Single-cell transcriptome atlases of soybean root and mature nodule reveal new regulatory programs that control the nodulation process.

The soybean root system is complex. In addition to being composed of various cell types, the soybean root system includes the primary root, the lateral roots, and the nodule, an organ in which mutualistic symbiosis with N-fixing rhizobia occurs. A mature soybean root nodule is characterized by a central infection zone where atmospheric nitrogen is fixed and assimilated by the symbiont, resulting from the close cooperation between the plant cell and the bacteria. To date, the transcriptome of individual cells isolated from developing soybean nodules has been established, but the transcriptomic signatures of cells from the mature soybean nodule have not yet been characterized. Using single-nucleus RNA-seq and Molecular Cartography technologies, we precisely characterized the transcriptomic signature of soybean root and mature nodule cell types and revealed the co-existence of different sub-populations of B. diazoefficiens-infected cells in the mature soybean nodule, including those actively involved in nitrogen fixation and those engaged in senescence. Mining of the single-cell-resolution nodule transcriptome atlas and the associated gene co-expression network confirmed the role of known nodulation-related genes and identified new genes that control the nodulation process. For instance, we functionally characterized the role of GmFWL3, a plasma membrane microdomain-associated protein that controls rhizobial infection. Our study reveals the unique cellular complexity of the mature soybean nodule and helps redefine the concept of cell types when considering the infection zone of the soybean nodule.

STCS-Net: a medical image segmentation network that fully utilizes multi-scale information.

In recent years, significant progress has been made in the field of medical image segmentation through the application of deep learning and neural networks. Numerous studies have focused on optimizing encoders to extract more comprehensive key information. However, the importance of decoders in directly influencing the final output of images cannot be overstated. The ability of decoders to effectively leverage diverse information and further refine crucial details is of paramount importance. This paper proposes a medical image segmentation architecture named STCS-Net. The designed decoder in STCS-Net facilitates multi-scale filtering and correction of information from the encoder, thereby enhancing the accuracy of extracting vital features. Additionally, an information enhancement module is introduced in skip connections to highlight essential features and improve the inter-layer information interaction capabilities. Comprehensive evaluations on the ISIC2016, ISIC2018, and Lung datasets validate the superiority of STCS-Net across different scenarios. Experimental results demonstrate the outstanding performance of STCS-Net on all three datasets. Comparative experiments highlight the advantages of our proposed network in terms of accuracy and parameter efficiency. Ablation studies confirm the effectiveness of the introduced decoder and skip connection module. This research introduces a novel approach to the field of medical image segmentation, providing new perspectives and solutions for future developments in medical image processing and analysis.

CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes.

Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe ( https://github.com/yao-laboratory/cellMarkerPipe ), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.

cellMarkerPipe: Cell Marker Identification and Evaluation Pipeline in Single Cell Transcriptomes.

Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe (https://github.com/yao-laboratory/cellMarkerPipe), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.