In:
Science, American Association for the Advancement of Science (AAAS), Vol. 377, No. 6606 ( 2022-08-05)
Abstract:
Single-cell technologies are a powerful means of studying metazoan development, enabling comprehensive surveys of cellular diversity at profiled time points and shedding light on the dynamics of regulatory element activity and gene expression changes during the in vivo emergence of each cell type. However, nearly all such whole-embryo atlases of embryogenesis remain limited by sampling density—i.e., the number of discrete time points at which individual embryos are harvested and cells or nuclei are collected. Given the rapidity with which molecular and cellular programs unfold, this limits the resolution at which regulatory transitions can be characterized. For example, in the mouse, there are typically 6 to 24 hours between sampled embryonic time points—gaps within which massive molecular and morphological changes take place. RATIONALE To construct an ungapped representation of embryogenesis in vivo, we would ideally sample embryos continuously. Although this is not practical for most model organisms, it is potentially possible in Drosophila melanogaster , where collections of timed and yet somewhat asynchronous embryos are easy to obtain, such that, at least in principle, one can achieve arbitrarily high temporal resolution. Drosophila could therefore serve as a test case to develop a framework for the inference of continuous regulatory and cellular trajectories of in vivo embryogenesis. Because Drosophila is a preeminent model organism that has yielded many advances in the biological and biomedical sciences, obtaining a single-cell atlas of Drosophila embryogenesis is also an important goal in itself. This includes its embryonic development, where the use of this model in conjunction with powerful genetic tools has transformed our understanding of the mechanisms by which developmental complexity is achieved, in addition to uncovering many general principles of both genetic and epigenetic gene regulation. RESULTS We profiled chromatin accessibility in almost 1 million nuclei and gene expression in half a million nuclei from eleven overlapping windows spanning the entirety of embryogenesis (0 to 20 hours). To exploit the developmental asynchronicity of embryos from each collection window, we applied deep neural network–based predictive modeling to more-precisely predict the developmental age of each nucleus within the dataset, resulting in continuous, multimodal views of molecular and cellular transitions in absolute time. With these data, the dynamics of enhancer usage and gene expression can be explored within and across lineages at the scale of minutes, including for precise transitions like zygotic genome activation. CONCLUSION This Drosophila embryonic atlas broadly informs the orchestration of cellular states during the most dynamic stages in the life cycle of metazoan organisms. The inclusion of predicted nuclear ages will facilitate the exploration of the precise time points at which genes become active in distinct tissues as well as how chromatin is remodeled across time. Characterizing the continuum of Drosophila embryogenesis. We collected staged Drosophila embryos from overlapping time windows across the first 20 hours of embryogenesis. Then we extracted nuclei and performed single-cell RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) profiling using combinatorial indexing (sci-RNA-seq and sci-ATAC-seq) to comprehensively map expressed genes and putatively active regulatory elements. We applied machine learning to infer a continuum of nuclear ages that is synchronized across unfolding lineages in absolute time. The continuous nuclear age predictions were used to annotate and then link cellular states at nonoverlapping 2-hour intervals, as well as to explore transcriptional regulatory dynamics across major cell lineages of embryonic development at fine-scale temporal resolution.
Type of Medium:
Online Resource
ISSN:
0036-8075
,
1095-9203
DOI:
10.1126/science.abn5800
Language:
English
Publisher:
American Association for the Advancement of Science (AAAS)
Publication Date:
2022
detail.hit.zdb_id:
128410-1
detail.hit.zdb_id:
2066996-3
detail.hit.zdb_id:
2060783-0
SSG:
11
Bookmarklink