Spatial-omics technologies have revolutionized the study of tissue cellular heterogeneity by enabling the simultaneous quantification of multiple targets, DNA (genomics), RNA (transcriptomics), metabolites (metabolomics), or protein (proteomics)1. The resolution of these different assays often varies, providing different limitations when measuring analytes, either with increased sparsity when measuring single cell2,3 or native spatial context4. In contrast, bulk methods such as RNA-sequencing only provide average gene expression across entire samples and obscure cellular heterogeneity2, or single-cell RNA-sequencing (scRNA-seq)2,3, which requires tissue dissociation for single-cell sequencing, often requires filtering. Therefore, these methods exclude large cells and aggregates, while bioinformatic methods try to remove fragments and doublets, all of which induce biases5,6,7. Spatial transcriptomics retains tissue architecture providing critical insights into cellular organization and interactions. Most of the non-targeted spatial sequencing methods do not have single-cell resolution, thus requiring deconvolution of spatial capture areas that containing 10 to 100 cells8,9,10. However, in the last decade, spatial transcriptomics has outpaced the needs for these methods, as there have been advancements in resolution, larger areas of capture, and the integration of multi-omics approaches, either using experimental strategy or novel techniques1,11,12.
Spatial Enhanced Resolution Omics-sequencing (Stereo-seq) provides nanoscale resolution (500 nm) and a centimeter-scale capture area, with species-agnostic whole transcriptomic capture allowing for the capture of coding, non-coding, and non-host RNA13. This method is currently the highest resolution for spatial transcriptomics and has options to provide the largest fields of view. Yet, this method has some notable limitations, including time and labor requirements of around 15 h hands-on time, spanning 4 days to complete the sequencing-ready libraries. In comparison, other sequencing-based spatial transcriptomics technologies, such as probe-based spatial transcriptomics, polyA-capture array-based spatial transcriptomics, Slide-seq, and deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) typically require 5 to 8 h of hands-on time over 2 to 3 days (Table 1). For many researchers, these advances outweigh the technical challenges and are ideal for constructing full transcriptomics spatial atlases to study disease progression at cellular and subcellular levels, particularly if layered with other technologies1.
The Stereo-seq protocol has possibilities beyond traditional mRNA transcriptomics, which relies on poly-T probes for capturing polyadenylated mRNA transcripts. These methods can be modified by adding polyadenylation to fresh tissues14 or species-specific probes for target predefined gene sets15 for spatial expression analysis. Instead, Stereo-seq uses random nucleotide probes to select diverse RNAs regardless of species, including microbial RNAs, fungal RNAs, viral RNAs, and non-polyadenylated RNAs such as enhancer RNAs, circular RNAs, and long non-coding RNAs (lncRNAs)16. Theoretically, it is possible to capture smaller RNAs or reparative elements such as small interfering RNAs (siRNAs), microRNAs (miRNAs), and transposons17. However, currently the bioinformatics and mapping of these RNA remain challenging and are often filtered out during the initial alignment , which typically uses 30 bp cutoff.
Outlined here is a detailed protocol, with our modifications for a manufacturer's random nucleotide capture on DNA nanoballs (DNB) chips to ensure better understanding and handling of a Stereo-seq protocol illustrated in Figure 1. This protocol includes strategies for molecular biologists for testing new FFPE tissues on Stereo-seq with random nucleotide capture to produce more consistent, higher yields from lower quality archived samples and fatty tissues. The outlined protocol is optimized for FFPE samples from most organs, regardless of species of origin. However, this protocol does not include current modifications needed to handle difficult tissues such as bone marrow, which require decalcification.