Abstract
To acquire a deeper understanding of malignant melanoma (MM), it is essential to study the proteome of patient tissues. In particular, phosphoproteomics of MM has become of significant importance because of the central role that phosphorylation plays in the development of MM. Investigating clinical samples, however, is an extremely challenging task as there is usually only very limited quantities of material available to perform targeted enrichment approaches. Here, an automated phosphopeptide enrichment protocol using the AssayMap Bravo platform was applied to MM tissues and assessed for performance. The strategy proved to be highly-sensitive, less prone to variability, less laborious than existing techniques and adequate for starting quantities at the microgram level. An Fe(III)-NTA-IMAC-based enrichment workflow was applied to a dilution series of MM tissue lysates. The workflow was efficient in terms of sensitivity, reproducibility and phosphosite localization; and from only 12.5 μg of sample, more than 1,000 phosphopeptides were identified. In addition, from 60 μg of protein material the number of identified phosphoproteins from individual MM samples was comparable to previous reports that used extensive fractionation methods. Our data set included key pathways that are involved in MM progression; such as MAPK, melanocyte development and integrin signaling. Moreover, tissue-specific immunological proteins were identified, that have not been previously observed in the proteome of MM-derived cell lines. In conclusion, this workflow is suitable to study large cohorts of clinical samples that demand automatic and careful handling.
Highlights
Malignant melanoma (MM) is a type of cancer that has a high rate of incidence in many countries [1]
To evaluate the sensitivity of the protocol, the phosphopeptide enrichment was assessed on increasing quantities of MM protein digests (12.5–200 μg) taken from a pool of eight MM tumor lysates
When higher quantities were injected onto the cartridges, linearity was compromised suggesting that the overall capacity was affected (S1 Fig)
Summary
Malignant melanoma (MM) is a type of cancer that has a high rate of incidence in many countries [1]. Along with an unfavorable prognosis, MM lacks any well-established biomarkers to aid in early detection, progression and treatment [1,2]. Automated phosphopeptide enrichment from malignant melanoma tissues. The funders (including the commercial company AstraZeneca) had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. AstraZeneca provided support in the form of salaries for author TM, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contributions’ section
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