Stereo-seq Analysis Workflows vs. Imaging-Based Workflows

When a research team decides to incorporate spatial context into their study, a fundamental choice arises in the methodology that will define their entire project pipeline. This choice, often between sequencing-based and imaging-based techniques, extends far beyond the lab bench into the realm of data handling. At STOmics, we focus on the complete journey of data, and we see distinct paths shaped by this initial decision. The Stereo-seq analysis workflow and workflows for imaging-based methods differ significantly in their demands, challenges, and the role of spatial omics software.

The Starting Point: Data Generation and Structure

The divergence begins at the very origin of the data. Imaging-based workflows, such as those using cyclic fluorescence, generate data as a series of high-resolution image files. The primary computational tasks involve image analysis, feature extraction, and signal decoding. In contrast, a Stereo-seq analysis workflow starts with raw sequencing reads (FASTQ files) that must be computationally integrated with high-resolution tissue images. This first critical step, aligning billions of reads to their precise spatial coordinates, is unique to sequencing-based approaches like ours. It requires specialized spatial omics software built to handle this massive, hybrid data fusion, a cornerstone of our platform's design.

Processing and Interpretation: A Question of Scale and Discovery

Following data generation, the analytical goals further separate these paths. Imaging workflows are typically targeted and protein-centric, bound by the pre-selected panel of markers. Analysis often revolves around cell segmentation and marker co-expression. The Stereo-seq analysis workflow, however, is fundamentally discovery-oriented. After spatial mapping, the resulting dataset is an unbiased, genome-wide transcriptome map at each coordinate. Here, spatial omics software must enable secondary analysis like spatial clustering, differential expression within regions, and trajectory inference—all while managing the scale of whole-transcriptome data. This software is not just for viewing; it's for interrogating the data to find novel, spatially defined cell states and interactions.

Visualization and Exploration: Navigating Different Data Landscapes

The final stage of visualization highlights another key difference. Imaging-based software often focuses on multi-channel overlay and quantitative cell phenotyping within a high-resolution optical view. For a Stereo-seq analysis workflow, visualization needs are broader. Researchers need to explore large-scale expression patterns, overlay clustering results onto tissue morphology, and drill down from a tissue-level view to subcellular detail. Our integrated spatial omics software, like StereoMap, is built for this exploration. It allows scientists to visualize the spatial gene expression map, interact with clustering results, and correlate transcriptional domains with histological features seamlessly, turning complex data into a navigable biological story.

The selection between a sequencing-based and an imaging-based method is, in effect, the selection of an entire analytical ecosystem. A Stereo-seq analysis workflow demands a powerful, integrated suite of spatial omics software capable of handling the unique pipeline from read alignment to spatial discovery. At STOmics, we provide this specialized, end-to-end bioinformatics environment—from SAW for primary analysis to StereoMap for visualization—ensuring that the complexity of the data never becomes a barrier to the insight it holds. Our tools are designed to support the complete journey of spatial discovery that our Stereo-seq technology enables.