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Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

BackgroundUsing a personality typing approach, we investigated the relationship between personality profiles and the prediction of longterm illness severity in patients with bipolar disorder (BD). While previous research suggests associations between BD and traits from the NEO-FFI profiles, the current study firstly aimed to identify latent classes of NEO-FFI profiles, and, secondly, to examine their impact on the longterm prognosis of BD.MethodsBased on the NEO-FFI profiles of 134 euthymic patients diagnosed with BD (64.2% female, mean age = 44.3 years), successive latent profile analyses were conducted. Subsequently, a subsample (n = 80) was examined prospectively by performing multiple regression analysis of the latent classes to evaluate the longitudinal course of the disease (mean: 54.7 weeks) measured using a modified Morbidity Index.ResultsThe latent profile analyses suggested a 3-class model typifying in a resilient (n = 68, 51%), vulnerable (n = 55, 41%) and highly vulnerable (n = 11, 8%) class. In the regression analysis, higher vulnerability predicted a higher longterm Morbidity Index (R2 = 0.28).ConclusionsSubgroups of patients with BD share a number of discrete personality features and their illness is characterized by a similar clinical course. This knowledge is valuable in a variety of clinical contexts including early detection, intervention planning and treatment process.

Formalin-fixed paraffin embedding (FFPE) of tissues has been assumed to alter the metabolite content or chemical state, hampering metabolomics studies. Here, Ly et al . describe reproducible metabolomic analysis of FFPE samples by mass spectrometry imaging. Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m / z species in the mass range m / z 50–1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ . The data acquired with this protocol can be used in research and clinical practice.

The non-steroidal anti-inflammatory drug aspirin is currently being developed as activatable contrast agent for chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI), for detection of its CEST MRI active metabolite salicylic acid (SA). This study refines and develops quantitative matrix-assisted laser desorption/ionization (QMALDI) imaging to investigate the distribution of aspirin metabolites including SA in triple-negative breast cancer (TNBC) models in mice. In this study, we established QMALDI imaging with norharmane (nH) matrix and assisted by the addition of 5 mM peracetic acid (PAA) for optimized SA detection. Deuterated D -SA was added as an internal standard to quantify SA detection. PAA was applied via spraying to improve matrix uniformity and reduce crystal size by forming hydrogen bonds with the nH matrix. Ultraviolet (UV) irradiation during MALDI imaging activated PAA, generating reactive radicals that facilitated the breakdown of nH matrix compounds, thereby reducing matrix-related noise. QMALDI imaging with 5 mM PAA-doped nH matrix and D -SA as internal standard revealed SA accumulation of 141.9 ± 22.6 pmol/mm² in the liver, 129.5 ± 7.8 pmol/mm² in the kidney, and 50.4 ± 3.0 pmol/mm² in TNBC tumors following intravenous injection of aspirin in mice. Precise spatial alignment, integration, and quantification of MALDI imaging, histology, and immunofluorescence images from CD31 staining for blood vessels allowed us to accurately evaluate the spatial distribution of SA in tissue regions enriched with blood vessels and in specific anatomical regions. This spatial data analysis revealed high SA accumulation in the kidney medulla, viable tumor rim containing CD31-stained blood vessels, and throughout the liver. This newly developed QMALDI imaging approach for detecting aspirin metabolites demonstrated high SA accumulation in the kidney medulla and tumor rim containing blood vessels within viable tumor regions following systemic aspirin injection in mice, consistent with our previous study using aspirin-generated SA as activated contrast agent for CEST MRI. This approach enhances the spatial and tissue structural accuracy of quantitative analysis, reinforcing the potential of QMALDI imaging for investigating contrast agents, drug distributions, and metabolism in various tissues.

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) is emerging as a powerful tool for the analysis of molecular distributions in biological samples in situ. When compared to classical histology, the major benefit of this method is the ability to identify and localize many molecules in a single tissue sample. MALDI-MSI spatial resolution currently falls short of traditional microscopic methods as it is limited by instrumentation and sample preparation. Tissue preparation steps, such as matrix deposition, are critical when considering strategies to further enhance the spatial resolution. The mammalian retina was selected as the tissue of choice for method development; its stratified anatomy renders it an ideal tissue to test high-resolution MALDI-MSI as the different layers correspond to specific neuronal classes and cellular structures. We compared alcohol-fixed, paraffin-embedded retina to fresh-frozen samples and matrix that had been deposited by spray or by sublimation. We present a lipid imaging method based on MALDI-MSI of frozen retinal sections with sublimated 2,5-dihydroxybenzoic acid matrix, which results in a highly advanced resolution compared to previous established methods. Hierarchical clustering of the primary data allows robust detection and differentiation of molecular distributions at a spatial resolution between 10 and 20 μm, thus approaching single-cell resolution.

Aims/hypothesis Diabetic retinopathy is a major complication of type 2 diabetes and the leading cause of blindness in adults of working age. Neuronal defects are known to occur early in disease, but the source of this dysfunction is unknown. The aim of this study was to examine differences in the retinal membrane proteome among non-diabetic mice and mouse models of diabetes either with or without metformin treatment. Methods Alterations in the retinal membrane proteome of 10-week-old diabetic db/db mice, diabetic db/db mice orally treated with the anti-hyperglycaemic metformin, and congenic wild-type littermates were examined using label-free mass spectrometry. Pathway enrichment analysis was completed with Genomatix and Ingenuity. Alterations in Slc17a7 mRNA and vesicular glutamate transporter 1 (VGLUT1) protein expression were evaluated using real-time quantitative PCR and immunofluorescence. Results A total of 98 proteins were significantly differentially abundant between db/db and wild-type animals. Pathway enrichment analysis indicated decreases in levels of proteins related to synaptic transmission and cell signalling. Metformin treatment produced 63 differentially abundant proteins compared with untreated db/db mice, of which only 43 proteins were found to occur in both datasets, suggesting that treatment only partially normalises the alterations induced by diabetes. VGLUT1, which is responsible for loading glutamate into synaptic vesicles, was found to be differentially abundant in db/db mice and was not normalised by metformin. The decrease in Slc17a7 /VGLUT1 was confirmed by transcriptomic and immunocytochemical analysis. Conclusions/interpretation These findings expand the knowledge of the protein changes in diabetic retinopathy and suggest that membrane-associated signalling proteins are susceptible to changes that are partially ameliorated by treatment.

Since DSCAM (Down Syndrome Cell Adhesion Molecule) was initially identified as a candidate gene for the neurological detects observed in Down Syndrome, it has emerged as a multi-faceted molecule integral to proper neurodevelopment. With a conserved domain structure among both vertebrates and invertebrates, DSCAM has been implicated in a wide range of neurodevelopment processes. Here we show that mammalian DSCAM can bind a classic axon guidance cue and mediate axon guidance. Additionally, we show that multiple DSCAM isoforms are dynamically regulated in different regions of the nervous system and most important we identified the first DSCAM isoforms that can mediate divergent functions. Early in vitro studies highlighted DSCAM's ability to engage in homotypic binding intrans, but we show in Chapter 1, that DSCAM is also capable of binding Netrin-1 and is involved in the development of the mouse spinal cord. Through in vitro and in vivo experiments, we show that DSCAM functions independently and in collaboration with DCC to mediate the outgrowth and turning of commissural axons, respectively, towards the netrin-1 gradient emanating from ventral floor plate. In Chapter 2, we explore DSCAM's protein expression pattern, primarily in the cerebellum and colliculus of wildtype mice. We then characterized a mutant DSCAM mouse line, DSCAMdel17 for potential phenotypes in these cerebral regions. We were surprised to discover the expression of multiple DSCAM proteins that are differentially expressed in different tissues and were not all affected in the mutant, suggesting the existence of alternatively spliced isoforms. Unlike the invertebrate counterpart, DSCAM in vertebrates have not been shown to have any functional isoforms. Our search for isoforms yielded new cytoplasmic domains that mediate different functions. These isoforms result from alternative splicing; the first includes an insertion of an extra exon and the other is a truncation resulting from retention of an intron. This work reveals that mammalian DSCAM does indeed undergo alternative splicing and conceptually expands the possible roles for DSCAM in neurodevelopment. These combined studies have deepened our understanding of how mammalian DSCAM contributes to the development of a functional neural circuit. Furthermore, it has highlighted exciting avenues of research to be pursued in uncovering the full potential of DSCAM, a molecule that never tails to surprise with its ever-expanding repertoire of axon guidance capabilities.

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

Murray Valley encephalitis virus (MVEV) is a mosquito-borne orthoflavivirus endemic to Australia that can cause fatal neurological disease. The enzootic focus of MVEV is believed to reside in northern Western Australia (WA). We sequenced whole genomes of 70 MVEV sampled over 51 years, 1969–2020, from locations across Australia and Papua New Guinea (PNG) and identified greater MVEV diversity than previously recognized. Genotype 1 (G1) demonstrated greatest intra-genotype diversity and was predominant over the sampling period with sub-lineage G1B circulating in WA and seeding activity across Australia. G1A included viruses sampled across northern WA, as well as the Northern Territory (NT). A newly identified sub-lineage G1C circulated in northern WA in 1993 and was detected again in 2003. G2 viruses were distributed across the Kimberley and Pilbara regions of northern WA, and in the NT. Although no new G3 and G4 viruses, previously identified only in PNG, were detected in the present study, other MVEV originating in PNG clustered with G1A. We confirm MVEV is enzootic in northern WA, with transmission occurring more frequently and across a wider geographical area than previously recognised. Additionally, we identify evidence of regular genotype replacement that has occurred over many decades where the major genotypes G1 and G2 have circulated in northern WA since the late 1960s. We also show that WA MVEV likely seeded an MVE outbreak in Victoria in 1974, further supporting the notion that the enzootic focus of MVEV lies in northern WA. Recent increases in MVEV detections, MVE cases and deaths in WA and across Australia highlight the need for enhanced surveillance and more frequent sampling to understand viral origin and genomic diversity, to identify potential virulence motifs, and to understand the ecological drivers that determine emergence of MVEV in northern WA and movement of MVEV across the country.

Pulmonary arterial hypertension (PAH) is characterized by a progressive accumulation of pulmonary artery smooth muscle cells (PA-SMCs) in pulmonary arterioles leading to the narrowing of the lumen, right heart failure, and death. Although most studies have supported the notion of a role for IL-6/glycoprotein 130 (gp130) signaling in PAH, it remains unclear how this signaling pathway determines the progression of the disease. Here, we identify ectopic upregulation of membrane-bound IL-6 receptor (IL6R) on PA-SMCs in PAH patients and in rodent models of pulmonary hypertension (PH) and demonstrate its key role for PA-SMC accumulation in vitro and in vivo. Using Sm22a-Cre Il6rfl/fl, which lack Il6r in SM22A-expressing cells, we found that these animals are protected against chronic hypoxia-induced PH with reduced PA-SMC accumulation, revealing the potent pro-survival potential of membrane-bound IL6R. Moreover, we determine that treatment with IL6R-specific antagonist reverses experimental PH in two rat models. This therapeutic strategy holds promise for future clinical studies in PAH.