Biology subjects

Planell, N.

Publications and source records attributed to Planell, N..

6 recordsLinked to original sources

bioRxiv2026

Mapping Disease Transitions from Premalignant, Asymptomatic to Advanced Myeloma through Integrative Epigenomic and Transcriptional Analyses

AbstractMultiple myeloma (MM) evolves from asymptomatic precursor conditions through progressive genetic and epigenetic remodeling, yet the regulatory mechanisms driving the development toward more active stages of the disease remain poorly understood. Here, we integrated bulk-based paired chromatin accessibility and activation, and transcriptomic profiling across disease stages to map regulatory remodeling during myeloma development. We identified a progressive increase in chromatin accessibility; furthermore, this epigenetic reconfiguration is accompanied by a stage-dependent shift from promoter-centered regulation in precursor states toward enhancer-dominated transcriptional control in active MM. Motif enrichment and regulatory network analyses identified both established and previously underappreciated transcription factors (TFs), including members of the IRF, MEF2, and FOX families, associated with disease-stage-specific transcriptional programs. Among these, MEF2D and FOXK2 emerged as candidate regulators of pathways involved in cell survival and chemotaxis. Functional perturbation demonstrated that MEF2D depletion markedly impaired MM cell viability, whereas inhibition of either MEF2D or FOXK2 reduced chemotactic migration. Together, these findings provide a stage-resolved framework of epigenetic and transcriptional remodeling across myeloma development, revealing regulatory programs established in precursor conditions and progressively reinforced during disease evolution, while identifying candidate transcriptional dependencies with potential biological and therapeutic relevance.

cancer biology↗

bioRxiv2025

An open CAR-T single-cell atlas to enable in-depth characterization and rational engineering of CAR-T products

BRIEF ABSTRACTWe built a CAR-T cell functional atlas from over one million cells across 13 studies, integrating data from patients and healthy donors. The atlas captures 11 phenotypes, links infusion product composition with clinical response, and reveals sex- and age-dependent effects, metabolic signatures, and rare ICANS-associated cells. This open-access resource provides a foundation to understand CAR-T cell function and guide the design of next-generation therapies.

bioinformatics↗

bioRxiv2024

Unravelling the molecular activation of the reparative cardiac fibroblasts after myocardial infarction

Activated cardiac fibroblasts (Postn+ CFs) are responsible for the healing of the heart tissue after a myocardial infarction (MI). However, so far little is known about the moment that CFs are activated, and the genes involved in this process. This is especially relevant in the context of CF heterogeneity and their role in the response to the damage. In this context, we have described a subpopulation of activated CFs responsible for the healing scar and for preventing the rupture of the ventricle after the damage: the Reparative Cardiac Fibroblasts (RCFs). Our new data indicate that RCFs directly derived from activated CFs, and this transcriptional shift happens in a close window after damage. Interestingly, our results exhibited two different molecular dynamics that would give rise to this activation and, consequently, the appearance of definitive RCFs. Using bulk RNA-Seq, RNAScope and Spatial Transcriptomics, we anatomically localized some of the genes related to both dynamics in the infarcted heart and highlight the potential role of Aspn as a new marker of this transcriptional transition in mice, pigs and patients.

bioinformatics↗

bioRxiv2023

Uncovering the cis-regulatory program of early human B-cell commitment and its implications in the pathogenesis of B-cell acute lymphoblastic leukemia

Dysregulation of the early stages of B-cell lymphopoiesis, orchestrating the development of cellular immunity, may induce malignant transformations. Therefore, it is essential to characterize the gene regulatory network (GRN) driving B-cell lymphopoiesis in healthy individuals to uncover malignancy mechanisms. To this end, we generated a dataset that included paired human data for chromatin accessibility and gene expression in eight B-cell precursor stages, providing the first deep characterization of early B-cell lymphopoiesis, including the identification of regulatory elements and the reconstruction of the GRN. Using this data, we recapitulated well-known regulatory elements and revealed new regulons, such as ELK3, enriched in pro-B cells with a putative role in cell cycle progression. Moreover, a single-cell multi-omics analysis validated and enhanced the resolution of the regulatory landscape recovered by bulk data, revealing MYBL2 and ZNF367 as specific regulons of cycling cell states, and CEBPA associated with lymphoid multipotent progenitors (LMPPs). Importantly, this dataset enabled us to uncover B-cell acute lymphoblastic leukemia (B-ALL) triggers. We identified different cellular origins of malignant transformation depending on the B-ALL subtype, including the association of the ETV6-RUNX1 with pro-B cells and the increased expression of ELK3 in this ALL subtype. Overall, our dataset provides the most comprehensive atlas to date of early human B-cell regulation (B-rex; https://translationalbio.shinyapps.io/brex/), facilitating further understanding of B-cell differentiation in health and disease.

immunology↗

bioRxiv2021

Deep deconvolution of the hematopoietic stem cell regulatory microenvironment reveals a high degree of specialization and conservation between mouse and human.

Understanding the regulation of normal and malignant human hematopoiesis requires comprehensive cell atlas of the hematopoietic stem cell (HSC) regulatory microenvironment. Here, we develop a tailored bioinformatic pipeline to integrate public and proprietary single-cell RNA sequencing (scRNA-seq) datasets. As a result, we robustly identify for the first time 14 intermediate cell states and 11 stages of differentiation in the endothelial and mesenchymal BM compartments, respectively. Our data provide the most comprehensive description to date of the murine HSC-regulatory microenvironment and suggests a higher level of specialization of the cellular circuits than previously anticipated. Furthermore, this deep characterization allows to infer conserved features in human, suggesting that the layers of microenvironmental regulation of hematopoiesis may also be shared between species. Our resource and methodology are a steppingstone towards a comprehensive cell atlas of the BM microenvironment.

immunology↗

bioRxiv2020

STATegra: Multi-omics data integration - A conceptual scheme with a bioinformatics pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.

bioinformatics↗