Randomized trial demonstrates scalable spatial protein profiling in human tissues, suggesting wider accessibility in research and clinical labs.
Immunofluorescence (IF) remains a central method for high-content spatial biology, but current approaches face several technical barriers. Indirect two-step IF is the most widely used but constrained by host-species requirements and lengthy workflows. Direct IF avoids this constraint but requires chemical conjugation of each primary antibody to a fluorophore, resulting in weaker signals, fixed antibody-fluorophore pairings, and reduced flexibility. Antibody-nanobody complexes provide a promising host-independent solution but have been limited by inconsistent performance and reduced efficiency. Commercial spatial biology platforms achieve high-plex imaging, but many rely on costly custom reagents or specialized microfluidics instrumentation, limiting broad accessibility. We introduce umIF, a strategy that leverages macromolecular crowding to enhance labeling efficiency across diverse IF workflows. It enables robust, host-independent multiplexing with antibody-nanobody complexes, while also improving performance across direct, one-step, and two-step IF workflows, including detection of weak targets. We demonstrate both single- and multicycle umIF in human tissue samples and mouse models, revealing epithelial, stromal, immune, and epigenetic organization in normal and disease contexts. umIF provides a versatile and accessible method for scalable spatial proteomics, lowering barriers to adoption in both research and clinical laboratories.
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Nguyen et al. (2026) studied this question.
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