Automation of single-cell techniques in neural tissue
Author(s)Steinmeyer, Joseph D. (Joseph Daly)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Meh met Fatih Yanik.
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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.
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).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.