A single-cell transcriptional timelapse of mouse embryonic development, from gastrula to pup

Zebrafish Models for Skeletal and Extraskeletal Osteogenesis Imperfecta Features: Unveiling Pathophysiology and Paving the Way for Drug Discovery

In the last decades, the easy genetic manipulation, the external fertilization, the high percentage of homology with human genes and the reduced husbandry costs compared to rodents, made zebrafish a valid model for studying human diseases and for developing new therapeutical strategies. Since zebrafish shares with mammals the same bone cells and ossification types, it became widely used to dissect mechanisms and possible new therapeutic approaches in the field of common and rare bone diseases, such as osteoporosis and osteogenesis imperfecta (OI), respectively. OI is a heritable skeletal disorder caused by defects in gene encoding collagen I or proteins/enzymes necessary for collagen I synthesis and secretion. Nevertheless, OI patients can be also characterized by extraskeletal manifestations such as dentinogenesis imperfecta, muscle weakness, cardiac valve and pulmonary abnormalities and skin laxity. In this review, we provide an overview of the available zebrafish models for both dominant and recessive forms of OI. An updated description of all the main similarities and differences between zebrafish and mammal skeleton, muscle, heart and skin, will be also discussed. Finally, a list of high- and low-throughput techniques available to exploit both larvae and adult OI zebrafish models as unique tools for the discovery of new therapeutic approaches will be presented.

Developmental mouse brain common coordinate framework

3D brain atlases are key resources to understand the brain's spatial organization and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of developing mouse brain 3D reference atlases hinders advancements in understanding brain development. Here, we present a 3D developmental common coordinate framework (DevCCF) spanning embryonic day (E)11.5, E13.5, E15.5, E18.5, and postnatal day (P)4, P14, and P56, featuring undistorted morphologically averaged atlas templates created from magnetic resonance imaging and co-registered high-resolution light sheet fluorescence microscopy templates. The DevCCF with 3D anatomical segmentations can be downloaded or explored via an interactive 3D web-visualizer. As a use case, we utilize the DevCCF to unveil GABAergic neuron emergence in embryonic brains. Moreover, we map the Allen CCFv3 and spatial transcriptome cell-type data to our stereotaxic P56 atlas. In summary, the DevCCF is an openly accessible resource for multi-study data integration to advance our understanding of brain development.

StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases

Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells' past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

James German and the Quest to Understand Human RECQ Helicase Deficiencies

James German's work to establish the natural history and cancer risk associated with Bloom syndrome (BS) has had a strong influence on the generation of scientists and clinicians working to understand other RECQ deficiencies and heritable cancer predisposition syndromes. I summarize work by us and others below, inspired by James German's precedents with BS, to understand and compare BS with the other heritable RECQ deficiency syndromes with a focus on Werner syndrome (WS). What we know, unanswered questions and new opportunities are discussed, as are potential ways to treat or modify WS-associated disease mechanisms and pathways.

Tracking early mammalian organogenesis - prediction and validation of differentiation trajectories at whole organism scale

Early organogenesis represents a key step in animal development, during which pluripotent cells diversify to initiate organ formation. Here, we sampled 300,000 single-cell transcriptomes from mouse embryos between E8.5 and E9.5 in 6-h intervals and combined this new dataset with our previous atlas (E6.5-E8.5) to produce a densely sampled timecourse of >400,000 cells from early gastrulation to organogenesis. Computational lineage reconstruction identified complex waves of blood and endothelial development, including a new programme for somite-derived endothelium. We also dissected the E7.5 primitive streak into four adjacent regions, performed scRNA-seq and predicted cell fates computationally. Finally, we defined developmental state/fate relationships by combining orthotopic grafting, microscopic analysis and scRNA-seq to transcriptionally determine cell fates of grafted primitive streak regions after 24 h of in vitro embryo culture. Experimentally determined fate outcomes were in good agreement with computationally predicted fates, demonstrating how classical grafting experiments can be revisited to establish high-resolution cell state/fate relationships. Such interdisciplinary approaches will benefit future studies in developmental biology and guide the in vitro production of cells for organ regeneration and repair.

Single-cell, whole-embryo phenotyping of mammalian developmental disorders

Mouse models are a critical tool for studying human diseases, particularly developmental disorders1. However, conventional approaches for phenotyping may fail to detect subtle defects throughout the developing mouse2. Here we set out to establish single-cell RNA sequencing of the whole embryo as a scalable platform for the systematic phenotyping of mouse genetic models. We applied combinatorial indexing-based single-cell RNA sequencing3 to profile 101 embryos of 22 mutant and 4 wild-type genotypes at embryonic day 13.5, altogether profiling more than 1.6 million nuclei. The 22 mutants represent a range of anticipated phenotypic severities, from established multisystem disorders to deletions of individual regulatory regions4,5. We developed and applied several analytical frameworks for detecting differences in composition and/or gene expression across 52 cell types or trajectories. Some mutants exhibit changes in dozens of trajectories whereas others exhibit changes in only a few cell types. We also identify differences between widely used wild-type strains, compare phenotyping of gain- versus loss-of-function mutants and characterize deletions of topological associating domain boundaries. Notably, some changes are shared among mutants, 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.