Mass Spectrometry Imaging Experiments Research Articles

Mass Spectrometry Imaging for Spatial Toxicology Research.

The spatial information of xenobiotics distribution, metabolism, and toxicity mechanisms insitu has drawn increasing attention in both pharmaceutical and environmental toxicology research to aid drug development and environmental risk assessments. Mass spectrometry imaging (MSI) provides a label-free, multiplexed, and high-throughput tool to characterize xenobiotics, their metabolites, and endogenous molecules insitu with spatial resolution, providing knowledge on spatially resolved absorption, distribution, metabolism, excretion, and toxicity on the molecular level. In this perspective, we briefly summarize applications of MSI in toxicology on xenobiotic distribution and metabolism, quantification, toxicity mechanisms, and biomarker discovery. We identified several challenges regarding how we can fully harness the power of MSI in both fundamental toxicology research and regulatory practices. First, how can we increase the coverage, sensitivity, and specificity in detecting xenobiotics and their metabolites in complex biological matrices? Second, how can we link the spatial molecular information of xenobiotics to toxicity consequences to understand toxicity mechanisms, predict exposure outcomes, and aid biomarker discovery? Finally, how can we standardize the MSI experiment and data analysis workflow to provide robust conclusions for regulation and drug development? With these questions in mind, we provide our perspectives on the future directions of MSI as a promising tool in spatial toxicology research.

Imaging Mass Spectrometry of Sulfatide Isomers from Rat Brain Tissue Using Gas-Phase Charge Inversion Ion/Ion Reactions.

Sulfatides are abundant components of the brain, and dysregulation of these molecules has been linked to several diseases. In sulfatide structures, a sugar is linked to a sphingoid backbone via an α-glycosidic or β-glycosidic linkage. While sulfatides are readily generated in negative ion mode imaging mass spectrometry experiments, resolving sulfatide diastereomers is challenging; therefore, identifications are usually reported as a single sulfatide. Herein, a gas-phase charge inversion ion/ion reaction between sulfatides and a strontium tris-phenanthroline [Sr(Phen)3]2+ reagent is performed to separate the diastereomers, as they form complexes containing different numbers of phenanthroline ligands. The ability to separate these diastereomers using the reaction alone, without the need for any further dissociation, allows for the workflow to be readily implemented in an imaging mass spectrometry experiment. Imaging mass spectrometry was performed on sulfatides generated directly from rat brain tissue, and both the α- and β-linked sulfatide images were obtained.

MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Rapid Authentication of Medicinal Plants: Exploring the Applicability of Mass Spectrometry Imaging for Species Discrimination.

In the past few years, mass spectrometry imaging (MSI) has brought many new inspirations to plant research. However, current MSI experiments usually include only a single batch of samples, casting doubts on the reproducibility of phytochemical distribution across different batches. Consequently, MSI has seldom been applied to conduct species discrimination. In this experiment, MSI was employed to discriminate between two taxonomically similar plants, Scutellaria baicalensis Georgi and Scutellaria rehderiana Diels. A new concept termed a "spatial marker" was proposed in this article, which referred to the phytochemical marker that presented both intraspecies similarity and interspecies dissimilarity. Multiple batches of S. baicalensis and S. rehderiana were analyzed using MSI, proving that the authentication protocol using spatial markers was reliable and reproducible. The observed spatial markers were further identified using on-tissue tandem mass spectrometry and liquid chromatography coupled with mass spectrometry. Additionally, the spectral data collected from MSI were utilized to set up algorithm models for species discrimination. External validation confirmed that the established random forest model was extrapolated well to unknown samples. Overall, this investigation successfully explored the analytical applicability of MSI, facilitating rapid authentication of medicinal plants.

Desorption Electrospray Ionization Cyclic Ion Mobility-Mass Spectrometry Imaging for Traumatic Brain Injury Spatial Metabolomics.

Lipidomics focuses on investigating alterations in a wide variety of lipids that harness important information on metabolic processes and disease pathology. However, the vast structural diversity of lipids and the presence of isobaric and isomeric species creates serious challenges in feature identification, particularly in mass spectrometry imaging experiments that lack front-end separations. Ion mobility has emerged as a potential solution to address some of these challenges and is increasingly being utilized as part of mass spectrometry imaging platforms. Here, we present the results of a pilot mass spectrometry imaging study on rat brains subjected to traumatic brain injury (TBI) to evaluate the depth and quality of the information yielded by desorption electrospray ionization cyclic ion mobility mass spectrometry (DESI cIM MSI). Imaging data were collected with one and six passes through the cIM cell. Increasing the number of passes increased the ion mobility resolving power and the resolution of isobaric lipids, enabling the creation of more specific maps. Interestingly, drift time data enabled the recognition of multiply charged phosphoinositide species in the complex data set generated. These species have not been previously reported in TBI MSI studies and were found to decrease in the hippocampus region following injury. These changes were attributed to increased enzymatic activity after TBI, releasing arachidonic acid that is converted to eicosanoids to control inflammation. A substantial reduction in NAD and alterations in other adenine metabolites were also observed, supporting the hypothesis that energy metabolism in the brain is severely disrupted in TBI.

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluated the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. Lung lobes from male and female BALB/c mice were cryosectioned and acquired by high resolution mass spectrometry imaging (MSI). Processed MSI peak annotations were validated by LC-MS/MS data from scraped tissue slides and microdissected lung tissue. Images were normalized and segmented into clusters. Interestingly, segmented clusters overlapped with stained serial tissue sections, enabling statistical analysis across biological replicates for morphologically relevant lung regions. Spatially distinct lipids had higher overall degree of unsaturated fatty acids in distal lung regions compared to proximal regions. Furthermore, the airway and alveolar epithelium exhibited significantly decreased sphingolipid and glycerophospholipid abundance in females, but not in males. We demonstrate the potential role of lipid saturation in healthy lung function and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future MSI experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

Enhancing Spatial Resolution in Tandem Mass Spectrometry Ion/Ion Reaction Imaging Experiments through Image Fusion.

We have recently developed a charge inversion ion/ion reaction to selectively derivatize phosphatidylserine lipids via gas-phase Schiff base formation. This tandem mass spectrometry (MS/MS) workflow enables the separation and detection of isobaric lipids in imaging mass spectrometry, but the images acquired using this workflow are limited to relatively poor spatial resolutions due to the current time and limit of detection requirements for these ion/ion reaction imaging mass spectrometry experiments. This trade-off between chemical specificity and spatial resolution can be overcome by using computational image fusion, which combines complementary information from multiple images. Herein, we demonstrate a proof-of-concept workflow that fuses a low spatial resolution (i.e., 125 μm) ion/ion reaction product ion image with higher spatial resolution (i.e., 25 μm) ion images from a full scan experiment performed using the same tissue section, which results in a predicted ion/ion reaction product ion image with a 5-fold improvement in spatial resolution. Linear regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN) predictive models were tested for this workflow. Linear regression and 2D CNN models proved optimal for predicted ion/ion images of PS 40:6 and SHexCer d38:1, respectively.

An analytical evaluation of tools for lipid isomer differentiation in imaging mass spectrometry

Imaging mass spectrometry has emerged as a powerful tool to map the spatial distributions of lipids and metabolites in biological tissues. However, these analyses are challenged by the multitude of isobaric (i.e., same nominal mass) and isomeric compounds present in most samples. Failure to adequately separate these compounds results in inaccurate or incomplete chemical identifications and produces composite images of spatial distribution arising from multiple compounds. A number of techniques have been developed to more completely resolve and identify this complex chemical milieu. These include methods that rely on condensed-phase chemical derivatization and gas-phase ion chemistry, or some combination thereof. This Young Scientist Feature focuses on summarizing the analytical figures of merit of these tools, highlighting their relative speeds, limits of detection, molecular specificities, and eases-of-use. It will also include current challenges and future perspectives for resolving structural isomers in imaging mass spectrometry experiments.

Imaging with mass spectrometry: Which ionization technique is best?

The use of mass spectrometry (MS) to acquire molecular images of biological tissues and other substrates has developed into an indispensable analytical tool over the past 25 years. Imaging mass spectrometry technologies are widely used today to study the in situ spatial distributions for a variety of analytes. Early MS images were acquired using secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. Researchers have also designed and developed other ionization techniques in recent years to probe surfaces and generate MS images, including desorption electrospray ionization (DESI), nanoDESI, laser ablation electrospray ionization, and infrared matrix-assisted laser desorption electrospray ionization. Investigators now have a plethora of ionization techniques to select from when performing imaging mass spectrometry experiments. This brief perspective will highlight the utility and relative figures of merit of these techniques within the context of their use in imaging mass spectrometry.

Lipid Isobar and Isomer Imaging Using Nanospray Desorption Electrospray Ionization Combined with Triple Quadrupole Mass Spectrometry.

Mass spectrometry imaging (MSI) is widely used for examining the spatial distributions of molecules in biological samples. Conventional MSI approaches, in which molecules extracted from the sample are distinguished based on their mass-to-charge ratio, cannot distinguish between isomeric species and some closely spaced isobars. To facilitate isobar separation, MSI is typically performed using high-resolution mass spectrometers. Nevertheless, the complexity of the mixture of biomolecules observed in each pixel of the image presents a challenge, even for modern mass spectrometers with the highest resolving power. Herein, we implement nanospray desorption electrospray ionization (nano-DESI) MSI on a triple quadrupole (QqQ) mass spectrometer for the spatial mapping of isobaric and isomeric species in biological tissues. We use multiple reaction monitoring acquisition mode (MRM) with unit mass resolution to demonstrate the performance of this new platform by imaging lipids in mouse brain and rat kidney tissues. We demonstrate that imaging in MRM mode may be used to distinguish between isobaric phospholipids requiring a mass resolving power of 3,800,000. Additionally, we have been able to image eicosanoid isomers, a largely unexplored class of signaling molecules present in tissues at low concentrations, in rat kidney tissue. This new capability substantially enhances the specificity and selectivity of MSI, enabling spatial localization of species that remain unresolved in conventional MSI experiments.

Extended similarity methods for efficient data mining in imaging mass spectrometry.

Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.

Multiple ion isolation and accumulation events for selective chemical noise reduction and dynamic range enhancement in MALDI imaging mass spectrometry.

Abundant chemical noise in MALDI imaging mass spectrometry experiments can impede the detection of less abundant compounds of interest. This chemical noise commonly originates from the MALDI matrix as well as other endogenous compounds present in high concentrations and/or with high ionization efficiencies. MALDI imaging mass spectrometry of biological tissues measures numerous biomolecular compounds that exist in a wide range of concentrations in vivo. When ion trapping instruments are used, highly abundant ions can dominate the charge capacity and lead to space charge effects that hinder the dynamic range and detection of lowly abundant compounds of interest. Gas-phase fractionation has been previously utilized in mass spectrometry to isolate and enrich target analytes. Herein, we have characterized the use of multiple continuous accumulations of selected ions (Multi CASI) to reduce the abundance of chemical noise and diminish the effects of space charge in MALDI imaging mass spectrometry experiments. Multi CASI utilizes the mass-resolving capability of a quadrupole mass filter to perform multiple sequential ion isolation events prior to a single mass analysis of the combined ion population. Multi CASI was used to improve metabolite and lipid detection in the MALDI imaging mass spectrometry analysis of rat brain tissue.

Mass Spectrometry Imaging of Cholesterol and Oxysterols.

Mass spectrometry imaging (MSI) is a new technique in the toolbox of the analytical biochemist. It allows the generation of a compound-specific image from a tissue slice where a measure of compound abundance is given pixel by pixel, usually displayed on a color scale. As mass spectra are recorded at each pixel, the data can be interrogated to generate images of multiple different compounds all in the same experiment. Mass spectrometry (MS) requires the ionization of analytes, but cholesterol and other neutral sterols tend to be poorly ionized by the techniques employed in most MSI experiments, so despite their high abundance in mammalian tissues, cholesterol is poorly represented in the MSI literature. In this chapter, we discuss some of the MSI studies where cholesterol has been imaged and introduce newer methods for its analysis by MSI. Disturbed cholesterol metabolism is linked to many disorders, and the potential of MSI to study cholesterol, its precursors, and its metabolites in animal models and from human biopsies will be discussed.

MSIpixel: a fully automated pipeline for compound annotation and quantitation in mass spectrometry imaging experiments.

Mass spectrometry imaging (MSI) is commonly used to map the spatial distribution of small molecules within complex biological matrices. One of the major challenges in imaging MS-based spatial metabolomics is molecular identification and metabolite annotation, to address this limitation, annotation is often complemented with parallel bulk LC-MS2-based metabolomics to confirm and validate identifications. Here we applied MSI method, utilizing data-dependent acquisition, to visualize and identify unknown molecules in a single instrument run. To reach this aim we developed MSIpixel, a fully automated pipeline for compound annotation and quantitation in MSI experiments. It overcomes challenges in molecular identification, and improving reliability and comprehensiveness in MSI-based spatial metabolomics.

Advanced motion tracking for interactive mass spectrometry imaging (IMSI)

Mass spectrometry imaging (MSI) is a powerful analytical technique for the two-dimensional (2D) localization of chemicals on surfaces. Conventional MSI experiments require to predefine the surface of interest based on photographic or microscopic images. Typically, these boundaries can no longer be changed or adjusted once the experiment has been started. In terms of a more interactive approach we recently developed a pen-like ionization interface which is directly connected to the mass spectrometer. The device allows the user to ionize chemicals by desorption electrospray ionization (DESI) and to freely move the interface over a surface of interest. A mini camera, which is mounted on the tip of the pen, magnifies the desorption area and enables a simple positioning of the pen. The combination of optical data from the camera module and chemical data obtained by mass analysis facilitates a novel type of imaging experiment: interactive mass spectrometry imaging (IMSI). For this application, we present a novel approach for a robust, optical flow-based motion detection. While the live video stream from the camera is used to track the pen's motion across the surface a post-acquisition algorithm correlates the coordinates of the pen trajectory with respective mass spectra obtained from a simultaneous mass spectrometric data acquisition. This algorithm is no longer dependent on a single, manually applied optical marker on the sample surface, which has to be visible on all video frames throughout the analysis. The advanced DESI-IMSI method was successfully tested on inkjet-printed letters as well as mouse brain tissue samples. Validation of the results was done by comparing DESI-IMSI with standard DESI-MSI data.

Low-melting point agarose as embedding medium for MALDI mass spectrometry imaging and laser-capture microdissection-based proteomics

The combination of MALDI mass spectrometry imaging, laser-capture microdissection, and quantitative proteomics allows the identification and characterization of molecularly distinct tissue compartments. Such workflows are typically performed using consecutive tissue sections, and so reliable sectioning and mounting of high-quality tissue sections is a prerequisite of such investigations. Embedding media facilitate the sectioning process but can introduce contaminants which may adversely affect either the mass spectrometry imaging or proteomics analyses. Seven low-temperature embedding media were tested in terms of embedding temperature and cutting performance. The two media that provided the best results (5% gelatin and 2% low-melting point agarose) were compared with non-embedded tissue by both MALDI mass spectrometry imaging of lipids and laser-capture microdissection followed by bottom-up proteomics. Two out of the seven tested media (5% gelatin and 2% low-melting point agarose) provided the best performances on terms of mechanical properties. These media allowed for low-temperature embedding and for the collection of high-quality consecutive sections. Comparisons with non-embedded tissues revealed that both embedding media had no discernable effect on proteomics analysis; 5% gelatin showed a light ion suppression effect in the MALDI mass spectrometry imaging experiments, 2% agarose performed similarly to the non-embedded tissue. 2% low-melting point agarose is proposed for tissue embedding in experiments involving MALDI mass spectrometry imaging of lipids and laser-capture microdissection, proteomics of consecutive tissue sections.

Imaging and Structural Characterization of Phosphatidylcholine Isomers from Rat Brain Tissue Using Sequential Collision-Induced Dissociation/Electron-Induced Dissociation.

The chemical complexity of biological tissues creates challenges in the analysis of lipids via imaging mass spectrometry. The presence of isobaric and isomeric compounds introduces chemical noise that makes it difficult to unambiguously identify and accurately map the spatial distributions of these compounds. Electron-induced dissociation (EID) has previously been shown to profile phosphatidylcholine (PCs) sn-isomers directly from rat brain tissue in matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry. However, the acquisition of true pixel-by-pixel images, as opposed to regional profiling measurements, using EID is difficult due to low fragmentation efficiency and precursor ion signal dilution into multiple fragment ion channels, resulting in low sensitivity. In this work, we have developed a sequential collision-induced dissociation (CID)/EID method to visualize the distribution of sn-isomers in MALDI imaging mass spectrometry experiments. Briefly, CID is performed on sodium-adducted PCs, which results in facile loss of the phosphocholine headgroup. This ion is then subjected to an EID analysis. Since the lipid headgroup is removed prior to EID, a major fragmentation pathway common to EID ion activation is eliminated, resulting in a more sensitive analysis. This sequential CID/EID workflow generates sn-specific fragment ions allowing for the assignment of the sn-positions. Carbon-carbon double-bond (C═C) positions are also localized along the fatty acyl tails by the presence of a 2 Da shift pattern in the fragment ions arising from carbon-carbon bond cleavages. Moreover, the integration of the CID/EID method into MALDI imaging mass spectrometry enables the mapping of the absolute and relative distribution of sn-isomers at every pixel. The localized relative abundances of sn-isomers vary throughout brain substructures and likely reflect different biological functions and metabolism.

NECTAR: A New Algorithm for Characterizing and Correcting Noise in QToF-Mass Spectrometry Imaging Data.

A typical mass spectrometry imaging experiment yields a very high number of detected peaks, many of which are noise and thus unwanted. To select only peaks of interest, data preprocessing tasks are applied to raw data. A statistical study to characterize three types of noise in MSI QToF data (random, chemical, and background noise) is presented through NECTAR, a new NoisE CorrecTion AlgoRithm. Random noise is confirmed to be dominant at lower m/z values (∼50-400 Da) while systematic chemical noise dominates at higher m/z values (>400 Da). A statistical approach is presented to demonstrate that chemical noise can be corrected to reduce its presence by a factor of ∼3. Reducing this effect helps to determine a more reliable baseline in the spectrum and therefore a more reliable noise level. Peaks are classified according to their spatial S/N on the single ion images, and background noise is thus removed from the list of peaks of interest. This new algorithm was applied to MALDI and DESI QToF data generated from the analysis of a mouse pancreatic tissue section to demonstrate its applicability and ability to filter out these types of noise in a relevant data set. PCA and t-SNE multivariate analysis reviews of the top 4000 peaks and the final 744 and 299 denoised peak list for MALDI and DESI, respectively, suggests an effective removal of uninformative peaks and proper selection of relevant peaks.

Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis.

Mass Spectrometry Imaging (MSI) is a technique used to identify the spatial distribution of molecules in tissues. An MSI experiment results in large amounts of high dimensional data, so efficient computational methods are needed to analyze the output. Topological Data Analysis (TDA) has proven to be effective in all kinds of applications. TDA focuses on the topology of the data in high dimensional space. Looking at the shape in a high dimensional data set can lead to new or different insights. In this work, we investigate the use of Mapper, a form of TDA, applied on MSI data. Mapper is used to find data clusters inside two healthy mouse pancreas data sets. The results are compared to previous work using UMAP for MSI data analysis on the same data sets. This work finds that the proposed technique discovers the same clusters in the data as UMAP and is also able to uncover new clusters, such as an additional ring structure inside the pancreatic islets and a better defined cluster containing blood vessels. The technique can be used for a large variety of data types and sizes and can be optimized for specific applications. It is also computationally similar to UMAP for clustering. Mapper is a very interesting method, especially its use in biomedical applications.