Sensitive spatial genome wide expression profiling at cellular resolution
Stickels, R. R.; Murray, E.; Kumar, P.; Li, J.; Marshall, J. L.; Di Bella, D.; Arlotta, P.; Macosko, E. Z.; Chen, F.
Show 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram 98%
- Photoselective sequencing: microscopically-guided genomic measurements with subcellular resolution 98%
- Search and Match across Spatial Omics Samples at Single-cell Resolution 97%
Similar papers in this journal
Similar papers in this journal
- Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states 97%
- ChIP-DIP: A multiplexed method for mapping hundreds of proteins to DNA uncovers diverse regulatory elements controlling gene expression 97%
- Transcriptional kinetics and molecular functions of long non-coding RNAs 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.