Early recognition of the risk of Alzheimer’s disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA–plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.

Alzheimer’s disease (AD) is the leading cause of dementia among the elderly. Neuropathologically, AD is characterized by the deposition of a 39- to 42-amino acid long β-amyloid (Aβ) peptide in the form of senile plaques. Several post-translational modifications (PTMs) in the N-terminal domain have been shown to increase the aggregation and cytotoxicity of Aβ, and specific Aβ proteoforms (e.g., Aβ with isomerized D7 (isoD7-Aβ)) are abundant in the senile plaques of AD patients. Animal models are indispensable tools for the study of disease pathogenesis, as well as preclinical testing. In the presented work, the accumulation dynamics of Aβ proteoforms in the brain of one of the most widely used amyloid-based mouse models (the 5xFAD line) was monitored. Mass spectrometry (MS) approaches, based on ion mobility separation and the characteristic fragment ion formation, were applied. The results indicated a gradual increase in the Aβ fraction of isoD7-Aβ, starting from approximately 8% at 7 months to approximately 30% by 23 months of age. Other specific PTMs, in particular, pyroglutamylation, deamidation, and oxidation, as well as phosphorylation, were also monitored. The results for mice of different ages demonstrated that the accumulation of Aβ proteoforms correlate with the formation of Aβ deposits. Although the mouse model cannot be a complete analogue of the processes occurring in the human brain in AD, and several of the observed parameters differ significantly from human values supposedly due to the limited lifespan of the model animals, this dynamic study provides evidence on at least one of the possible mechanisms that can trigger amyloidosis in AD, i.e., the hypothesis on the relationship between the accumulation of isoD7-Aβ and the progression of AD-like pathology.

The study of the molecular mechanisms of the pathogenesis of Alzheimer’s disease (AD) is extremely important for identifying potential therapeutic targets as well as early markers. In this regard, the study of the role of post-translational modifications (PTMs) of β-amyloid (Aβ) peptides is of particular relevance. Serine-8 phosphorylated forms (pSer8-Aβ) have been shown to have an increased aggregation capacity and may reflect the severity of amyloidosis. Here, an approach for quantitative assessment of pSer8-Aβ based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is proposed. The relative fraction of pSer8-Aβ was estimated in the total Aβ-pool with a detection limit of 1 fmol for pSer8-Aβ (1–16) and an accuracy of 2% for measurements in the reflectron mode. The sensitivity of the developed method is suitable for determining the proportion of phosphorylated peptides in biological samples.

Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a dataset of 124 high-resolution negative ionization mode lipid profiles of human brain tumors resected during neurosurgery. The dataset is supplemented with 38 non-tumor pathological brain tissue samples resected during elective surgery. The change in lipid composition alterations of brain tumors enables the possibility of discriminating between malignant and healthy tissues with the implementation of ambient mass spectrometry. On the other hand, the collection of clinical samples allows the comparison of the metabolism alteration patterns in animal models or in vitro models with natural tumor samples ex vivo. The presented dataset is intended to be a data sample for bioinformaticians to test various data analysis techniques with ambient mass spectrometry profiles, or to be a source of clinically relevant data for lipidomic research in oncology.

Early recognition of a risk of Alzheimer’s disease (AD) remains a global challenge, and blood proteomic markers are of particular interest for wide-scale diagnostic use. Quantitative multiple reaction monitoring (MRM) approach demonstrates good reproducibility in the characteristic changes in the levels of reported candidate biomarkers (CBs) in different cohorts in AD. Following up on our previous study, we performed a joint analysis of 331 blood plasma samples from two different clinical cohorts of participants, comprising a total of 95 samples from patients with AD, 136 samples from patients with mild cognitive impairment (MCI), and 100 samples from controls. The obtained results confirm the significance of 37 CBs. A logistic regression-based algorithm was used to build protein classifiers, and a total of 21 important proteins were selected, 13 of which (ORM1, APOA4, LBP, HP, FN1, BCHE, APOE, PZP, A1BG, TF, SERPINA7, TTR, and F12) formed a universal panel that demonstrated strong classification performance in distinguishing AD patients from controls (ROC-AUC = 0.90) and in separating stable and progressing patients with MCI (ROC-AUC = 0.81). Overall, the analysis confirms the high potential of the MRM method for validating CBs in independent cohorts.