Scaling up 3D imaging, analysis, and culture of complex brain models

The brain is the most complex human organ, containing components from the nanometer scale to the centimeter scale However, many experimental techniques in neuroscience have been optimized for small brain models This thesis summarizes a body of work aimed at scaling up 3D imaging, analysis, and tissue culture techniques for large-scale brain models We present a technique termed SWITCH that inhibits probe binding to allow for diffusion without the formation of a reaction front To improve imaging resolution, we present a tissue expansion technique called MAP that physically magnifies tissue samples for super-resolution imaging with conventional fluorescence microscopes Using these tools to achieve volumetric imaging of large-scale brain models generates petabyte-scale data, for which we present horizontally scalable image processing pipelines for analysis of intact mouse beams, marmoset bi am samples, and cerebral organoids The mouse brain pipeline allows region-based statistical analysis of protein expression and cell counts An efficient single-cell non-rigid coregistration algorithm for multiplexed volumetric fluorescence imaging based on matching corresponding nuclei between imaging founds is presented A multiscale phenotyping pipeline allows single-cell, cytoarchtectural, and morphological analyses to be combined into a hyperdimensional statistical analysis of cerebral organoids We use this pipeline to show phenotypic changes due to neurodevelopment, Zika virus infection, and changes in organoid culture protocols Current cerebral organoid cultures lack a vascular system and are limited by nutrient transport To address this issue in vitro, we fabricated synthetic vasculature by two-photon photopolymerization of polyethylene glycol-based resins Printed micro-vessels wee biocompatible, less than 100 [mu]m in outer diameter, and permeable to biomolecules through engineered pore structures Perfusion of vascularized cerebral organoids cultured for 30 days resulted neuronal differentiation as well as integration of the vascular network Future studies can use and build on these technical advances to further our understanding of the bi am through the use of large-scale brain models.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Chemical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Swaney, Justin M. (Justin Mark)

Advisor dc:contributor.advisor

• Kwanghun Chung.

• Chemical Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139721

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139721