High-Contrast Luminescent Immunohistochemistry Using PEGylated Lanthanide Complexes.

Immunohistochemistry (IHC) using fluorescent probes provides high resolution with multiplexing capability, but the imaging contrast is limited by the brightness of the fluorescent probe and the intrinsic autofluorescence background from tissues. Herein, we improved the contrast by high-density labeling of long-lifetime lanthanide complexes and time-gated imaging. As the large (∼280 nm) Stokes shift of lanthanide complexes effectively prevents the issue of concentration quenching, we succeeded in conjugating seven europium complexes to an eight-arm hydrophilic poly(ethylene glycol) (PEG) linker for signal amplification with improved water solubility to the level of up to 10 mg/mL. Moreover, we demonstrated that both human epidermal growth factor receptor 2 (HER2) in a formalin-fixed paraffin-embedded (FFPE) tissue section and cytokeratin 18 (CK18) in a frozen section can be resolved with the enhanced contrast by 2-fold and 3-fold, respectively. Furthermore, we show that the PEGylation of multiple lanthanide complexes is compatible with tyramide signal amplification (TSA). This work suggests new opportunities for sensitive imaging of low-abundance biomarkers in a tissue matrix.

Fourier Channel Attention Powered Lightweight Network for Image Segmentation.

The accuracy of image segmentation is critical for quantitative analysis. We report a lightweight network FRUNet based on the U-Net, which combines the advantages of Fourier channel attention (FCA Block) and Residual unit to improve the accuracy. FCA Block automatically assigns the weight of the learned frequency information to the spatial domain, paying more attention to the precise high-frequency information of diverse biomedical images. While FCA is widely used in image super-resolution with residual network backbones, its role in semantic segmentation is less explored. Here we study the combination of FCA and U-Net, the skip connection of which can fuse the encoder information with the decoder. Extensive experimental results of FRUNet on three public datasets show that the method outperforms other advanced medical image segmentation methods in terms of using fewer network parameters and improved accuracy. It excels in pathological Section segmentation of nuclei and glands.