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Overview of tools in BMKMANU S3000 spatial transcriptomic data analysis

Spatial transcriptomics offers a unique view of gene expression by preserving the expression location of RNA molecules within a tissue. To produce this information meaningful and accurately, the raw sequencing data and microscopic images must be processed together in a careful and coordinated way. At BMKGENE, we have developed a set of purpose-built tools that streamline this analysis and allow users to obtain reliable, high-resolution results from the BMKMANU S3000 system.

The core tool for S3000 data analysis

The core of our analysis pipeline is BSTMatrix, a command-line tool designed specifically for S3000 spatial transcriptomic data. The tool is remarkably simple to use—one line of command in the Linux environment is all that is needed to run the full workflow. Behind this simplicity, however, lies a complete set of analytical steps. BSTMatrix identifies barcodes and UMIs from the sequencing output, aligns the reads to a reference genome, and generates the gene expression matrix that serves as the foundation for all downstream analyses. In addition to processing the sequencing data, the same command also performs several key image-based steps, including tissue boundary recognition, cell segmentation, and matrix extraction under the tissue. These image-derived components are essential for linking expression back to specific tissue regions and for achieving single-cell-level resolution across the tissue sections. Once BSTMatrix completes its run, it produces a web-based report summarizing the results.

The assistant tools

To support users who prefer graphical interfaces, we provide several companion tools that require no programming experience or installation.

   • BMChiper is used at the beginning of the workflow to correct microscopy images and ensure that chip decoding and stitching are accurate. If you have used the spatial transcriptomic service at BMKGENE, then you can skip this step with BMChiper.

     • BSTViewer allows users to explore the results interactively, after BSTMatrix generates the first-hand results. With this viewer, researchers can trace the spatial location of individual genes, examine clusters within the tissue, and select specific regions of interest for a local re-analysis.

    • For projects that demand even higher precision, BSTCellViewer enables manual adjustments to cell boundaries and allows users to remove cells that should not be included in downstream interpretation.

Beyond the ready-to-use software, BMKGENE also provides supporting scripts in R and Python to help users integrate their results into third-party analysis environments. These scripts make it straightforward to perform tasks such as automated cell type annotation, cell-cell communication analysis, or cell development trajectory analysis with packages like Seurat.

Together, the BMKGENE tool suite—centered around BSTMatrix and supported by BMChiper, BSTViewer, and BSTCellViewer—offers a complete workflow for turning raw S3000 spatial transcriptomic data into a detailed, interpretable map of gene expression across complex tissues. To download these tools, please click here.