Cell blocks are an integral part of cytology, but their utility is recognized probably more now than ever before, largely owing to the significant role they play in ancillary testing, particularly ...molecular diagnostics. Modifications to improve the cell block method initially introduced more than a century ago have been made over the years. Though their value is acknowledged and they are widely used across laboratories, cell block preparations are not standardized and results of ancillary testing performed on them are inconsistent. This article reviews the state of cell blocks-summarizes the more common, currently available and used methods and their corresponding advantages and shortcomings, outlines the role of alternative techniques (eg, smears), and proposes methods to optimize results.
The novel coronavirus SARS-CoV-2 (coronavirus disease 19, or COVID-19) primarily causes pulmonary injury, but has been implicated to cause hepatic injury, both by serum markers and histologic ...evaluation. The histologic pattern of injury has not been completely described. Studies quantifying viral load in the liver are lacking. Here we report the clinical and histologic findings related to the liver in 40 patients who died of complications of COVID-19. A subset of liver tissue blocks were subjected to polymerase chain reaction (PCR) for viral ribonucleic acid (RNA). Peak levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were elevated; median ALT peak 68 U/l (normal up to 46 U/l) and median AST peak 102 U/l (normal up to 37 U/l). Macrovesicular steatosis was the most common finding, involving 30 patients (75%). Mild lobular necroinflammation and portal inflammation were present in 20 cases each (50%). Vascular pathology, including sinusoidal microthrombi, was infrequent, seen in six cases (15%). PCR of liver tissue was positive in 11 of 20 patients tested (55%). In conclusion, we found patients dying of COVID-19 had biochemical evidence of hepatitis (of variable severity) and demonstrated histologic findings of macrovesicular steatosis and mild acute hepatitis (lobular necroinflammation) and mild portal inflammation. We also identified viral RNA in a sizeable subset of liver tissue samples.
The pathogenesis of idiopathic pulmonary fibrosis (IPF), an intractable interstitial lung disease, is unclear. Recessive mutations in some genes implicated in Hermansky-Pudlak syndrome (HPS) cause ...HPS-associated interstitial pneumonia (HPSIP), a clinical entity that is similar to IPF. We previously reported that HPS1−/− embryonic stem cell-derived 3D lung organoids showed fibrotic changes. Here, we show that the introduction of all HPS mutations associated with HPSIP promotes fibrotic changes in lung organoids, while the deletion of HPS8, which is not associated with HPSIP, does not. Genome-wide expression analysis revealed the upregulation of interleukin-11 (IL-11) in epithelial cells from HPS mutant fibrotic organoids. IL-11 was detected predominantly in type 2 alveolar epithelial cells in end-stage IPF, but was expressed more broadly in HPSIP. Finally, IL-11 induced fibrosis in WT organoids, while its deletion prevented fibrosis in HPS4−/− organoids, suggesting IL-11 as a therapeutic target. hPSC-derived 3D lung organoids are, therefore, a valuable resource to model fibrotic lung disease.
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•HPS-associated pulmonary fibrosis can be modeled in ESC-derived lung organoids•Interleukin-11 is required for fibrosis in lung organoids•Interleukin expression is elevated in lungs of pulmonary fibrosis patients
Pulmonary fibrosis is an intractable disease that can be familial or idiopathic. Strikoudis et al. modeled pulmonary fibrosis in lung organoids generated from embryonic stem cells with mutations in Hermansky-Pudlak syndrome genes that strongly predispose to this disease and demonstrate an essential role for interleukin-11 in the fibrotic process.
Immune response dynamics in coronavirus disease 2019 (COVID-19) and their severe manifestations have largely been studied in circulation. Here, we examined the relationship between immune processes ...in the respiratory tract and circulation through longitudinal phenotypic, transcriptomic, and cytokine profiling of paired airway and blood samples from patients with severe COVID-19 relative to heathy controls. In COVID-19 airways, T cells exhibited activated, tissue-resident, and protective profiles; higher T cell frequencies correlated with survival and younger age. Myeloid cells in COVID-19 airways featured hyperinflammatory signatures, and higher frequencies of these cells correlated with mortality and older age. In COVID-19 blood, aberrant CD163+ monocytes predominated over conventional monocytes, and were found in corresponding airway samples and in damaged alveoli. High levels of myeloid chemoattractants in airways suggest recruitment of these cells through a CCL2-CCR2 chemokine axis. Our findings provide insights into immune processes driving COVID-19 lung pathology with therapeutic implications for targeting inflammation in the respiratory tract.
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•Airways show localized immune responses correlating to age and outcome in COVID-19•Airway T cells are activated and resident, while myeloid cells are hyperinflammatory•Aberrant CD163hi and HLA-DRlo monocytes predominate in COVID-19 blood•Monocytes infiltrate airways and lung alveoli potentially through a CCL2-CCR2 axis
Through longitudinal profiling of paired airways and blood from patients with severe COVID-19, Szabo et al. reveal airway immune responses that correlate with age and outcome. They further identify coordinate roles for T and myeloid cells in the respiratory tract and circulation in perpetuating lung pathology and disease pathogenesis.
Abstract Background The tumor microenvironment (TME) plays a key role in lung cancer initiation, proliferation, invasion, and metastasis. Artificial intelligence (AI) methods could potentially ...accelerate TME analysis. The aims of this study were to (1) assess the feasibility of using hematoxylin and eosin (H&E)-stained whole slide images (WSI) to develop an AI model for evaluating the TME and (2) to characterize the TME of adenocarcinoma (ADCA) and squamous cell carcinoma (SCCA) in fibrotic and non-fibrotic lung. Methods The cohort was derived from chest CT scans of patients presenting with lung neoplasms, with and without background fibrosis. WSI images were generated from slides of all 76 available pathology cases with ADCA ( n = 53) or SCCA ( n = 23) in fibrotic ( n = 47) or non-fibrotic ( n = 29) lung. Detailed ground-truth annotations, including of stroma (i.e., fibrosis, vessels, inflammation), necrosis and background, were performed on WSI and optimized via an expert-in-the-loop (EITL) iterative procedure using a lightweight random forest (RF) classifier. A convolution neural network (CNN)-based model was used to achieve tissue-level multiclass segmentation. The model was trained on 25 annotated WSI from 13 cases of ADCA and SCCA within and without fibrosis and then applied to the 76-case cohort. The TME analysis included tumor stroma ratio (TSR), tumor fibrosis ratio (TFR), tumor inflammation ratio (TIR), tumor vessel ratio (TVR), tumor necrosis ratio (TNR), and tumor background ratio (TBR). Results The model’s overall classification for precision, sensitivity, and F1-score were 94%, 90%, and 91%, respectively. Statistically significant differences were noted in TSR ( p = 0.041) and TFR ( p = 0.001) between fibrotic and non-fibrotic ADCA. Within fibrotic lung, statistically significant differences were present in TFR ( p = 0.039), TIR ( p = 0.003), TVR ( p = 0.041), TNR ( p = 0.0003), and TBR ( p = 0.020) between ADCA and SCCA. Conclusion The combined EITL—RF CNN model using only H&E WSI can facilitate multiclass evaluation and quantification of the TME. There are significant differences in the TME of ADCA and SCCA present within or without background fibrosis. Future studies are needed to determine the significance of TME on prognosis and treatment.
Recent advances in therapy for non-small cell lung carcinoma have shown that a personalized approach to treatment has the potential to significantly reduce lung cancer mortality. Concurrently, ...endoscopic ultrasound transbronchial needle aspiration has emerged as an accurate and sensitive tool for the diagnosis and staging of this disease. As knowledge of the molecular mechanisms that drive lung cancer progression increases, the amount of information that must be derived from a tumor specimen will also increase. Recent clinical studies have demonstrated that small specimens acquired by endoscopic ultrasound transbronchial needle aspiration are sufficient for molecular testing if specimen acquisition and processing are done with these needs in mind. Optimum use of this procedure requires a coordinated effort between the bronchoscopist and the cytopathologist to collect and triage specimens for diagnostic testing. When feasible, rapid onsite evaluation should be performed to assess the specimen for both diagnostic quality and quantity and to allocate the specimen for cell-block and possible immunohistochemistry and molecular studies. It is necessary for pulmonologists and bronchoscopists to understand the rationale for histologic and molecular testing of lung cancer diagnostic specimens and to ensure that specimens are acquired and processed in a fashion that provides information from small cytologic specimens that is sufficient to guide treatment in this era of targeted therapy.
SARS-CoV-2 infection causes severe pulmonary manifestations, with poorly understood mechanisms and limited treatment options. Hyperferritinemia and disrupted lung iron homeostasis in COVID-19 ...patients imply that ferroptosis, an iron-dependent cell death, may occur. Immunostaining and lipidomic analysis in COVID-19 lung autopsies reveal increases in ferroptosis markers, including transferrin receptor 1 and malondialdehyde accumulation in fatal cases. COVID-19 lungs display dysregulation of lipids involved in metabolism and ferroptosis. We find increased ferritin light chain associated with severe COVID-19 lung pathology. Iron overload promotes ferroptosis in both primary cells and cancerous lung epithelial cells. In addition, ferroptosis markers strongly correlate with lung injury severity in a COVID-19 lung disease model using male Syrian hamsters. These results reveal a role for ferroptosis in COVID-19 pulmonary disease; pharmacological ferroptosis inhibition may serve as an adjuvant therapy to prevent lung damage during SARS-CoV-2 infection.
Chest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung cancer risk, current clinical practice characterizes nodule ...risk of malignancy based on nodule size and smoking history; little consideration is given to the fibrotic microenvironment.
To evaluate the effect of incorporating fibrotic microenvironment into classifying malignancy of lung nodules in chest CT images using deep learning techniques.
We developed a visualizable 3D classification model trained with in-house CT dataset for the nodule malignancy classification task. Three slightly-modified datasets were created: (1) nodule alone (microenvironment removed); (2) nodule with surrounding lung microenvironment; and (3) nodule in microenvironment with semantic fibrosis metadata. For each of the models, tenfold cross-validation was performed. Results were evaluated using quantitative measures, such as accuracy, sensitivity, specificity, and area-under-curve (AUC), as well as qualitative assessments, such as attention maps and class activation maps (CAM).
The classification model trained with nodule alone achieved 75.61% accuracy, 50.00% sensitivity, 88.46% specificity, and 0.78 AUC; the model trained with nodule and microenvironment achieved 79.03% accuracy, 65.46% sensitivity, 85.86% specificity, and 0.84 AUC. The model trained with additional semantic fibrosis metadata achieved 80.84% accuracy, 74.67% sensitivity, 84.95% specificity, and 0.89 AUC. Our visual evaluation of attention maps and CAM suggested that both the nodules and the microenvironment contributed to the task.
The nodule malignancy classification performance was found to be improving with microenvironment data. Further improvement was found when incorporating semantic fibrosis information.
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