High-Spatial-Resolution Multi-Omics Atlas Sequencing of Mouse Embryos via Deterministic Barcoding in Tissue

Yang Liu, Mingyu Yang, Yanxiang Deng, Graham Su, Archibald Enninful, Cindy C. Guo, Toma Tebaldi, Di Zhang, Dongjoo Kim, Zhiliang Bai,

bioRxiv (Cold Spring Harbor Laboratory) · 2019 · 10 citations · 35 references

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Abstract

SUMMARY We present DBiT-seq –Deterministic Barcoding in Tissue for spatial omics sequencing – for co-mapping of mRNAs and proteins in a formaldehyde-fixed tissue slide via NGS sequencing. Parallel microfluidic channels were used to deliver DNA barcodes to the surface of a tissue slide and crossflow of two sets of barcodes A1-50 and B1-50 followed by ligation in situ yielded a 2D mosaic of tissue pixels, each containing a unique full barcode AB. Application to mouse embryos revealed major tissue types in early organogenesis as well as fine features like microvasculature in a brain and pigmented epithelium in an eye field. Gene expression profiles in 10μm pixels conformed into the clusters of single-cell transcriptomes, allowing for rapid identification of cell types and spatial distributions. DBiT-seq can be adopted by researchers with no experience in microfluidics and may find applications in a range of fields including developmental biology, cancer biology, neuroscience, and clinical pathology. In Brief Microfluidic deterministic barcoding of mRNAs and proteins in tissue slides followed by high-throughput sequencing enables the construction of a high-spatial-resolution multi-omics atlas at the genome scale. Application to mouse embryos (E10-12) identified major tissue types in early organogenesis and revealed fine tissue features such as retinal pigmented epithelium and endothelial microvasculature at the cellular level. Highlights Deterministic barcoding in tissue enables NGS-based spatial multi-omics mapping. DBiT-seq identified spatial patterning of major tissue types in mouse embryos. DBiT-seq revealed fine features such as retinal pigmented epithelium and microvascular endothelium at the cellular level. Direct integration with scRNA-seq data allows for rapid cell type identification.

References

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