We give a voice to each single cell of the body to show that diversity is an asset, not a liability.
OPEN POSITION for a computer science honours/PhD student on AI-driven single cell analysis.
Research
Fabilab is driven by people, not academic boundaries. We proudly work on biology, medicine, data science, computer science, network science and, sometimes, math. We are enthusiastic leaders in night science and love to generate new ideas and - why not - mix things up. If you like our work, shoot Fabio an email. Some of our current projects are listed below.
Virus-inclusive single cell RNA-Seq 2: predictive omics of infection
Dengue is the most widespread mosquito-transmitted viral disease with 400 million infections every year. We are developing new approaches to understand what different parts of the immune system are doing during severe dengue. We identified CD163 as a biomarker for severity in monocytes and patented new antibodies against dengue virus.
Single-cell gene expression with HTSeq 2.0
HTSeq is a software library to analyse high-throughput sequencing data in Python. It is particularly popular to quantify gene expression in bulk and single-cell RNA-Seq data via its htseq-count script. We have been maintaining HTSeq for many years and have developed HTSeq 2.0, adding specific support for single cell experiments, exon-level expression, and a dedicated new API to manage islands of dense genomic data sprinkled in data deserts.
Graph clustering guided by a cell atlas
Cell atlases are huge collections of single cell transcriptomes that describe in extreme molecular detail the composition of human tissues. We are pioneering new algorithms to leverage cell atlases to rapidly elucidate the composition of tumor biopsies.
Scalable network analysis with igraph
Graphs or networks are an essential mathematical object in modern systems biology. Together with some amazing folks, we develop igraph, a high-performance network analysis tool that underpins many software packages in single cell biology and beyond.
Gene networks regulating leukemic cell state transitions
Cancer cells are not equal in the face of therapeutics. Within a single patient, even within a single Petri dish, heterogeneity in gene expression and function distinguishes more stem-like cells, which are more likely to cause relapse, from more differentiated cells. Using our northstar algoritm, we developed data exploration techniques to reveal how the transition between these cancer cell states is regulated and how it can be perturbed chemically, with the vision to reduce relapse in acute myeloid leukemia (AML).
Single-cell biomedicine of the neonatal lung
The lung is a very special organ at birth, carrying pathogens and chemically reactive oxygen right into the center of our bodies. Our lab is mapping the staggering cellular diversity characterizing neonatal lungs in terms of gene expression (left) and anatomical location (right). A deeper understanding of this fundamental biological system will help us treat the lung conditions affecting thousands of newborn babies every year, such as bronchopulmonary dysplasia.