Secreted proteins mediate essential physiological processes. With conventional assays, it is challenging to map the spatial distribution of proteins secreted by single cells, to study cell-to-cell heterogeneity in secretion, or to detect proteins of low abundance or incipient secretion. Here, we introduce the "FluoroDOT assay," which uses an ultrabright nanoparticle plasmonic-fluor that enables high-resolution imaging of protein secretion. We find that plasmonic-fluors are 16,000-fold brighter, with nearly 30-fold higher signal-to-noise compared with conventional fluorescence labels. We demonstrate high-resolution imaging of different secreted cytokines in the single-plexed and spectrally multiplexed FluoroDOT assay that revealed cellular heterogeneity in secretion of multiple proteins simultaneously. Using diverse biochemical stimuli, including infection, and a variety of immune cells such as macrophages, dendritic cells (DCs), and DC-T cell co-culture, we demonstrate that the assay is versatile, facile, and widely adaptable for enhancing biological understanding of spatial and temporal dynamics of single-cell secretome.
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http://dx.doi.org/10.1016/j.crmeth.2022.100267 | DOI Listing |
Cancer Lett
January 2025
Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, P.R. China, 210029; The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, Jiangsu Province, China. Electronic address:
Preoperative detection of muscle-invasive bladder cancer (MIBC) remains a great challenge in practice. We aimed to develop and validate a deep Vesical Imaging Network (ViNet) model for the detection of MIBC using high-resolution Tweighted MR imaging (hrTWI) in a multicenter cohort. ViNet was designed using a modified 3D ResNet, in which, the encoder layers were pretrained using a self-supervised foundation model on over 40,000 cross-modal imaging datasets for transfer learning, and the classification modules were weakly supervised by an experiential knowledge-domain mask indicated by a nnUNet segmentation model.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFEur Arch Otorhinolaryngol
January 2025
Department of Radiology, Istanbul Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.
Purpose: Cochlear implantation (CI) surgery is essential for restoring hearing in individuals with severe sensorineural hearing loss. Accurate placement of the electrode within the cochlea is essential for successful auditory outcomes and minimizing complications. This study aims to analyze the relationship between the round window niche (RWN) alignment, its visibility during surgery, and the impact on surgical techniques and outcomes.
View Article and Find Full Text PDFUltrasound Med Biol
January 2025
PUC - Private Ultrasound Center Graz, Lassnitzhoehe, Austria; Medical University Vienna, Department of Biomedical Imaging and Image-guided Therapy, Vienna, Austria.
This is the first of a two-part article in which we focus on the Ultrasound (US) appearance of the normal median nerve (MN) and its main branches. The detailed anatomy and US anatomy of the MN course are presented with high-resolution images obtained with the latest-generation US machines and transducers. Variations are discussed to avoid misinterpretation of normal findings.
View Article and Find Full Text PDFNeuroimage
January 2025
Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
A fast BEM (boundary element method) based approach is developed to solve an EEG/MEG forward problem for a modern high-resolution head model. The method utilizes a charge-based BEM accelerated by the fast multipole method (BEM-FMM) with an adaptive mesh pre-refinement method (called b-refinement) close to the singular dipole source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models within 90 seconds after initial model assembly using a regular workstation.
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