target Cells without Washing Steps

Functional testing of cytotoxic lymphocytes is essential for research and quality control (QC), but most assays require freshly prepared target cells and extensive handling. A ready-to-thaw, no-wash, flow cytometry-based cytotoxicity assay was developed using pre-labeled K562 targets cryopreserved in STEM-CELLBANKER(R) EX (SCB) as suitably sized aliquots. SCB tolerability was evaluated in K562, NK-92, and primary natural killer (NK) cells; post-cryopreservation label stability of CellTrace Violet (CTV) and carboxyfluorescein succinimidyl ester (CFSE) was assessed; freezing and thawing conditions were optimized; and wash versus no-wash workflows were compared using viability-based and absolute-count readouts, across effector-to-target (E:T) ratios with NK donors and NK-92 cells. Effector viability remained high at SCB concentrations up to 10%, and 5% SCB was selected for assay design. After cryopreservation, CTV labeling remained stable over the tested storage period, whereas CFSE showed substantial signal loss. Warm-medium thawing performed comparably to water-bath thawing, and the consolidated protocol (SCB plus fetal calf serum and thermal buffering) maintained high post-thaw target viability and recovery. In killing assays, lysis increased with increasing E:T ratios; omission of the post-thaw wash had minimal impact, and 5% SCB did not impair cytotoxic function. This ready-to-thaw workflow reduces hands-on time and sample manipulation, while improving standardization for reproducible results and enabling high-throughput functional testing and QC.

immunology↗

bioRxiv2020

Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms

Drug sensitivity prediction models for human cancer cell lines constitute important tools in identifying potential driving factors of responsiveness in a pre-clinical setting. Integrating information derived from a range of heterogeneous data is crucial, but remains non-trivial, as differences in data structures may hinder fitting algorithms from assigning adequate weights to complementary information that is contained in distinct omics data. In order to counteract this effect that tends to lead to just one data type dominating supposedly multi-omics models, we developed a novel tool that enables users to train single-omics models separately in a first step and to integrate them into a multi-omics model in a second step. Extensive ablation studies are performed in order to facilitate an in-depth evaluation of the respective contributions of singular data types and of combinations thereof, effectively identifying redundancies and interdependencies between them. Moreover, the integration of the single-omics models is realized by a range of distinct classification algorithms, thus allowing for a performance comparison. Sets of molecular events and tissue types found to be related to significant shifts in drug sensitivity are returned to facilitate a comprehensive and straightforward analysis of potential drivers of drug responsiveness. Our two-step approach yields sets of actual multi-omics pan-cancer classification models that are highly predictive for a majority of drugs in the GDSC data base. In the context of targeted drugs with particular modes of action, its predictive performances compare favourably to those of classification models that incorporate multi-omics data in a simple one-step approach. Additionally, case studies demonstrate that it succeeds both in correctly identifying known key drivers of specific drug compounds as well as in providing sets of potential candidates for additional driving factors of drug sensitivity.

systems biology↗