A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes.

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside.

Lipidomic-Based Approach to 10 s Classification of Major Pediatric Brain Cancer Types with Picosecond Infrared Laser Mass Spectrometry.

Picosecond infrared laser mass spectrometry (PIRL-MS) is shown, through a retrospective patient tissue study, to differentiate medulloblastoma cancers from pilocytic astrocytoma and two molecular subtypes of ependymoma (PF-EPN-A, ST-EPN-RELA) using laser-extracted lipids profiled with PIRL-MS in 10 s of sampling and analysis time. The average sensitivity and specificity values for this classification, taking genomic profiling data as standard, were 96.41 and 99.54%, and this classification used many molecular features resolvable in 10 s PIRL-MS spectra. Data analysis and liquid chromatography coupled with tandem high-resolution mass spectrometry (LC-MS/MS) further allowed us to reduce the molecular feature list to only 18 metabolic lipid markers most strongly involved in this classification. The identified 'metabolite array' was comprised of a variety of phosphatidic and fatty acids, ceramides, and phosphatidylcholine/ethanolamine and could mediate the above-mentioned classification with average sensitivity and specificity values of 94.39 and 98.78%, respectively, at a 95% confidence in prediction probability threshold. Therefore, a rapid and accurate pathology classification of select pediatric brain cancer types from 10 s PIRL-MS analysis using known metabolic biomarkers can now be available to the neurosurgeon. Based on retrospective mining of 'survival' versus 'extent-of-resection' data, we further identified pediatric cancer types that may benefit from actionable 10 s PIRL-MS pathology feedback. In such cases, aggressiveness of the surgical resection can be optimized in a manner that is expected to benefit the patient's overall or progression-free survival. PIRL-MS is a promising tool to drive such personalized decision-making in the operating theater.

Metabolic Lipids in Melanoma Enable Rapid Determination of Actionable BRAF-V600E Mutation with Picosecond Infrared Laser Mass Spectrometry in 10 s.

Rapid molecular profiling of biological tissues with picosecond infrared laser mass spectrometry (PIRL-MS) has enabled the detection of clinically important histologic types and molecular subtypes of human cancers in as little as 10 s of data collection and analysis time. Utilizing an engineered cell line model of actionable BRAF-V600E mutation, we observed statistically significant differences in 10 s PIRL-MS molecular profiles between BRAF-V600E and BRAF-wt cells. Multivariate statistical analyses revealed a list of mass-to-charge (m/z) values most significantly responsible for the identification of BRAF-V600E mutation status in this engineered cell line that provided a highly controlled testbed for this observation. These metabolites predicted BRAF-V600E expression in human melanoma cell lines with greater than 98% accuracy. Through chromatography and tandem mass spectrometry analysis of cell line extracts, a 30-member "metabolite array" was characterized for determination of BRAF-V600E expression levels in subcutaneous melanoma xenografts with an average sensitivity and specificity of 95.6% with 10 s PIRL-MS analysis. This proof-of-principle work warrants a future large-scale study to identify a metabolite array for 10 s determination of actionable BRAF-V600E mutation in human tissue to guide patient care.

Picosecond Infrared Laser Mass Spectrometry Identifies a Metabolite Array for 10 s Diagnosis of Select Skin Cancer Types: A Proof-of-Concept Feasibility Study.

Currently, a large number of skin biopsies are taken for each true skin cancer case detected, creating a need for a rapid, high sensitivity, and specificity skin cancer detection tool to reduce the number of unnecessary biopsies taken from benign tissue. Picosecond infrared laser mass spectrometry (PIRL-MS) using a hand-held sampling probe is reported to detect and classify melanoma, squamous cell carcinoma, and normal skin with average sensitivity and specificity values of 86-95% and 91-98%, respectively (at a 95% confidence level) solely requiring 10 s or less of total data collection and analysis time. Classifications are not adversely affected by specimen's quantity of melanin pigments and are mediated by a number of metabolic lipids, further identified herein as potential biomarkers for skin cancer-type differentiation, 19 of which were sufficient here (as a fully characterized metabolite array) to provide high specificity and sensitivity classification of skin cancer types. In situ detection was demonstrated in an intradermal melanoma mouse model wherein in vivo sampling did not cause significant discomfort. PIRL-MS sampling is further shown to be compatible with downstream gross histopathologic evaluations despite loss of tissue from the immediate laser sampling site(s) and can be configured using selective laser pulses to avoid thermal damage to normal skin. Therefore, PIRL-MS may be employed as a decision-support tool to reduce both the subjectivity of clinical diagnosis and the number of unnecessary biopsies currently required for skin cancer screening.

Development of a Three-Dimensional Bioartificial Shoulder Joint Implant Mimetic of Periprosthetic Joint Infection.

Postsurgical infections of the shoulder joint involving Cutibacterium acnes are difficult to diagnose and manage. Despite the devastating clinical complications and costly health care burden of joint infections, the scarcity of joint infection models was identified as an unmet need by the 2019 International Consensus on Orthopedic Infections. In this study, we have developed a novel 3D shoulder joint implant mimetic (S-JIM) that includes a surgical metal surface and supports a co-culture of C. acnes and patient-derived shoulder capsule fibroblasts. Our findings indicate the S-JIM can generate a near anaerobic interior environment that allows for C. acnes proliferation and elicits fibroblast cell lysis responses that are consistent with clinical reports of tissue necrosis. Using the S-JIM, we have provided proof-of-concept for the use of mass spectrometry in real-time detection of C. acnes joint infections during surgery. The S-JIM is the first in vitro cell culture-based biomimetic of periprosthetic joint infection (PJI) that provides a preclinical method for the rapid and reliable testing of novel anti-PJI interventions. Impact statement We have developed the first 3D laboratory biomimetic of the postsurgical human shoulder joint to study periprosthetic joint infections.

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.

Breaking Through the Barrier: Regulatory Considerations Relevant to Ambient Mass Spectrometry at the Bedside.

Rapid characterization of tissue disorder using ambient mass spectrometry (MS) techniques, requiring little to no preanalytical preparations of sampled tissues, has been shown using a variety of ion sources and with many disease classes. A brief overview of ambient MS in clinical applications, the state of the art in regulatory affairs, and recommendations to facilitate adoption for use at the bedside are presented. Unique challenges in the validation of untargeted MS methods and additional safety and compliance requirements for deployment within a clinical setting are further discussed. Development of a harmonized validation strategy for ambient MS methods is emphasized.

Mass Spectrometry Imaging Reveals a Gradient of Cancer-like Metabolic States in the Vicinity of Cancer Not Seen in Morphometric Margins from Microscopy.

Spatially resolved ambient mass spectrometry imaging methods have gained popularity to characterize cancer sites and their borders using molecular changes in the lipidome. This utility, however, is predicated on metabolic homogeneity at the border, which would create a sharp molecular transition at the morphometric borders. We subjected murine models of human medulloblastoma brain cancer to mass spectrometry imaging, a technique that provides a direct readout of tissue molecular content in a spatially resolved manner. We discovered a distance-dependent gradient of cancer-like lipid molecule profiles in the brain tissue within 1.2 mm of the cancer border, suggesting that a cancer-like state progresses beyond the histologic border, into the healthy tissue. The results were further corroborated using orthogonal liquid chromatography and mass spectrometry (LC-MS) analysis of selected tissue regions subjected to laser capture microdissection. LC-MS/MS analysis for robust identification of the affected molecules implied changes in a number of different lipid classes, some of which are metabolized from the essential docosahexaenoic fatty acid (DHA) present in the interstitial fluid. Metabolic molecular borders are thus not as sharp as morphometric borders, and mass spectrometry imaging can reveal molecular nuances not observed with microscopy. Caution must be exercised in interpreting multimodal imaging results stipulated on a coincidental relationship between metabolic and morphometric borders of cancer, at least within animal models used in preclinical research.

Potential impact of tissue molecular heterogeneity on ambient mass spectrometry profiles: a note of caution in choosing the right disease model.

This review provides a summary of known molecular alterations in commonly used cancer models and strives to stipulate how they may affect ambient mass spectrometry profiles. Immortalized cell lines are known to accumulate mutations, and xenografts derived from cell lines are known to contain tumour microenvironment elements from the host animal. While the use of human specimens for mass spectrometry profiling studies is highly encouraged, patient-derived xenografts with low passage numbers could provide an alternative means of amplifying material for ambient MS research when needed. Similarly, genetic preservation of patient tissue seen in some organoid models, further verified by qualitative proteomic and transcriptomic analyses, may argue in favor of organoid suitability for certain ambient profiling studies. However, to choose the appropriate model, pre-evaluation of the model's molecular characteristics in the context of the research question(s) being asked will likely provide the most appropriate strategy to move research forward. This can be achieved by performing comparative ambient MS analysis of the disease model of choice against a small amount of patient tissue to verify concordance. Disease models, however, will continue to be useful tools to orthogonally validate metabolic states of patient tissues through controlled genetic alterations that are not possible with patient specimens.

Dual Laser and Desorption Electrospray Ionization Mass Spectrometry Imaging Using the Same Interface.

For a more comprehensive characterization of molecular heterogeneities of matter, multimodal mass spectrometry imaging must be developed to take advantage of the complementarity of information available through different ionization mechanisms. We report the design, implementation, and performance validation of a laser desorption imaging interface composed of add-on components that adapt a commercial Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) imaging interface for dual imaging of Picosecond Infrared Laser Mass Spectrometry (PIRL-MS) with DESI-MS. The interface utilizes hardware elements and data analysis pipelines already established for DESI-MS imaging, and was further validated in cancer margin assessments using human medulloblastoma cancers. The PIRL-MS images were robust and reproducible across multiple experimental runs on independently prepared xenograft tumors, and could be segmented into cancer and healthy regions in concordance with pathology using a variety of supervised and unsupervised clustering methods. The spectral quality and complexity obtained with this interface were examined with infiltrating and noninfiltrating tumors, and were comparable to other mass spectrometry analysis interfaces. The average PIRL-MS spectra from spatially resolved images could be used for robust cancer m/z model building to classify medulloblastoma cancer from healthy tissue without any misclassifications, an observation that held true over close to 70 sampling data points. While the unsupervised spectral analysis methods suggested a slight suppression of signal in the phospholipid range compared to the hand-held configuration, these changes were insufficient to hamper utility in cancer margin assessment with spatially resolved data obtained with our interface. Dual PIRL-MS and DESI-MS imaging of consecutive sections, as suggested by multivariate loading plots, revealed highly complementary molecular information with m/z values identifiable with one desorption method sufficient to reveal cancer regions being absent in another, further emphasizing the need for effective hardware and software interfaces for dual mass spectrometry imaging.

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.

Picosecond Infrared Laser Desorption Mass Spectrometry Identifies Medulloblastoma Subgroups on Intrasurgical Timescales.

Medulloblastoma (MB) is a pediatric malignant brain tumor composed of four different subgroups (WNT, SHH, Group 3, Group 4), each of which are a unique biological entity with distinct clinico-pathological, molecular, and prognostic characteristics. Although risk stratification of patients with MB based on molecular features may offer personalized therapies, conventional subgroup identification methods take too long and are unable to deliver subgroup information intraoperatively. This limitation prevents subgroup-specific adjustment of the extent or the aggressiveness of the tumor resection by the neurosurgeon. In this study, we investigated the potential of rapid tumor characterization with Picosecond infrared laser desorption mass spectrometry (PIRL-MS) for MB subgroup classification based on small molecule signatures. One hundred and thirteen ex vivo MB tumors from a local tissue bank were subjected to 10- to 15-second PIRL-MS data collection and principal component analysis with linear discriminant analysis (PCA-LDA). The MB subgroup model was established from 72 independent tumors; the remaining 41 de-identified unknown tumors were subjected to multiple, 10-second PIRL-MS samplings and real-time PCA-LDA analysis using the above model. The resultant 124 PIRL-MS spectra from each sampling event, after the application of a 95% PCA-LDA prediction probability threshold, yielded a 98.9% correct classification rate. Post-ablation histopathologic analysis suggested that intratumoral heterogeneity or sample damage prior to PIRL-MS sampling at the site of laser ablation was able to explain failed classifications. Therefore, upon translation, 10-seconds of PIRL-MS sampling is sufficient to allow personalized, subgroup-specific treatment of MB during surgery. SIGNIFICANCE: This study demonstrates that laser-extracted lipids allow immediate grading of medulloblastoma tumors into prognostically important subgroups in 10 seconds, providing medulloblastoma pathology in an actionable manner during surgery.

Platforms for rapid cancer characterization by ambient mass spectrometry: advancements, challenges and opportunities for improvement towards intrasurgical use.

Ambient Mass Spectrometry (MS) analysis is widely used to characterize biological and non-biological samples. Advancements that allow rapid analysis of samples by ambient methods such as Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) and Rapid Evaporative Ionization Mass Spectrometry (REIMS) are discussed. A short, non-comprehensive overview of ambient MS is provided that only contains example applications due to space limitations. A spatially encoded mass spectrometry analysis concept to plan cancer resection is introduced. The application of minimally destructive tissue ablation probes to survey the surgical field for sites of pathology using on-line analysis methods is discussed. The technological challenges that must be overcome for ambient MS to become a robust method for intrasurgical pathology assessments are reviewed.

Rapid determination of medulloblastoma subgroup affiliation with mass spectrometry using a handheld picosecond infrared laser desorption probe.

Medulloblastoma (MB), the most prevalent malignant childhood brain tumour, consists of at least 4 distinct subgroups each of which possesses a unique survival rate and response to treatment. To rapidly determine MB subgroup affiliation in a manner that would be actionable during surgery, we subjected murine xenograft tumours of two MB subgroups (SHH and Group 3) to Mass Spectrometry (MS) profiling using a handheld Picosecond InfraRed Laser (PIRL) desorption probe and interface developed by our group. This platform provides real time MS profiles of tissue based on laser desorbed lipids and small molecules with only 5-10 seconds of sampling. PIRL-MS analysis of ex vivo MB tumours offered a 98% success rate in subgroup determination, observed over 194 PIRL-MS datasets collected from 19 independent tumours (∼10 repetitions each) utilizing 6 different established MB cell lines. Robustness was verified by a 5%-leave-out-and-remodel test. PIRL ablated tissue material was collected on a filter paper and subjected to high resolution LC-MS to provide ion identity assignments for the m/z values that contribute most to the statistical discrimination between SHH and Group 3 MB. Based on this analysis, rapid classification of MB with PIRL-MS utilizes a variety of fatty acid chains, glycerophosphates, glycerophosphoglycerols and glycerophosphocholines rapidly extracted from the tumours. In this work, we provide evidence that 5-10 seconds of sampling from ex vivo MB tissue with PIRL-MS can allow robust tumour subgroup classification, and have identified several biomarker ions responsible for the statistical discrimination of MB Group 3 and the SHH subgroup. The existing PIRL-MS platform used herein offers capabilities for future in vivo use.

Correction: Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration.

Correction for 'Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration' by Michael Woolman et al., Analyst, 2017, 142, 3250-3260.

Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration.

Squamous cell carcinomas constitute a major class of head & neck cancers, where the tumour stroma ratio (TSR) carries prognostic information. Patients affected by stroma-rich tumours exhibit a poor prognosis and a higher chance of relapse. As such, there is a need for a technology platform that allows rapid determination of the tumour stroma ratio. In this work, we provide a proof-of-principle demonstration that Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) can be used to determine tumour stroma ratios. Slices from three independent mouse xenograft tumours from the human FaDu cell line were subjected to DESI-MS imaging, staining and detailed analysis using digital pathology methods. Using multivariate statistical methods we compared the MS profiles with those of isolated stromal cells. We found that m/z 773.53 [PG(18:1)(18:1) - H]-, m/z 835.53 [PI(34:1) - H]- and m/z 863.56 [PI(18:1)(18:0) - H]- are biomarker ions that can distinguish FaDu cancer from cancer associated fibroblast (CAF) cells. A comparison with DESI-MS analysis of controlled mixtures of the CAF and FaDu cells showed that the abundance of the biomarker ions above can be used to determine, with an error margin of close to 5% compared with quantitative pathology estimates, TSR values. This proof-of-principle demonstration is encouraging and must be further validated using human samples and a larger sample base. At maturity, DESI-MS thus may become a stand-alone molecular pathology tool providing an alternative rapid cancer assessment without the need for time-consuming staining and microscopy methods, potentially further conserving human resources.

Optimized Mass Spectrometry Analysis Workflow with Polarimetric Guidance for ex vivo and in situ Sampling of Biological Tissues.

Spatially Targeted Mass Spectrometry (MS) analysis using survey scans with an imaging modality often requires consecutive tissue slices, because of the tissue damage during survey scan or due to incompatible sample preparation requirements between the survey modality and MS. We report two spatially targeted MS analysis workflows based on polarized light imaging guidance that use the same tissue sample for survey and targeted analysis. The first workflow is applicable for thin-slice analysis, and uses transmission-polarimetry-guided Desorption ElectroSpray Ionization Mass Spectrometry (DESI-MS), and confirmatory H&E histopathology analysis on the same slice; this is validated using quantitative digital pathology methods. The second workflow explores a polarimetry-guided MS platform for thick tissue assessment by developing reflection-mode polarimetric imaging coupled with a hand-held Picosecond InfraRed Laser (PIRL) MS ablation probe that requires minimal tissue removal to produce detectable signal. Tissue differentiation within 5-10 s of sampling with the hand-held probe is shown using multivariate statistical methods of the MS profiles. Both workflows were tasked with differentiating necrotic cancer sites from viable cancers using a breast tumour model, and their performance was evaluated. The use of the same tissue surface addresses mismatches in guidance due to intrinsic changes in tissue morphology over consecutive sections.

An Assessment of the Utility of Tissue Smears in Rapid Cancer Profiling with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS).

Mass spectrometry imaging with desorption electrospray ionization mass spectrometry (DESI-MS) is used to characterize cancer from ex vivo slices of tissues. The process is time-consuming. The use of tissue smears for DESI-MS analysis has been proposed as it eliminates the time required to snap-freeze and section the tissue. To assess the utility of tissue smears for rapid cancer characterization, principal component analysis (PCA) was performed to evaluate the concordance between DESI-MS profiles of breast cancer from tissue slices and smears prepared on various surfaces. PCA suggested no statistical discrimination between DESI-MS profiles of tissue sections and tissue smears prepared on glass, polytetrafluoroethylene (PTFE), and porous PTFE. However, the abundances of cancer biomarker ions varied between sections and smears, with DESI-MS analysis of tissue sections yielding higher ion abundances of cancer biomarkers compared with smears. Coefficient of variance (CV) analysis suggests DESI-MS profiles from tissue smears are as reproducible as the ones from tissue sections. The limit of detection with smear samples from single pixel analysis is comparable to tissue sections that average the signal from a tissue area of 0.01 mm2. The smears prepared on the PTFE surface possessed a higher degree of homogeneity compared with the smears prepared on the glass surface. This allowed single MS scans (~1 s) from random positions across the surface of the smear to be used in rapid cancer typing with good reproducibility, providing pathologic information for cancer typing at speeds suitable for clinical utility. Graphical Abstract ᅟ.

Rapid Detection of Necrosis in Breast Cancer with Desorption Electrospray Ionization Mass Spectrometry.

Identification of necrosis in tumors is of prognostic value in treatment planning, as necrosis is associated with aggressive forms of cancer and unfavourable outcomes. To facilitate rapid detection of necrosis with Mass Spectrometry (MS), we report the lipid MS profile of necrotic breast cancer with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) imaging validated with statistical analysis and correlating pathology. This MS profile is characterized by (1) the presence of the ion of m/z 572.48 [Cer(d34:1) + Cl]- which is a ceramide absent from the viable cancer subregions; (2) the absence of the ion of m/z 391.25 which is present in small abundance only in viable cancer subregions; and (3) a slight increase in the relative intensity of known breast cancer biomarker ions of m/z 281.25 [FA(18:1)-H]- and 303.23 [FA(20:4)-H]-. Necrosis is accompanied by alterations in the tissue optical depolarization rate, allowing tissue polarimetry to guide DESI-MS analysis for rapid MS profiling or targeted MS imaging. This workflow, in combination with the MS profile of necrosis, may permit rapid characterization of necrotic tumors from tissue slices. Further, necrosis-specific biomarker ions are detected in seconds with single MS scans of necrotic tumor tissue smears, which further accelerates the identification workflow by avoiding tissue sectioning and slide preparation.

Variations in the Abundance of Lipid Biomarker Ions in Mass Spectrometry Images Correlate to Tissue Density.

While mass spectrometry (MS) imaging is widely used to investigate the molecular composition of ex vivo slices of cancerous tumors, little is known about how variations in the cellular properties of cancer tissue can influence cancer biomarker ion images. To better understand the basis for variations in the abundances of cancer biomarker ions seen in MS images of relatively homogeneous ex vivo tumor samples, sections of snap frozen human breast cancer murine xenografts were subjected to desorption electrospray ionization mass spectrometry (DESI-MS) imaging. Serial sections were then stained with hematoxylin and eosin (H&E) and subjected to detailed morphometric cellular analysis, using a commercial digital pathology platform augmented with custom-tailored image analysis algorithms developed in-house. Gross morphological heterogeneities due to stroma, vasculature, and noncancer cells were mapped in the tumor and found to not correlate with the areas of suppressed cancer biomarker abundance. Instead, the ion abundances of major breast cancer biomarkers were found to correlate with the cytoplasmic area of cancer cells that comprised the tumor tissue. Therefore, detailed cellular analyses can be used to rationalize subtle heterogeneities in ion abundance in MS images, not explained by the presence of gross morphological heterogeneities such as stroma.