Maintenance of Deep Lung Architecture and Automated Airway Segmentation for 3D Mass Spectrometry Imaging

Delineating lipidomic landscapes in human and mouse ovaries: Spatial signatures and chemically-induced alterations via MALDI mass spectrometry imaging: Spatial ovarian lipidomics

This study addresses the critical gap in understanding the ovarian lipidome's abundance, distribution, and vulnerability to environmental disruptors, a largely unexplored field. Leveraging the capabilities of matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI), we embarked on a novel exploration of the ovarian lipidome in both mouse and human healthy tissues. Our findings revealed that the obesogenic chemical tributyltin (TBT), at environmentally relevant exposures, exerts a profound and region-specific impact on the mouse ovarian lipidome. TBT exposure predominantly affects lipid species in antral follicles and oocytes, suggesting a targeted disruption of lipid homeostasis in these biologically relevant regions. Our comprehensive approach, integrating advanced lipidomic techniques and bioinformatic analyses, documented the disruptive effects of TBT, an environmental chemical, on the ovarian lipid landscape. Similar to mice, our research also unveiled distinct spatial lipidomic signatures corresponding to specific ovarian compartments in a healthy human ovary that may also be vulnerable to disruption by chemical exposures. Findings from this study not only underscore the vulnerability of the ovarian lipidome to environmental factors but also lay the groundwork for unraveling the molecular pathways underlying ovarian toxicity mediated through lipid dysregulation.

Interpretable dimensionality reduction and classification of mass spectrometry imaging data in a visceral pain model via non-negative matrix factorization

Mass spectrometry imaging (MSI) is a powerful scientific tool for understanding the spatial distribution of biochemical compounds in tissue structures. In this paper, we introduce three novel approaches in MSI data processing to perform the tasks of data augmentation, feature ranking, and image registration. We use these approaches in conjunction with non-negative matrix factorization (NMF) to resolve two of the biggest challenges in MSI data analysis, namely: 1) the large file sizes and associated computational resource requirements and 2) the complexity of interpreting the very high dimensional raw spectral data. There are many dimensionality reduction techniques that address the first challenge but do not necessarily result in readily interpretable features, leaving the second challenge unaddressed. We demonstrate that NMF is an effective dimensionality reduction algorithm that reduces the size of MSI datasets by three orders of magnitude with limited loss of information, yielding spatial and spectral components with meaningful correlation to tissue structure that may be used directly for subsequent data analysis without the need for additional clustering steps. This analysis is demonstrated on an MSI dataset from female Sprague-Dawley rats for an animal model of comorbid visceral pain hypersensitivity (CPH). We find that high-dimensional MSI data (∼ 100,000 ions per pixel) can be reduced to 20 spectral NMF components with < 20% loss in reconstruction accuracy. The resulting spatial NMF components are reproducible and correlate well with H&E-stained tissue images. These components may also be used to generate images with enhanced specificity for different tissue types. Small patches of NMF data (i.e., 20 spatial NMF components over 20 × 20 pixels) provide an accuracy of ∼ 87% in classifying CPH vs naïve control subjects. This paper presents the novel data processing methodologies that were used to produce these results, encompassing novel data processing pipelines for data augmentation to support training for classification, ranking of features according to their contribution to classification, and image registration to enhance tissue-specific imaging.

Characterization of spatial lipidomic signatures in tick-bitten guinea pig skin as a model for host-vector-pathogen interaction profiling

IMPORTANCE

Here, we demonstrate the adaptability of spatial "omics" methods to identify interphylum processes regulated at the vector-host interface of ticks during a mammalian blood meal. This approach enables a better understanding of complex bipartite or tripartite molecular interactions between hosts, arthropod vectors and transmitted pathogens, and contributes toward the development of spatially aware therapeutic target discovery and description.

Spatial lipidomics reveals biased phospholipid remodeling in acute Pseudomonas lung infection

Pseudomonas aeruginosa (Pa) is a pathogen causing chronic pulmonary infections in patients with cystic fibrosis (CF). Manipulation of lipids is an important feature of Pa infection and on a tissue-level scale is poorly understood. Using a mouse model of acute Pa pulmonary infection, we explored the whole-lung phospholipid response using mass spectrometry imaging (MSI) and spatial lipidomics. Using a histology-driven analysis, we isolated airways and parenchyma from both mock- and Pa-infected lungs and used systems biology tools to identify enriched metabolic pathways from the differential phospholipid identities. Infection was associated with a set of 26 ions, with 11 unique to parenchyma and 6 unique to airways. Acyl remodeling was differentially enriched in infected parenchyma as the predominant biological function. These functions correlated with markers of polymorphonuclear (PMN) cell influx, a defining feature of the lung response to Pa infection, implicating enzymes active in phospholipid remodeling.

An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue

Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.

Current challenges in metastasis research and future innovation for clinical translation

While immense strides have been made in understanding tumor biology and in developing effective treatments that have substantially improved the prognosis of cancer patients, metastasis remains the major cause of cancer-related death. Improvements in the detection and treatment of primary tumors are contributing to a growing, detailed understanding of the dynamics of metastatic progression. Yet challenges remain in detecting metastatic dissemination prior to the establishment of overt metastases and in predicting which patients are at the highest risk of developing metastatic disease. Further improvements in understanding the mechanisms governing metastasis have great potential to inform the adaptation of existing therapies and the development of novel approaches to more effectively control metastatic disease. This article presents a forward-looking perspective on the challenges that remain in the treatment of metastasis, and the exciting emerging approaches that promise to transform the treatment of metastasis in cancer patients.

Three-dimensional (3D) imaging of lipids in skin tissues with infrared matrix-assisted laser desorption electrospray ionization (MALDESI) mass spectrometry

Three-dimensional (3D) mass spectrometry imaging (MSI) has become a growing frontier as it has the potential to provide a 3D representation of analytes in a label-free, untargeted, and chemically specific manner. The most common 3D MSI is accomplished by the reconstruction of 2D MSI from serial cryosections; however, this presents significant challenges in image alignment and registration. An alternative method would be to sequentially image a sample by consecutive ablation events to create a 3D image. In this study, we describe the use of infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) in ablation-based 3D MSI for analyses of lipids within fresh frozen skin tissue. Depth resolution using different laser energy levels was explored with a confocal laser scanning microscope to establish the imaging parameters for skin. The lowest and highest laser energy level resulted in a depth resolution of 7 μm and 18 μm, respectively. A total of 594 lipids were putatively detected and detailed lipid profiles across different skin layers were revealed in a 56-layer 3D imaging experiment. Correlated with histological information, the skin structure was characterized with differential lipid distributions with a lateral resolution of 50 μm and a z resolution of 7 μm.

Streamlined Analysis of Cardiolipins in Prokaryotic and Eukaryotic Samples Using a Norharmane Matrix by MALDI-MSI

Cardiolipins (CLs) are an important, regulated lipid class both in prokaryotic and eukaryotic cells, yet they remain largely unexplored by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) in tissues. To date, no in-depth optimization studies of label-free visualization of CLs in complex biological samples have been reported. Here we report a streamlined modification to our previously reported MALDI-MSI method for detection of endogenous CLs in prokaryotic and eukaryotic cells based on preparation with norharmane (NRM) matrix. Notably, the use of NRM matrix permitted sensitive detection (4.7 pg/mm²) of spotted CL synthetic standards. By contrast, four other MALDI matrices commonly used for lipid analysis failed to generate CL ions. Using this NRM-based method, endogenous CLs were detected from two types of complex biological samples: dried bacterial arrays and mouse tissue sections. In both cases, using NRM resulted in a better signal/noise for CL ions than the other matrices. Furthermore, inclusion of a washing step improved CL detection from tissue and this combined tissue preparation method (washing and NRM matrix) was used to profile normal mouse lung. Mouse lung yielded 26 unique CLs that were mapped and identified. Consistent with previous findings, CLs containing polyunsaturated fatty acids (PUFAs) were found in abundance in the airway and vascular features of the lung. This work represents a comprehensive investigation of detection conditions for CL using MALDI-MSI in complex biological samples that resulted in a streamlined method that enables future studies of the biological role(s) of CL in tissue.

Integrating High-Resolution MALDI Imaging into the Development Pipeline of Anti-Tuberculosis Drugs

Successful treatment of tuberculosis (TB) requires antibiotics to reach their intended point of action, i.e., necrotizing granulomas in the lung. MALDI mass spectrometry imaging (MSI) is able to visualize the distribution of antibiotics in tissue, but resolving the small histological structures in mice, which are most commonly used in preclinical trials, requires high spatial resolution. We developed a MALDI MSI method to image antibiotics in the mouse lung with high mass resolution (240k @ m/z 200 fwhm) and high spatial resolution (10 μm pixel size). A crucial step was to develop a cryosectioning protocol that retains the distribution of water-soluble drugs in small and fragile murine lung lobes without inflation or embedding. Choice and application of matrices were optimized to detect human-equivalent drug concentrations in tissue, and measurement parameters were optimized to detect multiple drugs in a single tissue section. We succeeded in visualizing the distribution of all current first-line anti-TB drugs (pyrazinamide, rifampicin, ethambutol, isoniazid) and the second-line drugs moxifloxacin and clofazimine. Four of these compounds were imaged for the first time in the mouse lung. Accurate mass identification was confirmed by on-tissue MS/MS. Evaluation of fragmentation pathways revealed the structure of the double-protonated molecular ion of pyrazinamide. Clofazimine was imaged for the first time with 10 μm pixel size revealing clofazimine accumulation in lipid deposits around airways. In summary, we developed a platform to resolve the detailed histology in the murine lung and to reliably detect a range of anti-TB drugs at human-equivalent doses. Our workflow is currently being employed in preclinical mouse studies to evaluate the efficacy of novel anti-TB drugs.

On-Tissue Derivatization of Lipopolysaccharide for Detection of Lipid A Using MALDI-MSI

We developed a method to directly detect and map the Gram-negative bacterial virulence factor lipid A derived from lipopolysaccharide (LPS) by coupling acid hydrolysis with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). As the structure of lipid A (endotoxin) determines the innate immune outcome during infection, the ability to map its location within an infected organ or animal is needed to understand localized inflammatory responses that results during host-pathogen interactions. We previously demonstrated detection of free lipid A from infected tissue; however detection of lipid A derived from intact (smooth) LPS from host-pathogen MSI studies, proved elusive. Here, we detected LPS-derived lipid A from the Gram-negative pathogens, Escherichia coli (Ec, m/z 1797) and Pseudomonas aeruginosa (Pa, m/z 1446) using on-tissue acid hydrolysis to cleave the glycosidic linkage between the polysaccharide (core and O-antigen) and lipid A moieties of LPS. Using accurate mass methods, the ion corresponding to the major Ec and Pa lipid A species (m/z 1797 and 1446, respectively) were unambiguously discriminated from complex tissue substrates. Further, we evaluated potential delocalization and signal loss of other tissue lipids and found no evidence for either, making this LPS-to-Lipid A-MSI (LLA-MSI) method, compatible with simultaneous host-pathogen lipid imaging following acid hydrolysis. This spatially sensitive technique is the first step in mapping host-influenced de novo lipid A modifications, such as those associated with antimicrobial resistance phenotypes, during Gram-negative bacterial infection and will advance our understanding of the host-pathogen interface.