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What,-when,-where-mapping-gene-activity-in-a-2D-tissue

VIB research institute - 27.05.2020
Title: What, when, where: mapping gene activity in a 2D tissue Page Content Zone 1: Single cell technologies provide unprecedented insights into the dynamics of gene regulation of individual cells, but resolving spatiotemporal regulation networks has remained an experimental and computational challenge. A team of scientists lead by Stein Aerts (VIB-KU Leuven) came up with a new approach to map gene regulation in 2D tissues.
All the cells in our body share the same DNA, but only a fraction is put to use in a given cell. The identity of each individual cell is thus defined by which genes are active at any given time.  Aerts tries to decode the mechanisms underlying gene regulation, to better understand their role in health and disease. His team is one of many across the globe who have embraced single cell technology to study individual cells at unprecedented resolution. 
Chromatin access and gene expression “Single cell technologies provide new opportunities to study the mechanisms underlying cell identity. However, these techniques require the dissociation of the tissue, which means we lose information on the original location of individual cells in the tissue,” explains Aerts.  While new experimental techniques are arising that preserve spatial information when profiling single cells, they are mostly focused on transcriptomics, i.e. measuring gene expression in each cell. 
“One of the challenges in the field of single-cell regulatory genomics is how to integrate chromatin accessibility and transcriptome information,” says Aerts. “Chromatin accessibility serves as a readout of the DNA elements that control gene expression, while transcriptomics allows to measure gene expression itself in each cell. Our aim was to integrate both layers to infer spatial gene regulatory networks.” 
The developing fruit fly eye The team turned to the fruit fly, or Drosophila by its Latin name, a popular model organism for molecular biologists. More specifically, they studied the larval eye-antennal disc, which gives origin not only to the fly’s compound eyes but also to the head capsule and antennae. 
“The Drosophila eye-antennal disc provides an ideal biological system for the spatial modelling of gene regulation at single cell resolution: it comprises complex, dynamic, and spatially restricted cell populations in two dimensions,” explains Carmen Bravo González-Blas, PhD student in Aerts’ lab and first-author of the study.
She takes us through the newly developed approach: “First, we generated an epigenomic and a transcriptomic atlas of the eye-antennal disc. Taking advantage of the fact that the disc itself is a 2D tissue, we spatially map these single-cell profiles on a virtual eye-antennal disc. We use these virtual cells to derive links between enhancers and target genes using a new regression approach.”
The scientists investigated to what extent enhancers in a large space around the transcription start site of a gene can predict the expression of the gene. Bravo González-Blas: “We could conclude that genes are regulated by many enhancers, likely with a redundant function to ensure an accurate regulation of gene expression.”
A valuable resource The newly developed strategy to map transcriptomic and epigenomic information is also applicable to other tissues and organisms, and the software is freely available via GitHub. Currently, it is limited to 1D or 2D tissues and requires some a priori information about at least one landmark between the real and the virtual cells. Nevertheless, the spatial gene expression atlas Aerts and his team constructed accurately recapitulates known gene expression patterns, and allows to generate virtual gene expression profiles for any gene, at a good resolution. 
“We envision that our computational strategies and resources will be of value not only to the Drosophila research community, but also to the field of single-cell regulatory genomics in general,” says Aerts.
Publication Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics, Bravo González-Blas et al. 2020
News Date: 26/05/2020

VIB-single-cell-software-supported-by-a-Chan-Zuckerberg-Initiative-grant

VIB research institute - 26.05.2020
Title: VIB single-cell software supported by a Chan Zuckerberg Initiative grant Page Content Zone 1: Today, the Chan Zuckerberg Initiative (CZI) announced $3.8 million in funding for 23 grants to support open-source software projects essential to biomedical research, enabling software maintenance, growth, development, and community engagement.  Software by the lab of Prof. Yvan Saeys (VIB-UGent Center for Inflammation Research) for the analysis and visualization of single-cell data is one of the projects that will receive funding as part of CZI’s Essential Open Source Software for Science (EOSS) program. 
Open and transparent Open-source software is crucial to modern scientific research, advancing biology and medicine while providing reproducibility and transparency. Yet even the most widely used research software often lacks dedicated funding. The EOSS program enables scientists to hire additional developers, improve documentation, address usability, improve compatibility, attract contributors, convene communities, and more. 
One of the selected projects was the dynverse project developed by the Saeys lab. Prof. Yvan Saeys (VIB-UGent Center for Inflammation Research) explains: “The dynverse project provides an ecosystem of open-source software for analyzing and interpreting dynamic cellular processes measured at single-cell resolution. The project is spearheaded by postdocs Robrecht Cannoodt and Wouter Saelens, whose work in developing and benchmarking single-cell software has greatly impacted the field.”
Visualizing single-cell data Dr. Robrecht Cannoodt recalls the first steps of the project: “During our extensive review of software tools for analyzing cellular dynamics we developed a large library of internal tools to visualize single-cell data. We strongly believe in open-sourcing research projects from the get-go. Indeed, already long before the publication of our research in Nature Biotechnology, we received interest from other researchers in using the dynverse packages to enhance their analyses.”
Dr. Wouter Saelens adds: “Good software is one of the driving forces behind current biomedical research. More than ever, biological researchers need tools that are open, freely available, and trustworthy. Classical funding channels are often hesitant with software development projects because of the low research component. But in the long run, these tools will push the biomedical field even further. I'm really happy that the Chan Zuckerberg Initiative has recognized this, as their support will allow us to further refine our software and start new collaborations with research groups.” 
Related publication Saelens, W., Cannoodt, R., Todorov, H., Saeys, Y. (2019) A comparison of single-cell trajectory inference methods.  Nat. Biotechnol. 37(5):547-554  DOI: http://dx.doi.org/10.1038/s41587-019-0071-9 Software packages: https://dynverse.org   ​

News Date: 27/05/2020
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