STtools: a comprehensive software pipeline for ultra-high-resolution spatial transcriptomics data

Abstract

Motivation While there are many software pipelines for analyzing spatial transcriptomics data, few can process ultra high-resolution datasets generated by emerging technologies. There is a clear need for new software tools that can handle sub-micrometer resolution spatial transcriptomics data with computational scalability without compromising its resolution. Results We developed STtools, a software pipeline that provides a versatile framework to handle spatial transcriptomics datasets with various resolutions, such as the ones produced by Seq-Scope (<1μm), Slide-seq (10μm) and VISIUM (100μm). It automatically processes raw FASTQ files and runs downstream analyses at several folds higher resolution than existing methods. It also generates various visualizations including transcriptome density, cell type mapping, marker gene highlighting, and subcellular architectures. Availability STtools is publically available for download at https://github.com/seqscope/STtools Supplementary information Supplementary data are available at Bioinformatics Advances online.

Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics

“…The following public Stereo-seq datasets are used in this study: the axolotl brain regeneration dataset (Wei et al, 2022), MOSTA datasets (Chen et al, 2022), mouse hippocampus Slide-seq V2 dataset (Stickels et al, 2021) (https://singlecell.broadinstitute.org/single_cell/study/SCP815), mouse embryo seqFISH dataset (Lohoff et al, 2022) (https://crukci.shinyapps.io/SpatialMouseAtlas/), mouse hypothalamus dataset (Moffitt et al,2018) (https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248), mouse cortex STARmap dataset (Wang et al, 2018) and mouse liver seqScope dataset (Xi et al, 2022)). E14-16h and E16-18h Drosophila 3D Stereo-seq datasets are downloaded from the Flysta3D database https://db.cngb.org/stomics/flysta3d/download.html(Wang et al, 2022).…”

“…The following public Stereo-seq datasets are used in this study: the axolotl brain regeneration dataset (Wei et al, 2022), MOSTA datasets (Chen et al, 2022), mouse hippocampus Slide-seq V2 dataset (Stickels et al, 2021) (Wang et al, 2018) and mouse liver seqScope dataset (Xi et al, 2022)). E14-16h and E16-18h Drosophila 3D Stereo-seq datasets are downloaded from the Flysta3D database https://db.cngb.org/stomics/flysta3d/download.html .…”

“…The raw Seq-Scope data was downloaded from the example posted on the Seq-Scope companion repository, STtools (Xi et al, 2022). Using STtools, we converted data from the base format to a Seurat object, and to an AnnData object using Seurat (Butler et al, 2018) v4.1.1.…”

Exploring the Landscape of Spatial Transcriptome Analysis: Introducing STASH, a Database of Spatial Transcriptome Tools

“…In contrast, sST acquires gene expression matrix and spatial location information from sequenced nucleotide sequences, with some steps in common with scRNA-seq, but also requires additional steps such as bead barcode decoding and spot and image alignment. Raw data processing pipelines (e.g., ST Pipeline [17] and STtools [18] ) have been developed to process data from different ST technologies. Moreover, researchers have optimized specific steps of the pipeline, including cell segmentation.…”

FICTURE:Scalable segmentation-free analysis of submicron resolution spatial transcriptomics

“…While this was effective in analyzing Pixel-Seq brain data, it is difficult to generalize to many other tissue types where various cell types are densely packed together. Sliding window algorithms, as implemented in SSAM 20 and STtools 21 , partially mitigates the loss of resolution during segmentation, while the oversampling of transcripts limits the choice of clustering algorithms.…”