A microfluidic multiwell chip for enzyme-free detection of mRNA from few cells
| Autor: | Jan Hesse, Alois Sonnleitner, Michaela Haider, Thomas Haselgrübler, Bozhi Ji, Fritz Aberger |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Cell Biomedical Engineering Biophysics 02 engineering and technology Biology Sensitivity and Specificity 03 medical and health sciences Cancer stem cell Complementary DNA Cell Line Tumor Lab-On-A-Chip Devices Gene expression Electrochemistry medicine Biomarkers Tumor Humans RNA Messenger Gene In Situ Hybridization Fluorescence Messenger RNA Reproducibility of Results General Medicine Equipment Design 021001 nanoscience & nanotechnology Molecular biology Reverse transcriptase Enzymes Equipment Failure Analysis Pancreatic Neoplasms 030104 developmental biology medicine.anatomical_structure Cell culture Tissue Array Analysis Neoplastic Stem Cells 0210 nano-technology Biotechnology |
| Zdroj: | Biosensorsbioelectronics. 86 |
| ISSN: | 1873-4235 |
| Popis: | Isogenic cell populations possess heterogeneous gene expression patterns. Most methods for mRNA expression analysis start with the reverse transcription of mRNA into cDNA, a process that can introduce strong signal variations not related to the actual mRNA levels. Miniaturized lab-on-a-chip systems offer properties - e.g. low sample dilution, low contamination - that enable new reaction schemes for molecular analyses. To enable transcription-free mRNA expression analysis of few single cells, a one-step cell lysis, target labelling and hybridisation approach as well as a corresponding passive multiwell chip with a volume of 25.5 nL/well were developed. The method enabled the parallel analysis of up to 96 samples and 6 target genes per sample. Preceding light microscopy of the living cells allowed correlating mRNA levels and cell number. As a proof-of-principle, the pancreatic cancer cell line Panc-1 was investigated for expression heterogeneity of a reference gene plus 5 genes reported to be overexpressed in cancer stem cells (CSCs). A good correlation (r(51)=0.739, p |
| Databáze: | OpenAIRE |
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