Comparative Evaluation of Antibody-Oligonucleotide Conjugation Strategies for Multiplexed Imaging Applications
Chiara Caraccio1, Josie van de Klashorst2, Shelby Cherkas2
1Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California; Department of Pathology, Stanford University School of Medicine, Stanford, California; Harvard-MIT Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts; Department of Brain & Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Abstract:
Antibody-oligonucleotide conjugates (AOCs) have emerged as versatile tools with applications spanning diagnostics, therapeutics, and high-dimensional imaging. One major application of these is in multiplexed imaging techniques, such as CO-Detection by indEXing, which allow for the visualization of tissue networks at the single-cell level. In this study, we evaluated 4 methods-maleimide-modified, amine-modified, dibenzocyclooctyne (DBCO)-modified, and a site-specific enzyme-based method-to optimize the generation of AOCs for multiplexed imaging applications. Our assessment focused on key performance parameters, including conjugation efficiency, signal brightness, stability, reproducibility, and cost-effectiveness. Each conjugation chemistry proved effective, though the azide chemistry with DBCO oligonucleotides demonstrated more consistent conjugation success and stable signal retention over time. Compared with other protocols, this method produced reliably bright images and offered a more favorable cost profile, as further confirmed in a full-scale CO-Detection by indEXing multiplexed imaging experiment that yielded reproducible spatial data. The observed stability and reproducibility of the DBCO approach suggest that it may help reduce reagent waste and labor costs while facilitating the development of more comprehensive antibody panels. These findings indicate that the DBCO-modified oligonucleotide conjugation method is a valuable option for generating AOCs for multiplexed imaging and target current shortcomings, enabling more consistent, broader, and deeper multiplexed profiling.
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