A bstract Spatial transcriptomics (ST) technologies have gained increasing popularity due to their ability to provide positional context of gene expressions in a tissue. One major limitation of current commercially available ST methods such as the 10X Genomics Visium platform is the lack of single cell resolution. Cell type deconvolution for ST data is critical in order to fully reveal underlying biological mechanisms. Existing ST data deconvolution methods share two common limitations: first, few of them utilize spatial neighborhood information. Existing methods such as RCTD and SPOTlight intrinsically treat each spatial spot as independent of neighboring spots, although we anticipate nearby spots to share similar cell type compositions based on clinical evidence of tissue structures. Such limitation could be amplified when sequencing depths at single spots are relatively low so that borrowing information from neighboring spots is necessary in order to obtain reliable deconvolution results. Second, although Visium data provide us with a histological image which could add additional information regarding spot heterogeneity, most existing methods do not utilize this H&E image. To solve these two limitations, we developed Multiscale Adaptive ST Deconvolution (MAST-Decon), a smooth deconvolution method for ST data. MAST-Decon uses a weighted likelihood approach and incorporates both gene expression data, spatial neighborhood information and H&E image features by constructing different kernel functions to obtain a smooth deconvolution result. We showcased the strength of MAST-Decon through simulations based on real data, including a single-cell dataset of mouse brain primary visual cortex, and real-world Visium datasets to demonstrate its robust and superior performance compared with other state-of-the-art methods.
Research Article| March 01, 2015 GEOLOGY, Re-Os AGES, SULFUR AND LEAD ISOTOPES OF THE DIYANQINAMU PORPHYRY Mo DEPOSIT, INNER MONGOLIA, NE CHINA Cheng-Biao Leng; Cheng-Biao Leng † 1State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550002, China2ARC Centre of Excellence in Ore Deposits (CODES), School of Physical Sciences, University of Tasmania, Hobart 7001, Australia †Corresponding authors: e-mail, lengchengbiao@vip.gyig.ac.cn; huangzhi-long@vip.gyig.ac.cn Search for other works by this author on: GSW Google Scholar Xing-Chun Zhang; Xing-Chun Zhang 1State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550002, China Search for other works by this author on: GSW Google Scholar Zhi-Long Huang; Zhi-Long Huang † 1State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550002, China †Corresponding authors: e-mail, lengchengbiao@vip.gyig.ac.cn; huangzhi-long@vip.gyig.ac.cn Search for other works by this author on: GSW Google Scholar Qiu-Yue Huang; Qiu-Yue Huang 2ARC Centre of Excellence in Ore Deposits (CODES), School of Physical Sciences, University of Tasmania, Hobart 7001, Australia Search for other works by this author on: GSW Google Scholar Shou-Xu Wang; Shou-Xu Wang 3Shandong Gold Group Co. Ltd., Jinan 250014, China Search for other works by this author on: GSW Google Scholar De-Yun Ma; De-Yun Ma 3Shandong Gold Group Co. Ltd., Jinan 250014, China Search for other works by this author on: GSW Google Scholar Tai-Yi Luo; Tai-Yi Luo 1State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550002, China Search for other works by this author on: GSW Google Scholar Chao Li; Chao Li 4National Research Center of Geoanalysis, Beijing 100037, China Search for other works by this author on: GSW Google Scholar Wen-Bo Li Wen-Bo Li 5Laboratory of Crustal and Orogenic Evolution, Peking University, Beijing 100871, China Search for other works by this author on: GSW Google Scholar Economic Geology (2015) 110 (2): 557–574. https://doi.org/10.2113/econgeo.110.2.557 Article history received: 17 Jan 2014 accepted: 18 Jun 2014 first online: 09 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Cheng-Biao Leng, Xing-Chun Zhang, Zhi-Long Huang, Qiu-Yue Huang, Shou-Xu Wang, De-Yun Ma, Tai-Yi Luo, Chao Li, Wen-Bo Li; GEOLOGY, Re-Os AGES, SULFUR AND LEAD ISOTOPES OF THE DIYANQINAMU PORPHYRY Mo DEPOSIT, INNER MONGOLIA, NE CHINA. Economic Geology 2015;; 110 (2): 557–574. doi: https://doi.org/10.2113/econgeo.110.2.557 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEconomic Geology Search Advanced Search Abstract The Diyanqinamu porphyry Mo deposit in the southern Greater Khingan Range of the Central Asian orogenic belt contains 800 million metric tons (Mt) of ore with an average grade of 0.097% molybdenum. The deposit is hosted in Late Jurassic volcanic rocks of tuff, andesite, and volcanic breccia. Multiple-stage hydrothermal activities have resulted in propylitic, phyllic, and argillic alteration in this deposit. Five stages (I–V) of hydrothermal activity are identified. Stage I is represented by a mineral assemblage of epidote, chlorite, and magnetite, with some discontinuous barren veinlets of quartz + K-feldspar ± fluorite ± magnetite ± epidote ± chlorite. Stage II is marked by occurrence of quartz + fluorite + molybdenite + magnetite ± pyrite ± sericite ± siderite veinlets/veins with phyllic halos. Stage III consists of fluorite + siderite + quartz + molybdenite + pyrite ± ankerite ± calcite ± chalcopyrite veins that are commonly related to phyllic alteration and dissemination of fluorite in the altered rocks. Stage IV has an assemblage of fluorite + quartz + pyrite ± ankerite ± calcite ± molybdenite ± chalcopyrite ± sphalerite ± galena in coarse veins (10–20 mm wide). Stage V consists of narrow (≤5-mm wide) veinlets of calcite + fluorite + pyrite ± quartz. Molybdenite mainly occurs in Stages II and III.Re-Os dating results for molybdenite samples from these two stages yielded an isochron age of 156.2 ± 4.2 Ma (2σ, MSWD = 0.96, n = 10). Most molybdenite samples have high δ34S values (≥8.4‰) relative to other sulfide minerals (i.e., galena, sphalerite, pyrite, and chalcopyrite) of Stages II to V (δ34S = 2.5–8.3‰, n = 22). Molybdenite also has low 207Pb/204Pb and 208Pb/204Pb ratios relative to other sulfide minerals although there are minor overlaps. In a diagram of 206Pb/204Pb versus 207Pb/204Pb, these Pb isotope data display a positive trend transecting the growth curves of crustal lead, which could be invoked by mixing of crustal and mantle sources with distinct Pb isotopes. In combination with the S isotope data and mineral paragenesis, we suggest that magmas were the main source of molybdenum, whereas other metals (i.e., Pb, Zn, and Cu) were possibly sourced from the country rocks. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Background: Computed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes. Methods: From May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs. Results: In cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512–97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640–0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096–71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106–0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694–0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508–10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305–10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838–13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225–13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026–0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215–0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179–0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090–0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208–0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840–0.897; P<0.001). Conclusions: CT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.
Phosphatase and tensin homolog deleted on chromosome ten (PTEN) is a lipid and protein phosphatase and possesses an antitumor effect in lung cancers. miRNAs are reportedly abnormally expressed in human lung cancers. However, whether miRNA contributes to PTEN expression in non-small cell lung cancers (NSCLCs) has not been clearly clarified. In the present study, we found that miR-1297 probably binds with 3'UTR sequence of PTEN and negatively regulated the levels of PTEN in NSCLC cells. First, the expression levels of PTEN and Skp2 were detected by western blotting in NSCLC specimens and paired normal tissue specimens. The results showed that decreased levels of PTEN were detected in NSCLC tissues, compared with paired control tissues (**p < 0.01). The expression levels of PTEN were conversely correlated with the levels of Skp2 in clinical NSCLC specimens and NSCLC cell line. Transfection with miR-1297 mimic significantly promoted cell viability of A549 cells and NCI-H460 cells by downregulating the level of PTEN and upregulating the expression of Skp2. Interestingly, knockdown of Skp2 did not affect the expression of PTEN in A549 cells. Thus, miR-1297 might work as an oncogene by regulating PTEN/Akt/Skp2 signaling pathway in NSCLC cells. PTEN and Skp2 might be the potential targets in the clinical therapy of lung cancers.
Abstract Next-generation sequencing (NGS) of bulk cell populations is a useful and ubiquitous tool for the molecular characterization of clinical tumor samples. Bulk NGS reveals transcript abundance within a tumor sample and can further infer cell populations via deconvolution algorithms (PMID:31570899). However, it can’t ascribe the cellular context for a given gene’s expression or elucidate the spatial organization of tumor microenvironments. These additional features are critical to our understanding of tumor biology and are key to the development of immuno-oncology therapeutics. Spatial Transcriptomics (ST) is an emerging technology that characterizes gene expression within the spatial context of tissue. ST data can be generated directly from archival formalin fixed paraffin embedded samples, enabling the study of spatial gene expression in real-world clinical settings. We have studied a dataset comprising 6 samples from non-small cell lung cancer (NSCLC) patients and 1 core needle biopsy from a tumor of unknown origin. We used the 10X Visium CytAssist platform to generate ST data and additionally generated paired bulk RNAseq data. To test the interassay reliability of CytAssist on archival FFPE tissue sections, we compared ST results across 3 sample preparation conditions. We further studied the state of the tumor microenvironment by applying state-of-the-art computational approaches to deconvolve immune cell populations and produce super-resolution ST maps, validated using multiplex immunofluorescence (IF) via CODEX (PMID:30078711). We find key quality control metrics and spatial biomarkers are consistent across all 3 sample preparation conditions. When comparing deconvolution results between bulk and spatially-resolved methods we observe modest correlations for many cell types despite differences in sample preparation, supporting the idea that bulk and spatial samples contain complementary transcriptomic information. However, within samples, we find many of the correlations observed in bulk do not show strong spatial correlation. These comparisons indicate the importance of considering spatial context when studying the tumor immune microenvironment. Finally, we find an agreement between super-resolution ST and multiplex IF across key spatial biomarkers. These results demonstrate clinical archival FFPE samples yield high interassay reliability via the CytAssist platform. Results were consistent through 3 different H&E staining protocols and findings were consistent when superresolution deconvolution was utilized which further strongly correlated with high-resolution multiplex IF. Our findings demonstrate the feasibility and translational utility of ST to discover spatial signatures and the cellular context in retrospective clinical cohorts to empower discovery and translational efforts in precision oncology and therapeutic development. Citation Format: Mario G. Rosasco, Chi-Sing Ho, Tianyou Luo, Michelle M. Stein, Luca Lonini, Martin C. Stumpe, Jagadish Venkataraman, Sonal Khare, Ameen A. Salahudeen. Comparison of interassay similarity and cellular deconvolution in spatial transcriptomics data using Visum CytAssist. [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 4692.
Purpose: This study aimed to explore the differences in the magnetic resonance imaging (MRI) findings between intraspinal tuberculosis and metastatic cancer, which may aid in making the correct diagnosis. Patients and Methods: The clinical features and MRI findings of 15 patients with intraspinal tuberculosis and 11 patients with intraspinal metastatic cancers were retrospectively analyzed. Results: The mean ages of the patients with intraspinal tuberculosis and metastatic cancer were 26.3 (15– 42) and 52.1 (38– 67) years, respectively. All intraspinal tuberculosis cases were secondary to primary extraspinal tuberculosis, including tuberculous meningitis (11/15), as well as pulmonary (9/15), vertebral (5/15), urinary tract (1/15), abdominal (1/15), cervical lymph node (1/15), and multisystem tuberculosis (9/15). The intraspinal metastases originated from the breast (5/11), lung (3/11), kidney (1/11), ovarian (1/11), and nasopharyngeal cancers (1/11). Both intraspinal tuberculosis and metastatic cancers presented with multiple intra- and extramedullary lesions throughout all regional segments of the spinal canal, accompanied by irregularly thickened meninges. Intraspinal tuberculous lesions had indistinct edges that integrated with each other, most of them exhibiting obvious enhancement on MRI. Conversely, intraspinal metastatic lesions were distinctly separated with clear edges and exhibited lesser enhanced MRI than intraspinal tuberculosis. Conclusion: A combined analysis of clinical features and MRI findings may be helpful in differentiating intraspinal tuberculosis from metastatic cancer. Keywords: intraspinal tuberculosis, intraspinal metastatic cancer, intramedullary nodule, leptomeningitis
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