Single-Cell Metabolic Profiling of Macrophages Using 3D OrbiSIMS: Correlations with Phenotype

Macrophages are important immune cells that respond to environmental cues acquiring a range of activation statuses represented by pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes at each end of their spectrum. Characterizing the metabolic signature (metabolic profiling) of different macrophage subsets is a powerful tool to understand the response of the human immune system to different stimuli. Here, the recently developed 3D OrbiSIMS instrument is applied to yield useful insight into the metabolome from individual cells after in vitro differentiation of macrophages into naïve, M1, and M2 phenotypes using different cytokines. This analysis strategy not only requires more than 6 orders of magnitude less sample than traditional mass spectrometry approaches but also allows the study of cell-to-cell variance. Characteristic metabolites in macrophage subsets are identified using a targeted lipid and data-driven multivariate approach highlighting amino acids and other small molecules. The diamino acids alanylasparagine and lipid sphingomyelin SM(d18/16:0) are uniquely found in M1 macrophages, while pyridine and pyrimidine are observed at increased intensity in M2 macrophages, findings which link to known biological pathways. The first demonstration of this capability illustrates the great potential of direct cell analysis for in situ metabolite profiling with the 3D OrbiSIMS to probe functional phenotype at the single-cell level using molecular signatures and to understand the response of the human body to implanted devices and immune diseases

Untargeted Metabolomic Characterization of Glioblastoma Intra-Tumor Heterogeneity Using OrbiSIMS

Glioblastoma (GBM) is an incurable brain cancer with a median survival of less than two years from diagnosis. The standard treatment of GBM is multimodality therapy comprising surgical resection, radiation, and chemotherapy. However, prognosis remains poor, and there is an urgent need for effective anticancer drugs. Since different regions of a single GBM contain multiple cancer subpopulations ("intra-tumor heterogeneity"), this likely accounts for therapy failure as certain cancer cells can escape from immune surveillance and therapeutic threats. Here, we present metabolomic data generated using the Orbitrap secondary ion mass spectrometry (OrbiSIMS) technique to investigate brain tumor metabolism within its highly heterogeneous tumor microenvironment. Our results demonstrate that an OrbiSIMS-based untargeted metabolomics method was able to discriminate morphologically distinct regions (viable, necrotic, and non-cancerous) within single tumors from formalin-fixed paraffin-embedded tissue archives. Specifically, cancer cells from necrotic regions were separated from viable GBM cells based on a set of metabolites including cytosine, phosphate, purine, xanthine, and 8-hydroxy-7-methylguanine. Moreover, we mapped ubiquitous metabolites across necrotic and viable regions into metabolic pathways, which allowed for the discovery of tryptophan metabolism that was likely essential for GBM cellular survival. In summary, this study first demonstrated the capability of OrbiSIMS for in situ investigation of GBM intra-tumor heterogeneity, and the acquired information can potentially help improve our understanding of cancer metabolism and develop new therapies that can effectively target multiple subpopulations within a tumor

Label-free Chemical Characterization of Polarized Immune Cells in vitro and Host Response to Implanted Bio-instructive Polymers in vivo Using 3D OrbiSIMS

The Three-dimensional OrbiTrap Secondary Ion Mass Spectrometry (3D OrbiSIMS) is a secondary ion mass spectrometry instrument, a combination of a Time of Flight (ToF) instrument with an Orbitrap analyzer. The 3D OrbiSIMS technique is a powerful tool for metabolic profiling in biological samples. This can be achieved at subcellular spatial resolution, high sensitivity, and high mass-resolving power coupled with MS/MS analysis. Characterizing the metabolic signature of macrophage subsets within tissue sections offers great potential to understand the response of the human immune system to implanted biomaterials. Here, we describe a protocol for direct analysis of individual cells after in vitro differentiation of naïve monocytes into M1 and M2 phenotypes using cytokines. As a first step in vivo, we investigate explanted silicon catheter sections as a medical device in a rodent model of foreign body response. Protocols are presented to allow the host response to different immune instructive materials to be compared. The first demonstration of this capability illustrates the great potential of direct cell and tissue section analysis for in situ metabolite profiling to probe functional phenotypes using molecular signatures. Details of the in vitro cell approach, materials, sample preparation, and explant handling are presented, in addition to the data acquisition approaches and the data analysis pipelines required to achieve useful interpretation of these complex spectra. This method is useful for in situ characterization of both in vitro single cells and ex vivo tissue sections. This will aid the understanding of the immune response to medical implants by informing the design of immune-instructive biomaterials with positive interactions. It can also be used to investigate a broad range of other clinically relevant therapeutics and immune dysregulations

Spatially Resolved Molecular Compositions of Insoluble Multilayer Deposits Responsible for Increased Pollution from Internal Combustion Engines

Internal combustion engines are used heavily in diverse applications worldwide. Achieving the most efficient operation is key to improving air quality as society moves to a decarbonized energy system. Insoluble deposits that form within internal combustion engine components including fuel injectors and filters negatively impact CO2 and pollutant emissions. Understanding the composition, origins, and formation mechanisms of these complex materials will be key to their mitigation however, previous attempts only afforded nondiagnostic chemical assignments and limited knowledge toward this. Here, we uncover the identity and spatial distribution of molecular species from a gasoline direct injector, diesel injector, and filter deposit in situ using a new hyphenation of secondary ion mass spectrometry and the state-of-the-art Orbitrap mass analyzer (3D OrbiSIMS) and elemental analysis. Through a high mass resolving power and tandem MS we unambiguously uncovered the identity, distribution, and origin of species including alkylbenzyl sulfonates and provide evidence of deposit formation mechanisms including formation of longer chain sulfonates at the gasoline deposit’s surface as well as aromatization to form polycyclic aromatic hydrocarbons up to C66H20, which were prevalent in the lower depth of this deposit. Inorganic salts contributed significantly to the diesel injector deposit throughout its depth, suggesting contamination over multiple fueling cycles. Findings will enable several strategies to mitigate these insoluble materials such as implementing stricter worldwide fuel specifications, modifying additives with adverse reactivity, and synthesizing new fuel additives to solubilize deposits in the engine, thereby leading to less polluting vehicles

Spatially resolved molecular analysis of host response to medical device implantation using the 3D OrbiSIMS highlights a critical role for lipids

A key goal for implanted medical devices is that they do not elicit a detrimental immune response. Macrophages play critical roles in modulation of the host immune response and are the major cells responsible for persistent inflammatory reactions to implanted biomaterials. We investigate two novel immune-instructive polymers that stimulate pro- or anti-inflammatory responses from macrophages in vitro. These also modulate in vivo foreign body responses (FBR) when implanted subcutaneously in mice as coatings on biomedical grade silicone rubber. The tissue surrounding the implant is mechanically sectioned and imaged to assess the response of the polymers compared to silicone rubber. Immunofluorescent staining reveals responses consistent with pro- or anti-inflammatory responses previously described for these polymers. We apply 3D OrbiSIMS analysis to provide spatial analysis of the metabolite signature in the tissue surrounding the implant for the first time, providing molecular histology insight into the metabolite response in the host tissue. For the pro-inflammatory coating, monoacylglycerols (MG) and diacylglycerols (DG) are observed at increased intensity, while for the anti-inflammatory coating the number of phospholipid species detected decrease and pyridine and pyrimidine levels were elevated. These findings link to observations of small molecule signature from single cell studies of M2 macrophages in vitro where cell and tissue ion intensities were found to correlate suggesting potential for prediction. This illustrates the power of metabolite characterization by the 3D OrbiSIMS to gain insight into the mechanism of bio-instructive materials as medical devices and to inform on the FBR to biomaterials

Elucidating the molecular landscape of the stratum corneum

Characterization of the molecular structure of skin, especially the barrier layer, the stratum corneum, is a key research priority for generating understanding to improve diagnostics, aid pharmaceutical delivery, and prevent environmental damage. Our study uses the recently developed 3D OrbiSIMS technique to conduct in situ analysis of ex vivo human skin tissue and reveals the molecular chemistry of skin in unprecedented detail, as a result of the step change in high mass resolving power compared with previous studies. This characterization exposes the nonhomogeneity of the stratum corneum, both laterally and as a function of depth. Chemical variations relating to fundamental biological processes, such as the epidermal cholesterol sulfate cycle, are visualized using in situ analysis. We are able to resolve the debate around the chemical gradients present within the epidermis, for example, whether palmitic acid is of sebaceous origin or a true component of the stratum corneum. Through in situ depth analysis of cryogenically preserved samples, we are able to propose that it is actually a component of both surface sebum and the intrinsic lipid matrix. This approach also suggests similarity between the epidermis compounds found in human and porcine skin as a function of depth. Since porcine skin is a widely used model for permeation testing this result has clinical relevance. In addition to using this technique for endogenous species, we have used it to demonstrate the permeation of a commercially important antiaging peptide into the human stratum corneum. Due to its chemical similarity to native skin components and exceptionally low effective concentration, this information was previously unattainable

Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets

Modern mass spectrometry techniques produce a wealth of spectral data, and although this is an advantage in terms of the richness of the information available, the volume and complexity of data can prevent a thorough interpretation to reach useful conclusions. Application of molecular formula prediction (MFP) to produce annotated lists of ions that have been filtered by their elemental composition and considering structural double bond equivalence are widely used on high resolving power mass spectrometry datasets. However, this has not been applied to secondary ion mass spectrometry data. Here, we apply this data interpretation approach to 3D OrbiSIMS datasets, testing it for a series of increasingly complex samples. In an organic on inorganic sample, we successfully annotated the organic contaminant overlayer separately from the substrate. In a more challenging purely organic human serum sample we filtered out both proteins and lipids based on elemental compositions, 226 different lipids were identified and validated using existing databases, and we assigned amino acid sequences of abundant serum proteins including albumin, fibronectin, and transferrin. Finally, we tested the approach on depth profile data from layered carbonaceous engine deposits and annotated previously unidentified lubricating oil species. Application of an unsupervised machine learning method on filtered ions after performing MFP from this sample uniquely separated depth profiles of species, which were not observed when performing the method on the entire dataset. Overall, the chemical filtering approach using MFP has great potential in enabling full interpretation of complex 3D OrbiSIMS datasets from a plethora of material types

Unambiguous Identification of Key Molecular Species in Deposits Responsible for Increased Pollution from Internal Combustion Engines

Clean and efficient internal combustion engine performance will play a significant role in the move to a decarbonized energy system. Currently, fuel deposit formation on engine components negatively impacts CO2 and pollutant emissions, where previous attempts at deposit characterization afforded non-diagnostic chemical assignments. Here, we uncover the identity and 3D spatial distribution of molecular species from gasoline, diesel injector and filter deposits with the 3D OrbiSIMS technique. Alkylbenzyl sulfonates, derived from lubricant oil contamination in the engine fuel cycle, were common to samples, we evidence transformation of the native sulfonate to longer chain species by reaction with fuel fragments in the gasoline deposit. Inorganic salts, identified in both diesel deposits, were prevalent throughout the injector deposits depth. We identified common polycyclic aromatic hydrocarbons up to C66H20, these were prevalent in the gasoline deposits lower depths. This work will enable deposit mitigation by unravelling their chemical composition, spatial distribution, and origins.</p