Analysis of neuronal cardiolipin and monolysocardiolipin from biological samples with cyclic ion mobility mass spectrometry
Bibliographic record
Abstract
The mitochondrial phospholipid cardiolipin (CL) is essential for proper mitochondrial function and energy production. Cardiolipin has four distinct fatty acid tails with varying expression compositions, resulting in a highly variable tissue-specific distribution of isomer expression. Neuronal cardiolipin has a remarkable variety of subspecies and has recently been used as a biomarker to predict brain injury severity following cardiac arrest and traumatic brain injury. Multiple conditions have been associated with disordered cardiolipin remodeling, including Alzheimer's disease, Parkinson's disease, Barth syndrome, and astrocytoma. The clinical relevance of cardiolipin as a biomarker and the importance of the mechanistic role of cardiolipin remodeling in disease emphasize the demand for a reliable and accurate means of the identification and quantification of cardiolipin. In this study, we outline the use of a novel method of cardiolipin analysis using cyclic ion mobility mass spectrometry (cIMS-MS) to isolate and identify cardiolipin subspecies in several biological samples. Furthermore, cIMS-MS established the composition of the cardiolipin profile by individual subspecies across biological samples under basal conditions. Monolysocardiolipin (MLCL), the precursor of mature cardiolipin and a primary diagnostic biomarker of Barth syndrome, was isolated from cardiolipin and identified. The monolysocardiolipin:cardiolipin ratio was quantified in brain samples from tafazzin-knockout (KO) mice, demonstrating accumulation of MLCL and providing direct evidence for the validity of this cIMS-MS methodology through genetic loss-of-function. The novel, multiple-pass feature of cIMS-MS enabled the isolation and amplification of less abundant cardiolipin subspecies in both standards and biological samples. This protocol enables rapid analysis of biological samples, allowing researchers to further dissect the mechanistic role of cardiolipin in injury pathology, with simplified sample preparation and reduced potential for artifact introduction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".