Besemer, B. | Search bioRxiv

bioRxiv2026

Targeting poorly differentiated thyroid cancer with bispecific antibodies and CAR T cells from the human anti-TSHR autoantibody repertoire

Background: Poorly differentiated thyroid carcinoma (PDTC) has no effective systemic treatment option once radioactive iodine and tyrosine kinase inhibitors fail. The thyroid-stimulating hormone receptor (TSHR) is highly tissue-restricted and the anti-TSHR autoantibody repertoire of thyroid autoimmunity offers sequence-defined, fully human binders. We asked whether this repertoire can be converted into an off-the-shelf T cell-redirecting therapy for PDTC. Methods: TSHR expression was profiled on a 151-case thyroid cancer tissue microarray (TMA), 50 healthy controls and on fresh tumor material. Eight anti-TSHR clones, four of human autoimmune origin (M22, K1-18, K1-70, 5C9) and four murine (IRI-SAb2, IRI-SAb3, 9D33, 1H7), were reformatted as CD3-engaging bispecific antibodies (bsAbs; anti-TSHR Fab x anti-CD3 scFv) and, for leading clones, as chimeric antigen receptor (CAR) T cells. Activity was compared in vitro, across graded and mixed antigen densities and in a CAL-62-Luc-TSHR xenograft model, using healthy-donor (HD) or thyroid cancer-patient T cells. Results: TSHR was retained on a substantial fraction of PDTC, including metastatic lesions. Both formats mediated potent, antigen-selective killing and the leading bsAbs suppressed xenograft growth in vivo. For CAR T cells, scFv heavy/light-chain orientation was decisive. Anti-TSHR bsAbs cleared mixed cultures far beyond the antigen-positive fraction matching mosunetuzumab and indicating a modality-intrinsic bystander effect. Patient-derived T cells obtained on or after oncological treatment retained full effector function. Conclusions: The anti-TSHR autoantibody repertoire provides a promising source of binders for off-the-shelf bispecific antibodies targeting TSHR-positive thyroid cancer, while CAR T cells offer an autologous alternative.

cancer biology↗

bioRxiv2025

Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features

Microglia play critical roles in brain health and disease by adopting a spectrum of dynamic activation states. However, capturing this continuous heterogeneity in a scalable way remains a major challenge. To address this, we developed an imaging and data analysis framework to map the activation landscape of human iPSC-derived microglia (iMG) at single-cell resolution. We combined high-content imaging using two complementary strategies: a hypothesis-driven immunofluorescence (IF) panel targeting key activation markers (NF-{kappa}B, ASC, CD45) and a discovery-oriented, pan-morphological Cell Painting (CP) assay. Diverse phenotypes were captured through handcrafted and representation learning-based features. To classify cells, we applied Gaussian Mixture Models (GMMs) to image-derived features, enabling soft probabilistic assignments that capture transitional states between phenotypes. Compared to graph-based methods like the Leiden algorithm, GMMs provided comparable classification performance while offering a more nuanced and biologically interpretable view of microglial heterogeneity. We demonstrate that deep learning features from the targeted IF panel are most powerful, achieving high classification accuracy and strong correlation with biological states such as functional NLRP3 inflammasome activation. Our model system provides a robust and scalable platform for quantifying microglial heterogeneity, offering a new tool to identify novel disease-associated states and compounds that precisely modulate microglial phenotypes for therapeutic discovery.

neuroscience↗