Single cell, whole embryo phenotyping of pleiotropic disorders of mammalian development

Abstract

Mouse models are a critical tool for studying human diseases, particularly developmental disorders, as well as for advancing our general understanding of mammalian biology. However, it has long been suspected that conventional approaches for phenotyping are insufficiently sensitive to detect subtle defects throughout the developing mouse. Here we set out to establish single cell RNA sequencing (sc-RNA-seq) of the whole embryo as a scalable platform for the systematic molecular and cellular phenotyping of mouse genetic models. We applied combinatorial indexing-based sc-RNA-seq to profile 101 embryos of 26 genotypes at embryonic stage E13.5, altogether profiling gene expression in over 1.6M nuclei. The 26 genotypes include 22 mouse mutants representing a range of anticipated severities, from established multisystem disorders to deletions of individual enhancers, as well as the 4 wildtype backgrounds on which these mutants reside. We developed and applied several analytical frameworks for detecting differences in composition and/or gene expression across 52 cell types or trajectories. Some mutants exhibited changes in dozens of trajectories (e.g., the pleiotropic consequences of altering the Sox9 regulatory landscape) whereas others showed phenotypes affecting specific subsets of cells. We also identify differences between widely used wildtype strains, compare phenotyping of gain vs. loss of function mutants, and characterise deletions of topological associating domain (TAD) boundaries. Intriguingly, even among these 22 mutants, some changes are shared by heretofore unrelated models, suggesting that developmental pleiotropy might be "decomposable" through further scaling of this approach. Overall, our findings show how single cell profiling of whole embryos can enable the systematic molecular and cellular phenotypic characterization of mouse mutants with unprecedented breadth and resolution.

A single-cell multi-omic atlas spanning the adult rhesus macaque brain

“…To measure regional heterogeneity within cell types, we extended our recently developed statistic, lochNESS [41], to quantitatively measure enrichment of each region subclass or region within each cell’s neighborhood. For each cell type, we define lochNESS of cell n for region m as: where N is the total number of cells in the the cell type and k is the number of nearest neighbors for cell n .…”

A single-cell multi-omic atlas spanning the adult rhesus macaque brain

“…However, while astrocyte subtypes were widely distributed across multiple regions, the cell clusters did not correspond neatly to regions of origin, making claims about inter-region differences in cell composition difficult to systematically analyze across the many regions profiled. To address this complexity, as a third approach, we adapted our recently developed statistic, lochNESS ( 41 ), to quantitatively measure regional enrichment in each cell’s “neighborhood” of transcriptionally similar cells. Briefly, for each cell, we tally the number of cells from each brain region in its neighborhood and calculate a focal regional enrichment score (fig.…”

Single-cell RNA-based phenotyping reveals a pivotal role of thyroid hormone receptor alpha for hypothalamic development

“…Single-cell RNA sequencing has been gaining in popularity as a means to investigate cell-type composition, gene expression profiles, and cellular development/differentiation in tissues, organs, and even at the whole-animal scale ( ) ( Cao et al, 2019 , 2020 ; Huang et al, 2022 preprint; Smajić et al, 2022 ). Indeed, numerous single-cell studies of the hypothalamus have been carried out in recent years, starting with the cataloging of neuronal and non-neuronal cell types and the identification of gene expression dynamics in these cellular populations ( Campbell et al, 2017 ; Chen et al, 2017 ; Romanov et al, 2017 ).…”

“…The use of higher throughput technologies, such as single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) could be used to address the latter, albeit at the expense of reduced gene depth and increased cost. Methods such as the ‘local cellular heuristic neighbourhood enrichment specificity score’ (lochNESS) scoring system that are being currently developed could also improve unbiased analysis strategies ( Huang et al, 2022 preprint).…”

“…Single-cell genomics approaches offer powerful tools to quantify cellular composition changes and gene expression alterations in any tissue, organ or entire organism ( ) ( Cao et al, 2019 ; Cao et al, 2020 ; Huang et al, 2022 preprint; Smajić et al, 2022 ). With regard to the hypothalamus, several studies have been conducted on wild-type mice using this technique, but these reports focus largely on neurons ( Campbell et al, 2017 ; Chen et al, 2017 ; Hajdarovic et al, 2022 ; Kim et al, 2020 ; Mickelsen et al, 2019 ; Moffitt et al, 2018 ; Romanov et al, 2017 , 2020 ; Steuernagel et al, 2022 ).…”