Generalist 3D Cell Phenotyping for All-Type Tissues
Name
Gu-Xinyi-SM-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
17.16 MB
Format
Adobe PDF
Checksum (MD5)
f41b363e218ecb176cbcf09b0ec5205a
Author(s)
Gu, Xinyi
Advisor(s)
Chung, Kwanghun
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
Abstract
Tissue-clearing methods, light-sheet microscopy, and antibody labeling enable extracting cellular and subcellular information, producing large amount of image data needs to be analyzed. Hundreds of heterogeneous cell types were detected through the data obtained across species and types of tissues. We developed a novel approach that is generally applicable to a wide range of cell types in the large-scale 3D brain datasets, using a pipeline that performs accurate detection of cells regardless of image resolution, labeling pattern, and tissue processing techniques used. The pipeline is compatible with various labeling techniques including IHC, Fluorescence in situ hybridization (FISH), and genetic labeling and can be used for cellular level quantification in all types of tissues.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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