Early Cancer Biomarker Discovery Using DIA-MS Proteomic Analysis of EVs from Peripheral Blood

QuickProt: A bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasets

Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various software platforms. However, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results remains challenging due to the lack of integrated tools to facilitate the analyses. To address this challenge, we developed QuickProt, a series of Python-based Google Colab notebooks for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets. These pipelines are designed so that users with no coding expertise can utilize the tool. Furthermore, as open-source code, QuickProt notebooks can be customized and incorporated into existing workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets from a time-course study of human erythropoiesis. The analysis resulted in annotated tables and publication-ready figures revealing a dynamic rearrangement of the proteome during erythroid differentiation, with the abundance of proteins linked to gene regulation, metabolic, and chromatin remodeling pathways increasing early in erythropoiesis. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.

Liquid biopsy of oesophageal squamous cell carcinoma: implications in diagnosis, prognosis, and treatment monitoring

Oesophageal squamous cell carcinoma (ESCC) is a common malignant tumour of the gastrointestinal tract. Early detection and access to appropriate treatment are crucial for the long-term survival of patients. However, limited diagnostic and monitoring methods are available for identifying early stage ESCC. Endoscopic screening and surgical resection are commonly used to diagnose and treat early ESCC. However, these methods have disadvantages, such as high recurrence, lethality, and mortality rates. Therefore, methods to improve early diagnosis of ESCC and reduce its mortality rate are urgently required. In 1961, Gary et al. proposed a novel liquid biopsy approach for clinical diagnosis. This involved examining exosomes, circulating tumour cells, circulating free DNA, and circulating free RNA in body fluids. The ability of liquid biopsy to obtain samples repeatedly, wide detection range, and fast detection speed make it a feasible option for non-invasive tumour detection. In clinical practice, liquid biopsy technology has gained popularity for early screening, diagnosis, treatment efficacy monitoring, and prognosis assessment. Thus, this is a highly promising examination method. However, there have been no comprehensive reviews on the four factors of liquid biopsy in the context of ESCC. This review aimed to analyse the progress of liquid biopsy research for ESCC, including its classification, components, and potential future applications.

Explore & actuate: the future of personalized medicine in oncology through emerging technologies

PURPOSE OF REVIEW

The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings.

RECENT FINDINGS

To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation.

SUMMARY

The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.

Size-exclusion chromatography combined with DIA-MS enables deep proteome profiling of extracellular vesicles from melanoma plasma and serum

Extracellular vesicles (EVs) are important players in melanoma progression, but their use as clinical biomarkers has been limited by the difficulty of profiling blood-derived EV proteins with high depth of coverage, the requirement for large input amounts, and complex protocols. Here, we provide a streamlined and reproducible experimental workflow to identify plasma- and serum- derived EV proteins of healthy donors and melanoma patients using minimal amounts of sample input. SEC-DIA-MS couples size-exclusion chromatography to EV concentration and deep-proteomic profiling using data-independent acquisition. From as little as 200 µL of plasma per patient in a cohort of three healthy donors and six melanoma patients, we identified and quantified 2896 EV-associated proteins, achieving a 3.5-fold increase in depth compared to previously published melanoma studies. To compare the EV-proteome to unenriched blood, we employed an automated workflow to deplete the 14 most abundant proteins from plasma and serum and thereby approximately doubled protein group identifications versus native blood. The EV proteome diverged from corresponding unenriched plasma and serum, and unlike the latter, separated healthy donor and melanoma patient samples. Furthermore, known melanoma markers, such as MCAM, TNC, and TGFBI, were upregulated in melanoma EVs but not in depleted melanoma plasma, highlighting the specific information contained in EVs. Overall, EVs were significantly enriched in intact membrane proteins and proteins related to SNARE protein interactions and T-cell biology. Taken together, we demonstrated the increased sensitivity of an EV-based proteomic workflow that can be easily applied to larger melanoma cohorts and other indications.