Tissue profiling by mass spectrometry: a review of methodology and applications

Matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) has become a valuable tool to address a broad range of questions in many areas of biomedical research. One such application allows spectra to be obtained directly from intact tissues, termed "profiling" (low resolution) and "imaging" (high resolution). In light of the fact that MALDI tissue profiling allows over a thousand peptides and proteins to be rapidly detected from a variety of tissues, its application to disease processes is of special interest. For example, protein profiles from tumors may allow accurate prediction of tumor behavior, diagnosis, and prognosis and uncover etiologies underlying idiopathic diseases. MALDI MS, in conjunction with laser capture microdissection, is able to produce protein expression profiles from a relatively small number of cells from specific regions of heterogeneous tissue architectures. Imaging mass spectrometry enables the investigator to assess the spatial distribution of proteins, drugs, and their metabolites in intact tissues. This article provides an overview of several tissue profiling and imaging applications performed by MALDI MS, including sample preparation, matrix selection and application, histological staining prior to MALDI analysis, tissue profiling, imaging, and data analysis. Several applications represent direct translation of this technology to clinically relevant problems.     

Detergent addition to tryptic digests and ion mobility separation prior to MS/MS improves peptide yield and protein identification for in situ proteomic investigation of frozen and formalin‐fixed paraffin‐embedded adenocarcinoma tissue sections

The identification of proteins involved in tumour progression or which permit enhanced or novel therapeutic targeting is essential for cancer research. Direct MALDI analysis of tissue sections is rapidly demonstrating its potential for protein imaging and profiling in the investigation of a range of disease states including cancer. MALDI‐mass spectrometry imaging (MALDI‐MSI) has been used here for direct visualisation and in situ characterisation of proteins in breast tumour tissue section samples. Frozen MCF7 breast tumour xenograft and human formalin‐fixed paraffin‐embedded breast cancer tissue sections were used. An improved protocol for on‐tissue trypsin digestion is described incorporating the use of a detergent, which increases the yield of tryptic peptides for both fresh frozen and formalin‐fixed paraffin‐embedded tumour tissue sections. A novel approach combining MALDI‐MSI and ion mobility separation MALDI‐tandem mass spectrometry imaging for improving the detection of low‐abundance proteins that are difficult to detect by direct MALDI‐MSI analysis is described. In situ protein identification was carried out directly from the tissue section by MALDI‐MSI. Numerous protein signals were detected and some proteins including histone H3, H4 and Grp75 that were abundant in the tumour region were identified.     

Strategy combining separation of isotope-labeled unfolded proteins and matrix-assisted laser desorption/ionization mass spectrometry analysis enables quantification of a wide range of serum proteins

A novel strategy for the quantitative profiling of serum proteome is described. It includes an ammonium sulfate depletion of the serum, an affordable stable isotope labeling chemistry for samples with a large amount of protein, separation of the unfolded proteins, and relative quantification by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS). Labeling of unfolded proteins was performed using normal (D₀) acrylamide and deuterated (D₃) acrylamide. The workflow for separating the unfolded proteins includes whole gel elution and ion exchange liquid chromatography, and it combines electrophoretic separation based on the protein molecular weight followed by chromatographic separation in the presence of 8 M urea based on protein charge. This was followed by trypsinolysis and MALDI MS analysis, leading to the quantification of a large number of serum proteins, including those with an abundance of 10^-5 less than albumin. This robust and inexpensive workflow is suitable for the quantitative profiling of protein changes in serum associated with preanalytical variables.     

Sample preparation issues for tissue imaging by imaging MS

Imaging MS is a powerful technique that combines the chemical and spatial analysis of surfaces. It allows spatial localization of multiple different compounds that are recorded in parallel without the need of a label. It is currently one of the rapidly developing techniques in the proteomics toolbox. Different complementary imaging MS methods, i.e. MALDI and secondary ion MS imaging for direct tissue analysis, can be applied on exactly the same tissue sample. This allows the identification of small molecules, peptides and proteins present on the same sample surface. Sample preparation is crucial to obtain high quality, reliable and reproducible complementary molecular images. It is essential to optimize the conditions for each step in the sample preparation protocol, ranging from sample collection and storage to surface modification. In this article, we review and discuss the importance of correct sample treatment in case of MALDI and secondary ion MS imaging experiments and describe the experimental requirements for optimal sample preparation.     

Peptide and protein imaging mass spectrometry in cancer research

MALDI mass spectrometry is able to acquire protein profiles directly from tissue that can describe the levels of hundreds of distinct proteins. MALDI imaging MS can simultaneously reveal how each of these proteins varies in heterogeneous tissues. Numerous studies have now demonstrated how MALDI imaging MS can generate different protein profiles from the different cell types in a tumor, which can act as biomarker profiles or enable specific candidate protein biomarkers to be identified.     

MALDI-Mass Spectrometric Imaging for the Investigation of Metabolites in Medicago truncatula Root Nodules

Most techniques used to study small molecules, such as pharmaceutical drugs or endogenous metabolites, employ tissue extracts which require the homogenization of the tissue of interest that could potentially cause changes in the metabolic pathways being studied¹. Mass spectrometric imaging (MSI) is a powerful analytical tool that can provide spatial information of analytes within intact slices of biological tissue samples^1-5. This technique has been used extensively to study various types of compounds including proteins, peptides, lipids, and small molecules such as endogenous metabolites. With matrix-assisted laser desorption/ionization (MALDI)-MSI, spatial distributions of multiple metabolites can be simultaneously detected. Herein, a method developed specifically for conducting untargeted metabolomics MSI experiments on legume roots and root nodules is presented which could reveal insights into the biological processes taking place. The method presented here shows a typical MSI workflow, from sample preparation to image acquisition, and focuses on the matrix application step, demonstrating several matrix application techniques that are useful for detecting small molecules. Once the MS images are generated, the analysis and identification of metabolites of interest is discussed and demonstrated. The standard workflow presented here can be easily modified for different tissue types, molecular species, and instrumentation.     

High-throughput proteomic analysis of formalin-fixed paraffin-embedded tissue microarrays using MALDI imaging mass spectrometry

A novel method for high-throughput proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMA) is described using on-tissue tryptic digestion followed by MALDI imaging MS. A TMA section containing 112 needle core biopsies from lung-tumor patients was analyzed using MS and the data were correlated to a serial hematoxylin and eosin (H&E)-stained section having various histological regions marked, including cancer, non-cancer, and normal ones. By correlating each mass spectrum to a defined histological region, statistical classification models were generated that can sufficiently distinguish biopsies from adenocarcinoma from squamous cell carcinoma biopsies. These classification models were built using a training set of biopsies in the TMA and were then validated on the remaining biopsies. Peptide markers of interest were identified directly from the TMA section using MALDI MS/MS sequence analysis. The ability to detect and characterize tumor marker proteins for a large cohort of FFPE samples in a high-throughput approach will be of significant benefit not only to investigators studying tumor biology, but also to clinicians for diagnostic and prognostic purposes.     

Imaging mass spectrometry to visualize biomolecule distributions in mouse brain tissue following hemispheric cortical spreading depression

MALDI mass spectrometry can simultaneously measure hundreds of biomolecules directly from tissue. Using essentially the same technique but different sample preparation strategies, metabolites, lipids, peptides and proteins can be analyzed. Spatially correlated analysis, imaging MS, enables the distributions of these biomolecular ions to be simultaneously measured in tissues. A key advantage of imaging MS is that it can annotate tissues based on their MS profiles and thereby distinguish biomolecularly distinct regions even if they were unexpected or are not distinct using established histological and histochemical methods e.g. neuropeptide and metabolite changes following transient electrophysiological events such as cortical spreading depression (CSD), which are spreading events of massive neuronal and glial depolarisations that occur in one hemisphere of the brain and do not pass to the other hemisphere , enabling the contralateral hemisphere to act as an internal control. A proof-of-principle imaging MS study, including 2D and 3D datasets, revealed substantial metabolite and neuropeptide changes immediately following CSD events which were absent in the protein imaging datasets. The large high dimensionality 3D datasets make even rudimentary contralateral comparisons difficult to visualize. Instead non-negative matrix factorization (NNMF), a multivariate factorization tool that is adept at highlighting latent features, such as MS signatures associated with CSD events, was applied to the 3D datasets. NNMF confirmed that the protein dataset did not contain substantial contralateral differences, while these were present in the neuropeptide dataset.     

PAXgene fixation enables comprehensive metabolomic and proteomic analyses of tissue specimens by MALDI MSI

An alcohol-based non-crosslinking tissue fixative, PAXgene Tissue System, has been proposed as alternative fixation method to formalin, providing superior and morphological preservation. To date, metabolites have not been assessed in PAXgene-fixed tissues. The study focuses on a comparison between PAXgene and standard formalin fixation for metabolomic analysis by MALDI mass spectrometry imaging. Therefore, fifty-six samples from seven mice organs were fixed with PAXgene (PFPE) or formalin (FFPE), embedded in paraffin, and processed to a tissue microarray. PAXgene was able to spatially preserve metabolites in organs achieving an overlap of common metabolites ranging from 34 to 78% with FFPE. Highly similar signal intensities and visualization of molecules demonstrated negligible differences for metabolite imaging on PFPE compared to FFPE tissues. In addition, we performed proteomic analysis of intact proteins and peptides derived from enzymatic digestion. An overlap of 33 to 58% was found between FFPE and PFPE tissue samples in peptide analysis with a higher number of PFPE-specific peaks. Analysis of intact proteins achieved an overlap in the range of 0 to 28% owing to the poor detectability of cross-linked proteins in formalin-fixed tissues. Furthermore, metabolite and peptide profiles obtained from PFPE tissues were able to correctly classify organs independent of the fixation method, whereas a distinction of organs by protein profiles was only achieved by PAXgene fixation. Finally, we applied MALDI MSI to human biopsies by sequentially analyzing metabolites and peptides within the same tissue section. Concerning prospective studies, PAXgene can be used as an alternative fixative for multi-omic tissue analysis.     

Analysis of Human Plasma Proteome by 2DE‐ and 2D nanoLC‐Based Mass Spectrometry

Abstract

We compared the 2DE coupled to MALDI‐TOF‐MS and ESI‐MS/MS analysis (2DE‐MS) and the on‐line 2D nanoLC, followed by nanoESI‐MS/MS analysis (2DLC‐MS), for the separation and identification of proteins in high abundance protein‐depleted human plasma. Identification of proteins in the plasma by the two methods demonstrated that the majority of the identified protein set was unique to each method. Therefore, if a comprehensive coverage of the proteome identification is desired, it is ideal to apply both methods. The 2DE‐MS method is amenable to protein spot‐based quantitation, whereas the 2DLC‐MS method may provide an advantage of the high throughput application.     

Specific MALDI imaging and profiling for biomarker hunting and validation: fragment of the 11S proteasome activator complex, Reg alpha fragment, is a new potential ovary cancer biomarker

MALDI imaging mass spectrometry represents a new analytical tool to directly provide the spatial distribution and relative abundance of proteins in tissue. Twenty-five ovary carcinomas (stages III and IV) and 23 benign ovaries were directly analyzed using MALDI-TOF MS. The biomarker with the major prevalence (80%) has been fully identified using MALDI MS and nanoESI MS and MS/MS after separation by RP-HPLC and trypsin enzymatic digestion. This marker with an m/z of 9744 corresponds to 84 amino acid residues from the 11S proteasome activator complex, named PA28 or Reg-alpha. Validation of this marker has been performed using MALDI imaging, classical immunocytochemistry with an antibody raised against the C-terminal part of the protein, specific MALDI imaging, and Western blot analysis. The validation, using immunocytochemistry, confirmed the epithelial localization of this fragment with nucleus localization in benign epithelial cells and a cytoplasmic localization in carcinoma cells. This indicates that this antibody could be used to discriminate the borderline tumor cases. At this point, a multicentric study needs to be conducted in order to clearly establish the potential of this biomarker. Taken together these studies reflect that direct tissue analysis and specific MALDI imaging strategies facilitate biomarker hunting and validation which can be named pathological proteomics.     

Stopping the clock on proteomic degradation by heat treatment at the point of tissue excision

The effectiveness of rapid and controlled heating of intact tissue to inactivate native enzymatic activity and prevent proteome degradation has been evaluated. Mouse brains were bisected immediately following excision, with one hemisphere being heat treated followed by snap freezing in liquid nitrogen while the other hemisphere was snap frozen immediately. Sections were cut by cryostatic microtome and analyzed by MALDI‐MS imaging and minimal label 2‐D DIGE, to monitor time‐dependent relative changes in intensities of protein and peptide signals. Analysis by MALDI‐MS imaging demonstrated that the relative intensities of markers varied across a time course (0–5 min) when the tissues were not stabilized by heat treatment. However, the same markers were seen to be stabilized when the tissues were heat treated before snap freezing. Intensity profiles for proteins indicative of both degradation and stabilization were generated when samples of treated and nontreated tissues were analyzed by 2‐D DIGE, with protein extracted before and after a 10‐min warming of samples. Thus, heat treatment of tissues at the time of excision is shown to prevent subsequent uncontrolled degradation of tissues at the proteomic level before any quantitative analysis, and to be compatible with downstream proteomic analysis.     

MALDI imaging mass spectrometry: Discrimination of pathophysiological regions in traumatized skeletal muscle by characteristic peptide signatures

Due to formation of fibrosis and the loss of contractile muscle tissue, severe muscle injuries often result in insufficient healing marked by a significant reduction of muscle force and motor activity. Our previous studies demonstrated that the local transplantation of mesenchymal stromal cells into an injured skeletal muscle of the rat improves the functional outcome of the healing process. Since, due to the lack of sufficient markers, the accurate discrimination of pathophysiological regions in injured skeletal muscle is inadequate, underlying mechanisms of the beneficial effects of mesenchymal stromal cell transplantation on primary trauma and trauma adjacent muscle area remain elusive. For discrimination of these pathophysiological regions, formalin‐fixed injured skeletal muscle tissue was analyzed by MALDI imaging MS. By using two computational evaluation strategies, a supervised approach (ClinProTools) and unsupervised segmentation (SCiLS Lab), characteristic m/z species could be assigned to primary trauma and trauma adjacent muscle regions. Using “bottom‐up” MS for protein identification and validation of results by immunohistochemistry, we could identify two proteins, skeletal muscle alpha actin and carbonic anhydrase III, which discriminate between the secondary damage on adjacent tissue and the primary traumatized muscle area. Our results underscore the high potential of MALDI imaging MS to describe the spatial characteristics of pathophysiological changes in muscle.     

Exploring membrane and cytoplasm proteomic responses of Alkalimonas amylolytica N10 to different external pHs with combination strategy of de novo peptide sequencing

Identification of differentially proteomic responses to external pHs would pave an access for understanding of survival mechanisms of bacteria living at extreme pH environment. We cultured Alkalimonas amylolytica N10 (N10), a novel alkaliphilic bacterium found in Lake Chahannor, in media with three different pHs and extracted the correspondent membrane and cytoplasm proteins for proteomic analysis through 2‐DE. The differential 2‐DE spots corresponding to the altered pHs were delivered to MALDI TOF/TOF MS for protein identification. Since the genomic data of strain N10 was unavailable, we encountered a problem at low rate of protein identification with 18.1%. We employed, therefore, a combined strategy of de novo sequencing to analyze MS/MS signals generated from MALDI TOF/TOF MS. A significantly improved rate of protein identification was thus achieved at over than 70.0%. Furthermore, we extensively investigated the expression of these pH‐dependent N10 genes using Western blot and real‐time PCR. The conclusions drawn from immunoblot and mRNA measurements were mostly in agreement with the proteomic observations. We conducted the bioinformatic analysis to all the pH‐dependent N10 proteins and found that some membrane proteins participated in iron transport were differentially expressed as external pH elevated and most of differential proteins with increased or bell‐shape mode of pH‐dependence were involved in bioenergetic process and metabolism of carbohydrates, fatty acid, amino acids, and nucleotides. Our data thus provide a functional profile of the pH‐responsive proteins in alkaliphiles, leading to elucidation of alkaliphilic‐adaptive mechanism.     

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome

Background

Technological advances in next-generation sequencing (NGS) and chromatographic assays [e.g., liquid chromatography mass spectrometry (LC-MS)] have made it possible to identify thousands of microbe and metabolite species, and to measure their relative abundance. In this paper, we propose a sparse neural encoder-decoder network to predict metabolite abundances from microbe abundances.

Results

Using paired data from a cohort of inflammatory bowel disease (IBD) patients, we show that our neural encoder-decoder model outperforms linear univariate and multivariate methods in terms of accuracy, sparsity, and stability. Importantly, we show that our neural encoder-decoder model is not simply a black box designed to maximize predictive accuracy. Rather, the network’s hidden layer (i.e., the latent space, comprised only of sparsely weighted microbe counts) actually captures key microbe-metabolite relationships that are themselves clinically meaningful. Although this hidden layer is learned without any knowledge of the patient’s diagnosis, we show that the learned latent features are structured in a way that predicts IBD and treatment status with high accuracy.

Conclusions

By imposing a non-negative weights constraint, the network becomes a directed graph where each downstream node is interpretable as the additive combination of the upstream nodes. Here, the middle layer comprises distinct microbe-metabolite axes that relate key microbial biomarkers with metabolite biomarkers. By pre-processing the microbiome and metabolome data using compositional data analysis methods, we ensure that our proposed multi-omics workflow will generalize to any pair of -omics data. To the best of our knowledge, this work is the first application of neural encoder-decoders for the interpretable integration of multi-omics biological data.

     

Tracking and quantification of P-labeled phosphopeptides in liquid chromatography matrix-assisted laser desorption/ionization mass spectrometry

Phosphoamino acid modifications on substrate proteins are critical components of protein kinase signaling pathways. Thus, diverse methodologies have been developed and applied to identify the sites of phosphorylated amino acids within proteins. Despite significant progress in the field, even the determination of phosphorylated residues in a given highly purified protein is not a matter of routine and can be difficult and time-consuming. Here we present a practicable approach that integrates into a liquid chromatography matrix-assisted laser desorption/ionization mass spectrometry (LC–MALDI MS) workflow and allows localization and quantification of phosphorylated peptides on the MALDI target plate prior to MS analysis. Tryptic digests of radiolabeled proteins are fractionated by reversed-phase LC directly onto disposable MALDI target plates, followed by autoradiographic imaging. Visualization of the radiolabel enables focused analysis of selected spots, thereby accelerating the process of phosphorylation site mapping by decreasing the number of spectra to be acquired. Moreover, absolute quantification of the phosphorylated peptides is permitted by the use of appropriate standards. Finally, the manual sample handling is minimal, and consequently the risk of adsorptive sample loss is very low. Application of the procedure allowed the targeted identification of six novel autophosphorylation sites of AMP-activated protein kinase (AMPK) and displayed additional unknown phosphorylated peptide species not amenable to detection by MS. Furthermore, autoradiography revealed topologically inhomogeneous distribution of phosphorylated peptides within individual spots. However, accurate analysis of defined areas within single spots suggests that, rather than such quantitative differences, mainly the manner of matrix crystallization significantly affects ionization of phosphopeptides.     

Spatial Distribution of Endogenous Tissue Protease Activity in Gastric Carcinoma Mapped by MALDI Mass Spectrometry Imaging

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Highlights

• •New MALDI MS imaging sample preparation workflow reveals tissue protease activity.
• •Differential time- and inhibitor concentration-dependence confirm active proteases.
• •Mouse gastric tumor displays high protease activity compared to surrounding tissue.
• •Proteomic data and biochemical protease activity assay support MALDI MSI results.

     

Evaluation of sample preparation methods for MALDI-TOF MS identification of highly dangerous bacteria

Aims: To propose a universal workflow of sample preparation method for the identification of highly pathogenic bacteria by MALDI‐TOF MS. Methods and Results: Fifteen bacterial species, including highly virulent Gram‐positive (Bacillus anthracis and Clostridium botulinum) and Gram‐negative bacteria (Brucella melitensis, Burkholderia mallei, Francisella tularensis, Shigella dysenteriae, Vibrio cholerae, Yersinia pestis and Legionella pneumophila), were employed in the comparative study of four sample preparation methods compatible with MALDI‐TOF MS. The yield of bacterial proteins was determined by spectrophotometry, and the quality of the mass spectra, recorded in linear mode in the range of 2000–20 000 Da, was evaluated with respect to the information content (number of signals) and quality (S/N ratio). Conclusions: Based on the values of protein concentration and spectral quality, the method using combination of ethanol treatment followed by extraction with formic acid and acetonitrile was the most efficient sample preparation method for the identification of highly pathogenic bacteria using MALDI‐TOF MS. Significance and Impact of the Study: The method using ethanol/formic acid generally shows the highest extraction efficacy and the spectral quality with no detrimental effect caused by storage. Thus, this can be considered as a universal sample preparation method for the identification of highly virulent micro‐organisms by MALDI‐TOF mass spectrometry.     

Exploring proteins in Anopheles gambiae male and female antennae through MALDI mass spectrometry profiling

MALDI profiling and imaging mass spectrometry (IMS) are novel techniques for direct analysis of peptides and small proteins in biological tissues. In this work we applied them to the study of Anopheles gambiae antennae, with the aim of analysing expression of soluble proteins involved in olfaction perireceptor events. MALDI spectra obtained by direct profiling on single antennae and by the analysis of extracts, showed similar profiles, although spectra obtained through profiling had a richer ion population and higher signal to noise ratio. Male and female antennae showed distinct protein profiles. MALDI imaging experiments were also performed and differences were observed in the localization of some proteins. Two proteins were identified through high resolution measurement and top-down MS/MS experiments. A 8 kDa protein only present in the male antennae matched with an unannotated sequence of the An. gambiae genome, while the presence of odorant binding protein 9 (OBP-9) was confirmed through experiments of 2-DE, followed by MS and MS/MS analysis of digested spots. This work shows that MALDI MS profiling is a technique suitable for the analysis of proteins of small and medium MW in insect appendices, and allows obtaining data for several specimens which can be investigated for differences between groups. Proteins of interest can be identified through other complementary MS approaches.     

Identification of Protein Markers Specific for Papillary Renal Cell Carcinoma Using Imaging Mass Spectrometry

Since the emergence of proteomics methods, many proteins specific for renal cell carcinoma (RCC) have been identified. Despite their usefulness for the specific diagnosis of RCC, such proteins do not provide spatial information on the diseased tissue. Therefore, the identification of cancer-specific proteins that include information on their specific location is needed. Recently, matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) based imaging mass spectrometry (IMS) has emerged as a new tool for the analysis of spatial distribution as well as identification of either proteins or small molecules in tissues. In this report, surgical tissue sections of papillary RCC were analyzed using MALDI-IMS. Statistical analysis revealed several discriminative cancer-specific m/z-species between normal and diseased tissues. Among these m/z-species, two particular proteins, S100A11 and ferritin light chain, which are specific for papillary RCC cancer regions, were successfully identified using LC-MS/MS following protein extraction from independent RCC samples. The expressions of S100A11 and ferritin light chain were further validated by immunohistochemistry of human tissues and tissue microarrays (TMAs) of RCC. In conclusion, MALDI-IMS followed by LC-MS/MS analysis in human tissue identified that S100A11 and ferritin light chain are differentially expressed proteins in papillary RCC cancer regions.