Biology subjects

Manz, T.

Publications and source records attributed to Manz, T..

3 recordsLinked to original sources

bioRxiv2026

Bivalent bispecific CD28 antibodies reinforce T-cell responsiveness and revert anergy/quiescence in patients treated with bispecific CD3 antibodies

Bispecific T-cell engagers stimulating CD3 (TCEs) rapidly gain momentum in oncological therapy. We report that single-agent TCE-treatment of cancer patients induces T cell hyporesponsiveness including abolished proliferative and lytic capacity. Single-cell RNA-sequencing identified transcriptional features of anergy/quiescence like upregulation of CBLB and BACH2, and suppression of AP 1-dependent activation programs due to isolated provision of T cell signal-1. To restore T cell functionality, we engineered bispecific costimulators (BiCos) which deliver signal-2 via highly efficient bivalent CD28-binding yet maintain strictly target-restricted activity after binding antigens expressed not only by tumor cells but also the extracellular matrix. Preclinical analyses documented that BiCos potentiate efficacy of single-agent TCE treatment with activity exceeding that of univalent Knob-into-Hole CD28-costimulators and revert T cell hyporesponsiveness. In humanized mice, BiCos induced elimination of established tumors in combination with very low doses of TCE. Finally, BiCos reversed molecular hallmark features of quiescence such as high expression of BACH2 and KLF2 in T cells of TCE-treated patients resulting in complete restoration of cellular function, thereby reinstating durable antitumor immunity.

immunology↗

bioRxiv2024

scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution

Understanding how regulatory DNA elements shape gene expression across individual cells is a fundamental challenge in genomics. Joint RNA-seq and epigenomic profiling provides opportunities to build unifying models of gene regulation capturing sequence determinants across steps of gene expression. However, current models, developed primarily for bulk omics data, fail to capture the cellular heterogeneity and dynamic processes revealed by single-cell multi-modal technologies. Here, we introduce scooby, the first framework to model scRNA-seq coverage and scATAC-seq insertion profiles along the genome from sequence at single-cell resolution. For this, we leverage the pre-trained multi-omics profile predictor Borzoi as a foundation model, equip it with a cell-specific decoder, and fine-tune its sequence embeddings. Specifically, we condition the decoder on the cell position in a precomputed single-cell embedding resulting in strong generalization capability. Applied to a hematopoiesis dataset, scooby recapitulates cell-specific expression levels of held-out genes, and identifies regulators and their putative target genes through in silico motif deletion. Moreover, accurate variant effect prediction with scooby allows for breaking down bulk eQTL effects into single-cell effects and delineating their impact on chromatin accessibility and gene expression. We anticipate scooby to aid unraveling the complexities of gene regulation at the resolution of individual cells.

genomics↗

bioRxiv2023

OME-Zarr: a cloud-optimized bioimaging file format with international community support

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself - OME-Zarr - along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

bioinformatics↗