Sensitive spatial genome wide expression profiling at cellular resolution

Abstract

The precise spatial localization of molecular signals within tissues richly informs the mechanisms of tissue formation and function. Previously, we developed Slide-seq, a technology which enables transcriptome-wide measurements with 10-micron spatial resolution. Here, we report new modifications to Slide-seq library generation, bead synthesis, and array indexing that markedly improve the mRNA capture sensitivity of the technology, approaching the efficiency of droplet-based single-cell RNAseq techniques. We demonstrate how this modified protocol, which we have termed Slide-seqV2, can be used effectively in biological contexts where high detection sensitivity is important. First, we deploy Slide-seqV2 to identify new dendritically localized mRNAs in the mouse hippocampus. Second, we integrate the spatial information of Slide-seq data with single-cell trajectory analysis tools to characterize the spatiotemporal development of the mouse neocortex. The combination of near-cellular resolution and high transcript detection will enable broad utility of Slide-seq across many experimental contexts. Main text:The ab initio identification of spatially defined gene expression patterns can provide powerful insights into the development and maintenance of complex tissue architectures, and the molecular characterization of pathological states. Recently, we developed Slide-seq 1 , a spatial genomics technology that quantifies expression genome-wide with high (10-micron)

Robust decomposition of cell type mixtures in spatial transcriptomics

“…Slide-seq mouse cerebellum data was collected using the Slide-seqV2 protocol, developed and described recently (see supplementary methods for details) [1]. Slide-seqV2 hippocampus and Visium hippocampus data were used from previous studies [1,2]. Data pre-processing occurred using the Slide-seq tools pipeline [1].…”

“…Slide-seqV2 hippocampus and Visium hippocampus data were used from previous studies [1,2]. Data pre-processing occurred using the Slide-seq tools pipeline [1]. The region of interest was cropped prior to running RCTD, and spatial transcriptomic spots were filtered to have a minimum of 100 UMIs.…”

“…Tissues are composed of diverse cell types and states whose spatial organization governs interaction and function. Recent advances in spatial transcriptomics technologies [1][2][3] have enabled high throughput collection of RNA-sequencing coupled with spatial information in biological tissues. Using such technologies to spatially map cell types is fundamental to our understanding of tissue structure.…”

Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

“…Finally, as additional spatial transcriptomics technologies arise, Tangram can flexibly take them as input. For example, it could be applied to SLIDE-seq2 data 39 , which has a spatial resolution of 10 µm (compared to Visium's 50-55µm) and is more robust to technical dropouts 39 .…”

Single-cell and spatial transcriptomics approaches of cardiovascular development and disease

“…A higher spatial-resolution technique called Slide-seq was introduced in 2019 (12), and an improved second generation was described earlier this year (Slide-seq2) (38). Stickels et al report that modification of barcoded bead synthesis, the sequencing pipeline, and the cDNA-processing steps have markedly improved the mRNA capture sensitivity and efficiency, approaching the level of droplet-based scRNA-seq techniques (38). For further information on the early history and currently availability of ST methods and their applications have been previously reviewed (37).…”