An Optimised Protocol Harnessing Laser Capture Microdissection for Transcriptomic Analysis on Matched Primary and Metastatic Colorectal Tumours

利用激光捕获显微切割对匹配的原发性和转移性结直肠肿瘤进行转录组分析的优化方案

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作者:Chin-Ann J Ong #, Qiu Xuan Tan #, Hui Jun Lim #, Nicholas B Shannon, Weng Khong Lim, Josephine Hendrikson, Wai Har Ng, Joey W S Tan, Kelvin K N Koh, Seettha D Wasudevan, Cedric C Y Ng, Vikneswari Rajasegaran, Tony Kiat Hon Lim, Choon Kiat Ong, Oi Lian Kon, Bin Tean Teh, Grace H C Tan, Claramae Shuly

Abstract

Generation of large amounts of genomic data is now feasible and cost-effective with improvements in next generation sequencing (NGS) technology. Ribonucleic acid sequencing (RNA-Seq) is becoming the preferred method for comprehensively characterising global transcriptome activity. Unique to cytoreductive surgery (CRS), multiple spatially discrete tumour specimens could be systematically harvested for genomic analysis. To facilitate such downstream analyses, laser capture microdissection (LCM) could be utilized to obtain pure cell populations. The aim of this protocol study was to develop a methodology to obtain high-quality expression data from matched primary tumours and metastases by utilizing LCM to isolate pure cellular populations. We demonstrate an optimized LCM protocol which reproducibly delivered intact RNA used for RNA sequencing and quantitative polymerase chain reaction (qPCR). After pathologic annotation of normal epithelial, tumour and stromal components, LCM coupled with cDNA library generation provided for successful RNA sequencing. To illustrate our framework's potential to identify targets that would otherwise be missed with conventional bulk tumour sequencing, we performed qPCR and immunohistochemical technical validation to show that the genes identified were truly expressed only in certain sub-components. This study suggests that the combination of matched tissue specimens with tissue microdissection and NGS provides a viable platform to unmask hidden biomarkers and provides insight into tumour biology at a higher resolution.

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