Dalwigk, J. F. | bioRxiv Science

bioRxiv2026

Harmonized nucleoside mass spectrometry enables reproducible cross-platform RNA modification quantification

RNA modification analysis by LC-MS/MS is central to epitranscriptomics, yet quantitative comparison across laboratories and instrument platforms remains poorly standardized. Here, we performed a community-driven benchmarking study during the first Human RNome Project workshop to systematically evaluate cross-platform reproducibility of ribonucleoside mass spectrometry workflows. Using the same analytical column and gradient, standardized RNA samples, and shared reagents, we compared nucleoside quantification across quadrupole, time-of-flight, and orbitrap-based LC-MS platforms employing distinct acquisition strategies. While chromatographic separation was highly reproducible across systems, nucleoside-specific MS response behavior differed substantially between platforms and limited direct comparability of relative signal intensities. These response differences varied across analytes and concentration ranges, demonstrating that harmonized chromatography alone is insufficient for transferable quantitative analysis. Stable isotope-labeled internal standard (SILIS) normalization substantially reduced platform-and method-dependent response and improved agreement for most evaluated modifications. External calibration improved agreement between qTOF and Orbitrap workflows for a subset of modifications but did not fully resolve residual intersystem differences. Based on these findings, we establish benchmark-derived recommendations for harmonized relative and absolute RNA modification quantification, including guidance for calibration design, quality control, and data reporting. Together, this work provides a methodological framework for reproducible nucleoside LC-MS/MS workflows and establishes a foundation for large-scale comparative epitranscriptomic studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/739095v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@6e4e98org.highwire.dtl.DTLVardef@1ea13beorg.highwire.dtl.DTLVardef@b65858org.highwire.dtl.DTLVardef@1623337_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2026

Human 28S rRNA analysed by state-of-the-art oligonucleotide mass spectrometry: benchmarking current capabilities and a call to action for MS-Seq

Oligonucleotide mass spectrometry (MS-Seq) is emerging as a powerful approach for sequence-resolved RNA modification analysis, yet the field lacks standards for experimental workflows, data analysis and reporting. To assess current capabilities, the Human RNome Project Consortium conducted a cross-platform benchmarking study using a common RNA sample. A partial RNase T1 digest of human 28S rRNA was distributed to participating laboratories and analysed using existing LC-MS/MS workflows spanning different chromatographic strategies and mass spectrometers. To enable direct comparison, datasets were analysed using a harmonized NucleicAcidSearchEngine (NASE) workflow. Despite substantial methodological differences, laboratories recovered highly overlapping oligonucleotide sets and generated similar sequence coverage maps with a global coverage of 54.16%, demonstrating reproducible sequence information across platforms under standardized sample and analysis conditions. The benchmark further revealed incomplete sequence coverage, platform-specific differences in data architecture and increased assignment ambiguity during dynamic modification searches. Together with the community consensus developed during the HRPC workshop, these findings define priorities for the field, including improved sensitivity, standardized data analysis and reporting, community repositories, and robust bioinformatic workflows for confident de novo RNA modification discovery. This study provides an experimental benchmark and roadmap toward routine MS-based mapping of the human RNome. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/739151v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1fb1b9borg.highwire.dtl.DTLVardef@d172a5org.highwire.dtl.DTLVardef@bdc185org.highwire.dtl.DTLVardef@1ec1cc9_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗