Biology subjects

Nagai, J. S.

Publications and source records attributed to Nagai, J. S..

11 recordsLinked to original sources

bioRxiv2026

IntraTalker - Modelling Intracellular Signalling in1 Cellular Crosstalk

Single-cell sequencing has advanced the study of cell-cell communication, yet most methods focus on intercellular ligand-receptor interactions while neglecting downstream intracellular signalling cascades and the possibility that downstream target genes themselves encode ligands, thereby propagating communication across multiple cells. We present IntraTalker+CrossTalkeR that combines intracellular (IntraTalker) and intercellular (CrossTalkeR) signalling from multimodal single-cell data. IntraTalker infers cell-type-specific transcription factor activities and constructs receptomes that link receptors to downstream target genes, which are then integrated with ligand-receptor predictions in CrossTalkeR. To prioritize signalling receptors, the framework performs in silico receptor perturbation. In a murine bone marrow dataset, this recovered the known function of the Il1r1 receptor in driving myeloid progenitor cell-state changes. In a human myocardial infarction dataset, it predicted a novel role for IGF1R signalling in driving the differentiation of fibroblasts towards progenitor fibroblast states.

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↗

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↗

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

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods. FindingsO_LIBuilt a framework to systematically combine 15 resources and 6 methods to estimate cell-cell communication from single-cell RNA data C_LIO_LICell-cell communication resources are often built from the same original databases and very few interactions are unique to a single resource. Yet overlap varies among resources and certain biological terms are unevenly represented C_LIO_LIDifferent methods and resources provided notably different results C_LIO_LIThe observed disagreement among the methods could have a considerable impact on the interpretation of results C_LI

bioinformatics↗

bioRxiv2021

Dissecting CD8+ T cell pathology of severe SARS-CoV-2 infection by single-cell epitope mapping

The current COVID-19 pandemic represents a global challenge. A better understanding of the immune response against SARS-CoV-2 is key to unveil the differences in disease severity and to develop future vaccines targeting novel SARS-CoV-2 variants. Feature barcode technology combined with CITE-seq antibodies and DNA-barcoded peptide-MHC I Dextramer reagents enabled us to identify relevant SARS-CoV-2-derived epitopes and compare epitope-specific CD8+ T cell populations between mild and severe COVID-19. We identified a strong CD8+ T cell response against an S protein-derived epitope. CD8+ effector cells in severe COVID-19 displayed hyperactivation, T cell exhaustion and were missing characteristics of long-lived memory T cells. We identify A*0101 WTAGAAAYY as an immunogenic CD8+ T cell epitope with the ability to drive clonal expansion. We provide an in-depth characterization of the CD8+ T cell-mediated response to SARS-CoV-2 infection which will be relevant for the development of molecular and targeted therapies and potential adjustments of vaccination strategies.

immunology↗

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

The sugarcane and sorghum kinomes: insights into evolutionary expansion and diversification

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and is the core regulator of cellular signaling. Even considering this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, which were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between these species, this study offers insights about Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq, identifying significant similarities between Sbi and Ssp. Moreover, through coexpression networks, we inferred a core structure of kinase interactions with specific key elements. This study is the first to categorize the allele specificity of a kinome and provides a wide reservoir of molecular and genetic information, enhancing the understanding of the evolutionary history of Sbi and Ssp PKs. HighlightThis study describes the catalog of kinase gene family in Saccharum spontaneum and Sorghum bicolor, providing a reservoir of molecular features and expression patterns based on RNA-Seq and co-expression networks.

genomics↗

bioRxiv2020

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

bioinformatics↗