Decomposing spatially dependent and cell type specific contributions to cellular heterogeneity

Abstract

Both the intrinsic regulatory network and spatial environment are contributors of cellular identity and result in cell state variations. However, their individual contributions remain poorly understood. Here we present a systematic approach to integrate both sequencing-and imaging-based single-cell transcriptomic profiles, thereby combining whole-transcriptomic and spatial information from these assays. We applied this approach to dissect the cell-type and spatial domain associated heterogeneity within the mouse visual cortex region. Our analysis identified distinct spatially associated signatures within glutamatergic and astrocyte cell compartments, indicating strong interactions between cells and their surrounding environment. Using these signatures as a guide to analyze single cell RNAseq data, we identified previously unknown, but spatially associated subpopulations. As such, our integrated approach provides a powerful tool for dissecting the roles of intrinsic regulatory networks and spatial environment in the maintenance of cellular states.

Spatial Reconstruction of Single Enterocytes Uncovers Broad Zonation along the Intestinal Villus Axis

“…Landmark gene-based spatial reconstruction of single sequenced cells is another powerful approach for spatial reconstruction but requires a priori knowledge of the existence of zonated landmark genes. When such a set is identified, its spatial expression patterns can be precisely characterized using in situ approaches (Achim et al, 2015;Halpern et al, 2017;Codeluppi et al, 2018;Karaiskos et al, 2017;Moffitt et al, 2016;Satija et al, 2015;Shah et al, 2016;Zeisel et al, 2018;Zhu et al, 2018). LCM-RNA-seq is an alternative for extracting a large set of landmark genes in an unbiased manner, particularly useful when no prior knowledge of spatial expression patterns exists (Chen et al, 2017;Nichterwitz et al, 2016;Peng et al, 2016).…”

Regulatory network characterization in development: challenges and opportunities

“…Finally, the GRN in the native setting (that is, with the inputs from spatial and environmental interactions) has not been explored effectively ( Figure 1 ). Available methods rely on known spatial landmarks or computationally simulated spatial features to trace single cells to their spatial origins 48 – 51 . It is important that de novo identification of spatial coordinates and location mapping based on an unbiased method shall be established.…”