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.





Detection of early seeding of Richter transformation in chronic lymphocytic leukemia, Nat Med. 2022 Aug; 28(8):1662-1671. doi: 10.1038/s41591-022-01927-8


The emerging landscape of spatial profiling technologies, Nat Rev Genet. 2022 Dec;23(12):741-759. doi: 10.1038/s41576-022-00515-3


A single-cell tumor immune atlas for precision oncology, Genome Res. 2021 Oct;31(10):1913-1926. doi: 10.1101/gr.273300.120


Immune cell profiling of the cerebrospinal fluid enables the characterization of the brain metastasis microenvironment, Nat Commun. 2021 Mar 8;12(1):1503. doi: 10.1038/s41467-021-21789-x


SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes, Nucleic Acids Res. 2021 Feb 5;gkab043. doi: 10.1093/nar/gkab043. Online ahead of print


Building a high-quality Human Cell Atlas, Nat Biotechnol. 2021 Feb;39(2):149-153. doi: 10.1038/s41587-020-00812-4


Modeling Human TBX5 Haploinsufficiency Predicts Regulatory Networks for Congenital Heart Disease, Dev Cell. 2020 Dec 8;S1534-5807(20)30929-1.doi: 10.1016/j.devcel.2020.11.020


The order and logic of CD4 versus CD8 lineage choice and differentiation in mouse thymus, Nat Commun. 2021 Jan 4;12(1):99.doi: 10.1038/s41467-020-20306-w


Zonation of Ribosomal DNA Transcription Defines a Stem Cell Hierarchy in Colorectal Cancer, Cell Stem Cell. 2020 Jun 4;26(6):845-861.e12.


Sampling time-dependent artifacts in single-cell genomics studies, Genome Biol. 2020 May 11;21(1):112.


Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects, Nat Biotechnol. 2020 Jun;38(6):747-755.doi: 10.1038/s41587-020-0469-4


Single-cell transcriptomics unveils gene regulatory network plasticity, Genome Biol. 2019 Jun 4;20(1):110. doi: 10.1186/s13059-019-1713-4.


Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming, Elife. 2019 Mar 12;8. pii: e41627. doi: 10.7554/eLife.41627


Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies, Nat Protoc. 2018 Dec;13(12):2742-2757. doi: 10.1038/s41596-018-0073-y. Review.


Identity Noise and Adipogenic Traits Characterize Dermal Fibroblast Aging, Cell. 2018 Nov 29;175(6):1575-1590.e22. doi: 10.1016/j.cell.2018.10.012. Epub 2018 Nov 8


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


bigSCale: An Analytical Framework for Big-Scale Single-Cell Data, Genome Res. 2018 Jun;28(6):878-890. doi: 10.1101/gr.230771.117. Epub 2018 May 3


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 Apr 5;173(2):338-354.e15. doi: 10.1016/j.cell.2018.03.034


Single-cell transcriptome conservation in cryopreserved cells and tissues, Genome Biol. 2017 Mar 1;18(1):45. doi: 10.1186/s13059-017-1171-9











Holger Heyn

Team Leader

Giulia Lunazzi

Project Manager

Domenica Marchese

Head of Laboratory

Mohamed Abdalfttah

Data Analyst

Sergio Aguilar

PhD Student

Alexander Baxendale

Lab Technician

Will Blevins

Data Analyst

Ginevra Caratù

Lab Technician

Elena Domènech

Lab Technician

Marc Elosua

PhD Student

Laura Jiménez

PhD Student

Patricia Lordén

Lab Technician

Davide Maspero

Postdoctoral Fellow

Ramon Massoni

PhD Student

Juan Nieto

Postdoctoral Fellow

Paula Nieto

PhD Student

Sonal Rashmi

PhD Student

Sara Ruiz

Lab Technician

Irepan Salvador

Postdoctoral Fellow

Inés Sentís

Postdoctoral Fellow
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