Spatial and Temporal Genomics
Cells in complex mammalian tissues, such as the brain, are spatially organized and dynamic, yet almost all genomic tools lack temporal and spatial resolution. Current approaches for transcriptomic analysis involve grinding up or dissociating the tissue, while in situ hybridization (ISH) approaches are often limited to profiling one transcript at a time. However, mapping the spatial heterogeneity of complex tissues requires us to bridge the divide between spatial and molecular resolution. We are developing new tools and are interested in using these approaches to understand tissue organization with respect to communication between cells and cellular networks in development and pathology.
Slide-tags
A true single-cell spatial technology for multi-modal genomics
Slide-tags reconstructions of a mouse brain with different cell types colored accordingly switching between true spatial locations and UMAP space
Advancements in technology have revolutionized our understanding of complex tissue structures by allowing high-throughput quantification of gene expression and epigenetic regulation at the single-cell level. However, current methods lack the ability to easily determine the spatial localization of these profiled cells. To address this limitation, we are developing a novel technology called Slide-tags, which involves tagging single nuclei with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions.
Slide-tags demonstrated precise spatial positioning of nuclei at a resolution of less than 10 microns in the mouse hippocampus and meanwhile generate whole-transcriptome data of comparable quality to standard single-nucleus RNA sequencing. So far, Slide-tags has been applied to various tissues such as the brain, tonsil, and melanoma, providing cell-type-specific spatially varying gene expression patterns across cortical layers and revealing spatially contextualized receptor-ligand interactions involved in B-cell maturation of lymphoid tissue. Additionally, slide-tags also allowed multi-omic measurements of open chromatin, RNA, and T-cell receptor sequences in metastatic melanoma.
Slide-seq
A cellular form of GPS
Slide-seq reconstructions of tissues with different cell types colored accordingly
Slide-seq is an approach that enables high-resolution profiling of the transcriptome using spatially barcoded bead arrays, as well as highly multiplex microscopy methods. The technique begins with a rubber-coated glass slide, or “puck,” that is packed with microparticles, or “beads,” covered in unique DNA barcodes. We sequence each of those barcodes, generating data that allows users to determine where the sequencing reads originated on the bead array.
We then transfer slices of fresh-frozen tissue onto the bead surface and dissolve the tissue, leaving mRNA transcripts bound to barcoded beads. The barcoded RNA library is then sequenced on commercial instruments. Newly developed software assigns locations to each sequencing read, which can be plotted to generate high-resolution maps of cell types or gene expression, with richer information than standard microscopy images.
Slide-DNA-seq
Spatially resolved DNA sequencing from intact tissues
De novo identification of spatial tumor clones in primary human colorectal cancer.
Again, start with a spatially indexed array of 3-mm beads, as developed for the original slide-seq, we cryosection tissues and transfer a single 10-μm-thick fresh-frozen section onto the sequenced bead array. To enable unbiased capture of DNA, the tissue section is treated with HCl to remove histones and transposed with Tn5 to create genomic fragments flanked by custom adapter sequences. Photocleavable spatial barcodes are then released from the beads, ligate them to proximal genomic fragments, and PCR amplifies the resulting DNA sequencing library. These associations enable us to reconstruct the spatial organization of DNA in tissue without imaging the sample under a microscope.
We applied slide-DNA-seq to mouse metastasis models and primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumor microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance, and other cellular phenotypes.