Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues

Abstract

Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, and gene signatures associated with pathological features, and are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmarked the performance of three commercial iST platforms on serial sections from tissue microarrays (TMAs) containing 23 tumor and normal tissue types for both relative technical and biological performance. On matched genes, we found that 10x Xenium shows higher transcript counts per gene without sacrificing specificity, but that all three platforms concord to orthogonal RNA-seq datasets and can perform spatially resolved cell typing, albeit with different false discovery rates, cell segmentation error frequencies, and with varying degrees of sub-clustering for downstream biological analyses. Taken together, our analyses provide a comprehensive benchmark to guide the choice of iST method as researchers design studies with precious samples in this rapidly evolving field.

Comparative analysis of multiplexed in situ gene expression profiling technologies

“…While this is a commonly used tissue for evaluating spatial analyses platforms, it represents only a single biological context. Moreover, our analyses focused on fresh-frozen tissue, representing a complementary evaluation to two recent studies which specifically evaluated performance on FFPE tissues 33,34 . Finally, our Baysor analysis focused on cells in the mouse cortex, and as with all in situ benchmarking analyses, may be influenced by the specific composition of target genes and probes in the panel.…”

“…These experimental and computational differences can cause substantial variation in data quality, necessitating a systematic approach to benchmark and compare in situ technologies. Comparative benchmarking has been invaluable for researchers to select among competing single-cell RNA sequencing (scRNA-seq) technologies [30][31][32] , but similar efforts and benchmarking strategies are still in the process of being identified for imaging-based spatial technologies 33,34 .…”

A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

“…As more comprehensive data becomes available, the comparative methods used here can be extended to offer broader insights into the capabilities of various platforms. Tissue Type-Specific Effects: The impact of tissue types on spatial transcriptomics results is a significant factor. Other studies, including recent research from the Broad Institute 6 and SciLab 17 have highlighted the importance of tissue-specific considerations in interpreting results. Computational tools are quickly evolving to address this 18 . Biological Significance of Transcripts in Undefined Cells: An empiric but intriguing observation is the presence of transcripts in undefined cell regions, referred by our team to as the “nebula,” across different platforms.…”

“…FFPE samples present particular difficulties due to RNA degradation 5 . Achieving high sensitivity and specificity is crucial for accurate cell typing, and these challenges are particularly pronounced when dealing with FFPE tissue microarrays (TMAs), as was recently underscored 6 .…”

“…The biological relevance of these transcripts remains uncertain, yet it's crucial to consider their potential impact on the overall interpretation of the data. 6.…”