Structural amyloid plaque polymorphism is associated with distinct lipid accumulations revealed by trapped ion mobility mass spectrometry imaging

Understanding of Alzheimer's disease (AD) pathophysiology requires molecular assessment of how key pathological factors, specifically amyloid β (Aβ) plaques, influence the surrounding microenvironment. Here, neuronal lipids have been implicated in Aβ plaque pathology, though the lipid microenvironment in direct proximity to Aβ plaques is still not fully resolved. A further challenge is the microenvironmental molecular heterogeneity, across structurally polymorphic Aβ features, such as diffuse, immature, and mature, fibrillary aggregates, whose resolution requires the integration of advanced, multimodal chemical imaging tools. Herein, we used matrix‐assisted laser desorption/ionization trapped ion mobility spectrometry time‐of‐flight based mass spectrometry imaging (MALDI TIMS TOF MSI) in combination with hyperspectral confocal microscopy to probe the lipidomic microenvironment associated with structural polymorphism of Aβ plaques in transgenic Alzheimer's disease mice (tgAPPSWE). Using on tissue and ex situ validation, TIMS MS/MS facilitated unambiguous identification of isobaric lipid species that showed plaque pathology‐associated localizations. Integrated multivariate imaging data analysis revealed multiple, Aβ plaque‐enriched lipid patterns for gangliosides (GM), phosphoinositols (PI), phosphoethanolamines (PE), and phosphatidic acids (PA). Conversely, sulfatides (ST), cardiolipins (CL), and polyunsaturated fatty acid (PUFA)‐conjugated phosphoserines (PS), and PE were depleted at plaques. Hyperspectral amyloid imaging further delineated the unique distribution of PA and PE species to mature plaque core regions, while PI, LPI, GM2 and GM3 lipids localized to immature Aβ aggregates present within the periphery of Aβ plaques. Finally, we followed AD pathology‐associated lipid changes over time, identifying plaque‐ growth and maturation to be characterized by peripheral accumulation of PI (18:0/22:6). Together, these data demonstrate the potential of multimodal imaging approaches to overcome limitations associated with conventional advanced MS imaging applications. This allowed for the differentiation of both distinct lipid components in a complex micro‐environment as well as their correlation to disease‐relevant amyloid plaque polymorphs.


| INTRODUC TI ON
With the average age of the World's population increasing, the prevalence of age-associated diseases including neurodegenerative disorders is increasing as well. Alzheimer's disease (AD) is the most common form of dementia and the most common neurodegenerative disease affecting 12% over 65. The major pathologic hallmark of AD includes the formation of extracellular plaques consisting of amyloidβ (Aβ) peptides as well as neurofibrillary tangles formed by hyper-phosphorylated tau protein (Masters et al., 2015).
Aβ pathology has been established to precede other downstream pathologic features, suggesting that amyloid peptide secretion and aggregation are key pathogenic events in AD pathogenesis.
However, the exact chain of events underlying amyloid pathology and AD pathogenesis are still not fully understood hampering the development of any curative treatments with significant clinical and socioeconomic consequences (Masters et al., 2015;Scheltens et al., 2016).
A further challenge in understanding the biochemical basis underlying AD pathogenesis is the complex, phenotypic heterogeneity of AD pathology and Aβ plaque morphology in particular.
Polymorphic Aβ pathology involves structurally different amyloid aggregates within and across different plaque phenotypes (Rasmussen et al., 2017). Most prominently, mature plaques associated with AD pathology are characterized by the formation of a compact core that was found to consist mainly of Aβ 1-40 (Michno, Nystrom, et al., 2019). With background to the complexity of plaque pathology and the still limited knowledge thereof, it comes to no surprise that major pharmacotherapeutic strategies targeting Aβ plaque pathology have not been successful so far. This, in turn, highlights the immediate need to further elucidate the molecular architecture associated with plaque formation in AD. Indeed, key developments in bioanalytical techniques, including chemical imaging, have increased our understanding of the molecular basis of single Aβ deposit formation at subcellular scales.
Particularly, mass spectrometry imaging (MSI) has gained prominent relevance for studying biochemical traits of AD pathology warranted by the molecular specificity inherent to this technique (Enzlein et al., 2020;Hanrieder et al., 2013;Kaya, Brinet, Michno, Baskurt, et al., 2017;Kaya et al., 2018;Michno et al., 2018). Here, matrix-assisted laser desorption ionization (MALDI)based MSI has been demonstrated to be a valuable approach in retrieving novel chemical information on plaque pathology in both AD patient's brain and AD mouse models. This includes also molecular information on plaque-associated neuronal lipid dynamics, which is significant in that lipids have been implicated in AD pathology and more specific in Aβ plaque formation mechanisms before (Di Paolo & Kim, 2011). This is corroborated by the fact that the ε4 allele of the apolipoprotein E encoding gene (APOE), a lipid transporter, is the most prominent genetic risk factor for developing sporadic AD (Liu European  with structural polymorphism of Aβ plaques in transgenic Alzheimer's disease mice (tgAPP SWE ). Using on tissue and ex situ validation, TIMS MS/MS facilitated unambiguous identification of isobaric lipid species that showed plaque pathology-associated localizations. Integrated multivariate imaging data analysis revealed multiple, Aβ plaque-enriched lipid patterns for gangliosides (GM), phosphoinositols (PI), phosphoethanolamines (PE), and phosphatidic acids (PA). Conversely, sulfatides (ST), cardiolipins (CL), and polyunsaturated fatty acid (PUFA)-conjugated phosphoserines (PS), and PE were depleted at plaques. Hyperspectral amyloid imaging further delineated the unique distribution of PA and PE species to mature plaque core regions, while PI, LPI, GM2 and GM3 lipids localized to immature Aβ aggregates present within the periphery of Aβ plaques. Finally, we followed AD pathology-associated lipid changes over time, identifying plaque-growth and maturation to be characterized by peripheral accumulation of PI (18:0/22:6). Together, these data demonstrate the potential of multimodal imaging approaches to overcome limitations associated with conventional advanced MS imaging applications. This allowed for the differentiation of both distinct lipid components in a complex micro-environment as well as their correlation to disease-relevant amyloid plaque polymorphs.
The capacity of MSI has been significantly expanded due to the introduction of ion mobility into the MS system allowing further separation of structurally similar and even isomeric compounds, which is of great relevance given the complexity among different lipid classes (Gillig et al., 2000;Jackson et al., 2005Jackson et al., , 2007McLean et al., 2007;Trim et al., 2008).
In this study, we set out to elucidate plaque morphologyassociated lipid species in a transgenic AD mouse model (tgAPP SWE ) that displays polymorphic plaque pathology (Philipson et al., 2010).
We used a novel MALDI-MSI setup with trapped ion mobility spectrometry (TIMS) to further resolve plaque-associated lipid species.
The MSI experiments were interfaced with fluorescent amyloid staining on the same section using structure sensitive, electrooptic amyloid probes, luminescent-conjugated oligothiophenes (LCOs) (Nilsson, 2009) that allow to delineate structurally different amyloid aggregates (Nilsson et al., 2007;Nystrom et al., 2013;Rasmussen et al., 2017). This hyperspectral imaging approach identified distinct lipid localization patterns specific to polymorphic amyloid structures including mass overlapping compounds, which could be further resolved by ion mobility separation.

| Chemicals and reagents
All chemicals for matrix and solvent preparation were pro-analysis grade and obtained from Sigma-Aldrich/Merck unless otherwise specified. TissueTek optimal cutting temperature compound was purchased from Sakura Finetek (Cat. no: 4583; AJ Alphen aan den Rijn). Deionized water obtained by a Milli-Q purification system (Millipore Corporation).

| Animals
Transgenic male tgAPPSwe mice, 18 and 23 months of age (n = 3 per age group), carrying the Swedish (K670N, M671L) mutations of human APP were studied. The animals were reared ad libitum and housed in groups at an animal facility at Uppsala University, Sweden under a 12/12 light cycle. The animals were anesthetized with isoflurane and sacrificed by decapitation. The brains were dissected quickly within >3 min postmortem delay and frozen on dry ice. All animal procedures were approved by an ethical committee and performed in compliance with national and local animal care and use guidelines (DNr #C17/14 at Uppsala University). For this study, no randomization, blinding, and sample size calculations were performed. All animal experiments and reporting were performed according to the ARRIVE guidelines. This exploratory study was not preregistered and not randomized. No inclusion or exclusion criteria were applied. No sample size calculation was performed, though post-hoc power calculations were included for validation (see 2.6 Data Analysis). Before spraying, the solvent pump was purged with 70% acetonitrile (ACN, Cat. no.: 34851-1L; Sigma-Aldrich) at 500 μl/min for 10 min followed by a manual rinse of matrix loading loop using a syringe. A matrix solution containing 7 mg/ml NEDC in 70% MeOH (Cat. no. 34860; Sigma-Aldrich) was sprayed onto the tissue sections with the following instrumental parameters: nitrogen flow (5 psi), spray temperature (30°C), nozzle height (40 mm), 14 passes with offsets and rotations, and spray velocity (1200 mm/min), and isocratic flow of 60 μl/min using 70% ACN as a pushing solvent.

| MALDI-MSI analysis
Mass spectrometry imaging analysis of tissue sections was performed on a timsTOF fleX instrument (Bruker Daltonics). The instrument was equipped with frequency tripled, 10 kHz, Nd:YAG laser (355 nm) (SmartBeam 3D; Bruker Daltonics). Imaging MS was collected at 10 μm spatial resolution with beam scan off, using 200 laser shots per pixel. Data were collected in negative ion mode from m/z 400-2000 Da. TIMS was performed in the range from 1/K 0 0.9 to 1/K 0 2.13 V•s/cm 2 with a ramp time of 200 ms. External TOF calibration was performed with red phosphorous that was spotted next to the sections. TIMS was externally calibrated in electrospray ionization (ESI) mode using Tune Mix (Agilent). Single MS/MS spectra were acquired directly from tissue for some selected precursor ions at 50 µm laser pitch. For that, the mass range was set from m/z 50 to 1300 Da using a TIMS ramp from 1/K 0 0.5 to 1.7 V•s/cm 2 with 200 ms ramp time. Six hundred laser shots were acquired per pixel in negative ion mode. Quadrupole isolation windows and collision energies were adapted for each precursor individually. If required, spectra from several pixels were accumulated, and collision energies were stepped from lower to higher voltages.

| Hyperspectral amyloid imaging
Amyloid pathology was further delineated using double staining with LCOs and hyperspectral confocal imaging. The LCOs quadroformyl thiophene acetic acid (qFTAA) and heptamer-formyl thiophene acetic acid (hFTAA) were used for staining polymorphic plaques (Nystrom et al., 2013). Following MALDI analysis, tissues were rinsed in absolute EtOH for 60 s, fixed in absolute EtOH

| MSI data processing and analysis
Imaging data were processed in MATLAB R2020b with Bioinformatics Toolbox 4.14, Signal Processing Toolbox 8.4, and Image Processing Toolbox 11.1 (MathWorks, Inc.) installed.
Multivariate modeling was performed in SIMCA software (v.17.0; Sartorius Stedim Data Analytics AB-Umetrics). MALDI imaging raw data files were converted into the imzML format using SCiLS Lab (v.2021b, Bruker Daltonics) and imported into the MATLAB programming environment using the imzMLConverter by Race et al. (2012). Data were normalized to total ion count and, in addition, for multivariate modeling purposes, log-transformation was applied to address heteroscedasticity and skewness. Data were centered but not scaled for multivariate modeling. Spatial segmentation of MSI data sets was performed on score images from principal component analysis (PCA), whereby images with the most pronounced plaques or plaque core features, respectively, were used. Segmentation of plaque cores was achieved by thresholding pixel values followed by removal of off-plaque localizing pixels. Segmentation of plaque structures was achieved using Otsu's global threshold followed by a series refinement including image erosion, image dilation, active contours algorithm, and removal of off-plaque localizing pixels.
Image segmentation was further guided by hyperspectral fluorescence microscopy data.
Hyperspectral imaging raw data files in czi format were imported into MATLAB using code obtained from https://github.com/ Camac hoDej ay/czi_spec_im_load (CamachoDejay, czi_spec_im_load, 2020, github, https://github.com/Camac hoDej ay/czi_spec_im_ load) together with the Bio-Formats software tool (Linkert et al., 2010). Binary maps of each ROI were used to subset data sets by logical indexing and averaging pixel values for statistical data analysis.
Further, binary maps served as discriminator matrices for supervised multivariate analysis. Orthogonal projection to latent structurediscriminant analysis (OPLS-DA) was used to discriminate between ROIs such as plaques, plaque cores, and peripheral plaque regions.
OPLS-DA is a regression method that has evolved from Partial Least Squares (PLS) where class membership is supplied in a discriminator matrix (Y-matrix) to decompose the systematic variation in the data set (X-matrix) into Y-correlated (predictive) and Y-uncorrelated (orthogonal) variation (Bylesjö et al., 2006). The separation between the predictive and orthogonal components facilitates the interpretation of the predictive loading vector and provides a direct measure of the influence each of the variables has on the model. The number of relevant components for the models was estimated through seven-block cross-validation (CV). Orthogonal components based on CV were reduced to further prevent the overfitting of the OPLS-DA models.
The number of orthogonal components was then based on meaningful orthogonal variation pertaining to, for example, cores in OPLS-DA models for plaques versus nonplaque. Finally, the correlation-scaled loadings, p(corr), were examined in order to identify differences in the lipidomic profiles between areas of interest at both age groups.
Post-hoc power analyses were performed in MATLAB using the sampsizepwr function (Statistics and Machine Learning Toolbox).
High-loading variables were further subjected to t-test statistics and evaluated on single-ion images. Post-hoc power analyses and t statistics were performed in Matlab using the sampsizepwr function (Statistics and Machine Learning Toolbox). Power analysis was based on a two-sample pooled t test, after assessing for normally distributed data, with unknown standard deviation and equal variances. Mean and standard deviation of sample values from image-segmented arrays were used. Although we acknowledge that the usefulness of post-hoc power analyses is controversial, we included these results as retrospective assessments to safeguard against experimental error.
Together this approach allowed for the identification of significantly altered species which were further interrogated by single-ion images based on both m/z and their collisional cross-section (CCS).
For this, mass spectral and mobility information of the MALDI-TIMS imaging data were extracted using TIMS Data Viewer 1.0 (Bruker Daltonics) and visualized using SCiLS Lab 2021b.

| Lipid extraction and LC-TIMS analyses
Lipids were extracted according to a previously reported procedure with slight modifications (Marsching et al., 2014). Briefly, the tissue was homogenized for 2 min on ice in 2 ml methanol and 300 μl water with an Ultra Turrax T25 basic (IKA Labortechnik) at 24,000 rpm in a 15 ml polypropylene vial. The homogenate was transferred into a glass vial and the polypropylene vial was rinsed two times with 500 μl methanol. Afterward, 3 ml of chloroform (Cat. no. 650498; Sigma-Aldrich) were added to get a solvent mixture of chloroform/ methanol/water (10:10:1, v/v/v). The extract was centrifuged for 10 min at 1008 g, and the supernatant was collected in a separate glass vial. Extraction was completed by repetition of this procedure twice, one time with 3 ml chloroform/methanol/water (10:10:1, v/v/v) and the second time with a 30:60:8 (v/v/v) mixture. Every extraction step included 2 min of sonication. The pooled extracts were dried under a nitrogen stream (37°C). Finally, the extract was dissolved in 100 μl chloroform/methanol/water (10:10:1, v/v/v) per 100 mg brain wet weight and stored at −20°C.
For LC-MS an ultra-high-pressure chromatography system (Bruker Elute UHPLC) was used to separate lipids on a YMC Triart C18 column (100 × 2.1 mm, 1.9 μm), packed with 1.9 μm material. The column compartment was heated to 55°C, and lipids were separated with a binary gradient at a constant flow rate of 400 ml/min. Mobile phases A and B were ACN:H 2 O 60:40% (v/v) and IPA:ACN:H20 90: both buffered with 0.1% formic acid and 10 mM ammonium formate.
The 20-min LC-MS experiment started by ramping the mobile phase B from 40% to 43% within 2 min, to 50% within 2.1 min, to 54% within 12 min, to 70% within 12.1 min, and finally 99% within 18 min. It was decreased to 40% within 18.1 min and kept there until 20 min to reequilibrate the column. The injection volume was 5 μl.
The MS analysis was performed in negative-ion mode as previously described using the PASEF acquisition mode (Vasilopoulou et al., 2020). Briefly, the LC was coupled to a hybrid trapped ion mobility-

| Annotation of lipids in MALDI images
Lipids were annotated in MetaboScape (v.2021b, Bruker Daltonics) following the extraction of the four-dimensional data (m/z, ion mobility, intensity, and spatial coordinate). Briefly, spectra of the entire measurement region per sample were exported from SCiLS Lab (v.2021c) and imported into MetaboScape (v.2021b). Spectra from 10 × 10 pixels were averaged before feature extraction using the T-Rex 3 processing algorithm. An intensity threshold of 200 was used for peak detection and [M−H] − ions were selected as a primary ion, seed ions were forbidden.
Features were then annotated using an analyte list of mouse brain lipids that were previously identified using LC-ESI-PASEF allowing a mass error of 5 ppm and a CCS value deviation of 3% if available.
For additional confidence, MALDI TIMS MS/MS fragment spectra from some selected precursors were analyzed with MetaboScape (v.2021b). Fragment spectra were converted to *.mgf format and added to the corresponding entry in the existing feature table in MetaboScape. The lipid identification is based on comparisons against a spectral library or molecular formula generation based on exact mass and fragment prediction.

| Integrated MALDI TIMS TOF MSI and fluorescent amyloid imaging for multivariate classification of plaque pathology
We here employed a multidimensional MSI strategy using a novel MALDI-trapped ion mobility spectrometry TOF instrument to delineate plaque pathology-associated lipid species with increased identification confidence. This is achieved through the ion mobility dimension of the used instrument that expands the molecular coverage of the MSI analyses at reasonable analysis times and most importantly increases molecular specificity through additional molecular parameters, that is, mass accuracy and CCS. Using this advanced MSI approach, we investigated plaque-associated changes in lipid localization in transgenic AD mice (tgAPPswe) from two perspectives, including amyloid plaque polymorphism and age. For this, we multiplexed the TIMS MSI analyses with complemental structure sensitive amyloid staining performed on the same section and followed plaque pathology and plaque polymorphism in tgAPP SWE mice at different ages (18 and 23 months).
In TIMS, additional data are generated across the ion mobility dimension for each mass window in addition to the m/z dimension leading consequently to very large data file sizes. To approach these complex, highly dimensional data in an unbiased way, we performed multistage image data processing and image segmentation analysis (Figure 1).
Following data processing, we performed PCA of the data for image segmentation (Figures 1b and 2a,b). This allowed us to obtain PCA images containing plaque-specific single-ion information that we subsequently used for image segmentation. In detail, binary maps were generated for the classification of plaque structures into four regions of interest including (1) plaques and (2) background as well as (3) plaque core, and (4) plaque periphery. Binary maps were generated through a combination of thresholding and active contours segmentation to annotate the different ROI in an unbiased way across all single plaques within each tissue sample and mouse, respectively (Figure 2a-f).
F I G U R E 1 Schematic workflow for processing and statistical analysis of TIMS TOF MSI data. TIMS TOF MSI data were acquired on (a) cortical areas of transgenic AD mouse models (tgAPPSwe), (b) imaging raw data were processed, and PCA was performed in order to generate (scores) images for image segmentation. Image segmentation was performed by a combination of thresholding and active contours segmentation. Segmented image areas were either averaged for statistical analysis or were used to construct a Y-matrix for discriminant analysis. (c) OPLS-DA was performed on averaged ROI data or imaging data to examine analytes that drive class separation. Following ROI annotation and classification, the MSI pixel data were either averaged for statistical analysis or were used to construct a Y-matrix for discriminant analysis. OPLS-DA was used to discriminate between regions of interest both within and between data sets (Figure 3). Here the aim was to interrogate the lipid localization diversity accounting for differences between plaque versus background and between plaque cores and peripheries of individual plaques, respectively, and finally for differences between plaques and plaque regions of animals of 18 and 23 months of age.
For this, the data were analyzed through two independent approaches. First, the data were interrogated in a Y2 matrix, where either plaque versus background or periphery versus core of the plaques was analyzed across all animals both for 18-and 23-month-old animals ( Figure 3e,f,k,l; Figure S3). This analysis allowed for the extraction of key features separating the respective groups for all animals within a given age group. A limitation of this approach is the lack of possibility to visualize the unique features responsible for the separation of the fine plaque subcomponents in the context of a single animal. Therefore, we performed a second round of analysis that was based on a Y3-matrix.
This approach allowed for the extraction of unique features that underlie the separation of background from plaque cores and periphery within individual animals. Here, for the features that allow classification into the respective region of interest (ROI; i.e., plaque, core, periphery), no major difference was observed between the different age groups and between the different animals, confirming the validity of the first analysis based on averaged ROI data.
This approach, however, did allow to visualize the distribution of the subgroup separating components in the form of component ion images (Figure 3a,b,g,h). The generated score images further validated the accurate ROI annotation and classification procedure and were well in line with the OPLS-DA results obtained for the averaged pixel data that were used for groupwide comparisons.  (Figures S5 and S6). Data indicate mean ± SD, number of animals n = 3, N = 5-10 plaques. *p ≤ 0.05, paired ratio t-test F I G U R E 6 TIMS imaging allows for the identification of distinct phosphoinositol lipid changes associated with Aβ plaque maturation. Lyso-phosphoinositol LPI (18:0) (m/z 599.32 1/K 0 1.142 V•s/cm 2 ) (a, b) and PI (18:0/20:4) (m/z 885.54 1/K 0 1.440 V•s/cm 2 ) did not display age-related changes in localization to Aβ deposits (c, d), though PI (18:0/20:4) showed a trend (p = 0.06) toward relative increase in plaque periphery in older animals. PI (18:0/22:6) (e.2) (m/z 909.55 1/K 0 1.455 V•s/cm 2 ) showed a relative increase in the periphery compared with a core of Aβ plaques in 23-month-old animals compared with 18-month-old animals. This is illustrated by a decrease in the core/periphery signal ratio (d, f). Comparative statistics within each age group show relative higher peripheral localization of PI at 23 months but not 18 months for both PI (18:0/20:4) (i, j) and PI (18:0/22:6) (k, l), while no age differences were associated for LPI (18:0) (g, h), b, d, f: Data indicate mean ± SD, number of animals n = 3/age, N = 5-10 plaques. *p ≤ 0.05, Student's t-test. g-l: Data indicate mean ± SD, number of animals n = 3/age, N = 5-10 plaques. *p ≤ 0.05, paired ratio t-test each statistically different lipid compound were further validated through the offline analysis of mouse brain tissue extracts using liquid chromatography coupled to ESI TIMS TOF MS/MS. The TIMS MSI data (m/z/CCS) were matched against a generated database consisting of both the m/z/CCS and MS/MS data obtained by the LC-ESI TIMS experiments (Table S1). Together, this approach generated a list of confidently identified lipid species that were statistically significant in between the different groups compared, including plaques versus background, plaque core and plaque periphery, as well as 18-and 23-month-old mice.

| TIMS resolves plaque-associated lipids beyond mass
Next, we proceeded to inspect the OPLS-DA-derived and -identified lipid species that underlie a general Aβ plaque-associated localization pattern. Here, TIMS MSI allows for the generation of clean, singleion images without any interfering compounds as there is no other signal visible in the heat map display. The results showed a depletion of multiple species in the higher mass range (m/z 1400-2000).
Investigation of the trapped ion mobility MSI allowed for the identification of distinct cardiolipins (CL) (Figure 4a-d). Further, a general plaque-associated localization of mono-sialated gangliosides (GM), including GM2 and GM3 species was also observed (Figure 4e

| Plaque polymorphism is associated with distinct lipid localization patterns
Investigation of the TIMS mobilograms underlying the core-and periphery-enriched components revealed core-specific localization of several phospholipid species, such as phosphatidic acids (PA), including PA (32:0) and PA (34:1) (Figure 5a Figure S6). We have previously reported the localization of the ST (d36:1) to the periphery and outside of the Aβ plaques (Michno et al., 2018); however, definitive assignment of the ST species was not possible due to the presence of isobaric compounds. Here thanks to the CCS value, we were able to attribute the signal depletion at the plaque to PS (16:0/22:6) species and confidently annotate the specific localization toward the plaque rim to ST (d36:1), as further by on-tissue MALDI TIMS MS/MS as ST (16:0/20:1) ( Figure S6).

| Progressing plaque pathology is characterized by distinct lipid accumulation
Following these analyses, we proceeded to investigate age-related changes in plaque lipid microenvironment in 23 -months-old mice compared with 18-month-old animals. To assess changes in relative lipid localization, we compared plaque versus nonplaque tissue as well as core versus periphery within each group using OPLS-DA.
Here, no differences were observed between plaque-associated and plaque-depleted lipid localization patterns when comparing the significant loading values of the models generated for 18 and 23 months, respectively. Similarly, no absolute differences of lipid changes were observed for polymorphism-related models, core versus periphery, generated for 18-and 23 -months-old animals, though differences in loading values for certain lipid species were detected. To further assess these relative changes between the two age groups, we then compared the ratio of the core/periphery signal across the ages. Here, we observed mainly changes in phosphatidylinositols (PI). In more detail, lyso-phosphatidylinositol, LPI (18:0), did not display any significant, age-related changes (Figure 6a,b), while the larger, arachidonic acid-conjugated PI (18:0/20:4) species showed a trend toward a decrease in the core/periphery signal ratio with age (p = 0.06) (Figure 6c,d). This pattern was even more prominent for a previously unreported, docosahexaenoic acid-conjugated PI (18:0/22:6) (Figure 6e,f). This lipid showed a significant difference in relative localization in between the plaque regions with a relative increase in the periphery compared with the core in 23-month-old mice compared with this ratio in 18-month-old mice. The relative signal intensity of those inositol species in between core and periphery within the age groups showed that for all three species, a more pronounced relative localization to the periphery compared with the cores in the 23-month-old compared with the 18-month-old mice (Figure 6g-l).

| DISCUSS ION
The molecular mechanisms underlying AD pathogenesis are still not fully understood. Lipids have been implicated to play a central role in AD pathogenesis. Indeed, a global alteration is observed in cerebrospinal fluid, blood, and brain tissue extracts from AD patients before (Di Paolo & Kim, 2011). Multiple studies have also demonstrated alterations of various lipids and lipid metabolites both between Aβ plaque microenvironment and even within single Aβ plaques making use of the unique capabilities of MSI (Kaya, Brinet, Michno, Baskurt, et al., 2017;Kaya, Brinet, Michno, Syvanen, et al., 2017;Kaya et al., 2018;Michno et al., 2018).
In the present study, we extended MSI based spatial lipid analysis of AD plaque pathology towards TIMS--MSI. We further implemented this imaging modality with hyperspectral microscopy data based on conformation-sensitive amyloid probes acquired on the same tissues similar to a previous study performed in our lab (Michno et al., 2018). The novelty of the current study is the full integration of the multimodal imaging data in the multivariate data analysis workstream to identify and outline structurally different amyloid morphology within individual deposits and the associated lipid patterns, respectively.
On the general plaque level, TIMS MSI identified plaque-specific localization of gangliosides, while CL, phosphoserines, and sulfatides were found depleted at plaque regions. Previous MSI studies have found a general localization of GM2, and GM3 with C18:0 and C20:0 fatty acid (FA) moieties, to Aβ plaques that are independent of Aβ plaques morphology Michno et al., 2018).
Previous non-MSI studies have also identified enrichment of GM2 and GM3 species in both human AD and AD mouse models (Chan et al., 2012;Pernber et al., 2012). A plaque pathology-associated increase in gangliosides is in line with previous data that show that glycolipids enriched lipid rafts interact with beta-amyloid and hence promote aggregation.
In contrast, a plaque-associated depletion of cardiolipin is most likely a consequence of neuronal degradation. Brain cardiolipin has previously been outlined with MSI (Amoscato et al., 2014) and CL is highly enriched in the inner membrane of mitochondria of nerve cells. CL depletion consequently indicates neuronal degradation presumably a result of oxidative stress affecting the mitochondria.
Further in line with our data, impaired CL metabolism has been reported to be a consequence of amyloid pathology (Monteiro-Cardoso et al., 2015).
Along with the CL pattern, a plaque-associated depletion of poly-unsaturated FA-containing PE and PS lipids is observed. While this is in line with previously published results, indicating depletion of these species, these previous data were based solely on putative identification Michno et al., 2018). Our present study provides a clear separation and identification of these species facilitated through the ion mobility modality of TIMS MSI. The depletion of PUFA-containing phospholipids is well in line with previous studies that suggest PUFA-lipid degradation to be a result of lipid-peroxidation through amyloid pathology-induced oxidative stress (Butterfield & Lauderback, 2002).
The second part of our analysis comprised the delineation and identification of spatial lipid distributions associated with structural amyloid heterogeneity within individual deposits. For this, the TIMS MSI analyses were integrated with hyperspectral amyloid imaging followed by comprehensive data analysis using multivariate modeling. Here, the results revealed a distinct, plaque core-associated lipid pattern for PA and PE species.
PA is considered a key building block for phospholipid synthesis and also functions as a second messenger. Although debated, involvement of PA in AD has been suggested, partially due to the anionic nature of PA and hence its potential role as TREM2 ligand Specifically, some of the PLD isoforms have been shown to be responsible for PA synthesis from among other CL as well as phosphatidylcholine (PC) (Jang et al., 2012).
In addition to PA, some PE species showed characteristic distribution to the plaque core, which is in contrast to the depletion of DHA-containing PE species. PE is highly abundant in cholesterol-enriched detergent-resistant microdomains (DRM) of neuronal membranes. While the link of cholesterol and AD plaque pathology has been discussed for decades (Barrett et al., 2012;Chen et al., 2021;Di Paolo & Kim, 2011), PE has been directly linked to Aβ pathology through modulation of membraneassociated proteins both α-secretase and γ-secretase. Here, decreased PE levels were found to lead to reduced amyloidogenic processing of APP and reduced Aβ generation, respectively (Area-Gomez et al., 2012;Nesic et al., 2012). Plaque-associated PE therefore indicates a PE-APP/Aβ interaction, presumably of DRM-associated PE, that is aggravating Aβ secretion and aggregation into mature deposits.
In addition to the core-specific localizations observed for PA and PE, a putatively assigned sulfatide, ST (d36:1), was found to be increased at the outer periphery of the plaque. The signal distribution of this ST species was different to the general, signal depletion pattern observed for other, larger ST species, associated with demyelination (Kaya et al., 2020). Here, TIMS MSI resolved this ST species from PS and identified this compound as ST (16:0/20:1). This unique localization of ST with the short FA might highlight the presence of unique cell populations, such as astrocytes (Isaac et al., 2006), yet again highlighting the potential of TIMS MSI for studies of Aβ plaque molecular microenvironment.
Finally, we investigated relative changes in lipid localization associated with progressing plaque pathology in old mice at 23 months, which reflects very advanced and sever pathology and hence provides a good comparison point for these mice. The rationale of these analyses was that progressing plaque pathology with aging is associated with the accumulation of distinct lipids. Following OPLS-DA modeling of core periphery data with each age group yielded no difference with respect to the species that were different in between the plaque structures. This is in line with a previous study by our lab, where we followed amyloid peptide deposition over different ages of the tgSwe model (6, 9, 12, 18 and 23 months) .
To assess relative changes in spatial lipid localization, we investigated the top-loadings using univariate comparison of their portioning ratio in between core and periphery across the two age groups.
Here, we observed relatively increased levels of PI (18:0/22:6) in the periphery compared with the core in plaques at higher age compared with the core/periphery ratio of this species at 18 months. We have previously reported a clear localization of PI and lyso-PI (LPI), including LPI (18:0) and PI (18:0/20:4), to the periphery of Aβ deposits Michno et al., 2018). An increase of lipid localization, to intra-plaque structure heterogeneity as observed for PI (18:0, 22:6), might indicate plaque maturation. Interestingly, the here observed PI (18:0/22:6) exhibits a similar FA configuration as the secondary messenger, phosphoinositolbiphosphate (PIP2). These PI species have been linked to progressing amyloid pathology, where a decrease in PIP2 along with an increase in corresponding PI was observed in the context Aβ oligomerization (Arancio, 2008;Berman et al., 2008).
A major observation though for the age comparisons was a rather static pattern of plaque morphology-associated lipid localizations with progressing plaque pathology at advanced age. This is in line with previous data on relative Aβ peptide levels published by our lab , where plaque morphology-associated differences were more prominent rather than age-associated differences in plaque-associated Aβ content. This suggests that most plaques have reached a plateau in Aβ uptake, on plaque processing and plaque growth, respectively.
Based on our results, it is clear that in order to truly delineate spatial lipid dynamics, kinetic studies of lipid accumulation in Aβ proximity might be needed. Indeed, such approaches have been previously utilized, for instance, to follow surfactant metabolism in the lung (Ellis et al., 2021). Further, our lab recently utilized such a kinetic approach, in an imaging stable isotope labeling kinetic study (iSILK), where we followed Aβ peptide deposition during Aβ plaque maturation (Michno et al., 2021).

| CON CLUS ION
In summary, this work highlights the potential of in situ mapping and annotation of isobaric lipid species in the context of AD-specific Aβ plaque pathology. This was achieved through combined MALDI-MSI coupled with trapped ion mobility separation and structural Aβ conformational characterization through hyperspectral imaging. Using this approach, we were able to obtain well resolved single-ion patterns of multiple lipid classes (PS, PE, PA, GM, ST, CL, and PI) localizing to Aβ plaque pathology and intra-plaque amyloid heterogeneity, respectively. We further show that progressing plaque pathology is associated with relatively increased localization of PI to diffuse plaque structures. Together, this multimodal imaging approach combining hyperspectral conformational analysis and multidimensional MSI is demonstrated to be a powerful approach for studying molecular microenvironments of histologically confined pathologic features such as Aβ plaque pathology.

ACK N OWLED G EM ENTS
We thank Dr. Stina Syvänen and Dr. Dag Sehlin at Uppsala University for providing the tgAPPSwe mouse brain samples. The work was in part performed at the imaging MS infrastructure at the University of Gothenburg. We thank Prof. Per Hammarström and Prof. Peter