An integrated mass spectrometry imaging and digital pathology workflow for objective detection of colorectal tumours by unique atomic signatures

Integrative co-registration of elemental imaging and histopathology for enhanced spatial multimodal analysis of tissue sections through TRACE

Summary

Elemental imaging provides detailed profiling of metal bioaccumulation, offering more precision than bulk analysis by targeting specific tissue areas. However, accurately identifying comparable tissue regions from elemental maps is challenging, requiring the integration of hematoxylin and eosin (H&E) slides for effective comparison. Facilitating the streamlined co-registration of whole slide images (WSI) and elemental maps, TRACE enhances the analysis of tissue regions and elemental abundance in various pathological conditions. Through an interactive containerized web application, TRACE features real-time annotation editing, advanced statistical tools, and data export, supporting comprehensive spatial analysis. Notably, it allows for comparison of elemental abundances across annotated tissue structures and enables integration with other spatial data types through WSI co-registration.

Availability and implementation

Available on the following platforms-GitHub: jlevy44/trace_app, PyPI: trace_app, Docker: joshualevy44/trace_app, Singularity: docker://joshualevy44/trace_app.

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determinants of tumor entities and sub-entities. This is a task where artificial intelligence (AI)/machine learning (ML) systems could significantly contribute to help with tumor outcome prediction and the search for new diagnostic or treatment stratification biomarkers. AI systems are increasingly integrated into routine pathology workflows to improve accuracy, reproducibility, productivity and to reveal difficult-to-see features in complicated histological slides, including the quantification of important markers for tumor grading and staging. In this article, we review the infrastructure needed to facilitate digital and computational pathology. We address the barriers for its full deployment in the clinical setting and describe the use of AI in intraoperative or postoperative settings were frozen or formalin-fixed, paraffin-embedded materials are used. We also summarize quality assessment issues of slide digitization, new spatial biology approaches, and the determination of specific gene-expression from whole slide images. Finally, we highlight new innovative and future technologies, such as large language models, optical biopsies, and mass spectrometry imaging.

Integrated elemental analysis supports targeting copper perturbations as a therapeutic strategy in multiple sclerosis

Multiple sclerosis (MS) is a debilitating affliction of the central nervous system (CNS) that involves demyelination of neuronal axons and neurodegeneration resulting in disability that becomes more pronounced in progressive forms of the disease. The involvement of neurodegeneration in MS underscores the need for effective neuroprotective approaches necessitating identification of new therapeutic targets. Herein, we applied an integrated elemental analysis workflow to human MS-affected spinal cord tissue utilising multiple inductively coupled plasma-mass spectrometry methodologies. These analyses revealed shifts in atomic copper as a notable aspect of disease. Complementary gene expression and biochemical analyses demonstrated that changes in copper levels coincided with altered expression of copper handling genes and downstream functionality of cuproenzymes. Copper-related problems observed in the human MS spinal cord were largely reproduced in the experimental autoimmune encephalomyelitis (EAE) mouse model during the acute phase of disease characterised by axonal demyelination, lesion formation, and motor neuron loss. Treatment of EAE mice with the CNS-permeant copper modulating compound CuII(atsm) resulted in recovery of cuproenzyme function, improved myelination and lesion volume, and neuroprotection. These findings support targeting copper perturbations as a therapeutic strategy for MS with CuII(atsm) showing initial promise.

Elemental biomapping of human tissues suggests toxic metals such as mercury play a role in the pathogenesis of cancer

Toxic metals such as mercury, lead, and cadmium have multiple carcinogenic capacities, including the ability to damage DNA and incite inflammation. Environmental toxic metals have long been suspected to play a role in the pathogenesis of cancer, but convincing evidence from epidemiological studies that toxic metals are risk factors for common neoplasms has been difficult to gain. Another approach is to map the location of potentially toxic elements in normal human cells where common cancers originate, as well as in the cancers themselves. In this Perspective, studies are summarized that have used elemental biomapping to detect toxic metals such as mercury in human cells. Two elemental biomapping techniques, autometallography and laser ablation-inductively coupled-mass spectrometry imaging, have shown that multiple toxic metals exist in normal human cells that are particularly prone to developing cancer, and are also seen in neoplastic cells of breast and pancreatic tumors. Biomapping studies of animals exposed to toxic metals show that these animals take up toxic metals in the same cells as humans. The finding of toxic metals such as mercury in human cells prone to cancer could explain the increasing global incidence of many cancers since toxic metals continue to accumulate in the environment. The role of toxic metals in cancer remains to be confirmed experimentally, but to decrease cancer risk a precautionary approach would be to reduce emissions of mercury and other toxic metals into the environment from industrial and mining activities and from the burning of fossil fuels.

Integrative Co-Registration of Elemental Imaging and Histopathology for Enhanced Spatial Multimodal Analysis of Tissue Sections through TRACE

Summary

Elemental imaging provides detailed profiling of metal bioaccumulation, offering more precision than bulk analysis by targeting specific tissue areas. However, accurately identifying comparable tissue regions from elemental maps is challenging, requiring the integration of hematoxylin and eosin (H&E) slides for effective comparison. Facilitating the streamlined co-registration of Whole Slide Images (WSI) and elemental maps, TRACE enhances the analysis of tissue regions and elemental abundance in various pathological conditions. Through an interactive containerized web application, TRACE features real-time annotation editing, advanced statistical tools, and data export, supporting comprehensive spatial analysis. Notably, it allows for comparison of elemental abundances across annotated tissue structures and enables integration with other spatial data types through WSI co-registration.

Availability and Implementation

Available on the following platforms- GitHub: jlevy44/trace_app , PyPI: trace_app , Docker: joshualevy44/trace_app , Singularity: joshualevy44/trace_app .

Contact

joshua.levy@cshs.org.

Supplementary information

Supplementary data are available.

Mass spectrometry imaging of metals in tissues and cells: Methods and biological applications

BACKGROUND

Metals are pervasive throughout biological processes, where they play essential structural and catalytic roles. Metals can also exhibit deleterious effects on human health. Powerful analytical techniques, such as mass spectrometry imaging (MSI), are required to map metals due to their low concentrations within biological tissue.

SCOPE OF REVIEW

This Mini Review focuses on key MSI technology that can image metal distributions in situ, describing considerations for each technique (e.g., resolution, sensitivity, etc.). We highlight recent work using MSI for mapping trace metals in tissues, detecting metal-based drugs, and simultaneously imaging metals and biomolecules.

MAJOR CONCLUSIONS

MSI has enabled significant advances in locating bioactive metals at high spatial resolution and correlating their distributions with that of biomolecules. The use of metal-based immunochemistry has enabled simultaneous high-throughput protein and biomolecule imaging.

GENERAL SIGNIFICANCE

The techniques and examples described herein can be applied to many biological questions concerning the important biological roles of metals, metal toxicity, and localization of metal-based drugs.

Multimodal high-resolution nano-DESI MSI and immunofluorescence imaging reveal molecular signatures of skeletal muscle fiber types

The skeletal muscle is a highly heterogeneous tissue comprised of different fiber types with varying contractile and metabolic properties. The complexity in the analysis of skeletal muscle fibers associated with their small size (30-50 μm) and mosaic-like distribution across the tissue tnecessitates the use of high-resolution imaging to differentiate between fiber types. Herein, we use a multimodal approach to characterize the chemical composition of skeletal fibers in a limb muscle, the gastrocnemius. Specifically, we combine high-resolution nanospray desorption electrospray ionization (nano-DESI) mass spectrometry imaging (MSI) with immunofluorescence (IF)-based fiber type identification. Computational image registration and segmentation approaches are used to integrate the information obtained with both techniques. Our results indicate that the transition between oxidative and glycolytic fibers is associated with shallow chemical gradients (<2.5 fold change in signals). Interestingly, we did not find any fiber type-specific molecule. We hypothesize that these findings might be linked to muscle plasticity thereby facilitating a switch in the metabolic properties of fibers in response to different conditions such as exercise and diet, among others. Despite the shallow chemical gradients, cardiolipins (CLs), acylcarnitines (CAR), monoglycerides (MGs), fatty acids, highly polyunsaturated phospholipids, and oxidized phospholipids, were identified as molecular signatures of oxidative metabolism. In contrast, histidine-related compounds were found as molecular signatures of glycolytic fibers. Additionally, the presence of highly polyunsaturated acyl chains in phospholipids was found in oxidative fibers whereas more saturated acyl chains in phospholipids were found in glycolytic fibers which suggests an effect of the membrane fluidity on the metabolic properties of skeletal myofibers.

Emerging Computational Methods in Mass Spectrometry Imaging

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

Quantitative imaging approaches to understanding biological processing of metal ions

Faster, more sensitive, and higher resolution quantitative instrumentation are aiding a deeper understanding of how inorganic chemistry regulates key biological processes. Researchers can now image and quantify metals with subcellular resolution, leading to a vast array of new discoveries in organismal development, pathology, and disease. Metals have recently been implicated in several diseases such as Parkinson's, Alzheimers, ischemic stroke, and colorectal cancer that would not be possible without these advancements. In this review, instead of focusing on instrumentation we focus on recent applications of label-free elemental imaging and quantification and how these tools can lead to a broader understanding of metals role in systems biology and human pathology.

Self-supervised clustering of mass spectrometry imaging data using contrastive learning

Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. One of key challenges in molecular colocalization is that complex MSI data are too large for manual annotation but too small for training deep neural networks. Herein, we introduce a self-supervised clustering approach based on contrastive learning, which shows an excellent performance in clustering of MSI data. We train a deep convolutional neural network (CNN) using MSI data from a single experiment without manual annotations to effectively learn high-level spatial features from ion images and classify them based on molecular colocalizations. We demonstrate that contrastive learning generates ion image representations that form well-resolved clusters. Subsequent self-labeling is used to fine-tune both the CNN encoder and linear classifier based on confidently classified ion images. This new approach enables autonomous and high-throughput identification of co-localized species in MSI data, which will dramatically expand the application of spatial lipidomics, metabolomics, and proteomics in biological research.