Sequencing-based spatial transcriptomics technologies have introduced exciting new data and new challenges in data analysis. While these data share similarities with traditional single-cell RNAseq data, including feature-by-pixel expression matrices, they also present new complexities. Each pixel can represent more or less than a single cell, and spatial locations add new dimensions to define cellular hierarchy. The Curio Seeker Spatial Transcriptomics Kit, based on the Slide-Seq technology, captures the mRNA in a tissue section using a spatially indexed monolayer of 10 µm beads. In Seeker data, each pixel corresponds to a bead, capturing the mRNA from an average of 1-2 cells. This near single-cell resolution eliminates the need for segmentation or binning. In combination with applying tailored best practices—distinct from scRNAseq—user analyses and biological discoveries can be further accelerated.

In this webinar, we present a workflow designed to transform Seeker pipeline outputs into meaningful biological insights. We will discuss key steps and their rationales, including data pre-processing (background removal and normalization) and annotation (deconvolution, interactive ROI selection). Additionally, we will provide guidance for analyses tailored to various biological applications, such as alignment with H&E images, cell-cell communication, and time-series analysis. By comparing and highlighting tools proven effective for these tasks with Seeker data, we will offer clear guidelines to integrate Seeker pipeline outputs into these tools efficiently and outline the next steps to advance your analyses.