Background

Mass spectrometry (MS) analysis methods, including liquid chromatography-mass spectrometry (LC–MS) (A. Abuawad et al., 2020), liquid extraction surface analysis mass spectrometry (LESA–MS) (Eikel et al., 2011), matrix-assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry (SIMS), have been used to detect chemical and biological compounds, such as lipids, amino acids, peptides, and proteins from cells and tissue samples (Rauh, 2012; Cajka and Fiehn, 2014; Passarelli et al., 2017; Kotowska et al., 2020; Meurs et al., 2021). Classes of biomolecules, including proteins, lipids, and metabolites, are vital cellular components that have been characterized and designated to perform specific functions essential to life.

LC–MS-based metabolite analyses typically start with extraction of the metabolites from the biological samples. Analysis of metabolites using LC-MS has been limited because it requires initial liquid extraction procedures and a significant number of cells (1–6 million) to obtain a sufficient signal (Abuawad et al., 2020), leading to a lack of molecular spatial information (Tuli and Ressom, 2009).

The LESA–MS technique is a powerful tool for global, highly sensitive, and multi-analyte analysis ranging from small molecule metabolites to lipids and proteins. This technique has the limitation of involving a solvent-based approach, which allows lipid and small molecule metabolite injection into the analytical instrumentation, as well as poor spatial resolution (~1 mm), rendering single-cell level analysis impossible. For solvent extraction, sample preparation of cells or tissue for metabolomics has a unique protocol for each molecular class. Thus, solvent-extracted metabolites from cells or tissue samples are specific to that extraction protocol (Basu et al., 2018).

SIMS is a direct surface analysis technique that uses a primary ion beam that bombards the surface and generates neutral species and secondary ions (Sigmund, 1969), providing high lateral resolutions of < 100 nm and high surface sensitivity. An electrical field can be used to extract the charge species to obtain mass spectra, using the ion flight times, ion images, and depth profiles in both 2D and 3D (Walker, 2017). SIMS surface analysis has also been established for the quantification of small molecules in biological samples, such as cells and tissue samples.

Time-of-flight secondary ion MS (ToF–SIMS) is a surface analysis technique (Denbigh and Lockyer, 2015; Yoon and Lee, 2018) that provides information-rich mass, depth, and spatial resolution, along with chemical sensitivity. However, ToF–SIMS has insufficient mass accuracy and low mass resolving power for metabolite identification (Green et al., 2011; Shon et al., 2016). To overcome the pitfalls of low mass resolving power and accuracy, the 3D OrbiSIMS technique was developed, which utilizes the SIMS principle with a high mass resolving power (> 240,000 at m/z 200) and accuracy (< 2 ppm), along with the MS/MS capabilities of the OrbiTrap^TM mass analyzer (Passarelli et al., 2017).

Recently, the capabilities of the 3D OrbiSIMS instrument as a new means to assess the metabolomic profiles of biological samples have been investigated. For example, Kotowska et al. (2020) used 3D OrbiSIMS imaging and depth profiling to observe a protein monolayer biochip and the depth distribution of proteins in human skin. The platform has also proved its ability to identify metabolite profiling in macrophages treated with different concentrations of the drug amiodarone (Passarelli et al., 2017). Similarly, Suvannapruk et al. (2022) used the novel technique for metabolite identification in individual cells of macrophage subsets. This method of analysis is vital for gaining new insight into metabolomic processes for identifying the metabolites of biological samples.

The development of materials with cell-instructive properties could provide an effective biomaterial-based strategy for modulating cell behavior, to minimize adverse immune responses. Rostam et al. reported that changing the surface topography and chemistry of materials can impact macrophage adhesion and polarization (H. M. Rostam et al., 2015; Hassan M. Rostam et al., 2020). In this paper, we describe for the first time the development of a 3D OrbiSIMS methodology to investigate metabolic changes derived from single anti- and pro-inflammatory macrophages in vitro. We also compare this to metabolic profiles from in vivo anti- and pro-inflammatory macrophages generated at the site of a foreign body response in a mouse model, following exposure to a catheter section coated in known macrophage-instructive surface chemistries. This method aims to directly analyse the metabolic profiles of macrophage phenotypes from in vitro and in vivo studies, with minimal sample preparation steps.