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Antibodies are important tools with diverse uses in biomedical research. However, open access to reliable sources of well-characterized antibodies with unambiguous molecular identities remains an obstacle to research transparency and reproducibility. We propose here a community shift towards open-source antibodies, analogous to open-source computer software. The tenets of such antibodies are that 1) they are available to researchers in a ready to use form, 2) the renewable source of the antibody (e.g., hybridoma cells or plasmid) is also widely available ensuring reproducible and cost-effective access to the same antibody, and 3) the antibody sequence is publicly available. With these criteria met, the antibody can be widely used with the transparent assurance associated with a molecularly defined reagent, and the code can be edited to generate antibody variants to meet researchers' specific needs. We (the UC Davis/NIH NeuroMab Facility, the Development Studies Hybridoma Bank, and Addgene) have established a consortium to provide open-source access to a large collection of well characterized antibodies. As open-source software has benefitted both users and developers, we suggest open-source antibodies will have a similar positive impact on antibody based biomedical research. We encourage funding agencies to support initiatives to expand access to open-source antibody resources, and researchers to both utilize and to contribute to them, with a goal of enabling more reliable and cost-effective pursuit of research.
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http://dx.doi.org/10.1016/j.nbt.2025.04.004 | DOI Listing |
Cell Syst
September 2025
Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. Electronic address:
Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell-type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, the Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell-type annotation for images with a wide range of antibody panels without requiring additional model training or human intervention.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Understanding acute infectious disease dynamics at individual and population levels is critical for informing public health preparedness and response. Serological assays, which measure a range of biomarkers relating to humoral immunity, can provide a valuable window into immune responses generated by past infections and vaccinations. However, traditional methods for interpreting serological data, such as binary seropositivity and seroconversion thresholds, often rely on heuristics that fail to account for individual variability in antibody kinetics and timing of infection, potentially leading to biased estimates of infection rates and post-exposure immune responses.
View Article and Find Full Text PDFiScience
September 2025
Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia.
5-Ethynyl-2'-deoxyuridine (EdU) has revolutionized DNA replication and cell cycle analyses through fast, efficient click chemistry detection. However, commercial EdU kits suffer from high costs, proprietary formulations, limited antibody multiplexing capabilities, and difficulties with larger biological specimens. Here, we present OpenEMMU (Open-source EdU Multiplexing Methodology for Understanding DNA replication dynamics), an optimized, affordable, and user-friendly click chemistry platform utilizing off-the-shelf reagents.
View Article and Find Full Text PDFJ Vis Exp
August 2025
Department of Dermatology, Medical University of Vienna;
The spatial distribution of immune and non-immune cells within tissues and the expression of cell-specific markers provides essential information on cell function in situ. Human skin is a highly complex organ with defined compartments comprising different cells with diverse phenotypes. By using multi-fluorescence labeling, distinct skin cell populations can be determined and further characterized.
View Article and Find Full Text PDFLab Invest
July 2025
Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York; Dana-Farber Cancer Institute, Boston, Massachusetts; Massachusetts Institute of Technology, Cambridge, Massachusetts; Broad Institute of MIT and Harvard, Cambridge, Massachusetts. Electronic address: mloda@m
Conventional histopathology has traditionally been the cornerstone of disease diagnosis, relying on qualitative or semiquantitative visual inspection of tissue sections to detect pathological changes. Singleplex immunohistochemistry (IHC), although effective in detecting specific biomarkers, is often limited by its single-marker focus, which constrains its ability to capture the complexity of the tissue environment. The introduction of multiplexed imaging technologies, such as multiplex IHC and multiplex immunofluorescence, has been transformative, enabling the simultaneous visualization of multiple biomarkers within a single tissue section.
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