MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Doctoral Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Doctoral Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Automation of single-cell techniques in neural tissue

Author(s)
Steinmeyer, Joseph D. (Joseph Daly)
Thumbnail
DownloadFull printable version (13.45Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Meh met Fatih Yanik.
Terms of use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
The highly heterogeneous nature of cells in the context of native tissue environments necessitates the development of tools and techniques that can manipulate and analyze samples with single-cell resolution. While the past decades have seen significant progress in analyzing individual cells in tissue, both electrically and morphologically, the ability to genetically manipulate and biochemically analyze such cells in a high-throughput manner has seen only limited advances, and therefore a significant technological gap in accessing cells with single-cell specificity in tissue remains. We present a system design and workflow that fills in this gal) in technology through the implementation of precision automation and redesign of standard biological techniques, resulting in greatly improved throughput while maintaining single-cell accuracy and precision. This thesis comprises three parts: First we discuss the design and implementation of an expandable computer-controlled automation system enabling the rapid maneuvering and targeting of inicropipettes within tissue environments as well as a methodology for cleaning and reuse of these micropipettes to enable significant gains in throughput. Second we apply this automation to transfecting neurons in brain slices with DNA and RNA for subsequent analysis with greater throughput than previous methods. Third, we apply our automation to collecting the contents of single neurons embedded in relevant tissue environments for molecular analysis. The work presented greatly improves the throughput of traditional single-cell methods of transfection and cell-sampling by between one and two orders of magnitude and fills in a gap in the workflow of the rapidly expanding field of single-cell analysis.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 125-140).
 
Date issued
2014
URI
http://hdl.handle.net/1721.1/90004
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Doctoral Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.