Biology subjects

Ibarra, I. L.

Publications and source records attributed to Ibarra, I. L..

9 recordsLinked to original sources

bioRxiv2026

Cancer-myeloid cell invasive program in pediatric-type diffuse high-grade glioma

Pediatric-type diffuse high-grade gliomas (pHGGs) are aggressive, heterogeneous brain tumors shaped by intricate cancer-microenvironment cell-cell interactions. Here, we present an integrative multimodal pHGGmap, encompassing over 800,000 cells from 136 patients profiled across transcriptomic, epigenomic, and spatial modalities. Its analysis delineated robust cancer-myeloid cell programs that structured the tumor ecosystem and identified ten distinct cancer cell states, including previously unrecognized developmental and context-responsive programs. Among these, radial glial-like (RG-like) cells exhibited dual stress-adapted and infiltrative phenotypes. Tumor-associated monocyte-derived macrophages and resident microglia engaged in four distinct immunomodulatory programs aligned with specific cancer states. Three conserved multicellular communities were maintained across treatment, including a stable, spatially and transcriptionally linked RG-like/complement-macrophage niche, indicative of cellular co-option and adaptation to support invasion. Longitudinal profiling of a metastatic diffuse midline glioma case showed that RG-like cells predominate during dissemination and remain associated with complement-enriched macrophages, whose reprogramming restores immune activation. pHGGmap establishes a landmark resource for translational discovery. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/701142v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@7ae699org.highwire.dtl.DTLVardef@b95b92org.highwire.dtl.DTLVardef@12adcf9org.highwire.dtl.DTLVardef@1118703_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

UniversalEPI: Harnessing Attention Mechanisms to Decode Chromatin Interactions in Rare and Unexplored Cell Types

Enhancer-promoter interactions (EPIs) play a central role in gene regulation, but experimental techniques such as Hi-C for mapping these interactions remain costly and labor-intensive. Computational methods have been developed to predict EPIs in silico from DNA sequence and chromatin information; however, there are major challenges with the generalizability and accuracy of predictions by existing methods across cell types and conditions unseen during model training. We developed and validated UniversalEPI, an attention-based deep ensemble model that predicts EPIs up to 2 Mb apart using only DNA sequence and chromatin accessibility (ATAC-seq) data. Unlike models that reconstruct full Hi-C contact maps, UniversalEPI focuses on biologically relevant, sparse chromatin interactions between accessible regulatory elements. It generalizes across both bulk and single-cell ATAC-seq-derived pseudo-bulk datasets, delivering state-of-the-art performance while using fewer input modalities than existing approaches. By modeling predictive uncertainty, UniversalEPI enables statistically robust differential analysis of chromatin interactions across conditions. We demonstrate its utility by tracking dynamic EPIs during human macrophage activation and identifying regulatory differences between cancer cell states in esophageal adenocarcinoma. By providing precalculated Hi-C predictions for 157 ENCODE datasets, UniversalEPI expands the scope and applicability of in silico 3D genome modeling for studying gene regulation in development and disease.

genomics↗

bioRxiv2024

Learning sequence-based regulatory dynamics in single-cell genomics

Epigenomics assays, such as chromatin accessibility, can identify DNA-sequence-specific regulatory factors. Models that predict read counts from sequence features can explain cell-based readouts using specific DNA patterns (genomic motifs) but do not encode the changes in genomic regulation over time, which is crucial for understanding biological events during cell transitions. To bridge this gap, we present muBind, a deep learning model that accurately predicts genomic counts of single-cell datasets based on DNA sequence features, their cell-based activities, and cell relationships (graphs) in a single architecture, enhancing the interpretability of cell transitions due to the possibility of inspecting motif activities weighted by nearest neighbors. MuBind shows competitive performance in bulk and single-cell genomics. When complemented with graphs learned from RNA-based dynamical models used as injected priors in our model, muBind enhances through motif-graph interactions the identification of transcriptional regulators explaining cell transition events, including Sox9 in pancreatic endocrinogenesis scATAC-seq, and Gli3/Prdm16 in mouse neurogenesis and human organoids scRNA-seq, both supported by independent evidence, including associations between chromatin and motif activities over pseudotime, TF-gene expression patterns, and biological knowledge of these regulators. muBind advances our understanding of cell transitions by revealing regulatory motifs and their interactions, providing valuable insights for genomic research and gene regulatory network dynamics. It is available at https://github.com/theislab/mubind.

genomics↗

bioRxiv2023

Integrated multi-omics single cell atlas of the human retina

Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here, we present an integrated multimodal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 2.4 million cells from 55 donors, including 1.4 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) of transcriptome and chromatin accessibility, unveiling over 110 types. Engaging the retina community, we annotated each cluster, refined the Cell Ontology for the retina, identified distinct marker genes, and characterized cis-regulatory elements and gene regulatory networks (GRNs) for these cell types. Our analysis uncovered intriguing differences in transcriptome, chromatin, and GRNs across cell types. In addition, we modeled changes in gene expression and chromatin openness across gender and age. This integrated atlas also enabled the fine-mapping of GWAS and eQTL variants. Accessible through interactive browsers, this multimodal cross-donor and cross-lab HRCA, can facilitate a better understanding of retinal function and pathology.

molecular biology↗

bioRxiv2022

An integrated cell atlas of the human lung in health and disease

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

cell biology↗

bioRxiv2021

Comparative chromatin accessibility upon BDNF-induced neuronal activity delineates neuronal regulatory elements

Neuronal activity induced by brain-derived neurotrophic factor (BDNF) triggers gene expression, which is crucial for neuronal survival, differentiation, synaptic plasticity, memory formation, and neurocognitive health. However, its role in chromatin regulation is unclear. Here, using temporal profiling of chromatin accessibility and transcription in mouse primary cortical neurons upon either BDNF stimulation or depolarization (KCl), we identify features that define BDNF-specific chromatin-to-gene expression programs. Enhancer activation is an early event in the regulatory control of BDNF-treated neurons, where the bZIP motif-binding Fos protein pioneered chromatin opening and cooperated with co-regulatory transcription factors (Homeobox, EGRs, and CTCF) to induce transcription. Deleting cis-regulatory sequences decreased BDNF-mediated Arc expression, a regulator of synaptic plasticity. BDNF-induced accessible regions are linked to preferential exon usage by neurodevelopmental disorder-related genes and heritability of neuronal complex traits, which were validated in human iPSC-derived neurons. Thus, we provide a comprehensive view of BDNF-mediated genome regulatory features using comparative genomic approaches to dissect mammalian neuronal activity.

genomics↗

bioRxiv2021

Compositional perturbation autoencoder for single-cell response modeling

Recent advances in multiplexed single-cell transcriptomics experiments are facilitating the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug responses across different cell type, dose, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, which enables drug similarity analysis and predictions for unseen dosage and drug combinations. We show that CPA accurately models single-cell perturbations across compounds, doses, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying that it captures features that distinguish different interaction programs. Finally, we demonstrate that CPA can generate in-silico 5,329 missing genetic combination perturbations (97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design and hypothesis generation by enabling in-silico response prediction at the single-cell level, and thus accelerate therapeutic applications using single-cell technologies.

bioinformatics↗

bioRxiv2021

Squidpy: a scalable framework for spatial single cell analysis

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides both infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data.

bioinformatics↗

bioRxiv2020

APOBEC2 binds Chromatin and Represses Transcription during Myoblast Differentiation

The activation induced cytidine deaminase/apolipoprotein B editing complex (AID/APOBEC) family comprises several nucleic acid editors with roles ranging from antibody diversification to mRNA editing. APOBEC2, an evolutionarily conserved member of this family, has neither an established substrate nor a mechanism of action, however genetic evidence suggests functional relevance in tissues such as muscle. Here, we demonstrate that in muscle, APOBEC2 does not have any of the attributed molecular functions of the AID/APOBEC family, such as RNA editing, DNA demethylation, or DNA mutation. Instead, we show that APOBEC2 occupies chromatin at promoter regions of certain genes, whose expression is repressed during muscle cell differentiation. We further demonstrate that APOBEC2 on one hand binds promoter region DNA directly and in a sequence specific fashion, while on the other it interacts with HDAC transcriptional corepressor complexes. Therefore, APOBEC2, by actively repressing the expression of non-myogenesis pathway genes, plays a key role in enforcing the proper establishment of muscle cell fate.

molecular biology↗