Lasers & photonics
There is an array of examples of MALDI-based triumphs expressed in scientific literature, but one of the most contemporary relates to pancreatic cancer. Pancreatic cancer is one of the most lethal forms of the disease with overall five-year survival after diagnosis being less than 9%. A deeper understanding of the molecular changes involved could markedly improve individual prognostic assessment, diagnosis and even lead to better therapy.
MALDI has proved capable of identifying specific peptide signatures linked to established prognostic parameters of pancreatic cancer. In a study published last year by Florian Loch at the University of Berlin, MALDI imaging analysis was performed in addition to histopathological assessment. Tumour resection was performed on 18 patients with exocrine pancreatic cancer. The paper reads: “We were able to identify discriminative peptide signatures corresponding to nine proteins for the prognostic histopathological features lymphatic vessel invasion, lymph node metastasis and angioinvasion. This demonstrates the technical feasibility of MALDI-MS to identify peptide signatures with prognostic value through the workflows used in this study.” Cramer says the Berlin team’s findings emphasise the value and power of MALDI.
A separate group in Munich have modelled working on primary pancreatic ductal adenocarcinoma (PDAC) and turned to MALDI-MS for the rapid measurement of peptide/protein content, as well as its localisation within the tissue. Significantly, they identified peptide/protein content of tumour samples collected from patients diagnosed with PDAC who had also developed metastases. By adopting machine learning tools, the team predicted the ability of supervised classification models to differentiate between primary and metastatic tumour tissue. Based solely on the evaluation of tumour content, potential targets for the diagnosis of PDAC metastases were identified. Tentative identification of the certain molecular features showed that collagen fragments, referred to as COL1A1, COL1A2, and COL3A1, may play a fundamental role in tumour development.
Machine learning ahead of the field There has been cacophony about the growing role of artificial intelligence (AI) but, as Cramer points out, in the world of chemical and biochemical analysis, AI became an essential part of processes years ago. “We call it machine learning (ML), which is a sub-field of AI and presents computers associated with MALDI-TOF-MS and other systems with the ability to learn without being explicitly programmed,” he says. “In the last decade, machine
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learning programmes have been recognised as a fundamental resource to build informative and predictive models from what is highly complex biological data.” To take just one example, in the field of microbiology, several studies have shown how the combination of ML algorithms with mass spectra information can enhance discrimination between closely related sub-lineages within the genera Mycobacterium and Bacillus, as well as the identification of different antimicrobial resistant groups of Staphylococcus aureus. And, as Cramer adds, it’s on the frontline in the battle against Covid-19, as ML and MALDI have proved intrinsic to identifying some strains of the disease. The development of MALDI-based analytical techniques has come far since the original work of Hillenkamp and Karas gave some insight into its preeminent position in labs today. “The incorporation of photonics technologies into MALDI-MS instruments has significantly improved the sensitivity, the throughput and the ease of use of these systems,” says Cramer. “MALDI systems provide an attractive alternative to older, more complicated high-performance liquid chromatography (lc/ms) units.” The development of laser technology used in MALDI has evolved significantly too, resulting in more applications for it and other analysis methods, as Cramer notes: “Advances in laser technology have also enabled high-speed analysis, which significantly increases the throughput of say, protein identification in proteomics research laboratories. More recently, electrospray ionisation (ESI)-LC/MS has become the preferred method for proteomics, but MALDI-MS has found wider application in microbiology, pharmaceutical development and medical-related applications.” Also notable, the size of particles that can be analysed using MALDI today are far greater than the 34,472 daltons that won Tanaka the Nobel Prize, with limits of 150,000 being the standard for contemporary machines. When it comes to the interpretation of the spectrometry data, MALDI has had a bespoke option for sophisticated statistical open-source analysis. MALDIquant was developed about a decade ago to provide a complete open-source analysis pipeline, – comprising all steps from the importing of raw data, pre-processing (baseline removal), peak detection and non-linear peak alignment to calibration of mass spectra – making the use of MALDI-based analytical methods more accessible across the research community. They may not have won a Nobel Prize, but the world of medical science owes a great deal to Hillenkamp and Karas. ●
Medical Device Developments /
www.nsmedicaldevices.com
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