American Journal of Pathology最新文献

{"title":"Neurologic Consequences of Liver Disease: The Liver-Brain Axis.","authors":"Matthew McMillin, Kathryn Rhodes, Sharon DeMorrow","doi":"10.1016/j.ajpath.2026.07.014","DOIUrl":"10.1016/j.ajpath.2026.07.014","url":null,"abstract":"<p><p>The liver serves as a key metabolic and inflammatory organ involved in maintaining homeostasis of different organs and systems. This includes ensuring proper functioning of the central nervous system through a liver-brain axis that has bidirectional communication via signaling mediators and direct neural pathways. Damage to, or disease of, the liver from various etiologies leads to a loss of hepatic function, which then impacts cognitive, motor, and other neurologic functions. Severity, type, and duration of liver dysfunction can impact the brain in different ways via different metabolic and immune challenges. Some of the hepatic factors that impact the brain include ammonia, cytokines, and bile acids. These factors can induce more apparent neurologic changes, such as hepatic encephalopathy, whereas others induce more subtle changes, which typically fall outside of the classical characterization of hepatic encephalopathy. The aim of this review is to outline the current knowledge of the liver-brain axis in pathologic states. Specific emphasis is placed on how loss of liver function leads to systemic metabolic and inflammatory challenges and their subsequent impacts on hepatic and neural pathology. In addition, specific hepatic pathologies most associated with disruptions of the liver-brain axis will be focused on for more detailed analysis.</p>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148856876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Integrin-Mediated Focal Adhesion Is Critically Involved in the Macrophage-to-Myofibroblast Transition in Subretinal Fibrosis.","authors":"Wen Deng, Caijiao Yi, Jian Liu, Xuexue Cui, Changhua Ye, Zhongping Chen, Mei Chen, Heping Xu","doi":"10.1016/j.ajpath.2026.08.002","DOIUrl":"10.1016/j.ajpath.2026.08.002","url":null,"abstract":"<p><p>Fibrosis, a common end point of chronic inflammatory diseases across organs, remains a major therapeutic challenge. Macrophages are central regulators of fibrotic remodeling, with evidence suggesting their direct role through macrophage-to-myofibroblast transition. However, the upstream mechanisms governing macrophage fibrogenic reprogramming remain unclear. Using a laser-induced mouse model of subretinal fibrosis, whether adhesion-dependent mechanotransduction regulates macrophage-driven fibrosis was investigated. Bulk RNA sequencing of retinal pigment epithelium-choroid tissues revealed significant enrichment of adhesion-related genes and focal adhesion pathways, with up-regulation of integrins such as Itgb2, Itgal, and Itgax. Increased integrin expression and focal adhesion kinase (FAK) phosphorylation were detected in infiltrating F4/80⁺ macrophages within fibrotic lesions. Bone marrow-derived macrophages under high adherence conditions expressed higher levels of fibrosis-related genes and FAK-related genes such as Ptk2 and Ptk2b. FAK inhibitor PF562271 suppressed transforming growth factor-β1-induced up-regulation of fibrosis-related genes (Col1a1, Fn1, and Acta2) and α-smooth muscle actin in macrophages. I.P. administration of PF562271 reduced expression of adhesion molecules ITGB2 and ITGAL on circulating monocytes and alleviated subretinal fibrosis. These findings identify integrin-FAK-mediated mechanotransduction as a key regulator of macrophage fibrogenic reprogramming and macrophage-to-myofibroblast transition. Adhesion-dependent signaling is highlighted as a conserved pathway linking tissue remodeling to fibrotic macrophage activation and FAK as a potential therapeutic target.</p>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of Spatial Transcriptomics in Ischemic Stroke Research","authors":"Rafael Stacho,&nbsp;Daniel Zucha,&nbsp;Denisa Kirdajova,&nbsp;Lukas Valihrach","doi":"10.1016/j.ajpath.2026.01.008","DOIUrl":"10.1016/j.ajpath.2026.01.008","url":null,"abstract":"<div><div>Acute ischemic stroke is a complex disorder in which the damage goes beyond neuronal loss and involves dynamic responses from glial, vascular, stromal, and immune cells. Spatial transcriptomics (ST) has become a powerful tool to study these processes by preserving tissue architecture while revealing detailed gene expression patterns. This review describes how ST has advanced the understanding of cellular changes after stroke, focusing on microglia, astrocytes, and oligodendrocytes to showcase the complexity of stroke pathobiology. Research shows that the glial cells adopt different states depending on their location and time elapsed after injury, influencing both harmful and protective outcomes, such as inflammation, blood–brain barrier damage, remyelination, and tissue repair. By combining ST with single-cell and multiomics approaches, new therapeutic targets have been identified, including different types of activated glial states, and key signaling pathways involved in glial communication. Despite the recent progress in ST, challenges remain, particularly the need for multi–time point analyses, three-dimensional reconstructions and standardized data sets that can move the field closer to clinical applications. Future reference atlases, together with experimental validation, will be essential for developing precise, cell-targeted therapies. This review summarizes the results from the most recent ST studies and highlights the possible applications of spatial approaches to improving stroke research and therapy.</div></div>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":"196 7","pages":"Pages 1447-1457"},"PeriodicalIF":4.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Transcriptomics Profiling of Small Intestine Adenocarcinoma and Adenoma in Humans and a Murine Model with KRASG12D and Loss of CDKN2a-p16","authors":"Tyler D. Seckar,&nbsp;Elena V. Komissarova,&nbsp;Jing Sun,&nbsp;Narine N. Le Fevre,&nbsp;Zhong Ye,&nbsp;Tristan Jordan,&nbsp;Aylar Babaei,&nbsp;Armando Del Portillo,&nbsp;Jorge L. Sepulveda,&nbsp;Antonia R. Sepulveda","doi":"10.1016/j.ajpath.2026.01.013","DOIUrl":"10.1016/j.ajpath.2026.01.013","url":null,"abstract":"<div><div>Small intestine adenocarcinomas (SIACs) in humans occur predominantly as extra-ampullary SIAC [SIAC/extra-ampullary duodenal adenocarcinoma (EDA)], demonstrate intestinal- or gastric-type differentiation, and arise from precursor adenomas. The aims of this study were to determine spatial transcriptomics of SIAC/EDA differentiation subtypes and driver molecular pathways in humans and novel mouse models. Spatial transcriptomics and immunohistochemistry were performed in human and murine SIAC/EDA and adenomas. Mice with <em>Cdkn2a</em>/p16 conditional knockout, <em>Kras</em>^{<em>G12D</em>} expression, or both (p16KOKrasG12D) targeting leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5) cell progenitors were generated, and histologic alterations were examined. Spatial transcriptomics identified differentiation signatures of intestinal and gastric-type SIAC/EDA and adenomas, their microenvironment, cell-cell signaling interactions driving neoplasia progression, and susceptibility to targeted therapies. Activated signaling pathways were not specific to differentiation subtype. Algorithm-inferred susceptibility to mitogen-activated protein kinase kinase (MEK) inhibitors correlated with activation of MEK pathways in gastric-type SIAC. p16KOKrasG12D mice developed small intestine/duodenal adenomas by 11 to 15 months (10/14, 83%), and adenocarcinomas by 12 to 19 months, in contrast to wild-type mice (<em>P</em> = 0.0003). Spatial transcriptomics of mouse lesions largely replicated human SIAC/EDA differentiation signatures and signaling pathways. A novel genetically engineered mouse model that mimics gastric- and intestinal-type adenomas and gastric-type SIAC/EDA is reported. Spatial transcriptomics provided a detailed understanding of cellular differentiation lineages, cancer microenvironment related to SIAC subtypes, and driving pathways that may be leveraged to identify new molecular tools for pathologic diagnosis and potential targets for precision cancer therapies.</div></div>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":"196 7","pages":"Pages 1487-1513"},"PeriodicalIF":4.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147321232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Redefining Pathobiology with Spatial Multi-Omics at the Intersection of Biology, Computation, and Histopathological Assessment","authors":"Ankit Agrawal,&nbsp;Stefan Thomann","doi":"10.1016/j.ajpath.2026.01.007","DOIUrl":"10.1016/j.ajpath.2026.01.007","url":null,"abstract":"<div><div>Spatial profiling technologies are transforming our understanding of tissue organization by enabling high-resolution mapping of molecular features <em>in situ</em>. Spatial multi-omics platforms ranging from spot-based to single-cell and subcellular resolution are increasingly being integrated into pathobiological workflows, offering unprecedented insights into novel cellular states, the tissue microenvironment, cell–cell communication, drug resistance niches, and disease heterogeneity. This article discusses recent experimental and computational advances in spatial biology, highlighting how multimodal integration enables a more comprehensive understanding of tissue function and its dysregulation. The challenges and opportunities that arise in three-dimensional spatial mapping, the impact on biomarker discovery, therapeutic decision-making, and the translational implications of large-scale pathology foundation models trained on spatial omics data are explored. Finally, it’s highlighted that refined biological questions, combined with artificial intelligence, can unlock the full potential of spatial omics and reshape diagnostic workflows toward more precise clinical decision-making.</div></div>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":"196 7","pages":"Pages 1406-1426"},"PeriodicalIF":4.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moving Beyond Morphology to Multiplexed Molecular Imaging as the Next Frontier in Diagnostic Pathology","authors":"Andréanne Gagné,&nbsp;Jia-Ren Lin","doi":"10.1016/j.ajpath.2026.03.015","DOIUrl":"10.1016/j.ajpath.2026.03.015","url":null,"abstract":"<div><div>Diagnostic pathology has long relied on the morphologic interpretation of hematoxylin and eosin–stained tissues to guide diagnosis and assess prognostic features. Although pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics—including mass spectrometry–based imaging and cyclic immunofluorescence—now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histologic and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathologic perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.</div></div>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":"196 7","pages":"Pages 1458-1470"},"PeriodicalIF":4.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147809224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Pathobiology in the Omics Era","authors":"Madhumala K. Sadanandappa","doi":"10.1016/j.ajpath.2026.05.001","DOIUrl":"10.1016/j.ajpath.2026.05.001","url":null,"abstract":"","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":"196 7","pages":"Pages 1403-1405"},"PeriodicalIF":4.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning–Enhanced Biomarker Interpretation on Cytology Cell Blocks","authors":"Rong Xia,&nbsp;Nickolas G. Littlefield,&nbsp;Christopher Y. Park,&nbsp;Riyue Bao,&nbsp;Joan Cangiarella,&nbsp;Aylin Simsir,&nbsp;Qiangqiang Gu","doi":"10.1016/j.ajpath.2026.02.004","DOIUrl":"10.1016/j.ajpath.2026.02.004","url":null,"abstract":"<div><div>Cytology cell block specimens are essential for diagnosing patients with advanced-stage malignancy and often represents the only available strategy for therapeutic biomarker evaluation. The use of cell blocks preserves tumor cells, captures high-grade or metastatic populations, and retains meaningful microenvironmental context, making them well suited for IHC analysis. With the rapid expansion of computational pathology, deep learning–assisted biomarker interpretation in cell blocks is emerging as a promising frontier for improving reproducibility, reducing interobserver variability, and enabling quantitative assessment of spatial tumor–immune interactions. Because many treatment-defining biomarkers are routinely assessed on cytology cell blocks, this mini-review highlights artificial intelligence–based applications for PD-L1, HER2, ER/PR, Ki-67, ALK/ROS1, BRAF V600E, and p16 markers that directly inform decisions about immunotherapy, targeted therapy, and hormone therapy. Also reviewed are emerging predictive models that convey biomarker status directly from morphology, extending the utility of artificial intelligence beyond conventional IHC interpretation. Finally, spatial pathobiology–related opportunities afforded by cell block preparations are discussed, and future directions for integrating artificial intelligence–enabled analysis into cytology workflows are outlined. Together, these advances position cytology cell blocks as an important platform for computational biomarker interpretation and morphology-driven precision oncology.</div></div>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":"196 7","pages":"Pages 1439-1446"},"PeriodicalIF":4.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147321260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-Cell Sequencing Plus Spatial Transcriptomics","authors":"Yingjing Wang,&nbsp;Xia Wu,&nbsp;Xiao Cheng,&nbsp;Rong Fang,&nbsp;Ping Zhang,&nbsp;Heli Wang,&nbsp;Senxiao Yu,&nbsp;Huizhi Zhang,&nbsp;Ming Zhao","doi":"10.1016/j.ajpath.2026.04.001","DOIUrl":"10.1016/j.ajpath.2026.04.001","url":null,"abstract":"<div><div><em>ASPSCR1::TFE3</em> renal cell carcinoma (RCC) is a rare subtype with frequent lymph node metastasis and poor prognosis. Its obscure pathogenesis and immune microenvironment greatly limit clinical management. Here, this study integrated single-cell sequencing and high-resolution spatial transcriptomics to profile formalin-fixed paraffin-embedded specimens of <em>ASPSCR1</em>::<em>TFE3</em> RCC and clear cell RCC. Marked distinctions in tumor and immune landscapes were identified between the two subtypes. Clear cell RCC was enriched in interferon response and hypoxia pathways, while <em>ASPSCR1</em>::<em>TFE3</em> RCC favored oxidative phosphorylation and epithelial-mesenchymal transition. Tumor cells were classified into C3/C4 clusters; the metastasis-related C3 subtype highly expressed pro-invasive genes including <em>MDK</em>, <em>MMP7</em>, and <em>VCAN</em>. <em>ASPSCR1</em>::<em>TFE3</em> RCC displayed an immunosuppressive microenvironment with abundant macrophages and exhausted T/natural killer cells. <em>MDK-LRP1</em> crosstalk between tumor cells and macrophages facilitated immune escape. These findings define <em>ASPSCR1</em>::<em>TFE3</em> RCC as a highly metastatic, immunosuppressive malignancy driven by C3 enrichment and elevated <em>MDK</em>, offering novel targets for targeted and immunotherapeutic strategies.</div></div>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":"196 7","pages":"Pages 1514-1529"},"PeriodicalIF":4.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the Discordance between AT-Rich Interacting Domain 1A Protein and Genotype in Endometrioid-Type Endometrial Tumors","authors":"Savannah E. LaBuda,&nbsp;Katherine Spence,&nbsp;Rahul R. Niranjan,&nbsp;Isurika Weeranshige,&nbsp;Macy L. Osborne-Frazier,&nbsp;Russell R. Broaddus,&nbsp;Andrew B. Gladden","doi":"10.1016/j.ajpath.2026.04.010","DOIUrl":"10.1016/j.ajpath.2026.04.010","url":null,"abstract":"<div><div><em>ARID1A</em> is one of the most frequently mutated genes in endometrial cancer, with approximately 40% of patients harboring an <em>ARID1A</em> mutation. However, relatively little is known about how AT-rich interacting domain 1A (ARID1A) protein loss shapes endometrial cancer pathogenesis. Mounting evidence from other malignancies suggests that ARID1A protein can be regulated post-translationally, independent of genotype. However, most studies in endometrial cancer evaluate genotype alone, overlooking the potential for alternative mechanisms of ARID1A loss. To address this gap, ARID1A protein expression and genotype were examined in endometrioid tumors, and associated transcriptional changes were characterized. Evaluation of ARID1A protein in 71 human endometrioid tumors demonstrates that protein loss can occur regardless of <em>ARID1A</em> genotype. Retention or deficiency of ARID1A protein was not significantly related to variant allele frequency or location of mutation in human tumors with mutant <em>ARID1A</em>. A human endometrial cancer cell model suggests that ARID1A protein loss can occur through proteasomal degradation. Furthermore, ARID1A protein expression was found to be a predictor of worse overall survival in The Cancer Genome Atlas cohort of <em>ARID1A</em> wild-type endometrioid tumors. Spatial transcriptomics of 16 human endometrioid tumors revealed that both genotype and protein expression of <em>ARID1A</em> play a role in shaping unique transcriptional signatures in endometrial cancer and can be used to predict patient prognosis. Suggesting evaluation of ARID1A should not be done solely by sequencing techniques.</div></div>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":"196 7","pages":"Pages 1530-1545"},"PeriodicalIF":4.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147863407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}