Cost-effective and scalable single-cell analysis through spatial transcriptomics
Recently, a method called Single-cell Transcriptomics Analysis and Multimodal Profiling (STAMP) has been introduced that allows for large-scale profiling of individual cells. Instead of scRNA-seq, this technique involves spraying cells in suspension and reading them through image-based spatial transcriptomics (ST) and proteomics. Compared to conventional scRNA-seq, this method has the following advantages:
- it is more cost-effective,
- it can identify scarce cell populations, such as circulating tumor cells (CTCs), and
- it can now leverage data like tissue-specific environments and the subcellular localization of RNA within a spatial biology framework.
Per-cell cost — ST is ~50× cheaper
According to a reference from Genomics Colab at UCSF, the per-cell cost of scRNA-seq is significantly higher than Xenium. While one scRNA-seq sample typically covers around 10,000 cells, Xenium can analyze approximately 500,000 to 1,000,000 cells per sample. When comparing per-cell costs, image-based ST using Xenium is about 50 times cheaper than scRNA-seq. Image-based ST therefore offers a much more cost-effective approach on a per-cell basis.
Detecting rare cells — CTCs at 1:100,000
STAMP detected rare cell types such as circulating tumor cells (CTCs) by spiking MCF-7 cancer cells at dilutions of 1:100,000 and 1:50,000 into peripheral blood mononuclear cells (PBMCs), successfully identifying these rare populations with high sensitivity. Image-based ST in STAMP allows direct visual validation of rare cells through imaging in addition to molecular profiling. This non-destructive approach enables identification of rare cell types like CTCs by capturing spatial and morphological data — something difficult to achieve with sequencing alone. Consistent with this, a recent paper in Cancer Cell reported that a combined analysis of transcriptome and cell morphology can enable novel state and cell-type discovery and clinical prognosis.
Spatial context beyond cost
Although STAMP doesn’t explicitly address it, the advantages of spatial biology extend beyond cost. Spatially close cell-cell interaction and cell-type composition add information in the spatial context, as with Nicheformer. Subcellular RNA locations have been used in numerous cases.
Image-based ST has traditionally been known for its limited gene coverage, but significant improvements are being made — Xenium 5k and MERSCOPE 1k gene panels. Image-based ST now allows simultaneous protein profiling alongside transcriptome analysis, expanding its capabilities, as seen in STAMP. As a result, the decision to opt for ST over scRNA-seq is becoming increasingly popular, thanks to these advancements and the growing versatility of image-based approaches.
