New Frontiers for Mass Spectrometry Imaging Abstract:Mass spectrometry imaging has the unique ability to perform untargeted, spatial analysis of thousands of molecules in a single run. With improvements in instrumentation acquisition speeds and decreased spatial resolution, there is a large increase in the size of acquired data sets. This has led to a need for sophisticated software tools that can compress data and rapidly handle analysis workflows to obtain meaningful biological conclusions. Additionally, the utilization of mass spectrometry imaging in the clinical setting and pharmaceutical companies has led for a great need to develop robust quantitation strategies, as well as a coupling mass spectrometry imaging with other commonly used imaging modalities to answer new biological questions of interest. Here, we critically review the status of mass spectrometry imaging and discuss unique opportunities for new frontiers for mass spectrometry imaging in biomedicine.1. Introduction: Mass spectrometry imaging (MSI) is a powerful tool that allows the untargeted investigations of the spatial distribution of a molecules species of interest in a variety of sample sources. In a single experiment, it is capable of imaging thousands of molecules, such as metabolites, lipids, peptides, proteins, and glycans, without labeling. The combination of mass spectrometry with the ability spatially analyze thin sample sections creates a chemical analysis tool useful for biological characterization, essentially creating a chemical microscope. In general, after proper sample preparation, the thin sample section, a (x, y) grid is overlayed onto the tissue, with each square indicative of the spatial resolution dictated by the user. The MS instrument ionizes the molecules and collects a spectrum at each grid square on the section. After collecting all the spectra, computational software allows researchers to select a mass-to-charge (m/z) value from the overall, averaged spectrum for the tissue. The intensity of the m/z from each grid point (i.e., spectrum) is then extracted and combined into a colormetric image depicting the distribution of that m/z value. In order to determine the identity of that m/z value, on-section fragmentation can be done, and the fragments can be used to piece the structure of the unknown molecule. Otherwise, accurate mass matching to databases can be done as well to confirm the identity of the molecule within a certain mass error range. Based on technological advances in the past few years, MSI is becoming more routine tool in clinical practice and the pharmaceutical industry. Advances include improvements in reproducible sample preparation and instrumentation that allows for high acquisition speeds and lower spatial resolution. Additionally, the ability to provide absolute quantitative information in MSI experiments boasts its credibility. To help with large computational endeavors, statistical workflows and machine learning algorithms have been implemented to handle the large imaging datasets being produced with modern day instrumentation. MSI can also be combined with other imaging modalities, such as microscopy, Raman spectroscopy, and MRI to complement the high chemical specificity of MSI with high resolution structural information, which can be applied to clinical readouts of patient diagnosis and prognosis. Additionally, researchers have been able to expand MSI methodology beyond 2-dimensional (2D) sections. With both hardware and software improvements, 3-dimensional (3D) renderings and even single cell resolution using MSI are emerging as future frontiers. With all the advances in this field, MSI is still evolving and requires continuous developments to match the current demand. Overall, the aim of this review is to provide an informative resource for those in the MSI community who are interested in improving MSI data quality and analysis. Particularly, we discuss advances in sample preparation, instrumentation, quantitation, statistics, and multi-modal imaging that have allowed MSI to emerge as a powerful technique in the clinic. Also, several, novel biological applications will be highlighted. 2 Sample Preparation:2.1 The BasicsAs with any methodology, the most crucial step for analytical success is proper sample preparation. This is particularly true for mass spectrometry, as even subtle differences in sample integrity to density can have profound effects on the signal intensity, types of molecules ionized, or localizations. For example, in MSI, one of the greatest challenges is reducing delocalization of molecules, and this relies solely on proper sample preparation strategies. Researchers have even developed a new statistical scoring to determine sample preparation quality (independent assessment).Universally, after any necessary dissection, samples require a step to halt enzyme activity to reduce degradation and delocalization of molecules. Classically, this means flash freezing for MSI since many other preparations (e.g., formalin fixation (FF)) are not MS compatible for most molecular species, although some lipids are not cross-linked and thus allows for FF to preserve sample integrity (Inflation fixation). New method developments have made the abundant FF paraffin embedded (FFPE) samples more MSI accessible (see discussion below). Prior to sectioning, one unique preparation is the decellularization of the tissues, allowing for the improved signal of extracellular matrix \cite{26505774}. Next, these samples are sectioned thinly (6-20 micron), thaw mounted onto appropriate slides, and placed into a drying system (e.g., desiccator box). In many cases, tissues are fragile and do not section well without support, thus many researchers have adopted embedding tissues prior to sectioning. These range from things like optimal cutting temperature (OCT) material to gelatin (Precast molds, inflation fixation,), but, as always, MS-compatibility is a concern. OCT, for example, is popular among histologists but tends to contaminate MS spectra and is thus not recommend. Due to samples flaking or washing off the slide, O’Rourke et. al. recommend coating the slide in nitrocellulose as a “glue-like” substance to aid the sections in staying on the slides \cite{26212281}. Here, one major assumption made is that the samples described are 3D tissue samples, so these general steps are not accurate for all samples. In general, researchers have found ways to image analytes in imprinted \cite{25914940}, plant roots \cite{26990111}, and even agar \cite{26959280} \cite{26297185}. Others have gone beyond single tissues to whole body imaging, which obviously can have its own unique challenges \cite{26491885}.Several different ionization techniques are compatible with MSI, although each requires a unique process to preserve the corresponding sample. Matrix-assisted laser desorption/ionization (MALDI) is the most popular ionization technique for MSI, especially for its ability to image both small and large molecules (e.g., metabolites and proteins) (localization of ginsenosides). Its requirement of a matrix for proper ionization and production of only singly charged ions often limits its applicability to larger proteins. This has prompted the development of laserspray ionization and unique matrices (e.g., 2-NPG), although they have not found their niche in imaging workflows (in situ characterization). Obviously, no one matrix, application method, or analyte extraction process works for all molecules, so optimization is important and will be discussed later in this review. Other varieties of MALDI MSI exist, including scanning microprobe MALDI (SMALDI) (Phospholipid topography), IR-MALDESI \cite{27848143},\cite{26402586}, surface-assisted laser desorption/ionization (SALDI) (\cite{26705612}, although they are not as popular. Other techniques worth noting include desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS), which require minimal sample preparation in comparison to MALDI \cite{26545296}, \cite{25799886}, \cite{27270864}, \cite{26419771}, \cite{26859000}. Unfortunately, each of these is more limited in the molecules they ionize (peptides and metabolites, respectively). In the most general cases, both DESI and SIMS can be performed directly after sectioning, as they depend more on the instrument parameters for proper analyte extraction, although all the addition developments will be discussed. Even with all the ionization methods available researchers are still developing new methodology, such as laser electrospray \cite{26931651} . Each ionization has its own advantages and disadvantages, ranging from the molecules of interest to spatial resolution, the later to be discussed further on in this review. Finally, after proper preparation and ionization, the instrument itself (e.g., mass analyzer), is important to consider before determining your proper sample handling, and the confidence in being able to identify an analyte is just as important as the analyte being available for analysis.2.2 Improving the Basics2.2.1 Applying an Internal StandardWhile evaluation of different tissues or different analytes within a tissue was accepted previously, appropriate normalization and internal standards are expected if semi-quantitative comparisons are to be made. These standards could be included as early as dosing the animals/cells or right before the ions enter the instrument (direct targeted, quantitative mass spectrometry imaging, detection and mapping). For MALDI, the standards are classically applied prior to matrix application using the same automatic sprayer systems described below \cite{26544763}, \cite{28193015}, \cite{27263025}. Chumbley et. al. has done a comprehensive study to determine the proper inclusion of the standard (e.g., with matrix, under the tissue section, or sandwiching the section with matrix), and it was found that depositing the standards followed by matrix to be optimal for the drug rifampicin \cite{26814665} This sample protocol can also be applied to done tissue sections used in DESI experiments (applying prior to analysis), or standards can be added directly DESI extraction solvent for inclusion into sample analysis \cite{26859000}.2.2.2 Matrix Choice and Application (MALDI only)For MALDI ionization, a matrix is required to allow proper ionization of the molecules of interest. As the matrix crystalizes, analytes are extracted and co-crystalized. If analytes aren’t in this crystal structure, it is unlikely they will be ionized and then analyzed on the MS. Thus, the availability of the molecule, the matrix application, and the matrix itself can all have an effect on this process. It should be noted that all of preparations may be applicable for other ionizations if appropriate. For the case of some proteins, a fixation wash is necessary to make the molecules even available for co-crystalization \cite{26505774},\cite{26212281}. The Carnoy’s solution is a common wash used for. Other washes, such as ammonium citrate, have also been utilized to analyze low molecular weight species. Besides washing, pre-spraying with solvents can also aid in the extraction of peptides. The combination of ammonium citrate washes and pre-spraying with cyclohexane proved to be effective in extracting clozapine from rat brain sections (Pre-extraction). Vapor chambers have also been found to be effective, specifically TFA vapors for SIMS imaging of lipids \cite{25799886}.Several matricies have found popularity for their “universal analysis” including 2, 5-dihydroxybenzoic acid (DHB) and α-cyano-4-hydroxycinnamic acid (CHCA), especially for metabolites and peptides in positive mode. A 1:1 mixture of these matrices is also commonly used \cite{26962105}. Also for positive mode, sinaptic acid has been well vetted for proteins. On the other hand, negative mode has been found useful for metabolites, for which 1, 5-diaminonaphthalene (DAN) and 9-aminoacridine (9-AA) are the most accepted matrices \cite{28362367}. Based on the literature, little effort has been made on developing or discovering new matrices for MALDI, although the use of water as a “matrix” in MALDESI is has been done recently \cite{26402586}. Also, nanomaterials have been utilized as an alternative, although these are considered a different ionization all together (e.g., SALDI) \cite{26705612}. Matrix has also been used in enhance SIMS signals \cite{26419771}. Finally, since MALDI mainly produces singly-charged ions, some researchers have utilized matrices such as NPG-2 to produce multiply charged-ions using a commercial MALDI source, although their quick sublimation doesn’t allow for longer runs like imaging requires \cite{25273590}. In general, most of the focus for sample preparation has been on the matrix application process.When applying matrix, the best method would provide appropriate analyte extraction, small crystal size, and homogenous application. Unfortunately, no universal method exists. Classically, researchers would spray matrix over the tissue slide using a painter’s airbrush. While this can be reproducible within an individual, person-to-person variability is high, and there is little adjustability. For example, the “wetness” of the application itself defines the appropriate analyte extraction. An appropriate balance needs to be found, as a too “wet” application can cause molecular diffusion while a too “dry” method may not effectively extract the molecules. “Wet” vs. “dry” methods also have an effect on the crystal size, the wetter methods faltering towards larger crystal sizes. Substrate versus surrounding temperatures have also been thought to reduce heterogeneity, but this has been only applied to MALDI spots \cite{27126469}. Automated sprayers have allowed reproducible application methods across individuals and labs, thus their popularity has grown in the last few years \cite{26922843}). Several application notes for different vendors exist, but researchers should take time to optimize their application methods to their specific systems. Interestingly, alternative ionization methods (SIMS) have been used to characterize the analyte incorporation into spots, and, although difficult, imaging-based studies would be interesting \cite{26419771}. Homogenous application has also been a major focus, and researchers have utilized alternative application methods to improve this facet in the last few years. One example is electrospray deposition, for which units tend to be homebuilt. This dry application method usually requires an addition “incorporation spray” after the matrix has been applied \cite{28263004}. Some electrospray devices have allowed for control of the crystal size, which can directly relate to spatial resolution achievable \cite{26016507}. Other methods have also benefited from the inclusion of an electric field in decreasing crystal size and increase spatial resolution \cite{26016507}. Finally, the “driest” method used is sublimation, which is popular for its low-cost, low crystal size, high homogeneity. Commercial and partially modified apparatuses are highly published \cite{26212281}, \cite{26705612}, \cite{28362367}, \cite{28362367}. Moving forward, when individuals want to use several matrices on a tissue section or staining, they will tend to wash it off and reapply the new matrix, but this produces an expected signal loss and diffusion. As an alternative, using a commercial sprayer, Urbanek et. al. have developed a multigrid MALDI (mMALDI) methodology, where different matrices are “printed” into predefined dots on a grid. By targeting these specific matrix dots during the imaging run, a researcher is able to gather multiple datasets (e.g., metabolites, peptides, and proteins) from a single tissue section without washing \cite{27039200}. Finally, with all the different variations in equipment and methodology, a real emphasis needs to be on sharing automated matrix application methods and cross-lab communication to allow for reproducible results. The use of open source software and instrumentation is an example of this, although the ease of commercial instrumentation will continually compete with this notion \cite{25795163}.2.2.3 Chemical Derivatization/On-Tissue Labeling To those outside the field, mass spectrometry is seen as a “magic” technique, although there are several molecules that have a hard time being ionized and thus analyzed directly by mass spectrometry. The concept of derivitizingderivatizing molecules is commonly used in antibodye-based techniques, and its inclusion in mass spectrometry sample prep to aid in ionization was expected. The Girard T (GirT) reagent has been applied successful to several steroids, including testosterone and triamcinolone acetonide \cite{27676129} \cite{28193015}. Other steroids (e.g., THC) have also been targeted using 2-fluoro-1-methylpyridinium p-tolunesolfonate as a dervitization agent \cite{27648476}. N-glycans, fatty acids, and neurotransmitters have also all be targets through unique on-tissue assays \cite{25453841}, \cite{27181709}, \cite{27145236}. Compared to using the traditional spraying of the reagent, which usually produces poor (>100 micron) spatial resolutions, electrospray deposition has been successfully utilized to derivatize fatty acids but with a high (20 micron) spatial resolution \cite{27181709}. As you can see by the molecular species, most of targets for derivatization are smaller molecules. This may be due to their low ionization or the fact that they are the only molecules that have successfully been derivatized on-tissue.2.3 Specific Molecular Considerations2.3.1 On-Tissue DigestionMolecular imaging of proteins has been of major interest, but high mass resolution analysis of proteins has been out of reach due to the mass range limitations of current, mass analyzers (e.g., Orbitraps), especially for MALDI. This has been alleviated for extract analysis by the inclusion of an initial protein digestion, and naturally trypsin on-tissue protocols have been developed for MSI \cite{26544763}\cite{26505774}. Please note that, as with every method developed, the steps should all be optimized for the tissue type \cite{26544763} , \cite{27485623}. For example, Heijs et. al. has shown the appearance of different myelin basic protein fragments appearing over longer trypsin incubation times \cite{26544763}. Until recently, trypsin digestion has been analogous with on-tissue digestion experiments. With the recent boom of interest in glycans, PNGase F, which cleaves N-glycans, has found application into in situ digestion, and sequential enzyme application has even allowed the imaging of both glycans and protein fragments in one imaging run \cite{27373711}. Overall, while stains/immunolabelings are incredible effective, they can be non-specific, and MALDI MSI provides an interesting cross-validation of the labeling-based strategies. The trickiest part of in situ digestion is appropriately identifying the protein fragments. In some cases, on-tissue MS/MS is difficult depending on the instrumentation, and a complementary liquid chromatography experiment may be neededs to performed \citet*{26505774} decellularization, multimodal mass spec). It is worth noting that other ionization techniques (nanoDESI) allow for intact protein imaging up to 15 kDa on Orbitrap systems \cite{26509582}.2.3.2 Formalin-Fixed Paraffin Embedded SamplesWhile there is preference in obtaining freshly excised tissue, sometimes that isn’t possible for many hard to obtain biological samples, especially for rare, human-based samples. The large availability of FFPE tissues, which are not typically compatible with MS typically, researchers have been motivated to develop methods to release the analytes of interest to image these tissues \cite{27791282}. As also, optimization for a tissue type is important, and Oetjen et. al. has provided a comprehensive, guided study to do this (an approach to optimize). Unfortunately, not all molecular species can be extracted from these tissues, although Pietrowska et. al. has said that lipids can be analyzed by avoiding paraffin embedding after fixing the tissue with formalin \cite{27001204}. Most commonly proteins and peptides are targeted, mainly using the in situ digestion described above (tissue fixed with formalin, an approach to optimize sample prep). More recently, researchers have been able to extract metabolites and glycans \cite{27414759} \cite{25804891},\cite{27373711}. With more standardized protocols, the extensive FFPE samples available will be utilized more readily, allowing for a flood of new information to help guide researchers in future endeavors.3. Developments in InstrumentationMS imaging often requires specially developed instrumentation in order to address challenges unique to image acquisition, such as spatial resolution or surface homogeneity. Numerous advancements have been made in recent years to improve the quality and reproducibility of generated images. As the main distinction between imaging and LC-MS is related to the conservation of a spatial dimension, most instrumentation developments have been focused on the ionization source, with several exceptions related to ion accumulation. The two main ionization methods for MSI are laser based and secondary ion based, and most of the progress in recent years has focused on these sources. As such they will be the focus of discussion. 3.1 Laser-based ionization3.1.1 Spatial ResolutionArguably the most sought-after improvements in MSI are related to spatial resolution, which is the area of an imaged sample that comprises a single mass spectrum in an imaging acquisition . Improving the spatial resolution enables more discrete localization patterns to be observed throughout a tissue, but since improving spatial resolution decreases the area of tissue ionized, there is a tradeoff between spatial resolution and sensitivity. The resolution can be changed by adjusting the optics of the ionization source or otherwise changing the instrument’s geometry to decrease the laser diameter. Numerous groups have recently reported drastic improvements in spatial resolution. One paper reports a lateral spatial resolution of 1.4 micron on an atmospheric pressure MALDI source by adjusting its geometry, allowing for the visualization of subcellular lipid, metabolite, and peptide distributions \cite{27842060}. Another group achieved a spatial resolution of 5 microns on a vacuum pressure MALDI instrument by using a simple modification to the optical instrument. The system was easily interchangeable between various laser spot sizes, allowing for more selection in the tradeoff between sensitivity and resolution based on each individual experiment’s needs \cite{28050871}. These two papers highlight some of the current advances in spatial resolution recently.However, with the rapid developments in spatial resolution, it was found that spatial resolutionit was being defined differently between groups, instruments, and samples. As this makes it difficult to form a standard of comparison between methods and instruments, to developing a universal method for both defining, and measuring spatial resolution is crucial to proper data reporting and comparison of images acquired on different instruments with different sample preparation methods, or with different users. In this review, we define spatial resolution as Typically, the limiting factor in spatial resolution is the laser, as the laser width determines the ablation area. Therefore, investigations have looked into characterizing the ablation pattern in imaging experiments, particularly with MALDI-MSI, the most widespread imaging technique. It was found that laser ablation patterns follow a Gaussian distribution, with incomplete ionization around the outside of the pixel. Furthermore, there is the ability to “shear” matrix crystals, scattering debris across the sample after laser ablation. This finding led to the assertion that MSI resolution should be defined as (1) the homogeneity of the matrix crystals once they have been applied and co-crystallized with the analyte and (2) the effective ablation diameter of the laser {O'Rourke, 2017, The Characterization of Laser Ablation Patterns and a New Definition of Resolution in Matrix Assisted Laser Desorption Ionization Imaging Mass Spectrometry (MALDI-IMS)}. The hope is that this new definition will allow for more uniform reporting of spatial resolution between research laboratories on different instruments and with different sample preparation methodologies. Several research groups have developed methods for measuring the actual spatial resolution achievable from an instrument, which can differ from the reported pixel size of the instrument acquisition parameters. A simple and effective way to do this is with a standard slide that can be used to determine the working spatial resolution of an instrument based on user-defined instrumental parameters. One group developed such a slide that incorporated a pattern of crystal violate using lithography in order to measure the beam diameter in MALDI-MSI experiments by visually inspecting the ablation pattern \cite{27299987}. Another slide for measuring spatial resolution was developed using a slightly different technique, in which a sample solution can be dragged over the slide’s surface, allowing it to be automatically retained in hydrophilic grooves of the slide. The slide can then be imaged on the instrument in order to determine the lower threshold of the instrument’s spatial resolution \cite{26044268}. These devices can serve as a valuable method for testing the spatial resolution when adjusting instrumental parameters or performing quality assurance on images to ensure that proper resolution is being reported. 3.1.2 Matrix-free laser-based ionization Though highly beneficial in many regards, MALDI MSI’s requirement for a matrix coating is often a major drawback in imaging experiments. Matrix application can be a limitation because it requires an additional step in sample preparation, it suffers from poor homogeneity that can affect spatial resolution, and it results in excessive noise peaks in some ranges of the spectrum due to the interference of matrix ions. As a result, ionization sources are being developed to utilize a laser ablation techniques without the requirement of matrix. Laser desorption post-ionization mass spectrometry, though still in its early stages of development, has been demonstrated to have a promising potential as a complementary tool for in situ localization and quantification. It has the benefit of not requiring matrix application or sample preparation, though currently its resolution and mass accuracy are 500 micron and 300 ppm, respectively, which is not competitive with commercial instruments \cite{28294229}. However, with further development, it may earn its place as a prominent ionization source. Another method for ionization without the application of matrix is nanophotonic laser desorption ionization, which ionizes analytes from a highly uniform silicon nanopost array \cite{26929010}. This method has achieved 40 micron spatial resolution for over 80 molecular species, giving it the potential to be competitive with MALDI upon further exploration. 4.1.3 Throughput Another frequently cited challenge with MSI is the long analysis time typically required, which can range from several hours to several days, depending on the tissue area and pixel size. These long analysis times limit the practicality of MSI for routine applications, particularly in clinical settings. As a result, developments have been made in order to increase throughput without sacrificing image quality. One notable example involved utilizing a solid state laser with 5 kHz repetition rate to perform continuous laser raster sampling on a MALDI-TOF/TOF instrument. This method achieved an acquisition rate of up to 50 pixels per second, an 8 to 14-fold improvement over conventional lasers \cite{28239976}. Throughput becomes even more of a challenge when molecules in the same tissue ionize differently, thus requiring different polarities for acquisition. This is particularly the case with lipid analysis, as lipids are a diverse class with high structural variability. Methods have been developed for imaging in both positive and negative polarity while minimizing analysis time using high speed MALDI-MSI technology and precise laser control \cite{27041214}. The field is moving toward real-time imaging capabilities for immediate spatial analysis for guidance during surgeries. As an example, Fowble and colleagues have applied a laser ablation imaging approach in ambient conditions in order to obtain spatial distribution of metabolites with a range of polarities in real time without the use of any matrix or sample pretreatment \cite{28234459}. Another method couples a picosend IR laser to an ESI source in order to provide ambient MS imaging without causing thermal damage to tissue. This allows molecules to remain in their native state, allowing better insight into the tissue’s condition \cite{26561279}. These developments demonstrate great potential in moving MSI technology from laboratories to clinical settings for improved patient treatment.3.2 SIMS3.2.1 Resolution and Mass Accuracy The other most common method of ionization is SIMS, which has seen notable improvements in instrumentation. In SIMS imaging, spatial resolution is often quite good, but at the expense of sensitivity. This is largely a consequence of the ion beam, either due to low ionization probability or beam focusing difficulties. An Argon gas cluster ion beam is typically used for TOF-SIMS, but, despite its many benefits, it suffers from poor sensitivity, often causing a tradeoff between spatial resolution and mass resolution. Delayed extraction, a method widely used for MALDI, is becoming more prominent in TOF-SIMS imaging, and has been shown to be successful in maintaining high mass resolution and spatial resolution \cite{26395603}. By implementing external mass calibration, the mass accuracy can also be preserved \cite{26861497}. Methods involving delayed extraction have been explored as a means to improve resolution, but these methods often make mass calibration difficult, resulting in poor mass accuracy. Other groups have explored alternative primary ion sources, such as a CO2 cluster ion beam, which possesses many similarities to Argon, but improved the imaging resolution by more than a factor of 2 due to increased stability of the beam \cite{27324648}3.2.2 Parallel Imaging MS/MS With the inferior mass resolution of SIMS compared to other ionization methods, the mass accuracy is usually not high enough to make confident of detected molecules. Therefore, it is usually necessary to acquire MS2 spectra on ions in order to make identifications. Collecting MS2 spectra is difficult in imaging experiments, however, because performing sequential MS2 scans after a full-MS scan causes misalignment between spectra and spatial information. To address this, progress in parallel imaging MS/MS has been implemented, in which MS2 spectra are collected simultaneously with MS1 spectra using 2 mass analyzers. This acquisition method differs from traditional MS/MS acquisitions, in which all ions other than the precursor ions are discarded. As a result, MS1 and MS2 images are in perfect alignment with each other, allowing for more precise mapping of molecular distribution \cite{27181574};Fisher, 2016, Parallel imaging MS/MS TOF-SIMS instrument}. With fully optimized parallel imaging, identification confidence can be drastically improved without sacrificing the integrity of localization information. 3.2.3 Ambient/Low-vacuum TOF-SIMS As MSI is very commonly used for the analysis of biological tissue it is highly desirable for analyses to be conducted in near-native environments, such as in the presence of water, in order to get an accurate understanding of the chemical environment. Low-vacuum and ambient MALDI imaging have already been well-explored, but progress has recently been made with SIMS, denoted as Wet-SIMS {Seki, 2016, Ambient analysis of liquid materials with Wet-SIMS}. Currently, the technique is able to acquire images at 80 Pa in imaging experiments {Suzuki, 2016, Development of Low-vacuum SIMS instruments with large cluster Ion beam}. With further development, this technique could be used to analyze biomolecules in their native environment, allowing for analysis in biologically relevant experimental conditions. 3.4 Separation A significant limitation to MS imaging compared to LS-MS analysis is the lack of separation capabilities, as retaining spatial information typically requires ablating all ions present in a pixel of sample at the same time for a single scan. This often leads to problems such as ion suppression, but techniques that allow post-ionization separation are being developed to overcome this challenge. To separate analytes from noise or undesired compounds, a simple sample cleanup step was incorporated into MALDI MSI by first introducing laser ablation with vacuum capture followed by C18 elution onto the MALDI target plate. The method demonstrated an improved sample signal and decreased background interference compared to direct MALDI MSI, resulting in higher quality MS/MS data, cleaner spectra, and more confident identification power\cite{26374229}. For separation of analytes, ion mobility has been a popular choise, as it can and has been seamlessly integrated into MALDI MSI workflows. It has also been recently demonstrated to be highly effective for ambient ionization techniques, such as LESA and DESI \cite{27228471} \cite{27782388}. The results showed an increase in detected molecules and the ability to select specific classes to image. An alternative, pseudo-separation method has also been employed, in which subsequent MS scans covered differing m/z windows in order to detect low-intensity ions characteristic of specific ranges, providing the effect of gas-phase fractionation. By implementing a spiral plate motion during imaging, the integrity of spatial information was not lost with this method\cite{26438126}. 3.5 Depth profiling Another challenge specific to imaging is achieving uniform ionization over the surface of the tissue, something difficult to accomplish if the tissue is not perfectly flat. While extra care in sample preparation can help alleviate this to an extent in some sample types, often slight variations in the height of the tissue are unavoidable. To remedy this, modifications to instruments have been made that allow for height correction. For example, a novel LAESI source was recently developed that incorporated a confocal distance sensor that both moved the sample to a constant height and recorded the height information to generate a topography map {Bartels, 2017, Mapping metabolites from rough terrain: laser ablation electrospray ionization on non-flat samples}. Another method combined shear force microscopy with a nano-DESI source to measure and adjust the voltage magnitude to enable a stable feedback signal over surfaces with complex topographies {Nguyen, 2017, Constant-Distance Mode Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging of Biological Samples with Complex Topography}. If a uniform sampling can be ensured over the surface of a tissue, it not only preserves spatial integrity throughout the plane of the sample, but can also allow for three-dimensional imaging. With 3D imaging, it is imperative that the depth profile of the sample be preserved to ensure accurate record of the tissue profile. Several significant advances have been made in this respect in the area of elemental imaging, such the development of a femtosecond laser ionization source for multielemental imaging with a 7 micron depth resolution \cite{27976851}. Submicron depth resolution, down to 20 nm, has been demonstrated using extreme ultraviolet laser light, allowing for 3D imaging of bacterial colonies \cite{25903827}. It is expected that these capabilities will continue to be developed and applied to 3D imaging of more complex systems.4 Quantitation 4.1 Comparison to LC-ESI-MS/MS: The PastWith the push of multi-modal imaging, it is clear that obtaining several pieces of information from a single tissue is imperative. While MSI is mainly qualitative, with the appropriate conditions, processing, and software, quantitative information can be extracted, although this is still under question. Items such as tissue inhomogeneity, ion suppression, sample topography, etc. are all considered significant challenges in this field (aspects of quantitation). Before the development of quantitative MSI, the analytes of interest were separately extracted from another tissue section and run on a liquid chromatography (LC)-electrospray ionization (ESI)-based instrument, although this is still done regularly in MSI to aid in the identification of unknown, interesting m/z values \cite{27181709}. Once the absolute quantity of the analyte of calculated, these values can then be applied to the tissue of interest. This can also be a starting point of studies, allowing for more targeted imaging studies \cite{25542581}. This methodology is still in the current literature, although, it is more commonly utilized for confirmation of the MSI results, similar to Western blot for other LC-MS quantitative results \cite{26814665}. Quantitative MSI is now expected, as many application-based MSI publications focus on the comparison between two of more sample types. With proper sample preparation, comparisons can be made with the appropriate considerations.4.2 Relative4.2.1 Direct Comparison (with or without Normalization)As eluded to above, direct comparisons between different tissue sections is done commonly. While these “relative” comparison methods learn towards being “semi-quantitative,” several techniques and data processing strategies have perpetuated its use. For example, matrix effects and other interfering molecules tend to cause more deviation in the quantitative accuracy, although some researchers have shown that the correlation between MALDI-MSI and LC-MS/MS can be quantitative for fatty acids and proteins (On-tissue derv, a proof of concept). While these assessments are of different molecules in a single tissue are interesting, differences in ion suppression and ionization efficiencies between molecules should always be questioned, although the addition of an internal standard can aid in the normalization of the signal (spatial localization and quantitation). This can also be done with the same molecules within different tissues, and normalization still aids in more confident comparisons (spatial localization and quantitation). The inclusion of a normalization procedure in pre- and post-processing is now an expectation. This strategy is applicable for several other molecular species, including neurotransmitters, nucleotides, lipids, and tryptic peptides (direct targeted, MSI reveals, brain region specific). Almost all software available for MS imaging provides the ability to normalize. For example, the use of SciLS software tool allowed for normalization to the total ion current (TIC) before further statistical analysis (mass spectrometry imaging of metabolites). After differentiation, several metabolites were found to be different between the cortex, outer medulla, and inner medulla of the rat kidney between control and furosemide-treated (mass spectrometry imaging of metabolites). It should be noted that care should be taken when comparing different regions of a tissue, as their matrices can vary slightly (aspects of quantitation). It should be noted though that there are publications still make comparisons without normalization \cite{26475201}, pioneering ambient, imaging of proteins). Finally, software is obviously an important component in any imaging-based quantitative strategies, and Renslow et. al. have further developed tools to nanoSIMS transition from qualitative to quantitative for element incorporation into biofilms (quantifying elemental incorporation).4.2.2 On-tissue labeling – Using Reporter IonsFor ESI-based quantitation, two techniques are employed. Label-free directly compares samples in different runs, which is analogous to the “direct comparison” MSI described in the previous section. While label-free quantitation is commonly employed, instrument variability, instrument limitations, and other factors lead to inconsistent and incorrect comparisons. To compare, the incorporation of stable isotopes has allowed for same spectrum relative quantitation, although its application to MSI is extremely limited. One example in the literature entitled stable-isotope-label based mass spectrometric imaging (SILMSI) utilizes light and heavy chromogens to differentiate between different cancer biomarkers of interest (SILMSI). After labeling with a primary and secondary antibody, the addition of the chromogen produces an azo dye that, when ionized by the laser, fragmented into distinct, duplex reporter ions. The ratio of these reporter ions then can calculate their relative abundance compared to another molecule, in this case the estrogen receptor and progesterone receptor (SILMSI). While classically reporter ions are seen in the MS/MS spectra via isobaric labeling, this same concept is not done in MSI experiments, not only due to the poor fragmentation for ions but likely due to the incompatibly of the methods for relative quantitation. In comparison, MS1-based labeling methods can easily be transitioned to on-tissue MSI applications, although the process of derivitizing molecules on-tissue has primarily been used for increasing ionization of different molecules (direct targeted, on-tissue dervatization, linkage-specific).4.3 Absolute4.3.1 Internal StandardWhereas relative comparisons are common place, absolute quantitation is relatively underdeveloped. While obtaining the true concentration of a molecule is much more difficult, it is also more desired since it allows for true comparisons between different molecular species without worries of varying ionization efficiencies. As with ESI-based measurements, the easiest method is to incorporate a deuterated internal standard into the sample. As explained previously, internal standards are now being used extensively to normalize MSI data sets, and the inclusion of a very specific standard (e.g., deuterated version of an analyte of interest) facilitates absolute quantitation of that analyte of interest. This has been done primarily for DESI samples, with the standards incorporated into the solvent stream (quantitative mass spectrometry imaging of small).4.3.2 Calibration CurveIn general, the creation of a calibration curve is the most confident way to obtain the absolute quantity of an analyte. This has been done with ESI in separate and the same runs (iDiLeu). Initially, you would think producing an external, separately spotted calibration curve would work for MALDI, although the lack of sample matrix and matrix heterogeneity leads to inaccurate concentrations. Thus, researchers have adopted an on-tissue spotting technique that takes both of these considerations into account. The standard of interest (isotopic or non-isotopic) are spotted/applied on a separate, “control” section (absolute quantitation, direct targeted, direct imaging). This section is usually a serial section of the one being analyzed, as having the same matrix is important for accurate quantitation (aspects of quantitation). For example, many researchers chose liver tissue for initial optimization or studies, as it is considered extremely homogenous (aspects of quantitation, absolute quantitation). Interestingly, in the case of elemental analysis, before spotting on the sample, the sections are washed to remove excess elements (e.g., sodium) (direct imaging). To increase homogeneity of the areas where the standards are placed, researchers have developed methods where the standards are spiked into tissue homogenates themselves. These samples are then placed into a mold, frozen, sectioned, and placed near the imaged section, for which quantitation accuracy is similar, although it was noted that the dried droplet spotting method referenced above is much faster and easier (aspects of quantitation). All of these methods require sophisticated computational tools, and several software packages exist for processing region of interest quantitation (MsiReader, MSIQuant). msIQuant is an example software, which has been used to absolutely quantify drugs and neurotransmitters (msIQuant). 5. Data Analysis MSI data is difficult to process for a number of reasons, including the large size of the data files and the high degree of dimensionality, as acquisitions retain spatial information as well as other information. This is becoming more of a problem with the increase in spatial resolution causing an exponential growth in data files sizes. As such, key software developments have been made to address these challenges and ensure that effective analyses are being done without the loss of valuable information in the process. 5.1 VisualizationThe most important information obtained from an imaging experiment is a visualization of the distribution of various molecules throughout the tissue. As each pixel of an imaging experiment contains an entire mass spectrum, special software is required to handle this specific need in the field. While there have been numerous advancements in this respect, the influx of progress caused there to be a lack of uniformity. This means that typically the software could not be applied to large data sets, expensive commercial software would be required, or the software would require the end user to have some degree of programming knowledge to fit his or her data to the software input. However, recent efforts have been made to design open-source visualization tools that are user-friendly and applicable to multiple instrument platforms, particularly in the area of LA-ICP-MS, which is not as routinely implemented as MALDI-MSI or SIMS-TOF {Sforna, 2017, MapIT!: a simple and user-friendly MATLAB script to elaborate elemental distribution images from LA-ICP-MS data}\cite{27917244}{}{Uerlings, 2016, Reconstruction of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) spatial distribution images in Microsoft Excel 2007}. MSiReader is a key player in open source visualization, providing both a graphic user interface and MATLAB open source code for users\cite{23536269}. Additionally, even open source microscopy imaging software like ImageJ have plugins scripts capable of handling MSI data sets \cite{22347386}. These new open source tools show promise for making the processing of imaging data more widely accessible and customizable for to the broader mass spectrometry imaging community. New methods have also been explored for expanding the capabilities of visualization tools. For example, 3D MALDI imaging have been limited by inabilities to reconstruct 3D images, but Patterson and colleagues designed an open-source method for 3D reconstruction using multivariate segmentation \cite{26958804}. Others have expanded our knowledge gained in a different direction. Instead of using imaging to track a single molecule, they developed a tool to view the localization of biological indices (e.g. energy charge to indicate energy status in the cell), mapping the relationship between several specified molecules \cite{27542771}. An important note with visualization of data in MSI is critical to ensuring that the image shown is an accurate representation of the molecular distribution. .It has been found that cropping images to eliminate background can cause the emergence of distribution patterns not observed in the entire image. As a result, data can become skewed if the analyzed area is too small and does not contain sufficient background area for reference \cite{27730748}. With MS imaging making an increasing presence in biomedical applications as a diagnostic tool, appropriate representation and statistical analysis of visual data is essential. \cite{Bro_2014}5.2 PreprocessingPrior to data processing, several steps can be used to ensure accurate and efficient data analysis. These steps include normalization, baseline correction, spectra recalibration, smoothing, and data compression. Normalization is considered required for data analysis, while baseline correction, spectra recalibration, smoothing, and data compression (unsupervised and supervised) are considered optional prior to analysis, but may be necessary dependent upon chosen statistical analysis and the mass spectrometry instrumentation used to collect the data \cite{17541451}. The use of preprocessing steps can also depend on mass spectrometry and biological points of view of an individual project. Overall, preprocessing can help reduce experimental variance within the data set, and helps draw meaningful conclusions from subsequent statistical analysis. 5.2.1 Normalization\cite{21479971}Normalization is used to remove systematic artifacts that can affect the mass spectra. Sample preparation, matrix application, ion suppression, and differential ionization efficiencies in complex samples can influence the intensity peaks of mass spectra. Some of these random effects in data acquisition can be minimized by proper normalization. Not applying normalization can lead to misleading artifacts and ultimately can depict inaccurate ion distributions, statistical analysis, and conclusions about biological significance. There are a few different methods for normalization for mass spectrometry imaging data sets based on the purpose of the analysis. Normalization to the total ion current (TIC) is the most commonly implemented method. Normalization to the TIC ensures that all spectra have the same integrated area and is based on the assumption that there are comparable number of signal in each spectrum \cite{17541451} \cite{22148759}. However, in an imaging experiment, it cannot always be assumed that this condition is met. TIC normalization can improve the ability to compare expression levels across samples with similar sample type, however is not applicable when comparing very different tissue types. In addition to normalization to the TIC for similar sample types, the TIC normalized data can be further normalized to matrix related peaks for MALDI imaging experiments to correct for uneven matrix coating. This may be necessary depending on how the matrix is applied to the sample. For example, manually prayed air brush sprayed matrix applications cannot produce as homogenous of crystals across the whole tissue as matrix applied with an automated sprayer or automated microspotter\cite{25331774}. For samples with different tissues types, such as whole body imaging, an externally applied labeled calibration molecule similar to the compound of interest ideally is used a reference molecule and is applied during matrix application. For this normalization method, each spectra is normalized to the intensity of the reference molecule for analysis. Choice of reference molecule can be complicated by deposition methods and choice of compound that may require optimization. Normalization to an internal standard reduces the impact of ion suppression that arises from tissue inhomogeneity and improves pixel-to-pixel variability. TIC is not recommended for whole body imaging or for different samples compositions, where internal standard normalization is considered the best normalization option \cite{25318460}. Other options include normalization to an endogenous molecule that is expected to be consistently expressed throughout the whole tissue, such as a phospholipid head group. Additionally, some researchers have calculated a tissue extinction coefficients or relative response factors to determine the relative amount of a compound in whole body imaging or different tissue types. This tissue extinction coefficient takes into account ion suppression related to the compound of interest and the tissue of interest and is then compared to LC-MS/MS data \cite{22842155} . The advantage of this method is that no expensive labeled standards are needed of the compounds of interest, although accuracy of tissue extinction coefficients is still being investigated . 5.2.2 Baseline CorrectionSpectra resulting from imaging experiments can result in noisy data acquisitions and large variations in spectral intensity, even in within the same sample. Noise in the data is the baseline can affect peak detection algorithms and sample-to-sample comparisons. Typically, a baseline algorithm is implemented in the data preprocessing step to reduce this noise prior to statistical analysis. Baseline noise occurs because at low m/z values, there is more chemical noise, leading to the presence of a higher baseline than present at higher m/z values \cite{17541451}. The effect of chemical noise can be suppressed by estimating the baseline and then using a polynomial function or moving average to subtract the baseline. A new baseline is calculated and signal levels are adjusted. New sliding window baseline algorithms are being developed with automatic adjustments based on mass range \cite{27980460} . Even after correction, a residual baseline might still be observable in the low m/z ranges. A choice of baseline algorithm that best reduces that baseline of an individual dataset may depend on the complexity and acquisition of the data. 5.2.3 Spectral recalibrationSpectral recalibration, also called spectral realignment, is applied after data acquisition as a method to improve the mass accuracy of the data by calibrating to an internal standard or several internal standards to realign the spectra. This internal standard must be present in at least 90% of the spectra to be used for recalibration . Some use a matrix peak for MALDI imaging, consistently expressed m/z values in the tissue, or an applied internal standard to perform the recalibration as used in Heijs et al. \cite{26544763} . Multiple internal standard peaks should be selected if a large mass range is used in the data setup. The spectra are then realigned using a quadratic calibration algorithm based on the median value of the selected peaks used for calibration. This typically results in a 5-10 fold reduction of the range of centroid values following alignment \cite{17541451}. Spectral recalibration is especially important for instruments with low mass accuracy, such as a linear TOF instrument, where one might expect a 100-200 ppm mass accuracy \cite{17541451}. This can be compared with an Orbitrap, where one would expect 5 ppm mass accuracy, where spectral recalibration is not as necessary for m/z identifications. However, spectral recalibration can also help to correct for irregularities in the tissue thickness, which can further magnify variations in the mass measurement.5.2.4 SmoothingThe application of a smoothing algorithm can reduce fluctuation by increasing the signal-to-noise ratio. Mass spectrometry imaging can produce salt-and-pepper noise, where you see sharp, sudden disturbances in the image pixels that do not correspond to the signal seen surrounding this pixel. To help reduce these sudden fluctuations, denoising algorithms are applied to reduce pixel-to-pixel variability and to allow the local scale of features to be resolved. Commonly used algorithms include 1) Savitsky Golay Smoothing \cite{27791282} \cite{27256770}, which assumes a Gaussian distribution of the data and uses the polynomical order and the number of points to a compute a smoothed output value and the 2) Boxcar Smoothing \citet*{22743164}(also known as a moving average smoothing algorithm or the Gaussian kernel), which replaces each data point with the average of neighboring values\cite{26680279}. These work to reduce noisy data sets with significant inter-pixel variation. 5.2.5 Unsupervised data compressionAs MSI acquisitions tend to create large data files (up to several terabytes per sample), data processing becomes more difficult and requires more strenuous computational methods. To alleviate this problem and make the data files easier to handle and distribute, several compression strategies have been implemented to reduce the size of data, while still retaining the important information. Binning mass spectra for each pixel of an imaged tissue and compression based on region of interest (ROI) are the most successful methods, with ROI compression requiring the least amount of computation \cite{28842033}. Autoencoders have also been useful for unsupervised non-linear dimensionality reduction of imaging data by reducing each pixel one at a time to its core features {Thomas, 2016, Dimensionality Reduction of Mass Spectrometry Imaging Data using Autoencoders}. Once the size of data has been reduced, it can be more easily processed in subsequent steps of the processing pipeline. Unsupervised clustering of the data is also used to compress data into features for statistical analysis. Unsupervised analysis can be divided into manual, component, or segmentation analysis. Manual is carried out by selecting out m/z value unique to the region of interest, pulling out each image for a single m/z and manually cataloguing them. Component analysis requires a statistical or machine learning algorithm to cluster the data. Principal Component Analysis (PCA) is used to reduce the dimensionality of the dataset by converting possibly correlated variables into a set of linearly uncorrelated values, known as principal components. PCA an unsupervised statistical method to distinguish principal components that cause the greatest variance in the data. PCA plots the principal component that causes the greatest variation on the x axis and the principal component that causes the 2nd greatest amount of variation on the y-axis to induce groupings of related pixels in the data sets \cite{21980364} . PCA can also be used to remove signals which are poorly connected with variability between groups. Spatial segmentation helps bin together similar spectra into regions of interests and to identified co-localized m/z values. Hierarchical clustering segmentation partitions the image into its constituent regions at hierarchical levels of allowable dissimilarity between regions. Hierarchical clustering only requires a similarity between groups of data points. Hierarchical clustering is used to rearrange multiple variable to visualize possible groups in the data. This provides the possibility for rapid identification of specific markers from different histological samples. HC classifies the mass spectra according to similarities between their profiles and thus provides the ability to highlight regions containing differences in molecular content. Another segmentation methods is K-means clustering, which is the most commonly used for for mass spectrometry imaging. K-means clusters the number of partitions, n, into k number of clusters, where each cluster is based on the spatial distances between mass spectra. Following k-means clustering, each observation now belongs to the cluster with the nearest mean. K-means clustering to create spatially localized clusters to which feature extraction can be applied {Winderbaum, 2015, FEATURE EXTRACTION FOR PROTEOMICS IMAGING MASS SPECTROMETRY DATA} Bisecting k-Means is a combination of k-Means and hierarchical clustering, although computationally more complex. Bisecting k-means is a hierarchical clustering method that uses k-means repeatedly on the parent cluster to determine the best possible split to obtain the next two daughter clusters to obtain uniformly sized clusters. These methods can help to detect important, biologically relevant features that may otherwise go undetected due to the difficulty in extracting information and segmenting large data sets so that statistical analysis is computational reasonable. Cardinal, an R based statistics program can be used for data compression and statistical analysis \cite{25777525}. 5.2.6 Supervised data compressionSupervised clustering is better suited when a specified set of classes is known and the goal is to classify new data set into one of those classes. Supervised used predefined classes or categories, while unsupervised uses similarity between spectra to generate classes. Supervised classification is used to figure out if the groups are actually different, and what m/z values best differentiate the groups. Some studies use actually both supervised and unsupervised statistical analysis for analysis \cite{28361385}. Partial least squares regression is a supervised classification method, where classes of data are annotated with known labels \cite{25462628}. Partial least squares regression is similar to PCA, however instead of separating into components based on the maximum variance, it uses a linear regression model to project predicted variables and observable variables to a new space. This type of supervised clustering requires a training data set for the classification of groups.Both supervised and unsupervised classification methods reduce data down to the most important m/z value distributions. Data compression projects the data to a lower dimension subspace, while maintaining the essence of the data for statistical analysis. With the large degree of dimensionality associated with MS imaging data, especially of biomedical samples, extracting important, relevant features becomes increasingly difficult. Machine learning algorithms for feature detection applied to LC-MS data can be limiting with imaging data, as they don’t account for differences in spatial regions of the tissue of interest. A context aware feature mapping machine learning algorithm was recently developed that takes into account the spatial region of features when ranking \cite{27764717}. 5.3 Statistical Analysis5.3.1 Tests of SignificanceStatistical analysis of large data imaging sets is incredibly important for the implementation and utility of mass spectrometry imaging. Comparing samples significantly involves statistical hypothesis testing to determine if there is a certain difference that exists between samples or between spatial regions of the tissues. Univariate analysis tests that one m/z, identifying to a compound of interest, is different between different samples. If the data has a Gaussian distribution, a t-test is used to determine the difference between two samples, and ANOVA is used to determine if there is any difference in a group of samples \cite{27485623} \cite{Marczyk_2015} . Gaussian distribution of mean intensities cannot be assumed for clinical samples; mean values may still be used if the central limit theorem is satisfied. If the data has a non-Gaussian distribution, nonparametric tests like the Mann-Whitney U-test can be used as a statistical test of the hypothesis. These tests are useful for finding peaks with an observable change caused from the experiment design between different regions or experimental conditions.\cite{25877011}5.3.2 Discriminant AnalysisData reduction methods such as PCA or PLS are pre-processing steps to discriminant analysis. Together these analyses are commonly performed together and abbreviated as: PCA-DA or PLS-DA, respectively. Discriminant analysis is a statistical tool to assess the adequacy of a classification system. For any kind of discriminant analysis, the groups need to be assigned beforehand or in the case of PCA, preprocessed prior to discriminant analysis. Discriminant analysis is particularly useful in determining whether a set of variables is effective in predicting category membership. This is different from an ANOVA or multiple ANOVA, which is used to predict on or multiple continuous dependent variables by one or more independent categorical variable.\cite{26604989} 5.3.3 Biomarker TestsEven if statistical differences exist between two conditions for a single m/z, this does not necessarily mean that this m/z value can act as a biomarker to distinguish the two classes. For univariate biomarker analysis to confirm if a m/z can be used as a diagnostic test to distinguish two regions of interests, a receiver operator curve (ROC) analysis is performed. In ROC analysis, the true positive rate (sensitivity) is plotted in function of the false positive (specificity)\cite{20978390} \cite{20821157} \cite{16550707}. The area under the curve (AUC) in these plots can distinguish whether the m/z marker can be used for diagnostics. This is a test of accuracy, where an AUC value between .90-1 is excellent, .80-.90 is good, .70-.80 is fair, .60-.70 is poor, and .50-.60 is failed test. This test is used to discriminate the ability of a specific marker (m/z) to correctly classify groups of interest. MALDI imaging was used to reveal thymosin beta-4 as an independent biomarker in flash frozen colorectal cancer compared with normal using ClinPro Tools software to perform ROC analysis \cite{26556858}.However, often in biomarker discovery, one biomarker is not able to correctly classify groups with a high AUC for clinical analysis. In this case, multiple biomarkers (multiple m/z values) are used for analysis. This is known as multivariate analysis. Here, machine learning is used to look at multiple biomarkers to look for correlated structures in the mass spectra that also correlates with the target outcome. This multivariate analysis provides a single ROC curve that is derived from multiple biomarkers. Additionally, an indicator of how much each m/z contributes to the score from the resulting algorithm is calculated for each m/z value\cite{7628115} \cite{23054242}. For regression-based methods such as PLS, the importance of an m/z value is a direct result of the model’s loading vector. Additionally, colocalization of two individual m/z values in a tissue can be calculated in a correlation analysis to see how well m/z components of the multivariate analysis align based on special distributions\cite{18570456}. One problem for mass spectrometry imaging is salt adducts of the m/z values of interest are identified separately. Therefore, in biomarker analysis, it would be ideal to combine m/z values identifying to the same molecular compounds into a single peak for analysis For instance, two m/z values separated by 17mDa is indicative of the presence of that specific m/z plus a sodium ion. This can also happen for potassium salts, the loss of ammonia, the loss of water, oxidation of methionine, and other common modifications. This can complicate identification and statistical analysis as well as univariate and multivariate biomarker analysis. For MALDI, Alexandrov introduced a method called masses alignment which is used to group masses corresponding to a single peak and then represent them as one m/z value. This also reduces the size of the dataset, making computation and biological understanding of the data more attainable \cite{23176142}.5.3.4 Machine Learning AlgorithmsMachine learning is starting to play a larger role in developing algorithms to quantify relationships in mass spectrometry imaging and then using these identified data to make predictions for new data sets. First, data is converted from a population of profiles into a n by m data matrix, where n is individuals, and m is the biomolecule of interest. Following conversion, they can be analyzed using different algorithms that look for correlated structure in the measured data that also correlates with a target outcome.This is currently being implemented for automated decision making, modeling, and computer aided diagnosis. Supervised learning is being used to help the computer to identify patterns in the known categories. This can be done in two separate ways: classification and regression. Classification refers to decisions among a typically small and discrete set of choices (tumor vs. normal tissue), while regression refers to an estimation of possibly continuous-valued output variables (diagnosis of the severity of disease). Neuronal networks, support vector machine algorithms, recursive maximum margin criterion, and genetic algorithms build statistical models that use training data to perform to predict the classification of new data sets. This is commonly applied for tumor classification \cite{25750696}.\cite{27322705} 5.3.5 Complete data pipelines Because processing imaging data requires numerous different treatments than conventional LC-MS data, software with complete data analysis pipelines are useful for streamlining the entire data analysis process. While there are numerous open source and freely available software packages for processing data, functionality tends to be restricted and there typically aren’t export options for the data. A new MSI software package, SpectralAnalysis, strives to expand the reach of data processing by incorporating all processing steps from preprocessing to multivariate analysis, within a single package, allowing for the analysis of single experiments as well as large-scale experiments spanning multiple instruments and modalities \cite{27558772} . Improved data processing pipelines are also being developed in efforts to make full use of the spatial information unique to imaging experiments. One such pipeline, EXIMS, strives to reveal significant molecular distribution patterns by treating the dataset as a collection of intensity images for various m/z values. The process incorporates preprocessing, sliding window normalization, de-noising and contrast enhancement, spatial distribution-based peak-picking, and clustering of intensity images \cite{26063840}. This pipeline emphasizes the importance of special treatment for imaging data compared to LC-MS data. 5.4 Repositories Finally, data storage and sharing of the final results allow for the community to move forward and build upon the ever growing wealth of knowledge. In order to further drive this, imaging repositories are necessary for allowing researchers access to imaging data for comparison of results and for discovering new answers to biological questions. Previously, such repositories were difficult to implement due to the large requirement of space and computational powers, but technological advancements have allowed for the emergence of at least one such repository\cite{25542566}, with the promise of more becoming available in the near future. Currently the European project METASPACE for Bioinformatics for spatial metabolomics developed on online engine based on big-data technologies that automatically translates millions of ion images to molecular annotations. The estimated completion time for this project is June 2018. 6. Multi-modal Imaging Systems MSI is useful for analyzing the spatial distributions of small molecules, lipids, peptides, proteins, and glycans. The combination of MSI with other imaging modalities help to multiplex imaging analyses into a comprehensive analysis to answer biological questions that could not be otherwise not be analyzed with a single imaging modality. Multimodal technologies are very commonly implemented in diagnostic imaging techniques and the concept has been expanded into MSI analysis pipelines. MSI can serve as an essential complement for untargeted chemical analysis coupled with other imaging modalities. Because MSI has high chemical specificity, but lower spatial resolution compared with other imaging modalities, it is typically combined with modalities that complement these features. MSI is combined with imaging modalities that are low in chemical specificity, but high in spatial resolution or tissue structural information. The results from combining complementary imaging modalities is greater than the sum of its parts\cite{26070717}. Multi-modal imaging can be approached by either acquiring images at different times (asynchrosonous), where the images are fused in data processing step, or by simultaneously acquiring images (synchronous) and merging them during data acquisiton step \cite{20812286} . Asynchronous post-processing can present some difficulties which arise from the positioning of the same samples between different scans at different times, which can cause difficulties in co-registering images for analysis \cite{Meyer_2013}. Co-registration is especially difficult if data acquisitions are not acquired at the same spatial resolutions, however advances in computational annotation help to improve image analysis \cite{Eliceiri_2012}. Image co-registration can be achieved by aligning known regions of interest, using calibration points to perform a rigid regression, or by selecting a variety of points to perform moving least squares registration \cite{Huhdanpaa_2014}. Additionally, different imaging platforms have different sample preparation protocols, which can cause interference into different imaging modalities. Synchronous imaging is advantageous because consistency is achieved in both time and space, however combining instrumentation to accommodate synchronous acquisitions can required advanced skill and can be very expensive, especially for mass spectrometry instrumentation. The next steps for multimodal imaging involve integrating quantitative information from multiple existing functional modalities to create composites of not just two types of modalities, but integrating three, four, or even five imaging modalities into single data analysis pipeline. Additionally, advances in technology and instrumentation will allow for synchronous integration to be expanded for multiple imaging modalities.6.1 Microscopy Multi-ModalityMSI is often combined with microscopy to provide high resolution morphological and structural information, while MSI is used to visualize and identify distributions of specific molecules. Additionally, Plas et al. describes a method for using microscopy data to fuse with mass spectrometry imaging data to enable prediction of a molecular distribution both at high chemical specificity and a high spatial resolution \cite{Van_de_Plas_2015}. This is done post data acquisition using the microscopy data to sharpen and perform out-of-sample prediction \citet*{25707028}. Here, we describe the use of light and fluorescent microscopy to evaluate tissue structure and specific markers. Microscopy is the most common multi-modal system currently paired with mass spectrometry imaging and is particularly useful for identifying regions of interest . 6.1.1 HistologyAlthough tissue sections used for MSI can be scanned to produce an structural overlay, important structural information on the cellular level is obtained from histological analysis of a sample using light microscopy that can be important for region of interest analysis of MSI data. Light microscopy is used to see details and enlarged portions of a tissue section, which is then captured with a camera. Samples are stained with a specific dye to stain tissue structures. Histology overlay is the most common multimodal imaging system combined with mass spectrometry imaging currently applied in the current literature \cite{26216958} \cite{25488653} \cite{20170166}. The most traditional stain hematoxylin and eosin (H&E) stain distinguishes nucleic acids in blue and proteins in red. This allows the pathologist to visualize the difference between cells from the surrounding extracellular matrix\cite{21356829}. Other commonly used stains include Masson's trichrome stain used for connective tissue, Alcian Blue for mucins, and Periodic acid-Schiff reactions used for staining carbohydrate rich tissue region \cite{4184780}. Trained pathologists used stained slides to identify different disease states of the tissues. Tissue morphology, cell structure, and staining distribution is analyzed by pathologists to stratify patient specimens and provide diagnostic indices for the patient \cite{28416487} \cite{28117928}