Personal Information from Latent Fingerprints Using Desorption Electrospray Ionization Mass Spectrometry and Machine Learning

Rapid desorption and analysis for illicit drugs and chemical profiling of fingerprints by SICRIT ion source

Forensic analysis can encompass a wide variety of analytes from biological samples including DNA, blood, serum, and fingerprints to synthetic samples like drugs and explosives. In order to analyze this variety, there are various sample preparation techniques, which can be time-consuming and require multiple analytical instruments. With recent advancements in ambient ionization mass spectrometry (MS), plasma-based dielectric barrier discharge ionization (DBDI) sources have demonstrated to cover a wide range of these analytes. The flow-through design of this source also allows for easy connection to a thermal desorption type of sample introduction. We present an in-house built thermal desorption device where the sample is introduced via a glass slide, which gets heated and transferred to the DBDI-MS with nitrogen for identification and semi-quantification. Using a glass slide as an inexpensive sampling device, detection limits as low as 20 pg for fentanyl are demonstrated. Additionally, a very precise (>96% accuracy) identification of persons based on the chemical profile of their fingerprints is possible, establishing a direct analytical link of the drug trace to the individual in one measurement. We compared the DAG, TAG, sterol, and (semi-)volatile region of the averaged fingerprint spectra over multiple days, showing the best model accuracy for identification based on the DAG region. The combination of thermal desorption and DBDI-MS minimized sample preparation, leading to an ultrasensitive and rapid analysis of illicit drug traces and the identification of underlying personas based on fingerprints.

Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning

Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including autism, Alzheimer's disease, dementia, cardiovascular disease, dry eye disease, and diabetes, and thus are molecules worthy of biomarker exploration studies. Since triacylglycerols and wax esters make up the majority of skin-surface lipid secretions, a viable sampling method for these potential biomarkers would be that of groomed latent fingerprints. Currently, however, blood-based sampling protocols predominate in the field. The invasiveness of a blood draw limits its utility to protected populations, including children and the elderly. Herein we describe a noninvasive means for sample collection (from fingerprints) paired with fast MS data-acquisition (MassIVE data set MSV000092742) and efficient data analysis via machine learning. Using both supervised and unsupervised classification, we demonstrate the usefulness of this method in determining whether a variable of interest imparts measurable change within the lipidomic data set. As a proof-of-concept, we show that the method is capable of distinguishing between the fingerprints of different individuals as well as between anatomical sebum collection regions. This noninvasive, high-throughput approach enables future lipidomic biomarker researchers to more easily include underrepresented, protected populations, such as children and the elderly, thus moving the field closer to definitive disease diagnoses that apply to all.

Desorption Electrospray Ionization Mass Spectrometry Imaging of Powder-Treated Fingermarks on Forensic Gelatin Lifters and its Application for Separating Overlapping Fingermarks

Fingermarks are frequently collected at crime scenes by using gelatin lifters for preservation and transport of the marks to a forensic laboratory for inspection. The gelatin lifters preserve both the imprint of the fingermark pattern necessary for identification purposes and the chemical residue of the mark potentially useful for profiling the person who left the fingermark. The fingermark patterns are traditionally recorded using photography/optical imaging, but methods for chemical analysis of fingermark residues on gelatin lifters are scarce. Here we report the first method for the chemical analysis of fingermarks on gelatin lifters using desorption electrospray ionization mass spectrometry (DESI-MS) imaging. The imaging can be done directly on the gelatin support without any sample preparation, supporting immediate operational use of the method for fingermarks collected at crime scenes. Operational use of the method is further supported by successful chemical imaging of fingermarks enhanced by traditional dusting with forensic powders and lifted off different surfaces (glass, stainless steel, painted aluminum, polystyrene, cardboard, and plastic) as well as fingermarks lifted multiple times. We also demonstrate that the present method can be used to visually separate natural overlapping powder-treated fingermarks, and the chemical composition of the fingermarks can be analyzed on the gelatin support by DESI-MS/MS. The presented method has potential for integration into the traditional workflow for fingermark analysis, and will allow more fingermarks collected at crime scenes to be evaluated both visually and chemically.

Skin Surface Sebum Analysis by ESI-MS

The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow-from sample collection to data analysis-should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice.

Matrix- and surface-assisted laser desorption/ionization-mass spectrometry analysis of fingermark components for forensic studies: current trends and future prospects

The chemical analysis of fingermarks (FMs) has attracted considerable attention in the realm of forensic investigations. Techniques based on direct ionization of a sample by laser irradiation, specifically matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS), have provided excellent figures of merit for analyzing high molecular-weight compounds. However, it can be challenging to analyze low molecular-weight compounds using MALDI-MS owing to potential interference produced by the organic matrices in the low molecular-weight region, which can impede the detection of small molecules (m/z < 700 Da). Alternately, surface-assisted laser desorption/ionization-mass spectrometry (SALDI-MS) has shown great promise for small molecules analysis owing to the unique properties of the nanostructures used, particularly, minimal chemical background in low m/z region improved the production of ions involved in this method. The advancement of MALDI-MS and SALDI-MS has propelled their application in the analysis of FM components, focused on gaining deep insights into individual traits. This review aims to outline the current role of MALDI-MS and SALDI-MS in the chemical analysis of FMs. It also describes the latest achievements in forensic intelligence derived from fingermark analysis using these powerful methods. The accomplishments include the understanding of certain characteristics and lifestyles of donors. The review offers a comprehensive overview of the challenges and demands in this field. It suggests potential enhancements in this rapidly expanding domain to bridge the gap between research and practical police casework.

Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances

Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.

Desorption electrospray ionization mass spectrometry imaging of latent fingerprints revealed by Oil Red O

Mass spectrometry imaging (MSI) is increasingly used to produce chemical images of latent fingerprints. Yet, the actual benefits of MSI for real case studies have to be assessed for fingerprints previously processed by forensic techniques. Here, we have evaluated the compatibility of desorption electrospray ionization (DESI) with the fingerprint enhancement technique involving Oil Red O (ORO).

METHODS

To optimize the ionization step independently from surface extraction, the ORO reagent and its mixture with model compounds (triolein and linoleic acid) were first studied in solution using high-resolution electrospray ionization tandem mass spectrometry (ESI-MS/MS). Then, DESI-MSI experiments were performed in both polarity modes for ORO-processed fingermarks deposited on pieces of paper used as porous substrates.

RESULTS

ESI-MS of ORO reveals a complex mixture of azo dyes. Two main impurities detected beside the targeted species were characterized using MS/MS and then were usefully employed to produce DESI-MS images of fingermarks, decreasing the scanning rate to get sufficient ion abundance from natural fingerprints. ORO did not prevent chemical profiling, and one major added value of this pink dye was to produce MS images with contrast that cannot be obtained optically for some colored substrates.

CONCLUSIONS

DESI-MS has demonstrated imaging compatibility with the application of ORO used to enhance latent fingerprints on paper and could also enable chemical profiling in natural fingermarks. In addition, MS images of ORO impurities were of higher quality than optical ones for fingerprints revealed on colored paper.

Application of a Modified 3D-PCSI-MS Ion Source to On-Site, Trace Evidence Processing via Integrated Vacuum Collection

Trace evidence, including hair, fibers, soil/dust, and gunshot residue (GSR), can be recovered from a crime scene to help identify or associate a suspect with illegal activities via physical, chemical, and biological testing. Vacuum collection is one technique that is employed in recovering such trace evidence but is often done so in a targeted manner, leaving other complementary, chemical-specific information unexamined. Here, we describe a modified 3D-printed cone spray ionization (3D-PCSI) source with integrated vacuum collection for on-site, forensic evidence screening, allowing the processing of targeted physical traces and nontargeted chemical species alike. The reported form factor allows sample collection, onboard extraction, filtration, and spray-based ionization in a singular vessel with minimal handling of evidence by the operator. Utilizing authentic forensic evidence types and portable MS instrumentation, this new method was characterized through systematic studies that replicate CSI applications. Reliability in the form of false positive/negative response rates was determined from a modest, user-blinded data set, and other attributes, such as collection efficacy and detection limit, were examined.

TOF-SIMS study of latent fingerprints on challenging substrates with the aid of transfer films

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been applied in forensic science for fingerprint detection. However, due to limitations of the instrument, it is not always possible to directly sample fingerprints on certain substrates. In this report, we indirectly sampled fingerprints using transfer films. First, we optimized the experimental conditions and identified transfer films with better results. We then explored the feasibility of revealing fingerprints after transfer and successfully transferred and revealed the detailed features of fingerprints on several common objects that could not be directly sampled. Fingerprints transferred from smooth surfaces yield clearer feature details in ion images. Additionally, we analyzed the substances in the transferred fingerprints and detected components of morphine and MDMA(3,4-methylenedioxy-n-methylamphetamine). By combining feature details with identified chemical components, the identity of a person can be determined, linking suspects to the crime scene. This work provides a new approach for sample introduction in instrumental analysis, enabling TOF-SIMS to be applied in more scenarios.

Characterization of ginsenosides from Panax japonicus var. major (Zhu-Zi-Shen) based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry and desorption electrospray ionization-mass spectrometry imaging

BACKGROUND

Panax japonicus var. major (PJM) belongs to the well-known ginseng species used in west China for hundreds of years, which has the effects of lung tonifying and yin nourishing, and exerts the analgesic, antitussive, and hemostatic activities. Compared with the other Panax species, the chemical composition and the spatial tissue distribution of the bioactive ginsenosides in PJM have seldom been investigated.

METHODS

Ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS) and desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) were integrated for the systematic characterization and spatial tissue distribution studies of ginsenosides in the rhizome of PJM. Considering the great difficulty in exposing the minor saponins, apart from the conventional Auto MS/MS (M1), two different precursor ions list-including data-dependent acquisition (PIL-DDA) approaches, involving the direct input of an in-house library containing 579 known ginsenosides (M2) and the inclusion of the target precursors screened from the MS1 data by mass defect filtering (M3), were developed. The in situ spatial distribution of various ginsenosides in PJM was profiled based on DESI-MSI with a mass range of m/z 100-1500 in the negative ion mode, with the imaging data processed by the High Definition Imaging (HDI) software.

RESULTS

Under the optimized condition, 272 ginsenosides were identified or tentatively characterized, and 138 thereof were possibly not ever reported from the Panax genus. They were composed by 75 oleanolic acid type, 22 protopanaxadiol type, 52 protopanaxatriol type, 16 octillol type, 19 malonylated, 35 C-17 side-chain varied, and 53 others. In addition, the DESI-MSI experiment unveiled the differentiated distribution of saponins, but the main location in the cork layer and phloem of the rhizome. The abundance of the oleanolic acid ginsenosides was high in the rhizome slice of PJM, which was consistent with the results obtained by UHPLC/QTOF-MS.

CONCLUSION

Comprehensive characterization of the ginsenosides in the rhizome of PJM was achieved, with a large amount of unknown structures unveiled primarily. We, for the first time, reported the spatial tissue distribution of different subtypes of ginsenosides in the rhizome slice of PJM. These results can benefit the quality control and further development of PJM and the other ginseng species.

Analysis and classification of coffee beans using single coffee bean mass spectrometry with machine learning strategy

Coffee is a daily essential, with prices varying based on taste, aroma, and chemical composition. However, distinguishing between different coffee beans is challenging due to time-consuming and destructive sample pretreatment. This study presents a novel approach for directly analyzing single coffee beans through mass spectrometry (MS) without the need for sample pretreatment. Using a single coffee bean deposited with a solvent droplet containing methanol and deionized water, we generated electrospray to extract the main species for MS analysis. Mass spectra of single coffee beans were obtained in just a few seconds. To showcase the effectiveness of the developed method, we used palm civet coffee beans (kopi luwak), one of the most expensive coffee types, as model samples. Our approach distinguished palm civet coffee beans from regular ones with high accuracy, sensitivity, and selectivity. Moreover, we employed a machine learning strategy to rapidly classify coffee beans based on their mass spectra, achieving 99.58% accuracy, 98.75% sensitivity, and 100% selectivity in cross-validation. Our study highlights the potential of combining the single-bean MS method with machine learning for the rapid and non-destructive classification of coffee beans. This approach can help to detect low-priced coffee beans mixed with high-priced ones, benefiting both consumers and the coffee industry.

Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach

Background

Abuse of Synthetic Cannabinoids (SCs) has become a serious threat to public health. Due to the various structural and chemical group modified by criminals, their detection is a major challenge in forensic toxicological identification. Therefore, rapid and efficient identification of SCs is important for forensic toxicology and drug bans. The prediction of an analyte's retention time in liquid chromatography is an important index for the qualitative analysis of compounds and can provide informatics solutions for the interpretation of chromatographic data.

Methods

In this study, experimental data from high-resolution mass spectrometry (HRMS) are used to construct a regression model for predicting the retention time of SCs using machine learning methods. The prediction ability of the model is improved by adopting a strategy that combines different descriptors in different independent machine-learning methods.

Results

The best model was obtained with a method that combined Substructure Fingerprint Count and Finger printer features and the support vector regression (SVR) method, as it exhibited an R² value of 0.81 for the validation set and 0.83 for the test set. In addition, 4 new SCs were predicted by the optimized model, with a prediction error within 3%.

Conclusions

Our study provides a model that can predict the retention time of compounds and it can be used as a filter to reduce false-positive candidates when used in combination with LC-HRMS, especially in the absence of reference standards. This can improve the confidence of identification in non-targeted analysis and the reliability of identifying unknown substances.

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.

Physical visualization and squalene-based scanning electrochemical microscopy imaging of latent fingerprints on PVDF membrane

Fingerprints have long been the gold standard for personal identification in forensic science. However, realizing the high-resolution enhancement of eccrine LFPs is difficult using the traditional methods and the label-free detection of fingerprint residue information is also challenging. Herein, we propose two enhancement strategies for LFPs on PVDF membrane (LFPs/PVDF) using blue-black ink staining and scanning electrochemical microscopy (SECM). The blue-black ink staining method was used for the first time to develop three types (sebaceous, natural and eccrine) of LFPs/PVDF based on the difference in wettability between the fingerprint residues and PVDF membrane. The enhanced fingerprints clearly displayed levels 1-3 features with high contrast and low background interference. Furthermore, we achieved chemical imaging of the LFP/PVDF samples, where their possible visualization mechanisms were ascribed to the electrochemical reactivity of squalene and difference in wettability between the LFP and PVDF membrane, which was first proposed and investigated by SECM imaging and water contact angle (WCA) measurements, respectively. Significantly, SECM imaging not only provided fingerprint patterns without any labelling but also revealed the spatial distribution information of squalene in LFPs simultaneously. In addition, it was also demonstrated that LFPs deposited on various surfaces were first successfully transferred to the PVDF membrane, and then further developed with both methods, making them general for personal identity-related applications. Taken together, the blue-black ink staining method can easily and quickly obtain level 3 features of LFPs/PVDF and the SECM approach can non-invasively image the topography and chemical information of LFPs/PVDF, and thus they can be potentially selected according to various requirements in forensic scenarios.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Novel Ambient Oxidation Trends in Fingerprint Aging Discovered by Kendrick Mass Defect Analysis

A Kendrick mass defect (KMD) plot is an efficient way to disperse complex high-resolution mass spectral data in a visually informative two-dimensional format which allows for the rapid assignment of compound classes that differ by heteroatom content and/or unsaturation. Fingerprint lipid oxidation has the potential to be used to estimate the time since deposition of a fingerprint, but the mass spectra become extremely complex as the lipids degrade. We apply KMD plot analysis for the first time to sebaceous fingerprints aged for 0-7 days to characterize lipid degradation processes analyzed by MALDI-MS. In addition to the ambient ozonolysis of fingerprint lipids previously reported, we observed unique spectral features associated with epoxides and medium chain fatty acid degradation products that are correlated with fingerprint age. We propose an ambient epoxidation mechanism via a peroxyl radical intermediate and the prevalence of omega-10 fatty acyl chains in fingerprint lipids to explain the features observed by the KMD plot analysis. Our hypotheses are supported by an aging experiment performed in a sparse ozone condition and on-surface Paternò-Büchi reaction. A comprehensive understanding of fingerprint degradation processes, afforded by the KMD plots, provides crucial insights for considering which ions to monitor and which to avoid, when creating a robust model for time since deposition of fingerprints.

Role of lipidomics in assessing the functional lipid composition in breast milk

Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2–5% of the total breast milk composition and are a major energy source providing 50% of an infant’s energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.

Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning

Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.

Performing 2D-1D-2D Mass Spectrometry Imaging Using Strings

The mass spectrometry imaging (MSI) technique is widely used in several fields due to its ability to provide spatial information of samples. However, for existing MSI methods, the sample is typically placed on a two-dimensional (2D) platform and is scanned back and forth. As a result, the platform size limits the imaging size. This paper proposes a new MSI method that involves the initial imprinting of chemicals on a two-dimensional string plane area. The string plane was then unraveled to a one-dimensional (1D) string, and the chemicals imprinted on it were ionized using a lab-made ion source. Finally, a 2D MSI image was reconstructed through data processing (2D-1D-2D mass imaging). Compared with traditional MSI methods, the imaging size is no longer limited by the platform size, making it possible to perform the MSI of large samples. As proof of concept, this method was used to image an intact seedling of Broussonetia papyrifera. As a result, clear and overall MS images were obtained, demonstrating the ability of this method to analyze large samples.

Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns

Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.

Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations

The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species' biomarker potential, our workflow delivers spatially localized explanations of the classification model's decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species' potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.

Imaging mass spectrometry: a new way to distinguish dermal contact from administration of cocaine, using a single fingerprint

Here we show a new and significant application area for mass spectrometry imaging. The potential for fingerprints to reveal drug use has been widely reported, with potential applications in forensics and workplace drug testing. However, one unsolved issue is the inability to distinguish between drug administration and contamination by contact. Previous work using bulk mass spectrometry analysis has shown that this distinction can only be definitively made if the hands are washed prior to sample collection. Here, we illustrate how three mass spectrometry imaging approaches, desorption electrospray ionisation (DESI), matrix assisted laser desorption ionisation (MALDI) and time of flight secondary ion mass spectrometry (ToF-SIMS) can be used to visualise fingerprints at different pixel sizes, ranging from the whole fingerprint down to the pore structure. We show how each of these magnification scales can be used to distinguish between cocaine use and contact. We also demonstrate the first application of water cluster SIMS to a fingerprint sample, which was the sole method tested here that was capable of detecting excreted drug metabolites in fingerprints, while providing spatial resolution sufficient to resolve individual pore structure. We show that after administration of cocaine, lipids and salts in the fingerprint ridges spatially correlate with the cocaine metabolite, benzoylecgonine. In contrast after contact, we have observed that cocaine and its metabolite show a poor spatial correlation with the flow of the ridges.

The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data

One unifying challenge when classifying biological samples with mass spectrometry data is overcoming the obstacle of sample-to-sample variability so that differences between groups, such as between a healthy set and a disease set, can be identified. Similarly, when the same sample is re-analyzed under identical conditions, instrument signals can fluctuate by more than 10%. This signal inconsistency imposes difficulties in identifying subtle differences across a set of samples, and it weakens the mass spectrometrist’s ability to effectively leverage data in domains as diverse as proteomics, metabolomics, glycomics, and imaging. We selected challenging data sets in the fields of glycomics, mass spectrometry imaging, and bacterial typing to study the problem of within-group signal variability and adapted a 30 year old statistical approach to address the problem. The solution, “local-balanced model,” relies on using balanced subsets of training data to classify test samples. This analysis strategy was assessed on ESI-MS data of IgG-based glycopeptides and MALDI-MS imaging data of endogenous lipids, and MALDI-MS data of bacterial proteins. Two preliminary examples on non-mass spectrometry data sets are also included to show the potential generality of the method outside the field of MS analysis. We demonstrate that this approach is superior to simple normalization methods, generalizable to multiple mass spectrometry domains, and potentially appropriate in fields as diverse as physics and satellite imaging. In some cases, improvements in classification can be dramatic, with accuracy escalating from 60% with normalization alone to over 90% with the additional development described herein.

Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays

Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently "biorelevant," because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol's antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F2-isoprostanes. Support vector machines, artificial neural networks, and Bayesian probabilistic learning are some key algorithms that could be deployed. Such a model will represent a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, and thus facilitate the identification or design of antioxidant molecules. The approach will also help to fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research.

Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research

Shotgun lipidomics is an analytical approach for large-scale and systematic analysis of the composition, structure, and quantity of cellular lipids directly from lipid extracts of biological samples by mass spectrometry. This approach possesses advantages of high throughput and quantitative accuracy, especially in absolute quantification. As cancer research deepens at the level of quantitative biology and metabolomics, the demand for lipidomics approaches such as shotgun lipidomics is becoming greater. In this chapter, the principles, approaches, and some applications of shotgun lipidomics for cancer research are overviewed.

Mass spectrometry imaging of latent fingerprints using titanium oxide development powder as an existing matrix

Recent research has focused on increasing the evidentiary value of latent fingerprints through chemical analysis. Although researchers have optimized the use of organic and metal matrices for matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) of latent fingerprints, the use of development powders as matrices has not been fully investigated. Carbon forensic powder (CFP), a common nonporous development technique, was shown to be an efficient one-step matrix; however, a high-resolution mass spectrometer was required in the low mass range due to carbon clusters. Titanium oxide (TiO₂ ) is another commonly used development powder, especially for dark nonporous surfaces. Here, forensic TiO₂ powder is utilized as a single-step development and matrix technique for chemical imaging of latent fingerprints without the requirement of a high-resolution mass spectrometer. All studied compounds were successfully detected when TiO₂ was used as the matrix in positive mode, although, generally, the overall ion signals were lower than the previously studied CFP. TiO₂ provided quality mass spectrometry (MS) images of endogenous and exogenous latent fingerprint compounds. The subsequent addition of traditional matrices on top of the TiO₂ powder was ineffective for universal detection of latent fingerprint compounds. Forensic TiO₂ development powder works as an efficient single-step development and matrix technique for MALDI-MSI analysis of latent fingerprints in positive mode and does not require a high-resolution mass spectrometer for analysis.

LCMS Measurement of Steroid Biomarkers Collected from Palmar Sweat

Human eccrine sweat contains numerous biomarkers which can provide information on health, performance, and aging. Non-invasive collection and measurement of biomarkers has become especially important in recent times given viral outbreaks like SARS-CoV-2. In the current study we describe a method of sweat collection from palmar surfaces in participants via surface capture using glass beads and the resulting analysis of biomarkers from very low volumes of sweat using liquid chromatography mass spectrometry with selected ion monitoring. Study participants underwent a cognitive and physical stress task with easy and hard conditions with sweat being collected after each task. Resulting analysis found a signal for 22 steroid biomarkers and we report detailed information on selected biomarkers, given their applicability to timely real-world exemplars, including cortisol, dehydroepiandrosterone, allopregnanolone, estrone, aldosterone, and 20α/β-dihydrocortisone.

The current role of mass spectrometry in forensics and future prospects

Mass spectrometry (MS) techniques are highly prevalent in crime laboratories, particularly those coupled to chromatographic separations like gas chromatography (GC) and liquid chromatography (LC). These methods are considered "gold standard" analytical techniques for forensic analysis and have been extensively validated for producing prosecutorial evidentiary data. However, factors such as growing evidence backlogs and problematic evidence types (e.g., novel psychoactive substance (NPS) classes) have exposed limitations of these stalwart techniques. This critical review serves to delineate the current role of MS methods across the broad sub-disciplines of forensic science, providing insight on how governmental steering committees guide their implementation. Novel, developing techniques that seek to broaden applicability and enhance performance will also be highlighted, from unique modifications to traditional hyphenated MS methods to the newer "ambient" MS techniques that show promise for forensic analysis, but need further validation before incorporation into routine forensic workflows. This review also expounds on how recent improvements to MS instrumental design, scan modes, and data processing could cause a paradigm shift in how the future forensic practitioner collects and processes target evidence.

In situ tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality

Integration between a hand-held mass spectrometry desorption probe based on picosecond infrared laser technology (PIRL-MS) and an optical surgical tracking system demonstrates in situ tissue pathology from point-sampled mass spectrometry data. Spatially encoded pathology classifications are displayed at the site of laser sampling as color-coded pixels in an augmented reality video feed of the surgical field of view. This is enabled by two-way communication between surgical navigation and mass spectrometry data analysis platforms through a custom-built interface. Performance of the system was evaluated using murine models of human cancers sampled in situ in the presence of body fluids with a technical pixel error of 1.0 ± 0.2 mm, suggesting a 84% or 92% (excluding one outlier) cancer type classification rate across different molecular models that distinguish cell-lines of each class of breast, brain, head and neck murine models. Further, through end-point immunohistochemical staining for DNA damage, cell death and neuronal viability, spatially encoded PIRL-MS sampling is shown to produce classifiable mass spectral data from living murine brain tissue, with levels of neuronal damage that are comparable to those induced by a surgical scalpel. This highlights the potential of spatially encoded PIRL-MS analysis for in vivo use during neurosurgical applications of cancer type determination or point-sampling in vivo tissue during tumor bed examination to assess cancer removal. The interface developed herein for the analysis and the display of spatially encoded PIRL-MS data can be adapted to other hand-held mass spectrometry analysis probes currently available.

Integration between a hand-held mass spectrometry desorption probe based on picosecond infrared laser technology (PIRL-MS) and an optical surgical tracking system demonstrates in situ tissue pathology from point-sampled mass spectrometry data.

Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning

The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, cell function, and phenotype are not always understood. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry have enabled the high-throughput, multiplexed chemical analysis of single cells, capable of resolving hundreds of molecules in each mass spectrum. We developed a machine learning workflow to classify single cells according to their mass spectra based on cell groups of interest (GOI), e.g., neurons vs astrocytes. Three data sets from various cell groups were acquired on three different mass spectrometer platforms representing thousands of individual cell spectra that were collected and used to validate the single cell classification workflow. The trained models achieved >80% classification accuracy and were subjected to the recently developed instance-based model interpretation framework, SHapley Additive exPlanations (SHAP), which locally assigns feature importance for each single-cell spectrum. SHAP values were used for both local and global interpretations of our data sets, preserving the chemical heterogeneity uncovered by the single-cell analysis while offering the ability to perform supervised analysis. The top contributing mass features to each of the GOI were ranked and selected using mean absolute SHAP values, highlighting the features that are specific to the defined GOI. Our approach provides insight into discriminating the chemical profiles of the single cells through interpretable machine learning, facilitating downstream analysis and validation.

Interpol review of fingermarks and other body impressions 2016-2019

This review paper covers the forensic-relevant literature in fingerprint and bodily impression sciences from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20 Review%20 Papers%202019. pdf.

Methodologies Applied to Fingerprint Analysis

This systematic review deals with the last 10 years of research in analytical methodologies for the analysis of fingerprints, regarding their chemical and biological constituents. A total of 123 manuscripts, which fit the search criteria defined using the descriptor "latent fingermarks analysis," were selected. Its main instrumental areas (mass spectrometry, spectroscopy, and innovative methods) were analyzed and summarized in a specific table, highlighting its main analytical parameters. The results show that most studies in this field use mass spectrometry to identify the constituents of fingerprints, both to determine the chemical profile and for aging. There is also a marked use of mass spectrometry coupled with chromatographic methods, and it provides accurate results for a fatty acid profile. Additional significant results are achieved by spectroscopic methods, mainly Raman and infrared. It is noteworthy that spectroscopic methods using microscopy assist in the accuracy of the analyzed region of the fingerprint, contributing to more robust results. There was also a significant increase in studies using methods focused on finding new developers or identifying components present in fingerprints by rapid tests. This systematic review of analytical techniques applied to the detection of fingerprints explores different approaches to contribute to future studies in forensic identification, verifying new demands in the forensic sciences and assisting in the selection of studies for the progress of research.

Proof of concept for identifying cystic fibrosis from perspiration samples

The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method.

Early detection of unilateral ureteral obstruction by desorption electrospray ionization mass spectrometry

Desorption electrospray ionization mass spectrometry (DESI-MS) is an emerging analytical tool for rapid in situ assessment of metabolomic profiles on tissue sections without tissue pretreatment or labeling. We applied DESI-MS to identify candidate metabolic biomarkers associated with kidney injury at the early stage. DESI-MS was performed on sections of kidneys from 80 mice over a time course following unilateral ureteral obstruction (UUO) and compared to sham controls. A predictive model of renal damage was constructed using the LASSO (least absolute shrinkage and selection operator) method. Levels of lipid and small metabolites were significantly altered and glycerophospholipids comprised a significant fraction of altered species. These changes correlate with altered expression of lipid metabolic genes, with most genes showing decreased expression. However, rapid upregulation of PG(22:6/22:6) level appeared to be a hitherto unknown feature of the metabolic shift observed in UUO. Using LASSO and SAM (significance analysis of microarrays), we identified a set of well-measured metabolites that accurately predicted UUO-induced renal damage that was detectable by 12 h after UUO, prior to apparent histological changes. Thus, DESI-MS could serve as a useful adjunct to histology in identifying renal damage and demonstrates early and broad changes in membrane associated lipids.

Tutorial on lipidomics

The mainstream of lipidomics involves mass spectrometry-based, systematic, and large-scale studies of the structure, composition, and quantity of lipids in biological systems such as organs, cells, and body fluids. As increasingly more researchers in broad fields are beginning to pay attention to and actively learn about the lipidomic technology, some introduction on the topic is needed to help the newcomers to better understand the field. This tutorial seeks to introduce the basic knowledge about lipidomics and to provide readers with some core ideas and the most important approaches for studying the field.

Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks

When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction.

Study of the cyanoacrylate fuming mechanism by matrix-assisted laser desorption/ionization mass spectrometry

Despite cyanoacrylate fuming being widely used in the forensic science field, its mechanism is not well understood. In this study, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry is used to study latent fingerprints that have been cyanoacrylate fumed in an attempt to gain insight into the fuming mechanism. In the negative mode mass spectrometry data, four compounds related to the polymerization of cyanoacrylate are identified and their structures are determined from accurate mass and MS/MS. A mechanism is proposed for the formation of these compounds that are regarded as intermediates in the polymerization reaction. In addition, based on the fuming of standard endogenous compounds, we suggest that fatty acids and amino acids are the major catalytic nucleophiles that initiate the polymerization reactions.

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.

Combining Desorption Electrospray Ionization Mass Spectrometry Imaging and Machine Learning for Molecular Recognition of Myocardial Infarction

Lipid profile changes in heart muscle have been previously linked to cardiac ischemia and myocardial infarction, but the spatial distribution of lipids and metabolites in ischemic heart remains to be fully investigated. We performed desorption electrospray ionization mass spectrometry imaging of hearts from in vivo myocardial infarction mouse models. In these mice, myocardial ischemia was induced by blood supply restriction via a permanent ligation of left anterior descending coronary artery. We showed that applying the machine learning algorithm of gradient boosting tree ensemble to the ambient mass spectrometry imaging data allows us to distinguish segments of infarcted myocardium from normally perfused hearts on a pixel by pixel basis. The machine learning algorithm selected 62 molecular ion peaks important for classification of each 200 μm-diameter pixel of the cardiac tissue map as normally perfused or ischemic. This approach achieved very high average accuracy (97.4%), recall (95.8%), and precision (96.8%) at a spatial resolution of ∼200 μm. In addition, we determined the chemical identity of 27 species, mostly small metabolites and lipids, selected by the algorithm as the most significant for cardiac pathology classification. This molecular signature of myocardial infarction may provide new mechanistic insights into cardiac ischemia, assist with infarct size assessment, and point toward novel therapeutic interventions.

Covalent Organic Polymers for Rapid Fluorescence Imaging of Latent Fingerprints

Rapid, simple and highly sensitive identification of latent fingerprints (LFPs) is an important issue related to national security and recognition of potential crimes. Here, we synthesize a series of covalent organic polymers (COPs) with colorful fluorescence (from blue to green, pale yellow, bright yellow, and red) and further investigate their performance for fluorescence imaging of LFPs. Results indicate that the COP materials can be used as fluorescence probes to rapidly visualize the precision substructure of LFPs within 5 s by simply spraying method, and tunable fluorescent color makes the COP probes have a high contrast and low interference for fluorescence imaging of LFPs on different substrates (including glass slides, paper, aluminum foil, plastic, ironware) in different backgrounds. We also further reveal the mechanism of COP probes for fluorescence imaging of LFPs. Importantly, the COP probes show high stability and could successfully achieve the fluorescence imaging for LFPs after aged for 45 days or washed by water. In short, this is the first report on the porous polymers for fluorescence imaging of LFPs and expected that it can be also applied to the fluorescence imaging of other fields.

Recent advances in ambient mass spectrometry of trace explosives

Ambient mass spectrometry has evolved rapidly over the past decade, yielding a plethora of platforms and demonstrating scientific advancements across a range of fields from biological imaging to rapid quality control. These techniques have enabled real-time detection of target analytes in an open environment with no sample preparation and can be coupled to any mass analyzer with an atmospheric pressure interface; capabilities of clear interest to the defense, customs and border control, transportation security, and forensic science communities. This review aims to showcase and critically discuss advances in ambient mass spectrometry for the trace detection of explosives.