A Framework for 3D Mouse Brain Reconstruction: RNA-based Stitching of Adjacent Tissue Slices and Co-Registration of Multimodal Imaging Data

Mapping the brain’s complex neural networks requires tracing the long-distance pathways of individual axons, a task that demands a comprehensive 3D reconstruction of the brain. Recently, spatially resolved transcriptomics (SRT) methods enable the study of gene expression and biomolecule distribution in each neuron in its spatial context, opening the door to more thoroughly investigating cell-cell interactions between neurons. However, SRT methods are limited to slices of tissue; therefore, computational alignment is essential to reconstruct a cohesive 3D volume while correcting for both batch effects and inherent sample variability. This thesis presents a novel framework that addresses these challenges through three primary contributions. First, a memory-efficient, non-referenced-based algorithm was developed to align the superficial surfaces of adjacent, high-resolution tissue slices. Second, these surface transformations were interpolated through the tissue slices on a proof-of-concept dataset of three adjacent slices. Third, methods for co-transforming fluorescent protein imaging data were explored to fully resolve the cell boundaries between neurons. These three methods are necessary steps towards creating a fully-resolved, multimodal 3D model of the brain.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Pan, Jessica N.

Advisor dc:contributor.advisor

• Wang, Xiao

Statement dc:rights

• Attribution 4.0 International (CC BY 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164828

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164828