High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue

By Yang Liu, Mingyu Yang, Yanxiang Deng, Graham Su, Archibald Enninful, Cindy C. Guo, T. Tebaldi, Di Zhang, Dongjoo Kim, Zhiliang Bai and 6 more. Cell, 2020

782 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/aed50c5d7ea963658421e730079a8d7badc8efe9

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Citations in the corpus

Harnessing multimodal data integration to advance precision oncology (2021, background): “Emerging spatial genomics techniques 59–61 and complementary clinical and imaging modalities are opportunities to enrich these data and refine prognostication.”
Giotto: a toolbox for integrative analysis and visualization of spatial expression data (2021, background): “Recently, a number of lower-resolution spatial transcriptomic technologies have been developed, such as 10X Genomics Visium [26], Slide-seq [8], and DBiT-seq [27].”
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, neutral): “More recent sequencing-based technologies, including Slide-seq 20 , DBiT-seq 21 , Stereo-seq 22 , PIXEL-seq 23 , and Seq-Scope 24 , offer subcellular spatial resolution but suffer from high dropout events that give rise to a very sparse gene-spot matrix.”
Methods and applications for single-cell and spatial multi-omics (2023, methodology): “These methods are based on combining microfluidic deterministic barcoding in tissue (DBiT) strategies (Box 1) for spatial-ATAC-seq 101 or spatial-CUT&Tag 102 with DBiT-seq poly(A) transcript profiling 103 (Fig.”
The role of metabolism in chondrocyte dysfunction and the progression of osteoarthritis. (2020, background): “Future investigations should combine high-spatial-resolution multi-omics atlas sequencing, proteomics, metabolomics and lipidomics for deep phenotyping in OA (Liu et al., 2020b).”
Mapping Transcriptomic Vector Fields of Single Cells (2022, positive): “…from the recent developments of single cell multi-omics (Cao 2020; S. Ma et al. 2020), spatial transcriptomics (Moffitt et al. 2018; Rodriques et al. 2019), or both (Liu et al. 2020) into our framework will provide the opportunity to address the hidden variable problem in our vector field analysis.”