Preview Data: Atera In Situ Gene Expression, FFPE Human Breast Cancer
Atera dataset analyzed using Atera Onboard Analysis dev
Learn about Atera
Overview
This human FFPE breast cancer data showcases results using the pre-commercial version of the Atera Whole Transcriptome Assay (WTA), which is currently under development. The assay was designed to closely match the Chromium Flex Apex assay in terms of content and sensitivity, and includes 18,028 genes. The number of probes per gene were optimized to maximize sensitivity of lowly expressed genes and avoid optical crowding risk for highly expressed genes. The 10x Genomics in situ multimodal cell segmentation solution was used to generate cell segmentation boundaries.
This sample is from the same block as this Xenium v1 dataset (S1-bottom).
How to view data
This preview dataset can be opened in Xenium Explorer software (v4.1.1), but you may experience performance issues due to the large dataset size. See the Getting Started with Xenium Explorer page for general guidance navigating the web demo interface and these instructions to explore the aligned H&E image in the demo.
Download the files to explore further on your desktop. This preview dataset was converted to closely resemble Xenium Onboard Analysis v4 file formats for compatibility with existing analysis packages. It has been tested with seurat v5.4.0, scanpy v1.12.0, and spatialdata v0.7.2 + spatialdata_io v0.6.0. However due to minor differences in formats, it is not compatible with Xenium Ranger and not guaranteed to be compatible with other untested community-developed tools. Atera's official output data format will be optimized to integrate with the broader bioinformatics ecosystem and reduce data management overhead. Future Atera data releases will showcase the new file format to help customers transition.
Biomaterials
FFPE tissue blocks were purchased from BioIVT (breast cancer, DCIS: G3 (T1c N0 M0)).
Analysis metrics
| Metric | Breast Cancer |
|---|---|
| Median transcripts per cell | 2,116 |
| Cells detected | 170,057 |
| Nuclear transcripts per 100 µm² | 4,549.2 |
| Total high quality decoded transcripts | 624,095,990 |
| Region area (µm²) | 58,944,371.2 |
Custom Cell Groups
Differentially expressed genes from the graph-based clustering results were exported to annotate cell types. Major cell groups were annotated based on Kumar et al. (2023). Invasive versus DCIS (ductal carcinoma in situ) tumor cells were annotated based on molecular markers, myoepithelial cell number, and spatial localization. Similarly, DCIS-associated or invasive cancer-associated fibroblasts (CAFs) were annotated based on their spatial location. We relied partly on H&E to delineate amorphous DCIS and invasive regions. H&E proved insufficient for identifying structured basal-like DCIS which was staged as 'normal' by a pathologist, therefore, we exclusively utilized molecular markers. Cycling cells were validated using the CellCycleScoring function in Seurat. Apocrine cells were identified by histology and PIP expression (encodes prolactin-induced protein).
This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. 10x citation guidelines available here.