MALDI Imaging Mass Spectrometry (MALDI-IMS)―Application of Spatial Proteomics for Ovarian Cancer Classification and Diagnosis
Abstract
:1. Epidemiology of Ovarian Cancer
2. Early Detection of Ovarian Cancer
3. Molecular Classification of Ovarian Carcinomas
4. Application of Proteomics to Ovarian Cancer
5. Tissue Analysis by Mass Spectrometry
- Several hundred molecular features can be measured in a single experiment (see Figure 3a–c).
- No preliminary knowledge about tissue composition is required.
- No antibodies are required.
6. Methods for in Situ MALDI-TOF Analysis of Ovarian Cancer Tissue
7. Profiling Cancer Tissues Using MALDI-TOF MS
8. Profiling vs. Imaging
9. Software for Data Analysis
10. Automated Sample Preparation for Imaging Cancer Tissues
11. Peptide Imaging Provides Data Complementary to Protein Imaging
12. Using Histology to Guide Imaging Mass Spectrometry
13. Ovarian Cancer Biomarker Discovery Using Imaging Mass Spectrometry
14. Application of Tryptic Digestion to Formalin-Fixed Paraffin Embedded Ovarian Tissues
15. Conclusions and Future Prospects
References
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FIGO Stage | Prevalence (%) | Anatomical features |
---|---|---|
I | 25 | Limited to ovaries |
II | 11 | Pelvic extension |
III | 47 | Abdominal extension and/or positive lymph nodes |
IV | 17 | Distant metastases |
Grading system | Grade | Key features | Ref. | |||
---|---|---|---|---|---|---|
FIGO | 1 | Well differentiated | Grade based on % solid non-squamous growth, grade + 1 if nuclear atypia apparent | <5% solid growth | [10] | |
2 | Moderately differentiated | 6–50% solid growth | ||||
3 | Poorly differentiated | >50% solid growth | [13] | |||
3-tier universal grading | 1 | Grade based on sum of individual feature scores (see right) 1 = 3–5 points 2 = 6–7 points 3 = 8–9 points | Architecture based score | Glandular = 1 point Papillary = 2 points Solid = 3 points | ||
2 | Nuclear pleomorphism score | Slight = 1 point Moderate = 2 points Marked = 3 points | [12] | |||
3 | Mitotic activity score | 0–9 = 1 point 10–24 = 2 points ≥25 = 3 points | ||||
2-tier grading | Serous tumour | Low grade (type I) | Slow development | Low chromosomal instability | Gene mutation–KRAS, BRAF, ERBB2 | [14] |
High grade (type II) | Rapid development | High chromosomal instability | Gene mutation–P53 | |||
Endomet roid tumour | Low grade | Well differentiated, no necrosis | Solid glandular architecture | Gene mutation–Wnt, PI3K/Akt | [13] | |
High grade | Solid growth >50%, necrosis | Diffusely infiltrative or expansive growth, no glandular architecture | Gene mutation–TP53 | [16] |
Histology | IHC | Proteomics | ||
---|---|---|---|---|
Fractionation-MS | Direct tissue MS | |||
Methods | Cellular staining | Antibody directed staining of specific proteins | Liquid phase separation (i.e., liquid chromatography) | Direct measurement of peptides and proteins from tissue section |
Analysis | Tissue morphology assessment by light microscopy | Protein distribution across tissue sections | MS protein identification | MS profiles of tissue sections |
Quantitation using protein labelling | Peptide and protein intensity maps showing distribution across tissue sections | |||
Advantages | Easy staining methods | Highly specific | Highly sensitive | Rapid |
Cellular microscopy resolution | Cellular microscopy resolution | Thousands of proteins analysed at a time | Spatial proteome information | |
Well established | Well established | Heavily automated | Measurement of hundreds of molecular features at a time | |
Clinical personnel already available | Clinical personnel already available | Highly modular workflows | No antibodies required | |
Disadvantages | Reproducibility issues | Time consuming | Time consuming | Expensive equipment |
Based on visual assessment of morphology | Labor intensive | Labor intensive | Novel technology | |
Non-specific | Limited to 3–4 proteins | Removes spatial information | Requires fraction-MS based proteomics to identify peptide and protein features | |
Analysis is subjective | Dependent on antibody quality | Requires specialist personnel | Analytical resolution limited to a maximum of 20–50 μm |
Matrix | Chemical name | Biomolecule specificity |
---|---|---|
DHB | 2,5-dihydroxybenzoic acid | Lipids, peptides, <10 kDa proteins |
DHB/aniline | DHB + aniline | Lipids, peptides, <10 kDa proteins |
DHB/3-AP | DHB + 3-acetyl pyridine | Lipids, peptides, <10 kDa proteins |
CHCA | α-cyano-4-hydroxycinnamic acid | Peptides, small proteins (<10 kDa) |
CHCA/aniline | CHCA + aniline | Peptides, <10 kDa proteins |
SA | 3,5-dimethoxy-4-hydroxycinnamic acid | Proteins (>10 kDa) |
SA/aniline | SA + aniline | Proteins (>10 kDa) |
SA/3-AP | SA + 3-acetyl pyridine | Proteins (>10 kDa) |
SA/HFIP | SA + 1,1,1,3,3,3-hexafluoro-2-propanol | Proteins (>30 kDa) |
SA/TFE | SA + 2,2,2-trifluoroethanol | Proteins (>30 kDa) |
Nebulising instruments | Printers | |||
---|---|---|---|---|
Air brush | ImagePrep station | ChIP-1000 | Labcyte Portrait | |
Reproducibility | Poor | Good | Excellent | Excellent |
Acquisition resolution | ≥5 μm | ≥20 μm | ≥150 μm | ≥150 μm |
Advantages | Cheap | Automated | Automated | Automated |
High resolution MS acquisition | High resolution MS acquisition | Control over reagent volume deposited | Control over reagent volume deposited | |
Good for start up imaging MS laboratories | Default methods available but methods can be modified by user | High MS sensitivity | High MS sensitivity | |
Disadvantages | Lower peptide/protein incorporation into matrix | Lower peptide/protein incorporation into matrix | Expensive | Most expensive |
Requires experienced user | Requires experienced user | Time consuming preparation | Time consuming preparation | |
Manual preparation | Expensive | Lower data acquisition resolution than nebulised preparations | Lower data acquisition resolution than nebulised preparations |
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Gustafsson, J.O.R.; Oehler, M.K.; Ruszkiewicz, A.; McColl, S.R.; Hoffmann, P. MALDI Imaging Mass Spectrometry (MALDI-IMS)―Application of Spatial Proteomics for Ovarian Cancer Classification and Diagnosis. Int. J. Mol. Sci. 2011, 12, 773-794. https://doi.org/10.3390/ijms12010773
Gustafsson JOR, Oehler MK, Ruszkiewicz A, McColl SR, Hoffmann P. MALDI Imaging Mass Spectrometry (MALDI-IMS)―Application of Spatial Proteomics for Ovarian Cancer Classification and Diagnosis. International Journal of Molecular Sciences. 2011; 12(1):773-794. https://doi.org/10.3390/ijms12010773
Chicago/Turabian StyleGustafsson, Johan O. R., Martin K. Oehler, Andrew Ruszkiewicz, Shaun R. McColl, and Peter Hoffmann. 2011. "MALDI Imaging Mass Spectrometry (MALDI-IMS)―Application of Spatial Proteomics for Ovarian Cancer Classification and Diagnosis" International Journal of Molecular Sciences 12, no. 1: 773-794. https://doi.org/10.3390/ijms12010773
APA StyleGustafsson, J. O. R., Oehler, M. K., Ruszkiewicz, A., McColl, S. R., & Hoffmann, P. (2011). MALDI Imaging Mass Spectrometry (MALDI-IMS)―Application of Spatial Proteomics for Ovarian Cancer Classification and Diagnosis. International Journal of Molecular Sciences, 12(1), 773-794. https://doi.org/10.3390/ijms12010773