Perspectives in imaging using mass spectrometry

Imaging mass spectrometry (MS) allows a remarkable range of measurements including diagnosis of disease state of tissue based on detailed information on its chemical constituents, especially lipids and proteins. The recent emergence of ambient ionization allows imaging in the open environment without sample preparation. In this review, we briefly describe the history of imaging MS highlighting its main techniques and applications. We also demonstrate how the detailed molecular information obtained by imaging MS makes this technique suitable for a range of forensic and clinical applications with the potential to be successfully developed all the way to intra-surgical practice.     

Aerosol time-of-flight mass spectrometry data analysis: a benchmark of clustering algorithms

Airborne particulate matter is an important component of atmospheric pollution, affecting human health, climate, and visibility. Modern instruments allow single particles to be analyzed one-by-one in real time, and offer the promise of determining the sources of individual particles based on their mass spectral signatures. The large number of particles to be apportioned makes clustering a necessary step. The goal of this study is to compare using mass spectral data the accuracy and speed of several clustering algorithms: ART-2a, several variants of hierarchical clustering, and K-means. Repeated simulations with various algorithms and different levels of data preprocessing suggest that hierarchical clustering methods using derivatives of Ward's algorithm discriminate sources with fewer errors than ART-2a, which itself discriminates much better than point-wise hierarchical clustering methods. In most cases, K-means algorithms do almost as well as the best hierarchical clustering. These efficient algorithms (clustering derived from Ward's algorithm, ART-2a and K-means) are most accurate when the relative peak areas have been pre-scaled by taking the square root. Analysis times vary within a factor of 30, and when accuracy above 95% is required, run times scale up as the square of the number of particles. Algorithms derived from Ward's remain the most accurate under a wide range of conditions and conversely, for an equal accuracy, can deliver a shorter list of clusters, allowing faster and maybe on-the-fly classification.     

Compression strategies for the chemometric analysis of mass spectrometry imaging data

Application of chemometric methods to mass spectrometry imaging (MSI) data faces a bottleneck concerning the vast size of the experimental data sets. This drawback is critical when considering high‐resolution mass spectrometry data, which provide several thousand points for each considered pixel. In this work, different approaches have been tested to reduce the size of the analyzed data with the aim to allow the subsequent application of typical chemometric methods for image analysis. The standard approach for MSI data compression consists in binning mass spectra for each pixel to reduce the number of m/z values. In this work, a method is proposed to handle the huge size of MSI data based on the adaptation of a liquid chromatography‐mass spectrometry data compression method by the detection of regions of interest. Results showed that both approaches achieved high compression rates, although the proposed regions of interest–based method attains this reduction requiring lower computational requirements and keeping utter spectral information. For instance, typical compression rate reached values higher than 90% without loss of information in images and spectra.     

Probability-based protein identification by searching sequence databases using mass spectrometry data

Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.     

Matrix-free mass spectrometric imaging using laser desorption ionisation Fourier transform ion cyclotron resonance mass spectrometry

Mass spectrometry imaging (MSI) is a powerful tool in metabolomics and proteomics for the spatial localization and identification of pharmaceuticals, metabolites, lipids, peptides and proteins in biological tissues. However, sample preparation remains a crucial variable in obtaining the most accurate distributions. Common washing steps used to remove salts, and solvent-based matrix application, allow analyte spreading to occur. Solvent-free matrix applications can reduce this risk, but increase the possibility of ionisation bias due to matrix adhesion to tissue sections. We report here the use of matrix-free MSI using laser desorption ionisation performed on a 12 T Fourier transform ion cyclotron resonance (FTICR) mass spectrometer. We used unprocessed tissue with no post-processing following thaw-mounting on matrix-assisted laser desorption ionisation (MALDI) indium-tin oxide (ITO) target plates. The identification and distribution of a range of phospholipids in mouse brain and kidney sections are presented and compared with previously published MALDI time-of-flight (TOF) MSI distributions.     

Fast,high resolution mass spectrometry imaging using a medipix pixelated detector

In mass spectrometry imaging, spatial resolution is pushed to its limits with the use of ion microscope mass spectrometric imaging systems. An ion microscope magnifies and then projects the original spatial distribution of ions from a sample surface onto a position-sensitive detector, while retaining time-of-flight mass separation capabilities. Here, a new type of position-sensitive detector based on a chevron microchannel plate stack in combination with a 512 × 512 complementary metal-oxide-semiconductor based pixel detector is coupled to an ion microscope. Spatial resolving power better than 6 μm is demonstrated by secondary ion mass spectrometry and 8–10μm spatial resolving power is achieved with laser desorption ionization. A detailed evaluation of key performance criteria such as spatial resolution, acquisition speed, and data handling is presented.     

A concise review of mass spectrometry imaging

Mass spectrometric imaging allows the investigation of the spatial distribution of molecules at complex surfaces. The combination of molecular speciation with local analysis renders a chemical microscope that can be used for the direct biomolecular characterization of histological tissue surfaces. MS based imaging advantageously allows label-free detection and mapping of a wide-range of biological compounds whose presence or absence can be the direct result of disease pathology. Successful detection of the analytes of interest at the desired spatial resolution requires careful attention to several steps in the mass spectrometry imaging protocol. This review will describe and discuss a selected number of crucial developments in ionization, instrumentation, and application of this innovative technology. The focus of this review is on the latest developments in imaging MS. Selected biological applications are employed to illustrate some of the novel features discussed. Two commonly used MS imaging techniques, secondary ion mass spectrometric (SIMS) imaging and matrix-assisted laser desorption ionization (MALDI) mass spectrometric imaging, center this review. New instrumental developments are discussed that extend spatial resolution, mass resolving power, mass accuracy, tandem-MS capabilities, and offer new gas-phase separation capabilities for both imaging techniques. It will be shown how the success of MS imaging is crucially dependent on sample preparation protocols as they dictate the nature and mass range of detected biomolecules that can be imaged. Finally, developments in data analysis strategies for large imaging datasets will be briefly discussed.     

Subcellular imaging mass spectrometry of brain tissue

Imaging mass spectrometry provides both chemical information and the spatial distribution of each analyte detected. Here it is demonstrated how imaging mass spectrometry of tissue at subcellular resolution can be achieved by combining the high spatial resolution of secondary ion mass spectrometry (SIMS) with the sample preparation protocols of matrix-assisted laser desorption/ionization (MALDI). Despite mechanistic differences and sampling 10(5) times less material, matrix-enhanced (ME)-SIMS of tissue samples yields similar results to MALDI (up to m/z 2500), in agreement with previous studies on standard compounds. In this regard ME-SIMS represents an attractive alternative to polyatomic primary ions for increasing the molecular ion yield. ME-SIMS of whole organs and thin sections of the cerebral ganglia of Lymnaea stagnalis demonstrate the advantages of ME-SIMS for chemical imaging mass spectrometry. Subcellular distributions of cellular analytes are clearly obtained, and the matrix provides an in situ height map of the tissue, allowing the user to identify rapidly regions prone to topographical artifacts and to deconvolute topographical losses in mass resolution and signal-to-noise ratio.     

Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training set and a validation set. Spectra from the tumor and normal regions of each of the tissue sections in the training set were used for orthogonal projection to latent structures (O-PLS) treated partial least-square discriminate analysis (PLS-DA). This predictive model was then validated by using the validation set and showed a 5% error rate for classification and a misclassification rate of 12%. It was also used to create synthetic images of the tissue sections showing pixel-by-pixel disease classification of the tissue and these data agreed well with the independent classification that uses histological data by a certified pathologist. This represents the first application of multivariate statistical methods for classification by ambient ionization although these methods have been applied previously to other MS imaging methods. The results are encouraging in terms of the development of a method that could be utilized in a clinical setting through visualization and diagnosis of intact tissue.     

Three-dimensional visualization of protein expression in mouse brain structures using imaging mass spectrometry

We have developed a method to visualize matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) data aligned with optically determinable tissue structures in three dimensions. Details of the methodology are exemplified using the 3-D reconstruction of myelin basic protein (MBP) in the corpus callosum of a mouse brain. In this procedure, optical images obtained from serial coronal sections are first aligned to each other to reconstruct a surface of the corpus callosum from segmented contours of the aligned images. The MALDI IMS data are then coregistered to the optical images and superimposed into the surface to create the final 3-D visualization. Correlating proteomic data with anatomical structures provides a more comprehensive understanding of healthy and pathological brain functions, and holds promise to be utilized in more complex anatomical arrangements.     

Distribution measurement of amphetamine-type stimulants in organs using micropulverized extraction and liquid chromatography/tandem mass spectrometry to complement drug distribution using mass spectrometry imaging

Amphetamine-type stimulants (ATS) such as methamphetamine are widely abused and can cause toxic effects in the body. In this study, a simple and accurate analytical method for distribution measurement of drugs in organs was developed to visualize localization of ATS in organs and to complement drug distribution by mass spectrometry imaging (MSI). The brain, liver and kidney from rats to which ATS had been administered were segmented into blocks of 2×2×2 mm3 at -30°C. Each organ block was micropulverized with a stainless-steel bullet at -80°C. The concentrations of drugs in each block were measured by liquid chromatography/tandem mass spectrometry. The three-dimensional distribution of drugs in a whole organ was expressed using color gradation of drug concentration after reconstruction of all blocks to the original locations. The distribution was also compared with that obtained by MSI. This method enabled measurement of drug distribution in organs with simple and clean procedures and accurate quantification unlike autoradiography and MSI. The methamphetamine concentrations were different between parts in an organ, particularly in the kidney. This method could be applicable to the measurement of the distribution of compounds in various solid samples and could be used as a complementary method for the measurement of the distribution of compounds by MSI.     

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem—classifying reactions into distinct families—and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets, as well as those based on reaction fingerprints derived from masked language modelling. In addition to reaction classification, the effectiveness of the strategy is tested on regression datasets; the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels.

Contrastive pretraining of chemical reactions by matching augmented reaction representations to improve machine learning performance on small reaction datasets.