Efficient gene expression profiling of laser-microdissected melanoma metastases - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue Pigment Cell and Melanoma Research Année : 2012

Efficient gene expression profiling of laser-microdissected melanoma metastases

Résumé

Comparing the transcriptomes of primary and metastatic tumour tissues is a useful strategy for studying tumour progression. One factor limiting the interpretation of tissue-based transcriptomic data is the lack of cell-type purity. Laser capture microdissection (LCM) has been shown to be useful for overcoming this limitation. We established an efficient protocol for gene expression profiling of LCM and matched metastatic melanomas using a transgenic mouse model. This optimized workflow combines microsurgical recovery of mouse lungs, appropriate tissue freezing, laser microdissection of homogeneous tumour cell populations from cryosections, isolation of high-quality RNA and gene expression analysis. The RNA isolated from laser-microdissected material was not contaminated by stroma cells, was of excellent quality, and the synthesis of cDNAs was homogeneous and highly reproducible. Subsequent custom-based Taqman-low-density-array (TLDA)-based gene expression profiling identified stronger expression of five genes (M-MITF, TYR, STAT3, CCND1 and PAX3) in primary than metastatic melanoma. We detected only minor transcriptomic differences between primary and metastatic melanoma tissue. This optimized workflow could be very valuable for various studies requiring cell typespecific transcriptomic analysis.

Dates et versions

hal-01003352 , version 1 (10-06-2014)

Identifiants

Citer

Samira Makhzami, Florian Rambow, Veronique Delmas, Lionel Larue. Efficient gene expression profiling of laser-microdissected melanoma metastases. Pigment Cell and Melanoma Research, 2012, 25 (6), pp.783 - 791. ⟨10.1111/pcmr.12013⟩. ⟨hal-01003352⟩
145 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More