Understanding the spatial organization and interactions of cells within tissues is crucial for both basic research and clinical applications. Traditional methods, like multiplexed in situ imaging assays, allow for RNA expression measurement at subcellular resolution but are complex and require pre-selected gene panels. Spatial barcoding methods offer a more straightforward approach, enabling comprehensive whole-transcriptome analysis. Technologies such as 10× Genomics Visium have become popular for this purpose.
Despite advancements, many existing spatial transcriptomics techniques struggle with low spatial resolution, making it difficult to achieve single-cell resolution. Methods like HDST and Slide-seq have improved spatial RNA sequencing but are still limited in their ability to capture the fine details of cellular organization.
Recently, several techniques using nano-patterned arrays have emerged, promising subcellular resolution. For instance, Stereo-seq from BGI STomics uses randomly barcoded DNA nano-balls, while Seq-Scope repurposes Illumina MiSeq flow cells for spatial RNA sequencing. However, these methods face challenges in profiling large tissue sections due to their limited spatial footprints.
Researchers have developed Nova-ST, a new platform designed to enhance spatial sequencing of large tissue sections using high-density, patterned flow cells from Illumina. Nova-ST employs NovaSeq 6000 S4 flow cells, which offer a much larger area—around 6,000 mm²—for spatial sequencing. This approach aims to make spatial profiling more cost-effective and scalable.
Nova-ST’s development includes both experimental and computational workflows. Detailed protocols for implementing the Nova-ST experimental workflow are available online, as well as resources for the computational pipeline. This makes the method accessible to a broader range of researchers looking to perform high-throughput spatial sequencing.