Molecular Mapping Alzheimer's Disease: MALDI Imaging of Formalin-fixed, Paraffin-embedded Human Hippocampal Tissue

Validating MALDI-IMS Feasibility in Ex Vivo Brain Slices

Matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS) generates unique mass spectra in X/Y coordinates across a tissue sample, thus allowing for the spatial detection and relative quantification of biologic compounds in situ. The soft ionization of MALDI-IMS makes it an ideal technique for high-resolution imaging of complex lipid species. Lipid-based spatial chemical maps derived from MALDI-IMS provide critical insight into the unique molecular profiles of a variety of neurologic diseases. Ex vivo brain slice preparations are a prominent alternative to in vivo animal models for studying many different neurologic conditions. For the first time, we present a feasible protocol for achieving reproducible lipidomic MALDI-IMS data from ex vivo rat brain slices and provide evidence that ex vivo brain slices maintain spatiochemical lipidomic profiles representative of an intact whole brain. We conducted a methods comparison assessing the lipid profiles within the neocortex, striatum, and corpus callosum between coronal sections taken from ex vivo brain slices and the current gold standard tissue preparation method, fresh frozen whole brains. For the first time we demonstrate a technique by which 400 μm ex vivo brain slices can be extracted from an imaging chamber and prepared for MALDI-IMS in a way that preserves their lipidomic integrity. We demonstrate the feasibility of MALDI-IMS in ex vivo brain slices and provide a roadmap for MALDI-IMS utilization in uncharted neuroscience fields.

Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution

We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method's potential through its application toward delineating chemical traits of Alzheimer's disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.

Spatial proteomic alterations detected via MALDI-MS imaging implicate neuronal loss in a Huntington's disease mouse (YAC128) brain

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that occurs with the increase of CAG trinucleotide repeats in the huntingtin gene. To understand the mechanisms of HD, powerful proteomics techniques, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS) were employed. However, one major drawback of these methods is loss of the region-specific quantitative information of the proteins due to analysis of total tissue lysates. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a MS-based label-free technique that works directly on tissue sections and gathers m/z values with their respective regional information. In this study, we established a data processing protocol that includes several software programs and methods to determine spatial protein alterations between the brain samples of a 12 month-old YAC128 HD mouse model and their non-transgenic littermates. 22 differentially expressed proteins were revealed with their respective regional information, and possible relationships of several proteins were discussed. As a validation of the MALDI-MSI analysis, a differentially expressed protein (GFAP) was verified using immunohistochemical staining. Furthermore, since several proteins detected in this study have previously been associated with neuronal loss, neuronal loss in the cortical region was demonstrated using an anti-NeuN immunohistochemical staining method. In conclusion, the findings of this research have provided insights into the spatial proteomic changes between HD transgenic and non-transgenic littermates and therefore, we suggest that MALDI-MSI is a powerful technique to determine spatial proteomic alterations between biological samples, and the data processing that we present here can be employed as a complementary tool for the data analysis.

Chemical analysis of the human brain by imaging mass spectrometry

Analysis of the chemical makeup of the brain enables a deeper understanding of several neurological processes. Molecular imaging that deciphers the spatial distribution of neurochemicals with high specificity and sensitivity is an exciting avenue in this aspect. The past two decades have witnessed a significant surge of mass spectrometry imaging (MSI) that can simultaneously map the distribution of hundreds to thousands of biomolecules in the tissue specimen at a fairly high resolution, which is otherwise beyond the scope of other molecular imaging techniques. In this review, we have documented the evolution of MSI technologies in imaging the anatomical distribution of neurochemicals in the human brain in the context of several neuro diseases. This review also addresses the potential of MSI to be a next-generation molecular imaging technique with its promising applications in neuropathology.

Sample preparation of formalin-fixed paraffin-embedded tissue sections for MALDI-mass spectrometry imaging

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MALDI MSI) has become a powerful tool with a high potential relevance for the analysis of biomolecules in tissue samples in the context of diseases like cancer and cardiovascular or cardiorenal diseases. In recent years, significant progress has been made in the technology of MALDI MSI. However, a more systematic optimization of sample preparation would likely achieve an increase in the molecular information derived from MALDI MSI. Therefore, we have employed a systematic approach to develop, establish and validate an optimized "standard operating protocol" (SOP) for sample preparation in MALDI MSI of formalin-fixed paraffin-embedded (FFPE) tissue sample analyses within this study. The optimized parameters regarding the impact on the resulting signal-to-noise (S/N) ratio were as follows: (i) trypsin concentration, solvents, deposition method, and incubation time; (ii) tissue washing procedures and drying processes; and (iii) spray flow rate, number of layers of trypsin deposition, and grid size. The protocol was evaluated on interday variability and its applicability for analyzing the mouse kidney, aorta, and heart FFPE tissue samples. In conclusion, an optimized SOP for MALDI MSI of FFPE tissue sections was developed to generate high sensitivity, to enhance spatial resolution and reproducibility, and to increase its applicability for various tissue types. This optimized SOP will further increase the molecular information content and intensify the use of MSI in future basic research and diagnostic applications. Graphical Abstract.

Dual strategy for reduced signal-suppression effects in matrix-assisted laser desorption/ionization mass spectrometry imaging

RATIONALE

The molecular complexity of tissue features several signal-suppression effects which reduce the ionization of analytes significantly and thereby weakens the quality of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) imaging (MALDI imaging). We report a novel approach in MALDI imaging by reducing signal-suppression effects for the analysis of beta-amyloid (Aβ) plaques, one pathological hallmark of Alzheimer's disease (AD).

METHODS

We analyzed Aβ proteoforms from postmortem AD brains and brains from transgenic mice (APPPS1-21) overexpressing familial AD mutations by combining two techniques: (1) laser capture microdissection (LCM) to accumulate Aβ plaques and (2) phosphoric acid (PA) as additive to the super-2,5-dihydroxybenzoic acid matrix.

RESULTS

LCM and MALDI-MS enabled tandem mass spectrometric fragmentation of stained Aβ plaques. PA improved the signal-to-noise (S/N) ratio, especially of the Aβ1-42 peptide, by three-fold compared with the standard matrix additive trifluoroacetic acid. The beneficial effect of the PA matrix additive in MALDI imaging was particularly important for AD brain tissue. We identified several significant differences in Aβ plaque composition from AD compared with APPPS1-21, underlining the value of reducing signal-suppressing effects in MALDI imaging.

CONCLUSIONS

We present a novel strategy for overcoming signal-suppression effects in MALDI imaging of Aβ proteoforms.

Molecular imaging mass spectrometry for probing protein dynamics in neurodegenerative disease pathology

Recent advances in the understanding of basic pathological mechanisms in various neurological diseases depend directly on the development of novel bioanalytical technologies that allow sensitive and specific chemical imaging at high resolution in cells and tissues. Mass spectrometry-based molecular imaging (IMS) has gained increasing popularity in biomedical research for mapping the spatial distribution of molecular species in situ. The technology allows for comprehensive, untargeted delineation of in situ distribution profiles of metabolites, lipids, peptides and proteins. A major advantage of IMS over conventional histochemical techniques is its superior molecular specificity. Imaging mass spectrometry has therefore great potential for probing molecular regulations in CNS-derived tissues and cells for understanding neurodegenerative disease mechanism. The goal of this review is to familiarize the reader with the experimental workflow, instrumental developments and methodological challenges as well as to give a concise overview of the major advances and recent developments and applications of IMS-based protein and peptide profiling with particular focus on neurodegenerative diseases. This article is part of the Special Issue "Proteomics".

Recent advances in applying mass spectrometry and systems biology to determine brain dynamics

INTRODUCTION

Neurological disorders encompass various pathologies which disrupt normal brain physiology and function. Poor understanding of their underlying molecular mechanisms and their societal burden argues for the necessity of novel prevention strategies, early diagnostic techniques and alternative treatment options to reduce the scale of their expected increase. Areas covered: This review scrutinizes mass spectrometry based approaches used to investigate brain dynamics in various conditions, including neurodegenerative and neuropsychiatric disorders. Different proteomics workflows for isolation/enrichment of specific cell populations or brain regions, sample processing; mass spectrometry technologies, for differential proteome quantitation, analysis of post-translational modifications and imaging approaches in the brain are critically deliberated. Future directions, including analysis of cellular sub-compartments, targeted MS platforms (selected/parallel reaction monitoring) and use of mass cytometry are also discussed. Expert commentary: Here, we summarize and evaluate current mass spectrometry based approaches for determining brain dynamics in health and diseases states, with a focus on neurological disorders. Furthermore, we provide insight on current trends and new MS technologies with potential to improve this analysis.