Back

Ion-Pair-Free Nanoflow HILIC-MS With RNase Benchmarking for Native RNA

Qi, Y.; Li, C.; Yesiltac-Tosun, N.; Schicktanz, J.; Rusling, L.; Kaiser, S.; Wein, S. P.; Kaiser, S.

2025-11-18 molecular biology
10.1101/2025.11.18.689004 bioRxiv
Show abstract

RNA modifications play crucial roles in regulating cellular processes, but comprehensive mapping of the human RNome still remains limited by technological challenges. Mass spectrometry (MS) is a valuable tool to analyse RNA modifications complementing sequencing-based analysis. Current MS-based oligonucleotide workflows have limited sensitivity, requiring micrograms of RNA inputs and thus hindering studies on native RNAs. Additionally, environmentally toxic ion-pairing reagents are often required. Here, we report a highly sensitive, broadly applicable oligonucleotide-MS workflow that enables analysis of nanogram-scale RNA hydrolysates and we benchmark the substrate specificity of three nucleases: RNase T1, RNase 4, and colicin E5. We developed a nano-flow hydrophilic interaction liquid chromatography (HILIC) setup compatible with common MS buffers and coupled this with high-resolution MS. Using modified NucleicAcidSearchEngine (NASE), we confidently assigned RNA hydrolysates with diverse 3-end chemistries. Furthermore, we demonstrate that RNase 4 and colicin E5 efficiently cleave modified RNAs including pseudouridine-containing transcripts, enabling high sequence coverages. Using this workflow, we successfully mapped modifications in 25 ng of native yeast tRNAPhe and verified the sequence of 250 ng of a synthetic mRNA. Overall, our method provides a sensitive, high-resolution platform for oligonucleotide mass spectrometry, facilitating comprehensive analysis of RNA modifications and advancing efforts toward complete epitranscriptomic mapping. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/689004v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@ed3dc3org.highwire.dtl.DTLVardef@171d3d6org.highwire.dtl.DTLVardef@a9ce2org.highwire.dtl.DTLVardef@c1d940_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.