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The spatial resolution of omics dynamics is fundamental to understanding tissue biology. Spatial profiling of DNA methylation, which is a canonical epigenetic mark extensively implicated in transcriptional regulation, remains an unmet demand. Here, we introduce a method for whole genome spatial co-profiling of DNA methylation and transcriptome of the same tissue section at near single-cell resolution. Applying this technology to mouse embryogenesis and postnatal brain resulted in rich DNA-RNA bimodal tissue maps. These maps revealed the spatial context of known methylation biology and its interplay with gene expression. The two modalities' concordance and distinction in spatial patterns highlighted a synergistic molecular definition of cell identity in spatial programming of mammalian development and brain function. By integrating spatial maps of mouse embryos at two different developmental stages, we reconstructed the dynamics of both epigenome and transcriptome underlying mammalian embryogenesis, revealing details in sequence, cell type, and region-specific methylation-mediated transcriptional regulation. This method extends the scope of spatial omics to DNA cytosine methylation for a more comprehensive understanding of tissue biology over development and disease.
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http://dx.doi.org/10.1101/2025.07.01.662607 | DOI Listing |
Water Res
September 2025
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China. Electronic address:
Groundwater overextraction presents persistent challenges due to strategic interdependence among decentralized users. While game-theoretic models have advanced the analysis of individual incentives and collective outcomes, most frameworks assume fully rational agents and neglect the role of cognitive and social factors. This study proposes a coupled model that integrates opinion dynamics with a differential game of groundwater extraction, capturing the interaction between institutional authority and evolving stakeholder preferences.
View Article and Find Full Text PDFObjectiveThis work examined performance costs for a spatial integration task when two sources of information were presented at increasing eccentricities with an augmented-reality (AR) head-mounted display (HMD).BackgroundSeveral studies have noted that different types of tasks have varying costs associated with the spatial proximity of information that requires mental integration. Additionally, prior work has found a relatively negligible role of head movements associated with performance costs.
View Article and Find Full Text PDFMol Pharm
September 2025
Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Tissue factor (TF) has emerged as a promising target for the diagnosis and treatment of hepatocellular carcinoma (HCC). However, there is limited data available on TF-related PET imaging for longitudinal monitoring of the pathophysiological changes during HCC formation. Herein, we aimed to explore the TF-expression feature and compare a novel TF-targeted PET probe with F-FDG through longitudinal imaging in diethylnitrosamine (DEN)-induced rat HCC.
View Article and Find Full Text PDFBlood Adv
September 2025
BC Cancer, Vancouver, British Columbia, Canada.
Classical Hodgkin Lymphoma (CHL) is characterized by a complex tumor microenvironment (TME) that supports disease progression. While immune cell recruitment by Hodgkin and Reed-Sternberg (HRS) cells is well-documented, the role of non-malignant B cells in relapse remains unclear. Using single-cell RNA sequencing (scRNA-seq) on paired diagnostic and relapsed CHL samples, we identified distinct shifts in B-cell populations, particularly an enrichment of naïve B cells and a reduction of memory B cells in early-relapse compared to late-relapse and newly diagnosed CHL.
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science and Technology, Huaiyin Normal University, Huai'an, Jiangsu, China.
Previous studies have demonstrated that metric learning approaches yield remarkable performance in the field of kinship verification. Nevertheless, a prevalent limitation of most existing methods lies in their over-reliance on learning exclusively from specified types of given kin data, which frequently results in information isolation. Although generative-based metric learning methods present potential solutions to this problem, they are hindered by substantial computational costs.
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