Yang Xu

Spatial transcriptomics (ST) has rapidly expanded with the introduction of multiple high-resolution platforms, yet cross-platform benchmarking remains limited and largely focused on technical performance. Here we present SpatialBench , a matched multi-platform resource comprising Visium HD, Xenium and MERSCOPE data together with single-cell and single-nucleus references from a malaria-challenged wild-type and B cell-specific Tbx21 knockout mouse spleen model. In this system, loss of T-bet in B cells disrupts germinal center (GC) polarization and antibody maturation, providing a biologically grounded benchmark for technology comparison. We leveraged this system to systematically evaluate ST platform performance using technical and biological readouts. Across platforms, immune organization and Tbx21 -associated programs were consistently recovered, indicating robustness of major biological signals. Platforms instead differed in the level of biological resolution accessible. Visium HD enabled transcriptome-scale GC characterization and, together with Xenium, resolved dark and light zone organization, whereas GC zonation was not resolved in MERSCOPE, consistent with differences in transcript detection sensitivity. SpatialBench provides a biologically defined reference dataset for evaluation of ST technologies, method development, computational benchmarking, and studies of GC spatial organization in lymphoid tissue.

Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10x Visium, Slide-seq and Stereo-seq becoming widely used by researchers. Each platform brings its own set of protocols and customised data analysis pipelines which presents challenges when the goal is to obtain uniformly preprocessed data that is conveniently formatted for downstream analysis. To address the need for simpler, open-source solutions that deal with sequencing-based spatial transcriptomics (sST) data from different platforms, we present stPipe , a comprehensive and modular pipeline for analysing sST data. stPipe handles various analysis steps including (i) data processing from raw paired end FASTQ files to create a spatially resolved gene count matrix; (ii) the collation of relevant quality control metrics during preprocessing to ensure unwanted artefacts can be filtered from further analysis; and (iii) the adoption of standardised data storage containers to allow results to be easily passed on to a wide range of downstream analysis packages tailored to different goals (such as clustering, cell-cell communication analysis and differential expression analysis). stPipe is implemented as an R/Bioconductor package that builds upon functionality in the scPipe software, and offers a flexible preprocessing pipeline that can manage data from all current main-stream sST plaforms. A key use case for stPipe is in methods benchmarking, and we demonstrate how the uniform processing of sST data collected on reference tissue samples from the cadasSTre and SpatialBenchVisium projects is made easier, allowing comparisons between different technology platforms and downstream analysis tools. Our framework thus aims to advance the standardization and optimization of spatial transcriptomics analyses, fostering collaboration and innovation within the research community.

Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed important advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare 11 sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly affecting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.

A new phenylpropanoid-substituted epicatechin, (2 R,3S,9R)-methyl {2-(3,4-dihydroxyphenyl)-3,5,8a,4a-tetrahydroxy-3,4-dihydro-2H,12H-pyrano[2,3-α]xanthen-12-yl}acetate (1) was isolated from the rhizome of Smilax china, along with twelve known compounds (2 − 13), which were isolated from the Smilax genus for the first time. On the basis of chemical evidences and spectral data analysis (UV, ECD, 1 D and 2 D-NMR, HR-ESI-MS), the structures of compound 1 was elucidated. Furthermore, all compounds have been tested for their inhibitory effects on NO production in LPS-induced RAW 264.7 cells, and compounds 6, 7, 11 and 13 have obvious inhibitory effect, in which the IC₅₀ value of compound 7 reached 11.63 ± 1.29 μM. Through target screening and molecular docking, we can speculate that compound 7 may exert its anti-inflammatory effect by binding to MAPKAP kinase 2 and Leukocyte Proteases Cathepsin G & Chymase. Graphical Abstract