Fully electrical proof-of-concept biosensor for detecting RNA cancer biomarker sequences in solution at the single-molecule level. SciReports UMassLowell biosensor RNA cancer oncology biomarker
By Vijay Kumar MalesuAug 3 2023Reviewed by Sophia Coveney In a recent study published in Scientific Reports, a group of researchers investigated single-molecule electrical detection of ribonucleic acid cancer biomarkers, demonstrating high sensitivity and specificity in distinguishing mutants and paving the way for developing miniaturized electrical biosensors for cancer screening.
About the study In this study, the authors selected Kirsten rat sarcoma mutations as potential cancer biomarkers. The KRAS Exon 2 sequence was provided by collaborators from UMass Medical School. From this sequence, they identified two oligonucleotide sequences as deoxyribonucleic acid probes, which contained G12C and G12V mutations, with the mutated base situated in the center of each sequence.
STM-BJ measurements were performed at room temperature using a Pico-STM Molecular Imaging head and a Digital Instruments Nanoscope IIIa controller. Thiol groups in the DNA probes were reduced by Tris phosphine for conductance measurements, while the STM tip was created from a gold wire and coated with Apiezon wax.
With the assistance of the NCBI blast tool, alignments of prospective sequences ranging from 20–10 bases were carried out, determining the optimal short sequences which retained specificity to be 18 bases long. When no bound molecules were present, "steps" were not observed between the two electrodes, but they were noticeable when a single molecule was bound. After collecting thousands of conductance traces, a conductance histogram was created using curves with steps to identify the average conductance value for the specific sequence.
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ROGUE: an R Shiny app for RNA sequencing analysis and biomarker discovery - BMC BioinformaticsBackground The growing power and ever decreasing cost of RNA sequencing (RNA-Seq) technologies have resulted in an explosion of RNA-Seq data production. Comparing gene expression values within RNA-Seq datasets is relatively easy for many interdisciplinary biomedical researchers; however, user-friendly software applications increase the ability of biologists to efficiently explore available datasets. Results Here, we describe ROGUE (RNA-Seq Ontology Graphic User Environment, https://marisshiny.research.chop.edu/ROGUE/ ), a user-friendly R Shiny application that allows a biologist to perform differentially expressed gene analysis, gene ontology and pathway enrichment analysis, potential biomarker identification, and advanced statistical analyses. We use ROGUE to identify potential biomarkers and show unique enriched pathways between various immune cells. Conclusions User-friendly tools for the analysis of next generation sequencing data, such as ROGUE, will allow biologists to efficiently explore their datasets, discover expression patterns, and advance their research by allowing them to develop and test hypotheses.
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