Biology subjects

Kiani, N.

Publications and source records attributed to Kiani, N..

13 recordsLinked to original sources

bioRxiv2026

Single-cell analysis suggests coordinated immune-cell redistribution between blood and cerebrospinal fluid in early multiple sclerosis

Despite immune-cell infiltration being a hallmark of multiple sclerosis (MS), the interplay between the periphery and central nervous system immune responses is still incompletely characterized. We performed single-cell transcriptomic and V(D)J sequencing of paired blood and cerebrospinal fluid (CSF) immune cells from treatment-naive relapsing-remitting women with MS and compared them to age- and sex-matched healthy controls. Across major immune lineages, we identified coordinated, compartment-specific immune alterations, with enrichment of activated and memory lymphocyte populations in the CSF and concomitant depletion of related populations in peripheral blood, suggesting their recruitment from blood to CSF. Clonally expanded CD4 memory T-cells, together with activated, expanded IgM-positive B-cells, accumulated predominantly in the CSF of people with MS compared to healthy subjects. Interestingly, tissue-primed cytotoxic populations and CXCR3-associated memory populations were depleted from the CSF of MS, implying their recruitment to the target tissue in early disease. These findings reveal coordinated, compartment-specific immune changes in early MS and provide a systems-level view of immune-cell trafficking between peripheral and central nervous system compartments.

immunology↗

bioRxiv2026

A distal CTCF-binding site drives MYC expression plasticity in a negative feed-forward loop

Oncogene expression heterogeneity systemically diversifies cancer cell phenotypes, enabling selection and cancer evolution. Here we document that the level of MYC expression variation is coordinated by a negative feed-forward loop involving the non-coding CCAT1 eRNA and the MYC gating process. While CCAT1 eRNA indirectly antagonizes MYC gating by promoting MYC transcriptional initiation and elongation, the MYC protein promotes gated MYC expression in a feedback loop by inhibiting both MYC transcription and CCAT1 expression. This entire process is coordinated by a CTCF binding site positioned within CCAT1 at a distal, oncogenic super-enhancer, which functions as a master switch by coordinating both CCAT1 and gated MYC expression to diversify cells toward both low and high MYC levels, as determined by heterogeneity metrics. As hallmarks of this principle are frequent in breast cancer and colorectal tumors, the dynamics of this new principle may underlie transitions between therapy-resistant low-MYC, and proliferative high-MYC tumor cells. Highlights- Transcriptional rate regulates MYC gating frequency - CCAT1 expression indirectly inhibits MYC gating by promoting MYC transcription - High MYC protein levels promote gated expression by inhibiting both MYC transcription and CCAT1 expression - These features are controlled by a single CTCF binding in the distant super-enhancer to drive expression plasticity in colorectal cancer cells

cancer biology↗

bioRxiv2025

Fundamental Limits of Inferring Dynamical Gene Regulatory Models from Single-Cell Data

A long-standing goal in systems biology is to construct computational models that capture gene regulatory dynamics. On the one hand, graph-based regulatory network models rely on prior knowledge but are constrained by unknown kinetic parameters. On the other hand, recent advancements in single-cell sequencing technologies, particularly RNA velocity and pseudotime analysis, offer data-driven approaches to infer kinetic parameters. However, accurate modeling of biological dynamics requires the estimation of the Jacobian matrix, which captures signal propagation within a regulatory system. Several computational methods have recently emerged to infer Jacobians from single-cell data, including SpliceJAC, scMomentum, and Dynamo. Here, we evaluate these methods by analyzing Jacobian eigenvalues, assessing their ability to capture stability and oscillatory properties. Using simulated ground truth data, we find that methods such as Dynamo and scMomentum generate models that capture the behavioral properties of the cells, however, they do not reliably capture the structural properties. Our results highlight the limitations of dynamic GRN inference, namely, the inability of kinetics to alleviate the need for further structural constrains for the models to adequately reflect the structural properties of the GRN. Developing more robust approaches for Jacobian estimation from single-cell data will be essential for building accurate, large-scale dynamical models of gene regulation. Graphical AbstractOverview of Benchmarking Jacobian-based inference in single-cell RNA Data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/674509v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@13496e2org.highwire.dtl.DTLVardef@19e0250org.highwire.dtl.DTLVardef@a9db39org.highwire.dtl.DTLVardef@17789e2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Degeneracy in Astrocytic Potassium Buffering: A Minimal Model Capturing the Interplay Between Local and Long-Range Mechanisms

Maintaining extracellular potassium (K+) homeostasis is critical for neuronal function, and astrocytes achieve this through a combination of local uptake and long-range spatial buffering. While degeneracy--the ability of different mechanisms to achieve the same function--is a fundamental property of biological systems, its role in astrocytic potassium buffering has remained unexplored. We present a minimal mathematical model that identifies essential buffering mechanisms while ensuring tractability and interpretability. Incorporating Kir channels and gap junction coupling, the model reproduces experimentally observed astrocyte membrane dynamics under various pharmacological conditions Parameter exploration reveals two levels of degeneracy. At the single-cell level, multiple parameter configurations yield similar membrane potential dynamics, indicating flexibility in local and spatial buffering contributions. At the functional level, despite variations in astrocyte morphology and buffering efficiency, homeostasis of extracellular K+ is restored, demonstrating homeostatic degeneracy. These findings highlight the robustness of astrocytic potassium regulation, showing that diverse buffering strategies ensure stability. Our work establishes a theoretical framework for understanding how astrocytic heterogeneity contributes to robust ionic homeostasis and offers perspectives for studying pathological conditions where buffering mechanisms are impaired.

neuroscience↗

bioRxiv2024

PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific Graphs

Predicting the transcriptional response of chemical perturbations is crucial to understanding gene function and developing drug candidates, promising a streamlined drug development process. Single-cell sequencing has provided an ideal data basis for training machine learning models for this task. Recent advances in deep learning have led to significant improvements in predictions of chemical as well as genetic perturbations at the single cell level. Experiments have shown that different cell types exhibit distinct transcriptional patterns and responses to perturbation. This poses a fundamental problem for predicting transcriptional responses of drugs or cell types outside the training data. Accordingly, existing methods lack cell-type-specific modeling or do not explicitly provide an interpretable mechanism for the gene features. In this study, we introduce a novel approach that employs a network representation of various cell types as an inductive bias, improving prediction performance in scenarios with limited data while acknowledging cellular differences. We applied our framework to four small-scale single-cell perturbation datasets and one large-scale screening experiment, demonstrating that this representation can inherently generalize to previously unseen cell types. Furthermore, our method outperforms the state-of-the-art methods in predicting the post-perturbation response in unobserved cell types.

bioinformatics↗

bioRxiv2023

Joint Learning of Node Semantics and Graph Topology using a Transformer in the sparse network regime

The human interactome is a valuable tool for unraveling disease mechanisms, advancing precision medicine, facilitating drug discovery, and identifying biomarkers. Yet, current interactomes are incomplete, in part due to limited experimental coverage. Therefore, augmenting the human interactome by predicting missing links in the Protein-Protein interaction network (PPI), is a core challenge for precision medicine. This study proposes an end-to-end trainable transformer-based neural network for enhanced aggregation of Gene Ontology (GO) terms features. We augment the models predictive capabilities by incorporating semantic anc2vec features, complementing the structural node2vec embeddings specifically designed for sparse PPIs. As a result, by integrating semantic and graph features, we demonstrate superior performance in link prediction.

bioinformatics↗

bioRxiv2023

Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations

With the emergence of single-cell foundation models, an important question arises: how do these models perform when trained on datasets having an imbalance in cell type distribution due to rare cell types or biased sampling? We benchmark three foundation models, scGPT, scBERT, and Geneformer, using skewed single-cell cell-type distribution for cell-type annotation. While all models had reduced performance when challenged with rare cell types, scGPT and scBERT, performed better than Geneformer. Notably, in contrast to scGPT and scBERT, Geneformer uses ordinal positions of the tokenized genes rather than actual raw gene expression values. To mitigate the effect of a skewed distribution, we find that random oversampling, but not random undersampling, improved the performance for all three foundation models. Finally, scGPT, using FlashAttention, has the fastest computational speed, whereas scBERT is more memory-efficient. We conclude that tokenization and data representation are essential areas of research, and new strategies are needed to mitigate the effects of imbalanced learning in single-cell foundation models. Code and data for reproducibility are available at https://github.com/SabbaghCodes/ImbalancedLearningForSingleCellFoundationModels.

bioinformatics↗

bioRxiv2023

Uncovering the cis-regulatory program of early human B-cell commitment and its implications in the pathogenesis of B-cell acute lymphoblastic leukemia

Dysregulation of the early stages of B-cell lymphopoiesis, orchestrating the development of cellular immunity, may induce malignant transformations. Therefore, it is essential to characterize the gene regulatory network (GRN) driving B-cell lymphopoiesis in healthy individuals to uncover malignancy mechanisms. To this end, we generated a dataset that included paired human data for chromatin accessibility and gene expression in eight B-cell precursor stages, providing the first deep characterization of early B-cell lymphopoiesis, including the identification of regulatory elements and the reconstruction of the GRN. Using this data, we recapitulated well-known regulatory elements and revealed new regulons, such as ELK3, enriched in pro-B cells with a putative role in cell cycle progression. Moreover, a single-cell multi-omics analysis validated and enhanced the resolution of the regulatory landscape recovered by bulk data, revealing MYBL2 and ZNF367 as specific regulons of cycling cell states, and CEBPA associated with lymphoid multipotent progenitors (LMPPs). Importantly, this dataset enabled us to uncover B-cell acute lymphoblastic leukemia (B-ALL) triggers. We identified different cellular origins of malignant transformation depending on the B-ALL subtype, including the association of the ETV6-RUNX1 with pro-B cells and the increased expression of ELK3 in this ALL subtype. Overall, our dataset provides the most comprehensive atlas to date of early human B-cell regulation (B-rex; https://translationalbio.shinyapps.io/brex/), facilitating further understanding of B-cell differentiation in health and disease.

immunology↗

bioRxiv2023

LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions

AO_SCPLOWBSTRACTC_SCPLOWPredicting drug-target interactions is a tremendous challenge for drug development and lead optimization. Recent advances include training algorithms to learn drug-target interactions from data and molecular simulations. Here we utilize Evolutionary Scale Modeling (ESM-2) models to establish a Transformer protein language model for drug-target interaction predictions. Our architecture, LEP-AD, combines pre-trained ESM-2 and Transformer-GCN models predicting binding affinity values. We report new best-in-class state-of-the-art results compared to competing methods such as SimBoost, DeepCPI, Attention-DTA, GraphDTA, and more using multiple datasets, including Davis, KIBA, DTC, Metz, ToxCast, and STITCH. Finally, we find that a pre-trained model with embedding of proteins (the LED-AD) outperforms a model using an explicit alpha-fold 3D representation of proteins (e.g., LEP-AD supervised by Alphafold). The LEP-AD model scales favorably in performance with the size of training data. Code available at https://github.com/adaga06/LEP-AD

bioinformatics↗

bioRxiv2022

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics

A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than the placebo one, especially at homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than the placebo condition (p = 9 x 10-5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity--especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state. Author summaryIn this study, we aim to characterize two brain states (psychedelics-induced and placebo), as captured in functional magnetic resonance imaging (fMRI) data, with features derived from the Ising model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. Under the hypothesis that psychedelics drive the brain into a more disordered state, we study criticality features of brain dynamics under LSD in a within-subject study using the Ising model formalism and algorithmic complexity using Lempel-Ziv and the Block Decomposition methods. Personalized Ising models are created by first using BOLD fMRI data from all the subjects and conditions to create a single Ising "archetype" model--which we can interpret as the average model of the data at unit temperature--and then by adjusting the model temperature for each subject and condition. We find that the effects of LSD translate into increased BOLD signal complexity and Ising temperature, in agreement with earlier findings and predictions from existing theories of the effects of psychedelics, such as the relaxed beliefs under psychedelics (REBUS), the anarchic brain hypothesis [1], and the algorithmic information theory of consciousness (KT) [2, 3]. However, in contrast with some of the previously cited theories, we find that the system in the placebo condition is already in the paramagnetic phase--above the critical point--with ingestion of LSD resulting in a shift away from Ising criticality into a more disordered state. Finally, we highlight the fact that the structural connectome can be recovered to a good degree by fitting an Ising model and that the reduction of homotopic links appears to play an important role in the slide to disorder under psychedelics.

neuroscience↗

bioRxiv2022

scAEGAN: Unification of Single-Cell Genomics Data by Adversarial Learning of Latent Space Correspondences

Recent progress in Single-Cell Genomics have produced different library protocols and techniques for profiling of one or more data modalities in individual cells. Machine learning methods have separately addressed specific integration challenges (libraries, samples, paired-unpaired data modalities). We formulate an unifying data-driven methodology addressing all these challenges. To this end, we design a hybrid architecture using an autoencoder (AE) network together with adversarial learning by a cycleGAN (cGAN) network, jointly referred to as scAEGAN. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. The core insight is that the AE respects each samples uniqueness, whereas the cGAN exploits the distributional data similarity in the latent space. We evaluate scAEGAN using simulated data and real datasets of a single-modality (scRNA-seq), different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities such as paired scRNA-seq and scATAC-seq. We find that scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude scAEGAN surpasses current state-of-the-art methods across several seemingly different integration challenges.

bioinformatics↗

bioRxiv2020

scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes

Recent progress in single-cell genomics has generated multiple tools for cell clustering, annotation, and trajectory inference; yet, inferring their associated regulatory mechanisms is unresolved. Here we present scMomentum, a model-based data-driven formulation to predict gene regulatory networks and energy landscapes from single-cell transcriptomic data without requiring temporal or perturbation experiments. scMomentum provides significant advantages over existing methods with respect to computational efficiency, scalability, network structure, and biological application. AvailabilityscMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.git

systems biology↗

bioRxiv2018

The ultra-sensitive Nodewalk technique identifies stochastic from virtual, population-based enhancer hubs regulating MYC in 3D: Implications for the fitness of cancer cells

The relationship between stochastic transcriptional bursts and dynamic 3D chromatin states is not well understood due to poor sensitivity and/or resolution of current chromatin structure-based assays. Consequently, it is not well established if enhancers operate individually and/or in clusters to coordinate gene transcription. In the current study, we introduce Nodewalk, which uniquely combines high sensitivity with high resolution to enable the analysis of chromatin networks in minute input material. The >10,000-fold increase in sensitivity over other many-to-all competing methods uncovered that active chromatin hubs identified in large input material, corresponding to 10 000 cells, flanking the MYC locus are primarily virtual. Thus, the close agreement between chromatin interactomes generated from aliquots corresponding to less than 10 cells with randomly re-sampled interactomes, we find that numerous distal enhancers positioned within flanking topologically associating domains (TADs) converge on MYC in largely mutually exclusive manners. Moreover, when comparing with several enhancer baits, the assignment of the MYC locus as the node with the highest dynamic importance index, indicates that it is MYC targeting its enhancers, rather than vice versa. Dynamic changes in the configuration of the boundary between TADs flanking MYC underlie numerous stochastic encounters with a diverse set of enhancers to depict the plasticity of its transcriptional regulation. Such an arrangement might increase the fitness of the cancer cell by increasing the probability of MYC transcription in response to a wide range of environmental cues encountered by the cell during the neoplastic process.

genomics↗