Our work on Tomographical reconstruction of sequencing based technologies is now on the biorxiv. Enjoy!
We make use of highly quantitative single-cell genomics techniques and machine learning tools to address our scientific questions. For example, we have used the wealth of data provided by single cell RNA sequencing to characterize the developmental process in entire tissues. One of the long-term goals of the lab is to understand the complex gene regulatory programs that lead to the formation of cell types in the nervous system.
With our work, we have the ambition to contribute to the fight against neurodegenerative and blinding diseases. However we are wary of overselling our research as translational, what we do is basic research and we feel privileged of being granted this opportunity. We are convinced that our data-oriented and quantitative approach to biology is the way to build up information that can, then, immediatelly be used to support applied efforts.
★ Looking for a postdoc, internship, master or a PhD? Get in touch!
We welcome applications from both experimentalists and analysts, being curious about how the nervous system develops and a quantitative mindset are an important plus. Descriptions for some of the available projects can be found here.
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Popular science, News and Media
The lab is the recipient of one of the 79 grants awarded this year across all scientific disciplines. I am really honored by this recognition. This SNF grant will allow us to study, using cutting-edge technologies, the complex cellular dynamics of progenitor cells during brain development.
We are honored to have been warded a SNF SPARK grant to work on ‘A novel strategy for optics-free tomography-based spatial molecular profiling of tissues’ SPARK is a new funding scheme designed for projects that show unconventional thinking and introduce a unique approach.
Martin Weigert and Ana Marija Jakšić will be joining the EPFL as new ELISIR fellows. I was very pleased to hear that not one but two candidates have been selected! Looking forward to having them on campus. Exciting interactions ahead.
The Chan Zuckerberg Initiative (CZI) has awarded a grant to the lab of Gioele La Manno, together with the Karolinska Institute and The University of Edinburgh. The collaborative research aims at new therapies for multiple sclerosis, and will be part of the Human Cell Atlas project.
I have been appointed the first Scholar of the EPFL Life Sciences Early Independence Research program (ELSIR), a revolutionary fellowship that gives talented PhD graduates the kind of research independence they could usually only get much later in their career.
Researchers at Karolinska Institutet and Harvard Medical School report in the journal Nature that they have developed a technique for capturing dynamic processes in individual cells. Apart from studying disease processes, the method can be used to observe in detail how specialised cells are formed during embryonic development.
Two research teams at Karolinska Institutet have identified the dopamine-producing cells in the midbrain of mice and humans. They have also developed a method of assessing the quality of in-vitro cultured dopamine-producing cells, which can be of great benefit to research on Parkinson’s disease. The results are published in the academic journal Cell.
The sympathetic nerve system has long been thought to respond the same regardless of the physical or emotional stimulus triggering it. However, in a new study from Karolinska Institutet published in the journal Nature Neuroscience, scientists show that the system comprises different neurons that regulate specific physiological functions, such as erectile muscle control.
Researchers at Karolinska Institutet have made significant progress in the search for new treatments for Parkinson’s disease. By manipulating the gene expression of non-neuronal cells in the brain, they were able to produce new dopamine neurons. The study, performed on mice and human cells, is published in the prestigious scientific journal Nature Biotechnology.
Our work on Tomographical reconstruction of sequencing based technologies is now on the biorxiv. Enjoy!
Dimitri Lallement joins the lab for a Masters thesis project. He is enrolled in the Computer Science Engineering program, ENSIMAG at Grenoble and doing a exchange with EPFL. He has studied his Bachelor at the University of Bordeaux and studied as Data Scientist Junior at CNES. He wants to face challenging research problem and explore new methods that can help the interpretation of single-cell RNAseq datasets. Welcome Dimitri!
Halima Schede joins the lab for a Masters project. She is enrolled in the Bioengineering program and has studied in biology and mathematics at McGill. With her passion for machine learning and neural networks and her knowledge of anatomy and genetics she will turn a bunch of omics data in useful insights of the molecular anatomy of the brain. Welcome Halima!
Anurag Ranjak joins the lab for a Masters project. Anurag is a Chemical Engineer with industry experience in software engineering. He will adventure in spatial transcriptpomics and image reconstruction.He recently “converted” to biology and enrolled in EPFL Masters in Life Sciences. Welcome Anurag!
Our work on RNA velocity, the time derivative of gene expression, is now out in Nature. A lot of interesting new evidence and analysis added to the initial bioarxiv preprint. We put great attention to feature possibilities and the limits of the estimation approach. velocyto
software is mature and ready to use. Enjoy!
Below the scientific software I have been working on:
A package for the analysis of expression dynamics in single cell RNA seq data. For more information go to the velocyto homepage
A tool for building analysis pipeline for big single cell RNA sequencing projects
Core implementation the standard format to store and work with single cell expression data. For more information go to the loompy homepage
Negative binomial generalized linear model for single cell expression data
An alternative, lightweight, python interface to the Bayesian Modeling language Stan.
Reference implementation of the clustering algorithm described in Zeisel et al. 2015
An older file format designed to store smaller single-cell RNA seq datasets