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Single-Cell Spatial MIST for Versatile, Scalable Detection of Protein Markers
Arafat Meah1, Vadanasundari Vedarethinam1, Robert Bronstein2
1Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA.
Biosensors
|September 27, 2023
Summary
Spatial multiplex in situ tagging (MIST) offers high-multiplex protein detection in tissues at single-cell resolution. This advanced spatial biology method enables ultrahigh multiplexity for complex disease diagnostics.
Area of Science:
- Spatial biology
- Biomarker discovery
- Proteomics
Background:
- Traditional immunohistochemistry (IHC) has limitations for high-multiplex protein detection.
- Spatial biology approaches are gaining traction for detailed tissue analysis.
- Existing methods struggle with simultaneous detection of numerous protein biomarkers.
Purpose of the Study:
- To introduce and optimize spatial multiplex in situ tagging (MIST) for high-multiplex protein detection.
- To achieve single-cell resolution in spatial proteomics.
- To demonstrate the versatility and scalability of MIST for complex biological samples.
Main Methods:
- Spatial multiplex in situ tagging (MIST) transfers spatial protein information to an ultrahigh-density array.
- Optimization using smaller array units and 8-arm PEG polymer for single-cell resolution.
- Development of pseudocells (10 μm) for comprehensive tissue region profiling.
Main Results:
- Successfully visualized tissue cell nuclei (lamin B) and detected nine mouse brain markers.
- Detected 20 marker proteins in kidney samples with minimal time addition to standard IHC.
- Achieved ultrahigh multiplexity of 125 proteins per pseudocell through iterative staining over 5 rounds.
Conclusions:
- Spatial MIST is a versatile, single-cell resolution technology for high-multiplex protein detection.
- The method allows iterative staining and detection on the same tissue samples.
- Spatial MIST holds significant potential for advanced diagnostics in complex diseases.
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