Citations

Age-related gastrointestinal dysfunction is common, but the mechanisms of aging-associated smooth muscle failure remain unclear. We show that aging in mice slows whole gut and colonic transit, increases regional stiffness, and reduces smooth muscle contractility. Inducible smooth muscle cell (SMC)– specific deletion of Piezo1 preserved youthful transit and force generation, whereas Piezo1 activation in young mice phenocopied aging-associated transit delay. Single-cell transcriptomics, RNA velocity, stiffness-controlled cell and tissue cultures, and pharmacologic studies revealed that Piezo1 couples increased stiffness to Ca 2+ –calcineurin–NFAT signaling, loss of contractile gene programs, leading to age- related contractile loss and contractile-to-synthetic SMC remodeling and gut wall stiffening. Human intestinal SMCs supported conservation of this pathway, and PIEZO1 gain-of-function carriers showed a trend toward delayed colonic transit. Thus, maladaptive SMC Piezo1 mechanotransduction is a targetable mechanism of aging-associated gut dysmotility. Aging alters organ structure, yet how these changes drive functional decline remains poorly defined. We show that progressive stiffening of the gut with age activates the mechanosensitive ion channel Piezo1 in smooth muscle cells, initiating a maladaptive response that impairs contractility and slows intestinal transit. On the other hand, deletion of smooth muscle Piezo1 prevents age-related gut dysmotility. These findings reveal that age-related physical shifts actively drive functional decline through mechanotransduction pathways. By identifying Piezo1 as a critical link between tissue mechanics and organ aging, this work provides a framework for understanding how mechanical cues contribute to age-related dysfunction in the gut and possibly other smooth muscle organs.

Abstract Background Multiplexed immunofluorescence (MxIF) enables high-dimensional immune cell phenotyping and detailed characterization of the tumor immune microenvironment (TIME), but complex cyclic staining can introduce signal degradation and uncertainty in cell-level measurements. In routine practice, pathologists rely on hematoxylin and eosin (H&E) staining as the primary reference for tissue morphology and frequently cross-reference H&E to interpret MxIF findings. Methods We present a framework for aligning H&E and MxIF images at the tissue microarray (TMA) core level to support cross-modal analysis. Cell nucleus detections from each modality are used as anchor points, formulating the task as a point-set alignment problem. Coherent Point Drift (CPD) is applied for global alignment, followed by graph-matching–based refinement to improve local consistency. Evaluation on ovarian TMAs demonstrates consistent alignment performance for both restained and serial sections. We further explore the use of restained H&E as a reference for MxIF interpretation and investigate virtual H&E generation from MxIF data as a complementary approach when restained H&E is unavailable. Results The aligned images enable quantitative assessment of cross-modal feature concordance between MxIF-derived measurements and H&E morphology. The generated virtual H&E demonstrated similar cell population compared to the real H&E. Conclusions Cell-centric alignment can facilitate integrative multimodal histopathology analysis for TMA cores in spatial co-localization, cell feature validation, and virtual H&E generation.

ABSTRACT The enteric nervous system (ENS) is a complex peripheral neural network that coordinates gastrointestinal motility through highly organized synaptic communication. Although tissue-resident muscularis macrophages (MMs) closely associate with enteric neurons, whether they regulate enteric synaptic organization remains unknown. In the central nervous system (CNS), microglia sculpt neural circuits through complement-dependent synaptic remodeling, raising the possibility that analogous neuroimmune mechanisms operate in the gut. Here, we identify a previously unrecognized role for C1qa□ MMs in regulating enteric synaptic homeostasis and gastrointestinal motility. Using macrophage-specific constitutive and inducible C1qa deletion models, single-cell RNA sequencing, enteric synaptosome proteomics, physiology, and advanced imaging, we demonstrate that loss of MMs-derived C1qa increases enteric synaptic density without altering neuronal numbers. C1qa deficiency induced broad transcriptional changes in enteric neurons and macrophages, including altered synapse-associated, lysosomal, and endocytic programs. Proteomic analysis revealed that enteric synapses share a conserved molecular architecture with brain synapses while exhibiting distinct gastrointestinal-specific complement-associated synaptic networks enriched for structural and receptor-localization pathways. Functionally, macrophage-specific C1qa deletion altered excitatory and inhibitory enteric neurotransmission, enhanced cholinergic signaling, reduced nitrergic responses, and accelerated gastrointestinal transit, while smooth muscle responsiveness remained preserved. C1qa□ MMs displayed transcriptional and functional features consistent with a phagocytic synapse-remodeling phenotype, including enrichment of complement, lysosomal, and engulfment pathways. Loss of C1qa impaired macrophage phagocytic activity both in vitro and in vivo and was associated with synapse accumulation and altered macrophage morphology. Importantly, inducible deletion of C1qa in adulthood recapitulated the synaptic and motility phenotypes, demonstrating that C1qa□ MMs continuously regulate enteric synaptic organization beyond development. Together, these findings identify a complement-dependent neuroimmune mechanism that regulates enteric circuit organization and gut motility, establishing MMs as active modulators of adult ENS synaptic homeostasis. Abstract Figure

Abstract Background: Epithelial ovarian cancer (EOC), commonly known as ovarian cancer (OvCa), is the deadliest gynecological malignancy. High tumor expression of either NR3C1 mRNA (encoding the glucocorticoid receptor or GR) or GR protein (IHC) are associated with poor overall survival (OS) in patients (pts) with advanced stage OvCa. We hypothesized that high GR expression and associated GR transcriptional activity favor OvCa cell survival and chemotherapy resistance, thereby reducing OS. Here we investigated whether a GR transcriptome-based gene signature (GR-sig) would be a better prognosticator than tumor NR3C1 mRNA expression alone. Methods: mRNAs up- or downregulated >1.5 fold after glucocorticoid treatment (Dex) in one or both cultured OvCa cell lines (OVSAHO and HEYA8) were defined as differentially expressed genes (DEGs). DEGs were then identified in n=1247 unique OvCa microarrays from 15 publicly available Gene Expression Omnibus (GEO) datasets with matched pt OS (training set). We then used the OS data to train a Cox proportional hazards model. The “best subset selection” (BeSS) algorithm and bootstrap sampling identified those GR-regulated DEGs whose tumor mRNA expression correlated most significantly with pt OS, creating a GR-sig “risk score.” Subsequently, all n=522 microarray and n=374 RNA-seq publicly available samples from the OvCa TCGA were used as two independent validation sets. Microarray and RNA-seq data were normalized using the MAS5 and FPKM-UQ algorithms respectively. The two validation sets were independently stratified relative to the median training set risk score into low- and high-risk score groups. To examine the prognostic ability of NR3C1 mRNA expression alone, the median training set NR3C1 expression value was used to stratify validation set pts into low- and high-expression groups. In all cases, OS was estimated by the Kaplan-Meier method. Results: We identified n=2288 protein-coding DEGs in one or both cell lines. Of these genes, n=1251 were expressed in each GEO microarray sample (training set). A 10-gene GR-signature (GR-Sig) was then identified using the BeSS algorithm and bootstrap sampling, yielding a median training set risk score of 1.02. In both the microarray and RNA-seq validation datasets, the 10-gene GR-sig risk score was associated with significant OS differences between low- and high-risk score groups (microarray p = 0.018; RNA-seq p = 0.00049). In contrast, the OS difference between low- and high- NR3C1 mRNA expression groups did not reach significance (microarray p = 0.12; RNA-seq p = 0.28). Conclusion: In this retrospective analysis, a 10-gene GR-sig derived from OvCa GR-regulated DEGs performed better as a prognosticator than NR3C1 mRNA expression alone. Prospective application of the GR-sig may be useful in stratifying patients for treatment response with novel agents in OvCa clinical trials. Citation Format: Ananth V. Panchamukhi, Lei Huang, Eleanore P. Gray, Yuanhang Liu, Manisha Taya, Jennifer T. Veneris, Xiaonan Hou, S John Weroha, Balázs Győrffy, Gini F. Fleming, Chen Wang, Suzanne D. Conzen. Development of a prognostic ovarian cancer gene signature based on glucocorticoid receptor transcriptional activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4739.

Abstract Multiplexed Immunofluorescence (MxIF) enables detailed immune cell phenotyping, providing critical insights into cell behavior within the tumor immune microenvironment (TIME). However, signal integrity can be compromised due to the complex cyclic staining processes inherent to MxIF. Hematoxylin and Eosin (H&E) staining, on the other hand, offers complementary information through its depiction of cell morphology and texture patterns and is often visually cross-referenced with MxIF in clinical settings. In this study, we proposed a novel framework to align H&E and MxIF images for precise cross-modal cell feature validation. Using cell detection outputs from each modality as anchors, we formulated the multimodal image registration problem as point set alignment. Coherent Point Drift (CPD) is employed for initial alignment, followed by Graph Matching (GM) for refinement. Evaluations on ovarian cancer tissue microarrays (TMAs) demonstrate that our method achieves high alignment accuracy, enabling reliable validation of cell-level features across modalities for both restained and serial sections. Our results indicate that restained H&E enhances confidence in findings derived from MxIF. Additionally, we demonstrated the feasibility of generating high-quality virtual H&E images from MxIF data when restained H&E is unavailable, offering a viable alternative for integrated multimodal analysis.

AbstractBackgroundThe aggregation of α‐synuclein protein, encoded by the SNCA gene, forms Lewy bodies (LBs) in neurons and is a key pathological feature of Lewy body dementia (LBD). Interestingly, the apolipoprotein E gene (APOE), primarily expressed in glial cells, is the strongest genetic modifier for LBD. The ε4 allele of this gene (APOE4) notably increases the risk of LBD. However, the specific mechanisms through which APOE‐regulated glial cells affect α‐synuclein aggregation in neurons are not yet fully understood.MethodTo explore this, we conducted spatial transcriptomics using the 10X Genomics Visium CytAssist platform on Formalin‐Fixed Paraffin‐Embedded tissue slides from the temporal cortex of human LBD brains (N = 4 brains in controls and N = 6 brains in LBD group, with an equal ratio of APOE3/3 and APOE3/4 genotypes). We defined the neuronal layers of the gray matter (Layers 1 to 6, L1‐6) and white matter in each brain based on the gene signature of each spot. We also identified LBs from hematoxylin and eosin (H&E) staining on the same slides, and annotated LB‐positive (LB+), LB‐surrounding and LB‐negative (LB‐) spots in each brain using Loupe Browser. We evaluated SNCA and APOE gene expression in each layer and analyzed cell‐cell communication using the CellChat tool across the layers.ResultWe observed that the SNCA gene was highly expressed in L5, which corresponded to the areas where most LBs were found. Notably, within LB+ spots across all layers, both SNCA and APOE gene expression levels were elevated in APOE3/4 cases compared to APOE3/3 cases. Furthermore, we identified cell‐cell communication networks between LB+ and LB‐surrounding spots, particularly in L4 and L5. These networks were enriched by microglia‐related signaling pathways, including APOE signaling, migration inhibitory factor (MIF) signaling, and colony stimulating factor 1 (CSF1) signaling. Interestingly, APOE3/4 brains exhibited significantly reduced interactions and lower interaction strength in these networks compared to APOE3/3 samples.ConclusionOur findings indicate that impaired cell‐cell communication, such as microglia‐mediated immune responses around LB regions, may be associated with the increased risk presented by the APOE4 allele in LBD brains.

Abstract Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.

Liver fibrosis is characterized by the activation of perivascular hepatic stellate cells (HSCs), the release of fibrogenic nanosized extracellular vesicles (EVs), and increased HSC glycolysis. Nevertheless, how glycolysis in HSCs coordinates fibrosis amplification through tissue zone-specific pathways remains elusive. Here, we demonstrate that HSC-specific genetic inhibition of glycolysis reduced liver fibrosis. Moreover, spatial transcriptomics revealed a fibrosis-mediated up-regulation of EV-related pathways in the liver pericentral zone, which was abrogated by glycolysis genetic inhibition. Mechanistically, glycolysis in HSCs up-regulated the expression of EV-related genes such as Ras-related protein Rab-31 ( RAB31 ) by enhancing histone 3 lysine 9 acetylation on the promoter region, which increased EV release. Functionally, these glycolysis-dependent EVs increased fibrotic gene expression in recipient HSC. Furthermore, EVs derived from glycolysis-deficient mice abrogated liver fibrosis amplification in contrast to glycolysis-competent mouse EVs. In summary, glycolysis in HSCs amplifies liver fibrosis by promoting fibrogenic EV release in the hepatic pericentral zone, which represents a potential therapeutic target.

Background: Gastrointestinal (GI) disorders like chronic constipation are more prevalent with aging. Aging GI tract becomes stiffer and exhibits delayed GI transit. Smooth muscle cells (SMCs) are involved in controlling GI transit and show reduced contractility with age. Piezo1 is a mechanically activated ion channel that responds to membrane tension by opening its pore to allow flow of ions such as calcium (Ca2+) and regulates various cellular processes through Ca2+-calcineurin signaling pathway. We wondered GI stiffness changes might control SMC contractile to non-contractile phenotype through Piezo1. Objective: To determine how substrate stiffness regulates GI SMC contractile phenotype through Piezo1. Hypothesis: SMC Piezo1 senses GI tissue stiffness changes and alters SMC phenotype. Methods: Pathway analysis on human colon single cell sequencing data was performed using Gene Set Enrichment Analysis (GSEA) pre-ranked module with publicly available gene-sets from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Immortalized human jejunal SMCs (HuSMCs) and freshly dissociated SMCs from control (CTRL, Myh11-creERT2) and SMC specific Piezo1 knockout [Myh11-creERT2xPiezo1 Flox (Piezo1-KO)] mouse colons (primary SMCs) were cultured on soft (1kPa) and stiff (50kPa) substrates to model age-dependent shifts in intestinal stiffness for 48 hours either in the presence/absence of Piezo1 siRNA, Piezo1 agonist (Yoda1, 1μM), and calcineurin blocker (Cyclosporin A, CsA, 1μM) and used for Ca2+ imaging, RT-qPCR and immunostaining. Results: GSEA showed Piezo1+ve SMCs were significantly enriched in pathways involved in the regulation of Ca2+ signaling compared to Piezo1−ve SMCs (p<0.05). HuSMCs and primary SMCs showed increased Piezo1 expression (HuSMCs:1.1±0.2-fold, n=8, p<0.05; primary SMCs:2.1±0.1-fold, n=3, p<0.05) and cytoplasmic Ca2+ (3.6-fold, n=10, p<0.05) in response to Yoda1 on stiff substrates compared to soft. HuSMCs on stiff substrate showed decreased expression of contractile SMC markers smoothelin ( SMTN:0.7±0.2-fold, n=7, p<0.05) and transgelin ( TAGLN:0.6±0.1-fold, n=7, p<0.05) which was rescued by Piezo1 siRNA treatment ( SMTN:1.2±0.1-fold, n=2-7, p<0.05; TAGLN:1.15±0.02-fold, n=2-7, p<0.05) (Piezo1 is necessary). Stimulation by Yoda1 of HuSMCs on the soft substrate resulted in decreased expression of SMTN (0.7±0.3-fold, n=2-7, p<0.05) and TAGLN (0.7±0.1-fold, n=2-7, p<0.05) (Piezo1 is suffcient). CTRL primary SMCs on stiff substrate showed decreased expression of Smtn (0.4±0.1-fold, n=3, p<0.05) and Tagln (0.3±0.2-fold, n=3, p<0.05) compared to SMCs on soft substrate which was reversed when SMCs from Piezo1-KO mice were cultured on these substrates ( Smtn:1.8±0.5-fold, n=3, p<0.05; Tagln:2.4±0.07-fold, n=3, p<0.05). Treatment of CsA in HuSMCs cultured on stiff substrate also rescued from loss of SMTN (1.3±0.1-fold, n=3, p<0.05) and TAGLN (1.7±0.3-fold, n=3, p<0.05) expression and prevented nuclear factor of activated T cells (NFAT) nuclear translocation. Summary and Conclusion: GI SMC Piezo1 senses stiffness changes and regulates SMC phenotype through Ca2+-calcineurin signaling pathway. Targeting Piezo1 might be protective from age induced reduced GI SMC contractility. NIH DK052766, DK123549. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract Purpose: Benign breast disease (BBD) is viewed as a non-obligate precursor stage in the development of breast cancer (BC). Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks has been routinely used in BC research. However, RNA-seq of heterogeneous bulk samples represents an averaged expression profile across all cell mixtures. Downstream analyses, such as biomarker discovery, are likely confounded by differences in cell proportions. There is a lack of research in optimizing strategies for cell type deconvolution especially for FFPE samples. We evaluated and optimized strategies for cell type deconvolution using existing single cell RNA-seq (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) breast tissue references, and applied these to a cohort of BBD FFPE samples to assess risk of subsequent BC. Methods: 7 scRNA-seq and 3 snRNA-seq public samples from women’s breast tissue were normalized using SCTransform, then integrated using Harmony to form a complete reference set. Bioinformatics evaluations examined the performance of existing statistical and deep learning-based deconvolution methods. Computational simulations were conducted to evaluate those methods on three scenarios: 1) baseline; 2) incomplete reference set; 3) FFPE artifacts. FFPE artifacts were simulated based on a generalized additive model using 7 in-house paired FFPE-fresh frozen samples. Finally, we applied the best performing method to our BBD cohort of 62 samples. Differential expression analysis adjusting for cell proportions was performed to identify transcriptomic aberrations in BBD cases. Results: snRNA-seq was more sensitive in capturing adipocytes, while scRNA-seq was more sensitive in detecting lymphocytes in breast tissue. The median number of detectable genes per cell was higher for scRNA-seq than snRNA-seq before integration (1264 vs 558). Marker gene-based regression methods (e.g. CIBERSORT) displayed higher variance and lower bias compared to methods directly using scRNA-seq data (e.g. SCDC). Incomplete reference data led to increased bias and little effect on variance. FFPE artifacts had a higher impact on performance compared to incomplete reference data. Overall, Scaden, a deep learning-based method, was the top performing method across all simulation scenarios and was especially robust to FFPE artifacts. 59 differential expressed genes (DEGs) were identified with fold change &gt; 1.5 and p value &lt; 0.01. DEGs involved in BC, e.g. S100A8, WNT4 and HIST1H3H, would be left out if cell proportions were not properly adjusted. Conclusions: We constructed the first complete single cell reference data for women’s breast tissue and benchmarked existing cell type deconvolution methods. Compared to analyses not adjusting for cell proportions, we observed substantial differences in perturbed genes and pathways associated with BC risk for BBD patients. Citation Format: Yuanhang Liu, Chen Wang, Stacey J. Winham, Aditya Bhagwate, Robert A. Vierkant, Julie M. Cunningham, Bryan M. McCauley, William A. Jons, Derek C. Radisky, Amy C. Degnim. Deconvolution of cell type composition in FFPE tissue: Application to benign breast disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4291.