Multiplexed Targeted Spatial Mass Spectrometry Imaging Assays to monitor lipids and NAD+ metabolites in CD38 knockout mice exhibiting improved metabolism

Multiplexed Targeted Spatial Mass Spectrometry Imaging Assays to monitor lipids and NAD+ metabolites in CD38 knockout mice exhibiting improved metabolism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multiplexed Targeted Spatial Mass Spectrometry Imaging Assays to monitor lipids and NAD + metabolites in CD38 knockout mice exhibiting improved metabolism Birgit Schilling, Charles Schurman, Joanna Bons, Prasanna Kumaar, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6743284/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mass spectrometry imaging (MSI) is a rapidly advancing technology that provides mapping of the spatial molecular landscape of tissues for a variety of analytes. Matrix-assisted laser desorption/ionization (MALDI)-MSI is commonly employed, however, confident in situ identification and accurate quantification of analytes remain challenging. We present a novel imaging methodology combining trapped ion mobility spectrometry (TIMS)-based parallel accumulation-serial fragmentation (PASEF) with MALDI ionization for targeted imaging parallel reaction monitoring (iprm-PASEF). We investigated the spatial distribution of lipids and metabolites in liver tissues from wild-type and CD38 knockout mice (CD38 −/− ). CD38, an enzyme involved in nicotinamide adenine dinucleotide (NAD⁺) metabolism, significantly influences liver metabolic function and contributes to age-related NAD⁺ decline. Although CD38 deletion previously was linked to improved metabolic phenotypes, the underlying spatial metabolic mechanisms are poorly understood. The spatial iprm-PASEF workflow enabled confident identification and differentiation of lipid isomers at the MS2 fragment ion level and revealed increased NAD + and decreased adenosine diphosphate ribose (ADPR), a by-product of NAD + hydrolysis, in CD38 −/− livers. This approach provided confident, specific, and robust MS2-based identification and quantification of fragment ions in spatial MSI experiments. Additionally, the innovative iprm-PASEF opens unprecedented opportunities for spatial metabolomics and lipidomics, offering spatially resolved insights into molecular mechanisms. Biological sciences/Biological techniques/Mass spectrometry Biological sciences/Biochemistry/Lipids/Phospholipids Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mass spectrometry imaging (MSI) is a rapidly advancing application of mass spectrometry (MS) allowing for the unbiased spatial assessment of a variety of analytes and biological samples. MSI promises the capability to image thousands of molecules, such as metabolites, lipids, peptides, proteins, and glycans in tissues, while preserving their original spatial tissue localization. 1 This allows for the combination of the power, sensitivity, and specificity of mass spectrometry with spatial molecular details that can reveal novel biological regulation. Access to spatially resolved molecular features (proteins, glycans, lipids and metabolites) is instrumental to understand high tissue heterogeneity with localized hot spots of tissue damage, specifically during aging, (age-related) diseases and clinical applications. Using global proteomics/glycomics/lipidomics or metabolomics these spatial features are very challenging to capture, and indeed, often remain undetected or non-significant in ‘bulk-style’ analyses. On the other hand, sophisticated applications of spatial MS Imaging exist with multiple sources and ionization strategies, such as desorption electrospray ionization (DESI), laser ablation electrospray ionization (LAESI), liquid extraction surface analysis (LESA), liquid-micro junction-surface sampling probe (LMJ-SSP), infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI), and nanoscale secondary ion mass spectrometry (NanoSIMS). 2 , 3 Another popular approach for MSI is matrix-assisted laser desorption/ionization (MALDI)-MSI which utilizes a deposited matrix-enhanced analyte ionization in place of laser ablation. MALDI-MSI allows for the discrete collection of pixel-specific mass spectra that are then assembled to generate a visual representation of the molecular species throughout the imaged region. Several of the existing imaging techniques are limited to MS1 level analysis and do not utilize precursor ion fragmentation to increase identification confidence or improved quantification at the fragment ion (MS2) level. Or, in other cases MS/MS spatial imaging is often limited to selection of one analyte for MS/MS per acquisition covering a spatial region of interest (ROI). Powerful applications of MSI, such as DESI mass spectrometry, use a liquid-based probe to collect analytes in place and then analyze them by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a spatially resolved manner. 4 However, DESI-MS may present challenges with humidity and extraction efficiencies, as well as present resolution challenges that stem from the continuous solvent flow and the size of the liquid bridge formed between the tissue sample and the DESI probe. 5 Herein, we detail a novel workflow for MALDI-MSI that employs targeted imaging parallel reaction monitoring (iprm) in combination with PASEF that extends MALDI-MSI analysis from the MS1 level to the tandem mass spectrometry (MS2) dimension for in situ analysis of lipids and metabolites (or other analytes) in biological samples. Importantly, multiple MS/MS spectra can be acquired using iprm-PASEF in a single imaging tissue MSI workflow enabling multiplexed and targeted MS2-based spatial strategies. Parallel reaction monitoring (PRM) is a mass spectrometry technique used for the quantification and identification of targeted analytes in complex biological samples. 6 , 7 PRM is valued for its high sensitivity, specificity, and accuracy in proteomic studies. For high-resolution PRM, precursor ions (at specific m/z and retention times) are isolated in a targeted way and fragmented. The resulting fragment ions were subsequently analyzed with high-resolution MS detectors, such as Orbitraps or time-of-flight detectors (Q-TOF) 7 , 8 . This allows for multiplexed simultaneous analysis of all fragment ions with high resolution and high accuracy (post-acquisition), in contrast to low-resolution single reaction monitoring (SRM) or multiple reaction monitoring (MRM) which typically require extensive optimization of Q1/Q3 transitions 9 , 10 . Due to the high resolution and post-acquisition refinement of fragment ion selection for quantification, PRM-MS generally is not as prone to interferences, and it is particularly efficient for fast developments of multiplexed assays. The innovative, spatial imaging parallel reaction monitoring (iprm) for MALDI-MSI, presented in this study, uses trapped ion mobility spectrometry (TIMS) to separate precursor ions based on their collisional cross section (CCS) values 11 , 12 . The addition of ion mobility separation to MALDI-MSI provides an orthogonal dimension of analyte separation, reduction of complexity and specificity to MSI experiments. The TIMS dimension allows for another level of isolation of precursor ions (analytes), specifically differentiating isomeric lipids/metabolites (with identical m/z but different CCS), in addition to the isolation based on the analyte m/z only prior to the spatial tandem mass spectrometry (MS/MS) imaging. NAD + (nicotinamide adenine dinucleotide) metabolism is of key relevance for maintaining cellular health, playing crucial roles in energy metabolism, DNA repair, transcriptional regulation, immune function and inflammation. 13 , 14 Many clinical conditions implicate dysregulated NAD + metabolism, including neurodegeneration, cancer, immune disorders, and cardio and metabolic diseases. 15 – 17 Aging and age-related diseases and cellular conditions, such as cellular senescence, are also associated with declines in NAD + and disruptions in NAD + -related metabolic processes, positioning NAD + as a significant target for biomedical research. 17 , 18 In the liver, reduced NAD + levels have been associated with non-alcoholic fatty liver disease (NAFLD) and other age-related metabolic dysfunctions. 19 CD38, is a potent enzymatic consumer of NAD + , and shows increased expression with age in several tissues. 13 , 18 , 20 – 22 In liver tissue, CD38 is predominantly expressed in Kupffer cells, the resident macrophages of the liver sinusoids, where its activity drives cellular senescence and inflammation with aging. 23 Thus, CD38 is considered as a major driver of the age-related NAD + decline. Notably, CD38 knockout mice (CD38 −/− ) exhibit higher mitochondrial NAD + levels in the liver and resistance to age-related NAD + decline, suggesting improved metabolic regulation and protection against aging-associated metabolic deterioration. 20 Additionally, NAD + supplementation has emerged as a promising therapeutic strategy to enhance cellular function and mitigate age-related decline. 24 , 25 Given the importance of hepatic NAD + metabolism in systemic metabolic regulation, understanding how NAD⁺ and its related metabolites are spatially distributed within the liver could have significant therapeutic implications. We utilized our optimized iprm-PASEF assays to spatially map and precisely quantify lipids, NAD⁺, and related metabolites in liver tissues from wild-type (WT) and CD38 knockout (CD38⁻/⁻) mice. Here, we present our advanced multiplexed iprm-PASEF methodology, showcasing its capability for robust spatial MS imaging, identification, and accurate quantification of lipids and metabolites directly within liver tissue sections. Results Fresh livers from three male, 18-month-old CD38 −/− and control (WT) mice were collected, flash frozen, sectioned onto glass slides and prepared for MS imaging with the Bruker timsTOF fleX MALDI-2 mass spectrometer. CD38 −/− animals were chosen for the spatial profiling of lipids and metabolites ( Fig. 1 A ) , due to their well-characterized increase in hepatic NAD + levels with age compared to control (WT) counterparts 20 . Samples were first subjected to unbiased MS1 imaging in both negative and positive ionization modes for survey scans of the whole tissue cross-section. Preliminary identification of interesting candidate MS1 precursor ions, based on accurate m/z, isotope pattern, and ion mobility values (1/K 0 ), was completed with the T-ReX 3 feature finding algorithm in SCiLS Lab, and features were annotated directly in SCiLS by connecting to the MetaboScape 2025 REST API feature ( Fig. 1 B ) . To prioritize, candidate precursor ions that showed a predictive score difference of > 0.6 comparing CD38 −/− and WT liver tissues with the area under the receiving operator curve (AUROC) analysis were considered for the subsequent iprm-PASEF analysis. The iprm-PASEF target lists were assembled so that the candidate ions did not overlap in its 1/K 0 range based on the MS1 survey scans. For the iprm-PASEF acquisitions each candidate precursor ion was isolated first by their ion mobility 1/K 0 value in the TIMS cell, then by its m/z value in quadrupole Q1, and then finally fragmented in the Q2 collision cell. The MS/MS spectra were acquired in this targeted, sequential, and spatially resolved manner ( Fig. 1 C ) . All fragment ions for each analyte shared the same mobility isolation window as their precursor ion but had unique m/z values corresponding to lipid or metabolite fragment ions (as annotated in existing databases/spectral libraries, see methods). Spatial maps were generated for each fragment ion that matched the spatial distribution of the corresponding precursor ion. To demonstrate the ability for isolation and accurate identification of individual lipids and metabolites from complex biological matrices by iprm-PASEF, in our data sets, we first targeted a well-characterized and annotated feature at m/z 885.55 corresponding to phosphatidylinositol PI(18:0/20:4) as reported in previous studies. 26 In negative ion mode this feature at m/z 885.55 was abundant in the MS1 spectra for all liver sections imaged ( Fig. 2 A ). The collisional cross sections (CCS) that was observed for the m/z 885.55 in the MS1 survey scan showed a prominent feature at 1/K 0 1.47 Vs/cm 2 ( Fig. 2 B ) . This feature was then isolated (Supp. Figure 1 ) based on its unique CCS (ion mobility) and m/z value combination allowing distinct analysis of this molecular feature separated from other features present in the liver tissue. The isolated candidate precursor ion was fragmented resulting in multiple abundant fragment ions. Importantly, the fragment ions all shared the same 1/K 0 value as the isolated precursor ion, which allowed for generating a MS/MS spectrum of the fragmented [M-H] − precursor ion accordingly ( Fig. 2 C ) . Fragment ions were annotated based on m/z values from existing databases and spectral libraries for lipid fragmentation. Major characteristic fragment ions corresponded to structures, such as stearic acid C(18:0) at m/z 283.26 and arachidonic acid C(20:4) at m/z 303.23 as well as several ions containing unique combinations of the phosphatidylinositol head group with the characteristic lipid tails at m/z 419.26, 581.31, and 599.32, among others ( Fig. 2 C ) . Combined, these confidently confirmed the identity of the precursor ion as PI(18:0/20:4). At the given MS/MS settings the precursor [M-H] − ion at m/z 885.55 was still detectable even though at a significantly lower intensity than in the MS1 spectrum. MS/MS fragmentation was completed in a spatial raster manner at distinct tissue locations to generate the MS/MS imaging data set of fragment ions spatially across the liver sections ( Fig. 2 D ). This showed that each fragment ion generated from one precursor ion displayed the same spatial distribution, sharing similar areas of high and low abundance across the tissue. Going forward, iprm-PASEF quantification can thus be performed on the fragment ion level for higher specificity and accuracy. The survey MS1 scans revealed the regions in the composite MALDI-MS spectrum where different lipid isotope patterns were observed. One area of interest was the m/z range from m/z 857 to m/z 861 ( Fig. 3 A ) . Initial annotation (based on databases) of the intense peak at m/z 857.52 returned two possible isomeric phosphatidylinositols PI(36:4), each with 36 carbon atoms and a total of 4 unsaturated double bonds, PI 36:4(16:0/20:4) and PI 36:4(14:0/22:4). However, an overlapping isotopic distribution was observed, as the third isotopic peak (m/z 859.53) showed higher intensity than one would expect for a PI 36:4 (PI 36:4), suggesting the presence of a PI 36:3 (at m/z 859.53). Moreover, the spatial pattern for this ion at m/z 859.53, as determined by MALDI-MSI analysis, differed from that at m/z 857.52 (data not shown). The iprm-PASEF analysis was completed to ascertain the identities of these related lipid species. First, the feature at m/z 857.52 was isolated and fragmented for analysis. Characteristic fragment ions for PI 36:4(16:0/20:4) were detected in the isolated mobility window at 1/K 0 1.4308 ± 0.0093 Vs/cm 2 corresponding to palmitic acid C(16:0) at m/z 255.23 and arachidonic acid C(20:4) at m/z 303.23 ( Fig. 3 B ). Many other fragment ions were also detected that further confirmed the identity of this lipid, including several head group fragment ions validating the identity of the phosphatidylinositol lipid family (Supp. Figure 2 ) . Some fragment ions detected in the iprm mobility window were unique to this MALDI-MSI iprm-PASEF experiment and did not match to annotated fragment ions from LC-MS analysis of this lipid. Also, characteristic fragment ions of the potential isomer, PI 36:4(14:0/22:4), including features at m/z 227.20 (myristic Acid 14:0) and m/z 331.26 (cholesterol ester 22:4), were not detected on the iprm mobilogram for this isolated feature ( Fig. 3 B ) . This enabled us to resolve its identity as PI 36:4(16:0/20:4) and confidently differentiate it from its potential isomer PI 36:4(14:0/22:4). Similar analysis was completed for the precursor ion at m/z 859.53 (Supp. Figure 3 ) identifying this feature as a combination of two different PI isoforms (for PI 36:3, 3 double bonds), specifically: PI (18:1/18:2) and PI (16:0/20:3), which were distinct from the two Dalton lower mass identified at m/z 857.52 as PI 36:4(16:0/20:4) with 4 double bonds as described in Fig. 3 above. CD38 is a multifunctional enzyme with a predominant role as an NAD + glycohydrolase, catalyzing the hydrolysis of NAD + to ADP-ribose (ADPR) and nicotinamide. Additionally, CD38 exhibits ADP-ribosyl cyclase activity, generating cyclic ADP-ribose (cADPR) from NAD + , though this constitutes a minor fraction of its total catalytic function. Furthermore, CD38 functions as a cADPR hydrolase, hydrolyzing cADPR to ADPR. Collectively, these enzymatic activities contribute to the regulation of NAD + metabolism and calcium signaling, which are integral to cellular homeostasis. To identify optimal matrix conditions for MSI of NAD + and its metabolites, we analyzed synthetic standards of NAD+, ADPR, and cADPR, individually spotted onto glass slides using various commonly used matrices. Dihydroxyacetophenone (DHAP ) was used for positive ionization mode acquisitions and N-naphthylethylenediamine dihydrochloride (NEDC) was used for negative ionization mode. A representative mobilogram overview of the cADPR, ADPR, and NAD + standards highlighted their distinct ion mobility drift times (1/K 0 values) and indicated efficient separation in the TIMS dimension, while in situ fragmentation of the standards generated MS/MS spectra for each metabolite using iprm-PASEF ( Fig. 4 A ) . The fragmentation patterns obtained through iprm-PASEF showed strong concordance with those derived from conventional liquid chromatography-tandem mass spectrometry (LC-MS/MS) (Supp. Figure 4 ) , where both methodologies detected characteristic fragment ions for each metabolite investigated. Given that CD38 catalyzes NAD + hydrolysis, we hypothesized that CD38 ⁻/⁻ liver would exhibit elevated NAD + levels and a concurrent decrease in enzymatic by-products, such as ADPR and nicotinamide. To evaluate this, spatial MALDI MSI was performed for WT and CD38 ⁻/⁻ liver sections in negative ionization mode using NEDC matrix to monitor endogenous metabolites (n = 3 biological replicates per genotype). The MS1 survey scans revealed a marked reduction in ADPR (m/z 558.06) in CD38 ⁻/⁻ liver, with almost negligible signal intensity in CD38 ⁻/⁻ liver compared to WT liver ( Fig. 4 B ) . To further validate this observation, an ADPR standard was analyzed again, now in negative ionization mode ( Fig. 4 C ) which confirmed its characteristic fragmentation pattern. The optimized endogenous iprm-PASEF assay, incorporating MS2 quantification of the characteristic ADPR fragment ion at m/z 346.06 in situ , demonstrated a significant spatial reduction in ADPR abundance in the CD38 ⁻/⁻ tissue samples compared to WT: with 5.6-fold downregulation in CD38 −/− across 3 biologically distinct replicates ( Fig. 4 D ) . These findings provided a proof-of-principle demonstration for CD38-mediated NAD + metabolism in hepatic tissue and validated the iprm-PASEF approach as a robust technique for in situ spatial quantification of NAD + metabolites at the MS2 fragment ion level. Due to its highly polar and charged nature, NAD + presents challenges with consistent ionization, which can impact detection sensitivity and quantification accuracy, particularly in the presence of ion suppression from lipids. In addition, NAD + has in the past been shown to be prone to degradation thus this spatial imaging workflow may be advantageous due to very minimal sample handling 27 . In this study, we demonstrated efficient in situ detection and fragmentation of NAD + in tissue using the iprm-PASEF approach. The iprm-PASEF workflow exhibited the capability to isolate and fragment the NAD + precursor ion with [M + H] + at m/z 664.12 in positive ionization mode with 2,5-dihydroxybenzoic acid (DHB) matrix ( Fig. 5 A ). The presence of characteristic NAD + fragment ions in the ion mobility isolation window, for the isolated precursor at m/z 664.12, including fragment ions at m/z 136.06, 232.08, 348.07, and 428.04 confirmed the identity of the isolated metabolite as NAD + and subsequently allowed for accurate quantification. For the spatial imaging using iprm-PASEF, specifically, the MS2-level quantification and spatial imaging of each fragment ion revealed a significant increase in NAD + levels in CD38 ⁻/⁻ liver tissue compared to WT, ~ 20-fold with very high statistical significance p < 0.0001 ( Fig. 5 B ) . These analyses further validated the role of CD38 in NAD + metabolism and the robustness of the iprm-PASEF technique for spatially resolved metabolite quantification. Discussion The past decades have been marked by tremendous developments in omics technologies that have been instrumental for the in-depth exploration of complex biological systems, giving insights into molecular mechanisms and proposing novel clinical/therapeutic strategies. While very powerful, these technologies typically do not provide information about the native spatial context of biomolecules, which is highly relevant and critical to better appreciate the heterogeneity, architecture, and three-dimensional molecular organization of tissues. These needs have led to the rise of spatial omics technologies. Over the past five years, two spatial omics fields have been recognized as ‘Method of the Year’ by the journal Nature Methods , spatial transcriptomics in 2020 28 and spatial proteomics in 2024 29 , demonstrating the powerful and crucial roles of these cutting-edge technologies to advance our understanding of biological complexity. Currently, even further advances are being made to improve the capabilities of these mass spectrometry imaging (MSI) techniques, especially in their ability to accurately identify and quantify complex biomolecules in place on tissue. With respect to metabolomics and lipidomics, significant advances have been made in spatial mass spectrometry to investigate the spatial distribution of these molecules within tissues/organs. However, in situ identification remains very challenging. Here, we present the first study demonstrating the capabilities of imaging parallel reaction monitoring – Parallel Accumulation–Serial Fragmentation (iprm-PASEF) on lipids and metabolites for high-resolution, spatially resolved targeted MALDI-MS/MS Imaging. MALDI-MSI with iprm-PASEF technology allows for high accuracy analyte identification and quantification in real-time on the MS2 fragment ion level during imaging experiments. This novel workflow enables confident and accurate identification of complex and challenging molecules thanks to the acquisition of collision cross section (CCS)-enabled MS/MS spectra, while preserving their spatial information. Indeed, this strategy leverages the trapped ion mobility spectrometry (TIMS) technology to isolate molecular targets, first in the TIMS device based on their inverse reduced mobility (1/K 0 ) and then in the quadrupole based on their mass-to-charge ratio (m/z). Isolated precursor ions are subsequently fragmented to collect high-resolution, full MS/MS spectra, while providing spatial information of both precursor and fragment ions. Ion mobility separation for lipid precursor ions is essential for proper identification and differentiation among many potential isoforms. MS2-level analysis further increases identity confidence through isolation and fragmentation of precursor ions of interest and subsequent detection of fragment ions that can be individually verified. The iprm-PASEF workflow utilizes the full capabilities of the timsTOF fleX and leverages the combination of ion mobility separation and precursor ion isolation and fragmentation to identify and quantify complex molecular features accurately. This workflow achieves label-free multiplexed and spatial multi-omic MS/MS imaging and quantification within a single acquisition. The generated iprm-PASEF MS/MS spectra contain characteristic fragment ions that provide direct evidence for the in situ identification of lipids and metabolites present in tissues. The similar spatial distribution of the different fragment ions resulting from the same precursor ion provides an additional validation of the molecular identification. We showed that we can leverage the detection of characteristic fragment ions to decipher very complex and convoluted lipid profiles, as demonstrated for the well-studied and abundant Phosphatidylinositol PI(18:0/20:4) but also for more challenging and lower abundance features. The similarity in spatial patterns for identified fragment ions of PI(18:0/20:4) increased the confidence in isolated feature identification. Indeed, all fragment ions shared the same spatial pattern as the isolated precursor ion, signifying that all fragment ions were generated from a single precursor ion. Further, the ion mobility-resolved fragmentation pattern of the lipid PI 36:4(16:0/20:4) enabled highly-confident identification of this feature and enabled its differentiation from its structural isomer PI 36:4(14:0/22:4). This exemplified the ability for iprm-PASEF to accurately resolve lipids utilizing complex fragmentation patterns for multiple features across a dynamic abundance range. The ability to accurately identify analytes in place during iprm-PASEF experiments removes the need for orthogonal mass spectrometry technologies to confirm analyte identifications, significantly increasing the ability of MALDI-MSI in discovery-based studies. Besides confident identification, we demonstrated that the iprm-PASEF strategy can achieve quantification at the MS2 fragment ion level, using spatially resolved fragment ions, enabling more specific and sensitive performances. Similar to targeted LC-MS PRM assays developed for targeted (non-spatial) proteomic approaches, iprm-PASEF experiments can be refined post-acquisition to select the most intense, non-interfered, specific and selective, fragment ions for the analytes of interest for their accurate and robust quantification, while additionally providing a spatially defined context. This represents an asset in the context of complex and convoluted metabolome and lipidome analysis, as fragment ions differentiating co-fragmented species can be specifically used to ensure accurate quantification. We developed and optimized novel iprm-PASEF assays for in situ quantification of endogenous NAD and ADPR metabolites in mouse livers for. Through confident identification of these highly selective fragment ions, we demonstrated the ability for high confidence, specificity, and accuracy quantification for lipids and metabolites during MALDI-MSI experiments on the MS2 fragment ion level. Our application of the iprm-PASEF workflow to CD38 and NAD + biology demonstrates its power to better understand the underlying molecular mechanisms and address biological questions. NAD + is a central redox coenzyme critical for cellular metabolism, supporting key oxidative and reductive reactions and serving as a substrate for NAD + -consuming enzymes such as sirtuins, PARPs, and the NADase CD38. With age, NAD + levels decline in multiple tissues, including the liver and ovary, contributing to mitochondrial dysfunction and impaired metabolism 13 , 18 , 20 – 22 . Our findings, along with independent studies, implicate CD38 as a major driver of this decline through its NADase activity, which hydrolyzes NAD + primarily into nicotinamide and ADP-ribose (ADPR) 13 , 18 , 20 – 22 . While global (bulk) NAD + quantification by LC-MS is well established, understanding its spatial distribution is essential for elucidating compartment-specific metabolic regulation of NAD + and related metabolites. Mass spectrometry imaging offers spatial resolution but - in some cases - faces challenges in detecting small, labile metabolites like NAD + and ADPR due to ion suppression from abundant proteins and lipids, as well as difficulties in metabolite annotation. To overcome these limitations, prior studies have employed HRMS/MS and spiking of NAD + standards onto control tissue 30 , 31 . For instance, Lu et al. demonstrated that acidified methanol washes reduce ion suppression and, when combined with isotopic labeling, allow confident identification of NAD + and related metabolites 32 . In this study, we utilized the iprm-PASEF strategy to map the spatial distribution of NAD + and ADPR in wild-type and CD38 knockout mouse livers. Using refined fragment ions”, i.e. the fragment ion at m/z 346.06 for ADPR (in negative ionization mode) and the panel of fragment ions at m/z 136.06, m/z 232.08, m/z 348.07, and m/z 428.04 for NAD + (in positive ionization mode), we were able to obtain spatially resolved MS2-based quantification and further conduct statistical analysis to confirm their highly significant increase upon CD38 deletion in mouse livers. Additionally, we optimized matrix formulations utilizing both DHAP and DHB to enhance NAD + ionization during MALDI MS Imaging experiments from both slide-spotted standards and directly from tissue sections, further improving MSI sensitivity and reliability. We envision complementary, synergistic, and multi-modal approaches combining global LC-ESI-MS/MS and spatial MALDI-MS/MS (iprm-PASEF) Imaging to i) generate ESI-based spectral libraries to help interpret MALDI-MS/MS spectra and improve identification rate in MS-imaging assays, ii) inform on the spatial distribution of analytes of interest identified by ESI-MS/MS experiments, iii) aid in integrating multi-omics measurements and data. This novel iprm-PASEF workflow opens the way to novel strategies for advanced spatial metabolomics, lipidomics, and proteomics – including in situ tryptic peptides 33 or any other proteolytic peptides/proteins, that will be pivotal in obtaining a more holistic and refined picture of the dynamic organization and regulation of biological systems. With the multiplexed capabilities of iprm-PASEF in performing high-quality identification and simultaneous quantification for multiple complex lipids and biologically relevant small molecule metabolites, we have demonstrated the importance and future opportunities of MS2-based spatial mass spectrometry imaging. Specific future interests could be in spatial mapping of tissues highlighting therapeutic drug distribution, regional biomarker responses upon interventions, and overall tissue heterogeneity during aging and age-related diseases. Methods Chemicals and Samples Formic acid, acetonitrile (ACN), trifluoroacetic acid (TFA), methanol, water (all LCMS grade), 2,5-dihydroxyacetophenone (DHAP), 2,5-dihydroxybenzoic acid (DHB) N-naphthylethylenediamine dihydrochloride (NEDC), and chemical standards for nicotinamide adenine dinucleotide (NAD+), ADP-ribose (ADPR) and cyclic ADP-ribose (cADPR) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Livers from mice with global deletion of CD38 (CD38 -/- ) and control, litter mate wild-type (WT) mice (18 months old) were collected at the Buck Institute for Research on Aging. 20 Sample preparation for spatial iprm-PASEF imaging Livers were collected from CD38 -/- mice and WT controls (n = 3 biological replicates each) and flash frozen with liquid nitrogen to preserve lipids and metabolites. Frozen livers were mounted directly to a cryotome (Leica, Germany) pre-chilled to -20°C using a cellulose-based mounting media (M-1 Embedding Matrix, Epredia, Kalamazoo, MI). The portal vein was used to standardize the orientation of the left major lobe of each liver. Tissue sections with 10–12 µm thicknesses were mounted on charged microscope slides. Tissues slides were desiccated and then stored under vacuum at − 80°C prior to matrix application. Chemical standards for NAD + , ADPR, cADPR were dissolved in methanol at a concentration of 10 µM or 1 µM and spotted onto bare microscope slides with a pipette. Slides were desiccated to evaporate methanol. The HTX TM Sprayer M3 was used to apply matrix by aerosol 34 . For positive ionization mode experiments, 15 mg/mL DHAP or DHB in 80% ACN, 10% methanol in water was applied with the following parameters: flow rate 120 µL/min, number of passes 6, temperature 70°C, nitrogen gas pressure 10 psi, pattern CC, velocity 1200 mm/min, drying time 5 s. For negative ionization mode experiments, 7 mg/mL NEDC in 70% methanol in water was applied with the following parameters: flow rate 150 µL/min, number of passes 10, temperature 70°C, nitrogen gas pressure 10 psi, pattern HH, velocity 700 mm/min, drying time 0 s. MALDI-MS Imaging Analysis MALDI-TIMS-MS1 acquisitions MALDI-MS Imaging was performed using a Trapped Ion-Mobility Time-of-Flight Mass Spectrometer, the timsTOF fleX MALDI-2 (Bruker Scientific, LLC, Bremen, Germany) equipped with a dual ESI/MALDI source. Survey MS1 scans were acquired with single-scan spectra consisted of 200 accumulated laser shots at 1 kHz. MALDI-MS images were acquired at a 20 µm pixel resolution for both polarity ion imaging experiments. The SmartBeam 3D 10 kHz laser was set to 35% power, scan range of 16 µm for X and Y and resulting field size of 20 µm for X and Y. Additional instrument parameters for the studies included: an ion transfer time of 50 µs, a pre pulse storage time of 60–80 µs, a collision RF of 800–1000 Vpp, a collision energy of 10 eV, an ion energy in the quadrupole of 5.0 eV, a TIMS funnels 1 RF and 2 RF of 250 Vpp, and a multipole RF of 250 Vpp. Negative ionization mode acquisitions targeting lipids ranged from m/z 150–1100 and 1/K 0 0.4–1.8 VS/cm 2 with a 100-m/s ramp time. Positive ionization mode acquisitions, tuned for small molecule metabolites specifically, ranged from m/z 100–1000 and 1/K 0 0.4–1.4 VS/cm 2 with a 200-ms ramp time. iprm-PASEF acquisitions iprm-PASEF was performed in both positive and negative ionization acquisition modes. Main instrument settings were not adjusted between MS1 and MS2 acquisitions. Both modes used a mobility-based isolation strategy that isolated precursor ions by a 1/K 0 window first followed by isolation by m/z. Collision energy was determined by a linear fit based on precursor mobility ranging from 35 eV at 1/K 0 0.8 VS/cm 2 to 60 eV at 1/K 0 1.6 VS/cm 2 . No isolated precursor ions had a mobility greater than 1/K 0 1.6 VS/cm 2 . iprm-PASEF precursor isolation settings including m/z and mobility ranges can be found in Supplemental Table 1. MALDI-MSI data analysis After MS acquisition, the data was imported into SCiLS Lab Pro v2025a (Bruker Scientific, LLC, Bremen, Germany) for initial MS1 data visualization and iprm-PASEF target list assembly. Metaboscapev.2025, (Bruker, Germany) was used to annotation analytes using existing or customized libraries to build precursor ion list based on m/z, isotopic pattern, and ion mobility values. Subsequently, iprm-PASEF data was used to confirm analyte identification by comparing fragmentation spectra to LipidMaps and the MSDIAL-TandemMassSpectralAtlas for both positively- and negatively-charged analytes. Data was analyzed using SCiLS Lab Pro v2025a and Data Analysis v4.0 software (Bruker, Germany). iprm-PASEF data was further used for MS2-level quantification of selected fragment ions. A statistical analysis comparing the pixel intensity of the fragment ions was performed in Prism v10.4.2 (GraphPad), using a Student’s t-test. Quantitative results used for statistical analysis can be found in Supplemental Tables 2–3. LC-MS/MS Analysis Confirmation of NAD + and ADPR was performed by direct LC-MS analysis of pure standards obtained from Sigma Aldrich (N0632 for NAD + and A0752 for ADPR). Standards at 10-micromolar concentration were injected onto a Thermo Hypercarb column (5 µm, 100 × 2.1 mm) using a Vanquish UHPLC system. Chromatographic separation was carried out over a 12-minute gradient using solvent A (7.5 mM ammonium acetate with 0.05% ammonium hydroxide in water) and solvent B (0.05% ammonium hydroxide in acetonitrile) as follows: 0–1 min, 5% B; 1–6 min, linear gradient to 60% B; 6.1–7.5 min, 90% B; 7.6–12 min, re-equilibration at 5% B. Mass spectrometric detection was performed on a Thermo Q Exactive mass spectrometer operated in positive ion mode using a targeted SIM-PRM acquisition strategy. Targeted SIM (tSIM) scans were acquired across a mass range of m/z 50–750 at 70,000 resolution, AGC target of 1e6, and maximum injection time of 100 ms. PRM scans were performed at a resolution of 17,500, with an AGC target of 5e5, maximum injection time of 55 ms, and stepped normalized collision energies (NCE) of 20, 35, and 30. Data were visualized using Thermo Freestyle software. Declarations Animals are treated to all ethics standards, and following our IACUC protocol number A10285 at the Buck Institute (Dr. Verdin). Data Availability The spatial proteomic and liquid chromatography mass spectrometry raw MS data sets have been uploaded to the Center for Computational Mass Spectrometry, MassIVE, and can be downloaded using the following FTP link: ftp:// [email protected] or via the MassIVE website: https://massive.ucsd.edu/ProteoSAFe/private-dataset.jsp?task=4dbd4a7e3a4d4f66b90399a693974e2f(MassIVE ID: MSV000097855). [Note to the reviewers: To access the data repository MassIVE (UCSD) for MS data, please use: Username: MSV000097855_reviewer; Password: winter]. Acknowledgements We would like to acknowledge support from the following NIH funding sources: NIAMS R21 AR084303 (PI Schilling), NIA P01 AG066591 (PI Ellerby), NIA T32 AG000266 (PI Ellerby, fellowship to J. Fang). The authors would like to acknowledge Bruker for their technical support and generous access to instrumentation. Author Contributions. C.A.S and J.B. contributed equally to the study design, data acquisition and interpretation, manuscript and figure preparation, and revision and editing of the manuscript. P.V.A.K. contributed to data acquisition and analysis, manuscript and figure preparation, and editing of the manuscript. Authors J.F., A.R, G.V.H., and R.R.R. contributed to the murine study design and sample acquisition. N.T. contributed to study design, provided resources and data acquisition and manuscript editing. E.V. contributed to study design. B.S. implemented the study design and conceptual development, provided resources, contributed to data interpretation and manuscript preparation and revision. Competing Interests Dr. Birgit Schilling is on the advisory board for MOBILion Systems, Chadds Ford, PA. Dr. Nannan Tao is an employee of Bruker, San Jose, CA. Dr. Eric Verdin has research support from NIA/NIH, NIAID and Hevolution Foundation, and holds stock in; BioAge Labs, EDIFICE Health, Deciduous Therapeutics, Deep Longevity, Circulate, AVIV and DOC, and serves on an advisory board for; Longevity Clinic, Juvenescence, IHLAD, IHU, Ani Biome, Whoop, VERO, Lotus AI, Fountain Life, Longevity Science, and Longevity & Rejuvenation (The Royal Clinics of the Custodian of the Two Holy Mosques, Saudi Arabia). The terms of this arrangement have been reviewed and approved by the Buck Institute in accordance with its policy on objectivity in research. Additional Information Correspondence and requests for materials should be addressed to Dr. Birgit Schilling. References Buchberger, A. R., DeLaney, K., Johnson, J. & Li, L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. Anal Chem 90 , 240-265 (2018). Rivera, E. S., Djambazova, K. V., Neumann, E. K., Caprioli, R. M. & Spraggins, J. M. Integrating ion mobility and imaging mass spectrometry for comprehensive analysis of biological tissues: A brief review and perspective. J Mass Spectrom 55 , e4614 (2020). Spataro, S. et al. Stable isotope labeling and ultra-high-resolution NanoSIMS imaging reveal alpha-synuclein-induced changes in neuronal metabolism in vivo. Acta Neuropathol Commun 11 , 157 (2023). Wiseman, J. M. et al. Desorption electrospray ionization mass spectrometry: Imaging drugs and metabolites in tissues. Proc Natl Acad Sci U S A 105 , 18120-18125 (2008). Manikandan, M. et al. Biological Desorption Electrospray Ionization Mass Spectrometry (DESI MS) – unequivocal role of crucial ionization factors, solvent system and substrates. TrAC Trends in Analytical Chemistry 78 , 109-119 (2016). Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S. & Coon, J. J. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics 11 , 1475-1488 (2012). Gallien, S. et al. Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol Cell Proteomics 11 , 1709-1723 (2012). Schilling, B. et al. Multiplexed, Scheduled, High-Resolution Parallel Reaction Monitoring on a Full Scan QqTOF Instrument with Integrated Data-Dependent and Targeted Mass Spectrometric Workflows. Anal Chem 87 , 10222-10229 (2015). Ronsein, G. E. et al. Parallel reaction monitoring (PRM) and selected reaction monitoring (SRM) exhibit comparable linearity, dynamic range and precision for targeted quantitative HDL proteomics. J Proteomics 113 , 388-399 (2015). Picotti, P. & Aebersold, R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods 9 , 555-566 (2012). Helmer, P. O. et al. Complementing Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry Imaging with Chromatography Data for Improved Assignment of Isobaric and Isomeric Phospholipids Utilizing Trapped Ion Mobility-Mass Spectrometry. Anal Chem 93 , 2135-2143 (2021). Sanchez, L., Shepherd, R. A. & Luu, G. T. MALDI-TIMS-MS2 Imaging and Annotation of Natural Products in Fungal-Bacterial Co-Culture. bioRxiv , 2025.2005.2011.653367 (2025). Navas, L. E. & Carnero, A. NAD(+) metabolism, stemness, the immune response, and cancer. Signal Transduct Target Ther 6 , 2 (2021). Amjad, S. et al. Role of NAD(+) in regulating cellular and metabolic signaling pathways. Mol Metab 49 , 101195 (2021). Abdellatif, M. & Baur, J. A. NAD+ metabolism and cardiometabolic health: the human evidence. Cardiovasc Res 117 , e106-e109 (2021). Wu, J., Han, K. & Sack, M. N. Targeting NAD+ Metabolism to Modulate Autoimmunity and Inflammation. J Immunol 212 , 1043-1050 (2024). Kincaid, J. W. & Berger, N. A. NAD metabolism in aging and cancer. Exp Biol Med (Maywood) 245 , 1594-1614 (2020). Chini, C. C. S., Cordeiro, H. S., Tran, N. L. K. & Chini, E. N. NAD metabolism: Role in senescence regulation and aging. Aging Cell 23 , e13920 (2024). Dall, M., Hassing, A. S. & Treebak, J. T. NAD(+) and NAFLD - caution, causality and careful optimism. J Physiol 600 , 1135-1154 (2022). Covarrubias, A. J. et al. Senescent cells promote tissue NAD(+) decline during ageing via the activation of CD38(+) macrophages. Nat Metab 2 , 1265-1283 (2020). Li, L. et al. Multi-omics profiling of collagen-induced arthritis mouse model reveals early metabolic dysregulation via SIRT1 axis. Scientific Reports 12 (2022). Perrone, R. et al. CD38 regulates ovarian function and fecundity via NAD+ metabolism. iScience 26 (2023). Zhu, J. et al. Cell Type-Specific Roles of CD38 in the Interactions of Isoniazid with NAD(+) in the Liver. Drug Metab Dispos 48 , 1372-1379 (2020). Poljsak, B., Kovac, V., Spalj, S. & Milisav, I. The Central Role of the NAD+ Molecule in the Development of Aging and the Prevention of Chronic Age-Related Diseases: Strategies for NAD+ Modulation. Int J Mol Sci 24 (2023). Freeberg, K. A., Udovich, C. C., Martens, C. R., Seals, D. R. & Craighead, D. H. Dietary Supplementation With NAD+-Boosting Compounds in Humans: Current Knowledge and Future Directions. J Gerontol A Biol Sci Med Sci 78 , 2435-2448 (2023). Wang, H. J. & Hsu, F. F. Structural characterization of phospholipids and sphingolipids by in-source fragmentation MALDI/TOF mass spectrometry. Anal Bioanal Chem 414 , 2089-2102 (2022). Braidy, N., Villalva, M. D. & Grant, R. NADomics: Measuring NAD(+) and Related Metabolites Using Liquid Chromatography Mass Spectrometry. Life (Basel) 11 (2021). Method of the Year 2020: spatially resolved transcriptomics. Nat Methods 18 , 1 (2021). Method of the Year 2024: spatial proteomics. Nat Methods 21 , 2195-2196 (2024). Trammell, S. A. & Brenner, C. Targeted, LCMS-based Metabolomics for Quantitative Measurement of NAD(+) Metabolites. Comput Struct Biotechnol J 4 , e201301012 (2013). Chi, D. H. et al. NAD+Levels Are Augmented in Aortic Tissue of ApoE-/-Mice by Dietary Omega-3 Fatty Acids. Arteriosclerosis, Thrombosis, and Vascular Biology 42 , 395-406 (2022). Lu, W. et al. Acidic Methanol Treatment Facilitates Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry Imaging of Energy Metabolism. Analytical Chemistry 95 , 14879-14888 (2023). Li, M. J. et al. Spatial Proteomics by Parallel Accumulation-Serial Fragmentation Supported MALDI MS/MS Imaging: A First Glance Into Multiplexed and Spatial Peptide Identification. Rapid Commun Mass Spectrom 39 , e10006 (2025). Li, B., Comi, T. J., Si, T., Dunham, S. J. & Sweedler, J. V. A one-step matrix application method for MALDI mass spectrometry imaging of bacterial colony biofilms. J Mass Spectrom 51 , 1030-1035 (2016). Additional Declarations Yes there is potential Competing Interest. Dr. Birgit Schilling is on the advisory board for MOBILion Systems, Chadds Ford, PA. Dr. Nannan Tao is an employee of Bruker, San Jose, CA. Dr. Eric Verdin has research support from NIA/NIH, NIAID and Hevolution Foundation, and holds stock in; BioAge Labs, EDIFICE Health, Deciduous Therapeutics, Deep Longevity, Circulate, AVIV and DOC, and serves on an advisory board for; Longevity Clinic, Juvenescence, IHLAD, IHU, Ani Biome, Whoop, VERO, Lotus AI, Fountain Life, Longevity Science, and Longevity & Rejuvenation (The Royal Clinics of the Custodian of the Two Holy Mosques, Saudi Arabia). The terms of this arrangement have been reviewed and approved by the Buck Institute in accordance with its policy on objectivity in research. Supplementary Files AdditionalInformationlegends.docx SuppFigure1iprmisolation20250421v4.pptx Supplementary Figure 1 SuppFigure2iprmlipid364MSMS0421v6.pptx Supplementary Figure 2 SuppFigure3coisolation0421v5.pptx Supplementary Figure 3 SuppFigure4LCMSMSADPRcADPR250421v6.pptx Supplementary Figure 4 SupplementalTable1iprmIsolationSettingsv2.xlsx Supplementary Table 1 SupplementalTable2ADPRFragmentIonQuantificationv3.xlsx Supplementary Table 2 SupplementalTable3NADFragmentIonsQuantificationv3.xlsx Supplementary Table 3 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6743284","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":468412887,"identity":"bf01c7ad-6b01-450e-aea6-accb2573e248","order_by":0,"name":"Birgit Schilling","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-9907-2749","institution":"Buck Institute for Research on Aging","correspondingAuthor":true,"prefix":"","firstName":"Birgit","middleName":"","lastName":"Schilling","suffix":""},{"id":468412888,"identity":"bafa5c6c-af3c-4d8d-8128-3bae046733e8","order_by":1,"name":"Charles Schurman","email":"","orcid":"https://orcid.org/0000-0002-5739-8481","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Schurman","suffix":""},{"id":468412889,"identity":"77904c30-d4e6-4cc5-8457-8223a15caed9","order_by":2,"name":"Joanna Bons","email":"","orcid":"https://orcid.org/0000-0002-1110-4193","institution":"Buck Institute for Research on Aging","correspondingAuthor":false,"prefix":"","firstName":"Joanna","middleName":"","lastName":"Bons","suffix":""},{"id":468412890,"identity":"36384d10-f4d3-44d3-9b2f-7ed4cdba3981","order_by":3,"name":"Prasanna Kumaar","email":"","orcid":"","institution":"Buck Institute for Research on Aging","correspondingAuthor":false,"prefix":"","firstName":"Prasanna","middleName":"","lastName":"Kumaar","suffix":""},{"id":468412891,"identity":"b6055f7b-12fb-4636-844d-9f9a18a927d4","order_by":4,"name":"Jingji Fang","email":"","orcid":"","institution":"Buck Institute for Research on Aging","correspondingAuthor":false,"prefix":"","firstName":"Jingji","middleName":"","lastName":"Fang","suffix":""},{"id":468412892,"identity":"bd907159-3be5-4f6e-988a-48c72cf5dcac","order_by":5,"name":"Andrea Roberts","email":"","orcid":"","institution":"Buck Institute for Research on Aging","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Roberts","suffix":""},{"id":468412893,"identity":"c9cc31de-80e7-4258-b940-5b9579c1da05","order_by":6,"name":"Genesis Hormazabal","email":"","orcid":"","institution":"Buck Institute for Research on Aging","correspondingAuthor":false,"prefix":"","firstName":"Genesis","middleName":"","lastName":"Hormazabal","suffix":""},{"id":468412894,"identity":"8ec2c44d-9d98-4f9f-bd7a-d72f2c32cea7","order_by":7,"name":"Rebeccah Riley","email":"","orcid":"https://orcid.org/0000-0002-5870-6837","institution":"Buck Institute Research on Aging","correspondingAuthor":false,"prefix":"","firstName":"Rebeccah","middleName":"","lastName":"Riley","suffix":""},{"id":468412895,"identity":"34abb4e3-f636-4bde-b1dc-9adb267439d2","order_by":8,"name":"Nannan Tao","email":"","orcid":"","institution":"Bruker","correspondingAuthor":false,"prefix":"","firstName":"Nannan","middleName":"","lastName":"Tao","suffix":""},{"id":468412896,"identity":"1404f29b-cc85-4e78-ac7f-1c960bf63700","order_by":9,"name":"Eric Verdin","email":"","orcid":"https://orcid.org/0000-0003-3703-3183","institution":"Buck Institute for Research on Aging","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Verdin","suffix":""}],"badges":[],"createdAt":"2025-05-25 11:00:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6743284/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6743284/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84667249,"identity":"7a2abd51-a6a3-489c-bf9a-c3bcd3802a5d","added_by":"auto","created_at":"2025-06-16 05:54:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1289771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design for targeted MALDI-mass spectrometry imaging by iprm-PASEF of selected lipids and metabolites upon CD38 deletion in mouse liver.\u003c/strong\u003e A. CD38 is a potent enzymatic consumer of NAD\u003csup\u003e+\u003c/sup\u003e and CD38 knockout mice (CD38\u003csup\u003e-/-\u003c/sup\u003e) showed improved metabolism and lower cellular senescence in the liver, possibly through boosting the beneficial metabolic effects of NAD\u003csup\u003e+\u003c/sup\u003e. B. As a first discovery step, liver tissues from wild-type (WT) and CD38\u003csup\u003e-/-\u003c/sup\u003e mice (n = 3 each) were subjected to collision cross section-enabled matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry Imaging to spatially profile the lipidome and metabolome. Tissues sections were analyzed on a timsTOF flex, MALDI-2 mass spectrometer by MALDI-trapped ion mobility spectrometry (TIMS)-MS in both negative and positive ionization modes. Survey imaging scans were processed in Bruker SCiLS Lab using the T-ReX\u003csup\u003e3 \u003c/sup\u003efeature finding algorithm and the MetaboScape software. A list of precursor ion candidates, including lipids and metabolites, was generated. C. As a second validation step, targeted iprm-PASEF assays were developed and optimized in SCiLS Lab and timsControl software, extracting information on 1/K\u003csub\u003e0\u003c/sub\u003e, m/z and region of interest from the MALDI-TIMS-MS1 scans. Each precursor ion was ion mobility-aided isolated and \u003cem\u003ein situ\u003c/em\u003e fragmented sequentially. Collected iprm-PASEF data were analyzed in SCiLS Lab to apply the feature finding algorithm and generate MS/MS scans. This enabled to confirm lipid and metabolite candidate identification, to map the spatial distribution of their fragment ions in the tissues, and to perform multiplexed MS2-based quantification of the fragment ions.\u003c/p\u003e","description":"","filename":"Figure1Workflow20250327v12.png","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/58343fc3462e9d148e38562f.png"},{"id":84667262,"identity":"a95c69ab-75ee-4fdc-ba81-9f569058907d","added_by":"auto","created_at":"2025-06-16 05:54:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1521367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe iprm-PASEF strategy enables confident molecular identification with spatially resolved fragment ion distribution: example of Phosphatidylinositol PI(18:0/20:4).\u003c/strong\u003e A collision cross section (CCS)-enabled survey MALDI-MS Imaging scan was acquired at the MS1 level to identify precursor ion candidates for targeted iprm-PASEF MS/MS-level analysis. Individual features of interest were selected based on their 1/K­\u003csub\u003e0\u003c/sub\u003e and m/z values. Up to 25 individual precursor ions, which were non-overlapping in 1/K\u003csub\u003e0\u003c/sub\u003e, could be simultaneously targeted for iprm-PASEF isolation and analysis. The iprm-PASEF MS/MS spectra for isolated and fragmented precursor ions were generated in a spatial manner in real-time. A. In CD38\u003csup\u003e-/-\u003c/sup\u003e mouse liver, the composite survey MALDI-MS scan was acquired in negative ionization mode and showed the detection of a feature at m/z 885.55, that was highly abundant across the entire tissue section. B. This precursor ion candidate at m/z 885.55 and 1/K\u003csub\u003e0\u003c/sub\u003e 1.47 VS/cm\u003csup\u003e2 \u003c/sup\u003ewas selected for targeted iprm-PASEF isolation and fragmentation. C. The generated MS/MS spectrum showed species-specific fragment ions used for both accurate molecular identification and quantification of Phosphatidylinositol PI(18:0/20:4). The identified fragment ions were spatially mapped for representative images of wild-type (WT) and CD38\u003csup\u003e-/-\u003c/sup\u003e mouse liver tissue sections based on their m/z. The spatial distribution of the PI(18:0/20:4) fragment ions matched the distribution of the corresponding precursor ion.\u003c/p\u003e","description":"","filename":"Figure2ProofofConcept20250327v9.png","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/3c0c21d5c3a7b52d50fa6379.png"},{"id":84667253,"identity":"f2db8cd3-9c9b-46a5-9674-85b5465420e3","added_by":"auto","created_at":"2025-06-16 05:54:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1596202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of characteristic fragment ions for the spatially resolved identification of lipid isomers and isotopes in mouse liver tissues using iprm-PASEF.\u003c/strong\u003e A. The zoom into the m/z range 857-861 of the composite survey MALDI-MS scan collected for CD38\u003csup\u003e-/-\u003c/sup\u003e mouse liver tissue displayed an isotopic envelope for analytes (left). Potential lipid isomer discovery for the most intense ion at m/z 857.52 was initially annotated as PI (14:0/22:4) by MetaboScape software. B. The iprm-PASEF fragmentation pattern of the precursor ion [M-H]\u003csup\u003e-\u003c/sup\u003e at m/z 857.52 in the mobility isolation window 1.4308 ± 0.0093 Vs/cm\u003csup\u003e2\u003c/sup\u003e showing the detection of two characteristic fragment ions, m/z 255.23 (pink) and m/z 303.23 (orange), as evidenced for the identification of PI 36:4 (16:0/20:4). No presence of characteristic fragment ions for PI 36:4 (14:0/22:4) confirmed the absence of this species.\u003c/p\u003e","description":"","filename":"Figure3Isomers0327v6.png","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/7763ebfc6ed20c68f67d7538.png"},{"id":84667886,"identity":"5e6eb938-7bd5-4cb1-81e8-b9938f171a8b","added_by":"auto","created_at":"2025-06-16 06:02:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1719273,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConfident identification of NAD+ and related metabolites in WT and CD38\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-/-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mouse livers by iprm-PASEF.\u003c/strong\u003e A. The iprm-PASEF mobilogram and MS/MS spectra of synthetic cyclic adenosine 5′-diphosphate ribose (cADPR), ADPR, and nicotinamide \u003cstrong\u003eadenine dinucleotide (NAD) standards. Each standard was individually analyzed: in negative ionization mode (N-(1-naphthyl) ethylenediamine dihydrochloride (NEDC) matrix) for cADPR (1 μM) with precursor ion [M-H]\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e at m/z 540.06, in positive ionization mode (2,5-dihydroxyacetophenone (DHAP) matrix) for ADPR (10 μM) with precursor ion [M+H]\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e at m/z 560.08 and for NAD\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e (10 μM) with precursor ion [M+H]\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e at m/z 664.12. B. Spatial distribution of ADPR precursor ion (negative ionization mode, NEDC matrix, [M-H]\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e at m/z 558.06) for representative WT and CD38\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-/-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mouse liver sections analyzed by MALDI-MS Imaging. C. The iprm-PASEF mobilogram and MS/MS spectrum of the synthetic ADPR standard analyzed in negative ionization mode (1 μM) with precursor ion [M-H]\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e at m/z 558.06. D. ADPR was selected for targeted iprm-PASEF analysis in predefined regions of interest of WT and CD38\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-/-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mouse liver tissue sections. The fragmentation pattern of ADPR precursor ion [M-H]\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e at m/z 558.06 in the mobility isolation window 1.0045–\u003c/strong\u003e1.0233\u003cstrong\u003e Vs/cm\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e shows the detection of an ADPR fragment ion at m/z 346.06. This fragment ion was visualized \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein situ\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in the replicates of WT and CD38\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-/-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mouse liver and used for MS2-based quantification comparing average pixel intensity across the tissue sections. (\u003c/strong\u003eratio\u003cstrong\u003e \u003c/strong\u003eCD38\u003csup\u003e-/-\u003c/sup\u003e vs WT = 0.18, p = 0.0025 meaning 5.6-fold downregulation in CD38\u003csup\u003e-/-\u003c/sup\u003e vs WT, see \u003cstrong\u003eSupplemental Table 2). ** Student’s t-test p-value \u0026lt; 0.01 (n = 3 individual mice/biological replicates per condition).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4NADADPRcADPRDetection250519v9.png","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/4f3e7bbfdf1ec93c5f25ee55.png"},{"id":84667248,"identity":"b0faf76e-f298-429c-9118-c92514f8bc4a","added_by":"auto","created_at":"2025-06-16 05:54:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1287850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantification and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein situ\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e visualization of endogenous NAD+ fragment ions in WT and CD38\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-/-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mouse livers by iprm-PASEF.\u003c/strong\u003e A. The iprm-PASEF fragmentation pattern of the endogenous NAD+ precursor ion [M+H]\u003csup\u003e+\u003c/sup\u003e at m/z 664.12 in the mobility isolation window 1.1224-1.1400 Vs/cm\u003csup\u003e2\u003c/sup\u003e showed the detection of fragment ions at m/z 136.06, m/z 232.08, m/z 348.07, and m/z 428.04. B. Spatial distribution of the four NAD\u003csup\u003e+\u003c/sup\u003e fragment ions detected by iprm-PASEF in one WT and one CD38\u003csup\u003e-/-\u003c/sup\u003e mouse liver replicate. Each fragment ion was quantified \u003cem\u003ein situ\u003c/em\u003e using the pixel intensity within each tissue section, which all demonstrated an up-regulated level in CD38\u003csup\u003e-/-\u003c/sup\u003e vs WT mouse livers. The statistical analysis was performed by comparing the MS2 intensity of each fragment ion in each pixel which composed the CD38\u003csup\u003e-/-\u003c/sup\u003e vs WT image. **** Student’s t-test p-value \u0026lt; 0.0001 (n = 1885 pixels for the WT image and n = 2300 pixels for the WT image, see \u003cstrong\u003eSupplemental Table 3\u003c/strong\u003e; spatial tissue mapping for one mouse and one image replicate per condition). Fragmentation and \u003cstrong\u003ecleavage sites of identified NAD\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e fragment ions are mapped in the NAD\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e structure.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5NADQuantification20250519v12.png","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/94e887088aaffa64ee904637.png"},{"id":100370563,"identity":"f4bde9c0-ee0d-4365-a3b5-242ea72fcc04","added_by":"auto","created_at":"2026-01-16 08:06:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9409758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/621b56ee-c11b-4bf1-9c00-cfc19d15c485.pdf"},{"id":84667259,"identity":"695ef196-3f18-48bc-8c45-1a11f55bd176","added_by":"auto","created_at":"2025-06-16 05:54:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14429,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalInformationlegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/ab9ae51de22d456704b64f19.docx"},{"id":84667254,"identity":"bc407ed1-09f9-4d50-ba15-cf06875b6615","added_by":"auto","created_at":"2025-06-16 05:54:14","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":563625,"visible":true,"origin":"","legend":"Supplementary Figure 1","description":"","filename":"SuppFigure1iprmisolation20250421v4.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/1c9e70a825ff7c9aabdd796b.pptx"},{"id":84667263,"identity":"93248b98-1de7-4a5b-b7b1-011e8e07815e","added_by":"auto","created_at":"2025-06-16 05:54:15","extension":"pptx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":712027,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 2\u003c/p\u003e","description":"","filename":"SuppFigure2iprmlipid364MSMS0421v6.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/f70de36bc1d423ce40463048.pptx"},{"id":84667250,"identity":"83db9730-54b1-4da2-a70d-1512a80c79fa","added_by":"auto","created_at":"2025-06-16 05:54:14","extension":"pptx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":952291,"visible":true,"origin":"","legend":"Supplementary Figure 3","description":"","filename":"SuppFigure3coisolation0421v5.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/7d7f558a6583ac74361c2ebe.pptx"},{"id":84667256,"identity":"6f3d988e-0feb-4e1a-8e41-53408de8840e","added_by":"auto","created_at":"2025-06-16 05:54:14","extension":"pptx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":102750,"visible":true,"origin":"","legend":"Supplementary Figure 4","description":"","filename":"SuppFigure4LCMSMSADPRcADPR250421v6.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/2ab508050d264b02686dc009.pptx"},{"id":84667267,"identity":"d2d2b60b-f068-442b-ba57-856059523345","added_by":"auto","created_at":"2025-06-16 05:54:15","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11550,"visible":true,"origin":"","legend":"Supplementary Table 1","description":"","filename":"SupplementalTable1iprmIsolationSettingsv2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/38aa66ed4ca0a639915c1210.xlsx"},{"id":84667269,"identity":"05f01414-524c-4b44-866e-cf7d3888f86c","added_by":"auto","created_at":"2025-06-16 05:54:15","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1679158,"visible":true,"origin":"","legend":"Supplementary Table 2","description":"","filename":"SupplementalTable2ADPRFragmentIonQuantificationv3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/7f8105e279e987e05d747570.xlsx"},{"id":84667268,"identity":"eb9a52dd-8fb7-4b96-92ea-53db77eb6516","added_by":"auto","created_at":"2025-06-16 05:54:15","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":222130,"visible":true,"origin":"","legend":"Supplementary Table 3","description":"","filename":"SupplementalTable3NADFragmentIonsQuantificationv3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6743284/v1/7d8e15fc40580d7ad6509158.xlsx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nDr. Birgit Schilling is on the advisory board for MOBILion Systems, Chadds Ford, PA. Dr. Nannan Tao is an employee of Bruker, San Jose, CA. Dr. Eric Verdin has research support from NIA/NIH, NIAID and Hevolution Foundation, and holds stock in; BioAge Labs, EDIFICE Health, Deciduous Therapeutics, Deep Longevity, Circulate, AVIV and DOC, and serves on an advisory board for; Longevity Clinic, Juvenescence, IHLAD, IHU, Ani Biome, Whoop, VERO, Lotus AI, Fountain Life, Longevity Science, and Longevity \u0026 Rejuvenation (The Royal Clinics of the Custodian of the Two Holy Mosques, Saudi Arabia). The terms of this arrangement have been reviewed and approved by the Buck Institute in accordance with its policy on objectivity in research.","formattedTitle":"\u003cp\u003eMultiplexed Targeted Spatial Mass Spectrometry Imaging Assays to monitor lipids and NAD\u003csup\u003e+\u003c/sup\u003e metabolites in CD38 knockout mice exhibiting improved metabolism\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMass spectrometry imaging (MSI) is a rapidly advancing application of mass spectrometry (MS) allowing for the unbiased spatial assessment of a variety of analytes and biological samples. MSI promises the capability to image thousands of molecules, such as metabolites, lipids, peptides, proteins, and glycans in tissues, while preserving their original spatial tissue localization.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e This allows for the combination of the power, sensitivity, and specificity of mass spectrometry with spatial molecular details that can reveal novel biological regulation. Access to spatially resolved molecular features (proteins, glycans, lipids and metabolites) is instrumental to understand high tissue heterogeneity with localized hot spots of tissue damage, specifically during aging, (age-related) diseases and clinical applications. Using global proteomics/glycomics/lipidomics or metabolomics these spatial features are very challenging to capture, and indeed, often remain undetected or non-significant in \u0026lsquo;bulk-style\u0026rsquo; analyses. On the other hand, sophisticated applications of spatial MS Imaging exist with multiple sources and ionization strategies, such as desorption electrospray ionization (DESI), laser ablation electrospray ionization (LAESI), liquid extraction surface analysis (LESA), liquid-micro junction-surface sampling probe (LMJ-SSP), infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI), and nanoscale secondary ion mass spectrometry (NanoSIMS).\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Another popular approach for MSI is matrix-assisted laser desorption/ionization (MALDI)-MSI which utilizes a deposited matrix-enhanced analyte ionization in place of laser ablation. MALDI-MSI allows for the discrete collection of pixel-specific mass spectra that are then assembled to generate a visual representation of the molecular species throughout the imaged region.\u003c/p\u003e \u003cp\u003eSeveral of the existing imaging techniques are limited to MS1 level analysis and do not utilize precursor ion fragmentation to increase identification confidence or improved quantification at the fragment ion (MS2) level. Or, in other cases MS/MS spatial imaging is often limited to selection of one analyte for MS/MS per acquisition covering a spatial region of interest (ROI). Powerful applications of MSI, such as DESI mass spectrometry, use a liquid-based probe to collect analytes in place and then analyze them by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a spatially resolved manner.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e However, DESI-MS may present challenges with humidity and extraction efficiencies, as well as present resolution challenges that stem from the continuous solvent flow and the size of the liquid bridge formed between the tissue sample and the DESI probe.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Herein, we detail a novel workflow for MALDI-MSI that employs targeted imaging parallel reaction monitoring (iprm) in combination with PASEF that extends MALDI-MSI analysis from the MS1 level to the tandem mass spectrometry (MS2) dimension for \u003cem\u003ein situ\u003c/em\u003e analysis of lipids and metabolites (or other analytes) in biological samples. Importantly, multiple MS/MS spectra can be acquired using iprm-PASEF in a single imaging tissue MSI workflow enabling multiplexed and targeted MS2-based spatial strategies.\u003c/p\u003e \u003cp\u003eParallel reaction monitoring (PRM) is a mass spectrometry technique used for the quantification and identification of targeted analytes in complex biological samples.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e PRM is valued for its high sensitivity, specificity, and accuracy in proteomic studies. For high-resolution PRM, precursor ions (at specific m/z and retention times) are isolated in a targeted way and fragmented. The resulting fragment ions were subsequently analyzed with high-resolution MS detectors, such as Orbitraps or time-of-flight detectors (Q-TOF)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This allows for multiplexed simultaneous analysis of all fragment ions with high resolution and high accuracy (post-acquisition), in contrast to low-resolution single reaction monitoring (SRM) or multiple reaction monitoring (MRM) which typically require extensive optimization of Q1/Q3 transitions\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Due to the high resolution and post-acquisition refinement of fragment ion selection for quantification, PRM-MS generally is not as prone to interferences, and it is particularly efficient for fast developments of multiplexed assays. The innovative, spatial imaging parallel reaction monitoring (iprm) for MALDI-MSI, presented in this study, uses trapped ion mobility spectrometry (TIMS) to separate precursor ions based on their collisional cross section (CCS) values\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The addition of ion mobility separation to MALDI-MSI provides an orthogonal dimension of analyte separation, reduction of complexity and specificity to MSI experiments. The TIMS dimension allows for another level of isolation of precursor ions (analytes), specifically differentiating isomeric lipids/metabolites (with identical m/z but different CCS), in addition to the isolation based on the analyte m/z only prior to the spatial tandem mass spectrometry (MS/MS) imaging.\u003c/p\u003e \u003cp\u003eNAD\u003csup\u003e+\u003c/sup\u003e (nicotinamide adenine dinucleotide) metabolism is of key relevance for maintaining cellular health, playing crucial roles in energy metabolism, DNA repair, transcriptional regulation, immune function and inflammation.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Many clinical conditions implicate dysregulated NAD\u003csup\u003e+\u003c/sup\u003e metabolism, including neurodegeneration, cancer, immune disorders, and cardio and metabolic diseases.\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Aging and age-related diseases and cellular conditions, such as cellular senescence, are also associated with declines in NAD\u003csup\u003e+\u003c/sup\u003e and disruptions in NAD\u003csup\u003e+\u003c/sup\u003e-related metabolic processes, positioning NAD\u003csup\u003e+\u003c/sup\u003e as a significant target for biomedical research.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e In the liver, reduced NAD\u003csup\u003e+\u003c/sup\u003e levels have been associated with non-alcoholic fatty liver disease (NAFLD) and other age-related metabolic dysfunctions.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e CD38, is a potent enzymatic consumer of NAD\u003csup\u003e+\u003c/sup\u003e, and shows increased expression with age in several tissues.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In liver tissue, CD38 is predominantly expressed in Kupffer cells, the resident macrophages of the liver sinusoids, where its activity drives cellular senescence and inflammation with aging.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Thus, CD38 is considered as a major driver of the age-related NAD\u003csup\u003e+\u003c/sup\u003e decline. Notably, CD38 knockout mice (CD38\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e) exhibit higher mitochondrial NAD\u003csup\u003e+\u003c/sup\u003e levels in the liver and resistance to age-related NAD\u003csup\u003e+\u003c/sup\u003e decline, suggesting improved metabolic regulation and protection against aging-associated metabolic deterioration.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAdditionally, NAD\u003csup\u003e+\u003c/sup\u003e supplementation has emerged as a promising therapeutic strategy to enhance cellular function and mitigate age-related decline.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Given the importance of hepatic NAD\u003csup\u003e+\u003c/sup\u003e metabolism in systemic metabolic regulation, understanding how NAD⁺ and its related metabolites are spatially distributed within the liver could have significant therapeutic implications. We utilized our optimized iprm-PASEF assays to spatially map and precisely quantify lipids, NAD⁺, and related metabolites in liver tissues from wild-type (WT) and CD38 knockout (CD38⁻/⁻) mice. Here, we present our advanced multiplexed iprm-PASEF methodology, showcasing its capability for robust spatial MS imaging, identification, and accurate quantification of lipids and metabolites directly within liver tissue sections.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFresh livers from three male, 18-month-old CD38\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e and control (WT) mice were collected, flash frozen, sectioned onto glass slides and prepared for MS imaging with the Bruker timsTOF fleX MALDI-2 mass spectrometer. CD38\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e animals were chosen for the spatial profiling of lipids and metabolites \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, due to their well-characterized increase in hepatic NAD\u003csup\u003e+\u003c/sup\u003e levels with age compared to control (WT) counterparts\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Samples were first subjected to unbiased MS1 imaging in both negative and positive ionization modes for survey scans of the whole tissue cross-section. Preliminary identification of interesting candidate MS1 precursor ions, based on accurate m/z, isotope pattern, and ion mobility values (1/K\u003csub\u003e0\u003c/sub\u003e), was completed with the T-ReX\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e feature finding algorithm in SCiLS Lab, and features were annotated directly in SCiLS by connecting to the MetaboScape 2025 REST API feature \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. To prioritize, candidate precursor ions that showed a predictive score difference of \u0026gt;\u0026thinsp;0.6 comparing CD38\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e and WT liver tissues with the area under the receiving operator curve (AUROC) analysis were considered for the subsequent iprm-PASEF analysis. The iprm-PASEF target lists were assembled so that the candidate ions did not overlap in its 1/K\u003csub\u003e0\u003c/sub\u003e range based on the MS1 survey scans. For the iprm-PASEF acquisitions each candidate precursor ion was isolated first by their ion mobility 1/K\u003csub\u003e0\u003c/sub\u003e value in the TIMS cell, then by its m/z value in quadrupole Q1, and then finally fragmented in the Q2 collision cell. The MS/MS spectra were acquired in this targeted, sequential, and spatially resolved manner \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. All fragment ions for each analyte shared the same mobility isolation window as their precursor ion but had unique m/z values corresponding to lipid or metabolite fragment ions (as annotated in existing databases/spectral libraries, see methods). Spatial maps were generated for each fragment ion that matched the spatial distribution of the corresponding precursor ion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo demonstrate the ability for isolation and accurate identification of individual lipids and metabolites from complex biological matrices by iprm-PASEF, in our data sets, we first targeted a well-characterized and annotated feature at m/z 885.55 corresponding to phosphatidylinositol PI(18:0/20:4) as reported in previous studies.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e In negative ion mode this feature at m/z 885.55 was abundant in the MS1 spectra for all liver sections imaged \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e).\u003c/b\u003e The collisional cross sections (CCS) that was observed for the m/z 885.55 in the MS1 survey scan showed a prominent feature at 1/K\u003csub\u003e0\u003c/sub\u003e 1.47 Vs/cm\u003csup\u003e2\u003c/sup\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. This feature was then isolated \u003cb\u003e(Supp.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e based on its unique CCS (ion mobility) and m/z value combination allowing distinct analysis of this molecular feature separated from other features present in the liver tissue. The isolated candidate precursor ion was fragmented resulting in multiple abundant fragment ions. Importantly, the fragment ions all shared the same 1/K\u003csub\u003e0\u003c/sub\u003e value as the isolated precursor ion, which allowed for generating a MS/MS spectrum of the fragmented [M-H]\u003csup\u003e\u0026minus;\u003c/sup\u003e precursor ion accordingly \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Fragment ions were annotated based on m/z values from existing databases and spectral libraries for lipid fragmentation. Major characteristic fragment ions corresponded to structures, such as stearic acid C(18:0) at m/z 283.26 and arachidonic acid C(20:4) at m/z 303.23 as well as several ions containing unique combinations of the phosphatidylinositol head group with the characteristic lipid tails at m/z 419.26, 581.31, and 599.32, among others \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Combined, these confidently confirmed the identity of the precursor ion as PI(18:0/20:4). At the given MS/MS settings the precursor [M-H]\u003csup\u003e\u0026minus;\u003c/sup\u003e ion at m/z 885.55 was still detectable even though at a significantly lower intensity than in the MS1 spectrum. MS/MS fragmentation was completed in a spatial raster manner at distinct tissue locations to generate the MS/MS imaging data set of fragment ions spatially across the liver sections \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e).\u003c/b\u003e This showed that each fragment ion generated from one precursor ion displayed the same spatial distribution, sharing similar areas of high and low abundance across the tissue. Going forward, iprm-PASEF quantification can thus be performed on the fragment ion level for higher specificity and accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe survey MS1 scans revealed the regions in the composite MALDI-MS spectrum where different lipid isotope patterns were observed. One area of interest was the m/z range from m/z 857 to m/z 861 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Initial annotation (based on databases) of the intense peak at m/z 857.52 returned two possible isomeric phosphatidylinositols PI(36:4), each with 36 carbon atoms and a total of 4 unsaturated double bonds, PI 36:4(16:0/20:4) and PI 36:4(14:0/22:4). However, an overlapping isotopic distribution was observed, as the third isotopic peak (m/z 859.53) showed higher intensity than one would expect for a PI 36:4 (PI 36:4), suggesting the presence of a PI 36:3 (at m/z 859.53). Moreover, the spatial pattern for this ion at m/z 859.53, as determined by MALDI-MSI analysis, differed from that at m/z 857.52 (data not shown). The iprm-PASEF analysis was completed to ascertain the identities of these related lipid species. First, the feature at m/z 857.52 was isolated and fragmented for analysis. Characteristic fragment ions for PI 36:4(16:0/20:4) were detected in the isolated mobility window at 1/K\u003csub\u003e0\u003c/sub\u003e 1.4308\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0093 Vs/cm\u003csup\u003e2\u003c/sup\u003e corresponding to palmitic acid C(16:0) at m/z 255.23 and arachidonic acid C(20:4) at m/z 303.23 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e).\u003c/b\u003e Many other fragment ions were also detected that further confirmed the identity of this lipid, including several head group fragment ions validating the identity of the phosphatidylinositol lipid family \u003cb\u003e(Supp.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Some fragment ions detected in the iprm mobility window were unique to this MALDI-MSI iprm-PASEF experiment and did not match to annotated fragment ions from LC-MS analysis of this lipid. Also, characteristic fragment ions of the potential isomer, PI 36:4(14:0/22:4), including features at m/z 227.20 (myristic Acid 14:0) and m/z 331.26 (cholesterol ester 22:4), were not detected on the iprm mobilogram for this isolated feature \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. This enabled us to resolve its identity as PI 36:4(16:0/20:4) and confidently differentiate it from its potential isomer PI 36:4(14:0/22:4). Similar analysis was completed for the precursor ion at m/z 859.53 \u003cb\u003e(Supp.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e identifying this feature as a combination of two different PI isoforms (for PI 36:3, 3 double bonds), specifically: PI (18:1/18:2) and PI (16:0/20:3), which were distinct from the two Dalton lower mass identified at m/z 857.52 as PI 36:4(16:0/20:4) with 4 double bonds as described in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e above.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCD38 is a multifunctional enzyme with a predominant role as an NAD\u003csup\u003e+\u003c/sup\u003e glycohydrolase, catalyzing the hydrolysis of NAD\u0026thinsp;+\u0026thinsp;to ADP-ribose (ADPR) and nicotinamide. Additionally, CD38 exhibits ADP-ribosyl cyclase activity, generating cyclic ADP-ribose (cADPR) from NAD\u003csup\u003e+\u003c/sup\u003e, though this constitutes a minor fraction of its total catalytic function. Furthermore, CD38 functions as a cADPR hydrolase, hydrolyzing cADPR to ADPR. Collectively, these enzymatic activities contribute to the regulation of NAD\u003csup\u003e+\u003c/sup\u003e metabolism and calcium signaling, which are integral to cellular homeostasis. To identify optimal matrix conditions for MSI of NAD\u003csup\u003e+\u003c/sup\u003e and its metabolites, we analyzed synthetic standards of NAD+, ADPR, and cADPR, individually spotted onto glass slides using various commonly used matrices. Dihydroxyacetophenone (DHAP\u003cem\u003e)\u003c/em\u003e was used for positive ionization mode acquisitions and N-naphthylethylenediamine dihydrochloride (NEDC) was used for negative ionization mode. A representative mobilogram overview of the cADPR, ADPR, and NAD\u003csup\u003e+\u003c/sup\u003e standards highlighted their distinct ion mobility drift times (1/K\u003csub\u003e0\u003c/sub\u003e values) and indicated efficient separation in the TIMS dimension, while \u003cem\u003ein situ\u003c/em\u003e fragmentation of the standards generated MS/MS spectra for each metabolite using iprm-PASEF \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The fragmentation patterns obtained through iprm-PASEF showed strong concordance with those derived from conventional liquid chromatography-tandem mass spectrometry (LC-MS/MS) \u003cb\u003e(Supp.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, where both methodologies detected characteristic fragment ions for each metabolite investigated.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eGiven that CD38 catalyzes NAD\u003csup\u003e+\u003c/sup\u003e hydrolysis, we hypothesized that CD38\u003csup\u003e⁻/⁻\u003c/sup\u003e liver would exhibit elevated NAD\u003csup\u003e+\u003c/sup\u003e levels and a concurrent decrease in enzymatic by-products, such as ADPR and nicotinamide. To evaluate this, spatial MALDI MSI was performed for WT and CD38\u003csup\u003e⁻/⁻\u003c/sup\u003e liver sections in negative ionization mode using NEDC matrix to monitor endogenous metabolites (n\u0026thinsp;=\u0026thinsp;3 biological replicates per genotype). The MS1 survey scans revealed a marked reduction in ADPR (m/z 558.06) in CD38\u003csup\u003e⁻/⁻\u003c/sup\u003e liver, with almost negligible signal intensity in CD38\u003csup\u003e⁻/⁻\u003c/sup\u003e liver compared to WT liver \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. To further validate this observation, an ADPR standard was analyzed again, now in negative ionization mode \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e which confirmed its characteristic fragmentation pattern. The optimized endogenous iprm-PASEF assay, incorporating MS2 quantification of the characteristic ADPR fragment ion at m/z 346.06 \u003cem\u003ein situ\u003c/em\u003e, demonstrated a significant spatial reduction in ADPR abundance in the CD38\u003csup\u003e⁻/⁻\u003c/sup\u003e tissue samples compared to WT: with 5.6-fold downregulation in CD38\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e across 3 biologically distinct replicates \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. These findings provided a proof-of-principle demonstration for CD38-mediated NAD\u003csup\u003e+\u003c/sup\u003e metabolism in hepatic tissue and validated the iprm-PASEF approach as a robust technique for \u003cem\u003ein situ\u003c/em\u003e spatial quantification of NAD\u003csup\u003e+\u003c/sup\u003e metabolites at the MS2 fragment ion level.\u003c/p\u003e \u003cp\u003eDue to its highly polar and charged nature, NAD\u003csup\u003e+\u003c/sup\u003e presents challenges with consistent ionization, which can impact detection sensitivity and quantification accuracy, particularly in the presence of ion suppression from lipids. In addition, NAD\u003csup\u003e+\u003c/sup\u003e has in the past been shown to be prone to degradation thus this spatial imaging workflow may be advantageous due to very minimal sample handling\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In this study, we demonstrated efficient \u003cem\u003ein situ\u003c/em\u003e detection and fragmentation of NAD\u003csup\u003e+\u003c/sup\u003e in tissue using the iprm-PASEF approach. The iprm-PASEF workflow exhibited the capability to isolate and fragment the NAD\u003csup\u003e+\u003c/sup\u003e precursor ion with [M\u0026thinsp;+\u0026thinsp;H]\u003csup\u003e+\u003c/sup\u003e at m/z 664.12 in positive ionization mode with 2,5-dihydroxybenzoic acid (DHB) matrix \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e).\u003c/b\u003e The presence of characteristic NAD\u003csup\u003e+\u003c/sup\u003e fragment ions in the ion mobility isolation window, for the isolated precursor at m/z 664.12, including fragment ions at m/z 136.06, 232.08, 348.07, and 428.04 confirmed the identity of the isolated metabolite as NAD\u003csup\u003e+\u003c/sup\u003e and subsequently allowed for accurate quantification. For the spatial imaging using iprm-PASEF, specifically, the MS2-level quantification and spatial imaging of each fragment ion revealed a significant increase in NAD\u003csup\u003e+\u003c/sup\u003e levels in CD38\u003csup\u003e⁻/⁻\u003c/sup\u003e liver tissue compared to WT, ~\u0026thinsp;20-fold with very high statistical significance p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. These analyses further validated the role of CD38 in NAD\u003csup\u003e+\u003c/sup\u003e metabolism and the robustness of the iprm-PASEF technique for spatially resolved metabolite quantification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe past decades have been marked by tremendous developments in omics technologies that have been instrumental for the in-depth exploration of complex biological systems, giving insights into molecular mechanisms and proposing novel clinical/therapeutic strategies. While very powerful, these technologies typically do not provide information about the native spatial context of biomolecules, which is highly relevant and critical to better appreciate the heterogeneity, architecture, and three-dimensional molecular organization of tissues. These needs have led to the rise of spatial omics technologies. Over the past five years, two spatial omics fields have been recognized as \u0026lsquo;Method of the Year\u0026rsquo; by the journal \u003cem\u003eNature Methods\u003c/em\u003e, spatial transcriptomics in 2020\u003csup\u003e28\u003c/sup\u003e and spatial proteomics in 2024\u003csup\u003e29\u003c/sup\u003e, demonstrating the powerful and crucial roles of these cutting-edge technologies to advance our understanding of biological complexity. Currently, even further advances are being made to improve the capabilities of these mass spectrometry imaging (MSI) techniques, especially in their ability to accurately identify and quantify complex biomolecules in place on tissue.\u003c/p\u003e \u003cp\u003eWith respect to metabolomics and lipidomics, significant advances have been made in spatial mass spectrometry to investigate the spatial distribution of these molecules within tissues/organs. However, \u003cem\u003ein situ\u003c/em\u003e identification remains very challenging. Here, we present the first study demonstrating the capabilities of imaging parallel reaction monitoring \u003cb\u003e\u0026ndash;\u003c/b\u003e Parallel Accumulation\u0026ndash;Serial Fragmentation (iprm-PASEF) on lipids and metabolites for high-resolution, spatially resolved targeted MALDI-MS/MS Imaging. MALDI-MSI with iprm-PASEF technology allows for high accuracy analyte identification and quantification in real-time on the MS2 fragment ion level during imaging experiments. This novel workflow enables confident and accurate identification of complex and challenging molecules thanks to the acquisition of collision cross section (CCS)-enabled MS/MS spectra, while preserving their spatial information. Indeed, this strategy leverages the trapped ion mobility spectrometry (TIMS) technology to isolate molecular targets, first in the TIMS device based on their inverse reduced mobility (1/K\u003csub\u003e0\u003c/sub\u003e) and then in the quadrupole based on their mass-to-charge ratio (m/z). Isolated precursor ions are subsequently fragmented to collect high-resolution, full MS/MS spectra, while providing spatial information of both precursor and fragment ions. Ion mobility separation for lipid precursor ions is essential for proper identification and differentiation among many potential isoforms. MS2-level analysis further increases identity confidence through isolation and fragmentation of precursor ions of interest and subsequent detection of fragment ions that can be individually verified. The iprm-PASEF workflow utilizes the full capabilities of the timsTOF fleX and leverages the combination of ion mobility separation and precursor ion isolation and fragmentation to identify and quantify complex molecular features accurately. This workflow achieves label-free multiplexed and spatial multi-omic MS/MS imaging and quantification within a single acquisition.\u003c/p\u003e \u003cp\u003eThe generated iprm-PASEF MS/MS spectra contain characteristic fragment ions that provide direct evidence for the \u003cem\u003ein situ\u003c/em\u003e identification of lipids and metabolites present in tissues. The similar spatial distribution of the different fragment ions resulting from the same precursor ion provides an additional validation of the molecular identification. We showed that we can leverage the detection of characteristic fragment ions to decipher very complex and convoluted lipid profiles, as demonstrated for the well-studied and abundant Phosphatidylinositol PI(18:0/20:4) but also for more challenging and lower abundance features. The similarity in spatial patterns for identified fragment ions of PI(18:0/20:4) increased the confidence in isolated feature identification. Indeed, all fragment ions shared the same spatial pattern as the isolated precursor ion, signifying that all fragment ions were generated from a single precursor ion. Further, the ion mobility-resolved fragmentation pattern of the lipid PI 36:4(16:0/20:4) enabled highly-confident identification of this feature and enabled its differentiation from its structural isomer PI 36:4(14:0/22:4). This exemplified the ability for iprm-PASEF to accurately resolve lipids utilizing complex fragmentation patterns for multiple features across a dynamic abundance range. The ability to accurately identify analytes in place during iprm-PASEF experiments removes the need for orthogonal mass spectrometry technologies to confirm analyte identifications, significantly increasing the ability of MALDI-MSI in discovery-based studies.\u003c/p\u003e \u003cp\u003eBesides confident identification, we demonstrated that the iprm-PASEF strategy can achieve quantification at the MS2 fragment ion level, using spatially resolved fragment ions, enabling more specific and sensitive performances. Similar to targeted LC-MS PRM assays developed for targeted (non-spatial) proteomic approaches, iprm-PASEF experiments can be refined post-acquisition to select the most intense, non-interfered, specific and selective, fragment ions for the analytes of interest for their accurate and robust quantification, while additionally providing a spatially defined context. This represents an asset in the context of complex and convoluted metabolome and lipidome analysis, as fragment ions differentiating co-fragmented species can be specifically used to ensure accurate quantification. We developed and optimized novel iprm-PASEF assays for \u003cem\u003ein situ\u003c/em\u003e quantification of endogenous NAD and ADPR metabolites in mouse livers for. Through confident identification of these highly selective fragment ions, we demonstrated the ability for high confidence, specificity, and accuracy quantification for lipids and metabolites during MALDI-MSI experiments on the MS2 fragment ion level.\u003c/p\u003e \u003cp\u003eOur application of the iprm-PASEF workflow to CD38 and NAD\u003csup\u003e+\u003c/sup\u003e biology demonstrates its power to better understand the underlying molecular mechanisms and address biological questions. NAD\u003csup\u003e+\u003c/sup\u003e is a central redox coenzyme critical for cellular metabolism, supporting key oxidative and reductive reactions and serving as a substrate for NAD\u003csup\u003e+\u003c/sup\u003e-consuming enzymes such as sirtuins, PARPs, and the NADase CD38. With age, NAD\u003csup\u003e+\u003c/sup\u003e levels decline in multiple tissues, including the liver and ovary, contributing to mitochondrial dysfunction and impaired metabolism\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Our findings, along with independent studies, implicate CD38 as a major driver of this decline through its NADase activity, which hydrolyzes NAD\u003csup\u003e+\u003c/sup\u003e primarily into nicotinamide and ADP-ribose (ADPR) \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile global (bulk) NAD\u003csup\u003e+\u003c/sup\u003e quantification by LC-MS is well established, understanding its spatial distribution is essential for elucidating compartment-specific metabolic regulation of NAD\u003csup\u003e+\u003c/sup\u003e and related metabolites. Mass spectrometry imaging offers spatial resolution but - in some cases - faces challenges in detecting small, labile metabolites like NAD\u003csup\u003e+\u003c/sup\u003e and ADPR due to ion suppression from abundant proteins and lipids, as well as difficulties in metabolite annotation. To overcome these limitations, prior studies have employed HRMS/MS and spiking of NAD\u003csup\u003e+\u003c/sup\u003e standards onto control tissue\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For instance, Lu et al. demonstrated that acidified methanol washes reduce ion suppression and, when combined with isotopic labeling, allow confident identification of NAD\u003csup\u003e+\u003c/sup\u003e and related metabolites\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In this study, we utilized the iprm-PASEF strategy to map the spatial distribution of NAD\u003csup\u003e+\u003c/sup\u003e and ADPR in wild-type and CD38 knockout mouse livers. Using refined fragment ions\u0026rdquo;, \u003cem\u003ei.e.\u003c/em\u003e the fragment ion at m/z 346.06 for ADPR (in negative ionization mode) and the panel of fragment ions at m/z 136.06, m/z 232.08, m/z 348.07, and m/z 428.04 for NAD\u003csup\u003e+\u003c/sup\u003e (in positive ionization mode), we were able to obtain spatially resolved MS2-based quantification and further conduct statistical analysis to confirm their highly significant increase upon CD38 deletion in mouse livers. Additionally, we optimized matrix formulations utilizing both DHAP and DHB to enhance NAD\u003csup\u003e+\u003c/sup\u003e ionization during MALDI MS Imaging experiments from both slide-spotted standards and directly from tissue sections, further improving MSI sensitivity and reliability.\u003c/p\u003e \u003cp\u003eWe envision complementary, synergistic, and multi-modal approaches combining global LC-ESI-MS/MS and spatial MALDI-MS/MS (iprm-PASEF) Imaging to i) generate ESI-based spectral libraries to help interpret MALDI-MS/MS spectra and improve identification rate in MS-imaging assays, ii) inform on the spatial distribution of analytes of interest identified by ESI-MS/MS experiments, iii) aid in integrating multi-omics measurements and data. This novel iprm-PASEF workflow opens the way to novel strategies for advanced spatial metabolomics, lipidomics, and proteomics \u0026ndash; including \u003cem\u003ein situ\u003c/em\u003e tryptic peptides\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e or any other proteolytic peptides/proteins, that will be pivotal in obtaining a more holistic and refined picture of the dynamic organization and regulation of biological systems. With the multiplexed capabilities of iprm-PASEF in performing high-quality identification and simultaneous quantification for multiple complex lipids and biologically relevant small molecule metabolites, we have demonstrated the importance and future opportunities of MS2-based spatial mass spectrometry imaging. Specific future interests could be in spatial mapping of tissues highlighting therapeutic drug distribution, regional biomarker responses upon interventions, and overall tissue heterogeneity during aging and age-related diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eChemicals and Samples\u003c/h2\u003e \u003cp\u003eFormic acid, acetonitrile (ACN), trifluoroacetic acid (TFA), methanol, water (all LCMS grade), 2,5-dihydroxyacetophenone (DHAP), 2,5-dihydroxybenzoic acid (DHB) N-naphthylethylenediamine dihydrochloride (NEDC), and chemical standards for nicotinamide adenine dinucleotide (NAD+), ADP-ribose (ADPR) and cyclic ADP-ribose (cADPR) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Livers from mice with global deletion of CD38 (CD38\u003csup\u003e-/-\u003c/sup\u003e) and control, litter mate wild-type (WT) mice (18 months old) were collected at the Buck Institute for Research on Aging.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample preparation for spatial iprm-PASEF imaging\u003c/h3\u003e\n\u003cp\u003eLivers were collected from CD38\u003csup\u003e-/-\u003c/sup\u003e mice and WT controls (n\u0026thinsp;=\u0026thinsp;3 biological replicates each) and flash frozen with liquid nitrogen to preserve lipids and metabolites. Frozen livers were mounted directly to a cryotome (Leica, Germany) pre-chilled to -20\u0026deg;C using a cellulose-based mounting media (M-1 Embedding Matrix, Epredia, Kalamazoo, MI). The portal vein was used to standardize the orientation of the left major lobe of each liver. Tissue sections with 10\u0026ndash;12 \u0026micro;m thicknesses were mounted on charged microscope slides. Tissues slides were desiccated and then stored under vacuum at \u0026minus;\u0026thinsp;80\u0026deg;C prior to matrix application. Chemical standards for NAD\u003csup\u003e+\u003c/sup\u003e, ADPR, cADPR were dissolved in methanol at a concentration of 10 \u0026micro;M or 1 \u0026micro;M and spotted onto bare microscope slides with a pipette. Slides were desiccated to evaporate methanol. The HTX TM Sprayer M3 was used to apply matrix by aerosol\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. For positive ionization mode experiments, 15 mg/mL DHAP or DHB in 80% ACN, 10% methanol in water was applied with the following parameters: flow rate 120 \u0026micro;L/min, number of passes 6, temperature 70\u0026deg;C, nitrogen gas pressure 10 psi, pattern CC, velocity 1200 mm/min, drying time 5 s. For negative ionization mode experiments, 7 mg/mL NEDC in 70% methanol in water was applied with the following parameters: flow rate 150 \u0026micro;L/min, number of passes 10, temperature 70\u0026deg;C, nitrogen gas pressure 10 psi, pattern HH, velocity 700 mm/min, drying time 0 s.\u003c/p\u003e\n\u003ch3\u003eMALDI-MS Imaging Analysis\u003c/h3\u003e\n\u003cp\u003eMALDI-TIMS-MS1 acquisitions\u003c/p\u003e \u003cp\u003eMALDI-MS Imaging was performed using a Trapped Ion-Mobility Time-of-Flight Mass Spectrometer, the timsTOF fleX MALDI-2 (Bruker Scientific, LLC, Bremen, Germany) equipped with a dual ESI/MALDI source. Survey MS1 scans were acquired with single-scan spectra consisted of 200 accumulated laser shots at 1 kHz. MALDI-MS images were acquired at a 20 \u0026micro;m pixel resolution for both polarity ion imaging experiments. The SmartBeam 3D 10 kHz laser was set to 35% power, scan range of 16 \u0026micro;m for X and Y and resulting field size of 20 \u0026micro;m for X and Y. Additional instrument parameters for the studies included: an ion transfer time of 50 \u0026micro;s, a pre pulse storage time of 60\u0026ndash;80 \u0026micro;s, a collision RF of 800\u0026ndash;1000 Vpp, a collision energy of 10 eV, an ion energy in the quadrupole of 5.0 eV, a TIMS funnels 1 RF and 2 RF of 250 Vpp, and a multipole RF of 250 Vpp. Negative ionization mode acquisitions targeting lipids ranged from m/z 150\u0026ndash;1100 and 1/K\u003csub\u003e0\u003c/sub\u003e 0.4\u0026ndash;1.8 VS/cm\u003csup\u003e2\u003c/sup\u003e with a 100-m/s ramp time. Positive ionization mode acquisitions, tuned for small molecule metabolites specifically, ranged from m/z 100\u0026ndash;1000 and 1/K\u003csub\u003e0\u003c/sub\u003e 0.4\u0026ndash;1.4 VS/cm\u003csup\u003e2\u003c/sup\u003e with a 200-ms ramp time.\u003c/p\u003e \u003cp\u003eiprm-PASEF acquisitions\u003c/p\u003e \u003cp\u003eiprm-PASEF was performed in both positive and negative ionization acquisition modes. Main instrument settings were not adjusted between MS1 and MS2 acquisitions. Both modes used a mobility-based isolation strategy that isolated precursor ions by a 1/K\u003csub\u003e0\u003c/sub\u003e window first followed by isolation by m/z. Collision energy was determined by a linear fit based on precursor mobility ranging from 35 eV at 1/K\u003csub\u003e0\u003c/sub\u003e 0.8 VS/cm\u003csup\u003e2\u003c/sup\u003e to 60 eV at 1/K\u003csub\u003e0\u003c/sub\u003e 1.6 VS/cm\u003csup\u003e2\u003c/sup\u003e. No isolated precursor ions had a mobility greater than 1/K\u003csub\u003e0\u003c/sub\u003e 1.6 VS/cm\u003csup\u003e2\u003c/sup\u003e. iprm-PASEF precursor isolation settings including m/z and mobility ranges can be found in \u003cb\u003eSupplemental Table\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMALDI-MSI data analysis\u003c/p\u003e \u003cp\u003eAfter MS acquisition, the data was imported into SCiLS Lab Pro v2025a (Bruker Scientific, LLC, Bremen, Germany) for initial MS1 data visualization and iprm-PASEF target list assembly. Metaboscapev.2025, (Bruker, Germany) was used to annotation analytes using existing or customized libraries to build precursor ion list based on m/z, isotopic pattern, and ion mobility values. Subsequently, iprm-PASEF data was used to confirm analyte identification by comparing fragmentation spectra to LipidMaps and the MSDIAL-TandemMassSpectralAtlas for both positively- and negatively-charged analytes. Data was analyzed using SCiLS Lab Pro v2025a and Data Analysis v4.0 software (Bruker, Germany). iprm-PASEF data was further used for MS2-level quantification of selected fragment ions. A statistical analysis comparing the pixel intensity of the fragment ions was performed in Prism v10.4.2 (GraphPad), using a Student\u0026rsquo;s t-test. Quantitative results used for statistical analysis can be found in \u003cb\u003eSupplemental Tables\u0026nbsp;2\u0026ndash;3.\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLC-MS/MS Analysis\u003c/h2\u003e \u003cp\u003eConfirmation of NAD\u003csup\u003e+\u003c/sup\u003e and ADPR was performed by direct LC-MS analysis of pure standards obtained from Sigma Aldrich (N0632 for NAD\u003csup\u003e+\u003c/sup\u003e and A0752 for ADPR). Standards at 10-micromolar concentration were injected onto a Thermo Hypercarb column (5 \u0026micro;m, 100 \u0026times; 2.1 mm) using a Vanquish UHPLC system. Chromatographic separation was carried out over a 12-minute gradient using solvent A (7.5 mM ammonium acetate with 0.05% ammonium hydroxide in water) and solvent B (0.05% ammonium hydroxide in acetonitrile) as follows: 0\u0026ndash;1 min, 5% B; 1\u0026ndash;6 min, linear gradient to 60% B; 6.1\u0026ndash;7.5 min, 90% B; 7.6\u0026ndash;12 min, re-equilibration at 5% B. Mass spectrometric detection was performed on a Thermo Q Exactive mass spectrometer operated in positive ion mode using a targeted SIM-PRM acquisition strategy. Targeted SIM (tSIM) scans were acquired across a mass range of m/z 50\u0026ndash;750 at 70,000 resolution, AGC target of 1e6, and maximum injection time of 100 ms. PRM scans were performed at a resolution of 17,500, with an AGC target of 5e5, maximum injection time of 55 ms, and stepped normalized collision energies (NCE) of 20, 35, and 30. Data were visualized using Thermo Freestyle software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAnimals are treated to all ethics standards, and following our IACUC protocol number A10285 at the Buck Institute (Dr. Verdin).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial proteomic and liquid chromatography mass spectrometry raw MS data sets have been uploaded to the Center for Computational Mass Spectrometry, MassIVE, and can be downloaded using the following FTP link: ftp://[email protected] or via the MassIVE website: https://massive.ucsd.edu/ProteoSAFe/private-dataset.jsp?task=4dbd4a7e3a4d4f66b90399a693974e2f(MassIVE ID: MSV000097855).\u003c/p\u003e\n\u003cp\u003e[Note to the reviewers: To access the data repository MassIVE (UCSD) for MS data, please use: Username: MSV000097855_reviewer; Password: winter].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge support from the following NIH funding sources: NIAMS R21 AR084303 (PI Schilling), NIA P01 AG066591 (PI Ellerby), NIA T32 AG000266 (PI Ellerby, fellowship to J. Fang). The authors would like to acknowledge Bruker for their technical support and generous access to instrumentation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003cspan class=\"s1\"\u003e\u0026nbsp;Contributions.\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.A.S and J.B. contributed equally to the study design, data acquisition and interpretation, manuscript and figure preparation, and revision and editing of the manuscript. P.V.A.K. contributed to data acquisition and analysis, manuscript and figure preparation, and editing of the manuscript. Authors J.F., A.R, G.V.H., and R.R.R. contributed to the murine study design and sample acquisition. N.T. contributed to study design, provided resources and data acquisition and manuscript editing. E.V. contributed to study design. B.S. implemented the study design and conceptual development, provided resources, contributed to data interpretation and manuscript preparation and revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Birgit Schilling is on the advisory board for MOBILion Systems, Chadds Ford, PA. Dr. Nannan Tao is an employee of Bruker, \u003cem\u003eSan Jose, CA.\u0026nbsp;\u003c/em\u003eDr. Eric Verdin has research support from NIA/NIH, NIAID and Hevolution Foundation, and holds stock in; BioAge Labs, EDIFICE Health, Deciduous Therapeutics, Deep Longevity, Circulate, AVIV and DOC, and serves on an advisory board for; Longevity Clinic, Juvenescence, IHLAD, IHU, Ani Biome, Whoop, VERO, Lotus AI, Fountain Life, Longevity Science, and Longevity \u0026amp; Rejuvenation (The Royal Clinics of the Custodian of the Two Holy Mosques, Saudi Arabia). The terms of this arrangement have been reviewed and approved by the Buck Institute in accordance with its policy on objectivity in research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Dr. Birgit Schilling.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBuchberger, A. R., DeLaney, K., Johnson, J. \u0026amp; Li, L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. \u003cem\u003eAnal Chem\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 240-265 (2018). \u003c/li\u003e\n\u003cli\u003eRivera, E. S., Djambazova, K. V., Neumann, E. K., Caprioli, R. M. \u0026amp; Spraggins, J. M. 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A one-step matrix application method for MALDI mass spectrometry imaging of bacterial colony biofilms. \u003cem\u003eJ Mass Spectrom\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 1030-1035 (2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6743284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6743284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMass spectrometry imaging (MSI) is a rapidly advancing technology that provides mapping of the spatial molecular landscape of tissues for a variety of analytes. Matrix-assisted laser desorption/ionization (MALDI)-MSI is commonly employed, however, confident \u003cem\u003ein situ\u003c/em\u003e identification and accurate quantification of analytes remain challenging. We present a novel imaging methodology combining trapped ion mobility spectrometry (TIMS)-based parallel accumulation-serial fragmentation (PASEF) with MALDI ionization for targeted imaging parallel reaction monitoring (iprm-PASEF). We investigated the spatial distribution of lipids and metabolites in liver tissues from wild-type and CD38 knockout mice (CD38\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e). CD38, an enzyme involved in nicotinamide adenine dinucleotide (NAD⁺) metabolism, significantly influences liver metabolic function and contributes to age-related NAD⁺ decline. Although CD38 deletion previously was linked to improved metabolic phenotypes, the underlying spatial metabolic mechanisms are poorly understood. The spatial iprm-PASEF workflow enabled confident identification and differentiation of lipid isomers at the MS2 fragment ion level and revealed increased NAD\u003csup\u003e+\u003c/sup\u003e and decreased adenosine diphosphate ribose (ADPR), a by-product of NAD\u003csup\u003e+\u003c/sup\u003e hydrolysis, in CD38\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e livers. This approach provided confident, specific, and robust MS2-based identification and quantification of fragment ions in spatial MSI experiments. Additionally, the innovative iprm-PASEF opens unprecedented opportunities for spatial metabolomics and lipidomics, offering spatially resolved insights into molecular mechanisms.\u003c/p\u003e","manuscriptTitle":"Multiplexed Targeted Spatial Mass Spectrometry Imaging Assays to monitor lipids and NAD+ metabolites in CD38 knockout mice exhibiting improved metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 05:54:08","doi":"10.21203/rs.3.rs-6743284/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36996e3e-4093-4713-8a36-dc0a395a465e","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49987640,"name":"Biological sciences/Biological techniques/Mass spectrometry"},{"id":49987641,"name":"Biological sciences/Biochemistry/Lipids/Phospholipids"}],"tags":[],"updatedAt":"2026-01-14T03:45:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 05:54:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6743284","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6743284","identity":"rs-6743284","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}