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Schniederjohann, C.

Publications and source records attributed to Schniederjohann, C..

2 recordsLinked to original sources

Universal oligo adapters for high-efficiency DNA-barcoded antibody panel generation

DNA-barcoded antibodies are central to a broad range of spatial and dissociative assays and applications, including multiplexed imaging, single-cell profiling, and proximity detection. Direct modification of primary antibodies with defined DNA barcodes enables flexible panel design for multiplexed labeling of proteins. However, conventional antibody-oligonucleotide conjugation methods are inefficient, low-throughput, and prone to batch variability, limiting the reliable generation of orthogonally barcoded antibody panels. These challenges are particularly acute during initial panel development, where lengthy protocols, conjugation failures, large antibody input requirements, and the need for custom antibody formulations increase experimental cost and effort. Site-specific conjugation strategies based on antibody Fc-domain binders offer a promising alternative. We streamlined this foundational approach to establish its compatibility with multiplexed imaging in cells and tissues; however, generating a full barcoded library is still resource-intensive. This is because every unique DNA barcode must first be chemically linked to a separate binder before it can be attached to an antibody. To overcome the prominent bottleneck of rapidly and reliably generating DNA-barcoded antibody panels, we introduce the universal oligo adapter (UnO) strategy. UnO fundamentally changes the workflow from barcode-specific conjugation to universal barcode conversion. We build on the established photoreactive protein G binder and combine it with a universal oligonucleotide carrying a second ultrafast photocrosslinking group, 3-cyanovinylcarbazole (cnvK). This creates a dual-functional adapter: one photoreactive group enables covalent attachment to the antibody, while the cnvK-containing universal oligo simultaneously captures a user-defined DNA barcode through hybridization and UV crosslinking in a single step. Rather than preparing separate conjugation reactions for dozens of barcodes, UnO acts as a single reagent that covalently couples any desired barcode onto small quantities of off-the-shelf primary antibodies in minutes. We validate the generalizability and modularity of this approach across subcellular Immuno-SABER and tissue-based CODEX workflows for multiplex immunostaining. By converting antibody barcoding into a modular, one-step nucleic-acid adapter workflow, UnO reduces the cost, time, and complexity of generating DNA-barcoded antibody panels and provides an efficient, accessible solution to a central bottleneck in DNA-enabled multiplexed protein detection.

bioengineering↗

Spatialproteomics - an interoperable toolbox for analyzing highly multiplexed fluorescence image data

SummaryHighly multiplexed immunofluorescence imaging is a recent method to characterize tissues at single-cell resolution on the protein level, offering low cost, high scalability, and the ability to analyze paraffin-embedded tissue samples. However, the analysis of these data involves a sequence of steps, including segmentation, image processing, marker quantification, cell type classification, and neighborhood analysis, each of which involves a multitude of method and parameter choices that need to be adapted to the data and analytical objective at hand. Moreover, variations in data quality can be high and unpredictable, which necessitates further flexibility and interactivity. While individual components exist, there is an unmet need for a coherent toolbox that offers end-to-end coverage of the workflow, flexibility, and automation. We present spatialproteomics, a Python package that addresses these challenges. Built on top of xarray and dask, spatialproteomics can process images that are larger than the working memory. It supports synchronization of shared coordinates across data modalities such as images, segmentation masks, and expression matrices, which facilitates easy and safe subsetting and transformation. We demonstrate spatialproteomics on a set of images of reactive lymph nodes or different forms of B cell Non-Hodgkin lymphomas (BNHL) from 132 patients. We showcase an end-to-end analysis from raw images to statistical characterization of cell type composition and spatial distribution across indolent and aggressive lymphomas. Furthermore, we show how spatialproteomics can process gigapixel whole slide images. Altogether, we propose spatialproteomics as an easy-to-install, easy-to-learn, comprehensive toolbox for constructing powerful end-to-end image analysis solutions for highly multiplexed immunofluorescence imaging. Availability and ImplementationThe source code for spatialproteomics is freely available at https://github.com/sagar87/spatialproteomics under the MIT license. Contactwolfgang.huber@embl.org, Peter-Martin.Bruch@med.uni-duesseldorf.de

bioinformatics↗