Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics

Abstract

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are 'tagged' with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 micron spatial resolution, and delivered whole-transcriptome data that was indistinguishable in quality from ordinary snRNA-seq. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil, and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualised receptor-ligand interactions driving B-cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to virtually any single-cell measurement technology. As proof of principle, we performed multiomic measurements of open chromatin, RNA, and T-cell receptor sequences in the same cells from metastatic melanoma. We identified spatially distinct tumour subpopulations to be differentially infiltrated by an expanded T-cell clone and undergoing cell state transition driven by spatially clustered accessible transcription factor motifs. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

Identification of cell types, states and programs by learning gene set representations

“…The reduced performance of scDECAF and other marker list-based methods may be explained by the current lack of appropriate marker gene sets for mast cells and monocytes, and different cell states (classical and non-classical) for monocytes. Additionally, to demonstrate applications of the method in spatial transcriptomics, we applied scDECAF to annotate cell types in a slide-tags single-nucleus RNA (snRNA) of the human prefrontal cortex 36 ( Figure 2G ). scDECAF was able to annotate cell types in this spatial transcriptomic data with 92% accuracy ( Supplementary Figure 1 ).…”

Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data

“…The LSGI framework can be applied agnostically to technologies, as the only required inputs are spatial coordinates and gene expression levels. As single-cell resolution whole transcriptomic ST technologies 29 becomes increasingly available, we expect a relatively straightforward adaption of LSGI into new technologies. Lastly, although not demonstrated in this study, LSGI can easily fit three-dimensional ST data analysis through adding an additional 'Z' coordinate to the linear regression step.…”

Bering:joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings

“…For example, recording the transmission of neurotransmitters has been difficult in the past, but with high-resolution technology such as Ex-seq 7 , it is now possible to study neuron interactions within the synapse. Over the last few years, high-throughput sequencing-based spatial technologies, such as Slide-tag and Stereo-Seq have greatly enhanced the spatial resolution to near-single-cell or subcellular levels 25 . Additionally, the size of features in image- based spatial transcriptomics technologies has increased from 30 to 10,000 4 , making it increasingly feasible to use deep learning models.…”