Challenges in Spatial Metabolomics and Proteomics for Functional Tissue Unit and Single-Cell Resolution

SUMMARY

In the last decade, advanced developments of mass spectrometry–based assays have made spatial measurements of hundreds of metabolites and thousands of proteins not only possible, but routine. The information obtained from such mass spectrometry imaging experiments traces metabolic events and helps decipher feedback loops across anatomical regions, connecting genetic and metabolic networks that define phenotypes. Herein we overview developments in the field over the past decade, highlighting several case studies demonstrating direct measurement of metabolites, proteins, and proteoforms from thinly sliced tissues at the level of functional tissue units, approaching single-cell levels. Much of this work is feasible due to multidisciplinary team science, and we offer brief perspectives on paths forward and the challenges that persist with adoption and application of these spatial omics techniques at the single-cell level on mammalian kidneys. Data analysis and reanalysis still pose issues that plague spatial omics, but many mass spectrometry imaging platforms are commercially available. With greater harmonization across platforms and rigorous quality control, greater adoption of these platforms will undoubtedly provide major insights in complex diseases.

DEVELOPMENTS LEADING TO MODERN SPATIAL MASS SPECTROMETRY

Exponential growth of analytical instrumentation has made once impossible experiments not only feasible but routine. This is exemplified by rapid development, deployment, and diversification of measurement science. Techniques such as mass spectrometry (MS) have been bridged from once specialized assay performed only by analytical chemists into a booming multidisciplinary field with modern MS-based omics commonly performed in many translational laboratories.¹ Traditional pathology confirms morphological indicators of disease employing immunoassays to screen for various biomarkers. However, modern MS-based metabolomics and proteomics have been shown as robust alternatives for confirmatory results and novel insights.² Ultimately, MS-based assays are untargeted, multiplexed, highly selective, highly sensitive, and do not significantly underestimate or overestimate concentrations of biomolecules like common immunoassays (e.g., ELISA).² Previous reviews centered around traditional metabolomics and proteomics applied to acute kidney injury.³ However, additional insight can be gained when the localization of these biomolecules occurs within spatial analyses approaching the realm of singular cells.

The availability of clinical samples and strong potential of spatial multiomics empowers the next generation of spatial biomarker discovery and application toward precision medicine with molecular pathology and cellular phenotyping on the cellular scale.⁴ Additionally, broad dissemination of research outcomes are paramount to further adoption and broad use of spatial omics,⁵ although however, data analysis and reanalysis are now becoming a greater challenge than acquiring spatial omics datasets. Considering technologies to complete spatially resolved analyses directly from tissues have been initiated several decades after the first adoption of MS for clinical measurements,^{2, 6} the progress in modern instrumentation for preclinical and clinical imaging over the past decade is astounding. ⁶ Herein, recent advancements in MS imaging (MSI) and laser capture microdissection (LCM)-based spatial MS within kidney research are highlighted. Each methodology includes several steps after surgical resection or dissection of the tissue leading to quantitative liquid chromatography (LC)- MS and/or generation of ion images from MSI data, ultimately at the single-cell level (Fig. 1). LCM and MSI can identify hundreds if not thousands of metabolites, lipids, proteins, or proteoforms,⁷ and this level of coverage provides deep functional pathway analyses and access to gene ontology enrichment, which can help define and contextualize the analyzed regions, even though trends may begin to appear for the need for larger centers and multidisciplinary teams for better biological analyses.

Figure 1.

The sample preparation process for spatial omics, where (A) surgically resected or dissected tissues or biopsies with optional embedding are thinly sectioned by a cryostat and mounted onto various slides depending on the assay. Commonly for MSI (B), optically transparent and conductive or nonconductive glass slides receive thin cyrosections, which enables post-MSI histology. Or, in the case of LCM-based omics (C), sections are mounted onto polymer membrane slides or other alternatives, and histology or other microscopy guides LCM. After a variety of sample preparation steps geared toward detection of metabolites or proteins, MS-based data collection occurs with total ion current (TIC), intact mass (MS1), and tandem MS (MS2) outputs shown. Subsequent bioinformatics can produce and visualize qualitative or quantitative outputs. Subsequent ion images can be overlaid with histology, immunohistochemistry (IHC), or affinity-based immunofluorescence (IF) or any other spatial omic analysis. The visualizations shown here are based upon data generated by the HuBMAP Program. Zemaitis, Zhou, et al. (2023) “Tandem MALDI-MSI and LCM-LCMSMS of Healthy Human Kidney.” doi.org/10.35079/HBM334.DQWS.354 and doi.org/10.35079/HBM666.BBVZ.767, recently published.⁸

CURRENT METABOLOMIC APPROACHES WITHIN KIDNEY RESEARCH

The central dogma has been the fundamental ground truth of modern biomolecular discoveries, but this view of transcriptional regulation of proteins and metabolites oversimplifies complex underlying biology into rigid processes ending with catabolic and metabolic events. In fact, many transcripts have poor correlation to downstream biomolecules, and many metabolites have actually been found to actively modulate enzymatic processes⁹ and carrier proteins, and induce or regulate post-translational modifications (PTMs) and resulting proteoforms.¹⁰ Nevertheless, many researchers still utilize transcripts to broadly predict and infer metabolite abundance. While simplified notions are helpful for comprehension, enigmatic views of the proteome have also been in existence for decades.¹¹ Known biology can only account for a small portion of detected heterogeneity and flux. Tracing enzymatic paths for metabolic processing remains one of the main challenges of spatial approaches, as well as the need for more comprehensive and confident annotations to correlate spatial patterns of molecules to cellular type and molecular phenotypes.¹²

Within the realm of spatial metabolomics on mammalian kidneys, matrix-assisted laser desorption/ionization (MALDI)-MSI has been heavily utilized for the visualization of small molecules directly from tissues. Not exclusively used across the field, as there are several ambient ionization sources, but MALDI-MSI has reached the level of technical maturity that enables translational application.¹³ Many studies also combine multiple modes of MSI, spectroscopy, and histopathology for orthogonal validation, complementarity, and greater coverage,¹⁴ leading to novel markers even within well-characterized metabolites, and new preclinical and clinical assays as shown in an exemplary case within human kidney for adenine.¹⁵ Discovery-based MALDI-MSI detected increased abundance of adenine ex vivo on thin tissue sections (Fig. 2A). After much validation, this MSI-informed discovery resulted in translation of MS-based assays to the clinic for monitoring progression of diabetic kidney disease (DKD).¹⁶ While the resolution of the spatial omics performed was not at cellular levels, the kidney has many distinct anatomical regions and basic functional tissue units (FTUs) that are well defined by MSI at the scale of cellular neighborhoods. MALDI-MSI at these levels is a broadly enabling technology for investigating interactions within and between FTUs.

Figure 2.

(A) Tandem autofluorescence (AF) microscopy, spatial metabolomics by MALDI-MSI, and histology through periodic acid–Schiff (PAS) staining shows the anatomical regions where the metabolite adenine is highly abundant with diabetic kidney disease (DKD), demonstrating discovery-based imagery for biomarker analyses (bottom) relative to a healthy control (top). (B) Spatial metabolomics by MALDI-MSI was performed within a bilateral renal ischemia–reperfusion injury (bIRI) model analyzing cell type–specific repair where isotopic tracing in the central carbon was performed, enabling tracing of active metabolism through a heavy isotope labeled experiments. (A) Adapted with permissions from Figure 3 of Sharma et al.¹⁶ Copyright 2023 The Authors. (B) Adapted with permission from Figure 3 of Wang et al.¹⁷ Copyright 2022 The Authors.

Further extending these untargeted analyses beyond biomarkers, functional metabolomics can be completed using isotopic labeled metabolites to trace active metabolism. These pulse chase and stable isotope labeling experiments have been shown to inform on biosynthetic pathways and energy dynamics on a temporal scale, identifying a variety of labeled energy-related metabolites (i.e., ADP, ATP, NADH, NADPH, etc.) being actively produced throughout the cortex and medulla of the kidney.¹⁸ Down to the cellular scale with 5-μm spatial resolution, application of similar methods highlighted the potential to identify baseline metabolism of homeostatic processes across the diverse microenvironments in the kidney, enabling detection of hexoses, citric acid cycle (TCA) intermediates, and lipids (Fig. 2B).¹⁷ These case studies highlight the potential to trace active metabolic processes within FTUs and cellular neighborhoods, providing the ability to biochemically define largely unknown functional gradients of metabolites induced by and across disease microenvironments. Just recently, these methods have been applied to trace the metabolism of kidneys ex vivo perfused with media simulating storage prior to transplant. The presence of 13C-labeled TCA metabolites showed active metabolism beyond day 4 with perfusion of preservation fluid.¹⁹ This represents a milestone within kidney transplantation research, whereby other organs have also been successfully transplanted after similar treatment, enabling precious donor organs to be preserved rather than discarded.²⁰

While spatial metabolomics can offer vast insight into the active metabolism, probing cell-type and phenotype profiles, there are several limitations that are paramount to overcome. Isomeric complexity plagues spatial metabolomics as exemplified by bulking various isomers (i.e., glucose, galactose, fructose, etc.) under the sum annotation (e.g., hexoses).¹⁷ However, the use of ion mobility spectrometry (IMS), specifically via commercial trapped ion mobility spectrometry (TIMS)–enabled instrumentation, has become a powerful tool to resolve isomeric complexity.²¹ Additionally, measuring collisional cross-sections offers orthogonal means for more confident metabolite annotations.²² While commercial IMS-MS instrument platforms offer limited mass resolving power (<40k), which dictates the ability to resolve near isobars in the MS1 domain, at these levels millions of events are unresolved at the MS1 level (Fig. 3C). ^{23, 24} This is where application of in situ fragmentation (MS2) has been shown as a highly promising alternative for ultimate confidence in MSI annotations (Figure 3B). ²⁵ Ultimately, in situ MS2 at each pixel with orthogonal ultra-high mass resolving power analyses can resolve all levels of complexity, in turn enabling the highest levels of confidence for spatial metabolomics (Fig. 3A). ²⁶ Until a single instrument platform is developed that enables high resolving power (>200k at m/z 800) with in situ MS2 analysis at the pixel level, we must exercise caution and orthogonally validate all findings from our spatial omics with more traditional methods.

Figure 3.

(A) A schema bridging the level of confidence from mass spectral annotations from level 5, with formula-level assignments based on accurate mass measurements, to level 2 with fragmentation data and orthogonal measurements that can be extended to level 1 annotations with reference standards. (B) Overview of pixel-wise in situ tandem MS measurements using ion mobility separations that permit level 2 identifications from MALDI-MSI datasets. (C) Two MSI modalities showing differences within the levels of ambiguity that can be resolved for level 3 structural annotations based on molecular formula with ultra-high mass resolving power. If this level of resolving power is not used, overlapping signals in mass spectra will be merged with ion images and subsequent statistics and outcomes impacted. Reprinted with permissions from Figure 3 in Schrimpe-Rutledge et al.²⁶ Copyright 2016 American Society for Mass Spectrometry. (B) Reprinted with permissions from Figure 2 in Heukeroth et al.²⁵ Copyright 2023 The Authors. (C) Reprinted with permissions from Figure 3 in Vandergrift et al²³. Copyright 2023 American Chemical Society.

Regardless, these instrument and method developments have made cellular-scale imagery popular for discovery-based efforts, even though we still face the issue of poor dynamic range and limited metabolic coverage. Many portions of the metabolome are simply inaccessible to MSI instrumentation. The mammalian metabolome itself can be estimated at a depth of millions of unique metabolites without considering isomers, and from the inception of the Human Metabolome Database it has grown from roughly 2,000 metabolites²⁷ to more than 215,000 over roughly two decades.²⁸ To bridge this, researchers have turned to on-target or on-tissue chemical derivatization (OTCD), which allows for increased sensitivity and selectivity.²⁹ Here, reactive agents are applied directly to samples, incubated, and subsequently analyzed using specific chemistry to preferentially enhance ionization of specific metabolite classes. While OTCD is in its infancy, selectivity and differentiation of carbonyls have become routine,³⁰ targeting specific metabolites (e.g., vitamin D); subsequent derivatives can now be visualized,³¹ and visualization of many biogenic amines and neurotransmitters is now feasible.³² Although, these methods often require rigorous optimization, they can provide vast insight into previously dark portions of the metabolome and enable more robust quantitation of therapeutics and metabolic products.³³ When bridging to smaller spatial resolutions, this increase in sensitivity is essential. Finer resolution results in a quadratic loss of sensitivity: for example, going from 10-μm to 5-μm spatial resolution results in a fourfold reduction in ablated area, and approaching 600-nm resolution is roughly 278 times smaller. This correlates with losses in sensitivity, and to date, supplemental methods with postionization have been required to detect sufficient signal-to-noise for subcellular-resolution MALDI-MSI.³⁴ Furthermore, with cellular- and subcellular-scale probes, artifacts of tissue preparation such as tears or folds or delocalization from matrix application or sample embedding are critical considerations. Considering 5-μm spatial resolution is commercially viable on some MALDI-enabled instruments as of this writing, sample preparation concerns become a major issue among a host of batch effects.³⁵

CURRENT APPROACHES FOR PROFILING THE KIDNEY PROTEOME

MSI instrumentation advancements have been the primary driver of spatial metabolomics, but traditional LC-MS–based metabolomics has lagged behind proteomics in terms of sensitivity and metabolome coverage.³⁶ Landmark efforts such as the Human Proteome Project (HPP) from the Human Proteome Organization have credibly detected 93% of all theoretical proteins.³⁷ MS-based bottom-up proteomics (BUP), which uses proteolytic enzymes to cleave large intact proteins into smaller digested peptides, was used extensively in the HPP: 94.9% of the credibly detected 18,397 human proteins used MS-based proteomics. This has resulted in many LC-MS approaches being able to quantify over 10,000 proteins per experiment. Unfortunately, several million additional proteoforms are predicted to originate from these canonical proteins, whether these proteoforms result from alternative splicing, polymorphisms, or PTMs.³⁸ Proteoforms offer a more direct view into the function and have been broadly viewed as better biomarkers than proteins, as exemplified in the Blood Proteoform Atlas.³⁹ Due to many limitations, including a lower technical maturity of MS instrumentation for detection of intact proteins within top–down proteomics (TDP), spatial proteomics has been centered around BUP often integrated with LCM in spite of the great promise for TDP.^{40, 41}

These efforts have focused on unraveling the root causes of cell atrophy, senescence, and apoptosis where dozens of phenotypic states can be detected via proteomic fingerprints in various cell types. The current state-of-the art tissue-based spatial proteomics can quantify roughly a thousand proteins from a single cell dissected from tissues,⁴² although LCM workflows are at a reduced throughput of several to several dozen dissections a day. MSI plays a niche, but vital role in spatial proteomics workflows where LCM-BUP analyses have dominated the field, especially for the analyses of clinically relevant samples. This is largely due to the higher proteome coverage in comparison to other spatial proteomics methods, with specificity to determine subcellular function and ability to interrogate extracellular matrix with some level of proteoform specificity.⁴³ Due to many limitations, challenges exist in adopting bespoke LCM workflows, and efforts within large consortia have been focused on dissection of whole FTUs (e.g., glomeruli, tubules).^{5, 44, 45} Seminal publications have demonstrated the potential of low-input proteomics focusing on FTUs from tissues, where nanodroplet preparations (e.g., nanoPOTS) reduce sample losses and increase proteomic coverage.⁴⁶ Others have contributed vastly to LCM-based tissue workflows as well, where machine learning has enabled automated segmentation and LCM of cells to increase the throughput for deep spatial proteomics (Fig. 4).⁴¹

Figure 4.

(A) A spider projection of various spatial omics methods highlighting main characteristics of spatial proteomics in comparison. (B) Proteoform imaging using an ambient ionization probe that was able to differentiate a point mutation within GSTA2 and subsequent ion images. (C) Advanced development of laser ablation with a custom-deep

These research and development efforts have enabled robust processing at the near-cellular level with derived methods such as microdroplet processing in one pot for trace samples (microPOTS), where upon LCM of glomerular and proximal tubular regions 67 and 25 proteins were found to be differentially abundant, respectively.⁴⁷ This work confirmed cellular markers within the kidney known by MS-based proteomics, but many of these approaches can be considered beyond routine requiring specialized instrumentation and experienced staff. Broad dissemination efforts have sped up adoption, and collaborative efforts have been demonstrated across a wide array of systems. Recently, LCM-microPOTS and MALDI-MSI efforts were uniquely leveraged for spatial TDP, integrating broad field-of-view MALDI-MSI and dissection of FTUs for TDP to inspect homeostatic processes within healthy human kidney.⁴⁸ Despite the fact that previous microPOTS-BUP efforts identified roughly fivefold greater number of unique proteins from severalfold less tissue, TDP enabled the detection of unique events such as (combinatorial) PTMs and truncations, potentially with different functional outcomes. Where comparing dissected tubules with enriched proteoforms (Fig. 4), several were mitochondrial proteoforms (e.g., HSPE1, ATP5F1E, UQCRFS1, ATP5ME, and ATP5IF1), and while these were full-length proteoforms, HSPE1 and M(Ox) HSPE1 proteoforms were found with a log2 fold change greater than 3.8.⁴² Only 115 proteoforms could be quantified, versus >3,000 protein IDs from BUP, however this provides intriguing insight into healthy kidney metabolism and establishes baselines for FTUs for diseased TDP experiments. The expansion of LCM methodologies has a bright future for spatial proteomics within the kidney, especially considering that proteoforms are more accurate biomarkers of disease processes.³⁸

UV laser capture microscope advancing traditional cut-and-catapult LCM workflows in throughput while maintaining thousands of proteins at a subcellular level. (D) Advanced pipelines for LCM of glomeruli (*), tubules (**), and medullary rays (#) with subsequent top–down proteomics to capture proteoform distributions that are paired with MALDI-MSI. (A) Reprinted with permissions from Figure 4 of Mund et al.⁴³ with previous adaption. Copyright 2022 Elsevier Inc. (B) Reprinted with permissions from Figure 5 of Su et al.⁴⁹ Copyright 2022 The Authors. (C) Reprinted with permissions from Figure 3 of Xiang et al.⁴² Copyright 2023 The Authors. (D) Adapted with permissions from Figure 2 and 3 of Zemaitis et al⁸. Copyright 2025 The Authors.

Furthermore, the unique proteoforms by TDP were not shared with the BUP datasets, highlighting the complementarity and motivation to further pursue proteoforms, especially within the case of disease. While not as routine as LCM-BUP, MSI provides the path to much broader fields of view with fine spatial resolution for proteoform informed imaging. This does come at the cost of proteomic coverage, but viable information can be identified. For example, intact histone signatures (e.g., histone H4 N-Ac/ Ac and N-Ac/K20me2) were found to be differentially regulated throughout tumor margins of a renal cell carcinoma (RCC) and within chronic kidney injury with tubular atrophy.⁵⁰ These findings are consistent through multiple instances of histones being impacted by disease processes, just as was shown within microvascular invasions of hepatocellular carcinoma (HCC).⁵¹ Detecting such histone proteoforms at the cellular level by MALDI-MSI provides unique insight into epigenetics through histone code.⁵² Disease processes such as the cancers listed above are critically influenced histone H4 K20me2 proteoform localizations, where K20me2 is involved in DNA replication and DNA damage repair just as H4 K20me1 is.⁵³ BUP simply cannot inform on the complexity of histone proteoforms, and MSI can be used to contextualize the confident LCM-based proteomics. In recent works, this also has been shown to be incredibly valuable for functional metabolomics with the detection of enzymes that mediate metabolic processes, where microbial and fungal enzymes were colocalized with metabolic products showing processing of fuconate by L-fuconate dehydratase or glucose by hexokinases.⁵⁴ This level of depth is routinely missing from metabolic pathway analyses within sole application of MSI to spatial metabolomics.

Concurrently, focusing on intact proteoform signatures offers a high level of specificity, which eventually can be combined with spatial metabolomics approaches for a holistic epigenetic view of metabolic processes.^{55, 56} Since BUP approaches cannot detect combinatorial PTMs,⁵⁷ further expansion of instrumentation and methods for both TDP and middle-down proteomics is needed to usher in a new biological insight. One such approach is middle down proteomics (MDP), which utilizes highly specific enzymes to create larger proteolytic peptides, this enables the detection and quantification of multiple PTMs events (e.g., phosphorylation).⁵⁸ In similar efforts, new approaches for intact protein discovery from tissues have been reported and have greatly expanded the detectable mass range, enabling the visualization of glycolysis at the proteomics level.⁴⁹ The nanospray desorption electrospray (nanoDESI) probe used within this study was within the realm of FTU spatial resolution, but coverage of several hundred proteoforms up to 70 kDa was observed with visualization of several dozen unique PTMs and gene byproducts.⁴⁹ Both, nanoDESI and liquid surface extraction (LESA) have been shown to detect native protein–protein and protein–ligand complexes within thinly sliced tissues, albeit with more limited spatial resolution than MALDI studies.^{59, 60} Advancements in these ambient ionization techniques for MSI applications have come far within the past decade, eventually bridging down to the single-cell realm,⁶¹ with promise for reproducible and facile imagery in the future with ease of use similar to commercial MALDI-MSI instrumentation.

CHALLENGES TO BE OVERCOME FOR PRACTICAL ADOPTION

MS-based spatial omics approaches, including both LCM and MSI for metabolomics and proteomics, have been deployed in landmark consortia such as the Human BioMolecular Atlas Program (HuBMAP),⁶² Senescence Research Network (SenNet), and the Kidney Precision Medicine Project (KPMP).⁶³ However, there are many challenges when it comes to various quality control measures and batch effects across large-scale efforts.³⁵ This puts a large strain on experimental design especially for MSI with several opportunities to introduce a variety of batch effects,³⁵ and through these efforts the researchers have come to terms with the need not only for quality assurance and control, but also for validating and benchmarking these methods. Given that even validated organ-mapping antibody panels are distinctly biased to tissue preparation and the organ itself,⁶⁴ there is a need for all analytical methods to be thoroughly validated across large cohorts, not even accounting for biological variability (e.g., gender, age, ethnicity). Historically, this has meant spatial omics studies often include low replication (technical and experimental), which could encompass large biological noise. Currently, this means much of the effort within the field is going toward advancing the technology for sensitivity and making these methods more approachable with greater throughput. While there is no solution currently, a recently introduced technique for mass microscopy has enabled the generation of submicron gigapixel images within minutes in comparison to several hours of traditional instrumentation.⁶⁵ Whereas current-generation MSI instrumentation routinely operates between 0.3 and 40 Hz (instead of the 600 kHz recently demonstrated), we do see a path forward to noncompromising imagery without a sacrifice of spatial resolution, spectral resolution, or field of view while maintaining throughput.

As previously mentioned, the cellular domain has just recently been broached with spatial omics, where advanced approaches for MALDI have gone submicron,³⁴ but the imaging of biomolecules has been and will remain an interdisciplinary challenge requiring advanced expertise.⁶⁶ The assignment of molecular formula and putative annotations has been facilitated for small molecules with freely available tools such as METASPACE,⁶⁷ but the issue of primary database generation is becoming a more systemic issue as modern instrumentation can now routinely detect several dozen classes of exemplary lipids and hundreds to thousands of species per sample. Nomenclature and primary reference spectra are at various states of validation and accuracy.⁶⁸ Annotations of proteins and their proteoforms are also plagued by the issue of accurate identifications, since the outcomes of MSI are directly linked to the confidence within these annotations and all MSI methods are currently reliant on supplemental methods for confidence and accurate label transfers.⁶⁹ However, these technical and bioinformatic challenges are poised to be addressed over the coming decades, just as the needs for modern MS-based instrumentation have been addressed in the past several decades.

OPPORTUNITIES FOR TRANSLATIONAL RESEARCH STARTING WITH COLLABORATION

We have highlighted several exemplary case studies related to the kidney, spatial omics is a burgeoning field but is still lagging behind sequencing approaches while aggressively breaking down barriers to enter the clinical fields. Both spatial metabolomics and proteomics have had several success stories from method development to preclinical applications, with a few methods on the path toward clinical adoption. For example, even high-resolution accurate mass MS platforms are being retrofitted into portable formats for use within intraoperative procedures.⁷⁰ Much of this success has come from the fostering of interdisciplinary collaborations, leading to many key breakthroughs, including the use of MS-based approaches within a hospital setting.⁷¹ While cellular and near-cellular probes have been deployed to ex vivo tissues resected from patients, these analyses can still be considered too specialized in comparison to traditional histopathology. Ultimately, one solution to this is strengthening the relationships and collaborations between clinicians with access to surgical resected tissues and MS practitioners to realize clinical applications. When considering the current state of the art for spatial metabolomics or proteomics, the upfront investments are significant with dedicated personnel and specialized equipment for sample processing, not to mention the instrumentation and expertise to fine-tune them. Thus, the path forward relies on interdisciplinary study and team science, and we strongly urge clinicians and analytical scientists to form active collaborations for furthering the field of preclinical and clinical spatial metabolomics and proteomics.