Chapter title |
Quantitative Proteomic Analysis of Mass Limited Tissue Samples for Spatially Resolved Tissue Profiling
|
---|---|
Chapter number | 78 |
Book title |
Tissue Proteomics
|
Published in |
Methods in molecular biology, October 2017
|
DOI | 10.1007/7651_2017_78 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7852-6, 978-1-4939-7854-0
|
Authors |
Paul D. Piehowski, Rui Zhao, Ronald J. Moore, Geremy Clair, Charles Ansong, Piehowski, Paul D., Zhao, Rui, Moore, Ronald J., Clair, Geremy, Ansong, Charles |
Abstract |
Traditionally, proteomic studies have been carried out on whole tissues or organs enabling the profiling of thousands of proteins within a single LC-MS analysis. A disadvantage of this approach is that proteomes generated from whole tissues are an "average" that represents a blend of cell types and distinct anatomical regions which can obscure important biological phenomena. Laser capture microdissection (LCM) is an elegant method that allows tissue features of interest, as small as a single cell, to be identified and isolated for downstream analysis. Herein we describe an approach that utilizes an immobilized enzyme reactor (IMER) coupled directly to nanoLC-MS/MS for highly sensitive, automated, quantitative proteomic analysis of the microscopic tissue specimens generated by LCM. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 13 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 5 | 38% |
Professor > Associate Professor | 2 | 15% |
Unspecified | 1 | 8% |
Researcher | 1 | 8% |
Student > Master | 1 | 8% |
Other | 0 | 0% |
Unknown | 3 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 5 | 38% |
Biochemistry, Genetics and Molecular Biology | 2 | 15% |
Agricultural and Biological Sciences | 1 | 8% |
Unspecified | 1 | 8% |
Unknown | 4 | 31% |