The Single Cell Genomics Team focuses on the systematic integration of genomic data from individual cells to elucidate causalities underlying phenotype formation.


The mission of the team is the implementation of single-cell sequencing technologies and their application in a research and translational context. We established single-cell RNA sequencing processes for MARS-seq and SMART-seq and high-throughput protocols in microfluidic systems. Newly developed computational pipelines include methods to deconvolute tissue composition, identify cell type markers and track transcriptional dynamics. We are joining computational, statistical and biological knowledge in order to establish and apply best practices in single-cell research. The team combines collaborative research, development activities and follows an independent research line on translational cancer research.

We critically enlarged the scope of single-cell methods by implementing cryopreservation for sample transfer and archiving. A systematic comparison of different protocols pointed to large differences in sensitivity of molecule capture, with a high degree of accuracy across the methods. We applied single-cell RNA sequencing for cellular phenotyping, among others, during development, tumor evolution and aging. To be also able to characterize the single cell genome and epigenome, we are implementing new approaches for the identification of somatic alterations or open chromatin states.

Our research expertise is complemented with CNAG-CRG’s large next-generation sequencing capacity coupled with high performance supercomputer. We are also equipped with a microfluidic devices, an automated liquid handling platform and we collaborate with experienced FACS facilities. Our experience in single cell genomics is unique in Spain and suits research on virtual every species, tissue or disease context. We welcome partnerships and collaborations across all areas of life sciences as well as computational projects to tackle the analytic complexity of single cells. We are member of the Human Cell Atlas Project.




Single cell expression analysis uncouples transdifferentiation and reprogramming, BioRxiv 2018, June 20.


matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments, BioRxiv 2018, May 7.


bigSCale: An Analytical Framework for Big-Scale Single-Cell Data, Genome Research 2018, 28(6):878-890


PM20D1 methylation quantitative trait locus is associated with Alzheimer’s disease, Nature Medicine 2018, May 7. doi: 10.1038/s41591-018-0013-y. [Epub ahead of print]


Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation, Cell 2018, 173(2):338-354


Single-cell transcriptome conservation in cryopreserved cells and tissues, Genome Biology 2017, 18(1):45


A DNA Methylation Map of Human Cancer at Single Base-Pair Resolution, Oncogene 2017, 36(40):5648-5657


Mex3a Marks a Slowly Dividing Subpopulation of Lgr5+ Intestinal Stem Cells. Cell Stem Cell 2017, pii: S1934-5909(17)30040-1


Dual MET and ERBB inhibition overcomes intra-tumour plasticity in osimertinib resistant advanced non-small cell lung cancer (NSCLC). Annals of Oncology 2017, 28(10):2451-2457


Comparative analysis of single-cell RNA sequencing methods. Molecular Cell 2017, 65(4):631-643.e4



Holger Heyn

Team Leader

Giovanni Iacono

Postdoctoral Fellow

Atefeh Lafzi

PhD Student

Patricia Lordén

Lab Technician

Giulia Lunazzi

Lab Technician

Domenica Marchese

Lab Technician

Ramon Massoni

PhD Student

Elisabetta Mereu

Postdoctoral Fellow

Cátia Moutinho

Postdoctoral Fellow

Gustavo Rodríguez

Postdoctoral Fellow

Sara Ruiz

Lab Technician

Juan Luis Trincado

Postdoctoral Fellow
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