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Biology subjects

Sys, S.

Publications and source records attributed to Sys, S..

2 recordsLinked to original sources

Direct RNA sequencing (RNA004) allows for improved transcriptome assessment and near real-time tracking of methylation for medical applications

Direct RNA sequencing (DRS) is a nanopore-based technique for analyzing RNA in its native form, promising breakthroughs in diagnostics and biomarker development. Coupled to RNA002 sequencing chemistry, its clinical implementation has been challenging due to low throughput, low accuracy, and lack of large-scale RNA-modification models. In this study, we evaluate the improvements achieved by pairing the latest RNA004 chemistry with novel modified-base-calling models for pseudouridine and N6-methyladenosine using diverse RNA samples from cell lines, synthetic oligos, and human blood. Finally, we present the first clinical application of DRS by confirming the loss of RNA methylation in a patient carrying truncating mutations in the methyltransferase METTL5. Conclusively, the combined use of RNA004 chemistry with the base-calling models significantly improved the throughput, accuracy, and site-specific detection of modifications. From this perspective, we offer an outlook on the potential suitability of DRS for use in routine diagnostics, the first comprehensive benchmark of human peripheral blood and quality assessments of RNA therapeutics.

bioinformatics↗

ClearFinder: a Python GUI for annotating cells in cleared mouse brain

BackgroundTissue clearing combined with light-sheet microscopy is gaining popularity among neuroscientists interested in unbiased assessment of their samples in 3D volume. However, the analysis of such data remains a challenge. ClearMap and CellFinder are tools for analyzing neuronal activity maps in an intact volume of cleared mouse brains. However, these tools lack a user interface, restricting accessibility primarily to scientists proficient in advanced Python programming. The application presented here aims to bridge this gap and make data analysis accessible to a wider scientific community. ResultsWe developed an easy-to-adopt graphical user interface for cell quantification and group analysis of whole-cleared adult mouse brains. Fundamental statistical analysis, such as PCA and box plots, and additional visualization features allow for quick data evaluation and quality checks. Furthermore, we report significant differences in total cell counts between CellFinder and ClearMap when cross-analyzing the same samples, underscoring the need for optimizing reproducibility within the field. ConclusionsOur easily accessible tool allows more researchers to implement the methodology, troubleshoot arising issues, and develop quality checks, benchmarking, and standardized analysis pipelines for cell detection and region annotation in whole volumes of cleared brains.

bioinformatics↗