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Yong, M. C. R.

Publications and source records attributed to Yong, M. C. R..

2 recordsLinked to original sources

Longitudinal spatial profiling of neutrophils during adoptive T cell therapy in murine melanoma reveals distinct lymph node infiltration patterns across anatomical sites

Reactive neutrophil infiltration can restrain CD8+ T cell expansion in lymph nodes during adoptive T cell therapy (ACT), yet its spatiotemporal regulation remains incompletely understood. Levaraging flow cytometry and multiplex immunofluorescence data, we performed a time-resolved quantitative assessment of immune cell dynamics in tumor-draining lymph node (tdLN) and non-tumor-draining lymph node (non-tdLN) in a melanoma mouse model receiving ACT. Transferred tumor-reactive CD8+ T cells accumulated and expanded early after treatment initiation, showing the highest frequency of a favorable central memory CD8+ T cell phenotype in the tdLN. Enhancing innate immune signaling in melanomas increased neutrophil influx into lymph nodes, particularly the non-tdLN; however, within the tdLN, neutrophils were enriched in the T cell zone, which also contained the largest absolute reservoir of transferred CD8+ T cells. Together, these findings indicate that tdLN and non-tdLN differ in early neutrophil dynamics and compartmentalization during ACT, influenced by the strength of innate immune signaling in the tumor.

immunology↗

Development of AI-designed protein binders for detection and targeting of cancer cell surface proteins

Artificial intelligence (AI)-based protein design opens new avenues for the rapid generation of new research tools and therapeutics, but experimental validation lags behind the computational design throughput. Here, we present a scalable workflow for the discovery and validation of AI-designed protein binders. Leveraging the RFdiffusion protein design pipeline with a custom filter for stable alpha-helical bundle folds, we construct libraries of thousands of AI-binders against cancer-associated surface proteins. Mammalian cell-surface and phage display screening yield multiple high-affinity PD-L1 binders but fewer hits for CD276 (B7-H3) and VTCN1 (B7-H4), reflecting the target-dependent efficiency of RFdiffusion in generating high-quality designs. Using our experimentally validated AI-designed binder libraries, we benchmark freely available structure prediction models. We find that interface predicted template modelling (ipTM) scores by Chai-1 with ESM embedding correlate well with experimental success and even predict deleterious effects of binding interface mutations. To demonstrate the versatility of AI-binders as research tools, we deploy them in CAR-T cells and also assemble them with fluorophore-labeled streptavidin into tetravalent quattrobinders, which achieve antibody-comparable staining of endogenous PD-L1 by flow cytometry. With high production yields and accessible structural models, AI-designed quattrobinders are versatile and cost-effective research tools amenable to community-driven validation and optimization.

bioengineering↗