Biology subjects

Siahpirani, A. F.

Publications and source records attributed to Siahpirani, A. F..

5 recordsLinked to original sources

bioRxiv2025

Uncovering Functional Gene Regulatory Networks in Bulk and Single-Cell Data through Robust Transcription Factor Activity Estimation and Model-Guided Experimental Validation

Reconstructing genome-scale gene regulatory networks (GRNs) remains a difficult problem in systems biology, and many experimental and computational methods have been developed to address this problem. Recent computational methods have aimed to more accurately model GRNs by estimating the hidden Transcription Factor Activity (TFA) from prior knowledge of TF target regulatory connections, encoded as an input directed graph, to relax the assumption that mRNA level of the regulator correlates with the protein activity of the regulator. However, the noise in the prior knowledge can adversely affect the estimated TFA levels and the quality of the downstream inferred GRNs. Here, we present a new approach, MERLIN+P+TFA, that uses prior knowledge-guided sparsity regularization to robustly and accurately estimate TFA and downstream GRNs. We apply our method to simulated and real expression data in yeast and mammalian systems and show improved quality of inferred GRNs for both bulk and single-cell datasets. Regularized TFA offers benefits to a variety of other GRN inference algorithms, including those that have traditionally been used with expression alone, in both bulk and scRNA-seq settings. We used the inferred GRN to prioritize key regulators for the mouse Embryonic Stem Cell (mESC) state and validate 58 regulators experimentally. We identified both known and novel regulators of the mESC state and further validate the targets of 4 known and novel regulators. Our validation experiments suggest that computationally inferred networks can capture functional targets of TFs with higher precision than estimated in current benchmarks, however, it is important to generate context-specific gold standards.

bioinformatics↗

bioRxiv2025

sCIN: A Contrastive Learning Framework for Single-Cell Multi-omics Data Integration

The rapid advancement of single-cell omics technologies such as scRNA-seq and scATAC-seq has transformed our understanding of cellular heterogeneity and regulatory mechanisms. However, integrating these data types remains challenging due to distributional discrepancies and distinct feature spaces. To address this, we present a novel single-cell Contrastive INtegration framework (sCIN), that integrates different omics modalities into a shared low-dimensional latent space. sCIN uses modality-specific encoders and contrastive learning to generate latent representations for each modality, aligning cells across modalities and removing technology-specific biases. The framework was designed to rigorously prevent data leakage between training and testing, and was extensively evaluated on three real-world paired and unpaired datasets including SHARE-seq, 10X PBMC (10k version), and CITE-seq. Paired datasets refer to multi-omics data generated using technologies capable of capturing different omics features from the same individual cells whereas unpaired datasets are collected from different but similar cell populations within the same tissue, with each modality measured separately. Results on paired and unpaired datasets show that sCIN outperforms state-of-the-art models, including scGLUE, scBridge, sciCAN, Con-AAE, Harmony, and MOFA+, across multiple metrics: Average Silhouette Width (ASW) for clustering quality, Recall@k, Cell type@k, Cell type accuracy, and Median Rank for integration quality. Moreover, sCIN was evaluated on simulated unpaired datasets derived from paired data, demonstrating its ability to leverage available biological information for effective multimodal integration. In summary, sCIN reliably integrates omics modalities while preserving biological meaning in both paired and unpaired settings.

bioinformatics↗

bioRxiv2022

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

bioinformatics↗

bioRxiv2019

Human iPSC modeling elucidates mutation-specific responses to gene therapy in a genotypically diverse dominant maculopathy

Dominantly inherited disorders are not typically considered therapeutic candidates for gene augmentation. Here, we utilized patient-specific induced pluripotent stem cell-derived retinal pigment epithelium (iPSC-RPE) to test the potential of gene augmentation to treat Best disease, a dominant macular dystrophy caused by over 200 missense mutations in BEST1. Gene augmentation in iPSC-RPE fully restored BEST1 calcium-activated chloride channel activity and improved rhodopsin degradation in iPSC-RPE models of recessive bestrophinopathy and dominant Best disease caused by two different ion binding domain mutations. A dominant Best disease iPSC-RPE model that did not respond to gene augmentation showed normalization of BEST1 channel activity following CRISPR-Cas9 editing of the mutant allele. We then tested gene editing in all three dominant Best disease iPSC-RPE models, which produced premature stop codons exclusively within the mutant BEST1 alleles. Single-cell profiling demonstrated no adverse perturbation of RPE transcriptional programs in any model, although off-target analysis detected a silent genomic alteration in one model. These results suggest that gene augmentation is a viable first-line approach for some dominant Best disease patients and that non-responders are candidates for alternate approaches such as genome editing. However, testing genome editing strategies for on-target efficiency and off-target events using patient-matched iPSC-RPE model systems is warranted. In summary, personalized iPSC-RPE models can be used to select among a growing list of gene therapy options to maximize safety and efficacy while minimizing time and cost. Similar scenarios likely exist for other genotypically diverse channelopathies, expanding the therapeutic landscape for affected patients. SignificanceDominantly inherited disorders pose distinct challenges for gene therapies, particularly in the face of extreme mutational diversity. We tested whether a broad gene replacement strategy could reverse the cellular phenotype of Best disease, a dominant blinding condition that targets retinal pigment epithelium (RPE). Using RPE generated from patient-specific induced pluripotent stem cells (iPSCs), we show that gene replacement functionally overcomes some, but not all, of the tested mutations. In comparison, all dominant Best disease models tested were phenotypically corrected after mutation-specific genome editing, although one off-target genomic alteration was discovered. Our results support a two-tiered approach to gene therapy for Best disease, guided by safety and efficacy testing in iPSC-RPE models to maximize personal and public health value.

genetics↗

bioRxiv2018

Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available GTEx tissues and find 2356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

genetics↗