rMSIproc: an R package for mass spectrometry imaging data processing.

SUMMARY: Mass spectrometry imaging (MSI) can reveal biochemical information directly from a tissue section. MSI generates a large quantity of complex spectral data which is still challenging to translate into relevant biochemical information. Here, we present rMSIproc, an open-source R package that implements a full data processing workflow for MSI experiments performed using TOF or FT-based mass spectrometers. The package provides a novel strategy for spectral alignment and recalibration, which allows to process multiple datasets simultaneously. This enables to perform a confident statistical analysis with multiple datasets from one or several experiments. rMSIproc is designed to work with files larger than the computer memory capacity and the algorithms are implemented using a multi-threading strategy. rMSIproc is a powerful tool able to take full advantage of modern computer systems to completely develop the whole MSI potential. AVAILABILITY AND IMPLEMENTATION: rMSIproc is freely available at https://github.com/prafols/rMSIproc. CONTACT: pere.rafols@urv.cat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative Microproteomics Based Characterization of the Central and Peripheral Nervous System of a Mouse Model of Krabbe Disease.

Krabbe disease is a rare, childhood lysosomal storage disorder caused by a deficiency of galactosylceramide beta-galactosidase (GALC). The major effect of GALC deficiency is the accumulation of psychosine in the nervous system and widespread degeneration of oligodendrocytes and Schwann cells, causing rapid demyelination. The molecular mechanisms of Krabbe disease are not yet fully elucidated and a definite cure is still missing. Here we report the first in-depth characterization of the proteome of the Twitcher mouse, a spontaneous mouse model of Krabbe disease, to investigate the proteome changes in the Central and Peripheral Nervous System. We applied a TMT-based workflow to compare the proteomes of the corpus callosum, motor cortex and sciatic nerves of littermate homozygous Twitcher and wild-type mice. More than 400 protein groups exhibited differences in expression and included proteins involved in pathways that can be linked to Krabbe disease, such as inflammatory and defense response, lysosomal proteins accumulation, demyelination, reduced nervous system development and cell adhesion. These findings provide new insights on the molecular mechanisms of Krabbe disease, representing a starting point for future functional experiments to study the molecular pathogenesis of Krabbe disease. Data are available via ProteomeXchange with identifier PXD010594.

Proteomics Profiling of Neuron-Derived Small Extracellular Vesicles from Human Plasma: Enabling Single-Subject Analysis.

Small extracellular vesicles have been intensively studied as a source of biomarkers in neurodegenerative disorders. The possibility to isolate neuron-derived small extracellular vesicles (NDsEV) from blood represents a potential window into brain pathological processes. To date, the absence of sensitive NDsEV isolation and full proteome characterization methods has meant their protein content has been underexplored, particularly for individual patients. Here, we report a rapid method based on an immunoplate covalently coated with mouse monoclonal anti-L1CAM antibody for the isolation and the proteome characterization of plasma-NDsEV from individual Parkinson's disease (PD) patients. We isolated round-shaped vesicles with morphological characteristics consistent with exosomes. On average, 349 ± 38 protein groups were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, 20 of which are annotated in the Human Protein Atlas as being highly expressed in the brain, and 213 were shared with a reference NDsEV dataset obtained from cultured human neurons. Moreover, this approach enabled the identification of 23 proteins belonging to the Parkinson disease KEGG pathway, as well as proteins previously reported as PD circulating biomarkers.

Longitudinal Bottom-Up Proteomics of Serum, Serum Extracellular Vesicles, and Cerebrospinal Fluid Reveals Candidate Biomarkers for Early Detection of Glioblastoma in a Murine Model.

Glioblastoma Multiforme (GBM) is a brain tumor with a poor prognosis and low survival rates. GBM is diagnosed at an advanced stage, so little information is available on the early stage of the disease and few improvements have been made for earlier diagnosis. Longitudinal murine models are a promising platform for biomarker discovery as they allow access to the early stages of the disease. Nevertheless, their use in proteomics has been limited owing to the low sample amount that can be collected at each longitudinal time point. Here we used optimized microproteomics workflows to investigate longitudinal changes in the protein profile of serum, serum small extracellular vesicles (sEVs), and cerebrospinal fluid (CSF) in a GBM murine model. Baseline, pre-symptomatic, and symptomatic tumor stages were determined using non-invasive motor tests. Forty-four proteins displayed significant differences in signal intensities during GBM progression. Dysregulated proteins are involved in cell motility, cell growth, and angiogenesis. Most of the dysregulated proteins already exhibited a difference from baseline at the pre-symptomatic stage of the disease, suggesting that early effects of GBM might be detectable before symptom onset.

Molecular signatures of tumor progression in myxoid liposarcoma identified by N-glycan mass spectrometry imaging.

Myxoid liposarcoma (MLS) is the second most common subtype of liposarcoma, accounting for ~6% of all sarcomas. MLS is characterized by a pathognomonic FUS-DDIT3, or rarely EWSR1-DDIT3, gene fusion. The presence of ≥5% hypercellular round cell areas is associated with a worse prognosis for the patient and is considered high grade. The prognostic significance of areas with moderately increased cellularity (intermediate) is currently unknown. Here we have applied matrix-assisted laser desorption/ionization mass spectrometry imaging to analyze the spatial distribution of N-linked glycans on an MLS microarray in order to identify molecular markers for tumor progression. Comparison of the N-glycan profiles revealed that increased relative abundances of high-mannose type glycans were associated with tumor progression. Concomitantly, an increase of the average number of mannoses on high-mannose glycans was observed. Although overall levels of complex-type glycans decreased, an increase of tri- and tetra-antennary N-glycans was observed with morphological tumor progression and increased tumor histological grade. The high abundance of tri-antennary N-glycan species was also associated with poor disease-specific survival. These findings mirror recent observations in colorectal cancer, breast cancer, ovarian cancer, and cholangiocarcinoma, and are in line with a general role of high-mannose glycans and higher-antennary complex-type glycans in cancer progression.

Ultraviolet Photodissociation of ESI- and MALDI-Generated Protein Ions on a Q-Exactive Mass Spectrometer.

The identification of molecular ions produced by MALDI or ESI strongly relies on their fragmentation to structurally informative fragments. The widely diffused fragmentation techniques for ESI multiply charged ions are either incompatible (ECD and ETD) or show lower efficiency (CID, HCD), with the predominantly singly charged peptide and protein ions formed by MALDI. In-source decay has been successfully adopted to sequence MALDI-generated ions, but it further increases spectral complexity, and it is not compatible with mass-spectrometry imaging. Excellent UVPD performances, in terms of number of fragment ions and sequence coverage, has been demonstrated for electrospray ionization for multiple proteomics applications. UVPD showed a much lower charge-state dependence, and so protein ions produced by MALDI may exhibit equal propensity to fragment. Here we report UVPD implementation on an Orbitrap Q-Exactive Plus mass spectrometer equipped with an ESI/EP-MALDI. UVPD of MALDI-generated ions was benchmarked against MALDI-ISD, MALDI-HCD, and ESI-UVPD. MALDI-UVPD outperformed MALDI-HCD and ISD, efficiently sequencing small proteins ions. Moreover, the singly charged nature of MALDI-UVPD avoids the bioinformatics challenges associated with highly congested ESI-UVPD mass spectra. Our results demonstrate the ability of UVPD to further improve tandem mass spectrometry capabilities for MALDI-generated protein ions. Data are available via ProteomeXchange with identifier PXD011526.

Human Plasma N-glycosylation as Analyzed by Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance-MS Associates with Markers of Inflammation and Metabolic Health.

Glycosylation is an abundant co- and post-translational protein modification of importance to protein processing and activity. Although not template-defined, glycosylation does reflect the biological state of an organism and is a high-potential biomarker for disease and patient stratification. However, to interpret a complex but informative sample like the total plasma N-glycome, it is important to establish its baseline association with plasma protein levels and systemic processes. Thus far, large-scale studies (n >200) of the total plasma N-glycome have been performed with methods of chromatographic and electrophoretic separation, which, although being informative, are limited in resolving the structural complexity of plasma N-glycans. MS has the opportunity to contribute additional information on, among others, antennarity, sialylation, and the identity of high-mannose type species.Here, we have used matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-MS to study the total plasma N-glycome of 2144 healthy middle-aged individuals from the Leiden Longevity Study, to allow association analysis with markers of metabolic health and inflammation. To achieve this, N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-aminobenzoic acid, and following purification analyzed by negative ion mode intermediate pressure MALDI-FTICR-MS. In doing so, we achieved the relative quantification of 61 glycan compositions, ranging from Hex4HexNAc2 to Hex7HexNAc6dHex1Neu5Ac4, as well as that of 39 glycosylation traits derived thereof. Next to confirming known associations of glycosylation with age and sex by MALDI-FTICR-MS, we report novel associations with C-reactive protein (CRP), interleukin 6 (IL-6), body mass index (BMI), leptin, adiponectin, HDL cholesterol, triglycerides (TG), insulin, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and smoking. Overall, the bisection, galactosylation, and sialylation of diantennary species, the sialylation of tetraantennary species, and the size of high-mannose species proved to be important plasma characteristics associated with inflammation and metabolic health.

Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data.

The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.

Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution.

Technological advances in mass spectrometry imaging (MSI) have contributed to growing interest in 3D MSI. However, the large size of 3D MSI data sets has made their efficient analysis and visualization and the identification of informative molecular patterns computationally challenging. Hierarchical stochastic neighbor embedding (HSNE), a nonlinear dimensionality reduction technique that aims at finding hierarchical and multiscale representations of large data sets, is a recent development that enables the analysis of millions of data points, with manageable time and memory complexities. We demonstrate that HSNE can be used to analyze large 3D MSI data sets at full mass spectral and spatial resolution. To benchmark the technique as well as demonstrate its broad applicability, we have analyzed a number of publicly available 3D MSI data sets, recorded from various biological systems and spanning different mass-spectrometry ionization techniques. We demonstrate that HSNE is able to rapidly identify regions of interest within these large high-dimensionality data sets as well as aid the identification of molecular ions that characterize these regions of interest; furthermore, through clearly separating measurement artifacts, the HSNE analysis exhibits a degree of robustness to measurement batch effects, spatially correlated noise, and mass spectral misalignment.

Characterization of Degraded Proteins in Paintings Using Bottom-Up Proteomic Approaches: New Strategies for Protein Digestion and Analysis of Data.

Chemical hydrolysis assisted by microwave irradiation has been proposed as an alternative method for the analysis of proteins in highly insoluble matrices. In this work, chemical hydrolysis was applied for the first time to detect degraded proteins from paintings and polychromies. To evaluate the performance of this approach, the number of identified peptides, protein sequence coverage (%), and PSMs were compared with those obtained using two trypsin-based proteomics procedures used for the analysis of samples from cultural heritage objects. It was found that chemical hydrolysis allowed the successful identification of all proteinaceous materials in all paint samples analyzed except for egg proteins in one extremely degraded sample. Moreover, in one sample, casein was only identified by chemical digestion. In general, chemical hydrolysis identified more peptides, more PSM's, and greater sequence coverage in the samples containing caseins, and often also in animal glue, highlighting the great potential of this approach for the rapid digestion and identification of insoluble and degraded proteins from the field of the cultural heritage.

Mass spectrometry imaging: How will it affect clinical research in the future?

INTRODUCTION: Mass spectrometry imaging (MSI) is a label free, multiplex imaging technology able to simultaneously record the distributions of 100's to 1000's of species, and which may be configured to study metabolites, lipids, glycans, peptides, and proteins simply by changing the tissue preparation protocol. Areas covered: The capability of MSI to complement established histopathological practice through the identification of biomarkers for differential diagnosis, patient prognosis, and response to therapy; the capability of MSI to annotate tissues on the basis of each pixel's mass spectral signature; the development of reproducible MSI through multicenter studies. Expert commentary: We discuss how MSI can be combined with microsampling/microdissection technologies in order to investigate, with more depth of coverage, the molecular changes uncovered by MSI.

Round robin study of formalin-fixed paraffin-embedded tissues in mass spectrometry imaging.

Mass spectrometry imaging (MSI) has provided many results with translational character, which still have to be proven robust in large patient cohorts and across different centers. Although formalin-fixed paraffin-embedded (FFPE) specimens are most common in clinical practice, no MSI multicenter study has been reported for FFPE samples. Here, we report the results of the first round robin MSI study on FFPE tissues with the goal to investigate the consequences of inter- and intracenter technical variation on masking biological effects. A total of four centers were involved with similar MSI instrumentation and sample preparation equipment. A FFPE multi-organ tissue microarray containing eight different types of tissue was analyzed on a peptide and metabolite level, which enabled investigating different molecular and biological differences. Statistical analyses revealed that peptide intercenter variation was significantly lower and metabolite intercenter variation was significantly higher than the respective intracenter variations. When looking at relative univariate effects of mass signals with statistical discriminatory power, the metabolite data was more reproducible across centers compared to the peptide data. With respect to absolute effects (cross-center common intensity scale), multivariate classifiers were able to reach on average > 90% accuracy for peptides and > 80% for metabolites if trained with sufficient amount of cross-center data. Overall, our study showed that MSI data from FFPE samples could be reproduced to a high degree across centers. While metabolite data exhibited more reproducibility with respect to relative effects, peptide data-based classifiers were more directly transferable between centers and therefore more robust than expected. Graphical abstract ᅟ.

Mass Spectrometry Imaging, Laser Capture Microdissection, and LC-MS/MS of the Same Tissue Section.

Mass spectrometry imaging (MSI) is able to simultaneously record the distributions of hundreds of molecules directly from tissue. Rapid direct tissue analysis is essential for MSI in order to maintain spatial localization and acceptable measurement times. The absence of an explicit analyte separation/purification step means MSI lacks the depth of coverage of LC-MS/MS. In this work, we demonstrate how atmospheric pressure MALDI-MSI enables the same tissue section to be first analyzed by MSI, to identify regions of interest that exhibit distinct molecular signatures, followed by localized proteomics analysis using laser capture microdissection isolation and LC-MS/MS.

Design and Performance of a Novel Interface for Combined Matrix-Assisted Laser Desorption Ionization at Elevated Pressure and Electrospray Ionization with Orbitrap Mass Spectrometry.

Matrix-Assisted Laser Desorption Ionization, MALDI, has been increasingly used in a variety of biomedical applications, including tissue imaging of clinical tissue samples, and in drug discovery and development. These studies strongly depend on the performance of the analytical instrumentation and would drastically benefit from improved sensitivity, reproducibility, and mass/spatial resolution. In this work, we report on a novel combined MALDI/ESI interface, which was coupled to different Orbitrap mass spectrometers (Elite and Q Exactive Plus) and extensively characterized with peptide and protein standards, and in tissue imaging experiments. In our approach, MALDI is performed in the elevated pressure regime (5-8 Torr) at a spatial resolution of 15-30 µm, while ESI-generated ions are injected orthogonally to the interface axis. We have found that introduction of the MALDI-generated ions into an electrodynamic dual-funnel interface results in increased sensitivity characterized by a limit of detection of ∼400 zmol, while providing a mass measurement accuracy of 1 ppm and a mass resolving power of 120 000 in analysis of protein digests. In tissue imaging experiments, the MALDI/ESI interface has been employed in experiments with rat brain sections and was shown to be capable of visualizing and spatially characterizing very low abundance analytes separated only by 20 mDa. Comparison of imaging data has revealed excellent agreement between the MALDI and histological images.

Mass spectrometry imaging shows major derangements in neurogranin and in purine metabolism in the triple-knockout 3×Tg Alzheimer mouse model.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) can simultaneously measure hundreds of biomolecules directly from tissue. Using different sample preparation strategies, proteins and metabolites have been profiled to study the molecular changes in a 3×Tg mouse model of Alzheimer's disease. In comparison with wild-type (WT) control mice MALDI-MSI revealed Alzheimer's disease-specific protein profiles, highlighting dramatic reductions of a protein with m/z 7560, which was assigned to neurogranin and validated by immunohistochemistry. The analysis also revealed substantial metabolite changes, especially in metabolites related to the purine metabolic pathway, with a shift towards an increase in hypoxanthine/xanthine/uric acid in the 3×Tg AD mice accompanied by a decrease in AMP and adenine. Interestingly these changes were also associated with a decrease in ascorbic acid, consistent with oxidative stress. Furthermore, the metabolite N-arachidonyl taurine was increased in the diseased mouse brain sections, being highly abundant in the hippocampus. Overall, we describe an interesting shift towards pro-inflammatory molecules (uric acid) in the purinergic pathway associated with a decrease in anti-oxidant level (ascorbic acid). Together, these observations fit well with the increased oxidative stress and neuroinflammation commonly observed in AD. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

An experimental guideline for the analysis of histologically heterogeneous tumors by MALDI-TOF mass spectrometry imaging.

Mass spectrometry imaging (MSI) has been widely used for the direct molecular assessment of tissue samples and has demonstrated great potential to complement current histopathological methods in cancer research. It is now well established that tissue preparation is key to a successful MSI experiment; for histologically heterogeneous tumor tissues, other parts of the workflow are equally important to the experiment's success. To demonstrate these facets here we describe a matrix-assisted laser desorption/ionization MSI biomarker discovery investigation of high-grade, complex karyotype sarcomas, which often have histological overlap and moderate response to chemo-/radio-therapy. Multiple aspects of the workflow had to be optimized, ranging from the tissue preparation and data acquisition protocols, to the post-MSI histological staining method, data quality control, histology-defined data selection, data processing and statistical analysis. Only as a result of developing every step of the biomarker discovery workflow was it possible to identify a panel of protein signatures that could distinguish between different subtypes of sarcomas or could predict patient survival outcome. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Funnel-freezing versus heat-stabilization for the visualization of metabolites by mass spectrometry imaging in a mouse stroke model.

Tissue preparation is the key to a successful matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) experiment. Rapid post-mortem changes contribute a significant challenge to the use of MSI approaches for the analysis of peptides and metabolites. In this technical note we aimed to compare the tissue fixation method ex-vivo heat-stabilization with in-situ funnel-freezing in a middle cerebral artery occlusion (MCAo) mouse model of stroke, which causes profound alterations in metabolite concentrations. The influence of the duration of the thaw-mounting of the tissue sections on metabolite stability was also determined. We demonstrate improved stability and biomolecule visualization when funnel-freezing was used to sacrifice the mouse compared with heat-stabilization. Results were further improved when funnel-freezing was combined with fast thaw-mounting of the brain sections.

High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging.

The combination of high heterogeneity, both intratumoral and intertumoral, with their rarity has made diagnosis, prognosis of high-grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study, we investigated the ability of MALDI-MSI of proteins to distinguish between high-grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11 162), macrophage migration inhibitory factor (m/z 12 350), thioredoxin (m/z 11 608) and galectin-1 (m/z 14 633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11 314 and 11 355), indicative of poor survival for LMS patients.

Set of Novel Automated Quantitative Microproteomics Protocols for Small Sample Amounts and Its Application to Kidney Tissue Substructures.

Here we assessed the ability of an automated sample preparation device equipped with disposable microcolumns to prepare mass-limited samples for high-sensitivity quantitative proteomics, using both label-free and isobaric labeling approaches. First, we compared peptide label-free quantification reproducibility for 1.5-150 µg of cell lysates and found that labware preconditioning was essential for reproducible quantification of <7.5 µg digest. Second, in-solution and on-column tandem mass tag (TMT) labeling protocols were compared and optimized for 1 µg of sample. Surprisingly, standard methods for in-solution and on-column labeling showed poor TMT labeling (50-85%); however, novel optimized and automated protocols restored efficient labeling to >98%. Third, compared with a single long gradient experiment, a simple robotized high-pH fractionation protocol using only 6 µg of starting material doubled the number of unique peptides and increased proteome coverage 1.43-fold. To facilitate the analysis of heterogeneous tissue samples, such as those obtained from laser capture microdissection, a modified BCA protein assay was developed that consumes and detects down to 15 ng of protein. As a proof-of-principle, the modular automated workflow was applied to 0.5 and 1 mm2 mouse kidney cortex and medulla microdissections to show the method's potential for real-life small sample sources and to create kidney substructure-specific proteomes.

Multimodal Mass Spectrometry Imaging of N-Glycans and Proteins from the Same Tissue Section.

On-tissue digestion matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can be used to record spatially correlated molecular information from formalin-fixed, paraffin-embedded (FFPE) tissue sections. In this work, we present the in situ multimodal analysis of N-linked glycans and proteins from the same FFPE tissue section. The robustness and applicability of the method are demonstrated for several tumors, including epithelial and mesenchymal tumor types. Major analytical aspects, such as lateral diffusion of the analyte molecules and differences in measurement sensitivity due to the additional sample preparation methods, have been investigated for both N-glycans and proteolytic peptides. By combining the MSI approach with extract analysis, we were also able to assess which mass spectral peaks generated by MALDI-MSI could be assigned to unique N-glycan and peptide identities.