tRNA Direct Sequencing With LC-MS for Modification Mapping
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Solution Overview
Problem
Current RNA sequencing methods, particularly next-generation sequencing (NGS), fail to directly sequence RNA at single-nucleotide resolution and efficiently identify nucleotide modifications, especially in modification-rich tRNAs, and struggle with quantifying site-specific partial modifications and RNA isoforms.
Innovation Solution
The MLC-Seq method employs controlled acid hydrolysis of RNA samples to generate 5′ and 3′ ladder fragments, combined with LC-MS analysis and advanced algorithms like Homology Search, MassSum, GapFill, and Ladder Complementation, to directly sequence RNA and quantify nucleotide modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If NGS-based RNA sequencing methods are used, then high-throughput sequencing is achieved, but direct sequencing of modified nucleotides at single-nucleotide resolution is lost
Solution Approach 1:
The RNA sample is divided into multiple pools with different combinatorial barcodes assigned to specific nucleotide positions. This segmentation allows parallel sequencing of multiple positions simultaneously while maintaining the ability to resolve individual nucleotide identities and modifications through barcode-specific analysis
Solution Approach 2:
Combinatorial barcodes serve as intermediaries that link RNA molecules to their specific nucleotide positions and modification states. These barcodes enable the sequencing platform to indirectly detect modified nucleotides by capturing barcode-specific sequencing signals that correspond to modification presence at each position
2Measurement precision
If NGS-based methods with additional specific procedures are used, then some modified nucleotides can be identified, but only a small number of the over 170 known modified nucleotides can be detected
Solution Approach 1:
The combinatorial barcode system provides a universal platform that can detect any modified nucleotide across all 170+ known types without requiring modification-specific procedures. The system works uniformly for all nucleotide positions and modification types by relying on sequence-specific barcode assignment and comprehensive reference sequence matching
Solution Approach 2:
The method changes the detection parameter from modification-specific chemical or enzymatic properties to sequence-specific barcode identification. By assigning unique barcodes to each nucleotide position based on reference sequences, the system can identify any modification type through mass spectrometry or sequencing data that deviates from the expected barcode pattern
3Measurement precision
If nanopore-based direct sequencing is used, then mapping of modifications in long RNAs is achieved, but high error rates occur
Solution Approach 1:
The method incorporates iterative error correction through multiple sequencing passes and consensus building. By sequencing the same RNA population multiple times with combinatorial barcodes and comparing results against reference sequences and expected barcode patterns, systematic errors are identified and corrected, significantly improving sequencing accuracy
Solution Approach 2:
Reference sequences are predetermined and used to assign specific combinatorial barcodes to each nucleotide position before sequencing. This preliminary assignment creates an expected pattern that serves as a reference for error detection and correction during data analysis, improving reliability by comparing observed sequences against predetermined expectations
4Ease of operation
If conventional RNA sequencing is used, then canonical nucleotide sequences are obtained, but information on nucleotide modifications is removed
Solution Approach 1:
The method merges conventional sequencing approaches with modification detection by combining combinatorial barcode assignment with mass spectrometry or modified-nucleotide-aware sequencing. This integration allows simultaneous determination of both canonical sequence and modification status in a single experimental workflow, preserving modification information while maintaining operational simplicity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
MLC-Seq enables de novo sequencing of RNA with single-nucleotide resolution, revealing nucleotide identities, modification types, locations, and stoichiometry, even in complex RNA samples, and tracks modification dynamics in different cellular and disease contexts.
Implementation Method 1
controlled acid hydrolysis of RNA samples to generate 5′ and 3′ ladder fragments
Implementation Method 2
LC-MS analysis and advanced algorithms like Homology Search, MassSum, GapFill, and Ladder Complementation, to directly sequence RNA
Data Source
AI summary
The present disclosure provides a novel de novo sequencing method (herein referred to as MLC-Seq) of cellular RNAs within a sample including unbiased sequencing of nucleotide modifications, while also identifying site-specific stoichiometry of partial modifications. In one aspect, the method is used to sequence tRNAs and tRNA modifications within a mixed RNA sample.