We present a novel nuclear targeting nanoprobe based on peptide functionalized gold nanoparticles and its surface-enhanced Raman scattering (SERS) in living cells. For the first time, we probe an original SERS signal from the living cell nucleus by using high-selectivity functionalized gold nanoparticles. The gold nanoparticles conjugated with SV-40 large T nuclear localization signal (NLS) peptide successfully enter the cell nucleus after the incubation with Hela cells and deliver the spatially localized chemical information of the nucleus, as well as the signature of chemicals that intruded subsequently. This new targeted nanoprobe is a nontoxic, biocompatible method for biological research, provided with multiple functions comprising subcellular targeting, intracellular imaging, and real-time SERS detection.
Domain adaptation aims to transfer the enriched label knowledge from large amounts of source data to unlabeled target data. It has raised significant interest in multimedia analysis. Existing researches mainly focus on learning domain-wise transferable representations via statistical moment matching or adversarial adaptation techniques, while ignoring the class-wise mismatch across domains, resulting in inaccurate distribution alignment. To address this issue, we propose a Joint Adversarial Domain Adaptation (JADA) approach to simultaneously align domain-wise and classwise distributions across source and target in a unified adversarial learning process. Specifically, JADA attempts to solve two complementary minimax problems jointly. The feature generator aims to not only fool the well-trained domain discriminator to learn domain-invariant features, but also minimize the disagreement between two distinct task-specific classifiers' predictions to synthesize target features near the support of source class-wisely. As a result, the learned transferable features will be equipped with more discriminative structures, and effectively avoid mode collapse. Additionally, JADA enables an efficient end-to-end training manner via a simple back-propagation scheme. Extensive experiments on several real-world cross-domain benchmarks, including VisDA-2017, ImageCLEF, Office-31 and digits, verify that JADA can gain remarkable improvements over other state-of-the-art deep domain adaptation approaches. CCS CONCEPTS• Computing methodologies → Transfer learning; Image representations; • Computer systems organization → Neural networks.
Cells are complex chemical systems, where the molecular composition at different cellular locations and specific intracellular chemical interactions determine the biological function. An in-situ nondestructive characterization of the complicated chemical processes (like e.g. apoptosis) is the goal of our study. Here, we present the results of simultaneous and three-dimensional imaging of double organelles (nucleus and membrane) in single HeLa cells by means of either labelled or label-free surface-enhanced Raman spectroscopy (SERS). This combination of imaging with and without labels is not possible when using fluorescence microscopy. The SERS technique is used for a stereoscopic description of the intrinsic chemical nature of nuclei and the precise localization of folate (FA) and luteinizing hormone-releasing hormone (LHRH) on the membrane under highly confocal conditions. We also report on the time-dependent changes of cell nuclei as well as membrane receptor proteins during apoptosis analyzed by statistical multivariate methods. The multiplex three-dimensional SERS imaging technique allows for both temporal (real time) and spatial (multiple organelles and molecules in three-dimensional space) live-cell imaging and therefore provides a new and attractive 2D/3D tracing method in biomedicine on subcellular level.
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