Background: We have developed a new class of dual polarity molecules for matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) capable of acquiring 5 μm pixel sizes with high sensitivity toward polar lipids and metabolites. Aminated cinnamic acid analogs (ACAAs) are vacuum stable, have high extinction coefficients at 355 nm, are highly sensitive to polar lipids, have low toxicity, and are affordable. Current molecules used for high spatial resolution MALDI IMS of polar lipids have shown great success, but are plagued with issues such as low sensitivity at high spatial resolution, vacuum instability, and/or high toxicity. Results: ACAAs were evaluated as MALDI matrices, testing them for vacuum stability, absorption at 355 nm, crystal size, sensitivity, and molecular coverage. Among them, 4-aminocinnamic acid (ACA) and 4-(dimethylamino)cinnamic acid (DMACA) were found to perform better than conventional MALDI matrices for lipid IMS experiments. ACA generated fewer in-source fragments due to its high extinction coefficient at 355 nm. This leads to better discernment of thermally labile molecules such as gangliosides compared to typical ‘soft’ ionization matrices like DHA using murine brain tissue. On the other hand, DMACA showed better optical properties than ACA, giving it higher sensitivity from many lipid classes, such as phospholipids and sulfatides. DMACA outperformed DAN and DHA at their individually optimized laser power at small pixel sizes (≤10 μm). DMACA also allows for lower laser power to be used without compromising sensitivity, which reduced the laser spot size at the sample surface from ∼6 μm to ∼4.5 μm without hardware modifications. Significance: Improved sensitivity and absorption efficiency at 355 nm allow for 5 μm pixel size MALDI IMS without oversampling while maintaining high S/N on commercial mass spectrometry platforms. Performing MALDI experiments at reduced laser energies minimizes tissue damage, enabling advanced multimodal MALDI IMS studies to be performed on single tissue sections. Comparisons and optimized MALDI IMS methods were performed on murine tissues and human kidney samples as part of the Human Biomolecular Atlas Program.

Imaging mass spectrometry (IMS) yields high-dimensional and large data sets commonly exceeding 100,000 pixels, each reporting a mass spectrum of 200,000 intensity values or more. Reducing the dimensionality and size of IMS data is often necessary to enable downstream analysis, and matrix-factorization-based approaches are often used for this purpose. However, the model underlying most of these techniques, decomposing measurements into the sum of a low-rank term (presumed signal) and a small entry-wise residual term (presumed noise), is often not optimal for IMS. For example, while spatially or spectrally sparse signals are common in IMS data, they can heavily distort the low-rank approximation. Therefore, we propose capturing the IMS data structure using low-rank models that, in addition to a dense residual, allow for sparse variation to be captured separately. We implement two such methods, principal component pursuit (PCP) and stable principal component pursuit (SPCP), apply them to IMS data, and compare them to a classical factorization method, principal component analysis (PCA). We investigate their dimensionality and noise reduction performance on MALDI Q-TOF IMS measurements of human cornea and retina tissue since the human eye is a complex organ with lots of small, tightly packed tissue substructures that are spatially sparse. Our results suggest that if parameters are set adequately, PCP and SPCP enable stronger dimensionality reduction and higher compression of IMS data compared to PCA, while concurrently reducing signal overestimation.

Neutral lipids are vital to various cellular processes and disease pathologies. However, their characterization by matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) remains challenging due to poor ionization efficiency and difficulties distinguishing subtle structural differences among numerous isomeric and isobaric species. In this study, we enhanced neutral lipid detection by incorporating isotonic metal–cation washes into our MALDI IMS sample preparation workflow. Resulting salt adducts improved neutral lipid isobar and isomer separation by using trapped ion mobility spectrometry (TIMS). This approach increased both sensitivity and specificity for neutral lipid IMS experiments across multiple organ types, including murine brain, rabbit adrenal gland, human colon, and human kidney. Comparative analyses revealed that the most effective salt wash was tissue-dependent. However, the Na⁺carbonate buffer solution (CBS) wash showed the greatest overall increase in neutral lipid detection. These findings provide a robust framework for mapping neutral lipids across multiple tissues and disease states and allow for the detailed characterization of neutral lipid isomers and isobars in complex biological tissues.

Glomeruli filter blood through the coordination of podocytes, mesangial cells, fenestrated endothelial cells, and the glomerular basement membrane. Cellular changes, such as podocyte loss, are associated with pathologies like diabetic kidney disease. However, little is known regarding the in situ molecular profiles of specific cell types and how these profiles change with disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is well-suited for untargeted tissue mapping of a wide range of molecular classes. Importantly, additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Here, we integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 μm) was used to determine lipid distributions within human glomeruli from a normal portion of fresh-frozen kidney cancer nephrectomy tissue revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types and substructures through new methods that enable MxIF microscopy to be performed on the same tissue section following MALDI IMS, without sacrificing signal quality from either modality. Machine learning approaches were combined enabling cell type segmentation and identification based on MxIF data. This was followed by mining of cell type or cluster-associated MALDI IMS signatures using classification and interpretable machine learning. This allowed automated discovery of spatially specific molecular markers for glomerular cell types and substructures as well as lipids correlated to deep and superficial glomeruli. Overall, our work establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments providing a framework applicable to other kidney tissue features and organ systems.

Multimodal imaging by matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI MSI) and microscopy holds potential for understanding pathological mechanisms by mapping molecular signatures from the tissue microenvironment to specific cell populations. However, existing software solutions for MALDI MSI data analysis are incomplete, require programming skills and contain laborious manual steps, hindering broadly applicable, reproducible, and high-throughput analysis to generate impactful biological discoveries. Here, we present msiFlow, an accessible open-source, platform-independent and vendor-neutral software for end-to-end, high-throughput, transparent and reproducible analysis of multimodal imaging data. msiFlow integrates all necessary steps from raw data import to analytical visualisation along with state-of-the-art and self-developed algorithms into automated workflows. Using msiFlow, we unravel the molecular heterogeneity of leukocytes in infected tissues by spatial regulation of ether-linked phospholipids containing arachidonic acid. We anticipate that msiFlow will facilitate the broad applicability of MSI in multimodal imaging to uncover context-dependent cellular regulations in disease states.

Imaging mass spectrometry (IMS) enables untargeted, highly multiplexed mapping of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms lack microscopy-level spatial resolution and cellular morphological contrast, necessitating subsequent histochemical staining, microscopic imaging, and advanced image registration to correlate/link molecular distributions with specific tissue features and cell types. We present a diffusion model-based virtual histological staining approach that enhances spatial resolution and digitally introduces cellular morphological contrast into mass spectrometry images of label-free human tissue. Blind testing on human kidney tissue demonstrated that the virtually stained images of label-free samples closely match their histochemically stained counterparts (with periodic acid-Schiff staining), showing high concordance in identifying key renal pathology structures despite using IMS data with 10-fold larger pixel size. Additionally, our approach uses optimized noise sampling during the diffusion model's inference to achieve reliable and repeatable virtual staining. We believe this virtual staining method will open avenues for IMS-based biomedical research.

Motivation: Imaging mass spectrometry (IMS) has become an important tool for molecular characterization of biological tissue. However, IMS experiments tend to yield large datasets, routinely recording over 200 000 ion intensity values per mass spectrum and more than 100 000 pixels, i.e. spectra, per dataset. Traditionally, IMS data size challenges have been addressed by feature selection or extraction, such as by peak picking and peak integration. Selective data reduction techniques such as peak picking only retain certain parts of a mass spectrum, and often these describe only medium-to-high-abundance species. Since lower-intensity peaks and, for example, near-isobar species are sometimes missed, selective methods can potentially bias downstream analysis toward a subset of species in the data rather than considering all species measured. Results: We present an alternative to selective data reduction of IMS data that achieves similar data size reduction while better conserving the ion intensity profiles across all recorded m/z-bins, thereby preserving full spectrum information. Our method utilizes a low-rank matrix completion model combined with a randomized sparse-format-aware algorithm to approximate IMS datasets. This representation offers reduced dimensionality and a data footprint comparable to peak picking but also captures complete spectral profiles, enabling comprehensive analysis and compression. We demonstrate improved preservation of lower signal-to-noise ratio signals and near-isobars, mitigation of selection bias, and reduced information loss compared to current state-of-the-art data reduction methods in IMS. Availability and implementation: The source code is available at https://github.com/vandeplaslab/full_profile and data are available at https://doi.org/10.4121/a6efd47a-b4ec-493e-a742-70e8a369f788.

Tissue atlases provide foundational knowledge on the cellular organization and molecular distributions across molecular classes and spatial scales. Here, we construct a comprehensive spatiomolecular lipid atlas of the human kidney from 29 donor tissues using integrated multimodal molecular imaging. Our approach leverages high-spatial-resolution matrix-assisted laser desorption/ionization imaging mass spectrometry for untargeted lipid mapping, stained microscopy for histopathological assessment, and tissue segmentation using autofluorescence microscopy. With a combination of unsupervised, supervised, and interpretable machine learning, the atlas provides multivariate lipid profiles of specific multicellular functional tissue units (FTUs) of the nephron, including the glomerulus, proximal tubules, thick ascending limb, distal tubules, and collecting ducts. In total, the atlas consists of tens of thousands of FTUs and millions of mass spectrometry measurements. Detailed patient, clinical, and histopathologic information allowed molecular data to be mined on the basis of these features. As examples, we highlight the discovery of how lipid profiles are altered with sex and differences in body mass index.

Correction to: Nature Cell Biologyhttps://doi.org/10.1038/s41556-023-01194-w. Published online 19 July 2023. In the version of this article originally published, the name of Tianyang Xu was misspelled as Tiangyang Xu. The name has been corrected in the HTML and PDF versions of the article.

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney’s active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

The lack of standardization in antibody validation remains a major contributor to irreproducibility of human research. To address this, we have applied a standardized approach to validate a panel of antibodies to identify 18 major cell types and 5 extracellular matrix compartments in the human kidney by immunofluorescence (IF) microscopy. We have used these to generate an organ mapping antibody panel for two-dimensional (2-D) and three-dimensional (3-D) cyclical IF (CyCIF) to provide a more detailed method for evaluating tissue segmentation and volumes using a larger panel of markers than would normally be possible using standard fluorescence microscopy. CyCIF also makes it possible to perform multiplexed IF microscopy of whole slide images, which is a distinct advantage over other multiplexed imaging technologies that are applicable to limited fields of view. This enables a broader view of cell distributions across larger anatomical regions, allowing a better chance to capture localized regions of dysfunction in diseased tissues. These methods are broadly accessible to any laboratory with a fluorescence microscope, enabling spatial cellular phenotyping in normal and disease states. We also provide a detailed solution for image alignment between CyCIF cycles that can be used by investigators to perform these studies without programming experience using open-sourced software. This ability to perform multiplexed imaging without specialized instrumentation or computational skills opens the door to integration with more highly dimensional molecular imaging modalities such as spatial transcriptomics and imaging mass spectrometry, enabling the discovery of molecular markers of specific cell types, and how these are altered in disease.NEW & NOTEWORTHY We describe here validation criteria used to define on organ mapping panel of antibodies that can be used to define 18 cell types and five extracellular matrix compartments using cyclical immunofluorescence (CyCIF) microscopy. As CyCIF does not require specialized instrumentation, and image registration required to assemble CyCIF images can be performed by any laboratory without specialized computational skills, this technology is accessible to any laboratory with access to a fluorescence microscope and digital scanner.

Purpose: Mutations in the gene that encodes the enzyme acid sphingomyelinase (ASMase) are associated with Niemann-Pick disease, a lysosomal storage disorder. Mice that lack ASMase (ASMase-/-) exhibit age-related retinal degeneration and large increases in accumulation of lipofuscin in the retinal pigment epithelium (RPE). We examined which lipid species accumulate in the retina and the RPE of ASMase-/- mice and whether the retinal degeneration is associated with impaired photoreceptor metabolism and retinyl chromophore processing. Methods: NADPH availability and all-trans retinol formation after rhodopsin bleaching were measured in isolated single rod photoreceptors with fluorescence imaging; sphingolipid levels in retinas and RPEs were measured with LC/MS; relative abundances of different lipid species in different retinal layers were measured with MALDI imaging mass spectrometry. Results: There was no detectable difference in the kinetics of all-trans retinol formation or the NADPH-generating capacity between ASMase-/- and wild-type mice. Sphingomyelin levels were much higher in the retinas and RPEs of ASMase-/- animals compared to wild type, but there were no significant differences for ceramides. There was a large increase in the abundance of bis(monoacylglycero)phosphates (BMPs) in ASMase-/- mice, indicative of lysosomal dysfunction, but no substantial changes were detected for the bis-retinoid A2E. Conclusions: Lysosomal dysfunction and retinal degeneration in ASMase-/- mice are not associated with defects in rod photoreceptor metabolism that affect all-trans retinol formation and availability of NADPH. Lysosomal dysfunction in ASMase-/- mice is not associated with bis-retinoid A2E accumulation.

While probabilistic constellation shaping (PCS) enables rate and reach adaption with finer granularity [1] (Cho and Winzer, 2009), it imposes signal processing challenges at the receiver. Since the distribution of PCS-quadrature amplitude modulation (QAM) signals tends to be Gaussian, conventional blind polarization demultiplexing algorithms are not suitable for them [2] (Johnson et al., 1998). It is known that independently and identically distributed (iid) Gaussian signals, when mixed, cannot be recovered/separated from their mixture. For PCS-QAM signals, there are algorithms such as [3] and [4] Dris et al. (2019) and Athuraliya et al. (2004) which are designed by extending conventional blind algorithms used for uniform QAM signals. In these algorithms, an initialization point is obtained by processing only a part of the mixed signal, which have non-Gaussian statistics. In this article, we propose an alternative method wherein we add temporal correlations at the transmitter, which are subsequently exploited at the receiver in order to separate the polarizations. We will refer to the proposed method as frequency domain (FD) joint diagonalization (JD) probability aware-multi modulus algorithm (pr-MMA), and it is suited to channels with moderate polarization mode dispersion (PMD) effects. Furthermore, we extend our previously proposed JD-MMA [5] (Bajaj et al., 2022) by replacing the standard MMA with a pr-MMA, improving its performance. Both FDJD-pr-MMA and JD-pr-MMA are evaluated for a diverse range of PCS (entropy $\mathcal {H}$) of 64-QAM over a first-order PMD channel that is simulated in a proof-of-concept setup. A MMA initialized with a memoryless constant modulus algorithm (CMA) is used as a benchmark. We show that at a differential group delay (DGD) of 10% of symbol period T$_{\text{symb}}$ and 18 dB SNR/pol., JD-pr-MMA successfully demultiplexes the PCS signals, while CMA-MMA fails drastically. Furthermore, we demonstrate that the newly proposed FDJD-pr-MMA is robust against moderate PMD effects by evaluating it over a DGD of up to 40% of T$_{\text{symb}}$. Our results show that the proposed FDJD-pr-MMA successfully equalizes PMD channels with a DGD up to 20% of T$_{\text{symb}}$.

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

Macroscopic x-ray fluorescence imaging spectroscopy (MA-XRF) and reflectance imaging spectroscopy (RIS) are important tools in the analysis of cultural heritage objects, both for conservation and art historical research purposes. The elemental and molecular distributions provided by MA-XRF and RIS respectively, are particularly useful for the identification and mapping of pigments in easel paintings. While MA-XRF has relatively established data processing methods based on modeling of the underlying physics, RIS data cannot be modeled with sufficient precision and its processing has considerable room for improvements. This work seeks to improve RIS data processing workflows in the short wavelength infrared range (SWIR, 1000–2500 nm) with a novel method that fits Gaussian profiles to pigment-specific absorption features, and we compare its performance to MA-XRF for the task of semi-quantitative pigment mapping, evaluating their limits of detection (LODs) and the matrix effects that affect their signals. Two pigments are considered in this work, lead white and blue verditer, which are mapped in SWIR RIS using the first overtone of -OH stretching of their primary compounds, hydrocerussite (Pb₃(CO₃)₂(OH)₂) and azurite (Cu₃(CO₃)₂(OH)₂), at 1447 and 1497 nm respectively, and in MA-XRF using the Pb-L and Cu-K fluorescence signals. The methods are evaluated using two sets of custom-prepared paint samples, as well as a 16th-century painting, discussing the identification, mapping, and semi-quantitative analysis of the considered pigments. We found SWIR RIS to be a pigment-specific method with a longer linear range but inferior LODs and penetration depth when compared to MA-XRF, the latter is often not capable of discriminating between different pigments with identical elemental markers. We furthermore present a novel color scale that allows the simultaneous visualization of signals above and below a confidence limit.

Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.

Gangliosides are acidic glycosphingolipids, containing ceramide moieties and oligosaccharide chains with one or more sialic acid residue(s) and are highly diverse isomeric structures with distinct biological roles. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) enables the untargeted spatial analysis of gangliosides, among other biomolecules, directly from tissue sections. Integrating trapped ion mobility spectrometry with MALDI IMS allows for the analysis of isomeric lipid structures in situ. Here, we demonstrate the gas-phase separation and identification of disialoganglioside isomers GD1a and GD1b that differ in the position of a sialic acid residue, in multiple samples, including a standard mixture of both isomers, a biological extract, and directly from thin tissue sections. The unique spatial distributions of GD1a/b (d36:1) and GD1a/b (d38:1) isomers were determined in rat hippocampus and spinal cord tissue sections, demonstrating the ability to structurally characterize and spatially map gangliosides based on both the carbohydrate chain and ceramide moieties.

Imaging mass spectrometry (IMS) provides untargeted, highly multiplexed maps of molecular distributions in tissue. Ion images are routinely presented as heatmaps and can be overlaid onto complementary microscopy images that provide greater context. However, heatmaps use transparency blending to visualize both images, obscuring subtle quantitative differences and distribution gradients. Here, we developed a contour mapping approach that combines information from IMS ion intensity distributions with that of stained microscopy. As a case study, we applied this approach to imaging data from Staphylococcus aureus-infected murine kidney. In a univariate, or single molecular species, use-case of the contour map representation of IMS data, certain lipids colocalizing with regions of infection were selected using Pearson’s correlation coefficient. Contour maps of these lipids overlaid with stained microscopy showed enhanced visualization of lipid distributions and spatial gradients in and around the bacterial abscess as compared to traditional heatmaps. The full IMS data set comprising hundreds of individual ion images was then grouped into a smaller subset of representative patterns using non-negative matrix factorization (NMF). Contour maps of these multivariate NMF images revealed distinct molecular profiles of the major abscesses and surrounding immune response. This contour mapping workflow also enabled a molecular visualization of the transition zone at the host-pathogen interface, providing potential clues about the spatial molecular dynamics beyond what histological staining alone provides. In summary, we developed a new IMS-based contour mapping approach to augment classical stained microscopy images, providing an enhanced and more interpretable visualization of IMS-microscopy multimodal molecular imaging data sets.

The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) and k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease.

Introduction: Age related macular degeneration (AMD) causes legal blindness worldwide, with few therapeutic targets in early disease and no treatments for 80% of cases. Extracellular deposits, including drusen and subretinal drusenoid deposits (SDD; also called reticular pseudodrusen), disrupt cone and rod photoreceptor functions and strongly confer risk for advanced disease. Due to the differential cholesterol composition of drusen and SDD, lipid transfer and cycling between photoreceptors and support cells are candidate dysregulated pathways leading to deposit formation. The current study explores this hypothesis through a comprehensive lipid compositional analysis of SDD. Methods: Histology and transmission electron microscopy were used to characterize the morphology of SDD. Highly sensitive tools of imaging mass spectrometry (IMS) and nano liquid chromatography tandem mass spectrometry (nLC-MS/MS) in positive and negative ion modes were used to spatially map and identify SDD lipids, respectively. An interpretable supervised machine learning approach was utilized to compare the lipid composition of SDD to regions of uninvolved retina across 1873 IMS features and to automatically discern candidate markers for SDD. Immunohistochemistry (IHC) was used to localize secretory phospholipase A2 group 5 (PLA2G5). Results: Among the 1873 detected features in IMS data, three lipid classes, including lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE) and lysophosphatidic acid (LysoPA) were observed nearly exclusively in SDD while presumed precursors, including phosphatidylcholine (PC), phosphatidylethanolamine (PE) and phosphatidic acid (PA) lipids were detected in SDD and adjacent photoreceptor outer segments. Molecular signals specific to SDD were found in central retina and elsewhere. IHC results indicated abundant PLA2G5 in photoreceptors and retinal pigment epithelium (RPE). Discussion: The abundance of lysolipids in SDD implicates lipid remodeling or degradation in deposit formation, consistent with ultrastructural evidence of electron dense lipid-containing structures distinct from photoreceptor outer segment disks and immunolocalization of secretory PLA2G5 in photoreceptors and RPE. Further studies are required to understand the role of lipid signals observed in and around SDD.