An experimental guideline for the analysis of histologically heterogeneous tumors by MALDI-TOF mass spectrometry imaging

Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma

MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.

iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data

Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.

The metabolic landscape in chronic rotator cuff tear reveals tissue-region-specific signatures

BACKGROUND

Degeneration of shoulder muscle tissues often result in tearing, causing pain, disability and loss of independence. Differential muscle involvement patterns have been reported in tears of shoulder muscles, yet the molecules involved in this pathology are poorly understood. The spatial distribution of biomolecules across the affected tissue can be accurately obtained with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The goal of this pilot study was to decipher the metabolic landscape across shoulder muscle tissues and to identify signatures of degenerated muscles in chronic conditions.

METHODS

Paired biopsies of two rotator cuff muscles, torn infraspinatus and intact teres minor, together with an intact shoulder muscle, the deltoid, were collected during an open tendon transfer surgery. Five patients, average age 65.2 ± 3.8 years, were selected for spatial metabolic profiling using high-spatial resolution (MALDI-TOF) and high-mass resolution (MALDI-FTICR) MSI in negative or positive ion mode. Metabolic signatures were identified using data-driven analysis. Verifications of spatial localization for selected metabolic signatures were carried out using antibody immunohistology.

RESULTS

Data-driven analysis revealed major metabolic differences between intact and degenerated regions across all muscles. The area of degenerated regions, encompassed of fat, inflammation and fibrosis, significantly increased in both rotator cuff muscles, teres minor (27.9%) and infraspinatus (22.8%), compared with the deltoid (8.7%). The intact regions were characterized by 49 features, among which lipids were recognized. Several of the identified lipids were specifically enriched in certain myofiber types. Degenerated regions were specifically marked by the presence of 37 features. Heme was the most abundant metabolite in degenerated regions, whereas Heme oxygenase-1 (HO-1), which catabolizes heme, was found in intact regions. Higher HO-1 levels correlated with lower heme accumulation.

CONCLUSIONS

Degenerated regions are distinguished from intact regions by their metabolome profile. A muscle-specific metabolome profile was not identified. The area of tissue degeneration significantly differs between the three examined muscles. Higher HO-1 levels in intact regions concurred with lower heme levels in degenerated regions. Moreover, HO-1 levels discriminated between dysfunctional and functional rotator cuff muscles. Additionally, the enrichment of specific lipids in certain myofiber types suggests that lipid metabolism differs between myofiber types. The signature metabolites can open options to develop personalized treatments for chronic shoulder muscles degeneration.

Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations

MOTIVATION

Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance.

RESULTS

To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states.

AVAILABILITY AND IMPLEMENTATION

The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

Mass Spectrometry Imaging of Metabolites

Mass spectrometry imaging (MSI) is a technique which is gaining increasing interest in biomedical research due to its capacity to visualize molecules in tissues. First applied to the field of clinical proteomics, its potential for metabolite imaging in biomedical studies is now being recognized. Here we describe how to set up experiments for mass spectrometry imaging of metabolites in clinical tissues and how to tackle most of the obstacles in the subsequent analysis of the data.

Murine stroma adopts a human-like metabolic phenotype in the PDX model of colorectal cancer and liver metastases

Cancer research is increasingly dependent of patient-derived xenograft model (PDX). However, a major point of concern regarding the PDX model remains the replacement of the human stroma with murine counterpart. In the present work we aimed at clarifying the significance of the human-to-murine stromal replacement for the fidelity of colorectal cancer (CRC) and liver metastasis (CRC-LM) PDX model. We have conducted a comparative metabolic analysis between 6 patient tumors and corresponding PDX across 4 generations. Metabolic signatures of cancer cells and stroma were measured separately by MALDI-imaging, while metabolite changes in entire tumors were quantified using mass spectrometry approach. Measurement of glucose metabolism was also conducted in vivo using [¹⁸F]-fluorodeoxyglucose (FDG) and positron emission tomography (PET). In CRC/CRC-LM PDX model, human stroma was entirely replaced at the second generation. Despite this change, MALDI-imaging demonstrated that the metabolic profiles of both stromal and cancer cells remained stable for at least four generations in comparison to the original patient material. On the tumor level, profiles of 86 water-soluble metabolites as well as 93 lipid mediators underlined the functional stability of the PDX model. In vivo PET measurement of glucose uptake (reflecting tumor glucose metabolism) supported the ex vivo observations. Our data show for the first time that CRC/CRC-LM PDX model maintains the functional stability at the metabolic level despite the early replacement of the human stroma by murine cells. The findings demonstrate that human cancer cells actively educate murine stromal cells during PDX development to adopt the human-like phenotype.