Quantitative Analysis of Phosphorothioate Isomers in CRISPR sgRNA at Single-Residue Resolution Using Endonuclease Digestion Coupled with Liquid Chromatography Cyclic Ion Mobility Mass Spectrometry (LC/cIMS)
Bibliographic record
Abstract
Phosphorothioate (PS) modifications in single-guided RNA (sgRNA) are crucial for genome editing applications using the CRISPR/Cas9 system. These modifications may enhance sgRNA stability, pharmacokinetics, and binding to targets, thereby facilitating the desired genetic alterations. Incorporating multiple PS groups at varying positions may introduce chiral centers into the sgRNA backbone, resulting in a complex mixture of constitutional- and stereoisomers that challenges current analytical capabilities for reliable identification and quantification. In this study, we developed an innovative methodology that combines endonuclease digestion of sgRNA with ion pairing reversed-phase liquid chromatography coupled with cyclic ion mobility mass spectrometry (IPRP-LC/cIMS) to fully distinguish PS-induced isomers in a complex mixture. The relative abundance of each isomer was quantified using a two-step method, wherein the ion abundance was sequentially extracted from an LC/MS ion chromatogram and LC/cIMS two-dimensional ion mobiligram. This quantification method was thoroughly evaluated, demonstrating excellent sensitivity, precision, dynamic range, repeatability, and accuracy. In addition, this method enables the investigation of the kinetics of forming PS to phosphodiester (PO) impurities in sgRNA under oxidative stress conditions, offering unprecedented insights into PS stability at a single-residue resolution. In this context, this method highlights the in-depth characterization of PS, demonstrating its capability to support biomedical research, development, and production of sgRNA products.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".