2D-HELS MS SEQ: Общий метод на основе LC-MS для прямого и de novo секвенирования смесей РНК с различными модификациями нуклеотидов

Executive Industry Relevance

This LC-MS-based RNA sequencing method enables direct, de novo sequencing of modified RNA mixtures without cDNA synthesis, addressing a key bottleneck in epitranscriptomic target validation. By providing quantitative, modification-resolved sequencing data, it supports early discovery of RNA-based biomarkers and mechanistic de-risking of therapeutic oligonucleotides. The approach enhances predictive confidence in lead identification by delivering sequence-confirmed, modification-specific outputs directly applicable to screening and assay development workflows.

Strategic Applications in Biopharma R&D

Early Discovery & Target Validation

• Scientific Value: Enables interrogation of therapeutic hypotheses by directly sequencing RNA modifications such as pseudouridine and 5-methylcytosine in native contexts.
• Operational Value: Provides a general solution for simultaneous detection of canonical and modified nucleotides, reducing need for sequential modification-specific assays.
• Predictive Value: Supports target confidence through direct mass-retention time correlation of modified nucleobases, facilitating accurate site-specific modification mapping.

Screening & Assay Development

• Scientific Value: Generates quantitative mass and retention time outputs that enable reliable discrimination of RNA sequences in mixtures of up to 12 distinct oligonucleotides.
• Operational Value: Delivers reproducible, normalization-ready data compatible with standard high-resolution LC-MS systems, supporting assay standardization across short RNA panels.
• Scalability: Facilitates preparation of validated biological systems for downstream screening by yielding sequence-verified RNA standards with defined modification patterns.

Translational & Preclinical Research

• Translational Continuity: Supports sequence verification of modified therapeutic RNA oligonucleotides, linking discovery-phase modifications to preclinical candidate evaluation.
• Mechanistic De-risking: Enables detection of modification-induced mass shifts (e.g., CMC-pseudouridine adduct) to distinguish true signal from degradation artifacts in complex samples.
• Risk-Adjusted Advancement: Provides sequence confirmation without enzymatic bias, reducing false negatives in modification screening and improving go/no-go decision reliability.

Pipeline & Workflow Integration

The method integrates into early discovery workflows as a front-end sequencing tool for hypothesis-driven RNA analysis, feeding into lead identification by confirming sequence and modification status of synthetic or endogenous RNA targets prior to functional screening.

• Discovery Biology: Supports hypothesis testing and pathway clarification by delivering direct evidence of RNA sequence and modification co-occurrence in native or synthetic mixtures.
• Screening: Enables assay readiness through generation of mass-retention time ladders that serve as quantitative references for compound-induced RNA modifications or degradation profiling.
• Analytics: Produces centroid-based molecular feature data (mass, retention time, volume, quality score) exportable to Excel, enabling cross-condition comparison and statistical evaluation of modification stability.
• Translational Research: Connects to preclinical continuity by providing sequence-verified modified RNA standards suitable for biomarker alignment and functional validation in disease-relevant systems.
• Enterprise Reuse: Functions as a reusable LC-MS-based capability for rapid sequencing of short RNA variants (<35 nt), reducing dependency on outsourced sequencing and accelerating internal modification screening cycles.

Operational & Enterprise Impact

• Scientific Value: Delivers predictive confidence in target validation by eliminating cDNA-mediated errors and enabling direct observation of modification-specific mass signatures.
• Operational Value: Ensures reproducibility through standardized labeling, acid quenching, and normalization steps that minimize inter-run variability in complex RNA mixtures.
• Strategic Value: Improves capital efficiency by enabling internal sequencing of modified RNA, reducing reliance on external vendors and accelerating iteration cycles in lead optimization.
• Portfolio Impact: Supports risk-adjusted prioritization by delivering unambiguous sequence and modification data, decreasing late-stage attrition due to undetected RNA heterogeneity or misannotation.

Implementation Considerations

• Requires expertise in RNA handling, LC-MS operation, and MassHunter-based molecular feature extraction for accurate data interpretation.
• Depends on access to high-resolution LC-MS systems capable of centroid data acquisition with defined peak height (100–1000) and quality score (≥50) thresholds.
• Necessitates cross-team standardization of labeling efficiency, degradation timing, and normalization protocols to ensure consistent mass ladder generation across users and platforms.
• Involves adaptation considerations when extending beyond short RNA (<35 nt) or to complex biological samples, pending future algorithm and instrument improvements.
• Involves practical limitations including current throughput constraints and dependency on manual optimization of labeling reactions (e.g., AppCp-biotin, T4 ligase, DMSO) to maximize yield and ladder resolution.

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