Visualization and data exploration of chromosome conformation capture data using Voronoi diagrams with v3c-viz.

Chromosome conformation capture (3C) sequencing approaches, like Hi-C or micro-C, allow for an unbiased view of chromatin interactions. Most analysis methods rely on so-called interaction matrices, which are derived from counting read pairs in bins of fixed size. Here, we propose the Voronoi diagram, as implemented in Voronoi for chromosome conformation capture data visualization (v3c-viz) to visualize 3C data. The Voronoi diagram corresponds to an adaptive-binning strategy that adapts to the local densities of points. In this way, visualization of data obtained by moderate sequencing depth pinpoint many, if not most, interesting features such as high frequency contacts. The favorable visualization properties of the Voronoi diagram indicate that the Voronoi diagram as density estimator can be used to identify high frequency contacts at a resolution approaching the typical size of enhancers and promoters. v3c-viz is available at https://github.com/imbbLab/v3c-viz .

Screening of volatile organic compounds (VOCs) from liquid fungal cultures using ambient mass spectrometry.

The potential of fungi for use as biotechnological factories in the production of a range of valuable metabolites, such as enzymes, terpenes, and volatile aroma compounds, is high. Unlike other microorganisms, fungi mostly secrete secondary metabolites into the culture medium, allowing for easy extraction and analysis. To date, the most commonly used technique in the analysis of volatile organic compounds (VOCs) is gas chromatography, which is time and labour consuming. We propose an alternative ambient screening method that provides rapid chemical information for characterising the VOCs of filamentous fungi in liquid culture using a commercially available ambient dielectric barrier discharge ionisation (DBDI) source connected to a quadrupole-Orbitrap mass spectrometer. The effects of method parameters on measured peak intensities of a series of 8 selected aroma standards were optimised with the best conditions being selected for sample analysis. The developed method was then deployed to the screening of VOCs from samples of 13 fungal strains in three different types of complex growth media showing clear differences in VOC profiles across the different media, enabling determination of best culturing conditions for each compound-strain combination. Our findings underline the applicability of ambient DBDI for the direct detection and comparison of aroma compounds produced by filamentous fungi in liquid culture.

Do Anti-tuberculosis Drugs Reach Their Target?─High-Resolution Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Provides Information on Drug Penetration into Necrotic Granulomas.

Tuberculosis (TB) is characterized by mycobacteria-harboring centrally necrotizing granulomas. The efficacy of anti-TB drugs depends on their ability to reach the bacteria in the center of these lesions. Therefore, we developed a mass spectrometry (MS) imaging workflow to evaluate drug penetration in tissue. We employed a specific mouse model that─in contrast to regular inbred mice─strongly resembles human TB pathology. Mycobacterium tuberculosis was inactivated in lung sections of these mice by γ-irradiation using a protocol that was optimized to be compatible with high spatial resolution MS imaging. Different distributions in necrotic granulomas could be observed for the anti-TB drugs clofazimine, pyrazinamide, and rifampicin at a pixel size of 30 µm. Clofazimine, imaged here for the first time in necrotic granulomas of mice, showed higher intensities in the surrounding tissue than in necrotic granulomas, confirming data observed in TB patients. Using high spatial resolution drug and lipid imaging (5 µm pixel size) in combination with a newly developed data analysis tool, we found that clofazimine does penetrate to some extent into necrotic granulomas and accumulates in the macrophages inside the granulomas. These results demonstrate that our imaging platform improves the predictive power of preclinical animal models. Our workflow is currently being applied in preclinical studies for novel anti-TB drugs within the German Center for Infection Research (DZIF). It can also be extended to other applications in drug development and beyond. In particular, our data analysis approach can be used to investigate diffusion processes by MS imaging in general.

MALDI mass spectrometry imaging: From constituents in fresh food to ingredients, contaminants and additives in processed food.

Mass Spectrometry imaging (MS imaging) provides spatial information for a wide range of compound classes in different sample matrices. We used MS imaging to investigate the distribution of components in fresh and processed food, including meat, dairy and bakery products. The MS imaging workflow was optimized to cater to the specific properties and challenges of the individual samples. We successfully detected highly nonpolar and polar constituents such as beta-carotene and anthocyanins, respectively. For the first time, the distributions of a contaminant and a food additive were visualized in processed food. We detected acrylamide in German gingerbread and investigated the penetration of the preservative natamycin into cheese. For this purpose, a new data analysis tool was developed to study the penetration of analytes from uneven surfaces. Our results show that MS imaging has great potential in food analysis to provide relevant information about components' distributions, particularly those underlying official regulations.

Multi-modal molecular imaging maps the correlation between tumor microenvironments and nanomedicine distribution.

Gaining insight into the heterogeneity of nanoparticle drug distribution within tumors would improve both design and clinical translation of nanomedicines. There is little data showing the spatio-temporal behavior of nanomedicines in tissues as current methods are not able to provide a comprehensive view of the nanomedicine distribution, released drug or its effects in the context of a complex tissue microenvironment. Methods: A new experimental approach which integrates the molecular imaging and bioanalytical technologies MSI and IMC was developed to determine the biodistribution of total drug and drug metabolite delivered via PLA-PEG nanoparticles and to overlay this with imaging of the nanomedicine in the context of detailed tumor microenvironment markers. This was used to assess the nanomedicine AZD2811 in animals bearing three different pre-clinical PDX tumors. Results: This new approach delivered new insights into the nanoparticle/drug biodistribution. Mass spectrometry imaging was able to differentiate the tumor distribution of co-dosed deuterated non-nanoparticle-formulated free drug alongside the nanoparticle-formulated drug by directly visualizing both delivery approaches within the same animal or tissue. While the IV delivered free drug was uniformly distributed, the nanomedicine delivered drug was heterogeneous. By staining for multiple biomarkers of the tumor microenvironment on the same tumor sections using imaging mass cytometry, co-registering and integrating data from both imaging modalities it was possible to determine the features in regions with highest nanomedicine distribution. Nanomedicine delivered drug was associated with regions higher in macrophages, as well as more stromal regions of the tumor. Such a comparison of complementary molecular data allows delineation of drug abundance in individual cell types and in stroma. Conclusions: This multi-modal imaging solution offers researchers a better understanding of drug and nanocarrier distribution in complex tissues and enables data-driven drug carrier design.

An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities.

Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.

Method To Visualize the Intratumor Distribution and Impact of Gemcitabine in Pancreatic Ductal Adenocarcinoma by Multimodal Imaging.

Gemcitabine (dFdC) is a common treatment for pancreatic cancer; however, it is thought that treatment may fail because tumor stroma prevents drug distribution to tumor cells. Gemcitabine is a pro-drug with active metabolites generated intracellularly; therefore, visualizing the distribution of parent drug as well as its metabolites is important. A multimodal imaging approach was developed using spatially coregistered mass spectrometry imaging (MSI), imaging mass cytometry (IMC), multiplex immunofluorescence microscopy (mIF), and hematoxylin and eosin (H&E) staining to assess the local distribution and metabolism of gemcitabine in tumors from a genetically engineered mouse model of pancreatic cancer (KPC) allowing for comparisons between effects in the tumor tissue and its microenvironment. Mass spectrometry imaging (MSI) enabled the visualization of the distribution of gemcitabine (100 mg/kg), its phosphorylated metabolites dFdCMP, dFdCDP and dFdCTP, and the inactive metabolite dFdU. Distribution was compared to small-molecule ATR inhibitor AZD6738 (25 mg/kg), which was codosed. Gemcitabine metabolites showed heterogeneous distribution within the tumor, which was different from the parent compound. The highest abundance of dFdCMP, dFdCDP, and dFdCTP correlated with distribution of endogenous AMP, ADP, and ATP in viable tumor cell regions, showing that gemcitabine active metabolites are reaching the tumor cell compartment, while AZD6738 was located to nonviable tumor regions. The method revealed that the generation of active, phosphorylated dFdC metabolites as well as treatment-induced DNA damage primarily correlated with sites of high proliferation in KPC PDAC tumor tissue, rather than sites of high parent drug abundance.

Holistic Characterization of a Salmonella Typhimurium Infection Model Using Integrated Molecular Imaging.

A more complete and holistic view on host-microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium.

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration.

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

Method to Investigate the Distribution of Water-Soluble Drug-Delivery Systems in Fresh Frozen Tissues Using Imaging Mass Cytometry.

Imaging mass cytometry (IMC) offers the opportunity to image metal- and heavy halogen-containing xenobiotics in a highly multiplexed experiment with other immunochemistry-based reagents to distinguish uptake into different tissue structures or cell types. However, in practice, many xenobiotics are not amenable to this analysis, as any compound which is not bound to the tissue matrix will delocalize during aqueous sample-processing steps required for IMC analysis. Here, we present a strategy to perform IMC experiments on a water-soluble polysarcosine-modified dendrimer drug-delivery system (S-Dends). This strategy involves two consecutive imaging acquisitions on the same tissue section using the same instrumental platform, an initial laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MSI) experiment followed by tissue staining and a standard IMC experiment. We demonstrated that settings can be found for the initial ablation step that leave sufficient residual tissue for subsequent antibody staining and visualization. This workflow results in lateral resolution for the S-Dends of 2 µm followed by imaging of metal-tagged antibodies at 1 µm.

The amino acid transporter SLC7A5 is required for efficient growth of KRAS-mutant colorectal cancer.

Oncogenic KRAS mutations and inactivation of the APC tumor suppressor co-occur in colorectal cancer (CRC). Despite efforts to target mutant KRAS directly, most therapeutic approaches focus on downstream pathways, albeit with limited efficacy. Moreover, mutant KRAS alters the basal metabolism of cancer cells, increasing glutamine utilization to support proliferation. We show that concomitant mutation of Apc and Kras in the mouse intestinal epithelium profoundly rewires metabolism, increasing glutamine consumption. Furthermore, SLC7A5, a glutamine antiporter, is critical for colorectal tumorigenesis in models of both early- and late-stage metastatic disease. Mechanistically, SLC7A5 maintains intracellular amino acid levels following KRAS activation through transcriptional and metabolic reprogramming. This supports the increased demand for bulk protein synthesis that underpins the enhanced proliferation of KRAS-mutant cells. Moreover, targeting protein synthesis, via inhibition of the mTORC1 regulator, together with Slc7a5 deletion abrogates the growth of established Kras-mutant tumors. Together, these data suggest SLC7A5 as an attractive target for therapy-resistant KRAS-mutant CRC.

MALDI-2 at Atmospheric Pressure-Parameter Optimization and First Imaging Experiments.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful label-free technique for mapping the spatial distribution of biomolecules directly from tissue. However, like most other MSI techniques, it suffers from low ionization yields and ion suppression effects for biomolecules that might be of interest for a specific application at hand. Recently, a form of laser postionization was introduced (coined MALDI-2) that critically boosts the ion yield for many glyco- and phospholipids by several orders of magnitude and makes the detection of further biomolecular species possible. While the MALDI-2 technique is being increasingly applied by the MSI community, it is still only implemented in fine vacuum ion sources in a pressure range of about 1-10 mbar. Here, we show the first implementation of the technique to a custom-built atmospheric pressure ion source coupled to an Orbitrap Elite system. We present results from parameter optimization of MALDI-2 at atmospheric pressure, compare our findings to previously published fine vacuum data, and show first imaging results from mouse cerebellum with a 20 µm pixel size. Our findings broaden the feasibility of the technique to overall more flexible atmospheric pressure ion sources.

Correlative Hyperspectral Imaging Using a Dimensionality-Reduction-Based Image Fusion Method.

Chemical imaging techniques are increasingly being used in combination to achieve a greater understanding of a sample. This is especially true in the case of mass spectrometry imaging (MSI), where the use of different ionization sources allows detection of different classes of molecules across a range of spatial resolutions. There has been significant recent effort in the development of data fusion algorithms that attempt to combine the benefits of multiple techniques, such that the output provides additional information that would have not been present or obvious from the individual techniques alone. However, the majority of the data fusion methods currently in use rely on image registration to generate the fused data and therefore can suffer from artifacts caused by interpolation. Here, we present a method for data fusion that does not incorporate interpolation-based artifacts into the final fused data, applied to data acquired from multiple chemical imaging modalities. The method is evaluated using simulated data and a model polymer blend sample, before being applied to biological samples of mouse brain and lung.

The Influence of MS Imaging Parameters on UV-MALDI Desorption and Ion Yield.

Ultraviolet matrix-assisted laser desorption/ionization mass spectrometry imaging (UV-MALDI MSI) is a widely used technique for imaging molecular distributions within biological systems. While much work exists concerning desorption in UV-MALDI MS, the effects of commonly varied parameters for imaging applications (repetition rate, use of continuous raster mode and raster speed), which determine spatial resolution and limits of detection for the technique, remain largely unknown. We use multiple surface characterization modalities to obtain quantitative measurements of material desorption and analyte ion yield in thin film model systems of two matrix compounds, arising from different UV-MALDI MSI sampling conditions. Observed changes in resulting ablation feature point to matrix-dependent spatial resolution and laser-induced matrix modification effects. Analyte ion yields of 10-9 to 10-6 are observed. Complex changes in ion yield, between spot and raster sampling and arising from varied laser repetition rate and raster speed, are observed. Graphical Abstract.

Error-Free Data Visualization and Processing through imzML and mzML Validation.

Open data formats are key to facilitating data processing, sharing, and integration. The imzML format ( http://imzml.org/ ) has drastically improved these aspects of mass spectrometry imaging data. Efficient processing of data depends on data sets which are consistent and adhere to the specifications; however, this is not always the case. Here we present a validation tool for data stored in both imzML and the HUPO-PSI mass spectrometery counterpart, mzML, to identify any deviations from the published (i)mzML standard which could cause issues for the user when visualizing or processing data. The tool is released in two forms, a graphical user interface (GUI) for ease of use, and a command line version to fit into existing workflows and pipelines. When certain known issues are encountered, such as the presence of negative values for the location of the binary data, the validator resolves the issue automatically upon saving. The GUI version of the validator also allows editing of the metadata included within the (i)mzML files in order to resolve inconsistencies. We also present a means of performing conditional validation on the metadata within (i)mzML files, where user-defined rules are validated against depending on whether specific metadata are present (or not). For example, if the MALDI term is present, then additional rules related to MALDI (such as the requirement of inclusion of laser parameters) can be validated against this. This enables a flexible and more thorough automated validation of (i)mzML data. Such a system is necessary for validating data against more comprehensive sets of metadata such as minimum reporting guidelines or metadata requirements prior to submission and acceptance of data to data repositories. We demonstrate how this tool can be used to validate against the proposed minimum reporting guidelines in MSI as well as institute specific metadata criteria. The validator tool is endorsed for validation of imzML ( http://imzml.org/ ) and mzML ( http://www.psidev.info/mzml ) and is made available through the respective Web sites. The validator is also released as open source under Mozilla Public License 2.0 at https://gitlab.com/imzML/imzMLValidator .

Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images.

Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach that uses a graph-based algorithm with a two-phase sampling method that overcomes this limitation. We demonstrate the algorithm on a range of sample types and show that it can segment anatomical features that are not identified using commonly employed algorithms in MSI, and we validate our results on synthetic MSI data. We show that the algorithm is robust to fluctuations in data quality by successfully clustering data with a designed-in variance using data acquired with varying laser fluence. Finally, we show that this method is capable of generating accurate segmentations of large MSI data sets acquired on the newest generation of MSI instruments and evaluate these results by comparison with histopathology.

Raster-Mode Continuous-Flow Liquid Microjunction Mass Spectrometry Imaging of Proteins in Thin Tissue Sections.

Mass spectrometry imaging by use of continuous-flow liquid microjunction sampling at discrete locations (array mode) has previously been demonstrated. In this Letter, we demonstrate continuous-flow liquid microjunction mass spectrometry imaging of proteins from thin tissue sections in raster mode and discuss advantages (a 10-fold reduction in analysis time) and challenges (suitable solvent systems, data interpretation) of the approach. Visualization of data is nontrivial, requiring correlation of solvent-flow, mass spectral data acquisition rate, data quality, and liquid microjunction sampling area. The latter is particularly important for determining optimum pixel size. The minimum achievable pixel size is related to the scan time of the instrument used. Here we show a minimum achievable pixel size of 50 µm (x-dimension) when using an Orbitrap Elite; however a pixel size of 600 µm is recommended in order to minimize the effects of oversampling on image accuracy.

Analysis of Urine, Oral fluid and Fingerprints by Liquid Extraction Surface Analysis Coupled to High Resolution MS and MS/MS - Opportunities for Forensic and Biomedical Science.

Liquid Extraction Surface Analysis (LESA) is a new, high throughput tool for ambient mass spectrometry. A solvent droplet is deposited from a pipette tip onto a surface and maintains contact with both the surface and the pipette tip for a few seconds before being re-aspirated. The technique is particularly suited to the analysis of trace materials on surfaces due to its high sensitivity and low volume of sample removal. In this work, we assess the suitability of LESA for obtaining detailed chemical profiles of fingerprints, oral fluid and urine, which may be used in future for rapid medical diagnostics or metabolomics studies. We further show how LESA can be used to detect illicit drugs and their metabolites in urine, oral fluid and fingerprints. This makes LESA a potentially useful tool in the growing field of fingerprint chemical analysis, which is relevant not only to forensics but also to medical diagnostics. Finally, we show how LESA can be used to detect the explosive material RDX in contaminated artificial fingermarks.

Testing for Multivariate Normality in Mass Spectrometry Imaging Data: A Robust Statistical Approach for Clustering Evaluation and the Generation of Synthetic Mass Spectrometry Imaging Data Sets.

Spatial clustering is a powerful tool in mass spectrometry imaging (MSI) and has been demonstrated to be capable of differentiating tumor types, visualizing intratumor heterogeneity, and segmenting anatomical structures. Several clustering methods have been applied to mass spectrometry imaging data, but a principled comparison and evaluation of different clustering techniques presents a significant challenge. We propose that testing whether the data has a multivariate normal distribution within clusters can be used to evaluate the performance when using algorithms that assume normality in the data, such as k-means clustering. In cases where clustering has been performed using the cosine distance, conversion of the data to polar coordinates prior to normality testing should be performed to ensure normality is tested in the correct coordinate system. In addition to these evaluations of internal consistency, we demonstrate that the multivariate normal distribution can then be used as a basis for statistical modeling of MSI data. This allows the generation of synthetic MSI data sets with known ground truth, providing a means of external clustering evaluation. To demonstrate this, reference data from seven anatomical regions of an MSI image of a coronal section of mouse brain were modeled. From this, a set of synthetic data based on this model was generated. Results of r2 fitting of the chi-squared quantile-quantile plots on the seven anatomical regions confirmed that the data acquired from each spatial region was found to be closer to normally distributed in polar space than in Euclidean. Finally, principal component analysis was applied to a single data set that included synthetic and real data. No significant differences were found between the two data types, indicating the suitability of these methods for generating realistic synthetic data.

SpectralAnalysis: Software for the Masses.

The amount of data produced by spectral imaging techniques, such as mass spectrometry imaging, is rapidly increasing as technology and instrumentation advances. This, combined with an increasingly multimodal approach to analytical science, presents a significant challenge in the handling of large data from multiple sources. Here, we present software that can be used through the entire analysis workflow, from raw data through preprocessing (including a wide range of methods for smoothing, baseline correction, normalization, and image generation) to multivariate analysis (for example, memory efficient principal component analysis (PCA), non-negative matrix factorization (NMF), maximum autocorrelation factor (MAF), and probabilistic latent semantic analysis (PLSA)), for data sets acquired from single experiments to large multi-instrument, multimodality, and multicenter studies. SpectralAnalysis was also developed with extensibility in mind to stimulate development, comparisons, and evaluation of data analysis algorithms.