Biology subjects

Gesteira Costa Filho, I.

Publications and source records attributed to Gesteira Costa Filho, I..

At least 19 recordsLinked to original sources

bioRxiv2026

AtlasOT - The Fused Unbalanced Gromov-Wasserstein for Multimodal Integration of Disease Atlases

Single-cell and spatial multiomics technologies are transforming disease atlas construction, but computational integration of unpaired modalities remains challenging, as existing methods overlook two biological constraints of data with matching samples. First, cells should only be mapped across modalities within the same donor or biospecimen, and second, cell recovery frequently differs substantially between modalities. To address these gaps, we present AtlasOT, an optimal-transport framework based on the fused unbalanced Gromov-Wasserstein (FUGW) formulation for multi-modal integration. AtlasOT jointly models a shared cross-modality feature space and modality-specific geometric structures while restricting transport to within-sample cell pairs. Moreover, AtlasOT relaxes strict mass conservation of the balanced optimal transport optimization to accommodate unbalanced cell numbers. We benchmark AtlasOT against state-of-the-art methods on scRNA-scATAC and scRNA-spatial transcriptomics mapping scenarios. Our results indicate that AtlasOT outperforms baselines and state-of-the-art methods in all considered scenarios. Moreover, we demonstrate that AtlasOT's transport plan can be used to improve several relevant tasks such as the detection of rare cell populations, spot-level cell-type deconvolution, spatial gene imputation, and reconstruction of transcription-factor-driven spatial regulatory dynamics. These results establish AtlasOT as a unified, biologically constrained framework for multimodal integration in disease atlas studies, with broad applicability to label transfer, imputation, and regulatory network analysis.

bioinformatics↗

bioRxiv2026

Genome-wide multi-layered epigenomic profiling across human aging

Aging is characterized by highly reproducible alterations across multiple layers of the epigenetic landscape, including DNA methylation, chromatin accessibility, and histone modifications. However, it remains unclear to what extent these age-associated changes are interconnected and coordinated coherently. To investigate the genome-wide distribution and interplay of age-associated epigenetic alterations, we generated whole-genome bisulfite sequencing (WGBS) data from blood samples of 120 healthy donors. Integration with ATAC-seq data revealed no clear relationship between age-related changes in DNA methylation and chromatin accessibility. We further examined the association of these alterations with age-dependent changes in CTCF occupancy and histone modifications, including H3K27ac, H3K27me3, H3K4me1, H3K4me3, and H3K9me3, but observed very little corresponding changes in chromatin states. Collectively, our integrative genome-wide analysis revealed only limited association between age-associated epigenetic alterations in DNA methylation, chromatin accessibility, and histone modifications, arguing against a broadly coordinated remodeling of the aging epigenome.

genomics↗

bioRxiv2026

Multimodal single-cell analysis uncovers transcription factor networks underlying T-cell aging

Aging of the immune system is associated with chronic inflammation and impaired immune function, yet the regulatory mechanisms underlying these changes remain incompletely understood. Here, we generated paired single-cell transcriptomic and chromatin accessibility profiles from peripheral blood mononuclear cells of young and old healthy donors to characterize immune aging at single-cell resolution. Using an integrative computational framework for multi-omic single-cell analysis, we detected pronounced age-associated changes in T cells, including loss of naive CD8+ T cells and expansion of differentiated memory and effector populations. Aging was accompanied by increased inflammatory signaling and reduced oxidative phosphorylation programs. Enhancer-based gene regulatory network analyses identified a reduced role of TCF7 and increased activity of inflammatory regulators, including FOSL2, in aged T cells. Integration with genetic association and eQTL datasets further supported the functional relevance of age-associated regulatory regions and their target genes.

bioinformatics↗

bioRxiv2026

GraphHDBSCAN*: Graph-based Hierarchical Clustering on High Dimensional Single-cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is widely used to resolve cellular heterogeneity across thousands to millions of cells. A major challenge is to identify biologically meaningful cell populations while preserving their hierarchical organization, because broad cell types frequently split into more specialized subtypes. However, state-of-the-art approaches mostly focus on flat partitions and ignore the hierarchical structure of single-cell data. Here we introduce GraphHDBSCAN*, a graph-based, hyperparameter-free extension of HDBSCAN* that performs hierarchical density-based clustering on a graph representation of the data, enabling robust recovery of both single-level and hierarchical relationships in high-dimensional and sparse datasets. We evaluate GraphHDBSCAN* across multiple scRNA-seq datasets and show that it recovers biologically meaningful hierarchies that reveal fine-grained structure in complex data, including monocyte subpopulations. In addition, the method yields high-quality flat partitions that outperform widely used community-detection methods.

bioinformatics↗

bioRxiv2026

NicheSphere reveals Spp1⁺ macrophages as central hubs coordinating fibrotic remodeling in myeloproliferative neoplasms

Bone marrow fibrosis in myeloproliferative neoplasms arises from interactions between mutant hematopoietic clones and fibrosis-driving stromal cells. We identify Spp1 macrophages as central "communication hubs" integrating inflammatory and fibrotic programs via spatial proximity, ECM signaling, and cytokine activation. Using dual lineage-tracing, single-cell and multiplet RNA-sequencing, and a novel computational method for cell-colocalization and communication analysis named NicheSphere, we show that Spp1 macrophages form core communication hubs with osteoCAR cells, fibroblasts, and megakaryocytes. NicheSphere uncovered two distinct niches: macrophage-enriched compartments driving WNT, JAK-STAT, and TNF cytokine signaling, and a fibrosis-interacting core enriched in TGF-{beta} and ECM glycoproteins. Genetic ablation revealed cooperative roles of stromal and hematopoietic Spp1 in sustaining fibrosis and inflammation. Mechanistically, SPP1 promoted integrin-mediated adhesion, IL-1{beta} secretion, and stromal activation, while IL-1 cytokines induced Spp1 and collagen expression. Loss of Spp1 in hematopoietic progenitors reduced inflammation and restored macrophage function, establishing SPP1 macrophages as therapeutic targets in progressive bone marrow fibrosis.

cancer biology↗

bioRxiv2025

On the benchmarking of clustering algorithms and hyperparameter influence for cell type detection in single-cell RNA sequencing data.

Clustering single-cell RNA-seq (scRNA-seq) data and related protocols remains a major challenge due to high dimensionality, sparsity, and noise. Despite numerous benchmarking studies aiming to identify the most suitable clustering methods, many suffer from methodological flaws that can undermine their conclusions. A major challenge in benchmarking is selecting representative datasets that cover the diversity of scRNA-seq experiments and include laboratory-verified labels for reliable evaluation. Consistent preprocessing of all inputs to benchmarked algorithms is crucial, as it significantly impacts performance. Beyond selecting an algorithm, a thorough exploration of hyperparameters is also essential to assess robustness and identify configurations that maximize performance. We focus on proposing an improved benchmarking framework that addresses common methodological issues in prior studies. We illustrate our proposed methodology in a case study comparing the classic Leiden and Louvain clustering algorithms with extensive hyperparameters exploration on a carefully curated collection of real gold standard datasets. By evaluating clustering performance across different hyper-parameter selection scenarios, we show that benchmarking results can be misleading, either overestimating or underestimating performance depending on how the hyperparameter space is explored. In our illustrative case study, benchmarking results do not reveal any practically relevant performance differences between the Louvain and Leiden algorithms. In contrast, we show that overlooked factors such as graph construction and quality functions critically influence clustering outcomes, particularly un-der suboptimal settings of numerical hyperparameters--the neighbor-hood size k used for similarity graph construction and the resolution hyperparameter in graph-based clustering algorithms. While noticeable trends have been observed in terms of how different (dis)similarity functions affect performance, the impact of this choice is limited and, to some extent, overridden by the graph-building approach. Across different graphs, there is a noticeable trade-off between achieving optimal performance with ideally tuned numerical hyperparameters and maintaining robustness under more realistic, unsupervised, and suboptimal settings. All in all, the analysis of our illustrative benchmarking case study offers clear guidance and objective recommendations for practitioners in the field. Most importantly, as the main contribution of this manuscript, our proposed framework sets a foundation for more reliable scRNA-seq clustering evaluation and benchmarking in future studies.

bioinformatics↗

bioRxiv2024

IFNα Inhibits JAK2V617F-Driven Neoplastic Vasculogenesis and Endothelial-to-Mesenchymal Transition in a 3D iPSC-Based Bone Marrow Niche Model

The vascular niche is a critical regulator of hematopoiesis and disease progression in myeloproliferative neoplasms (MPN). The presence of JAK2V617F+ endothelial cells (EC) in MPN patients and their association with cardiovascular complications highlight the need to understand and therapeutically target this compartment. Using patient-specific induced pluripotent stem cells (iPSC) harboring JAK2WT or the MPN-driver JAK2V617F (heterozygous, JAK2V617FHET, or homozygous, JAK2V617FHOM), we identified zygosity-dependent transcriptional profiles in iPSC-derived EC (iEC) at baseline and following interferon-alpha (IFN) treatment. JAK2V617FHET iEC exhibited an endothelial-to-mesenchymal transition (EndMT) signature, while JAK2V617FHOM iEC showed suppression of translation and ribosome biogenesis. Leveraging iPSC-based 3D assembloids that mimic the bone marrow (BM) niche, we showed that JAK2V617F-driven EndMT is inhibited by tyrosine kinase inhibitors and IFN. In both JAK2V617F-driven polycythemia vera and TPO-driven myelofibrosis murine models, scRNA-seq analysis of the BM vascular niche consistently revealed inflammatory and EndMT-associated signatures in arterial and arteriolar EC. Notably, dysregulation of ribosome- and translation-related pathways emerged in the myelofibrosis model and at advanced disease stages in JAK2V617F-driven polycythemia vera, indicating progressive vascular remodeling with disease evolution. Chronic pegylated IFN treatment in vivo effectively reversed these pathological changes. IFNs anti-EndMT activity was further validated in BM biopsies from MPN patients undergoing IFN therapy. This is the first study to define MPN stage-dependent vascular remodeling and zygosity-specific endothelial effects of JAK2V617F, and to directly link IFN-mediated EndMT inhibition as a novel antifibrotic mechanism. Our 3D assembloids provide a translational platform for mechanistic studies and therapeutic targeting of the BM microenvironment in MPN. Bullet PointsO_LIArterial vascular remodeling emerges as a novel hallmark of MPN, characterized by TNF-inflammation, ribosomal dysregulation and EndMT. C_LIO_LIIFN restores neoplastic endothelial dysfunction, highlighting its role as a vascular niche-modulating and anti-fibrotic agent in MPN. C_LI

cancer biology↗

bioRxiv2024

PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian

Multi-modal single-cell sequencing, which captures changes in chromatin and gene expression in the same cells, is a game changer in the study of gene regulation in cellular differentiation processes. Computational trajectory analysis is a key computational task for inferring differentiation trees from this single-cell data, though current methods struggle with complex, multi-branching trees and multi-modal data. To address this, PHLOWER (decomPosition of the Hodge Laplacian for inferring trajectOries from floWs of cEll diffeRentiation) leverages the harmonic component of the Hodge decomposition on simplicial complexes to infer trajectory embeddings. These natural representations of cell differentiation facilitate the estimation of their underlying differentiation trees. We evaluate PHLOWER through benchmarking with multi-branching differentiation trees and using novel kidney organoid multi-modal and spatial single-cell data. These demonstrate the power of PHLOWER in both the inference of complex trees and the identification of transcription factors regulating off-target cells in kidney organoids.

bioinformatics↗

bioRxiv2024

scACCorDiON: A clustering approach for explainable patient level cell cell communication graph analysis

MotivationThe combination of single-cell sequencing with ligand-receptor analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs stand out as a natural representation of cell-cell communication events. However, current computational methods cannot analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the non-linear nature of cell-cell communication and the high variability presented by the patient-specific single-cell RNAseq datasets. ResultsHere, we present scACCorDiON (single-cell Analysis of Cell-Cell Communication in Disease clusters using Optimal transport in Directed Networks), an optimal transport algorithm exploring node distances on the Markov Chain as the ground metric between directed weighted graphs. Additionally, we derive a k-barycenter algorithm using the Wasserstein-based distance, which is able to cluster directed weighted graphs. We compare our approach with competing methods in several large cohorts of scRNA-seq data. Our results show that scACCorDiON can predict clusters better, matching the disease status of samples. Moreover, we show that barycenters provide a robust and explainable representation of cell cell communication events related to the detected clusters. We also provide a case study of pancreas adenocarcinoma, where scACCorDion detects a sub-cluster of disease samples associated with changes in the tumor microenvironment. AvailabilityThe code of scACCorDiON is available at https://scaccordion.readthedocs.io/en/latest Contactivan.costa@rwth-aachen.de

systems biology↗

bioRxiv2023

EphrinA5 regulates cell motility by modulating the targeting of DNMT1 to the Ncam1 promoter via lncRNA/DNA triplex formation

Cell-cell communication is mediated by membrane receptors and their cognate ligands, such as the Eph/ephrin system, and dictates physiological processes, including cell proliferation and migration. However, whether and how Eph/ephrin signaling culminates in transcriptional regulation is largely unknown. Epigenetic mechanisms are key for integrating external "signals", e.g., from neighboring cells, into the transcriptome. We have previously reported that ephrinA5 stimulation of immortalized cerebellar granule (CB) cells elicits transcriptional changes of lncRNAs and protein-coding genes. LncRNAs represent important adaptors for epigenetic writers through which they regulate gene expression. Here, we investigate the interaction of lncRNA with protein-coding genes by the combined power of in silico modeling of RNA/DNA interactions and respective wet lab approaches, in the context of ephrinA5-dependent regulation of cellular motility. We found that Snhg15, a cancer-related lncRNA, forms a triplex structure with the Ncam1 promoter and interacts with DNMT1. EphrinA5 stimulation leads to reduced Snhg15 expression, diminished Snhg15/DNMT1 interaction and decreased DNMT1 association with the Ncam1 promoter. These findings can explain the attenuated Ncam1 promoter methylation and elevated Ncam1 expression that in turn elicits decreased cell motility of CB cells. Hence, we propose that ephrinA5 influences gene transcription via lncRNA-targeted DNA methylation underlying the regulation of cellular motility.

molecular biology↗

bioRxiv2023

RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data

BackgroundMassive amounts of data are produced by combining next-generation sequencing (NGS) with complex biochemistry techniques to characterize regulatory genomics profiles, such as protein-DNA interaction and chromatin accessibility. Interpretation of such high-throughput data typically requires different computation methods. However, existing tools are usually developed for a specific task, which makes it challenging to analyze the data in an integrative manner. ResultsWe here describe the Regulatory Genomics Toolbox (RGT), a computational library for the integrative analysis of regulatory genomics data. RGT provides different functionalities to handle genomic signals and regions. Based on that, we developed several tools to perform distinct downstream analyses, including the prediction of transcription factor binding sites using ATAC-seq data, identification of differential peaks from ChIP-seq data, and detection of triple helix mediated RNA and DNA interactions, visualization, and finding an association between distinct regulatory factors. ConclusionWe present here RGT; a framework to facilitate the customization of computational methods to analyze genomic data for specific regulatory genomics problems. RGT is a comprehensive and flexible Python package for analyzing high throughput regulatory genomics data and is available at: https://github.com/CostaLab/reg-gen. The documentation is available at: https://reg-gen.readthedocs.io

bioinformatics↗

bioRxiv2022

CimpleG: Finding simple CpG methylation signatures

DNA methylation (DNAm) at specific CG dinucleotides (CpG sites) in the genome provides powerful biomarkers for a wide range of applications, such as epigenetic clocks, cell type deconvolution, and stratification of cancer patients. Such epigenetic signatures are usually based on multivariate approaches that require hundreds of DNAm sites for predictions. In contrast, targeted assays for only a few selected CpGs may facilitate faster turnover, better standardisation, and reduced costs. Here, we propose a computational framework named CimpleG for the detection of small CpG methylation signatures, exemplarily used for cell type classification. We evaluate the performance of CimpleG and competing methods on two distinct datasets in regard to the cell-type classification of blood cells and of other somatic cells. Furthermore, we evaluate how well these classifiers perform cellular deconvolution of blood cell mixtures. We show that CimpleG is both time efficient and performs as well as Elastic Net, while basing its prediction on a single DNAm site per cell type. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution.

bioinformatics↗

bioRxiv2022

A lncRNA identifies IRF8 enhancer element in negative feedback control of dendritic cell differentiation

Transcription factors play a determining role in lineage commitment and cell differentiation. Interferon regulatory factor 8 (IRF8), a hematopoietic transcription factor, is prominently upregulated in dendritic cells (DC) by autoactivation and controls DC differentiation and function. However, it is unclear how IRF8 autoactivation is controlled and eventually limited. Here we identified a novel long non-coding RNA transcribed from the +32 kb enhancer downstream of IRF8 transcription start site and expressed specifically in plasmacytoid DC (pDC), referred to as lncIRF8. A sequence element of the lncIRF8 promoter, but not lncIRF8 itself, is crucial for pDC and classical DC type 1 (cDC1) differentiation. In DC development IRF8 autoactivation is first initiated by flanking enhancers and then second controlled by feedback inhibition through the lncIRF8 promoter element in the +32 kb enhancer. Our work reveals a previously unrecognized negative feedback loop of IRF8 that orchestrates its own expression and thereby controls DC differentiation.

cell biology↗

bioRxiv2022

MOJITOO: a fast and universal method for integration of multimodal single cell data

The advent of multi-modal single cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities. We present a single cell multi-modal integration method, named MOJITOO (Multi-mOdal Joint IntegraTion of cOmpOnents). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single cell data sets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering.

bioinformatics↗

bioRxiv2021

Human pluripotent stem cell-derived kidney organoids for personalized congenital and idiopathic nephrotic syndrome modeling

Nephrotic syndrome (NS) is characterized by severe proteinuria as a consequence of kidney glomerular injury due to podocyte damage. In vitro models mimicking in vivo podocyte characteristics are a prerequisite to resolve NS pathogenesis. Here, we report human induced pluripotent stem cell derived kidney organoids containing a podocyte population that heads towards adult podocytes and were superior compared to 2D counterparts, based on scRNA sequencing, super-resolution imaging and electron microscopy. In this study, these next-generation podocytes in kidney organoids enabled personalized idiopathic nephrotic syndrome modeling as shown by activated slit diaphragm signaling and podocyte injury following protamine sulfate treatment and exposure to NS plasma containing pathogenic permeability factors. Organoids cultured from cells of a patient with heterozygous NPHS2 mutations showed poor NPHS2 expression and aberrant NPHS1 localization, which was reversible after genetic correction. Repaired organoids displayed increased VEGFA pathway activity and transcription factor activity known to be essential for podocyte physiology, as shown by RNA sequencing. This study shows that organoids are the preferred model of choice to study idiopathic and congenital podocytopathies. Summary StatementKidney organoid podocytes allow personalized nephrotic sydrome modeling,

molecular biology↗

bioRxiv2021

CrossTalkeR: Analysis and Visualisation of Ligand Receptor Networks

MotivationLigand-receptor (LR) network analysis allows the characterization of cellular crosstalk based on single cell RNA-seq data. However, current methods typically provide a list of inferred LR interactions and do not allow the researcher to focus on specific cell types, ligands or receptors. In addition, most of these methods cannot quantify changes in crosstalk between two biological phenotypes. ResultsCrossTalkeR is a framework for network analysis and visualisation of LR interactions. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological phenotypes, i.e. disease vs. homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterisation of changes in cellular crosstalk in disease. Availability and ImplementationCrosstalkeR is an R package available at https://github.com/CostaLab/CrossTalkeR.

systems biology↗

bioRxiv2020

Spatial multi-omic map of human myocardial infarction

Myocardial infarction is a leading cause of mortality. While advances in the acute treatment have been made, the late-stage mortality is still high, driven by an incomplete understanding of cardiac remodeling processes1,2. Here we used single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of different physiological zones and timepoints of human myocardial infarction and human control myocardium to generate an integrative high-resolution map of cardiac remodeling. This approach allowed us to increase spatial resolution of cell-type composition and provide spatially resolved insights into the cardiac transcriptome and epigenome with identification of distinct cellular zones of injury, repair and remodeling. We here identified and validated mechanisms of fibroblast to myofibroblast differentiation that drive cardiac fibrosis. Our study provides an integrative molecular map of human myocardial infarction and represents a reference to advance mechanistic and therapeutic studies of cardiac disease.

systems biology↗

bioRxiv2020

Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites

BackgroundThe complex composition of different cell types within a tissue can be estimated by deconvolution of omics datasets. For example, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. In this study, we investigated if deconvolution is also feasible with individual cell-type-specific CG dinucleotides (CpG sites), which can be addressed by targeted analysis, such as pyrosequencing. ResultsWe compiled and curated a dataset of 579 samples from Illumina 450k BeadChip technology that comprised 14 different purified and characterized human cell types. A training and validation strategy was applied to identify and test cell-type-specific CpGs. Initially, the amount of fibroblasts was estimated using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. This FibroScore correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts and induced pluripotent stem cells. Using previously published BeadChip datasets with cell mixtures the accuracy of this eight CpG signature was comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types. ConclusionThis proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.

genomics↗