Elisabetta Mereu

Group Leader of the Cellular Systems Genomics Group

Josep Carreras Research Institute (Barcelona, Spain)


From 2021 I’m Group Leader of the Cellular Systems Genomics Group at the Josep Carreras Institute, in Barcelona. The group focus is to define the spatial-temporal organization of complex tissues in health and disease. We are particularly interested in inflammatory disorders and autoimmune diseases. By adopting a single-cell perspective, we will provide new machine learning tools and computational methods in order to unlock key regulatory mechanisms hidden in large-scale multi-omics datasets. During my postdoc at the National Centre of Genomic Analysis of Spain, I worked as computational scientist at the Single Cell Genomics Team, in which I gained experience on the analysis of multi-modal data that are from individual cells. Being in a high throughput sequencing center allowed me to be involved in a wide range of projects in collaboration with local and international research groups. As part of the Human Cell Atlas consortium, I work on the benchmarking of scRNA-seq protocols and the integration of multi-modality sequencing data (e.g. sc/scRNA-seq, bulk-RNA-seq, spatial transcriptomics and scMNT-seq) in order to quantify reproducibility and complementarity across these methods. I’m also part of the Expression and Spatial analysis Pancreas Atlas Consortium Europe (ESPACE) for the generation of the first version of the Human Cell Atlas of the Pancreas.


  • Single-cell and Spatial Genomics
  • Computational biology
  • Inflammation and Autoimmunity
  • Cancer Genomics


  • PhD in Statistical Genetics, 2015

    University of Cagliari

  • MSc in Mathematics, 2009

    University of Cagliari

  • BSc in Mathematics, 2007

    University of Cagliari



Group Leader of the Cellular Systems Genomics Group

Josep Carreras Research Institute

Jan 2021 – Present Barcelona (Spain)

Computational Postdoc

National Centre of Genomic Analysis (CNAG-CRG)

May 2016 – Dec 2020 Barcelona (Spain)
Responsibilities include:

  • Data analysis of scRNA-seq data for biological discovery.
  • Identification of cell types and states under different treatments, conditions and/or genetic backgrounds.
  • Develop statistical tools for the visualization, analysis and biological interpretation.
  • Systematic evaluation of sequencing technologies used in single cell genomics (scRNA-seq, scATAC-seq).


Data scientist


Jun 2015 – Sep 2015 Italy
Development of optimized machine learning algorithms (mostly Convolutional Neural Networks and Support Vector Machine) for industrial applications.

Visiting Scientist

Neurology Department of the University of California San Francisco (UCSF)

Oct 2014 – Apr 2015 San Franscisco (CA)
Integration of genomic screening and genetic regulatory information in the context of Multiple Sclerosis (MS) susceptibility.

PhD Visiting Student

Institute of Genetics and Biomedical Research (Italian Research Council, IRGB-CNR)

Jan 2013 – May 2015 Monserrato, Cagliari (Italy)
Statistical analysis of genetic data from whole exome sequencing in trios and multigenerational families segregating Type 1 Diabetes and Multiple Sclerosis.



Benchmarking scRNA-seq protocols for cell atlas projects.

In this study we systematically compared 13 scRNA-seq protocols in many aspects that are relevant for cell atlas projects.

Dissecting the molecular signatures of invasion in Glioblastoma.

In this study we compared three different genetic models to invastigate about the molecular mechanism of invasion in glioblastomas.

ESPACE – the expression and spatial analysis pancreas atlas consortium europe.

Three prior HCA early pilot studies of the pancreas have now come together to form the “Expression and Spatial analysis Pancreas Atlas …


matchSCore2 is a user-friendly R package that allows a fast and robust annotation of cell identity by using a reference dataset.

Zonation of ribosomal DNA transcription defines a stem cell hierarchy in colorectal cancer.

In this study we used scRNA-seq to describe a previously unappreciated heterogeneity in colorectal cancer cells (NEW!).

Recent Publications

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