High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue

Abstract

Single cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical tissue samples. Commercially available methods that characterize either single cell or spatial gene expression are currently limited by low sample throughput and/or gene plexy, lack of on-instrument analysis, and the destruction of histological features and epitopes during the workflow. Here, we analyzed large, serial formalin-fixed, paraffin-embedded (FFPE) human breast cancer sections using a novel FFPE-compatible single cell gene expression workflow (Chromium Fixed RNA Profiling; scFFPE-seq), spatial transcriptomics (Visium CytAssist), and automated microscopy-based in situ technology using a 313-plex gene panel (Xenium In Situ). Whole transcriptome profiling of the FFPE tissue using scFFPE-seq and Visium facilitated the identification of 17 different cell types. Xenium allowed us to spatially resolve these cell types and their gene expression profiles with single cell resolution. Due to the non-destructive nature of the Xenium workflow, we were able to perform H&E staining and immunofluorescence on the same section post-processing which allowed us to spatially register protein, histological, and RNA data together into a single image. Integration of data from Chromium scFFPE-seq, Visium, and Xenium across serial sections allowed us to do extensive benchmarking of sensitivity and specificity between the technologies. Furthermore, data integration inspired the interrogation of three molecularly distinct tumor subtypes (low-grade and high-grade ductal carcinoma in situ (DCIS), and invasive carcinoma). We used Xenium to characterize the cellular composition and differentially expressed genes within these subtypes. This analysis allowed us to draw biological insights about DCIS progression to infiltrating carcinoma, as the myoepithelial layer degrades and tumor cells invade the surrounding stroma. Xenium also allowed us to further predict the hormone receptor status of tumor subtypes, including a small 0.1 mm2 DCIS region that was triple positive for ESR1 (estrogen receptor), PGR (progesterone receptor) and ERBB2 (human epidermal growth factor receptor 2, a.k.a. HER2) RNA. In order to derive whole transcriptome information about these cells, we used Xenium data to interpolate the cell composition of Visium spots, and leveraged Visium whole transcriptome information to discover new biomarkers of breast tumor subtypes. We demonstrate that scFFPE-seq, Visium, and Xenium independently provide information about molecular signatures relevant to understanding cancer heterogeneity. However, it is the integration of these technologies that leads to even deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

“…From these data we predicted the proportion of cell types in each spot, allowing us to achieve cellular resolution for our spatio-molecular map. While other imaging-based, spatially-resolved transcriptomics platforms, such as Xenium and MERFISH (36,37), directly measure transcripts in individual cells, only a limited number of genes can currently be probed. In contrast, the discovery-based approach afforded by Visium, as well as its scalability to a large number of samples, allowed for robust identification of novel spatial marker genes across many donors.…”

Methods and applications for single-cell and spatial multi-omics

“…Additionally, combined spatial transcriptome and protein readouts on the same tissue section are crucial to correlate transcript with protein expression, localization and interactions, to help unravel the cellular mechanisms that govern specific cell types and states. Several platforms that recently became commercially available for automated imaging-based spatial transcriptome profiling of tissue sections at single-cell resolution, including the NanoString CosMx (smFISH-based) 126 , Vizgen MERSCOPE (MERFISH-based) 127 and 10x Genomics Xenium (ISS-based) 128 platforms, will allow for imaging-based co-profiling of a few to tens of proteins, making these technologies more accessible.…”

Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows

“…Among those we find Xenium, a 10X Genomics product based on in situ sequencing (ISS), that promises to generate maps of hundreds of transcripts at a subcellular resolution. Although Xenium datasets have been used by 10X Genomics to show the potential of the technology 12 , an independent exploration and evaluation of the platform is required. In this study, we explore the characteristics, capacities and limitations of data from the Xenium platform.…”