Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution

Abstract

The spatial organization of cells in tissue has a profound influence on their function, yet a highthroughput, genome-wide readout of gene expression with cellular resolution is lacking. Here, we introduce Slide-seq, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 µm spatial resolution, comparable to the size of individual cells. In Slide-seq, RNA is transferred from freshly frozen tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the spatial locations of the RNA to be inferred by sequencing. To demonstrate Slide-seq's utility, we localized cell types identified by large-scale scRNA-seq datasets within the cerebellum and hippocampus. We next systematically characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, identifying new axes of variation across Purkinje cell compartments. Finally, we used Slide-seq to define the temporal evolution of cell-type-specific responses in a mouse model

Molecular Atlas of the Adult Mouse Brain

“…Thereby, the molecular atlas is the basis for also establishing a specific regional palette for any region of interest, to guide spatial annotation of subregions and borders using existing methods to map gene expression in tissue (28)(29)(30)(31)(32)(33). We anticipate that technological advancements will lead to increase spatial and sequencing resolution (34,35), potentially also leading to a more detailed molecular classification of cell types and brain regions. We do not know the function of the molecules that define spatial subdivisions, but we provide evidence that certain key gene ontologies (related to dendrite and axon processes, nervous system development, and glutamatergic neurotransmission) are probably major contributors to the spatial classification in the adult brain.…”

Questions and (some) answers on reactive astrocytes

“…There are now many techniques and models to study astrocytes, some mentioned in the previous paragraphs [e.g., new transgenic mice, purification methods, and human induced pluripotent stem cells (iPSCs)‐derived astrocytes (Almad & Maragakis, ; Guttenplan & Liddelow, )]. In addition, refined cellular and molecular methods developed in other fields could prove very useful to study reactive astrocytes like spatial transcriptomics (Rodriques et al, ; Wang et al, ), scRNAseq (Svensson et al, ), Cas9 genome editing and screening (Shalem, Sanjana, & Zhang, ), and multiplexed immunostainings (Goltsev et al, ). Miniaturization (in terms of quantity of input sample), extension to large brain regions and multiplexing to hundreds or thousands of target genes or proteins will achieve unprecedented resolution to decipher how astrocytes react in a given disease context and define better molecular markers (see Section 6).…”

Cellular Plasticity in Musculoskeletal Development, Regeneration, and Disease

“…Further studies using scRNAseq or even newer technologies (e.g., that maintain spatial information in the single‐cell sequencing process) have the potential to reveal much more about cell plasticity in regeneration. Moreover, computational methods exist to determine the lineage relationship of individual cells within a scRNAseq data set, as well as to discover rare cell populations .…”