We apply mostly computational approaches, particularly those involving the analysis of high-throughput genomic and transcriptomic data, to fundamental questions in biomedical research. We have a long-term interest in the systems-level transcriptional regulation underlying mammalian cell specification, often perturbed in disease. We aim to understand how RNA-level (transcription initiation, splicing, etc) changes in (mostly) human tissues increase proneness to diseases, namely cancer, neurodegenerative disorders and other ageing-related pathologies. We thereby aim to identify molecular targets for functional exploration in vitro and in vivo. We also combine molecular and clinical information for the unveiling of novel candidate prognostic factors and therapeutic targets. Along the way, we develop some tools for assisting non-computational scientists in their analyses of transcriptomic data.

These are some of the projects being developed in the Lab, with additional [keywords] highlighting our main interests:

RNA (de)regulation in disease contexts

Cancer

The role of tumour-infiltrating myeloid cells in breast cancer at the single-cell level [immuno-senescence, scRNA-seq analysis]
Lab members involved: Marta Bica, Nuno Barbosa-Morais (Co-PI)
Collaborators: Karine Serre (PI)

CDKN2A in senescence and cancer [alternative splicing, senescence]
Lab members involved: Rita Silva, Maria Guerra, José Ferrão (alumnus), Mariana Ascensão-Ferreira, Nuno Barbosa-Morais (PI)
Collaborators: Maria Vivo

Uncovering the stressome: a computational approach to define a stress granule signature and its implication in cancer
Lab members involved: Alexandre Kaizeler, Nuno Barbosa-Morais (PI)

Alternative transcription of CELF2 in colorectal cancer progression and therapeutics [alternative splicing]
Lab members involved: Francisca Xara-Brasil, Lina Gallego (alumna) (Co-PI), Marta Bica, Nuno Barbosa-Morais (PI)
Collaborators: Marta Martins (Co-PI), Luís Costa, Ana Rita Grosso

Alternative splicing during oncogene-induced senescence [alternative splicing, senescence]
Lab members involved: Mariana Ascensão-Ferreira, Nuno Barbosa-Morais (Co-PI)
Collaborators: Jesús Gil (PI)

Selected publications:
Moreno-Marin N, et al. bioRxiv, 2023.03.13.532472.
Conde J, et al. ACS Central Science, 2021 April 14;7(5):868–881. [PUBMED]
Gomes I, et al. Oncotarget, 2020 May 12;11(19):1714-1728. [PUBMED]
Munkley J, et al. eLife, 2019 Sep 3;8:e47678. [PUBMED]
Rodrigues T, et al.Chem Commun (Camb), 2019 May 30;55(45):6369-6372. [PUBMED]
Baker C, et al. Bioorg Med Chem, 2019 Jun 15;27(12):2531-2536. [PUBMED]
de Almeida BP, et al. PLoS Computational Biology, 2019 Mar 11;15(3):e1006832. [PUBMED]
Braun S, et al. Nat Commun, 2018 Aug 17;9(1):3315. [PUBMED]
Georgilis A, et al. Cancer Cell, 2018 Jul 9;34(1):85-102.e9. [PUBMED]
Marteil G, et al. Nat Commun, 2018 Mar 28;9(1):1258. [PUBMED]

Neuro-biology

Investigating circadian disruption in a submarine crew: the health impact of alternating shift work in low-light and confined environments
Lab members involved: Daniel Marques, Nuno Barbosa-Morais (PI)
Collaborators: Cátia Reis (Co-PI)

Selected publications:
Sousa NS, et al. bioRxiv, 2023.11.07.565995, Cell Reports (in press).
Martins I, et al. Int J Mol Sci, 2023;24(7):6433. [PUBMED]
Goulielmaki E, et al. Nature Communications, 2021 May 26;12(1):3153. [PUBMED]
Bordone MC & Barbosa-Morais NL. Frontiers in Neuroscience, 2020 Dec 9;14:607215. [PUBMED]
Rathore OS, et al. RNA, 2020 Dec;26(12):1935-1956. [PUBMED]
Godinho-Silva C, et al. Nature, 2019 Oct;574(7777):254-258. [PUBMED]
Leznicki P, et al. J Cell Sci. 2018 May 16;131(10):jcs.212753. [PUBMED]
Cardoso V, et al. Nature, 2017 Sep 14;549(7671):277–281. [PUBMED]
Gallego-Paez LM, et al. Human Genetics, 2017 Sep;136(9):1015-1042. [PUBMED]
Braunschweig U, et al. Genome Res, 2014 Nov;24(11):1774-86. [PUBMED]
Han H, et al. Nature, 2013 Jun 13;498(7453):241-5. [PUBMED]
Ward MC, et al. Mol Cell, 2013 Jan 24;49(2):262-72. [PUBMED]

Bioinformatics tools for the analysis of RNA-seq data

cTRAP: identification of candidate causal perturbations from differential gene expression data [pharmaco-transcriptomics]
Lab members involved: Bernardo Almeida (alumnus), Nuno Saraiva-Agostinho (alumnus) (co-PI), Juan Carlos Gómez Verjan (alumnus), Nuno Barbosa-Morais (PI)

scStudio: Shiny-based interactive graphical-interface for the exploration of pre-processed single-cell transcriptomics data [immuno-senescence, scRNA-seq analysis]
Lab members involved: Marta Bica, Nuno Barbosa-Morais (PI)
Collaborators: Karine Serre, Michael Gotthardt

Selected publications:
Schneider AL*, Martins-Silva R*, Kaizeler A*, Saraiva-Agostinho N, Barbosa-Morais NL. eLife, 2024;12:RP88623. [PUBMED]
Ascensao-Ferreira M*, Martins-Silva R*, Saraiva-Agostinho N, Barbosa-Morais NL. RNA, 2024;30(4):337-353. [PUBMED]
Saraiva-Agostinho N & Barbosa-Morais NL. Methods in Molecular Biology, 2020;2117:179-205. [PUBMED]
Saraiva-Agostinho N & Barbosa-Morais NL. Nucleic Acids Research, 2019 Jan 25;47(2):e7. [PUBMED]

Funding