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Updated: Feb 23, 2026

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays
Published on: January 16, 2018
Profiling of the metabolic transcriptome via single molecule molecular inversion probes
Tessa de Bitter1, Carlijn van de Water1, Corina van den Heuvel1
1Dept of Pathology, Radboud University Medical Centre, Nijmegen, The Netherlands.
A novel RNA sequencing method using single molecule molecular inversion probes (smMIPs) effectively profiles cancer metabolism. This cost-effective technique aids in identifying therapeutic targets for individual cancers in clinical settings.
Area of Science:
- Biochemistry
- Molecular Biology
- Oncology
Background:
- Cancer cells exhibit unique metabolic alterations, presenting potential therapeutic targets.
- Identifying these cancer-specific metabolic pathways requires robust and accessible methods.
- Current methods like 13C-tracing are costly and complex, limiting their clinical use.
Purpose of the Study:
- To validate a novel metabolic profiling technique using multiplex targeted next-generation RNA sequencing with single molecule molecular inversion probes (smMIPs).
- To assess the utility of smMIP-profiling for measuring the activity and mutations of genes encoding metabolic enzymes.
- To evaluate the potential of this method for routine patient care.
Main Methods:
- Multiplex targeted next-generation RNA sequencing utilizing single molecule molecular inversion probes (smMIPs).
- Profiling of an isogenic cell line pair with differing Von Hippel Lindau protein expression.
- Measurement of gene activity and mutations in genes encoding metabolic enzymes.
Main Results:
- smMIP-profiling successfully provided relevant information on active metabolic pathways.
- The technique demonstrated its ability to measure gene activity and mutations in metabolic enzymes.
- The method is cost-effective and suitable for medium-to-high throughput analysis.
Conclusions:
- smMIP-based targeted RNA sequencing is a validated, cost-effective method for metabolic profiling.
- This approach can identify active metabolic pathways and gene alterations in cancer.
- Its suitability for medium-high throughput analysis makes it promising for routine patient care and cancer metabolism research.
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