Biology subjects

Dimitrov, D.

Publications and source records attributed to Dimitrov, D..

12 recordsLinked to original sources

bioRxiv2025

OmniPath: integrated knowledgebase for multi-omics analysis

Analysis and interpretation of omics data largely benefit from the use of prior knowledge. However, this knowledge is fragmented across resources and often is not directly accessible for analytical methods. We developed OmniPath (https://omnipathdb.org/), a database combining diverse molecular knowledge from 168 resources. It covers causal protein-protein, gene regulatory, miRNA, and enzyme-PTM (post-translational modification) interactions, cell-cell communication, protein complexes, and information about the function, localization, structure, and many other aspects of biomolecules. It prioritizes literature curated data, and complements it with predictions and large scale databases. To enable interactive browsing of this large corpus of knowledge, we developed OmniPath Explorer, which also includes a large language model (LLM) agent that has direct access to the database. Python and R/Bioconductor client packages and a Cytoscape plugin create easy access to customized prior knowledge for omics analysis environments, such as scverse. OmniPath can be broadly used for the analysis of bulk, single-cell and spatial multi-omics data, especially for mechanistic and causal modeling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/675512v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17c2b6borg.highwire.dtl.DTLVardef@1069835org.highwire.dtl.DTLVardef@1f2ce76org.highwire.dtl.DTLVardef@1d0b34f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Rational redesign of antigen binding domain improves in vivo efficacy of CD22-CAR T cells

Chimeric antigen receptor (CAR) T cells targeted to CD19 are an effective therapy for B-lineage malignancies. However, about half of patients relapse and this therapeutic, often with antigen-negative disease, warranting the targeting of other antigens. CD22 represents another promising target, with highly restricted but ubiquitous expression across the B-lineage. However, despite promising preclinical work by several groups with CD22-targeted CAR T cells targeting of this antigen in the clinic has proven difficult, with many patients relapsing with CD22Lo leukemia, contrasting to complete loss of CD19 expression post CD19-CAR. While prior work has demonstrated that a CAR with so-called "tonic" antigen-independent signaling properties has proven to be highly efficacious, tonic signaling has been shown be detrimental to long-term T cell function. Here, we demonstrate a balance between binding affinity and antigen-independent tonic signaling (as determined by length of flexible linker) in determining CAR function. We show that maximal CAR function in the settings CD22Lo and WT leukemia is maintained by boosting binding affinity without shortening flexible linker to induce tonic signaling, establishing rational modification of antigen binding domain as an important approach for modulating the function of cellular therapeutics. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=179 HEIGHT=200 SRC="FIGDIR/small/643183v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1d543e3org.highwire.dtl.DTLVardef@5bf226org.highwire.dtl.DTLVardef@76cd2forg.highwire.dtl.DTLVardef@198fd37_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

RIDDEN: Data-driven inference of receptor activity from transcriptomic data

Intracellular signaling initiated from ligand bound receptors plays a fundamental role in both physiological regulation and development of disease states, making receptors one of the most frequent drug targets. Systems level analysis of receptor activity can help to identify cell and disease type specific receptor activity alterations. While recently several computational methods have been developed to analyse ligand -receptor interactions based on transcriptomics data, none of them focuses directly on the receptor side of these interactions. Also, most of the methods use directly the expression of ligands and receptors to infer active interaction, while co-expression of genes does not necessarily indicate functional interactions or activated state. To address these problems, we developed RIDDEN (Receptor actIvity Data Driven inferENce), a computational tool, which predicts receptor activities from the receptor-regulated gene expression profiles, and not from the expressions of ligand and receptor genes. We collected 14463 perturbation gene expression profiles for 229 different receptors. Using these data, we trained the RIDDEN model, which can effectively predict receptor activity for new bulk and single-cell transcriptomics datasets. We validated RIDDENs performance on independent in vitro and in vivo receptor perturbation data, showing that RIDDENs model weights correspond to known regulatory interactions between receptors and transcription factors, and that predicted receptor activities correlate with receptor and ligand expressions in in vivo datasets. We also show that RIDDEN can be used to identify mechanistic biomarkers in an immune checkpoint blockade-treated cancer patient cohort. RIDDEN, the largest transcriptomics-based receptor activity inference model, can be used to identify cell populations with altered receptor activity and, in turn, foster the study of cell-cell communication using transcriptomics data. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/626558v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@155d87dorg.highwire.dtl.DTLVardef@f80ed2org.highwire.dtl.DTLVardef@195140eorg.highwire.dtl.DTLVardef@57ccfa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions

Interactions between proteins and metabolites are key for cellular function, from the catalytic breakdown of nutrients to signaling. An important case is cell-cell communication, where cellular metabolites are secreted into the microenvironment and initiate a signaling cascade by binding to an intra- or extracellular receptor of another cell. While protein-protein mediated cell-cell communication is routinely inferred from transcriptomic data, for metabolite-protein interactions this is challenging due to the limitations of high-throughput single-cell and spatial metabolomics technologies, together with the absence of comprehensive prior knowledge resources that include metabolites. Here we report MetalinksDB, a comprehensive and flexible database of intercellular metabolite-protein interactions that is a magnitude larger than existing ones. MetalinksDB can be tailored to specific biological contexts such as diseases, pathways, or tissue/cellular locations by querying subsets of interactions using the web interface (https://metalinks.omnipathdb.org/) or the knowledge graph adapters. We showcase the use of MetalinksDB by identifying deregulated processes in renal cancer patients from multi-omics data as well as inferring metabolite-mediated cell-cell communication events driving acute kidney injury from spatial transcriptomic data. We anticipate that MetalinksDB will facilitate the study of metabolite-mediated communication processes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/573715v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@18c78aaorg.highwire.dtl.DTLVardef@145f21borg.highwire.dtl.DTLVardef@119d16borg.highwire.dtl.DTLVardef@3febb_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

LIANA+: an all-in-one cell-cell communication framework

The growing availability of single-cell and spatially-resolved transcriptomics has led to the rapidly growing popularity of methods to infer cell-cell communication. Many approaches have emerged, each capturing only a partial view of the complex landscape of cell-cell communication. Here, we present LIANA+, a scalable framework to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially-resolved data. Beyond integrating and extending established methodologies and a rich knowledge base, LIANA+ enables novel analyses using diverse molecular mediators, including those measured in multi-omics data. Accessible as an open-source Python package at https://github.com/saezlab/liana-py, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/553863v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@16486ceorg.highwire.dtl.DTLVardef@ce6821org.highwire.dtl.DTLVardef@1165e54org.highwire.dtl.DTLVardef@18add5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes [~]1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of [~]63k cells, 10 cell types, and 12 samples.

bioinformatics↗

bioRxiv2023

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

Single-cell atlases across conditions are essential in the characterization of human disease. In these complex experimental designs, patient samples are profiled across distinct cell-types and clinical conditions to describe disease processes at the cellular level. However, most of the current analysis tools are limited to pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes and the effects of other biological and technical factors in the variation of gene expression. Here we propose a computational framework for an unsupervised analysis of samples from cross-condition single-cell atlases and for the identification of multicellular programs associated with disease. Our strategy, that repurposes multi-omics factor analysis, incorporates the variation of patient samples across cell-types and enables the joint analysis of multiple patient cohorts, facilitating integration of atlases. We applied our analysis to a collection of acute and chronic human heart failure single-cell datasets and described multicellular processes of cardiac remodeling that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlas and allows for the integration of the measurements of patient cohorts across distinct data modalities, facilitating the generation of comprehensive tissue-centric understanding of disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/529642v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@72269org.highwire.dtl.DTLVardef@645808org.highwire.dtl.DTLVardef@1cf9d6dorg.highwire.dtl.DTLVardef@1686e65_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Multiple paths towards repeated phenotypic evolution in the spiny-leg adaptive radiation (Tetragnatha; Hawaii)

The repeated evolution of phenotypes is ubiquitous in nature and offers some of the clearest evidence of the role of natural selection in evolution. The genomic basis of repeated phenotypic evolution is often complex and can arise from a combination of gene flow, shared ancestral polymorphism and de novo mutation. Here, we investigate the genomic basis of repeated ecomorph evolution in the adaptive radiation of the Hawaiian spiny-leg Tetragnatha. This radiation comprises four ecomorphs that are microhabitat-specialists, and differ in body pigmentation and size (Green, Large Brown, Maroon, and Small Brown). Using 76 newly generated low-coverage, whole-genome resequencing samples, coupled with population genomic and phylogenomic tools, we studied the evolutionary history of the radiation to understand the evolution of the spiny-leg lineage and the genetic underpinnings of ecomorph evolution. Congruent with previous works, we find that each ecomorph has evolved twice, with the exception of the Small Brown ecomorph, which has evolved three times. The evolution of the Maroon and the Small Brown ecomorphs likely involved ancestral hybridization events, whereas the Green and the Large Brown ecomorphs likely evolved because of either standing genetic variation or de novo mutation. Pairwise comparisons of ecomorphs based on the fixation index (FST) show that divergent genomic regions include genes with functions associated with pigmentation (melanization), learning, neuronal and synapse activity, and circadian rhythms. These results show that the repeated evolution of ecomorphs in the Hawaiian spiny-leg Tetragnatha is linked to multiple genomic regions and suggests a previously unknown role of learning and circadian rhythms in ecomorph.

evolutionary biology↗

bioRxiv2021

decoupleR: Ensemble of computational methods to infer biological activities from omics data

SummaryMany 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 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. 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 methods perform better than other methods at predicting perturbed regulators. Availability and ImplementationdecoupleR is open source available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html). 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). ContactJulio Saez-Rodriguez at pub.saez@uni-heidelberg.de.

bioinformatics↗

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↗

bioRxiv2020

BingleSeq: A user-friendly R package for Bulk and Single-cell RNA-Seq Data Analysis

RNA sequencing is a high-throughput sequencing technique considered as an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is Differential Expression analysis and it is used to determine genetic loci with distinct expression across different conditions. On the other hand, an emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both these types of analyses include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that both require programming expertise. BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface and incorporating three state-of-the-art software packages for each type of the aforementioned analyses, alongside additional features such as key visualisation techniques, functional gene annotation analysis and rank-based consensus for differential gene analysis results, among others. As a result, BingleSeq puts the best and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programming experience.

bioinformatics↗

bioRxiv2019

Contrasting biogeographic patterns of bacterial and archaeal diversity in the top- and subsoils of temperate grasslands

Biogeographic patterns and drivers of soil microbial diversity have been extensively studied in the past few decades. However, most research has focused on the topsoil while the subsoil is assumed to have similar microbial diversity patterns as the topsoil. Here we compare patterns and drivers of microbial diversity in the top- (0-10 cm) versus subsoils (30-50 cm) of temperate grasslands in Inner Mongolia of China along an aridity gradient covering a ~1500-km transect from arid to mesic ecosystems. Counter to the conventional assumption, we find contrasting biogeographic patterns of diversity and influencing factors for different bacterial and archaeal groups and between depths. While bacterial diversity increases with increasing aridity, archaeal diversity decreases. Microbial diversity in the topsoil is most strongly influenced by aboveground vegetation, but is most strongly influenced by historical temperature anomaly since the Last Glacial Maximum (LGM) in the subsoil. Moreover, the biogeographic patterns of top-subsoil diversity difference varies for different microbial groups and is overall most strongly influenced by soil fertility difference between depths and historical temperature anomaly. These findings suggest that diversity patterns observed in the topsoil may not be readily applied to the subsoil horizons. For the subsoil in particular, historical climate plays a vital role in the distribution of various microbial groups. Overall, our study provides novel information for understanding and predicting soil microbial diversity patterns at depth.\n\nIMPORTANCEExploring the biogeographic patterns of soil microbial diversity is critical for understanding mechanisms underlying the response of soil processes to climate change. Using top- and subsoils from a ~1500-km temperature grassland transect, we find divergent patterns of microbial diversity and its determinants in the top-versus subsoils. Furthermore, we find important legacy effect of historical climate change on the microbial diversity of subsoil but not topsoil. Our findings challenge the conventional assumption of similar geographic patterns of soil microbial diversity along soil profiles and help to improve our understanding of how soil microbial communities may respond to future climate change in different regions with varied climate history.

microbiology↗