Ambient ionization mass spectrometry has become one of the most promising approaches for rapid and high-throughput screening of small molecules in complex biological matrices for emergency medicine, forensics, and food and agriculture applications. The simple procedures for sample collection and ionization without additional pretreatment are vital in these fields. Many efforts have been devoted to modifying various ambient ionization techniques to simplify the procedures and improve the robustness and sensitivity of the methods. Here, we demonstrate the implementation of rigid spherical sampler probes to improve the robustness of touch spray ionization mass spectrometry. The sphericity of the probes increases the stability of the cone-jet mode of electrospray, reduces the requirements for fine positioning of a sampler in the ion source, and decreases the possibility of corona discharge occurrence. The utilization of spherical sampler probes allows fast, non-invasive sampling, followed by rapid analysis for various drugs of different chemical classes in complex biological matrices, such as the whole blood or sebum collected from the skin surface. The linearity of the analytical signal response from drug concentration confirms the possibility of creating a simple semiquantitative method for small molecules monitoring using spherical sampler probes.

The use of postmortem (autopsy) material in fundamental and applied biomedical research significantly facilitates the collection of biomaterial for statistically robust sample cohorts. However, natural adaptive processes to developing cellular stress in the early postmortem period, caused by oxygen and nutrient deprivation, trigger the activation of numerous genes promoting cell survival under stress. Many of these activated pathways are also crucial for tumor cell survival in vivo, as evidenced by various transcriptomic studies. This study aimed to investigate the potential influence of postmortem interval (PMI) duration on gene expression in normal and tumor tissues. Using a model of chemically induced hepatocellular carcinoma in mouse liver, we comparatively analyzed the dynamics of transcript levels for several genes (BRCA1, BRCA2, CHEK1, CHEK2, ATM, CDK12) in paired samples of normal and tumor tissue over a 24-h PMI using RT-qPCR. In normal tissue, gene expression increased significantly, while tumor tissue demonstrated relative transcriptional stability, with no substantial changes in the studied transcript levels. A critical finding was the observed convergence of expression profiles: initial differences between the tissues were completely eliminated by 24 h PMI. This pattern developed despite formally adequate RNA quality (RQN) and the absence of clear signs of progressive autolysis in histology, indicating the insufficiency of standard quality criteria for detecting postmortem changes. These findings collectively underscore the critical importance of minimizing and controlling PMI during the biobanking of oncological samples for reliable transcriptomic research.

The aim of this review was to analyze the accumulated data on the use of mass spectrometry in diagnosing, treating, and prognosing cancer from the perspective of precision medicine. Currently, universally accepted methods for early cancer diagnosis are not available, primarily due to low molecular specificity of pathological changes at early stages of cancer development. Additionally, the existing diagnostic modalities are notably limited in sensitivity. However, early detection is imperative for selection of the most suitable cancer treatment strategy and its successful implementation. In the realm of oncology, mass spectrometry approaches show great potential for advancement and utilization. Mass spectrometry is becoming an indispensable tool in basic and applied research due to its sensitivity, specificity, and accuracy. It allows for efficient analysis of complex biological compounds, even at low concentrations. Moreover, contemporary mass spectrometry technology is capable of automating the analysis, thereby facilitating its diverse clinical applications in diagnosis, drug therapy selection, and even potential assistance to surgical oncologists in the operating room. Considering all these characteristics and advantages, mass spectrometry methods for the analysis of biological samples can be defined as some of the most promising and dynamically developing tools in precision medicine, as they are capable of providing clinically valuable information based on omics technologies, taking into account personal characteristics of the patient. Over the next decade, introduction of mass spectrometry-based methods into clinical practice based on the principles of precision medicine is expected to optimize selection of personalized treatment strategies for cancer patients and provide significant economic benefits by reducing morbidity, disability, and mortality.This comprehensive review presents the analysis of 65 scientific publications, highlighting the results of clinical and experimental studies utilizing mass spectrometry methods for diagnosing cancer, investigating the underlying mechanisms of disease development, and evaluating the efficacy of therapeutic interventions. The review encompasses original articles published from January 1, 2018 to November 30, 2023. The majority of studies back the potential of mass spectrometry as a valuable tool for cancer diagnosis and treatment monitoring. Broadening application of mass spectrometry techniques in the field of oncology holds significant promise and represents a relevant area for future research.

The demand for automated systems assisting surgeons in decision-making drives researchers to develop novel analytical tools for clinical use. Ambient ionization mass spectrometry (AIMS) provides an attractive possibility through its ability to analyze intact tissue samples in real time. Despite the variety of ionization methods and sampling techniques, these approaches generally provide a specific type of multidimensional data: profile spectra rich in signals because they lack molecular separation prior to mass spectra acquisition. Thus, machine learning techniques are essential for analyzing such data. However, sample cohort imbalances, which lead to substantial model overfitting, complicate their application in clinical settings, especially in oncology, where control samples are often limited for ethical reasons. The presented dataset was generated to address this problem and to test the following hypothesis: “Can analyzing autopsy material improve classification efficiency for intraoperative implementation of AIMS?” The dataset consists of molecular profiles of 412 liver tissues from a balanced cohort of 52 mice with induced hepatocellular carcinoma and 50 healthy controls, with varying time between sacrifice and necropsy. The dataset can be used not only for its primary goal, but also to implement and test novel machine learning approaches on AIMS data that imitate clinically obtained data but without inherited cohort imbalances.

Intraoperative determination of tumor margins reduces the volume of resected tissue while ensuring complete removal of malignant tissue without increasing the risk of recurrence. The existing approaches, including intraoperative histology or tomography, are highly effective, but they extend the time of the surgery. Therefore, real-time analysis methods are a particularly interesting avenue. One such solution is mass spectrometric profiling based on ambient ionization mass spectrometry. The molecular profile of the examined tissue is compared with a reference database of healthy and pathologically altered tissues. For many human organs, samples for this database can only be obtained from autopsy material. However, due to the unavoidable time delay between the moment of death and sample collection, tissue degradation may change the picture of pathological process. This experimental quantitative study investigated the stability of polar lipid profiles in the liver and brain tissues of healthy BALB/c mice (n = 23) during the early postmortem period (0–72 hours). It has been shown that lysolipids and free fatty acids correlate with the postmortem interval (r > 0.6 overall; r = 0.75 for FA 20:4 and FA 22:6); therefore, they cannot be used as molecular markers in diagnostic models based on autopsy samples. At the same time, the profile of phospholipids in tissue cell membranes remains largely unchanged in the early postmortem period, which preserves their value as biomarkers detectable in both biopsy and autopsy specimens.