Multidimensional chromatography offers enhanced chromatographic resolution and peak capacity, which are crucial for analyzing complex samples. This study presents a novel comprehensive online multidimensional chromatography method for the lipidomic analysis of biological samples, combining lipid class and lipid species separation approaches. The method combines optimized reversed-phase ultrahigh-performance liquid chromatography (RP-UHPLC) in the first dimension, utilizing a 150 mm long C18 column, with ultrahigh-performance supercritical fluid chromatography (UHPSFC) in the second dimension, using a 10 mm long silica column, both with sub-2 μm particles. A key advantage of employing UHPSFC in the second dimension is its ability to perform ultrafast analysis using gradient elution with a sampling time of 0.55 min. This approach offers a significant increase in the peak capacity. Compared to our routinely used 1D methods, the peak capacity of the 4D system is 10 times higher than RP-UHPLC and 18 times higher than UHPSFC. The entire chromatographic system is coupled with a high-resolution quadrupole-time-of-flight (QTOF) mass analyzer using electrospray ionization (ESI) in both full-scan and tandem mass spectrometry (MS/MS) and with positive- and negative-ion polarities, enabling the detailed characterization of the lipidome. The confident identification of lipid species is achieved through characteristic ions in both polarity modes, information from MS elevated energy (MSE) and fast data-dependent analysis scans, and mass accuracy below 5 ppm. This analytical method has been used to characterize the lipidomic profile of the total lipid extract from human plasma, which has led to the identification of 298 lipid species from 16 lipid subclasses.

• MeSH
• lidé MeSH
• lipidomika * metody   MeSH
• lipidy * analýza   MeSH
• superkritická fluidní chromatografie metody   MeSH
• tandemová hmotnostní spektrometrie * metody   MeSH
• vysokoúčinná kapalinová chromatografie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• lipidy * MeSH

Pancreatic ductal adenocarcinoma (PDAC) is one of the most common causes of cancer-related deaths worldwide, accounting for 90% of primary pancreatic tumors with an average 5-year survival rate of less than 10%. PDAC exhibits aggressive biology, which, together with late detection, results in most PDAC patients presenting with unresectable, locally advanced, or metastatic disease. In-depth lipid profiling and screening of potential biomarkers currently appear to be a promising approach for early detection of PDAC or other cancers. Here, we isolated and characterized complex glycosphingolipids (GSL) from normal and tumor pancreatic tissues of patients with PDAC using a combination of TLC, chemical staining, carbohydrate-recognized ligand-binding assay, and LC/ESI-MS2. The major neutral GSL identified were GSL with the terminal blood groups A, B, H, Lea, Leb, Lex, Ley, P1, and PX2 determinants together with globo- (Gb3 and Gb4) and neolacto-series GSL (nLc4 and nLc6). We also revealed that the neutral GSL profiles and their relative amounts differ between normal and tumor tissues. Additionally, the normal and tumor pancreatic tissues differ in type 1/2 core chains. Sulfatides and GM3 gangliosides were the predominant acidic GSL along with the minor sialyl-nLc4/nLc6 and sialyl-Lea/Lex. The comprehensive analysis of GSL in human PDAC tissues extends the GSL coverage and provides an important platform for further studies of GSL alterations; therefore, it could contribute to the development of new biomarkers and therapeutic approaches.

• Klíčová slova
• Lipidomics, Tandem mass spectrometry, chromatogram binding assay, glycolipids, lipids, liquid chromatography, monoclonal antibodies, pancreatic cancer, sphingolipids, thin-layer chromatography,
• MeSH
• chromatografie kapalinová MeSH
• chromatografie na tenké vrstvě MeSH
• duktální karcinom slinivky břišní diagnóza   patofyziologie   MeSH
• gangliosidy chemie   MeSH
• glykosfingolipidy * analýza   chemie   MeSH
• lidé MeSH
• nádorové biomarkery metabolismus   MeSH
• nádory slinivky břišní * diagnóza   patofyziologie   MeSH
• sulfoglykosfingolipidy chemie   MeSH
• tandemová hmotnostní spektrometrie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• gangliosidy MeSH
• glykosfingolipidy * MeSH
• nádorové biomarkery MeSH
• sulfoglykosfingolipidy MeSH

PURPOSE: RCC, the most common type of kidney cancer, is associated with high mortality. A non-invasive diagnostic test remains unavailable due to the lack of RCC-specific biomarkers in body fluids. We have previously described a significantly altered profile of sulfatides in RCC tumor tissues, motivating us to investigate whether these alterations are reflected in collectible body fluids and whether they can enable RCC detection. METHODS: We collected and further analyzed 143 plasma, 100 urine, and 154 tissue samples from 155 kidney cancer patients, together with 207 plasma and 70 urine samples from 214 healthy controls. RESULTS: For the first time, we show elevated concentrations of lactosylsulfatides and decreased levels of sulfatides with hydroxylated fatty acyls in body fluids of RCC patients compared to controls. These alterations are emphasized in patients with the advanced tumor stage. Classification models are able to distinguish between controls and patients with RCC. In the case of all plasma samples, the AUC for the testing set was 0.903 (0.844-0.954), while for urine samples it was 0.867 (0.763-0.953). The models are able to efficiently detect patients with early- and late-stage RCC based on plasma samples as well. The test set sensitivities were 80.6% and 90%, and AUC values were 0.899 (0.832-0.952) and 0.981 (0.956-0.998), respectively. CONCLUSION: Similar trends in body fluids and tissues indicate that RCC influences lipid metabolism, and highlight the potential of the studied lipids for minimally-invasive cancer detection, including patients with early tumor stages, as demonstrated by the predictive ability of the applied classification models.

• Klíčová slova
• cancer biomarkers, classification models, early detection, lipidomics, renal cell carcinoma, sphingomyelins, sulfatide,
• Publikační typ
• časopisecké články MeSH