Session: 668 Neurobiology and neuronal signaling I
(668.7) Paper-MAP: A Novel Tool for Super-resolution Imaging of Pathological Tissues by Rapidly Tissue Expansion and Clearing
Monday, April 4, 2022
12:30 PM – 1:45 PM
Location: Exhibit/Poster Hall A-B - Pennsylvania Convention Center
Poster Board Number: A349
Mirae Lee (Yonsei University College of Medicine), Jiwon Woo (Yonsei University College of Medicine), Jin Woo Chang (Yonsei University College of Medicine), Jeong-Yoon Park (Yonsei University College of Medicine)
Presenting Author Yonsei University College of Medicine Seoul
Super-resolution mapping of three-dimensional (3D) structures in intact tissues is essential in many biological studies. Biological tissues has very complex structure of functional networking with various cell population/organization. Since most biological tissues are not transparent, and imaging processes are limited by light scatter, imaging deep within the tissues is problematic. In this work, we developed the novel method as call Paper-MAP for rapidly and simple clearing/expansion of based on MAP (Magnified Analysis of Proteome) using very thin tissue. The Paper-MAP can be analyzed to super-resolution 3D imaging of hybrid tissue with rapidly 4-fold clearing/expansion in under 14 hours, while preserving their overall architecture and protein content of subcellular details after hydrogel embedding. Using a Paper-MAP, we performed the super-resolution imaging of pathological samples, such as injured spinal cord, brain hippocampus, salivary gland organoid, tumor sphere, and glioblastoma tumor of human patients. This technique provides an ideal way for researchers to examine the pathologic patterns as well as various intact biological samples.
This research was supported by the Brain Korea 21 FOUR Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.amp;nbsp;This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1A6A3A01097969).