Ganglioside Detection from Formalin-Fixed Human Brain Tissue Utilizing MALDI Imaging Mass Spectrometry
Bibliographic record
Abstract
Matrix assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is used to perform mass spectrometric analysis directly on biological samples providing visual and anatomical spatial information on molecules within tissues. A current obscuration of MALDI-IMS is that it is largely performed on fresh frozen tissue, whereas clinical tissue samples stored long-term are fixed in formalin, and the fixation process is thought to cause signal suppression for lipid molecules. Studies have shown that fresh frozen tissue sections applied with an ammonium formate (AF) wash prior to matrix application in the MALDI-IMS procedure display an increase in observed signal intensity and sensitivity for lipid molecules detected within the brain while maintaining the spatial distribution of molecules throughout the tissue. In this work, we investigate the viability of formalin-fixed tissue imaging in a clinical setting by comparing MALDI data of fresh frozen and postfixed rat brain samples, along with postfixed human brain samples washed with AF to assess the capabilities of ganglioside analysis in MALDI imaging of formalin-fixed tissue. Results herein demonstrate that MALDI-IMS spectra for gangliosides, including GM1, were significantly enhanced in fresh frozen rat brain, formalin-fixed rat brain, and formalin-fixed human brain samples through the use of an AF wash. Improvements in MALDI-IMS image quality were demonstrated, and the spatial distribution of molecules was retained. Results indicate that this method will allow for the analysis of gangliosides from formalin-fixed clinical samples, which can open additional avenues for neurodegenerative disease research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".