Enhancement of Lipid Signal in MALDI MS imaging obtained from Formalin Fixed Human Brain Tissue
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 accurate visual/anatomical spatial information of molecules within the tissue. A current limitation of MALDI‐IMS is that it is largely performed on fresh frozen tissue whereas clinical tissue samples stored long term are fixed in formalin. It has been 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 in the brain while maintaining the special distribution of molecules throughout the tissue. In this work we investigate the effectiveness of this AF wash on post‐fixed rat and human brain tissue sections in an effort to increase the viability of formalin fixed tissue imaging in a clinical setting. Results herein demonstrate that the AF wash significantly improved MALDI‐IMS spectra for gangliosides, including GM1 in fresh frozen rat brain, formalin‐fixed rat brain and formalin fixed human brain samples. AF wash also demonstrated improvements in MALDI‐IMS image quality while retaining the spatial distribution of molecules. Results indicate that this method will allow analysis of gangliosides from formalin‐fixed clinical samples, which can open additional avenues for neurodegenerative disease research. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".