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

Chin-Ann Johnny OngQiu Xuan TanHui Jun LimNicholas B ShannonWeng Khong LimJosephine HendriksonWai Har NgJoey W S TanKelvin K N KohSeettha D WasudevanCedric Chuan Young NgVikneswari RajasegaranTony Kiat Hon LimChoon-Kiat OngOi Lian KonBin Tean TehGrace H C TanClaramae Shulyn ChiaKhee Chee SooMelissa C C Teo
Published in: Scientific reports (2020)
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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