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Surgical adhesions are bands of scar tissues that abnormally conjoin organ surfaces. Adhesions are a major cause of post-operative and dialysis-related complications, yet their patho-mechanism remains elusive, and prevention agents in clinical trials have thus far failed to achieve efficacy. Here, we uncover the adhesion initiation mechanism by coating beads with human mesothelial cells that normally line organ surfaces, and viewing them under adhesion stimuli. We document expansive membrane protrusions from mesothelia that tether beads with massive accompanying adherence forces. Membrane protrusions precede matrix deposition, and can transmit adhesion stimuli to healthy surfaces. We identify cytoskeletal effectors and calcium signaling as molecular triggers that initiate surgical adhesions. A single, localized dose targeting these early germinal events completely prevented adhesions in a preclinical mouse model, and in human assays. Our findings classifies the adhesion pathology as originating from mesothelial membrane bridges and offer a radically new therapeutic approach to treat adhesions. Surgical adhesions are organ-joining bands of scar tissue that remain clinically untreatable. Here, the authors show that adhesions are formed through expansive mesothelial membrane bridges, and that blocking these with small molecules prevents formation of adhesions in mice.

BackgroundPancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies to date. The impressively developed stroma that surrounds and modulates the behavior of cancer cells is one of the main factors regulating the PDAC growth, metastasis and therapy resistance. Here, we postulate that stromal and cancer cell compartments differentiate in protein/lipid glycosylation patterns and analyze differences in glycan fragments in those compartments with clinicopathologic correlates. ResultsWe analyzed native glycan fragments in 109 human FFPE PDAC samples using high mass resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometric imaging (MALDI-FT-ICR-MSI). Our method allows detection of native glycan fragments without previous digestion with PNGase or any other biochemical reaction. With this method, 8 and 18 native glycans were identified as uniquely expressed in only stromal or only cancer cell compartment, respectively. Kaplan–Meier survival model identified glycan fragments that are expressed in cancer cell or stromal compartment and significantly associated with patient outcome. Among cancer cell region-specific glycans, 10 predicted better and 6 worse patient survival. In the stroma, 1 glycan predicted good and 4 poor patient survival. Using factor analysis as a dimension reduction method, we were able to group the identified glycans in 2 factors. Multivariate analysis revealed that these factors can be used as independent survival prognostic elements with regard to the established Union for International Cancer Control (UICC) classification both in tumor and stroma regions. ConclusionOur method allows in situ detection of naturally occurring glycans in FFPE samples of human PDAC tissue and highlights the differences among glycans found in stromal and cancer cell compartment offering a basis for further exploration on the role of specific glycans in cancer–stroma communication.

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.

Background/objectivesIndividuals carrying loss-of-function gene mutations for the adipocyte hormone leptin are morbidly obese, but respond favorably to replacement therapy. Recombinant leptin is however largely ineffective for the vast majority of obese individuals due to leptin resistance. One theory underlying leptin resistance is impaired leptin transport across the blood–brain-barrier (BBB). Here, we aim to gain new insights into the mechanisms of leptin BBB transport, and its role in leptin resistance.MethodsWe developed a novel tool for visualizing leptin transport using infrared fluorescently labeled leptin, combined with tissue clearing and light-sheet fluorescence microscopy. We corroborated these data using western blotting.ResultsUsing 3D whole brain imaging, we display comparable leptin accumulation in circumventricular organs of lean and obese mice, predominantly in the choroid plexus (CP). Protein quantification revealed comparable leptin levels in microdissected mediobasal hypothalami (MBH) of lean and obese mice (p = 0.99). We further found increased leptin receptor expression in the CP (p = 0.025, p = 0.0002) and a trend toward elevated leptin protein levels in the MBH (p = 0.17, p = 0.078) of obese mice undergoing weight loss interventions by calorie restriction or exendin-4 treatment.ConclusionsOverall, our findings suggest a crucial role for the CP in controlling the transport of leptin into the cerebrospinal fluid and from there to target areas such as the MBH, potentially mediated via the leptin receptor. Similar leptin levels in circumventricular organs and the MBH of lean and obese mice further suggest intact leptin BBB transport in leptin resistant mice.

Background Obese subjects undergoing weight loss often fear the Yoyo dieting effect, which involves regaining or even surpassing their initial weight. To date, our understanding of such long-term obesity and weight cycling effects is still limited and often based on only short-term murine weight gain and loss studies. This study aimed to investigate the long-term impacts of weight cycling on glycemic control and metabolic health, focusing on adipose tissue, liver, and hypothalamus. Methods Chow-fed mice and mice subjected to prolonged high-fat diet (HFD) consumption for 20 weeks, followed by 24 weeks of dietary interventions to either induce weight gain, weight loss, or weight cycling were monitored for perturbations in feeding efficiency and glucose homeostasis. Post-mortem analyses included qPCR, Western Blotting, biochemical and microscopical assessments for hepatic steatosis and insulin resistance, hypothalamic and adipose tissue inflammation, and circulating lipid, leptin and IL-6 levels. Results Weight cycling led to hyperphagia and rapid weight regain, matching the weights of mice continuously on HFD. Despite weight loss, adipose tissue inflammation persisted with elevated pro-inflammatory markers, macrophage infiltration, and impaired Glut4 expression. HFD-induced dysregulation in hypothalamic expression of orexigenic peptides and synaptic plasticity markers persisted also after weight normalization suggesting long-lasting neural alterations. Weight-cycled mice exhibited higher circulating IL-6 and leptin levels, increased hepatic lipid storage, and dysregulated glucose metabolism compared to those with consistent diets, indicating worsened metabolic effects by Yoyo dieting. Conclusion In sum, our study highlights significant metabolic risks associated with weight cycling, particularly following prolonged obesity. Persistent adipose tissue inflammation, perturbed neural peptide and plasticity markers and impaired glucose tolerance emphasize the need for effective and sustainable weight loss strategies to mitigate the adverse outcomes of weight regain and improve long-term metabolic health.

Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consuming, labor-intensive, and non-standardized. To address this problem, we present HistoGPT, a vision language model that generates pathology reports from a patient’s multiple full-resolution histology images. It is trained on 15,129 whole slide images from 6705 dermatology patients with corresponding pathology reports. The generated reports match the quality of human-written reports for common and homogeneous malignancies, as confirmed by natural language processing metrics and domain expert analysis. We evaluate HistoGPT in an international, multi-center clinical study and show that it can accurately predict tumor subtypes, tumor thickness, and tumor margins in a zero-shot fashion. Our model demonstrates the potential of artificial intelligence to assist pathologists in evaluating, reporting, and understanding routine dermatopathology cases. Machine learning models represent an opportunity for the automatic generation of histopathology reports. Here, the authors develop HistoGPT, a vision language model that can generate reports from multiple gigapixel-sized whole slide images and also predict tumour thickness, subtypes, and margins, among other diseases.

Multimodal tissue analyses that combine two or more detection technologies provide synergistic value compared to single methods and are employed increasingly in the field of tissue-based diagnostics and research. Here, we report a technical pipeline that describes a combined approach of HER2/CEP17 fluorescence in situ hybridization (FISH) analysis with MALDI imaging on the very same section of formalin-fixed and paraffin-embedded (FFPE) tissue. FFPE biopsies and a tissue microarray of human gastroesophageal adenocarcinoma were analyzed by MALDI imaging. Subsequently, the very same section was hybridized by HER2/CEP17 FISH. We found that tissue morphology of both, the biopsies and the tissue microarray, was unaffected by MALDI imaging and the HER2 and CEP17 FISH signals were analyzable. In comparison with FISH analysis of samples without MALDI imaging, we observed no difference in terms of fluorescence signal intensity and gene copy number. Our combined approach revealed adenosine monophosphate, measured by MALDI imaging, as a prognostic marker. HER2 amplification, which was detected by FISH, is a stratifier between good and poor patient prognosis. By integrating both stratification parameters on the basis of our combined approach, we were able to strikingly improve the prognostic effect. Combining molecules detected by MALDI imaging with the gene copy number detected by HER2/CEP17 FISH, we found a synergistic effect, which enhances patient prognosis. This study shows that our combined approach allows the detection of genetic and metabolic properties from one very same FFPE tissue section, which are specific for HER2 and hence suitable for prognosis. Furthermore, this synergism might be useful for response prediction in tumors.

Rainbow trout ( Oncorhynchus mykiss ) are frequently used as experimental animals in ecotoxicological studies, in which they are experimentally exposed to defined concentrations of test substances, such as heavy metals, pesticides, or pharmaceuticals. Following exposure to a broad variety of aquatic pollutants, early morphologically detectable toxic effects often manifest in alterations of the gills. Suitable methods for an accurate and unbiased quantitative characterization of the type and the extent of morphological gill alterations are therefore essential prerequisites for recognition, objective evaluation and comparison of the severity of gill lesions. The aim of the present guidelines is to provide practicable, standardized and detailed protocols for the application of unbiased quantitative stereological analyses of relevant morphological parameters of the gills of rainbow trout. These gill parameters inter alia include the total volume of the primary and secondary gill lamellae, the surface area of the secondary gill lamellae epithelium ( i . e ., the respiratory surface) and the thickness of the diffusion barrier. The featured protocols are adapted to fish of frequently used body size classes (300–2000 g). They include well-established, conventional sampling methods, probes and test systems for unbiased quantitative stereological analyses of light- and electron microscopic 2-D gill sections, as well as the application of modern 3-D light sheet fluorescence microscopy (LSFM) of optically cleared gill samples as an innovative, fast and efficient quantitative morphological analysis approach. The methods shown here provide a basis for standardized and representative state-of-the-art quantitative morphological analyses of trout gills, ensuring the unbiasedness and reproducibility, as well as the intra- and inter-study comparability of analyses results. Their broad implementation will therefore significantly contribute to the reliable identification of no observed effect concentration (NOEC) limits in ecotoxicological studies and, moreover, to limit the number of experimental animals by reduction of unnecessary repetition of experiments.

The development of single-molecule co-agonists for the glucagon-like peptide-1 (GLP-1) receptor (GLP-1R) and glucose-dependent insulinotropic polypeptide (GIP) receptor (GIPR) is considered a breakthrough in the treatment of obesity and type 2 diabetes. But although GIPR–GLP-1R co-agonism decreases body weight with superior efficacy relative to GLP-1R agonism alone in preclinical 1 – 3 and clinical studies 4 , 5 , the role of GIP in regulating energy metabolism remains enigmatic. Increasing evidence suggests that long-acting GIPR agonists act in the brain to decrease body weight through the inhibition of food intake 3 , 6 – 8 ; however, the mechanisms and neuronal populations through which GIP affects metabolism remain to be identified. Here, we report that long-acting GIPR agonists and GIPR–GLP-1R co-agonists decrease body weight and food intake via inhibitory GABAergic neurons. We show that acyl-GIP decreases body weight and food intake in male diet-induced obese wild-type mice, but not in mice with deletion of Gipr in Vgat (also known as Slc32a1 )-expressing GABAergic neurons ( Vgat-Gipr knockout). Whereas the GIPR–GLP-1R co-agonist MAR709 leads, in male diet-induced obese wild-type mice, to greater weight loss and further inhibition of food intake relative to a pharmacokinetically matched acyl-GLP-1 control, this superiority over GLP-1 vanishes in Vgat-Gipr knockout mice. Our data demonstrate that long-acting GIPR agonists crucially depend on GIPR signaling in inhibitory GABAergic neurons to decrease body weight and food intake. Long-acting GIPR agonists, individually or as part of GIPR–GLP-1R co-agonists, are shown to require GIPR signaling in inhibitory GABAergic neurons to decrease body weight and food intake in mice.

Background The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non‐small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read‐out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning. Methods Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high‐resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy‐naïve patients with NSCLC (n = 85). Results The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716–8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004–4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445–1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977–1.550) were not independent prognostic factors. Using Kaplan‐Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non‐responders (P < 0.001). Conclusions Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology‐based assessment of tumor response.