Crown-like structures (CLSs) are adipose microenvironments of macrophages engulfing adipocytes. Their histological density in visceral adipose tissue (VAT) predicts metabolic disorder progression in obesity and is believed to initiate obesity comorbidities. Here, we use three-dimensional (3D) light sheet microscopy and deep learning to quantify 3D features of VAT CLSs in lean and obese states. Obese CLS densities are significantly higher, composing 3.9% of tissue volume compared with 0.46% in lean tissue. Across the states, individual CLS structural characteristics span similar ranges; however, subpopulations are distinguishable. Obese VAT contains large CLSs absent from lean tissues, located near the tissue center, while lean CLSs have higher volumetric cell densities and prolate shapes. These features are consistent with inefficient adipocyte elimination in obesity that contributes to chronic inflammation, representing histological biomarkers to assess adipose pathogenesis. This tissue processing, imaging, and analysis pipeline can be applied to quantitatively classify 3D microenvironments across diverse tissues.
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http://dx.doi.org/10.1126/sciadv.abe2480 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Center for Complexity and Biosystems, Department of Environmental Science and Policy, University of Milan, 20133 Milan, Italy.
Collective migration of cancer cells is often interpreted using concepts derived from the physics of active matter, but the experimental evidence is mostly restricted to observations made in vitro. Here, we study collective invasion of metastatic cancer cells injected into the mouse deep dermis using intravital multiphoton microscopy combined with a skin window technique and three-dimensional quantitative image analysis. We observe a multicellular but low-cohesive migration mode characterized by rotational patterns which self-organize into antiparallel persistent tracks with orientational nematic order.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, Beijing, 100084, China.
Single nanoparticle analysis is crucial for various applications in biology, materials, and energy. However, precisely profiling and monitoring weakly scattering nanoparticles remains challenging. Here, it is demonstrated that deep learning-empowered plasmonic microscopy (Deep-SM) enables precise sizing and collision detection of functional chemical and biological nanoparticles.
View Article and Find Full Text PDFJ Transl Med
January 2025
Dental School, The University of Western Australia, 17 Monash Avenue, Nedlands, WA, 6009, Australia.
Background: Treatment of deep carious lesions poses significant challenges in dentistry, as complete lesion removal risks compromising pulp vitality, while selective removal often reduces the longevity of restorations. Herein, we propose a minimally invasive approach using High-Intensity Focused Ultrasound (HIFU) for microscale removal of carious dentine. Concurrently, HIFU's antimicrobial effects against associated cariogenic biofilms and the corresponding thermal and biological impacts on surrounding tissues were investigated.
View Article and Find Full Text PDFNature
January 2025
Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, CA, USA.
Microscopy and crystallography are two essential experimental methodologies for advancing modern science. They complement one another, with microscopy typically relying on lenses to image the local structures of samples, and crystallography using diffraction to determine the global atomic structure of crystals. Over the past two decades, computational microscopy, encompassing coherent diffractive imaging (CDI) and ptychography, has advanced rapidly, unifying microscopy and crystallography to overcome their limitations.
View Article and Find Full Text PDFPharm Res
January 2025
Synthetic Molecule Pharmaceutical Sciences, Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA.
Purpose: The purpose of this study is to present a correlative microscopy-tomography approach in conjunction with machine learning-based image segmentation techniques, with the goal of enabling quantitative structural and compositional elucidation of real-world pharmaceutical tablets.
Methods: Specifically, the approach involves three sequential steps: 1) user-oriented tablet constituent identification and characterization using correlative mosaic field-of-view SEM and energy dispersive X-ray spectroscopy techniques, 2) phase contrast synchrotron X-ray micro-computed tomography (SyncCT) characterization of a large, representative volume of the tablet, and 3) constituent segmentation and quantification of the imaging data through user-guided, iterative supervised machine learning and deep learning.
Results: This approach was implemented on a real-world tablet containing 15% API and multiple common excipients.
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