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PubMed2026

DREAMS illuminates spatial DNA and RNA modification landscapes.

DNA and RNA modifications regulate gene expression and RNA processing, but their spatial organization in complex tissues remains elusive. Here we developed DNA RNA Elements Areal Mass Spectrometry (DREAMS), a mass spectrometry imaging platform that spatially maps diverse nucleic acid modifications simultaneously. Applying DREAMS to TET-deficient mouse brains (Tet1Δ/Δ and triple Tet1/2/3Δ/Δ), we uncover TET1's unexpected role in modulating N1-methyladenosine (m1A), a pivotal RNA modification. While DREAMS reveals broad modification landscapes altered across TET knockouts, we identify TET1-mediated changes in m1A that correlate with transcriptome alterations. Our work establishes DREAMS as a transformative tool for spatial epigenomics/epitranscriptomics and suggests that TET enzymes could influence multiple DNA and RNA modifications with potential gatekeeping roles in nucleic acid regulation.

Animals↗

PubMed2025

Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach.

MOTIVATION: Bottom-up mass spectrometry-based proteomics studies changes in protein abundance and structure across conditions. Since the currency of these experiments are peptides, i.e. subsets of protein sequences that carry the quantitative information, conclusions at a different level must be computationally inferred. The inference is particularly challenging in situations where the peptides are shared by multiple proteins or post-translational modifications. While many approaches infer the underlying abundances from unique peptides, there is a need to distinguish the quantitative patterns when peptides are shared. RESULTS: We propose a statistical approach for estimating protein abundances, as well as site occupancies of post-translational modifications, based on quantitative information from shared peptides. The approach treats the quantitative patterns of shared peptides as convex combinations of abundances of individual proteins or modification sites, and estimates the abundance of each source in a sample together with the weights of the combination. In simulation-based evaluations, the proposed approach improved the precision of estimated fold changes between conditions. We further demonstrated the practical utility of the approach in experiments with diverse biological objectives, ranging from protein degradation and thermal proteome stability, to changes in protein post-translational modifications. AVAILABILITY AND IMPLEMENTATION: The approach is implemented in an open-source R package MSstatsWeightedSummary. The package is currently available at https://github.com/Vitek-Lab/MSstatsWeightedSummary (doi: 10.5281/zenodo.14662989). Code required to reproduce the results presented in this article can be found in a repository https://github.com/mstaniak/MWS_reproduction (doi: 10.5281/zenodo.14656053).

Protein Processing, Post-Translational↗