MeXpose—A Modular Imaging Pipeline for the Quantitative Assessment of Cellular Metal Bioaccumulation

MeXpose is an end-to-end image analysis pipeline designed for mechanistic studies of metal exposure, providing spatial single-cell metallomics using laser ablation-inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOFMS). It leverages the high-resolution capabilities of low-dispersion laser ablation setups, a standardized approach to quantitative bioimaging, and the toolbox of immunohistochemistry using metal-labeled antibodies for cellular phenotyping. MeXpose uniquely unravels quantitative metal bioaccumulation (sub-fg range per cell) in phenotypically characterized tissue. Furthermore, the full scope of single-cell metallomics is offered through an extended mass range accessible by ICP-TOFMS instrumentation (covering isotopes from m/z 14–256). As a showcase, an ex vivo human skin model exposed to cobalt chloride (CoCl2) was investigated. For the first time, metal permeation was studied at single-cell resolution, showing high cobalt (Co) accumulation in the epidermis, particularly in mitotic basal cells, which correlated with DNA damage. Significant Co deposits were also observed in vascular cells, with notably lower levels in dermal fibers. MeXpose provides unprecedented insights into metal bioaccumulation with the ability to explore relationships between metal exposure and cellular responses on a single-cell level, paving the way for advanced toxicological and therapeutic studies.


■ INTRODUCTION
Metal exposure in human populations is increasing, driven by anthropogenic activities, modern industrialization, and medical use.−4 The toxic effect of metals depends on their chemical form, dose, and route of exposure.Accumulation patterns of metal species differ significantly among various tissue and cell types. 5,6More importantly, metal exposure influences dynamic cellular properties, thereby potentially modifying the behavior of cellular metal accumulation. 7,8−12 In keratinocytes, Co accumulates in the cell nucleus and in perinuclear structures, causing multiform toxicity related to interaction with genomic DNA and nuclear proteins, as well as altered zinc and magnesium homeostasis. 13herefore, we hypothesize that metal activity and patterns of toxicity could be better understood by measuring the characteristics of metal tissue distribution at single-cell resolution.Retaining spatial information is essential, as metal concentration gradients within the tissue arise upon routes of exposure, and cell activities are affected by position-dependent interactions and nutrient exchange.
−19 Time-resolved inductively coupled plasma mass spectrometry (ICP-MS) measurements enable the quantification of metals in thousands of cells at high throughput.The underlying analysis principle involves the production of single-cell events in a flow cytometer from cell suspensions which are subsequently detected by ICP-MS. 20,21As a drawback, the tissue context is not retained.−27 Thus, metal uptake quantified at the single-cell level can only be correlated to predominant cellular bulk properties, such as the cell cycle in synchronized cell cultures. 28Multiplexed analysis of single cells was achieved upon the introduction of time-of-flight-based ICP-MS instruments equipped with a quasi-simultaneous mass analyzer recording an entire spectrum of elements from a single ion pulse. 20When combined with antibody labeling strategies using metal isotope tags, the mass spectrometric pendant of fluorescence-based cytometry boosted the number of readouts per cell, allowing in-depth phenotypic screenings of cell populations. 21,29−35 Imaging mass cytometry (IMC) creates unprecedented molecular and cellular maps by boosting the number of potential read-outs per single cell as compared to fluorescence-based techniques.−43 Many of these solutions are subject to continuous improvement since existing pipelines show a combination of various shortcomings, such as (a) inability to provide a complete end-to-end data analysis workflow; (b) high level of required technical entry knowledge; and (c) lack of interactivity or scalability.Most importantly, none of the current available image analysis solutions contain the functionality to provide quantified absolute metal contents for tissue imaging. 44he term bioimaging in LA-ICP-MS refers to the measurement of endogenous/exogenous metal distributions in tissue. 45,46Recent standardization strategies 47−51 have supported the assessment of quantitative metal maps.−55 Cell-based metal abundances (fg per cell) can only be derived upon integrating cell segmentation in the image analysis pipeline, which in turn requires adequate spatial resolution (down to 1 μm for singlecell analysis in tissue) and multiplexed measurements.As a minimum prerequisite, at least one membrane and one nucleus marker as well as the accumulating metal must be measured simultaneously.The majority of quantitative bioimaging studies do not meet all the technological requirements, impeding quantitative single-cell analysis in tissue.
We present MeXpose, a laser ablation-inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOFMS) imaging pipeline, linking quantitative metal distribution at single-cell resolution with the identification of cellular phenotypes in tissues.It exploits the full scope of IMC, identifies cells preferentially accumulating metals, and quantifies their metal content.While IMC was established on an ICP-TOFMS instrument with a truncated mass range (m/z > 75 Da), 20,21 boosting the performance for the developed mass tags, the ICP-TOFMS instrumentation fundamental to MeXpose offers an expanded mass range toward lower masses (m/z range 14− 256).−58 Thus, potential toxic metal species such as Co, Cr, Ni, and Ti species can be investigated.Another key component is the quantitative bioimaging capability.Each imaging experiment is accompanied by a day-to-day fully traceable calibration routine. 49MeXpose integrates, for the first time, this quantification strategy and IMC into one stringent end-to-end workflow.The single-cell analysis strategy is tailored to investigate metal distribution in tissue as a result of metal exposure.Beyond merely locating the metalaccumulating cells in tissue, these cells are phenotypically characterized by utilizing metal-labeled antibodies.This way, MeXpose paves the way for novel research on metal toxicity, biology, and medicine, aiming to provide a mechanistic understanding of metal activity for both "wanted" and "unwanted" metal exposures.Metals present as macromolecular complexes in tissue can be quantitatively assessed since their distribution remains intact upon immunostaining.Whether absolute concentrations can be inferred depends on the metal species present in the tissue and on the analytical figures of merit of the LA-ICP-TOF-MS approach.Thus, orthogonal validation experiments proving the approach fit for absolute quantification are required for each metal species under investigation.
The MeXpose toolbox is showcased in a proof-of-concept study, showing the power of mechanistic exposure studies on metals in ex vivo human skin tissue.Several metals such as Ni, Cr, Co, and Au are known to elicit contact allergies upon direct skin contact in sensitized individuals. 59,60There is no clear-cut correlation between sensitizing potencies of different metals, their binding kinetics, and the resulting penetration depth into the skin layers, indicating that the mechanisms of skin penetration, as a first step in contact allergy, might be more complex than assumed. 61,62In this work, CoCl 2 , a clinically highly relevant metal cation with proven skin penetration capacity, was selected and studied by MeXpose in an ex vivo human skin model. 61,63,64For the first time, studies on Co permeation in human skin include the quantification of the metal uptake of single cells in tissue, together with their spatial distribution and phenotypical characterization.In fact, MeXpose uniquely discovered distinct cellular phenotypes and tissue locations, preferentially accumulating Co once the metal penetrated the skin barrier.

Step by Step�from Measurement to Data Analysis
−67 The ICP-TOFMS system covers a mass range of m/z = 14−256, allowing for the investigation of metal exposure for lower mass elements such as Cr, Co, and Ni, as well as the cellular metallome, including Fe, Cu, and Zn.Overall, metal concentration levels as low as < fg per single cell can be imaged at the spatial resolution required for single-cell analysis in tissue. 68issue sections are stained with metal-conjugated antibodies and measured in a sequence with multielemental calibration standards based on gelatin microdroplets, dispensed on surfaces by robotics. 49The droplets feature precisely characterized pL volume and (multi)elemental concentrations.If complete ablation of the microdroplet is achieved, an accurate elemental amount per microdroplet can be inferred and used for constructing an external calibration function.The calibration sequences, consisting typically of a blank and 5 standards of different concentration levels, can be measured within few minutes by LA-ICP-TOFMS.The linear dynamic working range typically covers 2 orders of magnitude (fg amounts).As in any quantification exercise using ICP-MS, the standardization measurement is performed on a day-to-day routine.The acquired data is then evaluated using MeXpose, which provides comprehensive tools for single-cell analysis.Figure 1 gives an overview of the entire analysis procedure.

MeXpose Data Analysis
MeXpose deploys a dedicated high-dimensional multiplexed image analysis pipeline combining various third-party software tools with additional newly developed Python scripts into a userfriendly, flexible containerized platform.Figure 2 outlines the workflow and key features.The elemental images obtained by LA-ICP-TOFMS are exported in single-or multichannel tiff format and submitted to the technology agnostic workflow.MeXpose data evaluation offers both an interactive and a scalable semiautomated version deployed as a single docker container.The latter was developed to meet the requirements of large-scale studies.The interactive mode was designed to enable immediate control on preprocessing, segmentation, and exploratory data analysis.This approach ensures optimal cell segmentation for different tissue types, which is a key prerequisite for quantitative image analysis.Depending on the individual needs, all included applications can be executed in "GUI-mode" (Graphical User Interface) by simply launching them using "-gui" as a suffix to the desired application name.The core functionality of both workflows (interactive and scalable) is the same and utilizes identical software with the exception being that the interactive Jupyter notebooks are replaced by command line Python scripts in the automated workflow.
Both MeXpose versions�i.e., the interactive and scalable workflow�offer essential functionalities to obtain tabular cellular data characterized by object-based integrated channel/ isotope intensities and morphology for downstream quantitative analysis.For data preprocessing, cell segmentation, exploratory data analysis, and visualization, state-of-the-art software packages dedicated to each individual step were combined in a modular end-to-end pipeline.MeXpose deploys Fiji (v1.54f) image processing software for preprocessing and multichannel image visualization, Cellpose 2.0 (v2.2.3) for deep learningassisted cell segmentation, Cellprofiler (v4.2.1) for feature extraction (intensities and morphology) of cellular objects as well as preprocessing, and newly developed in-house Python scripts (v3.8.8) and JupyterLab notebooks (JupyterLab v3.5.3;−73 Each module can be replaced with other software at will, which allows for seamless integration into existing image analysis workflows.The obtained tabular single-cell data are exported for further quantitative image analysis.Scripts for absolute metal quantification and statistical analysis are integrated.The latter task involves established strategies of image analysis, such as unsupervised clustering and/or dimensionality reduction.Phenograph (v1.5.7), an unsupervised graph clustering algorithm based on the Leiden algorithm, is used to categorize segmented cells into clusters depending on their feature intensities and inter-versus intracluster connections. 74Simplified, when represented as a graph, cells that form a subpopulation with a high number of internal connections and a lower number of external connections to other subpopulations are designated as a cluster.MeXpose allows us to visualize these high-dimensional clusters by projecting them as a two-dimensional embedding using UMAP (v0.5.4). 75This enables visual inspection of cluster quality.By creating a cluster heat map, MeXpose permits indepth analysis of the channel expression profile and relative intensities within each cluster, enabling the characterization of the various cellular phenotypes within tissue.Data interpretation is facilitated by the MeXpose option of projecting the corresponding segmented objects onto elemental images as a channel heatmap for visual inspection of spatial arrangements.
MeXpose uniquely allows us to apply a calibration function to the obtained data (including pixel-based and cell-based data), resulting in object-based quantitative-integrated elemental amounts.Following standardization, the color intensity code in the images can be converted to a color concentration code.Upon visualizing segmented cells on the images with a color concentration code, quantitative bioaccumulation resulting from metal exposure can be observed at the single-cell level.The images showing quantitative single-cell metal data can be generated for a user-defined cell population.Finally, the singlecell metal quantities can be plotted as histograms either for the complete imaged cell populations or again for certain subpopulations, e.g., cell phenotypes as revealed by statistical analysis.Producing histograms and plotting quantified cellular objects for distinct cellular subpopulations greatly aid in the The samples, and subsequently the multielement calibration standards, are subjected to LA-ICP-TOFMS analysis.Data analysis is performed using the MeXpose workflow.
Figure 2. MeXpose�a pipeline for image analysis at the single-cell level.User-defined elemental channels are stacked, preprocessed, and used to run a pretrained network to generate cell masks.Consequently, a feature matrix is extracted from the raw data using the segmentation mask.The extracted data can be used for image analysis at the single-cell level by downstream statistical analysis.The quantitative module integrates an external calibration function, which can be applied on the data to obtain metal quantities per cell.discovery of so far missing links between cellular metal enrichment and cellular phenotypes and/or tissue architecture.

Revealing the Structural Landscape of Human Skin through Multiplexed Imaging
To make the case for single-cell metallomics, CoCl 2 permeation in human skin was studied for the first time at single-cell resolution.Skin represents one of the major metal exposure routes in occupational settings.A cutting-edge ex vivo human skin model, NativeSkin, was exposed to CoCl 2 .The model closely resembles in vivo skin permeation upon exposure as it preserves tissue architecture in controlled environments. 76igure 3 shows LA-ICP-TOFMS images of the ex vivo skin model obtained by overlay visualizations of selected image channel intensities.The overlay images unveiling the skin tissue structural architecture relied on five markers along with one marker for cell nuclei (6 channels).The selected antibody panel (Table S1) characterized the different layers of the epidermis and the dermis together with essential structural features such as blood vessels, sweat glands, and muscle tissue.

Single-Cell Analysis in Human Skin Tissue
Following the interactive branch of the MeXpose image analysis pipeline, single-cell data were extracted for human skin tissue sections.Stacking required the selection of two channels, (1) a selective membrane and (2) a nuclei marker.In human skin, CD44 proved to be a valuable membrane marker, while the established Ir-based DNA intercalator was selected as a nuclei marker (Figure S1).Before stacking, the two channel images were preprocessed using a combination of steps, including contrast enhancement and intensity thresholding, outlier filtering, and median/Gaussian filtering.MeXpose deploys Cellpose 2.0 for cellular segmentation, leveraging its integrated pretrained "model zoo" as well as its interactive GUI (interactive workflow only).It also inherently includes segmentation quality control through visualization and the ability to interactively correct segmentation errors and add custom annotations.Upon completion, Cellpose generates PNG cell masks which are successively used to extract multiparametric tabular single-cell data (characterized by morphology and integrated intensities) from raw images using Cellprofiler.
MeXpose image analysis of human skin areas in the mm 2 range revealed cell numbers in the 10 3 orders of magnitude with an average cell diameter of 10 μm.  in the epidermis, while the dermal layers showed fewer cells embedded in the collagenous matrix.
The applied downstream statistical analysis resorted to a marker panel of 9 metal-conjugated antibodies (Table S1) and the endogenous metal iron (Fe).Phenograph clustering revealed seven clusters, which could be assigned to cellular phenotypes, typical for the different epidermal and dermal layers.Keratinocytes (basal, spinous, and granular), macrophages, fibroblasts, and smooth muscle cells could be assigned (Figure 4).MeXpose uniquely allows the consideration of endogenous elements upon cellular phenotyping.As has already been shown, intrinsic elements such as Fe cannot be accurately quantified due to the applied staining protocols. 68,77However, they can be used as complementary markers if their distribution is maintained.In the case of human skin, adding cellular Fe was beneficial for characterizing epidermal keratinocytes.Basal cells, which have the highest blood flow due to their proximity to capillaries, could be differentiated from other keratinocytes using Fe (Figure S2). 78,79Larger blood vessels such as arteries and veins, sweat glands, and hair follicles also showed characteristic Fe distributions that are useful for cell pheno-/ subtyping (Figure S3).

Quantitative Analysis Calls for Validation
Whether the applied histochemistry allows for simultaneous phenotyping and cellular metal enrichment needs to be evaluated for each specific exposure study.As in any quantitative approach, additional experiments are required proving the method fit for purpose.The effect of sample preparation has been investigated using pixel data, as staining is required for the analysis of single cells in tissue.A dedicated script for pixel-based image analysis is therefore provided with MeXpose.As shown in Figure 5, two consecutive skin sections exposed to Co were measured, one following the immunostaining procedures and the other omitting the sample preparation steps.K-means clustering demonstrated the validity of the quantitative approach, as both samples showed excellent agreement regarding Co quantity and location.

Quantifying Metal Accumulation at the Single-Cell Level
The human skin model exposed to CoCl 2 showed pronounced metal permeation into the epidermis.High bioaccumulation was found down to a depth of approximately 100 μm; however, the highest Co amount was observed within the cornified layer at a permeation depth of 20 μm (Figure S4).Only small amounts of   4).Cluster 2 (basal cells) in particular showed significantly higher Co content compared to the other cell types (Figure S6).
Co were found to reach the dermal layer of the skin.By applying the external calibration function (obtained from the measurement of gelatin-based microdroplets) (Figure 6A), both pixelbased and cell-based intensity data on Co were converted into absolute amounts (Figure 6B,C).Within the MeXpose pipeline, the obtained single-cell quantities can be plotted as histograms showing the distribution of cellular metal content for subpopulations or the entire cell population.For visualization, projections of segmented cells on top of the image are associated with a color-coded concentration scale.The entire imaged cell population of 1782 cells revealed a mean Co content of 2.4 fg per cell (Figure 6D).A procedural limit of detection of 0.38 fg Co per cell was estimated by studying control skin tissue samples (not exposed to CoCl 2 ) that were stained with the metalconjugated antibody panel (Figure S5).The cellular Co intensities constituting the blank values were converted into quantities and treated as Poisson distributed to infer the limit of detection. 80

Cellular Phenotypes of Co Accumulation
The human skin characteristics of Co bioaccumulation were investigated by assessing the quantitative single-cell Co data and their distribution for the observed cell phenotypes, respectively (Figure 6E).Among the 7 subpopulations obtained from unsupervised phenograph clustering, mitotic basal cells of the epidermis (cluster 2 in Figure 4) showed pronounced Co bioaccumulation, with an average of 3.8 fg/cell.When applying the null hypothesis (Mann−Whitney U), this subpopulation was shown to differ significantly from other clusters (Figure S6).Besides basal cells, spinous and granular cells (cluster 1) showed high metal bioaccumulation.In these epidermal cells, high Co levels were correlated with an increase in DNA damage (Figure S7).Co accumulation was also pronounced in vascular endothelial cells (Figure S8), with an average of 2.8 fg/cell, particularly in capillaries of the papillary dermis characterized by loose connective tissue (Figure S9).Clusters associated with arrector pili smooth muscle cells and fibroblasts showed comparatively lower Co accumulation (clusters 3 and 4).Macrophages in cluster 6 showed a similar distribution with an average Co content of 2.1 fg/cell.The remaining "unspecified" cell population of the dermis, not characterized by the selected antibody panel (cluster 0), showed the lowest Co bioaccumulation (mean value of 1.2 fg/cell).

■ DISCUSSION
MeXpose introduces cutting-edge single-cell metallomics in tissue, combining quantitative bioimaging by LA-ICP-TOFMS and the comprehensive toolbox of IMC into an integrated endto-end workflow.This approach couples comprehensive tissue characterization and cell population assessment with quantitative cellular metal bioaccumulation analysis.Additionally, the standardization concept allows to infer absolute amounts of metals on a single-cell level in tissue.As with any quantification procedure, validation is essential and has to be performed for each metal species and tissue type, respectively.Generally, the method is fit-for-purpose for metals forming macromolecular complexes in tissue upon exposure.Metal accumulation in the sub-fg range per cell can be studied.
The prime example of Co exposure and its effects, studied in NativeSkin models, demonstrated the value of the approach.Co skin permeation was investigated in unprecedented detail, i.e., at the single-cell level.The application of Co in the form of CoCl 2 to the skin resulted in a predominant accumulation in the cornified layer, a key skin barrier.However, our investigation also revealed a notable ability of Co to penetrate deeper layers of the skin, primarily targeting the epidermis and, to a lesser extent, the dermis.This transdermal permeation resulted in significant Co accumulation within the basal layer, correlating with increased DNA damage at the single-cell level.Due to the close proximity of basal cells to dermal blood vessels, there is a risk that Co could be transported further into the bloodstream.This concern was supported by the elevated levels of Co in vascular endothelial cells.High levels of Co in the systemic circulation could potentially contribute to organ damage due to its ability to generate reactive oxygen species.

Exposure of Ex Vivo Skin Model to CoCl 2
Upon arrival of the skin model, the media (supplied by Genoskin) was added under sterile conditions and incubated for 1 h at 37 °C, 5% CO 2 , 95% relative humidity (according to the user manual).A 100 mg mL −1 stock solution of CoCl 2 •6H 2 O was prepared by dissolving 1 g in 10 mL of ultrapure water, followed by sterile filtration.For the target dose of 2.5 μg μL −1 (based on prior results from patch testing of Co allergic individuals), 25 μL of the stock solution was mixed with 975 μL of sterile ultrapure water and vortexed prior to exposure. 63,81The CoCl 2 solution was added to the model by pipetting 20 μL.The solution was homogeneously distributed on the apical side of the model.The plate was then placed in an incubator (37 °C, 5% CO 2 , 95% relative humidity).The next day the media was changed.Samples were collected after 48 h, later embedded in FFPE, and sectioned at 5 μm.

Immunostaining of Skin Tissue Sections
Skin tissue sections were deparaffinized by treatment with fresh xylene for 20 min.Descending grades of ethanol (100−70%) were used for rehydration.After rinsing with ultrapure water, heat-mediated antigen retrieval was performed at 96 °C for 30 min using a Tris-EDTA buffer at pH 9. Slides were then cooled and washed with ultrapure water and TBS/0.05%Tween.To prevent nonspecific binding, SuperBlock buffer was applied to the skin tissue for 30 min at RT, followed by CD16/32 treatment for 10 min.The sections were then exposed to a cocktail of metal-conjugated antibodies overnight in a hydration chamber at 4 °C.Details of the metal-conjugated antibodies used are given in Table S1.The antibodies (1:100 dilution) were prepared in a mixture of 0.5% BSA, 1:100 CD16/32 in TBS/0.05%Tween.To prevent aggregation, the antibodies were centrifuged at 13,000 g for 2 min prior to use.Tissue sections were then stained with the Ir-based DNA-intercalator (125 μM) using a 1:100 dilution in TBS/0.05%Tween.After incubation for 5 min at RT in a hydration chamber, slides were thoroughly washed with ultrapure water and air-dried.Microscopic images were taken prior to LA-ICP-TOFMS analysis to provide an overview of the tissue sections.

Calibration Standards for LA-ICP-TOFMS Analysis
Quantitative analysis was carried out by LA-ICP-TOFMS with the use of gelatin microdroplets, as described previously. 49,82For this purpose, multielement standard solutions were prepared gravimetrically using commercial standard stock solutions and mixed with a gelatin solution.The resulting solutions were then transferred to the wells of a 384-well plate.The plate served as the sample source for a microspotter system.
The CellenONE X1 microspotter (Cellenion, Lyon, France) was used for generating arrays of gelatin microdroplet standards on glass slides.These droplets were approximately 200 μm in diameter and had a volume of 400 ± 10 pL.The software of the instrument evaluated the size of the droplets, which was then used for normalization to determine the absolute amounts of elements in the droplets.The entire microdroplets were subjected to quantitative and selective ablation, followed by multielement analysis using LA-ICP-TOFMS.

LA-ICP-TOFMS Analysis
An Iridia 193 nm LA system from Teledyne Photon Machines (Bozeman, MT, USA) was coupled to an icpTOF 2R ICP-TOFMS instrument from TOFWERK AG (Thun, Switzerland).The LA system consisted of an ultrafast, low-dispersion cell in a cobalt ablation chamber. 65,66The cell was connected to the ICP-TOFMS using an aerosol rapid introduction system (ARIS).An argon makeup gas flow (∼0.90 L min −1 ) was introduced through the low-dispersion mixing bulb of the ARIS into the He carrier gas flow (0.60 L min −1 ) before entering the plasma.The NIST SRM612 glass-certified reference material (National Institute for Standards and Technology, Gaithersburg, MD, USA) was used for daily tuning.Tuning was aimed at high intensities of certain ions ( 59 Co + , 115 In + , and 238 U + ), minimal oxide formation ( 238 U 16 O + / 238 U + ratio <2%), and low elemental fractionation ( 238 U + / 232 Th + ratio ∼1).In addition, aerosol dispersion was minimized by optimization of the pulse response time for 238 U+ with the FW0.01 M criterion.LA sampling was performed in fixed dosage mode 2 with a 200 Hz repetition rate.A circular spot size of 2 μm was used with a line spacing of 1 μm, resulting in a pixel size of 1 μm × 1 μm.By using energy densities above the ablation threshold of the samples but below that of glass, selective ablation of the samples was achieved. 83Gelatin microdroplets and skin tissue were quantitatively removed using a fluence of 1.0 J cm −2 .The icpTOF 2R ICP-TOFMS instrument offers a specified mass resolution of 6000 (R = m/Δm) and detects ions in the m/z range of 14 to 256.Its integration and readout rates were in line with the LA repetition rate.The instrument was equipped with a torch injector of 2.5 mm inner diameter and nickel sample and skimmer cones (with a 2.8 mm skimmer cone insert).It was operated with a radiofrequency power of 1440 W, an auxiliary Ar gas flow rate of ∼0.80 L min −1 , and a plasma Ar gas flow rate of 14 L min −1 .All measurements were performed in collision cell technology (CCT) mode, with the collision cell pressurized with a H 2 /He gas mixture (93% He (v/v), 7% H 2 (v/v)) at a flow rate of 4.2 mL min −1 .Detailed instrumental parameters for LA-ICP-TOFMS measurements are given in Table S2.

Data Acquisition and Processing of LA-ICP-TOFMS data
LA-ICP-TOFMS data were acquired using TofPilot 2.10.3.0 from TOFWERK AG (Thun, Switzerland) and stored in the open-source hierarchical data format (HDF5, www.hdfgroup.org).Subsequent data processing was done using Tofware v3.2.2.1 (TOFWERK AG, Thun, Switzerland), used as an add-on to IgorPro (Wavemetric Inc., Oregon, USA).This processing included three steps: (1) correction of mass peak position drift across the spectra with time-dependent mass calibration; (2) determination of peak shapes; and (3) fitting and subtraction of the mass spectral baseline.The data were further processed using HDIP version 1.8.4.120 from Teledyne Photon Machines (Bozeman, MT, USA).A built-in script was used to automate the processing of files generated by Tofware, producing 2D elemental distribution maps.Thus, acquired LA-ICP-TOFMS data were processed in HDIP and exported as separate TIFF files for each isotope to enable single-cell-based analysis or in CSV format for pixelbased image analysis.

MeXpose Image Analysis
TIFF files or CSV files were imported for single-cell-or pixel-based image analysis, respectively.

MeXpose Implementation
MeXpose contains all the software and scripts needed to run the workflow and was designed to be used as an all-in-one Docker container.The MeXpose image can be found on the Docker Hub under the name MeXpose.It contains both the interactive and scalable versions of the workflow.

MeXpose Description
In principle, the workflow of the MeXpose data analysis consists of four steps: Data preprocessing, cell segmentation, data extraction, and downstream statistical analysis.Our pipeline provides near seamless integration into existing analysis workflows by allowing substitution and individual execution of each step.The pipeline is available in two versions: an interactive graphical user interface (GUI)-based version for initial exploratory data analysis and a scalable version using the same software but streamlined to run with minimal user input for efficient analysis of large data sets.In addition to in-depth single-cell-based analysis, basic pixel-based analysis, k-means clustering, visualization, and pixel-based quantification are provided.All analysis steps can be performed from the container's command line.
For single-cell-based analysis, raw LA-ICP-TOFMS data is processed in HDIP.Isotope channel images are exported as individual 16-bit TIFF files for analysis with the MeXpose workflow.Single-or multichannel TIFF images can be provided from any multiplexed imaging modality.Fiji can be used to preprocess the raw images.The interactive mode allows the user to access the full range of image processing tools in Fiji and to view the effects live on their image set in real-time.The scalable mode provides user-selectable Fiji macros for outlier/hot pixel removal, Gaussian or median filtering for speckle removal, channel stacking, and image tilling.The resulting images are saved as stacks or processed copies of the raw isotope images in 16-bit TIFF format.Cell segmentation requires a 2-channel TIFF stack consisting of a nucleus and a membrane/cytoplasm channel.Cellpose 2.0 is used in both the interactive and scalable versions of MeXpose to provide cutting-edge cell segmentation performance.Cellpose provides multiple pretrained deep neural network models based on the Cellpose generalist segmentation algorithm, from which the user can freely select the best-performing model. 69,70In addition, the interactive workflow enables the use of the Cellpose human-in-the-loop pipeline.This feature allows the user to make manual adjustments such as adding/ removing object annotations and then retraining the model, resulting in a refined model with improved segmentation performance.By using this approach in conjunction with the Fiji tiling macro, it is possible to make iterative improvements to small subsets of the final data until a satisfactory set of results and a specialist model are obtained.Finetuning a model typically requires 3−5 iterations, as recommended in the Cellpose documentation.Successful segmentation results are saved as PNG masks and used to extract morphological features and intensity information from individual cells using Cellprofiler.Similar to preprocessing, the user has access to Cellprofiler functionality in interactive mode.A basic Cellprofiler pipeline file is provided as a template.The user is encouraged to perform the initial setup of a Cellprofiler pipeline either in interactive mode or by running Cellprofiler as stand-alone software.Marker panels and image channels are subjective to the experiment and require manual configuration.The scalable workflow uses a user-preconfigured Cellprofiler pipeline that is executed from the command line.Cellmasks are imported into Cellprofiler along with raw images of all elemental channels of interest.Within Cellprofiler, the user can assign isotopes to image channel names, filter objects that touch the image boundary, filter objects by size, measure object intensities and morphological features, and finally export the data in a tabular format.
MeXpose uses custom Python scripts to perform exploratory data analysis and quantification.These scripts can be deployed either as Jupyter notebooks for interactive analysis or as command line executable scripts for scalable analysis.There are three interactive Jupyter notebooks and three corresponding Python scripts provided, a phenotyping notebook/script containing all the necessary modules for single-cell data analysis, a quantif ication notebook/script, and a pixel notebook/script including modules to analyze image data at the pixel level.All scripts can be run independently.The exploratory analysis of the data includes visual inspection using heatmap overlays and histograms, as well as cell phenotyping.Phenotyping is performed by combining unsupervised phenograph clustering and heatmap-based cluster analysis on scaled and normalized single-cell data.The user can select relevant channels as needed.The heatmap visualization provides quick identification of relevant cellular phenotypes.A typical analysis workflow would start with the phenotyping notebook and later feed cell phenotype-specific data into the quantification script.In summary, the structure of the phenotyping notebook consists of the following functions: 1. Initial setting and loading of the working directory and singlecell data 2. (Optional) Inspection of raw histograms 3. Size-normalization (based on "real world" pixel size) and outlier filtering 4. Heatmap visualization of isotope intensities at the single-cell level (whole sample) 5. Data scaling for accurate clustering 6. Phenograph clustering of the data 7. Dimensionality reduction using UMAP 8. Visualization of clusters and UMAP embedding 9. Heatmap visualization of isotope intensities at the single-cell level (per cluster) 10.Data export The quantif ication script can be run after clustering or as a standalone method without clustering, depending on the needs of the user.It uses two input files, a file containing the standard measurements and calibration function, and one or more files containing the single-cell data to be quantified.After setting the working directory and loading all necessary files, the elemental channels for quantification are selected and divided by their corresponding calibration factors.Histograms of the selected channels can then be plotted and saved, and the quantified data can be exported as CSV files.
Pixel-based analysis can be conducted using the pixel script by importing a CSV file with intensity information organized in columns, where each row represents a pixel.The user needs to specify the width and height dimensions of the image in pixels and the desired number of clusters for k-means clustering.The resulting image will show each pixel colored according to its cluster assignment.Using Spearman's rank correlation coefficients, it is also possible to generate a correlation heatmap for user-selected channels.

MeXpose Image Analysis Showcased in Human Skin
Specific to our workflow, we perform the following preprocessing steps for human skin samples: (i) Stacking of nuclei (Ir DNA-Intercalator) and membrane/cytoplasm channels (CD44); (ii) application of a median filter with a pixel size of 1−2 depending on the size of artifacts present; (iii) (optional) splitting image stacks into tiles of ∼300 × 300 pixels for iterative improvements of the initial segmentation model.
Within Cellpose, the membrane/cytoplasm channel is designated as the primary segmentation channel, with the nuclei channel as an optional secondary channel.The automatic size estimation function is used to determine an appropriate object diameter if no morphological information about the cells is available.To select the initial model, we perform several segmentation runs on a subset of the initial image using the different available models and all default settings.After visual inspection, the most effective model is chosen to perform 3−5 iterations of manual annotation and retraining.For this purpose, we use the interactive annotation feature of Cellpose to correct, remove, or add annotations to the cells, and then retrain the model with our additional annotations.After achieving satisfactory segmentation results on image subsets, we apply the final specialized model to all images of that tissue/ cell type.
Cellmasks obtained from Cellpose segmentation are imported into Cellprofiler along with raw images of all relevant channels.In Cellprofiler, potential hot pixels in the raw elemental channel images are first removed using the Smooth Multichannel module 84 with a Neighborhood f ilter size of 3.0 and a Hot pixel threshold of 50.Next, cell masks are transformed into image objects, and after removing objects that intersect with image boundaries, single-cell elemental intensities and morphological data are exported as a CSV file for downstream analysis.

Figure 1 .
Figure 1.Overview of the workflow to assess metal exposure at the single-cell level.Metal accumulation experiments are carried out on biological tissues.The tissues are embedded in FFPE (formalin-fixed, paraffin-embedded), sectioned, and labeled with a panel of metal-conjugated antibodies.The samples, and subsequently the multielement calibration standards, are subjected to LA-ICP-TOFMS analysis.Data analysis is performed using the MeXpose workflow.

Figure 3 .
Figure 3. Skin tissue architecture.Following FFPE treatment and sectioning, the skin thin sections were labeled with metal-conjugated antibodies and analyzed by LA-ICP-TOFMS (1 μm pixel size, 200 Hz pixel acquisition rate).The applied histochemistry and the DNA intercalator allowed us to visualize the epidermal and dermal layers together with essential structural features, including blood vessels, sweat glands, and muscle tissue.The marker panel included α-SMA (specifically labels smooth muscle cells), vimentin (mesenchymal cells, encompassing fibroblasts of the papillary dermis, as well as the endothelium of blood vessels), pan-keratin (cornified layer and keratinocytes in the epidermis), CD44 (cell surface receptor for hyaluronic acid, membrane marker for both epidermal and dermal cells), collagen (extracellular matrix of the dermis), and DNA intercalator (cell nuclei).(A) shows the signal intensity maps of the individual markers, (B) visualizes an overlay of the 6 channels, and (C) shows structural features of regions of interest of the skin tissue together with H&E staining of consecutive FFPE sections for comparison.
Figure S1 shows the obtained segmented cells in the human skin model, projected on top of the LA-ICP-TOFMS image.High cell numbers were observed

Figure 4 .
Figure 4. Downstream statistical analysis.(A) Heatmap and (B) UMAP are shown for the 1782 segmented cellular objects.The marker panel of collagen type I, pan keratin, E-cadherin, CD44, vimentin, α-SMA, CD68, KI-67, pHistone H3, and endogenous Fe revealed 7 distinct cell populations (0−6).Cluster 1 and Cluster 2 relating to the epidermis were well separated from the dermal clusters (0, 3−6).(C) Visualization of the phenograph clusters in the segmentation mask, revealing the location of the cells within the skin tissue.

Figure 5 .
Figure 5. Imaging of Co in consecutive sections of human skin, (A) unlabeled and (B) labeled, showing the calculated amounts of Co obtained by kmeans clustering (n = 3).

Figure 6 .
Figure 6.Quantitative Co imaging in human skin at the single-cell level (LA-ICP-TOFMS analysis, 1 μm pixel size, 200 Hz pixel acquisition rate).Ex vivo skin models were incubated with 100 μg cm −2 CoCl 2 solution (dose chosen based on previous results from patch tests in Co allergic subjects) for 48 h.(A) Quantification of Co was performed using gelatin-based calibration standards which were measured along with the skin thin sections.(B) Co signal intensity map of the tissue was converted to absolute Co amounts using the calibration factors.Co was enriched in the cornified layer but penetrated the epidermis, whereas the dermis showed significantly lower Co quantities.(C) Pixel data were transformed into cellular data to better visualize the Co accumulation within cells.The histogram in (D) shows the Co distribution in all segmented cells.The average Co amount per cell is 2.4 ± 1.8 fg.(E) Histograms for each individual cell cluster revealed by unsupervised clustering (Figure4).Cluster 2 (basal cells) in particular showed significantly higher Co content compared to the other cell types (FigureS6).