Biology subjects

Abdollahi, N.

Publications and source records attributed to Abdollahi, N..

9 recordsLinked to original sources

bioRxiv2026

Ancestra: A lineage-explicit simulator for benchmarking B-cell receptor repertoire and lineage inference methods

AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput B-cell receptor sequencing has transformed the analysis of adaptive immunity, but benchmarking clonal grouping and lineage reconstruction methods remains limited by the absence of datasets with known evolutionary histories. Here we present Ancestra, a lineage-explicit simulator of B-cell receptor heavy-chain affinity maturation. Ancestra models stochastic V(D)J recombination, context-dependent somatic hypermutation, affinity-based selection and clonal expansion while recording complete parent-child relationships and mutation events. The framework generates BCR heavy-chain sequence datasets together with their corresponding ground-truth lineage trees, enabling direct benchmarking of lineage-aware analytical methods. Across simulations, Ancestra recapitulates key properties of human repertoires, including complementarity-determining region 3 length distributions, amino-acid usage patterns, junctional mutation patterns consistent with IMGT criteria and heterogeneous branching topologies. Simulated lineages also reveal multi-label lineage trees, in which identical nucleotide sequences can arise independently along distinct evolutionary paths. Ancestra provides a practical foundation for rigorous benchmarking of lineage-aware immune repertoire analysis.

bioinformatics↗

bioRxiv2026

UnivAIRRse: A Unified Framework for Organizing and Comparing Adaptive Immune Receptor Repertoire Simulators

Adaptive immune receptor repertoire sequencing (AIRR-seq) enables large-scale profiling of B- and T-cell receptor diversity and has become a cornerstone of modern computational immunology. However, AIRR-seq provides only a partial and lossy molecular snapshot of immune dynamics, lacking explicit ground truth for clonal ancestry, lineage trajectories, antigen specificity, and longitudinal immune evolution. This limitation complicates benchmarking, method validation, and mechanistic interpretation of repertoire analysis pipelines. Here, we introduce UnivAIRRse, a unified hierarchical framework that organizes AIRR simulators within a shared conceptual coordinate system spanning five operational levels, from observed sequence data to the theoretical generative potential of the adaptive immune system. By explicitly distinguishing sequence-, clonal-, specificity-, repertoire-, and generative-level representations, UnivAIRRse enables systematic comparison of simulator assumptions, biological scope, abstraction level, and application focus. To our knowledge, this is the first review to formalize such a unified structure across biological, computational, and functional layers of AIRR simulation. Using this framework, we review how simulation supports benchmarking, strengthens computational inference, and enables multi-scale investigation of immune repertoire formation and evolution. We identify persistent limitations in existing simulators, including incomplete biological context, limited modularity, restricted interoperability, and overreliance on AIRR-seq as a molecular proxy for complex spatiotemporal immune processes. To operationalize this framework, we provide an interactive web-based AIRR Simulation Landscape Explorer (publicly available at https://www.imgt.org/AIRR-Simulator/) that enables dynamic filtering and comparison of simulators across biological scope, abstraction level, output fidelity, and application focus. Finally, we outline emerging directions toward digital-twin-ready immune simulation, emphasizing modular architectures, longitudinal multi-omic integration, uncertainty quantification, and dynamic model updating. By providing a coherent conceptual and operational coordinate system, UnivAIRRse establishes a foundation for reproducible, interpretable, and clinically actionable modeling of adaptive immune repertoires, bridging current simulation practices with the next generation of predictive and personalized immunological modeling. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/706510v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@7a5a95org.highwire.dtl.DTLVardef@d127f1org.highwire.dtl.DTLVardef@19545c9org.highwire.dtl.DTLVardef@118cc74_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Impact of extracellular current flow on action potential propagation in myelinated axons

Myelinated axons conduct action potentials, or spikes, in a saltatory manner. Inward current caused by a spike occurring at one node of Ranvier spreads axially to the next node, which regenerates the spike when depolarized enough for voltage-gated sodium channels to activate, and so on. The rate at which this process progresses dictates the velocity at which the spike is conducted, and depends on several factors including axial resistivity and axon diameter that directly affect axial current. Here we show through computational simulations in modified double-cable axon models that conduction velocity also depends on extracellular factors whose effects can be explained by their indirect influence on axial current. Specifically, we show that a conventional double-cable model, with its outside layer connected to ground, transmits less axial current than a model whose outside layer is less absorptive. A more resistive barrier exists when an axon is packed tightly between other myelinated fibers, for example. We show that realistically resistive boundary conditions can significantly increase the velocity and energy efficiency of spike propagation, while also protecting against propagation failure. Certain factors like myelin thickness may be less important than typically thought if extracellular conditions are more resistive than normally considered. We also show how realistically resistive boundary conditions affect ephaptic interactions. Overall, these results highlight the unappreciated importance of extracellular conditions for axon function. SIGNIFICANCE STATEMENTAxons transmit spikes over long distances. Transmission is sped up and made more efficient by myelination, which allows spikes to jump between nodes of Ranvier without activating the intervening (internodal) membrane. Conduction velocity depends on the current transmitted axially from one node to the next. Axial current is known to depend on a variety of features intrinsic to myelinated fibers (e.g. axon diameter, myelin thickness) but we show here, through detailed biophysical simulations, how extracellular conditions (e.g. axon packing density) are also important. The effects ultimately boil down to the variety of paths current can follow, and the amount of current taking alternative paths rather than flowing directly from one node to the next.

neuroscience↗

bioRxiv2023

Paresthesia during spinal cord stimulation depends on synchrony of dorsal column axon activation

Spinal cord stimulation (SCS) reduces chronic pain. Conventional (40-60 Hz) SCS engages spinal inhibitory mechanisms by activating low-threshold mechanoreceptive afferents with axons in the dorsal columns (DCs). But activating DC axons typically causes a buzzing sensation (paresthesia) that can be uncomfortable. Kilohertz-frequency (1-10 kHz) SCS produces analgesia without paresthesia and is thought, therefore, not to activate DC axons, leaving its mechanism unclear. Here we show in rats that kilohertz-frequency SCS activates DC axons but causes them to spike less synchronously than conventional SCS. Spikes desynchronize because axons entrain irregularly when stimulated at intervals shorter than their refractory period, a phenomenon we call overdrive desynchronization. Effects of overdrive desynchronization on evoked compound action potentials were verified in simulations, rats, pigs, and a chronic pain patient. Whereas synchronous spiking in DC axons is necessary for paresthesia, asynchronous spiking is sufficient to produce analgesia. Asynchronous activation of DC axons thus produces paresthesia-free analgesia.

neuroscience↗

bioRxiv2022

ViCloD, an interactive web tool for visualizing B cell repertoires and analyzing intra-clonal diversities: application to human B-cell tumors

High throughput sequencing of adaptive immune receptor repertoire (AIRR-seq) has provided numerous human immunoglobulin (IG) sequences allowing specific B cell receptor (BCR) studies such as the antigen-driven evolution of antibodies (soluble forms of the membrane-bound IG part of the BCR). AIRR-seq data allows researchers to examine intraclonal differences caused primarily by somatic hypermutations in IG genes and affinity maturation. Exploring this essential adaptive immunity process could help elucidate the generation of antibodies with high affinity or broadly neutralizing activities. Retracing their evolutionary history could also help to clarify how vaccines or pathogen exposition drive the humoral immune response. Computational methods are necessary for large-scale analysis of AIRR-seq properties. However, there is no efficient and interactive tool for analyzing intraclonal diversity, permitting users to explore adaptive immune receptor repertoires in biological and clinical applications. Here we developed ViCloD, a web server for large-scale visual analysis of repertoire clonality and intraclonal diversity. ViCloD uses data preprocessed by IMGT/HighV-QUEST and performs clonal grouping and evolutionary analyses, producing a collection of useful plots. The web server presents diverse functionalities, including repertoire navigation, clonal abundance analysis, and intraclonal evolutionary tree reconstruction. Users can download the analyzed data in different table formats and save the generated plots as images. ViCloD is a simple, versatile, and user-friendly tool that can help researchers and clinicians to analyze B cell intraclonal diversity. Moreover, its pipeline is optimized to process hundreds of thousands of sequences within a few minutes, allowing an efficient investigation of large and complex repertoires. Availability and implementationThe web server is available at http://www.lcqb.upmc.fr/viclod/. The pipeline is available at github and allows users to run analyses locally https://github.com/julibinho/ViCLoD

bioinformatics↗

bioRxiv2022

Reconstructing the evolutionary history of a BCR lineage with minimum spanning tree and clonotype abundances

B cell receptor (BCR) genes exposed to an antigen undergo somatic hypermutations and Darwinian antigen selection, generating a large BCR-antibody diversity. This process, known as B cell affinity maturation, increases antibody affinity, forming a specific B cell lineage that includes the unmutated ancestor and mutated variants. In a B cell lineage, cells with a higher antigen affinity will undergo clonal expansion, while those with a lower affinity will not proliferate and probably be eliminated. Therefore, cellular (genotype) abundance provides a valuable perspective on the ongoing evolutionary process. Phylogenetic tree inference is often used to reconstruct B cell lineage trees and represents the evolutionary dynamic of BCR affinity maturation. However, such methods should process B cell population data derived from experimental sampling that might contain different cellular abundances. There are a few phylogenetic methods for reconstructing the evolutionary history of B cell lineages; best-performing solutions are time-demanding and restricted to analyzing a reduced number of BCR IGH sequences, while time-efficient methods do not consider cellular abundances. We propose ClonalTree, a low-complexity and accurate approach to reconstruct B cell lineage trees that incorporates genotype abundances into minimum spanning tree (MST) algorithms. Using both simulated and experimental data, we demonstrated that ClonalTree outperforms MST-based algorithms and achieves a similar performance compared to a method that explores tree generating space exhaustively. However, ClonalTree has a lower running time, being more convenient for reconstructing phylogenetic lineage trees from high-throughput BCR sequencing data, mainly in biomedical applications, where a lower computational time is appreciable. It is hundreds to thousands of times faster than exhaustive approaches, enabling the analysis of a large set of sequences within minutes or seconds and without loss of accuracy. The source code is freely available at github.com/julibinho/ClonalTree.

bioinformatics↗

bioRxiv2021

Spike initiation properties in the axon support high-fidelity signal transmission

The axon initial segment (AIS) converts graded depolarization into all-or-none spikes that are transmitted by the axon to downstream neurons. Analog-to-digital transduction and digital signal transmission call for distinct spike initiation properties (filters) and those filters should, therefore, differ between the AIS and distal axon. Here we show that unlike the AIS, which spikes repetitively during sustained depolarization, the axon spikes transiently and only if depolarization reaches threshold before KV1 channels activate. Rate of depolarization is critical. This was shown by optogenetically evoking spikes in the distal axon of CA1 pyramidal neurons using different photostimulus waveforms and pharmacological conditions while recording antidromically propagated spikes at the soma, thus circumventing the prohibitive difficulty of patching intact axons. Computational modeling shows that KV1 channels in the axon implement a high-pass filter that is matched to the axial current waveform associated with spike propagation, thus maximizing the signal-to-noise ratio to ensure high-fidelity transmission of spike-based signals.

neuroscience↗

bioRxiv2021

A multi-objective based clustering for identifying clonally-related sequences from high-throughput B cell repertoire data

The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clones. A high number of clones in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity within clones can be related to antigens selective pressure. Identifying clones is fundamental to many repertoire studies, including repertoire comparisons, clonal tracking and statistical analysis. Several methods have been developed to group sequences from high-throughput B cell repertoire data. Current methods use clustering algorithms to group clonally-related sequences based on their similarities or distances. Such approaches create groups by optimizing a single objective that typically minimizes intra-clonal distances. However, optimizing several objective functions can be advantageous and boost the algorithm convergence rate. Here we propose a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial clones and iteratively applies two objective functions that optimize cohesion and separation within clones simultaneously. We show that under simulations with varied mutation rates, our method greatly improves clonal grouping as compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools. The method based on multi-objective clustering can accurately identify clone members, has fewer parameter settings and presents the lowest running time among existing tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context to unravel the mechanisms involved in the development and evolution of B cell malignancies.

bioinformatics↗

bioRxiv2020

Automatic generation of ground truth data for the evaluation of clonal grouping methods in B-cell populations

MotivationThe adaptive B-cell response is driven by the expansion, somatic hypermutation, and selection of B-cell clones. Their number, size and sequence diversity are essential characteristics of B-cell populations. Identifying clones in B-cell populations is central to several repertoire studies such as statistical analysis, repertoire comparisons, and clonal tracking. Several clonal grouping methods have been developed to group sequences from B-cell immune repertoires. Such methods have been principally evaluated on simulated benchmarks since experimental data containing clonally related sequences can be difficult to obtain. However, experimental data might contains multiple sources of sequence variability hampering their artificial reproduction. Therefore, the generation of high precision ground truth data that preserves real repertoire distributions is necessary to accurately evaluate clonal grouping methods. ResultsWe proposed a novel methodology to generate ground truth data sets from real repertoires. Our procedure requires V(D)J annotations to obtain the initial clones, and iteratively apply an optimisation step that moves sequences among clones to increase their cohesion and separation. We first showed that our method was able to identify clonally-related sequences in simulated repertoires with higher mutation rates, accurately. Next, we demonstrated how real benchmarks (generated by our method) constitute a challenge for clonal grouping methods, when comparing the performance of a widely used clonal grouping algorithm on several generated benchmarks. Our method can be used to generate a high number of benchmarks and contribute to construct more accurate clonal grouping tools. Availability and implementationThe source code and generated data sets are freely available at github.com/NikaAb/BCR_GTG

bioinformatics↗