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Mass spectrometry imaging (MSI) encompasses a powerful suit of techniques which provide spatially resolved atomic and molecular information from almost any sample type. MSI is now widely used in preclinical research to provide insight into metabolic phenotypes of disease. Typically, fresh-frozen tissue preparations are considered optimal for biological MSI and other traditional preservation methods such as formalin fixation, alone or with paraffin embedding (FFPE), are considered less optimal or even incompatible. Due to the prevalence of FFPE tissue storage, particularly for rare and therefore high-value tissue samples, there is substantial motivation for optimizing MSI methods for analysis of FFPE tissue. Here, we present a novel modality, atmospheric-pressure infrared laser-ablation plasma postionization (AP-IR-LA-PPI), with the first proof-of-concept examples of MSI for FFPE and fresh-frozen tissues, with no post-sectioning sample preparation. We present ion images from FFPE and fresh tissues in positive and negative ion modes. Molecular annotations (via the Metaspace annotation engine) and on-tissue MS/MS provide additional confidence that the detected ions arise from a broad range of metabolite and lipid classes from both FFPE and fresh-frozen tissues.

Gemcitabine (dFdC) is a common treatment for pancreatic cancer; however, it is thought that treatment may fail because tumor stroma prevents drug distribution to tumor cells. Gemcitabine is a pro-drug with active metabolites generated intracellularly; therefore, visualizing the distribution of parent drug as well as its metabolites is important. A multimodal imaging approach was developed using spatially coregistered mass spectrometry imaging (MSI), imaging mass cytometry (IMC), multiplex immunofluorescence microscopy (mIF), and hematoxylin and eosin (H&E) staining to assess the local distribution and metabolism of gemcitabine in tumors from a genetically engineered mouse model of pancreatic cancer (KPC) allowing for comparisons between effects in the tumor tissue and its microenvironment. Mass spectrometry imaging (MSI) enabled the visualization of the distribution of gemcitabine (100 mg/kg), its phosphorylated metabolites dFdCMP, dFdCDP and dFdCTP, and the inactive metabolite dFdU. Distribution was compared to small-molecule ATR inhibitor AZD6738 (25 mg/kg), which was codosed. Gemcitabine metabolites showed heterogeneous distribution within the tumor, which was different from the parent compound. The highest abundance of dFdCMP, dFdCDP, and dFdCTP correlated with distribution of endogenous AMP, ADP, and ATP in viable tumor cell regions, showing that gemcitabine active metabolites are reaching the tumor cell compartment, while AZD6738 was located to nonviable tumor regions. The method revealed that the generation of active, phosphorylated dFdC metabolites as well as treatment-induced DNA damage primarily correlated with sites of high proliferation in KPC PDAC tumor tissue, rather than sites of high parent drug abundance.

Imaging mass cytometry (IMC) offers the opportunity to image metal- and heavy halogen-containing xenobiotics in a highly multiplexed experiment with other immunochemistry-based reagents to distinguish uptake into different tissue structures or cell types. However, in practice, many xenobiotics are not amenable to this analysis, as any compound which is not bound to the tissue matrix will delocalize during aqueous sample-processing steps required for IMC analysis. Here, we present a strategy to perform IMC experiments on a water-soluble polysarcosine-modified dendrimer drug-delivery system (S-Dends). This strategy involves two consecutive imaging acquisitions on the same tissue section using the same instrumental platform, an initial laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MSI) experiment followed by tissue staining and a standard IMC experiment. We demonstrated that settings can be found for the initial ablation step that leave sufficient residual tissue for subsequent antibody staining and visualization. This workflow results in lateral resolution for the S-Dends of 2 μm followed by imaging of metal-tagged antibodies at 1 μm.

Clinical tissue specimens are often unscreened, and preparation of tissue sections for analysis by mass spectrometry imaging (MSI) can cause aerosolization of particles potentially carrying an infectious load. We here present a decontamination approach based on ultraviolet-C (UV-C) light to inactivate clinically relevant pathogens such as herpesviridae, papovaviridae human immunodeficiency virus, or SARS-CoV-2, which may be present in human tissue samples while preserving the biodistributions of analytes within the tissue. High doses of UV-C required for high-level disinfection were found to cause oxidation and photodegradation of endogenous species. Lower UV-C doses maintaining inactivation of clinically relevant pathogens to a level of increased operator safety were found to be less destructive to the tissue metabolome and xenobiotics. These doses caused less alterations of the tissue metabolome and allowed elucidation of the biodistribution of the endogenous metabolites. Additionally, we were able to determine the spatially integrated abundances of the ATR inhibitor ceralasertib from decontaminated human biopsies using desorption electrospray ionization-MSI (DESI-MSI).

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.

Extracellular pH (pHe) is an important chemical factor in many cellular processes and disease pathologies. The routine sampling of pHe in vitro could lead to innovative advances in therapeutics. To this end, we have fabricated a novel gold-coated polymer mesh, which facilitates the real-time measurement of pHe via surface-enhanced Raman scattering (SERS). In this proof of concept study, we apply our SERS sensor to measure metabolically induced changes in the pHe of carcinoma-derived cell line HepG2/C3A. We demonstrate that gold-coated polyurethane electrospun nanofibers (AuNF) have strong and reproducible SERS spectra of surface-adsorbed analytes. By functionalizing AuNF with pH-responsive reporter 4-mercaptobenzoic acid (MBA), we have developed an accurate pH SERS sensor for the extracellular microenvironment. We cultured HepG2/C3A on the surface of MBA-AuNF and measured an acidic shift in pHe at the cell-fiber interface. Following exposure to staurosporine, an apoptosis-inducing drug, we observed changes in the HepG2/C3A cellular morphology indicative of controlled cell death, and detected an increase in the pHe of HepG2/C3A. These results demonstrate how subtle changes in pHe, induced by the metabolic activity of cells, can be measured with our novel SERS sensor MBA-AuNF. The excellent pH measurement performance of MBA-AuNF provides a unique platform to study extracellular pH on the microscale and will help to deepen our understanding of pHe in disease pathology.

Elemental and molecular imaging play a crucial role in understanding disease pathogenesis. To accurately correlate elemental and molecular markers, it is desirable to perform sequential elemental and molecular imaging on a single-tissue section. However, very little is known about the impact of performing these measurements in sequence. In this work, we highlight some of the challenges and successes associated with performing elemental mapping in sequence with mass spectrometry imaging. Specifically, the feasibility of molecular mapping using the mass spectrometry imaging (MSI) techniques matrix-assisted laser desorption ionization (MALDI) and desorption electrospray ionization (DESI) in sequence with the elemental mapping technique particle-induced X-ray emission (PIXE) is explored. Challenges for integration include substrate compatibility, as well as delocalization and spectral changes. We demonstrate that while sequential imaging comes with some compromises, sequential DESI-PIXE imaging is sufficient to correlate sulfur, iron, and lipid markers in a single tissue section at the 50 μm scale.

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

A new tissue sample embedding and processing method is presented that provides downstream compatibility with numerous different histological, molecular biology, and analytical techniques. The methodology is based on the low temperature embedding of fresh frozen specimens into a hydrogel matrix composed of hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) and sectioning using a cryomicrotome. The hydrogel was expected not to interfere with standard tissue characterization methods, histologically or analytically. We assessed the compatibility of this protocol with various mass spectrometric imaging methods including matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS). We also demonstrated the suitability of the universal protocol for extraction based molecular biology techniques such as rt-PCR. The integration of multiple analytical modalities through this universal sample preparation protocol offers the ability to study tissues at a systems biology level and directly linking results to tissue morphology and cellular phenotype.

We have previously demonstrated liquid extraction surface analysis (LESA) high field asymmetric waveform ion mobility spectrometry (FAIMS) mass spectrometry imaging of proteins in thin tissue sections of brain and liver. Here, we present an improved approach that makes use of multiple static FAIMS parameters at each sampled location and allows a significant improvement in the number of proteins, lipids, and drugs that can be imaged simultaneously. The approach is applied to the mass spectrometry imaging of control and cassette-dosed rat kidneys. Mass spectrometry imaging of kidneys typically requires washing to remove excess hemoglobin; however, that is not necessary with this approach. Multistep static FAIMS mass spectrometry resulted in a 6- to 16-fold increase in the number of proteins detected in the absence of FAIMS, in addition to smaller increases over single step static FAIMS (chosen for optimum transmission of total protein ions). The benefits of multistep static FAIMS mass spectrometry for protein detection are also shown for sections of testes. The numbers of proteins detected following multistep FAIMS increased between 2- and 3-fold over single step FAIMS and between 2- and 14-fold over LESA alone. Finally, to date, LESA mass spectrometry of proteins in tissue has been undertaken solely on fresh frozen samples. In this work, we demonstrate that heat-preserved tissues are also suitable for these analyses. Heat preservation of tissue improved the number of proteins detected by LESA MS for both kidney and testes tissue (by between 2- and 4-fold). For both tissue types, the majority of the proteins additionally detected in the heat-treated samples were subsequently detected in the frozen samples when FAIMS was incorporated. Improvements in the numbers of proteins detected were observed for LESA FAIMS MS for the kidney tissue; for testes tissue, fewer total proteins were detected following heat preservation; however, approximately one-third were unique to the heat-preserved samples.

Described is a quantitative-mass-spectrometryimaging\ud (qMSI) methodology for the analysis of lactate and\ud glutamate distributions in order to delineate heterogeneity\ud among mouse tumor models used to support drug-discovery\ud efficacy testing. We evaluate and report on preanalysisstabilization\ud methods aimed at improving the reproducibility\ud and efficiency of quantitative assessments of endogenous\ud molecules in tissues. Stability experiments demonstrate that\ud optimum stabilization protocols consist of frozen-tissue\ud embedding, post-tissue-sectioning desiccation, and storage at\ud −80 °C of tissue sections sealed in vacuum-tight containers.\ud Optimized stabilization protocols are used in combination with qMSI methodology for the absolute quantitation of lactate and\ud glutamate in tumors, incorporating the use of two different stable-isotope-labeled versions of each analyte and spectral-clustering\ud performed on each tissue section using k-means clustering to allow region-specific, pixel-by-pixel quantitation. Region-specific\ud qMSI was used to screen different tumor models and identify a phenotype that has low lactate heterogeneity, which will enable\ud accurate measurements of lactate modulation in future drug-discovery studies. We conclude that using optimized qMSI\ud protocols, it is possible to quantify endogenous metabolites within tumors, and region-specific quantitation can provide valuable\ud insight into tissue heterogeneity and the tumor microenvironment.

Absolute quantification of proteins in tissue is important for numerous fields of study. Liquid chromatography–mass spectrometry (LC–MS) methods are the norm but typically involve lengthy sample preparation including tissue homogenization, which results in the loss of information relating to spatial distribution. Here, we propose liquid extraction surface analysis (LESA) mass spectrometry (MS) of stable isotope labeled mimetic tissue models for the spatially resolved quantification of intact ubiquitin in rat and mouse brain tissue. Measured ubiquitin concentrations are in agreement with values found in the literature. Images of rat and mouse brain tissue demonstrate spatial variation in the concentration of ubiquitin and demonstrate the utility of spatially resolved quantitative measurement of proteins in tissue. Although we have focused on ubiquitin, the method has the potential for broader application to the absolute quantitation of any endogenous protein or protein-based drug in tissue.

Multicellular tumor spheroids (MTS) are a well-established model system for drug development and are a valuable in vitro research tool for use prior to employing animal models. These 3D-cell cultures are thought to display chemical gradients of oxygen and nutrients throughout their structure, giving rise to distinct microenvironments in radial layers, thus, mimicking the pathophysiological environment of a tumor. Little is known about the localized distributions of metabolites within these microenvironments. To address this, here we utilize high spectral resolution Fourier-transform ion cyclotron resonance (FT-ICR), MALDI mass spectrometry imaging (MSI) to image the distribution of endogenous metabolites in breast cancer MCF-7 spheroids. We show that known specific metabolite markers (adenosine phosphates and glutathione) indicate that the central region of these cell culture models experiences increased hypoxic and oxidative stress. By using discriminatory analysis, we have identified which m/z values localize toward the outer proliferative or central hypoxic regions of an MTS. Elemental formulae were assigned with sub-ppm mass accuracy, allowing metabolite assignment. Using this information, we have mapped these metabolites back to distinct pathways to improve our understanding of the molecular environment and biochemistry of these tumor models.

Direct analyte-probed nano-extraction (DAPNe) is a method of extracting material from a microscale region of a sample and provides the opportunity for detailed mass spectrometry analysis of extracted analytes from a small area. The technique has been shown to provide enhanced sensitivity compared with bulk analysis by selectively removing analytes from their matrix and has been applied for selective analysis of single cells and even single organelles. However, the quantitative capabilities of the technique are yet to be fully evaluated. In this study, various normalisation techniques were investigated in order to improve the quantitative capabilities of the technique. Two methods of internal standard incorporation were applied to test substrates, which were designed to replicate biological sample matrices. Additionally, normalisation to the extraction spot area and matrix compounds were investigated for suitability in situations when an internal standard is not available. The variability observed can be significantly reduced by using a sprayed internal standard, and in some cases, by normalising to the extracted area.

Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach that uses a graph-based algorithm with a two-phase sampling method that overcomes this limitation. We demonstrate the algorithm on a range of sample types and show that it can segment anatomical features that are not identified using commonly employed algorithms in MSI, and we validate our results on synthetic MSI data. We show that the algorithm is robust to fluctuations in data quality by successfully clustering data with a designed-in variance using data acquired with varying laser fluence. Finally, we show that this method is capable of generating accurate segmentations of large MSI data sets acquired on the newest generation of MSI instruments and evaluate these results by comparison with histopathology.

Androgens are essential for male development and reproductive function. They are transported to their site of action as blood-borne endocrine hormones but can also be produced within tissues to act in intracrine and paracrine fashions. Because of this, circulating concentrations may not accurately reflect the androgenic influence within specific tissue microenvironments. Mass spectrometry imaging permits regional analysis of small molecular species directly from tissue surfaces. However, due to poor ionization and localized ion suppression, steroid hormones are difficult to detect. Here, derivatization with Girard T reagent was used to charge-tag testosterone and 5α-dihydrotestosterone allowing direct detection of these steroids in mouse testes, in both basal and maximally stimulated states, and in rat prostate. Limits of detection were ∼0.1 pg for testosterone. Exemplary detection of endogenous steroids was achieved by matrix-assisted laser desorption ionization and either Fourier transform ion cyclotron resonance detection (at 150 μm spatial resolution) or quadrupole-time-of-flight detection (at 50 μm spatial resolution). Structural confirmation was achieved by collision induced fragmentation following liquid extraction surface analysis and electrospray ionization. This application broadens the scope for derivatization strategies on tissue surfaces to elucidate local endocrine signaling in health and disease.

D(4)-α-Cyano-4-hydroxycinnamic acid (D(4)-CHCA) has been synthesized for use as a matrix for matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) and MALDI-MS imaging (MSI) of small molecule drugs and endogenous compounds. MALDI-MS analysis of small molecules has historically been hindered by interference from matrix ion clusters and fragment peaks that mask signals of low molecular weight compounds of interest. By using D(4)-CHCA, the cluster and fragment peaks of CHCA, the most common matrix for analysis of small molecules, are shifted by + 4, + 8 and + 12 Da, which expose signals across areas of the previously concealed low mass range. Here, obscured MALDI-MS signals of a synthetic small molecule pharmaceutical, a naturally occurring isoquinoline alkaloid, and endogenous compounds including the neurotransmitter acetylcholine have been unmasked and imaged directly from biological tissue sections.

The distributions of positron emission tomography (PET) ligands in rat brain tissue sections were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). The detection of the PET ligands was possible following the use of a solvent-free dry MALDI matrix application method employing finely ground dry α-cyano-4-hydroxycinnamic acid (CHCA). The D2 dopamine receptor antagonist 3,5-dichloro-N-{[(2S)-1-ethylpyrrolidin-2-yl]methyl}-2-hydroxy-6-methoxybenzamide (raclopride) and the D1 dopamine receptor antagonist 7-chloro-3-methyl-1-phenyl-1,2,4,5-tetrahydro-3-benzazepin-8-ol (SCH 23390) were both detected at decreasing abundance at increasing period postdosing. Confirmation of the compound identifications and distributions was achieved by a combination of mass-to-charge ratio accurate mass, isotope distribution, and MS/MS fragmentation imaging directly from tissue sections (performed using MALDI TOF/TOF, MALDI q-TOF, and 12T MALDI-FT-ICR mass spectrometers). Quantitative data was obtained by comparing signal abundances from tissues to those obtained from quantitation control spots of the target compound applied to adjacent vehicle control tissue sections (analyzed during the same experiment). Following a single intravenous dose of raclopride (7.5 mg/kg), an average tissue concentration of approximately 60 nM was detected compared to 15 nM when the drug was dosed at 2 mg/kg, indicating a linear response between dose and detected abundance. SCH 23390 was established to have an average tissue concentration of approximately 15 μM following a single intravenous dose at 5 mg/kg. Both target compounds were also detected in kidney tissue sections when employing the same MSI methodology. This study illustrates that a MSI may well be readily applied to PET ligand research development when using a solvent-free dry matrix coating.

Liquid extraction surface analysis mass spectrometry (LESA-MS) is a surface sampling technique that incorporates liquid extraction from the surface of tissue sections with nanoelectrospray mass spectrometry. Traditional tissue analysis techniques usually require homogenization of the sample prior to analysis via high-performance liquid chromatography mass spectrometry (HPLC-MS), but an intrinsic weakness of this is a loss of all spatial information and the inability of the technique to distinguish between actual tissue penetration and response caused by residual blood contamination. LESA-MS, in contrast, has the ability to spatially resolve drug distributions and has historically been used to profile discrete spots on the surface of tissue sections. Here, we use the technique as a mass spectrometry imaging (MSI) tool, extracting points at 1 mm spatial resolution across tissue sections to build an image of xenobiotic and endogenous compound distribution to assess drug blood-brain barrier penetration into brain tissue. A selection of penetrant and "nonpenetrant" drugs were dosed to rats via oral and intravenous administration. Whole brains were snap-frozen at necropsy and were subsequently sectioned prior to analysis by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and LESA-MSI. MALDI-MSI, as expected, was shown to effectively map the distribution of brain penetrative compounds but lacked sufficient sensitivity when compounds were marginally penetrative. LESA-MSI was used to effectively map the distribution of these poorly penetrative compounds, highlighting its value as a complementary technique to MALDI-MSI. The technique also showed benefits when compared to traditional homogenization, particularly for drugs that were considered nonpenetrant by homogenization but were shown to have a measurable penetration using LESA-MSI.

The limit of detection of low-molecular weight compounds in tissue sections, analyzed by matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), was significantly improved by employing sample washing using a pH-controlled buffer solution. The pH of the washing solutions were set at values whereby the target analytes would have low solubility. Washing the tissue sections in the buffered solution resulted in removal of endogenous soluble ionization-suppressing compounds and salts, while the target compound remained in situ with minor or no delocalization during the buffered washing procedure. Two pharmaceutical compounds (cimetidine and imipramine) and one new protease inhibitor compound were successfully used to evaluate the feasibility of the pH-controlled tissue washing protocol for MALDI-MSI. Enhancement in signal-to-noise ratio was achieved by a factor of up to 10.