Our new preprint is online: https://www.biorxiv.org/content/10.1101/2022.03.03.482826v1
We imaged chromosome folding dynamics at high spatial and temporal resolution for several hours in living mESC and discovered how cohesin and CTCF control looping timing and duration!
We are looking for Master students!
We propose Master projects (min. 6 months) on these two themes:
1. To measure promoter transcription as a function of enhancer location and their mutual interaction probabilities, we recently developed a new assay enabling an enhancer to be positioned at large numbers of chromosomal sites around its target promoter (see our recent preprint). We would like a student to contribute to the development of a new version of this assay allowing to measure many enhancers and promoters in parallel.
2. Understanding enhancer-promoter communication mechanistically ultimately requires to measure how they interact in single living cells. To this end we use live-cell imaging techniques allowing us to track the position of an enhancer and a promoter in real time within the nucleus, as well as the RNA that is produced by the promoter. We are looking for a student to help with cell line engineering, microscopy and data analysis.
The student(s) working on these projects will:
- Use CRISPR/Cas9 and transposons to engineer mouse embryonic stem cells
- Perform live-cell imaging on state-of-the-art microscope systems
- Learn basic methods of quantitative imaging data analysis.
We are looking for a highly motivated and self-driven student with knowledge of basic molecular biology techniques. Experience with cell culture, microscopy and image processing is an advantage. Candidates should apply by email with a CV listing courses and internships taken during Masters studies.
Our deep learning method to detect diffraction limited is out on Nucleic Acid Research
Check our deep learning paper on spot detection in microscopy data here
Overview of deepBlink’s functionality. deepBlink requires a pre-trained model that can be obtained by training from scratch using custom images and coordinate labels (1–3) or downloaded directly (4). To predict on new data, deepBlink takes in raw microscopy images (5) and the aforementioned pre-trained model (6) to predict (7) spot coordinates. The output is saved as a CSV file (8) which can easily be used in further analysis workflows (9). An example use case is shown for a smFISH analysis with blue indicating DAPI staining.
New preprint: Nonlinear control of transcription through enhancer-promoter interactions
Jessica’s and Gregory’s project is finally out on bioRxiv: https://www.biorxiv.org/content/10.1101/2021.04.22.440891v1
We use a combination of genomic engineering, quantitative measurements and mathematical modelling to address the long-standing question of how changes in enhancer-promoter genomic distance and contact frequency modulates transcription.
New preprint! A deep-learning method (deepBlink) to automatically detect and localise spots in FISH and live cell imaging data
deepBlink can detect spots in images with varying background
deepBlink does not need manual adjustment of thresholds
deepBlink outperforms benchmarking methods (TrackMate, detNet)
deepBlink is easy to use via our web interface http://deepblink.org/
The preprint of our method is out, check here https://www.biorxiv.org/content/10.1101/2020.12.14.422631v1.
The lab is growing!
Welcome to Gergely, our brand new PhD student!
Gergely has recently joined our lab to study how transcription is regulated by distal enhancers. You can read more about Gergely in the Lab members section.
We wish him a great journey with us throughout his PhD project!
Our review on 3C methods and beyond published in Molecular Cell!
The final version of our Review on methods and concepts in chromosome structure is out in Molecular Cell and can be found here.
