Abstract
Single cell analysis techniques have great potential in the cancer genomics field. The detection and characterization of circulating tumour cells are important for identifying metastatic disease at an early stage and monitoring it. This protocol is based on transcript profiling using Reverse Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA), which is a specific method for simultaneous detection of multiple mRNA transcripts. Because of the small amount of (circulating) tumour cells, a pre-amplification reaction is performed after reverse transcription to generate a sufficient number of target molecules for the MLPA reaction. We designed a highly sensitive method for detecting and quantifying a panel of seven genes whose expression patterns are associated with breast cancer, and optimized the method for single cell analysis. For detection we used a fluorescence-dependent semi-quantitative method involving hybridization of unique barcodes to an array. We evaluated the method using three human breast cancer cell lines and identified specific gene expression profiles for each line. Furthermore, we applied the method to single cells and confirmed the heterogeneity of a cell population. Successful gene detection from cancer cells in human blood from metastatic breast cancer patients supports the use of RT-MLPA as a diagnostic tool for cancer genomics.
Highlights
More than ten years ago Cristofanilli et al used the CellSearch platform to show that circulating tumour cells (CTCs) have prognostic value in metastatic breast cancer patients[1]
The subsequent MLPA reaction uses target-specific MLPA probes that consist of two synthetic oligonucleotides: a left hybridization oligonucleotide (LHO) and a right hybridization oligonucleotide (RHO)
Optimization of the Reverse Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA) protocol for single cell sensitivity showed that 25 pre-amplification cycles were needed for low amounts of total RNA to gain a signal comparable to the positive control, when using chip-based capillary electrophoresis or fluorescence-dependent semi-quantitative method with hybridization of unique barcodes
Summary
More than ten years ago Cristofanilli et al used the CellSearch platform to show that circulating tumour cells (CTCs) have prognostic value in metastatic breast cancer patients[1]. Reverse transcription quantitative PCR (RT-qPCR) is currently the main method used for molecular analysis of CTCs9, transcriptome analysis using RNA sequencing (RNA-seq) is advancing[10]. We have used a variant of the Reverse Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA) assay developed at MRC-Holland. Because of the small amount of tumour cells in this study, a pre-amplification reaction is performed after reverse transcription to generate a sufficient number of target molecules for the MLPA reaction. The MLPA method is based on sequence-specific probe hybridization to reverse transcribed RNA targets. As one MLPA probe oligonucleotide contains a specific barcode sequence, the amplification products can be distinguished by a fluorescence-dependent semi-quantitative detection method with hybridization of unique barcodes to an array. We have designed a set of MLPA probes for detecting and quantifying the gene expression, with single cell sensitivity. In the future we hope that our method will be useful for the molecular characterization of CTCs in patient blood samples
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
