Integrated and scalable molecular brain mapping
Name
1003292047-MIT.pdf
Description
Full printable version
Size
15.52 MB
Format
Adobe PDF
Checksum (MD5)
b78afec0259b689abb0681708cff0a16
Author(s)
Cho, Jae H. (Jae Hun)
Advisor(s)
Kwanghun Chung.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Understanding the brain requires integrative knowledge of its cellular-, network-, and system-level architectures. Existing volume imaging techniques have proven the potential to provide such information, but the lack of technology to label large volumes for visualization has limited their utility. Here, we address this challenge by developing technologies -- stochastic electrotransport and SWITCH -- to extend multiplexed labeling methods to larger volumes. Stochastic electrotransport selectively expedites transport of molecular probes into the tissue without damaging it. SWITCH synchronizes the labeling reaction to achieve consistent and uniform labeling. These technologies are demonstrated by successfully visualizing several molecular markers in adult mouse brain tissues, which have been previously infeasible in time and cost. Although our focus is on neuroscience, the concepts and methods described in this thesis are quite general. Stochastic electrotransport will be applicable to any nonlinear transport problems, and SWITCH will be applicable to any problem requiring synchronization of reaction kinetics across long distances..
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 143-158).
Subjects
Chemical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
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