Single Cell Proteomics to Spatial Multiomics in Cardiovascular Disease: Foundations, Technologies, and Biological Insights

Abstract

It is now apparent that cardiac form and function are governed at a systems level by an integrated collective of heterogeneous single-cell programs that together regulate tissue homeostasis, drive disease emergence, and shape individualized responses to therapy. Current advances in single-cell proteomics (SCP) and spatial multiomics by mass spectrometry imaging (MSI) allow systematic dissection of clinically defined cardiovascular tissues with an unprecedented molecular resolving power, yet remain relatively under-utilized in cardiovascular research. This compendium review works to stimulate new research into spatial regulation of the heart, emphasizing integration of single cell proteomic regulation with comprehensive multiomic MSI studies. In this context, we outline conceptual foundations, technological innovations, and biological insights that have resulted in the current success of single-cell proteomic and spatial multiomic analyses in cardiovascular disease. We provide an experimental design knowledgebase of critical components in SCP and spatial workflows by MSI, essential for generating robust and reproducible datasets that are interpretable by advanced computational methods. Key cardiovascular discoveries by single-cell proteomics and multiomic MSI are reviewed, highlighting how these approaches have provided new molecular insights into cardiac cell programming.

Introduction

Cardiovascular disease (CVD) remains the single largest contributor to global morbidity and mortality^1,2. CVD impacted global disease burden with 19.2 million deaths in 2023 and in the same year accounted for 437 million disability-adjusted life years¹. The global disease burden presented by CVD is largely due to the continued impact of modifiable life style risk factors and aging, with complex and distinctive contributions arising from biological sex and inequalities in access to health care^1,3–7. The complexities surrounding individual CVD risk reflect a multifaceted biological interplay between genetics, epigenetics, lifestyle, and environmental factors that cumulatively contributes to CVD development and progression and extend from the heart to the vascular system. The last decade has therefore leveraged significant advances in ‘Omic capabilities to produce a multiomic understanding to CVD development and treatment^8–10. It is therefore noteworthy that, in spite of these advances, CVD remains a major global burden, underscoring the need to leverage more targeted approaches to both research and personalized therapy such as single-cell biology.

Cardiovascular disease epitomizes the concept of form follows function where single cells distributed in a three-dimensional extracellular matrix (ECM) microenvironment, or the form, collectively contribute to the overall function of the heart. Cell populations such as fibroblasts, endothelial cells, vascular smooth muscle cells, pericytes, immune cells, all contribute to a multicellular network that becomes a maladaptive, feedforward system in CVD to decrease heart function^8,11,12. These cells are spatially distributed, presenting a dimensional integrative system biology that drives molecular pathways of CVD pathology such as chronic inflammation, fibrotic remodeling, immune cell flux, cellular differentiation, and lipid alterations^13–16. Within the last 5 years, there have been major advances in single cell sequencing and spatial profiling that make it feasible to evaluate spatial single cell biology from individual patients^17–19. For CVD, single cell approaches have primarily been at the transcriptional level; these important studies show that even within a single cardiovascular cell type there is a broad range of diversification, plasticity, heterogeneity, all which demonstrate the specificity of cellular mechanisms required for appropriate heart function^20–25. A significant gap in understanding CVD towards personalized medicine is the lack of studies addressing spatial protein-level dynamics, including post-translational modifications, especially those of the extracellular microenvironment. Proteins present real-time monitors of cellular function and are the majority composition of the extracellular matrix (ECM), the dimensional microenvironment through which cells migrate, differentiate, and signal between cell types. Post-translational modifications are dynamic, metabolically-driven changes to proteins that occur during or immediately after protein synthesis and are also a significant component to cell sensing within the extracellular microenvironment. ECM proteomics rarely correlates with transcriptional profiling due to in part to protein and post-translational modification, processing, and degradation^26,27. Proteins thus represent the real-time processing of the cardiovascular system, reflecting early changes towards CVD pathology. Although single cell to spatial proteomic expression has high potential to in reporting biomarkers of prognosis and diagnosis, there are limited studies leveraging this technology in cardiac research. this compendium review focuses on current state-of-the art advances and resources specifically for cardiac tissue analysis (not the vascular system) research by single-cell proteomics and spatial proteomics by mass spectrometry imaging (MSI) towards stimulating advances that mitigate the high global burden of CVD.

Single Cell Proteomics and Spatial Proteomics

Single cell proteomics and spatial proteomics by mass spectrometry imaging evolved through a series of parallel technological advances both working towards personalized medicine. Single cell proteomics is a technology that reports all of the proteins from an isolated single cell through a combination of high throughput chromatography coupled to highly sensitivity mass spectrometry instrumentation to generate sequences of amino acids and map those sequences back to proteins. Single cell proteomics produces quantitative, cell-specific measurements of protein abundance, post-translational modifications, and signaling pathways, and can account for functional heterogeneity among individual cells that appear similar at a transcriptional level. Spatial proteomic approaches maintain the cell localization within the context of the tissue microenvironment to report spatial relationships between cell types, understand molecular signatures of regional pathology, and evaluate structure-function relationships. Spatial proteomics is thus a powerful approach to localized protein interaction within a dimensional microenvironment. Spatial proteomics in the context named as the 2024 Method of the Year²⁸ relates to a broad range of methods where multiple proteins are detected using antibody-based approaches. Examples of such technologies are microfluidic-driven²⁹ sequential immunofluorescence (COMET)^30,31, DNA conjugated antibodies (co-detection by indexing; CODEX), the fluorometriciterative bleaching extends multiplexity (IBEX), , rare metal conjugated antibodies with detection by mass spectrometry (multiplexed ion beam imaging; MIBI or imaging mass cytometry; IMC). This broad definition of spatial proteomics includes methods using early work using laser capture microdissection to spatially extract cells and tissue regions prior to proteomics^32–34 and extends to deep learning artificial intelligence to identify cell populations in a region, followed by laser dissection of the region to collect individual cells and mass spectrometry-based proteomics (deep visual proteomics; DVP^35,36). Another type of spatial proteomics uses mass spectrometry imaging (MSI), directly detecting proteins or peptides from tissue sections by mass spectrometry. MSI is an especially powerful technique for CVD as spatial proteomics can be collected along with metabolomics, lipidomics, glycomics, and transcriptomics from the same tissue section. For CVD, this is an especially important advantage as the multiomic capabilities by MSI produces an understanding of the highly complex molecular interplay that produces specific cardiovascular pathologies. When integrated together, single-cell proteomics defines the functional protein status of each cell and spatial multiomics by MSI has the capability to anchor cellular states with precise microenvironmental contexts (Figure 1). The combined framework can be used to link cellular phenotypes to histological/pathological landscapes, identify microenvironment-dependent signaling pathways, and uncover how localized cues such as fibrosis, hypoxia, inflammation, or vascular proximity shape disease progression and outcome. Such integration is particularly powerful in cardiovascular research, where mechanical forces, regional injury, and extracellular matrix remodeling generate highly localized protein-level differences that cannot be captured by singular transcriptomics or spatial transcriptomics.

Figure 1.

Single cell proteomics and spatialomics technologies in cardiovascular research. Single cell proteomics have emerged that allow functional reporting of single cell status towards identifying druggable cell targets. Multiomic spatialomics can now report multiple analyte types from a single tissue section, define cell and region-specific pathologies and biomarkers. MALDI MSI may be used for arrays of cells or cell models to uncover and investigate biomarkers found in tissue imaging. The current use of artificial intelligence in both fields amplifies the synergy of both technologies for personalized medicine.

Single Cell Proteomics

Single-cell proteomics (SCP) is the quantitative survey of the working proteins from a single cell, down to single digit protein copy numbers. SCP has advanced rapidly since the introduction of the Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) approach by Slavov and colleagues in 2018³⁷ (Table 1), which demonstrated that proteomic signatures can be used to distinguish cellular subpopulations. Despite this progress, only a limited number of studies have analyzed cells directly isolated from tissues, including cardiac tissue. Working with cardiac tissue cells, such as cardiomyocytes, presents a unique set of challenges inherent to their morphology. Cardiomyocytes are very large (~30-150 μm in length), specialized rod-shaped cells that become extremely fragile upon tissue dissociation and isolation into suspension. Existing techniques, such as Fluorescence-Activated Cell Sorting (FACS), reduce their viability due to the shear stress they undergo during sorting. On the other hand, manual isolation techniques, such as patch clamping, too low throughput for consistent use in cell isolation. Additionally, cardiomyocytes exhibit a highly specialized proteome in which around ~50 most abundant contractile and mitochondrial proteins dominate, thereby masking the detection of the low-abundance proteins. The development of mass–spectrometry–based SCP workflows (Figure 2) have been driven by the need to address four major challenges:(i) generating enough ion copies for accurate MS quantification; (ii) achieving reliable amino acid sequence identification for quantified ions; (iii) scaling the analysis to thousands of single cells at an affordable cost; and (iv) enabling sensitivity for label-free workflows.

Table 1.

A selected list of literature marking the timepoints in the development of SCP-MS with focus on studies contributing to cardiovascular research.

YEAR		PROTEINS QUANTIFIED/CELL	RUN TIME (MIN/CELL)	DATA ACQUISITION
2015	Murgia M et al.	6500	270	DDA
2017	Murgia M et al.	2100	120	DDA
2018	Budnik B et al.	1526	170	DDA
2018	Zhu Y et al.	1500-3000	210	DDA
2021	Specht H et al.	3042	95	DDA
2022	Gebreyesus S et al.	1500	90	DIA-LFQ
2022	Webber K et al.	986	30	DDA
2023	Derks J et al.	1000	10	DIA-LFQ
2023	Thielert M et al.	2377	18	DIA-LFQ
2023	Momenzadeh A et al.	868	15	DIA-LFQ
2023	Kreimer et al.	1145	15	DIA-LFQ
2023	Johnston S et al.	2374	95	DDA
2023	Ctorecka et al.	1957	80	DDA-TMT
2024	Ctorecka et al.	3500	36	DIA-LFQ
2024	Wang Y et al.	3008	21	DIA-LFQ
2024	Fulcher et al.	2934	60	DDA
2024	Guzman U et al.	4591	16	DIA-LFQ
2024	Leduc A et al.	1000-3200	41	DDA-TMT/DIA-LFQ
2024	Onat B et al.	723	15	DIA-LFQ
2024	Lizhuo A et al.	2742	10	DIA-LFQ
2025	Chazarin B et al.	1327	15	DIA-LFQ
2025	Saddic L et al.	2746	10	DIA-LFQ
2025	Ye Z et al.	5204	36	DIA-LFQ
2025	Bubis JA et al.	5300	25	DIA-LFQ
2025	Petrosius V et al.	1300-3500	21	DIA-LFQ

Figure 2.

Single Cell Mass Spectrometry Proteomics Workflow. This generalized workflow highlights different choices how single dissociated cells are obtained from the biological system and sorted using different sample preparation platforms; cells are further lysed and treated with trypsin that optionally can be followed by tagging with isobaric amine reactive multiplexing reagents: next the resulting pools of peptides can be analyzed by different liquid chromatography setups coupled to mass spectrometry for quantitation.

Emergence of Single-Cell Proteomics

Early single cell proteomic studies focused on giant cells such as oocytes, which yielded ~450-800 proteins per cell^38,39, and on single skeletal muscle fibers, which yielded ~2,100 proteins per fiber^40,41. However, these early approaches lacked sufficient sensitivity to detect low-abundance signaling proteins, transcription factors, and cell-surface markers present at only a few thousand copies per cells. Throughput was also limited to roughly multiple hours per sample, restricting its broader application.

Relatively recently, in 2018, the ability to prepare single-cell sample preparation by was transformed by incorporating a “carrier” or “booster” proteome labeled with tandem mass tags (TMT)⁴². Quantification in data-dependent acquisition (DDA) with TMT was achieved by measuring the relative intensities of these reporter ions in MS2 or MS3 scans, allowing for multiplexed sample processing with up to 35 channels in modern workflows (Figure3A). This strategy reduced sample loss, improved ion detectability, and provided fragment ions that supported confident peptide identification - enabling quantification of more than 1,000 proteins in single embryonic stem cells. To further enhance quantification and throughput, the Slavov group introduced Single Cell ProtEomics version 2 (SCoPE2) in 2021⁴³. This approach integrated automated, miniaturized sample preparation into the workflow, reducing both cost and hands-on time. Using this method, Specht et al. quantified ~2,000 proteins in monocytes and macrophages, adapting the process to a 384-well format. To address sample-preparation limitations more broadly, the minimal proteomic sample preparation (mPOP) workflow was developed by the Slavov group and reduced reagent volumes nearly tenfold (to ~1 μL per cell), including enzymes, buffers, and TMT reagents, minimizing the surface area that contacts the sample and reducing protein loss from adsorption. Several groups have built upon this concept, reducing volumes into the hundreds of nanoliters. Notably, nanodroplet processing in one pot for trace samples⁴⁴(nanoPOTS) developed in Kelly group enabled highly miniaturized workflows and has been used to prepare multiple (10-100 cells⁴⁴) and single cells with isobarically labeled carriers³⁷. Ctorecka et al presented an automated nanowell-array platform designed to improve sample preparation for multiplexed SCP⁴⁵. This approach supported ultra-low-volume processing within nano-wells and high-throughput preparation, enabling quantitative analysis of multiple single cells simultaneously while minimizing sample loss. Conceptually, SCP analysis and bulk proteomics ask slightly different questions of the heterogeneity within a cell population, versus compared to different between conditions. This paradigm shift changes quantification assumptions, statistical modeling and considerations for normalization, missingness imputation and lastly biological inferences⁴⁶. In bulk proteomics, quantification is generally stable, signal is rich from high protein inputs, whereas in SCP, low ion copy numbers compete with technical noise, missingness is frequent and intensity-dependent, requiring extensive quality filtering and cell-level normalization^47–49. Isobaric labeling strategies in SCP commonly use a carrier channel to boost peptide identification, which introduces additional considerations such as ratio compression from precursor co-isolation and reporter ion noise, which when compared to bulk proteomics are only moderate and data matrices are relatively complete.

Figure 3.

Single Cell Proteomics mass spectrometry methodologies. Distinct SCP MS approaches differ primarily in their data acquisition and quantification strategies. A) DDA is most often paired with peptide labeling by tandem mass tags (TMTs), in which individual single cell peptides are tagged with reporter ions and pooled for acquisition in a single MS run. Multiplexed samples allow for up to 35 channels in modern workflows and require >75,000 resolving power to distinguish 3mDa differences in isobaric tag m/z values, typically achieved with Orbitrap mass analyzers. B) DIA is often paired with label-free quantification (LFQ) and fragments all precursor ions within a specified mass range, capturing comprehensive MS/MS spectra in a single run per single cell. This approach enhances the quantitative reproducibility and has a wider dynamic range and reduced reagents cost, but data complexity requires more processing resources. C) There are also DIA approaches leveraging peptide multiplexing – plexDIA or mDIA which use 3-plex peptide labeling reagents to achieve triple the LFQ-DIA throughput. The quantitative accuracy remains like that of separate DIA injections. The data completeness using this approach is as high as 98% across 3-plexes.

Another approach for SCP uses data-independent acquisition (DIA), typically with label-free quantification (LFQ), in which all precursor ions within a defined mass-to-charge window are fragmented in a systematic manner, generating complete MS/MS information in a single analysis (Figure 3B). This strategy allows for consistent, unbiased sampling of peptides, minimizing the randomness inherent to DDA-based acquisition and improving the reproducibility of quantitative measurements. Here, peptide intensities are obtained independently for each single cell sample without the labeling required in DDA approaches; DIA approaches were introduced later as the SCP technology in microfluidic sample preparation and Bruker’s high sensitivity MS instrumentation progressed^50–53. Despite the improvements in in quantification, sensitivity, and dynamic range by DIA, data analysis workflows for SCP still involve working with sparse data, relying on dimensionality reduction (PCA, UMAP⁵⁴), Leiden clustering^55–57, differential abundance across clusters⁵⁸, cell type/trajectory inference (pseudotime analysis^59,60) and other methods adapted from single-cell transcriptomics. Contrasting, data dependent acquisition technology depends on relative quantification across multiplexed samples with extra computational steps to align and correct signals, however it is still broadly used in the SCP field and continuously built upon. It is important to acknowledge that distinguishing technical from biological variance is far more challenging in SCP than in bulk proteomics, which oftentimes use linear models (e.g. Limma⁶¹) on robust datasets Those are fundamentally different quantification assumptions, data processing strategies, and statistical frameworks which in SCP aims at resolving cellular heterogeneity within same cell populations.

Most recently, studies have revisited MS1-based multiplexing strategies, adapting them for use with DIA in SCP multiplexing (Figure 3C). These methods leverage one channel as a carrier to enhance sensitivity by minimizing absorption losses. One method, plexDIA⁶², employs 3-plex peptide labeling, effectively tripling throughput for low-input samples while maintaining quantitative accuracy comparable to separate DIA injections. Using this approach, Derks et al. quantified approximately 1,000 protein groups per plex (up to three single cells per injection) with a trapped ion mobility SCP mass spectrometer, using 30-minute injections (10 minutes per cell). This strategy was later proposed to scale up to a 9-plex format⁶³. The second approach, mDIA⁶⁴, uses dimethyl peptide labeling. In its 3-plex implementation, with one reference channel and two single-cell channels, it doubled the number of quantified protein groups to a median of 2,377. This was achieved using low input trapped ion mobility instrumentation developed specifically for single cell proteomic analysis, with an injection throughput of 40 samples per day, corresponding to 80 single cell proteomes per day. A next-generation automated platform called nPOP⁶⁵ has been developed, capable of preparing thousands of single cells simultaneously. nPOP employs piezo-acoustic dispensing to isolate individual cells into ~300-picoliter droplets and carries out multiple sample preparation steps, including lysis, digestion, and labeling within 8-20 nanoliter volumes on a glass slide. This technology has been demonstrated to work with various multiplexed workflows, supporting both non-isobaric and isobaric tagging approaches (super-heavy tandem mass tag) that allow 11-plex, 18-plex, 32-plex, 35-plex, and with potentially adaptations using diethyl, dimethyl, and dimethyl leucine labeling. The ultra-low-volume handling with high-throughput automation, enhances sensitivity, reproducibility, and scalability, and has advanced SCP toward routine biological and clinical applications.

Key Technologies in Single Cell Proteomics

SCP progress has been tightly linked to innovations in chromatographic systems and mass spectrometry instrumentation designed to enhance sensitivity, robustness, and throughput for extremely low-input samples. A major advance came from the development of nanoflow dual-trap single-column LC configurations, as demonstrated Kreimer and colleagues⁵³. Their system decoupled sample loading and elution using parallel trapping columns at nanoliter-per-minute flow rates, allowing for reduced overhead time between injections. Further improvements in low-flow chromatographic performance were reported by Mechtler group, by introducing a high-sensitivity, low-nanoflow LC-MS setup optimizing emitter geometry and ionization efficiency, through maximized ion delivery to the mass spectrometer⁶⁶. In 2022 and 2024, Kelly and colleagues introduced a multicolumn nanoflow LC system with accelerated offline gradient generation^67,68. This approach minimized downtime between runs and improved robustness by distributing wear and tear across multiple columns. Building on this concept, Kelly’s group also demonstrated a preprint⁶⁹ in 2025 that multicolumn nanoLC could be paired with ultra-short, 5-minute gradients to achieve label-free SCP at unprecedented throughput. Another important set of innovations from Kelly group demonstrated that gradient-elution nanoflow LC could be achieved without a traditional binary pump by using smoothed step gradients⁷⁰. This pump-free design reduced system complexity and cost, enhanced operational stability, and still provided reproducible and efficient separations required for SCP. On the mass spectrometry side, significant advances in analyzer technology have enabled deeper and more accurate proteomic profiling from single cells. Current SCP work uses multireflectron time of flight analyzer, called the Astral analyzer, for high-sensitivity, label-free proteomic profiling studies with enhanced proteome coverage using optimized workflow^71,72. These studies evaluated small single-cell datasets from different cell types to illustrate that achievable proteome depth varies with cell type and size, while also capturing biologically meaningful protein patterns. Another landmark study used the Astral mass analyzer to report ~5,300 proteins per single cell while maintaining high quantitative accuracy⁷³. This level of proteome depth has pushed SCP toward comprehensive characterization of cellular heterogeneity. Collectively, these instrumentation advance have fundamentally reshaped what is experimentally possible in single-cell proteomics in systems biology, developmental biology, and clinical research.

Experimental Design in Single Cell Proteomics

As single-cell proteomics matures, careful attention to experimental design is essential for generating reliable, reproducible, and biologically interpretable data. In 2023 experts from multiple proteomics laboratories emphasized that SCP experiments must be evaluated across the entire workflow from cell isolation to data analysis as each step can introduce variability that is amplified at the single-cell level⁷⁴. Key considerations in SCP experiments work to ensure accurate cell handling, minimizing sample loss, maintaining rigorous controls, and reporting all experimental parameters transparently (including sample preparation volumes, carrier channel composition, batch performance, chromatography settings, instrument acquisition parameters, and computational pipelines).

Cell preservation and storage represent another critical source of variability, as demonstrated by Onat et al⁷⁵ who examined how storage conditions affect single-cell proteomes. The authors showed that factors such as freeze–thaw cycles, temperature fluctuations, and duration of storage can alter protein abundance patterns and degrade low-abundance targets. As these effects can confound biological differences between samples, their findings underscore the need to standardize storage protocols, particularly important for multiple batches collected over time. Another important confounder in SCP measurements is cell isolation, which can cause cell lysis or leakage, contaminate the single cell proteome with other proteomes and debris (e.g. extracellular proteome), or alter intracellular stoichiometry⁷⁶. Cell stress, prolonged handling, enzymatic dissociation, and suboptimal buffers can lead to partial lysis or membrane permeability, allowing proteins to diffuse into the surrounding medium. To mitigate these effects, gentle cell isolation procedures, rapid processing, careful selection of buffers, and implementation of cytosolic markers abundance monitoring in supernatants. Mass spectrometry compatible detergents can minimize sample loss during the single cell proteome experiment⁷⁷. Standardization in cell handling, storage, and preparation, paired with transparent reporting and benchmarking, ensures that observed proteomic differences reflect true biological variation rather than technical artifacts. As SCP continues to expand into more diverse tissues and clinical applications, such guidelines will be essential for ensuring data quality, comparability across institutions, and confidence in the biological insights derived from single-cell proteome measurements.

Advances and Key SCP Discoveries in Cardiovascular Research

Single-cell proteomics has progressed rapidly through the above-mentioned advances in ultrasensitive mass-spectrometry workflows, but the studies in the field of single–muscle-fiber proteomics^40,41,78 laid the groundwork and understanding how to approach the single cardiomyocyte proteomic analysis in more recent years. Starting with the work of Murgia and colleagues in 2015⁴⁰, the group demonstrated that LC-MS/MS-based proteomics could quantify thousands of proteins from individual human muscle fibers and revealed previously unappreciated fiber-type-specific mitochondrial specialization. Their data showed that oxidative and glycolytic fibers differed not only in canonical metabolic enzymes but also in the stoichiometry and composition of mitochondrial respiratory chain components, emphasizing the functional heterogeneity encoded at the proteome level. Building on this foundation, the same group applied single-fiber proteomics to examine how human muscles age and compared proteomes from type I and type II fibers across donors of different ages⁴¹. The study described a fiber-type-specific pattern of proteomic remodeling with aging, and importantly, it showed that aging does not affect all fiber types uniformly, providing molecular evidence for selective vulnerability of fast fibers and underscoring the value of single-cell-scale resolution in studying muscle plasticity and decline. To scale these approaches for broader application, subsequent work from Meyer group focused on robustness⁷⁸ and presented a complete high-throughput workflow enabling the analysis of hundreds of human muscle fibers innovating with methodology designed for high-throughput which opened the door to population-level studies of muscle heterogeneity in exercise adaptation, disease, and aging. In parallel, improved single cardiomyocyte sample prep work⁷⁹ and separation technique by Kreimer et al⁵³, enabled high-throughput single-cell proteomics of heterogeneous myocardium-derived single cell populations. This methodology showed that deep proteome coverage could be achieved from diverse human and mouse primary cell types, featuring first report of single cardiomyocyte proteome⁵³.

These advances shifted the technical design towards throughput for single-cell proteomics to more complex tissues and cellular models in cardiovascular systems. In 2025 Ai et al⁸⁰ extended these methods to cardiomyocytes derived from human iPSCs and adult hearts. Using optimized low-input proteomics, the authors characterized distinct cardiomyocyte subpopulations, uncovering proteomic signatures associated with maturation state, metabolic specialization, and electrophysiological phenotypes. The ability to distinguish cardiomyocyte subtypes at the proteome level has important implications for regenerative medicine, disease modeling, and quality control of stem-cell-derived cardiac cells. Complementary work of the same group in Van Eyk laboratory established Single Cardiomyocyte Proteomics (SCarP), a workflow for proteomic characterization of primary mouse cardiomyocytes at single-cell resolution⁸¹. This study addressed the intrinsic heterogeneity of cardiomyocytes - cells known to vary in size, metabolic program, contractile protein composition, and stress responses. By applying high-sensitivity mass spectrometry to freshly isolated adult cardiomyocytes, the authors identified proteomic clusters corresponding to distinct cellular phenotypes, highlighting how single-cell proteomics can refine our understanding of cardiac functional diversity. Finally, a 2025 preprint⁸² extended these approaches to vascular disease, applying single-cell proteomics to dissect cellular phenotypes in Marfan mouse aneurysm tissue. The study used optimized low-input workflows to analyze proteomes of individual cells within the aortic wall, revealing previously unrecognized cell states associated with extracellular matrix remodeling, inflammation, and aneurysm progression. By providing proteomic resolution of disease-associated phenotypes, this work illustrates the promise of single-cell proteomics for uncovering pathogenic mechanisms in cardiovascular disorders.

To make SCP broadly useful in cardiovascular research the efforts should not necessarily focus on better instrumentation but better-quality measurements and inference under cardiomyocyte-specific constraints (large, fragile, sarcomere-dominated and spatially heterogenous tissue). Cardiac tissue dissociation disproportionately damages cardiomyocytes and biases what survives, and this could lead to enriching other cell types like fibroblasts, immune or endothelial cells. The analytical development here involves QC metrics and models that allow for distinction between a cell that is stressed or partially lysed and account for it instead of discarding large proportions of the dataset. Reporting of QC cell status (stress or partial lysis), batch effects metrics, missingness profiles, interference scores (for labeling approaches), and minimal filtering standards for declaring a protein quantifiable in single cells are work in progress for standardization. SCP needs better ways to recover low-abundance biology which could potentially be addressed by smarter acquisition methods that reduce co-isolation, implement improved ion utilization, and computational strategies that extract quantitative signal from very low ion counts. Data matrices are sparse and intensity-dependent – missing data points often means it was not sampled, rather than not present. Cardiovascular datasets add another layer because cell size and sarcomeric/mitochondrial content vary dramatically across cell types and disease states. A long-term but pragmatic effort for cardiovascular endpoints could involve a targeted SCP panel including hybrid discovery and targeted cell-specific peptide lists or protein isoform panels. Finally, valuable heart tissue is frozen or banked in FFPE collections, therefore analytical advances in recovering single cells from archival samples with reliable quantification and pipelines that control for that analytical variation would be key steps to access precious human cardiovascular cohorts.

Integration with other Omics: Multiomics

The integration of SCP with other omics modalities has emerged as a critical strategy for resolving cellular heterogeneity and bridging molecular regulation with functional phenotypes. SCP uniquely captures protein abundance, modifications, and signaling states that are not always predictable from nucleic acid data alone. When combined with transcriptomic, epigenomic, and other single-cell omics layers, proteomics enables a more comprehensive and mechanistic understanding of cell states and transitions⁸³. Recent efforts, such as the PSCS framework described by Meyer group⁸⁴, highlight the importance of unified data standards and shared analytical infrastructures to support scalable, reproducible multi-omics integration across single-cell datasets, facilitating cross-study comparisons and systems-level biological inference. Technological advances are now showing first endeavors at allowing a coordinated measurement of proteins and transcripts from the same individual cells, strengthening direct correspondence between gene expression and protein output. Recent preprint by Dawar et al.⁸⁵ demonstrate a high-throughput nanoliter-scale spin-transfer approach that enables joint single-cell proteomic and transcriptomic profiling, reducing sampling bias and improving alignment between molecular layers. Such approaches are particularly powerful for dissecting post-transcriptional regulation, protein turnover, and signaling pathway activation, which are central to understanding dynamic cellular responses in development and disease.

In cardiovascular research, multi-omics strategies combining single-cell proteomics with transcriptomic and epigenomic profiling have revealed specialized cell populations and regulatory programs underlying tissue injury and repair^86,87. These studies illustrate how integrating multiple omics layers at single-cell resolution uncovers distinct fibroblast and immune cell subtypes with unique regulatory and functional signatures in myocardial infarction and myocarditis. While these studies primarily emphasize transcriptomic and epigenomic data, the incorporation of proteomic information is increasingly recognized as essential for validating effector functions, immune activation states, and signaling networks. Collectively, these advances underscore the growing role of SCP as a key pillar of multi-omics integration, enabling deeper biological insight and more precise identification of therapeutic targets.

Spatial Proteomics by Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) uses an ion source that is capable of scanning across a surface to produce ions at a specific x,y coordinates with detection by a mass spectrometer. For tissue imaging, the ion source is scanned across a tissue section, collecting a mass spectrum at every x, y coordinate; the mass spectrum at a single x,y point is the MSI data pixel (Figure 4). Each peak detected within a mass spectrum may be mapped across the entire plane of x and y coordinates, reporting changes in intensity as a heatmap distribution across the imaged area. The spatial resolution depends on the ability of the ion source to focus to a small region from single cells to clusters of cells. The spatial resolution is also dependent on the stepsize, or spacing between each data pixel collected. For high spatial resolution imaging, current images can be collected from single cells with sampling tightly spaced between each x,y coordinate (<10 μm in step size), or use larger step sizes (20-80 μm) over tissue sections that are centimeters in size. Total data acquisition time per pixel is highly dependent on the type of mass spectrometer coupled to the ion source. There are many types of ion sources used in MSI⁸⁸. Of these ion sources, matrix-assisted laser desorption/ionization (MALDI) has been broadly implemented over decades for multiomic detection of cellular and extracellular proteomics from tissue sections, extended from single cell types to single cells^89–95 and to visual array analysis^96,97.

Figure 4.

Overview of MALDI MSI acquisition demonstrated using tryptic proteomic imaging on mouse heart, courtesy of Dr. Christine Kern at Medical University of South Carolina. A) A laser is scanned across the tissue, collecting a mass spectrum at every x and y coordinate. B) Total ion current across the entire heart. Peaks that appear low intensity are highly localized, observed in C. C) Peptide images with corresponding hematoxylin and eosin (H&E) stain. Peptides are distinct to different anatomical features of the heart. D) MALDI MSI is highly multiplexed within a single analysis. Shown are 110 images out of 1,359 peptide peaks. Data were collected on a MALDI FT-ICR (Solarix, Bruker) at 60 μm step size.

Integrative Strategies in MSI Approaches

Sample preparation exerts a major influence on the success of MALDI MSI studies, particularly when detecting multiple types of analytes across scales ranging from tissues to single cells. MALDI MSI relies on the choice of chemical matrix, a small organic molecule that absorbs and transfers energy from the laser to the analyte, desorbing the analyte into the gas phase during the process⁹⁸. Target analyte ionization is highly dependent on the quantized energy distributed from a MALDI matrix during laser desorption and thus different matrices are used for ionizing metabolites, lipids, glycans, peptides and proteins^99–101. Secondary to the desorption process that produces ionization energy, the ability of the MALDI matrix to be ablated from a defined region of single cell dimensions is critical to producing a high-definition spatial map of the analytes. A smaller matrix crystal size increases the signal to noise from the detected analyte and improves the ability to achieve precise single cell-sized desorption from the sample. Currently, crystal sizes of 1 μm or less are obtainable for single cell imaging techniques^92,102,103. MALDI matrix application is now highly automated and commercial sprayers and sublimators are used to produce consistently small and uniform matrix crystals for spatialomics studies.

The field of MALDI MSI has shown a significant focus on multiomic studies or detecting multiple types of analytes from the same tissue section or from single cells^91,104–109. Sequential analyses strategies take into account the type of analyte and the parameters required for detection and can include other modalities such as spatial transcriptomics or second harmonic generation microscopy prior to MALD MSI^{105,110–112}. For multiomic studies that include metabolites, lipids, or drugs, a fresh frozen section is used to produce high quality spatial maps of tissue histology. Typically, metabolites and lipids are collected in positive ion mode and negative ion mode to increase the depth of biological information. After imaging of metabolites and lipids, MALDI matrix and the previously imaged analytes are removed through carefully designed washing steps that improve detection of the next analyte targets. Enzymes are used in cyclical patterns to target specific post-translational modifications (PTM) and protein populations from the same tissue section or single cell (Figure 5). The specificity of enzymes allows targeted multiplex approaches to be applied to either fresh frozen or FFPE tissues after antigen retrieval to expose specific sites for enzyme activity. The pH and chemical composition of the antigen retrieval buffer is evaluated per enzyme and per disease state. Enzyme targeted imaging sequentially deploys enzymes in a way that systematically mines the biology and has analytical benefits. Glycans are metabolically sensitive PTMs that also represent both chemical and steric hindrance to other proteases used in MALDI MSI. N-glycan imaging¹¹³ using PNGase F is a general first step in imaging tissue that reports metabolic information and increases access by proteases to target proteomes. Other enzymes such as chondriotinase¹⁰⁷ have also been used early in multiomic workflows to improve later protease activity. Elastase and collagenase type III may be used to target respective extracellular matrix proteomes^114,115 with increased signal after N-glycan imaging¹¹⁶. Since multiomic approaches that target the extracellular matrix have significant effect on the pathology, the immunohistochemical and pathological stains are completed and digitally scanned before MALDI MSI^116,117. Bottom-up proteomic imaging by MALDI MSI first used Carboxy Peptidase Y and later developed tryptic approaches for clinically archived FFPE tissue^118–120. General protease enzymes such as trypsin and LysC are now used in sequential combination with collagenase and elastase to report the spatial proteome that includes cellular and extracellular composition. Combinations of enzymes that target the extracellular matrix and cellular populations including chondriotinase, PNGase F, elastase, collagenase and tryptic imaging have been used to evaluate the complete tissue microenvironment^107,121. Tissue expansion followed by MALDI MSI is a recent development in single cell level imaging of lipids, N-glycans, and tryptic peptides that works to expand the tissue without disrupting biological distribution¹²². Last, immunohistochemistry approaches using antibodies that are labeled with photocleavable mass-tags may be detected by MSI. This approach is abbreviated as MALDI IHC, with dozens of antibodies can be detectable in a single MALDI MSI run¹²³. The combination of both enzymatic digestion and proteins detected by antibodies synergistically increases the information and ability to uncover molecular pathology in tissue sections¹²⁴.

Figure 5.

Mining spatial biology by multimodal, multiomic MALDI MSI. The figure shows a common schematic where tissue sections are imaged by other modalities prior to MALDI MSI. MALDI MSI workflows, especially for clinically archived FFPE tissue, deploy cyclical enzyme imaging to report multiple levels of spatial biology. Enzyme 1 images N-glycans, a sugar post-translational modification tied to metabolic status. Enzyme 2 targets the extracellular matrix proteome using collagenase. Enzyme 3 is trypsin, targets primarily cellular proteins with some reporting of extracellular matrix proteins. This can be continued for multiple enzymes to report multiomic data.

Beyond tissue imaging, the MALDI MSI platform has been leveraged as a way to visualize array data of glycosylation patterns, target proteins, and single cells^{91,96,97,125,126}. Target proteins and single cells are captured on slide prior to imaging by MALDI MSI using modified slide surfaces such as nitrocellulose or amine reactive hydrogel coating. Antibodies that have been validated in quantitative capture of target analytes when bound to solid substrate are used in the absence of reagents such as glycerol that increase analyte delocalization and increase ion suppression. Affinity capture reagents such as antibodies or lectins may be printed or stamped onto the slide^91,127 to increase specificity by capturing target proteins and cells from highly complex biofluids. After blocking in detergents that are mass spectrometry compatible, slides are incubated with target biofluid or cells and later rinsed to increase sensitivity. For cells, a brief fixation may be used to maintain cell integrity and location on the slide prior to profiling for metabolites or lipids. Enzymes can again be used in a sequential manner on either proteins or cells. For evaluating N-glycosylation on target proteins, PNGase F may be sprayed onto the printed antibody array after affinity capture of glycoproteins. Trypsin digestion may be used afterwards to assess sequence information and determine N-glycan site occupancy. After enzyme incubation, MALDI matrix is sprayed onto the array slide in lower amounts (about 5x less) compared to the matrix used for tissue imaging. Automated spraying and sublimation produce smaller crystal sizes which enhance signal to noise detection from nanoliter array spots. MALD MSI approaches to evaluating single cell types in culture have also been developed that include evaluation of the extracellular matrix proteome from as few as 3,000 cells and advanced methods that use stable isotopic labeling of N-glycans for studies investigating absolute turnover of N-glycosylation and glycosylation of extracellular matrix proteins^89,128,129. The accumulated techniques have developed MALDI MSI as a complete analytical platform for biological studies from multiomic spatial studies down to target proteins and single cells at a patient specific level. Thus, in the context of cardiovascular translational or pre-clinical studies, MALDI MSI has the capability to probe prognostic or diagnostic samples as well as defining basic mechanisms of cardiovascular biology.

Technologies in MALDI MSI

Like SCP, the ability of MALDI MSI to achieve high spatial resolution down to single cells is connected to rapid innovation and commercialization of instrumentation^88,130. A pivotable advancement in spatialomics by MALDI MSI was the development of a high repetition 10-20 kHz Nd:YAG laser dynamically focused onto the sample by Galvo mirrors to result in a uniform square impact geometry. This also created a tunable pixel size from 5-100 μm² in size¹³¹, allowing a broad range of applications from single cells to large surgical tissue sections¹³². Current lasers facilitate rapid collection of more than 150k pixels per hour with negligible damage to the tissue, allowing pathology staining and interpretation of the same sampled tissue section after acquisition. Rapid continuous stage movement was an essential innovation that, combined with high repetition tunable lasers, that now allows minutes to hours of acquisition times for single cell imaging over millimeter to centimeter sized tissue sections^133–135. A further advancement in MALDI MSI was the development of a UV laser used in secondary ionization, termed MALDI2¹³⁶. This is a second laser focused on the plume generated by the initial laser to increase ionization processes and enhance sensitivity, particularly powerful for single cell imaging studies. Landmark innovations in mass analyzers have centered around high-definition reporting of thousands of analytes from a single pixel. Hardware and software advancements in Fourier transform ion cyclotron resonance (FT-ICR) technology, where the cell acts as both mass analyzer and detector, have resulted in image acquisition with mass resolving powers >1.6 million with mass accuracies below 100 parts per billion¹³⁷. Trapped ion mobility (tims) developed by M. Park and colleagues¹³⁸ was a pivotable development for MALDI MSI¹³⁹, producing a new dimension of post-ionization separation for image analysis. Significant work has developed MALDI MS/MS imaging capabilities¹³³, and parallel accumulation of serial fragmentation¹⁴⁰ coupled with trapped ion mobility is a seminal innovation supporting metabolite, lipid, and peptide MS/MS in MALDI imaging studies^141,142. Current imaging studies use MALDI sources can be coupled with many configurations of instrumentation. MALDI coupled with ion extraction to a time of flight tube (MALDI TOF) was the foundational instrumentation used in MALDI MSI¹²⁰ and is a mainstay for economic image analysis of lipids, peptides, and proteins. The MALDI quadrupole time of flight (MALDI QTOF) uses an ion funnel to collect ions and a quadrupole mass filter to guide ions to a multichannel detector for high throughput, high mass resolution image detection. The versatility in a broad range of complex analyte acquisition from tissues to cells has positioned MALDI MSI as a central technology for spatialomics easily leveraged for multiomic spatial studies.

Experimental Design in MALDI MSI Studies

The success of a MALDI MSI experiment is largely decided before a sample ever reaches the instrument, with experimental design defining the path to reproducible and high-quality image data (Figure 6). In experimental design, the scope of the biological question to be defined along with histological or immunohistochemistry of target biology defines the required spatial resolution, to either single cell level or broad sampling over the entire tissue section. Studies may use a strategy where sampling over the entire tissue section is done prior to defining a target area for single cell imaging. The anatomical site localization and time of collection between all samples should be consistent for the most reproducible results. The three-dimensional anatomical origin of the sample tissue section or single cell has a profound effect on signal variation by MALDI MSI, particularly true for cardiac studies. Chemical staining or immunohistochemical staining of cell markers of additional tissue sections are required to define the biological status of the tissue. At minimum, a section with pathological annotation by hematoxylin (nuclear staining) and eosin (cytoplasm and ECM) demonstrates histological patterns within the tissue section can be annotated by a pathologist for regional pathologies. Strategies intersperse digital scanning of chemical stains or cell marker stains between MALDI MSI of tissue sections for co-registration of specific biological markers with imaging data. MALDI MSI in combination with pre-clinical to clinical imaging such as radiologic data may contribute to study endpoints, thus requires strategies that include positive and negative controls that validate sample preparation. Automated sprayers and sublimators used in sample preparation include documented system suitability criteria that allow reproducibility across multicenter studies. The type of instrument to be used in the study dictates the substrate the sample will be mounted on. For clinically archived samples, the tissue sections are frequently already mounted on a standard microscope slide, and instrumentation with ion funnels (MALDI QTOF, MALDI FT-ICR) may be used. For samples that can be placed de novo onto a substrate, a conductive slide allows use of an extraction voltage source (MALDI TOF). MALDI MSI is highly reproducible, and the use of internal standards can be used to support normalization, evaluate instrument performance, and adjust for potential batch variation^{117,143–145}. Sample power analyses for MALDI MSI studies consider biological variation as a higher variable in experimental design compared to technical variation. The quality metric on evaluating sample preparation includes the use of tissue mimetics^146,147, with spike-in standards, evaluation of prominent known peaks, e.g., common lipid peaks, common tryptic autolysis products, common endogenous peptides or positive/negative control tissues. In the application of multiomic approaches, endogenous peaks routinely detected across tissues are used in assessing quality of the image analysis. For general lipidomics, PI 38:4 at 885.5488 (C47H83O13P) in negative mode or glycerophosphocholines 760.5851 (C42H82NO8P) in positive mode is a target for assessment. For N-glycan imaging, the peak 1809.6393 (Hex5dHex1HexNAc4 + 1Na) is a good general tissue marker useful in evaluating imaging analysis. Post-acquisition evaluation of image quality takes into evaluates for high peak signal-to-noise ratio, peak symmetry and resolution, localization of the analytes relative to the applied spatial resolution, and minimal MALDI matrix peaks. For cardiovascular research, a carefully integrated experimental design includes consideration of site localized cardiac anatomy, regional heterogeneity, temporal biology, biological variability, and includes integrative histological studies, instrumentation considerations with quality control over the study lifetime. These points are essential for MALDI MSI studies that are technically robust and produce biologically meaningful data that is translatable across experimental models and clinical cohorts.

Figure 6.

Schematic overview of experimental design considerations for a MALD MSI experiment. A) For routine analysis, fresh frozen tissue may be used for spatial reporting of drugs, metabolites, lipids, glycans, peptides, and intact proteins. Clinically archived formalin fixed, paraffin embedded tissue is useful for routine reporting for glycans and proteases that targeting different proteomes to produce peptides. B) Co-studies that report target biology are chosen and the order and number of co-study stains interspersing with MALDI MSI determined. C) Rigor focuses on biological vs technical replicates. Study components for quality control are determined, at minimum using internal standards during sample preparation. D) the type of matrix chosen depends on the analyte. This table reports common matrices, E) The analyte population also determines mass resolution. For drugs, small molecules, metabolites, lipids, peptides, mass resolution >60,000 on-tissue is ideal for separating isomers. The type of instrument also determine the substrate the sample will need to be mounted on: requires a conductive coating (MALDI TOF) or does not require conductive coating (MALDI QTOF or MALDI FT-ICR). F) Spatial resolution required determines the type of matric application used. For images over 10 μm step size, a liquid matrix is sprayed onto the sample. For image studies under 10 μm step size, matrix is sublimated onto the sample. G) Data analysis involves types of visualization software available. Images are usually in the 10-30 Gb per image so data storage should be considered. Computational resources available determine the ability to perform complex tasks within image data sets.

Key Advances and Discoveries by MALDI MSI in Cardiovascular Research

The development of MALD MSI occurred parallel with adoption of the technique in cardiovascular research. These advances have resulted in an established foundation for cardiac spatial analysis of multiple analytes, that when combined with multiomics or clinical modalities, is quickly becoming a powerful tool for basic to clinical research. Here, we highlight key examples in cardiovascular research alongside the progress in MALDI MSI emergence. Lipids were a first target for MALDI MSI¹⁴⁸ and early cardiovascular research on prototype MALDI MSI instrumentation reported single cell lipid layers uniquely localized in murine aortic valve or aortic valve hinge¹⁴⁹. This initial lipid spatialomics data on cardiac tissue demonstrated that molecular pathology found by MALDI MSI could reveal novel subanatomic patterns within the heart. MALDI MSI unveiled the first images of cardiovascular complexity due to lipid regulation, with the first MS/MS lipid images reporting fatty acid chain variation localized to infarcted heart¹⁵⁰. Glycerophospholipids were found to have a distinctive spatial localization within the ventricular tissue compared to aortic anatomical features¹⁵¹, implicating a functional role for spatial regulation of the cardiovascular lipid population. A further report demonstrated that glycerophospholipids are distinctively regulated within the extracellular matrix compartments of healthy aortic valves from humans and sheep¹⁵², tying spatial lipid regulation to extracellular matrix and tissue function. Spatial lipidomics as a singular imaging analysis of the heart^153,154 or combined with proteomic imaging^155,156 continues to yield critical insights into cardiac biology and disease. Single cell studies combining lipidomics with MALDI IHC, have been used to report changes in lung fibroblast senescence; however, this has yet to be applied to cardiovascular fibroblasts¹⁵⁷. Sub anatomical imaging of intact proteins revealed that small proteins were spatially segregated in lesions of aortic valve stenosis¹⁵⁸ and in chick heart models¹⁵⁹, supporting a systems biology form-to-function role in cardiac spatial regulation. Intact protein imaging illustrated significant spatial flux in protein regulation during cardiac ischemia/reperfusion¹⁶⁰. ECM had historically been only accessible by chemical staining and researchers quickly leveraged MALDI MSI to understand cardiovascular regulation of ECM. In what may be the first multimodal report of MALDI MSI used in cardiovascular research, Lohöfer and colleagues reported processes of ECM synthesis in mouse myocardial infarct healing using a combination of magnetic resonance imaging (MRI), MALDI MSI, laser-ablation-inductively-coupled plasma mass spectrometry imaging and conventional chemical staining by Verhoeff van Gieson¹⁶¹. This study showed that Gadoflourine P, a contrast reagent that binds to collagens and other glycoproteins, could be detected and quantified by both MRI and MALDI MSI towards understanding functional deposition of ECM in myocardial infarct healing with applications to the clinic¹⁶². For studies involving human investigations, tissue from disease states is routinely stored as formalin-fixed, paraffin-embedded (FFPE) samples; the fixation process limits practical use of decellularization as an approach targeted ECM analysis. Although trypsin had been developed early in MALDI MSI to access FFPE tissue¹¹⁸, in application to cardiovascular research, this enzyme primarily reports on abundant cellular proteins, used in innovative proteomic pathway imaging of aortic aneurysms by Buerger et al¹⁶³. The extracellular matrix proteome within clinically archived FFPE aortic valve tissue was targeted for spatial proteomics by MALDI MSI using enzymes elastase and collagenase type III¹¹⁴. This study described high molecular proteomic variation, including post-translational modifications, from the elastin and collagen protein network found in porcine aortic valve tissue. The potential in enzyme targeting of the ECM in cardiac processes was shown by therapeutic application of injection of collagen peptides to myocardial infarct site in murine heart¹⁶⁴ and uncovered spatial regulation of ECM centered around the injection site. Here, therapeutic peptides which were human in origin compared to endogenous deposition of collagen peptides by the mouse host were defined by MS/MS and ion mobility processes to trace specific healing processes. The enzyme targeting technique found useful for ECM later led to multiplexing up to 7 enzymes to report spatial localization of glycosylation, ECM and cellular proteins from a single tissue section of pediatric aortic valve tissue¹⁰⁷, showing that tissue sparing approaches can be used to assess molecular pathology in cardiac tissue. The ECM is highly glycosylated and over 75% of human ECM proteins contain on average 4.3 consensus sites (N-X-S/T, where X≠P) for N-glycosylation. Significant work pioneering N-glycan imaging¹⁶⁵ has led to robust protocols in FFPE, frozen tissue, or on printed arrays^166,167. The initial N-glycan imaging approaches have been further modified to distinguish sialic acids isomers by chemical derivatization^126,168 or define fucose isomers by enzymatic strategies^169,170. In cardiac tissue, aortic valve function was linked to anatomic compartmentalization of N-glycosylation with abundant, but localized core fucosylation and 2,3 sialic acid linkages in pediatric congenital aortic valve stenosis¹⁷¹. Current strategies at minimum consider multiplexing N-glycan imaging with ECM imaging on heart tissue (Figure 7). Spatial analysis of small molecule drug localization is a key strength of MALDI mass spectrometry imaging that has been applied to cardiac studies. Quantitative imaging analysis of the anticancer drug doxorubicin using on-tissue chemical derivatization showed rapid clearance in hear compared to other organ tissues¹⁷². Here, spatial analysis revealed high uptake in the atria compared to ventricles and lower clearance from the atria after 24 hours, supporting why doxorubicin causes atrial fibrillation in cardiotoxicity. MALDI MSI has been significant in uncovering metabolic processing and was used to evaluate metabolic flux in a rat model diabetic cardiomyopathy¹⁷³. Here integrating with MALDI MSI with air-flow-assisted desorption electrospray imaging, pathology staining, and trichrome staining showed significant changes in molecular pathology from metals, metabolites, and amino acids. In this study, therapeutic application of ferulic acid, a natural compound small molecule, was found to restore diabetic cardiomyopathy to molecular levels similar to control tissue with anti-fibrotic effects. The MALDI MSI platform was also applied to cultured human aortic endothelial cells to evaluate N-glycan regulation in oxidative stress⁸⁹. This study used stable isotopic labeling of the amide nitrogen glutamine^174,175 to incorporate a single label into N-acetylglucosamine, sialic acid, or N-acetyl galactosamine in a time course exposure to oxidative stress modeled by hydrogen peroxide. Data showed changes in N-glycosylation turnover, with the emergence of highly branched N-glycans appearing at 55 hours⁸⁹ and accompanying increases in TGFβ1, COL1A1, and CHOP, implicating cell phenotypic differences that include N-glycan changes in oxidative stress. Currently there are few studies leveraging multiomic cell type protocols using the state-of-the-art MALDI MSI platform^89,91,128 against cardiac originating cell types. The totality of the highlighted MALDI MSI studies demonstrates that MALDI MSI is an impactful approach to uncovering spatial regulation across multiple analytes, in different cardiac sample types and is unbiased to species. The range of approaches that have been developed for cardiovascular research lead to the current multimodal studies focused on combining powerful MALDI MSI with more clinically driven imaging^176,177 that could, in the near future, be translatable to monitor and solve human cardiovascular disease.

Figure 7.

Multiplexed MALDI MSI of N-glycans and collagen peptides on a single tissue section of cardiac tissue (Rat). Data reports novel spatial variation by post-translational modifications. A) Photomicrograph greyscale image of tissue section prior to acquiring MALDI MSI data. B) N-glycomic spatial regulation. Combined N-glycan image with a single fucose showing different spatial patterns. C) Colorized and heatmap images of the same N-glycans differing by a single fucose structure. D) ECM-targeted spatial proteomics was used to show collagen proteomic network spatial modulation. Example mapping of species conserved cell binding domains from collagen alpha 1(I) protein. Discoidin domain binding and integrin binding domains are shown and highlight spatial variability in mapping the collagen network. O indicates proline hydroxylation site. E) Colorized and heatmap images of two peptides from the same collagen. F) Hematoxylin and eosin stain after multiplexed mass spectrometry imaging.

Computational Opportunities in Cardiovascular Research by MALDI MSI

Any review of MSI spatialomics would be remiss if data analysis were not discussed. The amount and breadth of work that has continually been done in MALDI MSI data analysis is can only peripherally summarized here; this field has rapidly moved from first painstaking data points charted in excel¹²⁰ to current use of artificial intelligence¹⁷⁸. A long-time goal of the MALDI MSI field is to apply the data-rich information for clinical use. As such, pioneering studies using machine learning techniques have been applied to evaluate MALDI MSI derived signatures from large patient datasets for well over a decade^179–188, driven by the unique MALDI MSI ability to scan proteomic signatures from FFPE tissue microarrays that contain hundreds of patient samples. Significant advances in MALDI MSI image analysis have been made with software statistical evaluation by spatially-derived regions^189–191, image fusion for multimodality studies¹⁹², three-dimensional image construction^193,194, searchable and quality controlled spatialomics databases¹⁹⁵, and prediction of single cell metabolic states¹⁹⁶. Current multiomic deep learning tools are beginning truly leverage MALDI MSI in a multiomic capacity¹⁹⁷ and integrate MALDI MSI data of any analyte with spatial transcriptomics^110,198. However, it is noteworthy that very few of these approaches have been applied to cardiovascular research and this emphasizes the untapped potential for mining the dynamic spatial biology of the heart.

Conclusions

From a systems-level perspective, cardiovascular disease (CVD) is a fundamentally multicellular and multiomic pathology, in which spatially regulated dynamics govern disease initiation, progression, and remodeling. SCP and MSI are converging to rapid real-time reporting from clinically relevant CVD pathology, cells and biofluids. In the near future, the application of artificial intelligence will be used to integrate SCP and multiomic pathology by MSI, defining single cell dynamics at a systems biology level. This data will be able to rapidly integrate transcriptomic, metabolomic, and proteomic data from cell-cell, cell-matrix neighborhood communication networks with clinical data. Such an approach holds promise for earlier detection of CVD, identifying therapeutic strategies that promote adaptive, functional remodeling rather than maladaptive, nonfunctional outcomes. Ultimately, a central goal is to integrate multiomic spatial strategies down to a single cell level and capture the dynamic emergence of CVD in real time, thereby allowing therapeutic interventions to be deployed at the right moment and with appropriate temporal precision to optimize individual cardiovascular health.

Non-standard Abbreviations and Acronyms (In order of appearance in text)

MS

Mass spectrometry

MSI

mass spectrometry imaging

SCP

Single-cell proteomics

TMT

tandem mass tags

SCoPE2

Single Cell ProtEomics version 2

mpop

minimal proteomic sample preparation

nanoPOTS

nanodroplet processing in one pot for trace samples

DIA

data-independent acquisition

LFQ

label-free quantification

PCA

Principal components analysis

UMAP

Uniform Manifold Approximation and Projection

plexDIA

Multiplexed data independent acquisition

LC

Liquid chromatography

nanoLC

Nanoliter flow

LC-MS/MS

Liquid chromatography coupled to tandem mass spectrometry

SCarP

Single Cardiomyocyte Proteomics

QC

Quality control

MALDI

Matrix-Assisted Laser Desorption/Ionization

MS/MS

tandem mass spectrometry

ECM

Extracellular matrix

MALDI QTOF

Matrix-Assisted Laser Desorption/Ionization Quadrupole Time of Flight

MALDI FT-ICR

Matrix-Assisted Laser Desorption/Ionization Fourier Transform-Ion Cyclotron Resonance

MALDI TOF

Matrix-Assisted Laser Desorption/Ionization Time of Flight

MALDI IHC

Matrix-Assisted Laser Desorption/Ionization Immunohistochemistry

MRI

Magnetic Resonance Imaging

FFPE

formalin-fixed, paraffin-embedded