With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

An exceptionally easy to assemble source for ambient mass spectrometry is described. Based on Venturi easy ambient sonic-spray ionization (V-EASI), the source was further simplified by the use of a can of compressed air which simultaneously provides solution or solvent Venturi self-pumping and continuous, stable and abundant low-noise ion signal via voltage-free sonic-spraying. Further simplification was also attained by the use of inexpensive and readily commercially available parts: a surgical 2-way catheter, an aerosol can of compressed air, a 30 cm long fused-silica capillary and a hypodermic needle. This “Spartan” V-EASI source seems to offer one of the easiest and cheapest ways to make ions for ambient desorption/ionization mass spectrometry analysis of both liquid and solid samples.

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

This review is the first to summarize a decade of studies testing the uses of easy ambient sonic-spray ionization mass spectrometry (EASI-MS) and its several sister techniques, Venturi (V-EASI), thermal imprinting (TI-EASI) and Spartan (S-EASI), in food quality control and authentication.