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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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A Versatile Multiplexed Immunofluorescence Strategy for Efficient, Host-Independent, and Scalable Spatial Protein

Phuong Nguyen¹, Hongqiang Ma¹, Dimitrios S Gotsis²

¹Grainger College of Engineering, Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.

|June 17, 2026

Summary

We developed umIF, a novel immunofluorescence method using macromolecular crowding to improve labeling efficiency. This versatile technique enhances various IF workflows, enabling robust, host-independent multiplexing for scalable spatial proteomics.

Keywords:

cyclic immunofluorescence host‐independent labeling immunofluorescence staining spatial proteomics

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Area of Science:

Spatial biology
Immunofluorescence techniques
Proteomics

Background:

Current immunofluorescence (IF) methods face barriers like host-species constraints, lengthy workflows, and limited flexibility.
Direct IF requires chemical conjugation, leading to weaker signals and fixed pairings.
Antibody-nanobody complexes offer host independence but have inconsistent performance.

Purpose of the Study:

To introduce umIF, a strategy leveraging macromolecular crowding to enhance IF labeling efficiency.
To enable robust, host-independent multiplexing across diverse IF workflows.
To improve accessibility and scalability of spatial proteomics.

Main Methods:

Leveraging macromolecular crowding to enhance labeling efficiency in immunofluorescence.
Utilizing antibody-nanobody complexes for host-independent multiplexing.
Applying umIF to single- and multicycle imaging in human tissues and mouse models.

Main Results:

umIF enhances labeling efficiency across direct, one-step, and two-step IF workflows.
Improved detection of weak targets and robust multiplexing with antibody-nanobody complexes.
Demonstrated umIF in human and mouse samples, revealing tissue organization in normal and disease states.

Conclusions:

umIF is a versatile and accessible method for scalable spatial proteomics.
The technique lowers adoption barriers for research and clinical laboratories.
Enhances high-content spatial biology through improved IF performance.