INSIHGT - A Scalable, Accessible, Homogeneous Deep Multiplexed Immunolabelling Platform
- Abstract number
- 29
- Presentation Form
- Poster
- DOI
- 10.22443/rms.elmi2024.29
- Corresponding Email
- [email protected]
- Session
- Poster Session
- Authors
- Hei Ming Lai (1), Tin Shing Hung (1), Chun Ngo Yau (1)
- Affiliations
-
1. The Chinese University of Hong Kong
- Keywords
Three-dimensional histology, imaging across scales, 3D tissue imaging, immunohistochemistry
- Abstract text
Three-dimensional (3D) quantitative maps of tissue molecules and cells offer integrative views of organ-wide biology. However, the penetration of macromolecular probes, notably antibody-based reagents, suffered from limited and inhomogeneous tissue penetration, resulting in biased fluorescent intensities despite advanced tissue clearing and light microscopy methods. We discovered a novel class of compounds that can flexibly and precisely modulate protein-protein interactions without causing denaturation, allowing the antibodies to overcome reaction-diffusion constraints in complex tissue matrices for their homogeneous penetration. Based on our new chemistry, we developed INSIHGT for scalable, accessible, quantitative 3D histology. INSIHGT can provide uniform, 3D multiplexed images of entire mouse organs within 5–6 days from dissection, with only 3 days of immunostaining. INSIHGT only requires incubating tissues with off-the-shelf primary antibodies and secondary antibodies at room temperature without any specialized equipment, making it highly scalable, accessible, and automatable. 309 out of 343 tested antibodies (90.1%) are INSIHGT-compatible. Multi-round immunostaining is also possible for higher multiplexing with conventional formaldehyde-fixed tissues. As a demonstration, we obtained a 28-plex 3D map of a millimeter-thick mouse hypothalamus, quantitative maps of neuronal expression markers and GABA contents in the mouse brain, and a centimeter-thick human hemi-brainstem mapped to its whole-brain MRI image for patho-radiological correlation. With advancements in tissue clearing, microscopy, image analysis, and machine learning, we believe INSIHGT can accelerate progress in biology and medicine.