| Chapter title |
Laser Capture Microdissection of Epithelium from a Wound Healing Model for MicroRNA Analysis
|
|---|---|
| Chapter number | 19 |
| Book title |
MicroRNA Protocols
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7601-0_19 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7600-3, 978-1-4939-7601-0
|
| Authors |
Alyne Simões, Zujian Chen, Yan Zhao, Lin Chen, Virgilia Macias, Luisa A. DiPietro, Xiaofeng Zhou, Simões, Alyne, Chen, Zujian, Zhao, Yan, Chen, Lin, Macias, Virgilia, DiPietro, Luisa A., Zhou, Xiaofeng |
| Abstract |
MicroRNAs are ~22 nucleotide-long noncoding RNAs influencing many cellular processes (including wound healing) by their regulatory functions on gene expression. The ability to analyze microRNA in different cells at the wound site is essential for understanding the critical role(s) of microRNA during various phases of wound healing. Laser capture micro-dissection (LCM) is an effective method to distinguish between relevant and non-relevant cells or tissues and enables the researcher to obtain homogeneous, ultra-pure samples from heterogeneous starting material. We present here our protocol for procuring epithelial cells from a mouse wound healing model using a Leica LMD7000 Laser Microdissection system, as well as the RNA isolation and downstream microRNA analysis. Using this method, researchers can selectively and routinely analyze regions of interest down to single cells to obtain results that are relevant, reproducible, and specific. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
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|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
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|---|---|---|
| Researcher | 4 | 31% |
| Unspecified | 2 | 15% |
| Student > Bachelor | 2 | 15% |
| Other | 1 | 8% |
| Student > Master | 1 | 8% |
| Other | 3 | 23% |
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| Unspecified | 2 | 15% |
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| Neuroscience | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 1 | 8% |