Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets

Applications of MALDI mass spectrometry in forensic science

Forensic chemistry literature has grown exponentially, with many analytical techniques being used to provide valuable information to help solve criminal cases. Among them, matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS), particularly MALDI MS imaging (MALDI MSI), has shown much potential in forensic applications. Due to its high specificity, MALDI MSI can analyze a wide variety of compounds in complex samples without extensive sample preparation, providing chemical profiles and spatial distributions of given analyte(s). This review introduces MALDI MS(I) to forensic scientists with a focus on its basic principles and the applications of MALDI MS(I) to the analysis of fingerprints, drugs of abuse, and their metabolites in hair, medicine samples, animal tissues, and inks in documents.

Quantitative Mass Spectrometry Imaging Using Multivariate Curve Resolution and Deep Learning: A Case Study

In the present contribution, a novel approach based on multivariate curve resolution and deep learning (DL) is proposed for quantitative mass spectrometry imaging (MSI) as a potent technique for identifying different compounds and creating their distribution maps in biological tissues without need for sample preparation. As a case study, chlordecone as a carcinogenic pesticide was quantitatively determined in mouse liver using matrix-assisted laser desorption ionization-MSI (MALDI-MSI). For this purpose, data from seven standard spots containing 0 to 20 picomoles of chlordecone and four unknown tissues from the mouse livers infected with chlordecone for 1, 5, and 10 days were analyzed using a convolutional neural network (CNN). To solve the lack of sufficient data for CNN model training, each pixel was considered as a sample, the designed CNN models were trained by pixels in training sets, and their corresponding amounts of chlordecone were obtained by multivariate curve resolution-alternating least-squares (MCR-ALS). The trained models were then externally evaluated using calibration pixels in test sets for 1, 5, and 10 days of exposure, respectively. Prediction R² for all three data sets ranged from 0.93 to 0.96, which was superior to support vector machine (SVM) and partial least-squares (PLS). The trained CNN models were finally used to predict the amount of chlordecone in mouse liver tissues, and their results were compared with MALDI-MSI and GC-MS methods, which were comparable. Inspection of the results confirmed the validity of the proposed method.

A Review of Pharmaceutical Robot based on Hyperspectral Technology

The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review

OBJECTIVE

A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines.

METHODS

We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies.

RESULTS

Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively.

CONCLUSION

Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible.

SIGNIFICANCE

By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.

Unsupervised methods in LC-MS data treatment: Application for potential chemotaxonomic markers search

The combination of Liquid Chromatography and Mass Spectrometry (LC-MS) is commonly used to determine and characterize biologically active compounds because of its high resolution and sensitivity. In this work we explore the interpretation of LC-MS data using multivariate statistical analysis algorithms to extract useful chemical information and identify clusters of similar samples. Samples of leaves from 19 plants belonging to the Apiaceae family were analyzed in unified LC conditions by high- and low-resolution mass spectrometry in a wide range scan mode. LC-MS data preprocessing was performed followed by statistical analysis using tensor decomposition in the form of Parallel Factor Analysis (PARAFAC); matrix factorization following tensor unfolding with principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF); or unsupervised feature selection (UFS). The optimal number of components for each of these methods were found and results were compared using four different metrics: silhouette score, Davies-Bouldin index, computational time, number of noisy components. It was found that PCA, ICA and UFS give the best results across the majority of the criteria for both low- and high-resolution data. An algorithm for biomarker signal selection is suggested and 23 potential chemotaxonomic markers were tentatively identified using MS² data. Dendrograms constructed by the methods were compared to the molecular phylogenic tree by calculating pixel-wise mean square error (MSE). Therefore, the suggested approach can support chemotaxonomic studies and yield valuable chemical information for biomarker discovery.

Diagnosis of Agglomeration and Crystallinity of Active Pharmaceutical Ingredients in Over the Counter Headache Medication by Electrospray Laser Desorption Ionization Mass Spectrometry Imaging

Agglomeration of active pharmaceutical ingredients (API) in tablets can lead to decreased bioavailability in some enabling formulations. In a previous study, we determined that crystalline APIs can be detected as agglomeration in tablets formulated with amorphous acetaminophen tablets. Multiple method advancements are presented to better resolve agglomeration caused by crystallinity in standard tablets. In this study, we also evaluate three “budget” over-the-counter headache medications (subsequently labeled as brands A, B, and C) for agglomeration of the three APIs in the formulation: Acetaminophen, aspirin, and caffeine. Electrospray laser desorption ionization mass spectrometry imaging (ELDI-MSI) was used to diagnose agglomeration in the tablets by creating molecular images and observing the spatial distributions of the APIs. Brand A had virtually no agglomeration or clustering of the active ingredients. Brand B had extensive clustering of aspirin and caffeine, but acetaminophen was observed in near equal abundance across the tablet. Brand C also had extensive clustering of aspirin and caffeine, and minor clustering of acetaminophen. These results show that agglomeration with active ingredients in over-the-counter tablets can be simultaneously detected using ELDI-MS imaging.

A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging

MOTIVATION

Probabilistic latent semantic analysis (pLSA) is commonly applied to describe mass spectra (MS) images. However, the method does not provide certain outputs necessary for the quantitative scientific interpretation of data. In particular, it lacks assessment of statistical uncertainty and the ability to perform hypothesis testing. We show how linear Poisson modelling advances pLSA, giving covariances on model parameters and supporting χ2 testing for the presence/absence of MS signal components. As an example, this is useful for the identification of pathology in MALDI biological samples. We also show potential wider applicability, beyond MS, using magnetic resonance imaging (MRI) data from colorectal xenograft models.

RESULTS

Simulations and MALDI spectra of a stroke-damaged rat brain show MS signals from pathological tissue can be quantified. MRI diffusion data of control and radiotherapy-treated tumours further show high sensitivity hypothesis testing for treatment effects. Successful χ2 and degrees-of-freedom are computed, allowing null-hypothesis thresholding at high levels of confidence.

AVAILABILITY AND IMPLEMENTATION

Open-source image analysis software available from TINA Vision, www.tina-vision.net.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

137Cs γ Ray and 28Si Irradiation Induced Murine Hepatocellular Carcinoma Lipid Changes in Liver Assessed by MALDI-MSI Combined with Spatial Shrunken Centroid Clustering Algorithm: A Pilot Study

Characterization of lipids by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is of great interest because not only are lipids important structural molecules in both the cell and internal organelle membranes, but they are also important signaling molecules. MALDI-MSI combined with spatial image segmentation has been previously used to identify tumor heterogeneities within tissues with distinct anatomical regions such as the brain. However, there has been no systematic study utilizing MALDI-MSI combined with spatial image segmentation to assess the tumor microenvironment in the liver. Here, we present that image segmentation can be used to evaluate the tumor microenvironment in the liver. In particular, to better understand the molecular mechanisms of irradiation-induced hepatic carcinogenesis, we used MALDI-MSI in the negative ion mode to identify lipid changes 12 months post exposure to low dose ²⁸Si and ¹³⁷Cs γ ray irradiation. We report here the changes in the lipid profiles of male C3H/HeNCrl mice liver tissues after exposure to irradiation and analyzed using the spatial shrunken centroid clustering algorithm. These findings provide valuable information as astronauts will be exposed to high-charge high-energy (HZE) particles and low-energy γ-ray irradiation during deep space travel. Even at low doses, exposure to these irradiations can lead to cancer. Previous studies infer that irradiation of mice with low-dose HZE particles induces oxidative damage and microenvironmental changes that are thought to play roles in the pathophysiology of hepatocellular carcinoma.

Visualization of the tissue distribution of fullerenols in zebrafish (Danio rerio) using imaging mass spectrometry

With the wide application of fullerenols in biomedicine, their environmental exposure risks and toxicity to organisms have been extensively studied. However, there is still a lack of knowledge about the distribution of fullerenols in organisms as an important aspect of their mechanism of toxicity. High-resolution matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an emerging technology for researching the distribution of molecules in biological tissue samples. Using this high-resolution technique, we map the distribution of fullerenols in zebrafish tissues, and the results suggest that fullerenols enter the gill, intestine, and muscle tissues and even permeate the blood-brain barrier, reaching the brain of zebrafish after aquatic exposure. Moreover, from the MS images of fullerenols, the distribution amount of fullerenols is highest in the gill, followed by that in the intestine and the small amount in muscle and brain tissues. As an emerging environmental pollutant, the establishment of this research method will provide a new method for the study of the environmental toxicity of carbon nanomaterials. Our results also indicated that this high-resolution imaging method could be applied to explore the mechanism of interaction between carbon nanomaterials and biological systems at the cellular level in the future.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

Perioperative margin detection in basal cell carcinoma using a deep learning framework: a feasibility study

PURPOSE

Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure to solar radiation and the aging population. Reduction of positive margin rates when removing BCC leads to fewer revision surgeries and consequently lower health care costs, improved cosmetic outcomes and better patient care. In this study, we propose the first use of a perioperative mass spectrometry technology (iKnife) along with a deep learning framework for detection of BCC signatures from tissue burns.

METHODS

Resected surgical specimen were collected and inspected by a pathologist. With their guidance, data were collected by burning regions of the specimen labeled as BCC or normal, with the iKnife. Data included 190 scans of which 127 were normal and 63 were BCC. A data augmentation approach was proposed by modifying the location and intensity of the peaks of the original spectra, through noise addition in the time and frequency domains. A symmetric autoencoder was built by simultaneously optimizing the spectral reconstruction error and the classification accuracy. Using t-SNE, the latent space was visualized.

RESULTS

The autoencoder achieved an accuracy (standard deviation) of 96.62 (1.35%) when classifying BCC and normal scans, a statistically significant improvement over the baseline state-of-the-art approach used in the literature. The t-SNE plot of the latent space distinctly showed the separability between BCC and normal data, not visible with the original data. Augmented data resulted in significant improvements to the classification accuracy of the baseline model.

CONCLUSION

We demonstrate the utility of a deep learning framework applied to mass spectrometry data for surgical margin detection. We apply the proposed framework to an application with light surgical overhead and high incidence, the removal of BCC. The learnt models can accurately separate BCC from normal tissue.

In situ analysis of oxytetracycline tablets based on matrix-assisted laser desorption/ionization mass spectrometry imaging

RATIONALE

A thorough understanding of the content and distribution of active ingredients in pharmaceuticals is essential for drug efficacy and safety. Technological advancements in mass spectrometry imaging present an opportunity for methodological innovation by providing qualification and quantification analysis, as well as spatial information, in the same assay, which has great potential for applications in the rapid analysis and quality control of drugs.

METHODS

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was employed to directly analyze oxytetracycline tablets in order to map the distribution of the active constituent within the whole tablet. Quantitative analysis was capable of differentiating tablets containing various doses of the active pharmaceutical ingredient.

RESULTS

To establish the methodology, detailed factors that influence matrix spraying and spatial resolution during sample preparation and the data acquisition process were optimized systematically. Quantitative analysis could differentiate the tablets containing various doses of the active compound. The proposed method was successfully applied to analyze real commercial tablets.

CONCLUSIONS

The developed method could successfully achieve the spatial location of oxytetracycline in actual tablet samples. These results could contribute to pharmaceutical tracing technology, especially the formulation process of tablets, which is helpful for monitoring the quality of pharmaceutical products and guaranteeing drug security.

Rapid diagnosis of drug agglomeration and crystallinity in pharmaceutical preparations by electrospray laser desorption ionization mass spectrometry imaging

In this study we evaluate the applicability of electrospray laser desorption ionization mass spectrometry imaging (ELDI-MSI) to interrogate tablet formulations for the spatial distributions of ingredients. Tablet formulations with varying amounts of crystalline acetaminophen (the active pharmaceutical ingredient, API) were analyzed to determine if crystallinity could be evaluated via ELDI-MSI. ELDI-MSI concurrently imaged the (API, binders, and surfactants. The spatial distributions of amorphous API were very similar to that of the surfactants and different from that of crystalline API. The higher the crystallinity in the tablet formulation, the more agglomeration of the active ingredient was observed by ELDI-MSI. This study shows the capability of ELDI-MSI to diagnose agglomeration and crystallinity content in pharmaceutical preparations with little to no sample preparation. The ability to concurrently image APIs with other components provides valuable information as to their form in the tablet.

Use of physiological information based on grayscale images to improve mass spectrometry imaging data analysis from biological tissues

The characterization of cancer tissues by matrix-assisted laser desorption ionization-mass spectrometry images (MALDI-MSI) is of great interest because of the power of MALDI-MS to understand the composition of biological samples and the imaging side that allows for setting spatial boundaries among tissues of different nature based on their compositional differences. In tissue-based cancer research, information on the spatial location of necrotic/tumoral cell populations can be approximately known from grayscale images of the scanned tissue slices. This study proposes as a major novelty the introduction of this physiologically-based information to help in the performance of unmixing methods, oriented to extract the MS signatures and distribution maps of the different tissues present in biological samples. Specifically, the information gathered from grayscale images will be used as a local rank constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the analysis of MALDI-MSI of cancer tissues. The use of this constraint, setting absence of certain kind of tissues only in clear zones of the image, will help to improve the performance of MCR-ALS and to provide a more reliable definition of the chemical MS fingerprint and location of the tissues of interest. The general strategy to address the analysis of MALDI-MSI of cancer tissues will involve the study of the MCR-ALS results and the posterior use of MCR-ALS scores as dimensionality reduction for image segmentation based on K-means clustering. The resolution method will provide the MS signatures and their distribution maps for each tissue in the sample. Then, the resolved distribution maps for each biological component (MCR scores) will be submitted as initial information to K-means clustering for image segmentation to obtain information on the boundaries of the different tissular regions in the samples studied. MCR-ALS prior to K-means not only provides the desired dimensionality reduction, but additionally resolved non-biological signal contributions are not used and the weight given to the different biological components in the segmentation process can be modulated by suitable preprocessing methods.

Applications of MALDI-TOF mass spectrometry in clinical proteomics

INTRODUCTION

The development of precision medicine requires advanced technologies to address the multifactorial disease stratification and to support personalized treatments. Among omics techniques, proteomics based on Mass Spectrometry (MS) is becoming increasingly relevant in clinical practice allowing a phenotypic characterization of the dynamic functional status of the organism. From this perspective, Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) MS is a suitable platform for providing a high-throughput support to clinics. Areas covered: This review aims to provide an updated overview of MALDI-TOF MS applications in clinical proteomics. The most relevant features of this analysis have been discussed, highlighting both pre-analytical and analytical factors that are crucial in proteomics studies. Particular emphasis is placed on biofluids proteomics for biomarkers discovery and on recent progresses in clinical microbiology, drug monitoring, and minimal residual disease (MRD). Expert commentary: Despite some analytical limitations, the latest technological advances together with the easiness of use, the low time and low cost consuming and the high throughput are making MALDI-TOF MS instruments very attractive for the clinical practice. These features offer a significant potential for the routine of the clinical laboratory and ultimately for personalized medicine.

Towards a spray-coating method for the detection of low-dose compounds in pharmaceutical tablets using surface-enhanced Raman chemical imaging (SER-CI)

Surface-enhanced Raman chemical imaging (SER-CI) is a highly sensitive analytical tool recently used in the pharmaceutical field owing to the possibility to obtain high sensitivity along with spatial information. However, the covering method of the pharmaceutical samples such as tablets with metallic nanoparticles is a major issue for SER-CI analyses due to the difficulty to obtain a homogeneous covering of tablet surface with the SERS substrates. In this context, a spray-coating method was proposed in order to fully exploit the potential of SER-CI. A homemade apparatus has been developed from an electrospray ionization (ESI) probe in order to cover the pharmaceutical tablets with the colloidal suspension in a homogeneous way. The silver substrate was pulled through the airbrush by a syringe pump which was then nebulized into small droplets due to the contact of the solution with the gas flow turbulence. A robust optimization of the process was carried out by adjusting experimental parameters such as the liquid flow rate and the spraying time. Besides, the performances of this spraying technique were compared with two others covering methods found in the literature which are drop casting and absorption coating. A homogeneity study, conducted by SER-CI and matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) applied to the different covering techniques was performed. The influence of the metallic nanoparticles deposit on soluble compounds was also investigated in order to highlight the advantages of using this new spray coating approach.

In vivo bioconcentration of a metal mixture by Danio rerio eleutheroembryos

Exposure to heavy metals has represented one of the most serious health risks of environmental pollution over the last 50 years. Most of the bioconcentration studies that have been carried out to date explored only individual contaminants, unlike the real situations that occur in the environment. In this work, zebrafish eleutheroembryos were exposed to a mixture of CH₃Hg(II), iAs(III), Ag(I) and Cd(II), and new BCFs were calculated and compared with those calculated from single metal exposures. In both cases, experimental conditions meet the OECD Test 305 conditions established for aquatic systems. In addition, spatial imaging obtained by laser ablation coupled to inductively plasma mass spectrometry (LA-ICP/MS), has been directly performed in these samples providing complementary information. The new BCF's have revealed some differences compared to single metal exposures when eleutheroembryos were exposed to the metal mixture, especially for iAs(III) and Cd(II). LA-ICP/MS images are in good agreement with the BFC's found, representing an interesting approach to get spatial distribution of metals that reinforces the toxicokinetic information.

Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra

Motivation

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian.

Results

We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth.

Availability and implementation

Software is available via the open source image analysis system TINA Vision, www.tina-vision.net.

Contact

paul.tar@manchester.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Quantitative analysis of multiple high-resolution mass spectrometry images using chemometric methods: quantitation of chlordecone in mouse liver

In this work, a chemometrics-based strategy is developed for quantitative mass spectrometry imaging (MSI).

Fighting falsified medicines: The analytical approach

Given the harm to human health, the fight against falsified medicines has become a priority issue that involves numerous actors. Analytical laboratories contribute by performing analyses to chemically characterise falsified samples and assess their hazards for patients. A wide range of techniques can be used to obtain individual information on the organic and inorganic composition, the presence of an active substance or impurities, or the crystalline arrangement of the formulation's compound. After a presentation of these individual techniques, this review puts forward a methodology to combine them. In order to illustrate this approach, examples from the scientific literature (products used for erectile dysfunction treatment, weight loss and malaria) are placed in the centre of the proposed methodology. Combining analytical techniques allows the analyst to conclude on the falsification of a sample, on its compliance in terms of pharmaceutical quality and finally on the safety for patients.

Recent Advances in Solid-State Analysis of Pharmaceuticals

Current analytical techniques for characterizing solid-state pharmaceuticals include powder x-ray diffraction, differential scanning calorimetry, thermogravimetric analysis, infrared spectroscopy, Raman spectroscopy, electron microscopy and nuclear magnetic resonance. Powder x-ray diffraction and differential scanning calorimetry are mainstream techniques but they lack spatial resolution. Scanning electron microscopy and micro-Raman spectroscopy provide good chemical and optical characterization but they are not capable of analysing very small nanoparticles. Transmission electron microscopy and nano-thermal analysis can provide explicit characterization of nanoparticles but they are invasive. Nuclear magnetic resonance offers good spatial resolution but its use is mainly limited by poor sensitivity and high costs. In view of the many challenges posed by existing methods, new and novel techniques are being continually researched and developed to cater to the growing number of solid formulations in the pipeline and in the market. Some of the recent advances attained in the solid-state analysis of pharmaceutical are summarized in this review article.