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Biology subjects

Rollenske, T.

Publications and source records attributed to Rollenske, T..

2 recordsLinked to original sources

IgA plasma cells co-secrete monomeric and dimeric IgA

Dimeric immunoglobulin A (dIgA) is generated from IgA monomers (mIgA) via JCHAIN-dependent polymerization. DIgA is transported across epithelial barriers by the poly Ig receptor (PIGR) and confers mucosal protection, while serum contains substantial amounts of IgA monomers. Distinct plasma cell subsets have been proposed to produce either monomeric or dimeric IgA, with bone marrow plasma cells as a primary source of mIgA. Here, we addressed whether IgA plasma cell populations segregate based on mIgA or dIgA production. Flow cytometric analysis of antibody-secreting cells from bone marrow, lymphoid and mucosal tissues revealed universal intracellular JCHAIN expression across isotypes and failed to identify a discrete JCHAIN-negative IgA plasma cell population. To detect polymeric IgA, we generated a recombinant soluble PIGR that selectively bound JCHAIN-containing dIgA in Western blot, ELISA, and flow cytometry. Soluble PIGR binding was detected in all IgA plasma cells irrespective of tissue origin, arguing against a dedicated mIgA-producing plasma cell subset incapable of dIgA formation. Ex vivo cultures and single-cell DropMap secretion assays demonstrated that bone marrow and lamina propria IgA antibody-secreting cells co-secrete mIgA and dIgA. These findings suggest that dIgA assembly and secretion are general properties of IgA plasma cells and disfavor a dedicated mIgA-producing population. HighlightsO_LIAll plasma cells express JCHAIN protein C_LIO_LIRecombinant poly Ig receptor detects dimeric IgA in all IgA plasma cells C_LIO_LIBone marrow plasma cells secrete dimeric IgA C_LIO_LIIgA plasma cells co-secrete mono- and dimeric IgA C_LI

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↗