Chapter title |
Laser-Capture Microdissection of Maize Kernel Compartments for RNA-Seq-Based Expression Analysis
|
---|---|
Chapter number | 9 |
Book title |
Maize
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7315-6_9 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7314-9, 978-1-4939-7315-6
|
Authors |
Shanshan Zhang, Dhiraj Thakare, Ramin Yadegari |
Abstract |
Laser-capture microdissection (LCM) enables isolation of single cells or groups of cells for a variety of downstream applications including transcriptome profiling. Recently, this methodology has found a more widespread use particularly with the advent of next-generation sequencing techniques that enable deep profiling of the limited amounts of RNA obtained from fixed or frozen sections. When used with fixed tissues, a major experimental challenge is to balance the tissue integrity needed for microscopic visualization of the cell types of interest with that of the RNA quality necessary for deep profiling. Complex biological structures such as seeds or kernels pose an especially difficult case in this context as in many instances the key internal structures such as the embryo and the endosperm are relatively inaccessible. Here, we present an optimized LCM protocol for maize kernel that has been developed specifically to enable profiling of the early stages of endosperm development using RNA-Seq. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 50% |
France | 1 | 17% |
Unknown | 2 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 50% |
Scientists | 2 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Other | 2 | 25% |
Student > Ph. D. Student | 1 | 13% |
Researcher | 1 | 13% |
Student > Doctoral Student | 1 | 13% |
Unknown | 3 | 38% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 2 | 25% |
Biochemistry, Genetics and Molecular Biology | 1 | 13% |
Social Sciences | 1 | 13% |
Design | 1 | 13% |
Unknown | 3 | 38% |