Single-cell approaches and deep learning to map all stages of fruit fly embryo development - Genetics News

Single-cell approaches and deep learning to map all stages of fruit fly embryo development – Genetics News

Scientists have constructed the most comprehensive and detailed single-cell map of embryonic development in any animal to date, using the fruit fly as a model organism.

Posted in Science, this study, co-led by Eileen Furlong of EMBL and Jay Shendure of the University of Washington, leverages data from more than one million embryonic cells spanning all stages of embryonic development and represents a significant advance to several levels. This basic research also helps scientists’ ability to dig deeper into questions such as how mutations lead to different developmental defects. Furthermore, it provides a pathway to understand the large non-coding part of our genome that contains most disease-associated mutations.

“Just capturing the entirety of embryogenesis – all stages and all cell types – to get a more complete view of the cellular states and molecular changes that accompany development is a feat in itself,” said Eileen Furlong, EMBL Genome Manager. Biology Unit. “But what really excites me is using deep learning to get a continuous view of the molecular changes driving embryonic development, down to the minute. »

Embryonic development begins with the fertilization of an egg, followed by a series of cell divisions and decisions that give rise to a highly complex, multicellular embryo that can move, eat, feel, and interact with its environment. Researchers have studied this process of embryonic development for more than a hundred years, but it is only in the last decade that new technologies have allowed scientists to identify the molecular changes that accompany cellular transitions at the level of a single cell.

These single-cell studies have generated enormous excitement as they have demonstrated the complexity of cell types in tissues, even identifying new cell types, and revealed their developmental trajectories in addition to underlying molecular changes. However, attempts to profile the entire development of the embryo at single-cell resolution have been out of reach due to numerous technical challenges in sampling, costs, and technologies.

In this regard, the fruit fly (Drosophila melanogaster), a preeminent model organism in developmental biology, gene regulation, and chromatin biology, has key advantages when it comes to developing new approaches to address this problem. Fruit fly embryonic development occurs extremely rapidly; in just 20 hours after fertilization, all the tissues have formed, including the brain, intestine and heart, so the organism can crawl and eat. This, combined with the many discoveries made in fruit flies that have propelled the understanding of how genes work and their products, encouraged the Furlong lab and its collaborators to take up this challenge.

“Our goal was to get a continuous view of all stages of embryogenesis, to capture all the dynamics and changes as an embryo develops, not only at the RNA level but also control elements that regulate this process,” the co-author said. Stefano Secchia, PhD student in the Furlong group.

Preliminary work with ‘enhancers’

In 2018, the Furlong and Shendure groups showed the feasibility of “open” chromatin profiling at single-cell resolution in embryos and how these regions of DNA often represent active developmental enhancers. “Enhancers” are segments of DNA that act as control switches to turn genes on and off. The data showed which cell types in the embryo are using which activators at any given time and how this use changes over time. Such a map is essential for understanding what drives specific aspects of embryonic development.

“I was really excited when I saw these results,” Furlong said. “Going beyond RNA to look upstream at these regulatory switches in individual cells was something I didn’t think possible for a long time. »

Beyond “snapshots”

The 2018 study was state-of-the-art at the time, profiling around 20,000 cells in three different windows of embryo development (early, middle and late). However, this work has so far only yielded snapshots of cellular diversity and regulation at precise, discrete times. The team therefore explored the potential of using samples from overlapping time windows and, as a proof of principle, applied the concept to a specific lineage – muscle.

This then set the stage for dramatic scaling using new technology developed in the Shendure lab. The team’s current work has profiled the open chromatin of nearly a million cells and the RNA of half a million cells at overlapping time points and spanning the entire development of the embryo from the fruit fly.

Using a type of machine learning, the researchers took advantage of overlapping time points to predict the weather with much finer resolution. Co-author Diego Calderon, a postdoctoral researcher in the Shendure lab, trained a neural network to predict the precise development time of each cell.

“Even if the samples collected contained embryos of slightly different ages within a 2 or 4 hour time window, this method allows you to zoom in on any part of this embryogenesis timeline on a scale of minutes” , Calderon said.

Shendure added, “I was amazed at how well it works. We could capture molecular changes that happen very quickly over time, within minutes, which previous researchers had discovered by removing embryos every three minutes. »

In the future, such an approach would not only save time, but could also serve as a benchmark for normal embryo development to see how things might change in different mutant embryos. This could determine exactly when and in which cell type a mutant phenotype appears, as the researchers have shown in muscle. In other words, this work not only helps understand how development normally happens, but also opens the door to understanding how different mutations can mess it up.

The new predictive potential that this research portends, based on samples from much larger time windows, could be used as a framework for other model systems. For example, the development of mammalian embryos, in vitro cell differentiation, or even post-drug treatment in diseased cells, where deviations in sample times can be engineered to facilitate optimal temporal prediction at research resolution.

In the future, the team plans to explore the predictive powers of the atlas.

“By combining all the new tools available to us in single-cell genomics, computer science and genetic engineering, I would like to see if we could predict what happens to the fates of individual cells. live due to a genetic mutation,” Furlong said. ” … But we are not there yet. However, before this project, I also thought that the current work would not be possible anytime soon. »

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