MALDI-MSI and Ovarian Cancer Biomarkers
Bibliographic record
Abstract
Advances in Cancer Management 212 MALDI Mass Spectrometry Imaging (MALDI-MSI)Over these past ten years, important technical improvements in mass spectrometry instrumentation together with the growing importance of this method for compound identification had lead to the development of direct analysis of tissue samples.Mass spectrometry has become an analytical tool allowing identification of compounds directly from tissues without any extraction or separation and adding the essential and time saving spatial resolution to the analysis.Furthermore, in a single experiment, molecular information on hundreds of chemical or biological molecules can be retrieved.By automation of this method and powerful data processing, molecular maps are generated from single tissue sections.Another major advantage is the sensitivity of mass spectrometry instruments giving access to hundreds of compound molecular images after one set acquisition.Matrix-assisted laser desorption/ionization (MALDI) ion sources are well suited for this application as they can provide data on a range of biomolecular families ranging from small molecule drugs, peptides, proteins, oligonucleotides, sugars or lipids with a spatial resolution that approaches near cellular resolution.MALDI-imaging mass spectrometry (MALDI-MSI) was first introduced by Caprioli and coll.12 but major improvements have been developed in by other groups seeking to improve sample preparations, instrumentation, image spatial resolution , as well as develop new fields of applications 2,12-14 .For example, MALDI-MSI technology has been used for biomarkers hunting, drug biodistribution tissue interactions in drug discovery as well as for the molecular diagnosis through biopsy analyses in pathology.The translational nature of this technology provides unique challenges and as yet unimagined opportunities that promise to transform the way disease is detected, treated, and managed.Rather than focusing on genetic alterations that may lead to a particular disease, it is emerging that changes in protein expression patterns are the most accurate way to identify diseases in their early stages and to determine the most effective course of treatment.Indeed, genome sequences fails to provide certainty for post-translational modification events such as glycosylation, phosphorylation, acylation or partial proteolysis.One of the most common objectives in proteomics is the study of protein expression patterns (e.g., protein profiling) associated with diseases.Pathologies that cause changes in signal transduction pathways generally result in changes in specific cell phenotypes.Using MALDI-MSI in this context does not have knowledge prerequisite of the studied system due to the non-targeted nature of the analysis.Such data leads to the establishment of a classification of cell phenotypic changes at the molecular level and in this way can provide a better understanding of pathologies, can lead to new diagnostic biomarkers or even new therapeutic targets.The capacity of generating multidimensional pictures with a spatial resolution that can approach the cellular level, allows monitoring, in the same analysis, of the localization of drugs compounds and the changes in biomarkers expression 2 .In the context of the present discussion, there is a single clear advantage of MALDI_MSI, that is the spatial localization of identified compounds, that tremendously increases the predictive potential of which markers are most likely to be successful at the clinical level.There are additional advantages to the MALDI-MSI approach for biomarker hunting.MALDI ion sources can identify a wide range of biomolecular families including small molecule drugs, peptides, proteins, sugars or lipids with a spatial resolutions that approaches the cellular level.Due to its high data acquisition, MALDI_MSI can permit the establishment of a classification of cell phenotypic changes at the molecular level, which can www.intechopen.com
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".