Evan Murray

TL;DR: Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells, and defines the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury.

TL;DR: Slide-seqV2, a technology that enables transcriptome-wide detection of RNAs with a spatial resolution of 10 μm, is reported, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency ~50% that of single-cell RNA-seq data (~10-fold greater than Slide-seq).

TL;DR: In this paper, the authors propose robust cell type decomposition (RCTD) to detect mixtures and identify cell types on simulated datasets, which can accurately reproduce known cell type and subtype localization patterns in Slide-seq and Visium datasets.

TL;DR: Robust Cell Type Decomposition (RCTD) is developed, a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies.

TL;DR: In situ genome sequencing (IGS) as discussed by the authors is a method for simultaneously sequencing and imaging genomes within intact biological samples, which can directly connect sequence and structure across length scales from single base pairs to whole organisms.