Biology subjects

Hisada, S.

Publications and source records attributed to Hisada, S..

4 recordsLinked to original sources

bioRxiv2026

A practical workflow for CAR screening in primary T cells using fitness-guided design and mRNA electroporation

Chimeric antigen receptor (CAR) T cell therapy has achieved remarkable therapeutic outcomes in hematological cancers. However, broader clinical use has uncovered substantial challenges arising from intrinsic properties of both T cells and tumor tissues. As the functional phenotype of CAR T cells is affected by the CAR molecular architecture, optimizing CAR constructs continues to be a critical and ongoing task. Here, we present a practical workflow for scalable screening of CAR variants in primary T cells using fitness-guided design and mRNA electroporation. Using a CD19-targeted second-generation CAR, we built a library of point mutants that focused mutagenesis on hinge and costimulatory domains. Amino acid substitutions were prioritized using the sequence-based zero-shot fitness predictor to enrich evolutionarily tolerated variants. From 340 designed variants, we electroporated mRNA encoding 85 constructs into primary human CD8+ T cells and quantified cytotoxicity against CD19-positive Nalm6 cells. Twenty-four variants reproducibly exceeded wild-type cytotoxicity across three runs, and three hits were selected for lentiviral validation. One of the selected variants showed significantly improved cytotoxicity despite lower expression frequency and exhibited higher CD62L within CAR-positive cells, suggesting enhanced intrinsic function with a less differentiated phenotype. This approach enables scalable, rapid discovery of improved CAR domain variants directly in primary T cells.

bioengineering↗

bioRxiv2026

Cytotoxicity-based High-throughput Screening System for CAR T Cell

Some chimeric antigen receptor (CAR) T cell therapies have shown strong clinical efficacy, yet systematic screening of new CAR designs remains constrained by labor-intensive, low-throughput evaluation methods. To address this limitation, we developed a cytotoxicity-centered, high-throughput screening platform that integrates single-cell pooled screening with fully automated arrayed screening enabling both large-scale library handling and quantitative functional resolution for systematic CAR design exploration. Using a mutation-based CAR design approach guided by protein fitness prediction, we generated a 4-1BB-based CAR library with approximately 10 theoretical variants while minimizing the prevalence of low-activity designs. In pooled screening, CAR T cells were evaluated at the single-cell level based on cytotoxicity and proliferation, enabling rapid enrichment of high-performing variants from a highly diverse library. Subsequent automated arrayed screening quantitatively measured cytotoxicity with high reproducibility, providing high-resolution functional data suitable for comparative ranking. Selected CAR variants demonstrated superior antitumor efficacy in a leukemia xenograft model compared with a template CAR. Furthermore, systematic analysis of mutation sites from an enhanced CAR variant identified essential mutation combinations underlying functional enhancement. Together, this study establishes a cytotoxicity-focused screening framework that provides a robust approach for optimizing CAR architectures and accelerating the development of CAR T-cell therapies.

synthetic biology↗

bioRxiv2025

Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences

Chimeric antigen receptor (CAR)-T cell therapy has shown remarkable success in treating hematological malignancies; however, several challenges remain, including limited efficacy against solid tumors, T cell exhaustion, and lack of T cell persistence, which have restricted its clinical efficacy across various indications. Sequence optimization of CAR constructs offers a promising strategy to enhance therapeutic efficacy of CAR-T cells. Recent advances in machine learning, especially protein language models (PLMs), enable prediction of mutational effects based on sequence representations. Nevertheless, applying PLMs to CARs is challenging due to the artificial nature of CARs and the absence of comprehensive CAR sequence databases. In this study, we developed a computational framework to predict CAR-T cell activity by fine-tuning ESM-2 with the CAR sequences generated using sequence augmentation. These CAR sequences were constructed by recombining homologous domains of CARs, enabling task-specific adaptation of the model. To evaluate prediction performance, we experimentally assessed the cytotoxicity of CAR-T cells expressing mutated CAR variants and compared these results with model predictions. Our results demonstrated that fine-tuned ESM-2 significantly improves prediction performance of CAR-T cell activity. Furthermore, we showed that training parameters--such as sequence diversity, number of training steps, and model size--substantially influence prediction performance. This work highlights the potential of combining sequence augmentation with fine-tuning PLMs to advance data-driven CAR-T cell design.

bioinformatics↗

bioRxiv2023

Bayesian Approach Enabled Objective Comparison of Multiple Human iPSC-derived Cardiomyocytes' Proarrhythmia Sensitivities.

The one-size-fits-all approach has been the mainstream in medicine, and the well-defined standards support the development of safe and effective therapies for many years. Advancing technologies, however, enabled precision medicine to treat a targeted patient population (e.g., HER2+ cancer). In safety pharmacology, computational population modeling has been successfully applied in virtual clinical trials to predict drug-induced proarrhythmia risks against a wide range of pseudo cohorts. In the meantime, population modeling in safety pharmacology experiments has been challenging. Here, we used five commercially available human iPSC-derived cardiomyocytes growing in 384-well plates and analyzed the effects of ten potential proarrhythmic compounds with four concentrations on their calcium transients (CaTs). All the cell lines exhibited an expected elongation or shortening of calcium transient duration with various degrees. Depending on compounds inhibiting several ion channels, such as hERG, peak and late sodium and L-type calcium or IKs channels, some of the cell lines exhibited irregular, discontinuous beating that was not predicted by computational simulations. To analyze the shapes of CaTs and irregularities of beat patterns comprehensively, we defined six parameters to characterize compound-induced CaT waveform changes, successfully visualizing the similarities and differences in compound-induced proarrhythmic sensitivities of different cell lines. We applied Bayesian statistics to predict sample populations based on experimental data to overcome the limited number of experimental replicates in high-throughput assays. This process facilitated the principal component analysis to classify compound-induced sensitivities of cell lines objectively. Finally, the association of sensitivities in compound-induced changes between phenotypic parameters and ion channel inhibitions measured using patch clamp recording was analyzed. Successful ranking of compound-induced sensitivity of cell lines was validated by visual inspection of raw data. Author SummaryCardiac safety is one of the most stringent regulatory risk monitoring during drug development. Regulatory agencies, including the Food and Drug Administration (FDA), require drug developers to conduct thorough preclinical and clinical studies to evaluate drug-induced proarrhythmia risks. The CiPA (Comprehensive in vitro Proarrhythmia Assay) initiative led by the FDA validated the applications of human cardiomyocytes derived from induced pluripotent stem cells in proarrhythmia risk assessments. The assay can be cost effective, and use of high-throughput approaches enables scale-up analysis. However, limited diversity in cell lines to recapitulate heterogeneity of human patients are still lacking in the cardiac safety analysis. We applied various computational tools to maximize capacity for analyzing diverse response of commercially available five cell lines against reference chemical compounds. Limited number of experimental data made it difficult to predict similarities and differences in drug responses among different cell lines. By applying Bayesian approach, our analysis could intuitively grasp the probabilities of expecting different responses from different cell lines.

pharmacology and toxicology↗