Pau Badia i Mompel

All Publications

• Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions. Nature neuroscience Lerma-Martin, C., Badia-I-Mompel, P., Ramirez Flores, R. O., Sekol, P., Schäfer, P. S., Riedl, C. J., Hofmann, A., Thäwel, T., Wünnemann, F., Ibarra-Arellano, M. A., Trobisch, T., Eisele, P., Schapiro, D., Haeussler, M., Hametner, S., Saez-Rodriguez, J., Schirmer, L. 2024; 27 (12): 2354-2365

  Abstract

  Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. Inflammation is gradually compartmentalized and restricted to specific tissue niches such as the lesion rim. However, the precise cell type composition of such niches, their interactions and changes between chronic active and inactive stages are incompletely understood. We used single-nucleus and spatial transcriptomics from subcortical MS and corresponding control tissues to map cell types and associated pathways to lesion and nonlesion areas. We identified niches such as perivascular spaces, the inflamed lesion rim or the lesion core that are associated with the glial scar and a cilia-forming astrocyte subtype. Focusing on the inflamed rim of chronic active lesions, we uncovered cell-cell communication events between myeloid, endothelial and glial cell types. Our results provide insight into the cellular composition, multicellular programs and intercellular communication in tissue niches along the conversion from a homeostatic to a dysfunctional state underlying lesion progression in MS.

  View details for DOI 10.1038/s41593-024-01796-z

  View details for PubMedID 39501036

  View details for PubMedCentralID PMC11614744

• Defining the molecular response to ischemia-reperfusion injury and remote ischemic preconditioning in human kidney transplantation PLOS ONE Nordstrom, J., Badia-I-Mompel, P., Witasp, A., Schwarz, A., Evenepoel, P., Moor, M. B., Wennberg, L., Saez-Rodriguez, J., Wernerson, A., Olauson, H. 2024; 19 (10): e0311613

  Abstract

  Ischemia-reperfusion injury (IRI) inevitably occurs during kidney transplantation and extended ischemia is associated with delayed graft function and poor outcomes. Remote ischemic preconditioning (RIPC) is a simple, noninvasive procedure aimed at reducing IRI and improving graft function. Experimental studies have implicated the kynurenine pathway as a protective mechanism behind RIPC.First, paired biopsies from 11 living kidney donors were analyzed to characterize the acute transcriptomic response to IRI. Second, 16 living kidney donors were subjected to either RIPC (n = 9) or no pretreatment (n = 7) to evaluate the impact of RIPC on the transcriptomic response to IRI. Finally, the effect of RIPC on plasma metabolites was analyzed in 49 healthy subjects.There was a robust immediate response to IRI in the renal transcriptomes of living-donor kidney transplantation, including activation of the mitogen-activated protein kinase (MAPK) and epidermal growth factor receptor (EGFR) pathways. Preconditioning with RIPC did not significantly alter the transcriptomic response to IRI or the concentration of plasma metabolites.The present data validate living-donor kidney transplantation as a suitable model for mechanistic studies of IRI in human kidneys. The failure of RIPC to alter transcriptomic responses or metabolites in the kynurenine pathway raises the question of the robustness of the standard procedure used to induce RIPC, and might explain the mixed results in clinical trials evaluating RIPC as a method to attenuate IRI.

  View details for DOI 10.1371/journal.pone.0311613

  View details for Web of Science ID 001348616300011

  View details for PubMedID 39471208

  View details for PubMedCentralID PMC11521294

• LIANA plus provides an all-in-one framework for cell-cell communication inference NATURE CELL BIOLOGY Dimitrov, D., Schaefer, P., Farr, E., Rodriguez-Mier, P., Lobentanzer, S., Badia-i-Mompel, P., Dugourd, A., Tanevski, J., Flores, R., Saez-Rodriguez, J. 2024; 26 (9): 1613-1622

  Abstract

  The growing availability of single-cell and spatially resolved transcriptomics has led to the development of many approaches to infer cell-cell communication, each capturing only a partial view of the complex landscape of intercellular signalling. Here we present LIANA+, a scalable framework built around a rich knowledge base to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially resolved data. By extending and unifying established methodologies, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication via diverse molecular mediators, including those measured in multi-omics data. LIANA+ is accessible at https://github.com/saezlab/liana-py with extensive vignettes ( https://liana-py.readthedocs.io/ ) and provides an all-in-one solution to intercellular communication inference.

  View details for DOI 10.1038/s41556-024-01469-w

  View details for Web of Science ID 001303026700002

  View details for PubMedID 39223377

  View details for PubMedCentralID PMC11392821

• Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results NUCLEIC ACIDS RESEARCH Paton, V., Ramirez Flores, R., Gabor, A., Badia-i-Mompel, P., Tanevski, J., Garrido-Rodriguez, M., Saez-Rodriguez, J. 2024; 52 (14): 8100-8111

  Abstract

  Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

  View details for DOI 10.1093/nar/gkae552

  View details for Web of Science ID 001257427400001

  View details for PubMedID 38943333

  View details for PubMedCentralID PMC11317128

• Spatially resolved multiomics on the neuronal effects induced by spaceflight in mice NATURE COMMUNICATIONS Masarapu, Y., Cekanaviciute, E., Andrusivova, Z., Westholm, J. O., Bjorklund, A., Fallegger, R., Badia-i-Mompel, P., Boyko, V., Vasisht, S., Saravia-Butler, A., Gebre, S., Lazar, E., Graziano, M., Frapard, S., Hinshaw, R. G., Bergmann, O., Taylor, D. M., Wallace, D. C., Sylven, C., Meletis, K., Saez-Rodriguez, J., Galazka, J. M., Costes, S. V., Giacomello, S. 2024; 15 (1): 4778

  Abstract

  Impairment of the central nervous system (CNS) poses a significant health risk for astronauts during long-duration space missions. In this study, we employed an innovative approach by integrating single-cell multiomics (transcriptomics and chromatin accessibility) with spatial transcriptomics to elucidate the impact of spaceflight on the mouse brain in female mice. Our comparative analysis between ground control and spaceflight-exposed animals revealed significant alterations in essential brain processes including neurogenesis, synaptogenesis and synaptic transmission, particularly affecting the cortex, hippocampus, striatum and neuroendocrine structures. Additionally, we observed astrocyte activation and signs of immune dysfunction. At the pathway level, some spaceflight-induced changes in the brain exhibit similarities with neurodegenerative disorders, marked by oxidative stress and protein misfolding. Our integrated spatial multiomics approach serves as a stepping stone towards understanding spaceflight-induced CNS impairments at the level of individual brain regions and cell types, and provides a basis for comparison in future spaceflight studies. For broader scientific impact, all datasets from this study are available through an interactive data portal, as well as the National Aeronautics and Space Administration (NASA) Open Science Data Repository (OSDR).

  View details for DOI 10.1038/s41467-024-48916-8

  View details for Web of Science ID 001245213500033

  View details for PubMedID 38862479

  View details for PubMedCentralID PMC11166911

• Gene regulatory network inference in the era of single-cell multi-omics. Nature reviews. Genetics Badia-I-Mompel, P., Wessels, L., Müller-Dott, S., Trimbour, R., Ramirez Flores, R. O., Argelaguet, R., Saez-Rodriguez, J. 2023; 24 (11): 739-754

  Abstract

  The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

  View details for DOI 10.1038/s41576-023-00618-5

  View details for PubMedID 37365273

  View details for PubMedCentralID 3219227

• Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities NUCLEIC ACIDS RESEARCH Mueller-Dott, S., Tsirvouli, E., Vazquez, M., Flores, R., Badia-i-Mompel, P., Fallegger, R., Tuerei, D., Laegreid, A., Saez-Rodriguez, J. 2023: 10934-10949

  Abstract

  Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

  View details for DOI 10.1093/nar/gkad841

  View details for Web of Science ID 001089866000001

  View details for PubMedID 37843125

  View details for PubMedCentralID PMC10639077

• Molecular consequences of SARS-CoV-2 liver tropism NATURE METABOLISM Wanner, N., Andrieux, G., Badia-I-Mompel, P., Edler, C., Pfefferle, S., Lindenmeyer, M. T., Schmidt-Lauber, C., Czogalla, J., Wong, M. N., Okabayashi, Y., Braun, F., Luetgehetmann, M., Meister, E., Lu, S., Noriega, M. M., Guenther, T., Grundhoff, A., Fischer, N., Braeuninger, H., Lindner, D., Westermann, D., Haas, F., Roedl, K., Kluge, S., Addo, M. M., Huber, S., Lohse, A. W., Reiser, J., Ondruschka, B., Sperhake, J. P., Saez-Rodriguez, J., Boerries, M., Hayek, S. S., Aepfelbacher, M., Scaturro, P., Puelles, V. G., Huber, T. B. 2022; 4 (3): 310-+

  Abstract

  Extrapulmonary manifestations of COVID-19 have gained attention due to their links to clinical outcomes and their potential long-term sequelae1. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) displays tropism towards several organs, including the heart and kidney. Whether it also directly affects the liver has been debated2,3. Here we provide clinical, histopathological, molecular and bioinformatic evidence for the hepatic tropism of SARS-CoV-2. We find that liver injury, indicated by a high frequency of abnormal liver function tests, is a common clinical feature of COVID-19 in two independent cohorts of patients with COVID-19 requiring hospitalization. Using autopsy samples obtained from a third patient cohort, we provide multiple levels of evidence for SARS-CoV-2 liver tropism, including viral RNA detection in 69% of autopsy liver specimens, and successful isolation of infectious SARS-CoV-2 from liver tissue postmortem. Furthermore, we identify transcription-, proteomic- and transcription factor-based activity profiles in hepatic autopsy samples, revealing similarities to the signatures associated with multiple other viral infections of the human liver. Together, we provide a comprehensive multimodal analysis of SARS-CoV-2 liver tropism, which increases our understanding of the molecular consequences of severe COVID-19 and could be useful for the identification of organ-specific pharmacological targets.

  View details for DOI 10.1038/s42255-022-00552-6

  View details for Web of Science ID 000773844400003

  View details for PubMedID 35347318

  View details for PubMedCentralID PMC8964418

• decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics advances Badia-I-Mompel, P., Vélez Santiago, J., Braunger, J., Geiss, C., Dimitrov, D., Müller-Dott, S., Taus, P., Dugourd, A., Holland, C. H., Ramirez Flores, R. O., Saez-Rodriguez, J. 2022; 2 (1): vbac016

  Abstract

  Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions, which are not present in other frameworks. Moreover, it leverages OmniPath, a meta-resource comprising over 100 databases of prior knowledge. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across top methods perform better than other methods at predicting perturbed regulators.decoupleR's open-source code is available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html) for R and in GitHub (https://github.com/saezlab/decoupler-py) for Python. The code to reproduce the results is in GitHub (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208).Supplementary data are available at Bioinformatics Advances online.

  View details for DOI 10.1093/bioadv/vbac016

  View details for PubMedID 36699385

  View details for PubMedCentralID PMC9710656

• A community challenge for a pancancer drug mechanism of action inference from perturbational profile data CELL REPORTS MEDICINE Douglass, E. F., Allaway, R. J., Szalai, B., Wang, W., Tian, T., Fernandez-Torras, A., Realubit, R., Karan, C., Zheng, S., Pessia, A., Tanoli, Z., Jafari, M., Wan, F., Li, S., Xiong, Y., Duran-Frigola, M., Bertoni, M., Badia-I-Mompel, P., Mateo, L., Guitart-Pla, O., Chung, V., Tang, J., Zeng, J., Aloy, P., Saez-Rodriguez, J., Guinney, J., Gerhard, D. S., Califano, A. 2022; 3 (1): 100492

  Abstract

  The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.

  View details for DOI 10.1016/j.xcrm.2021.100492

  View details for Web of Science ID 000745037100009

  View details for PubMedID 35106508

  View details for PubMedCentralID PMC8784774

• Bioactivity descriptors for uncharacterized chemical compounds. Nature communications Bertoni, M., Duran-Frigola, M., Badia-I-Mompel, P., Pauls, E., Orozco-Ruiz, M., Guitart-Pla, O., Alcalde, V., Diaz, V. M., Berenguer-Llergo, A., Brun-Heath, I., Villegas, N., de Herreros, A. G., Aloy, P. 2021; 12 (1): 3932

  Abstract

  Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.

  View details for DOI 10.1038/s41467-021-24150-4

  View details for PubMedID 34168145

  View details for PubMedCentralID PMC8225676

• Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs JOURNAL OF CHEMICAL INFORMATION AND MODELING Duran-Frigola, M., Bertoni, M., Blanco, R., Martinez, V., Pauls, E., Alcalde, V., Turon, G., Villegas, N., Fernandez-Torras, A., Pons, C., Mateo, L., Guitart-Pla, O., Badia-i-Mompel, P., Gimeno, A., Soler, N., Brun-Heath, I., Zaragoza, H., Aloy, P. 2020; 60 (12): 5730-5734

  Abstract

  Until a vaccine becomes available, the current repertoire of drugs is our only therapeutic asset to fight the SARS-CoV-2 outbreak. Indeed, emergency clinical trials have been launched to assess the effectiveness of many marketed drugs, tackling the decrease of viral load through several mechanisms. Here, we present an online resource, based on small-molecule bioactivity signatures and natural language processing, to expand the portfolio of compounds with potential to treat COVID-19. By comparing the set of drugs reported to be potentially active against SARS-CoV-2 to a universe of 1 million bioactive molecules, we identify compounds that display analogous chemical and functional features to the current COVID-19 candidates. Searches can be filtered by level of evidence and mechanism of action, and results can be restricted to drug molecules or include the much broader space of bioactive compounds. Moreover, we allow users to contribute COVID-19 drug candidates, which are automatically incorporated to the pipeline once per day. The computational platform, as well as the source code, is available at https://sbnb.irbbarcelona.org/covid19.

  View details for DOI 10.1021/acs.jcim.0c00420

  View details for Web of Science ID 000608875100011

  View details for PubMedID 32672454