About

Golnaz Vahedi, Ph.D., is a Professor of Genetics at the University of Pennsylvania's Perelman School of Medicine. She is a member of the Abramson Cancer Center, the Institute for Diabetes, Obesity and Metabolism, and serves as Deputy Director of the Institute for Immunology & Immune Health. Additionally, she is Co-Director of the Penn Epigenetics Institute. Her research focuses on understanding the molecular mechanisms through which genomic information in immune cells is interpreted during normal development and how genetic variation can lead to misinterpretation in immune-mediated diseases. Her laboratory employs multidisciplinary approaches, combining computational and experimental methods to generate unbiased maps of genome organization in primary human and mouse immune cells. Her work involves dissecting the mechanisms underlying gene regulation and chromatin organization, often utilizing genome editing techniques in mice and cell lines to explore the link between genetics and chromatin structure.

Research topics

• Computational biology
• Biology
• Cell biology
• Genetics

Selected publications

• Joint profiling of gene expression and chromatin accessibility in pancreatic lymph nodes and spleens in human type 1 diabetes

  Science Immunology · 2025-11-21 · 2 citations

  articleOpen accessSenior authorCorresponding

  Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of insulin-producing β cells in the pancreas. While current therapies focus on managing the disease, a deeper understanding of the underlying molecular mechanisms is crucial for developing disease-modifying interventions. In this study, we conducted a comprehensive analysis of gene expression and chromatin accessibility in nearly 1 million immune cells from the pancreatic lymph nodes and spleens of 43 individuals with and without T1D. We found a distinct subset of CD4 T cells specifically present in the pancreatic lymph nodes of organ donors representing the active disease stage. These cells exhibited elevated activity of NFKB1 and BACH2 , along with extensive chromatin remodeling associated with these transcription factors, which we also corroborated in a mouse model of T1D. A better understanding of these NFKB1-BACH2 –expressing CD4 T cells may lead to therapeutic avenues for preventing or delaying T1D onset.

  PublisherOA PDFDOI

• Artificial intelligence in metabolic research

  Nature Metabolism · 2025-10-02 · 3 citations

  articleCorresponding
  PublisherDOI

• Inference of multi-enhancer interactions in T lymphocytes using Hi-Cociety

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-17

  preprintOpen accessSenior author

  Three-dimensional (3D) enhancer communities are key regulators of gene expression, shaping cell fate decisions and contributing to disease pathogenesis. Assays such as H3K27ac HiChIP have been used to map enhancer-enhancer interactions and define enhancer communities; however, their reliance on antibody-based enrichment restricts scalability and cross-cell-type applicability. In contrast, Hi-C provides an unbiased, genomewide view of chromatin architecture but lacks direct annotation of regulatory elements, limiting its utility for enhancer-focused analyses. To bridge this gap, we introduce Hi-Cociety-a graph-based computational framework and accompanying R package that infers 3D enhancer communities directly from Hi-C data, without relying on histone modification or chromatin accessibility measurements. Hi-Cociety constructs a network of significant interactions and applies clustering algorithms to define chromatin interaction modules. Applying Hi-Cociety to Hi-C measurements in T lymphocytes, we identified highly connected modules enriched for active transcription, chromatin accessibility, and histone acetylation. Notably, modules identified in T cells pinpoint critical genes central to T cell biology. Hi-Cociety also detects cell-type-specific differences in chromatin organization, highlighting dynamic regulatory rewiring across T cell states. Our findings underscore the importance of network properties- connectivity, transitivity, and centrality-in shaping gene regulation through 3D genome organization. Hi-Cociety provides a scalable and versatile tool for mapping enhancer communities at scale, advancing our understanding of immune cell identity and the regulatory logic encoded in 3D chromatin structure.

  PublisherDOI

• OLIVE provides rapid visualization and analysis of chromatin tracing experiments

  Cell Reports Methods · 2025-10-21 · 1 citations

  articleOpen access

  Optical chromatin tracing experiments directly capture the three-dimensional folding of thousands of individual alleles, highlighting the need for a tool that enables fast, interactive, and analytical browsing of such data. Here, we introduce optical looping interactive viewing engine (OLIVE), the first web-based application designed for high-throughput ball-and-stick chromatin tracing data studies that functions similarly to genome browsers. OLIVE allows users, regardless of computational expertise, to input their own data for automated reconstruction of chromatin fibers at individual alleles or to browse and analyze annotated publicly available datasets. Using OLIVE's functionalities, users can interact with three-dimensional presentation of traced alleles and query them based on spatial features, including pairwise distances and perimeters between their segments. Finally, OLIVE calculates and presents several polymer physics metrics of each allele, providing quantitative summaries for hypothesis-driven studies. OLIVE is an open-source project accessible at https://faryabilab.github.io/chromatin-traces-vis/.

  PublisherDOI

• The epigenetic landscape of fate decisions in T cells

  Nature Immunology · 2025-03-19 · 15 citations

  reviewSenior author
  PublisherDOI

• OLIVE Provides Rapid Visualization and Analysis of Chromatin Tracing Experiments

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-20

  preprintOpen access

  Optical chromatin tracing experiments directly capture the three-dimensional folding of thousands of individual alleles, highlighting the need for a tool that enables fast, interactive, and analytical browsing of such data. Here, we introduce Optical Looping Interactive Viewing Engine (OLIVE), a web-based tool designed for high-throughput chromatin tracing data that functions similarly to genome browsers. OLIVE allows users, regardless of computational expertise, to input their own data for automated reconstruction of chromatin fibers at individual alleles or to browse annotated publicly available datasets. Using OLIVE's functionalities, users can interact with three-dimensional presentation of traced alleles and query them based on spatial features, including pairwise distances and perimeters between their segments. Finally, OLIVE calculates and presents several polymer physics metrics of each allele, providing quantitative summaries for hypothesis-driven studies. OLIVE is an open-source project accessible at https://faryabilab.github.io/chromatin-traces-vis/.

  PublisherDOI

• Lineage-determining transcription factors constrain cohesin to drive multi-enhancer oncogene regulation

  Nature Cell Biology · 2025-12-02 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• Single-Allele Chromatin Tracing Reveals Genomic Clustering of Paralogous Transcription Factors as a Mechanism for Developmental Robustness in T Cells

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-02

  preprintOpen accessSenior authorCorresponding

  Abstract In metazoans, gene duplication has given rise to paralogous transcription factors, which have functionally diversified to control cellular differentiation. While the majority of paralogous TFs are dispersed across different chromosomes, some remain clustered raising the question of whether genomic proximity confers any evolutionary advantage for TF clusters. To address this, we investigated a ∼1 Mbp locus containing two ETS family paralogs, Ets1 and Fli1 . Using a sub-diffraction sequential imaging technique called Optical Reconstruction of Chromatin Architecture (ORCA), we traced the 3D organization of this region in single alleles of T cells from genetically engineered mice with targeted deletions of key regulatory elements. In wild-type T cells, the predominant chromatin conformation spatially links Ets1 to its proximal super-enhancer, segregating Ets1 from Fli1 . This topology correlates with high Ets1 and low Fli1 expression. Deletion of the Ets1 super-enhancer abolishes this configuration, triggering locus-wide architectural rewiring that increases Ets1-Fli1 promoter-promoter interactions and subsequently the co-expression of two genes within individual cells. Remarkably, this compensatory interaction bypasses insulated chromatin domains, sustaining Ets1 levels necessary for T cell development despite enhancer loss. Our results reveal that genomic clustering of TF paralogs enables dynamic architectural plasticity: while a super-enhancer fine-tunes paralog expression balance in wild-type contexts, its deletion unmasks latent promoter-driven coordination, suggesting that proximity safeguards functional redundancy and transcriptional resilience critical for cellular fitness.

  PublisherOA PDFDOI

• FAIR sharing of Chromatin Tracing datasets using the newly developed 4DN FISH Omics Format.

  PubMed · 2025-08-21

  preprintOpen access

  and are ideally suited for promoting reuse, exchange, further processing, and integrative modeling. Furthermore, the manuscript will present examples of analysis pipelines that could be applied more widely due to the existence of the FOF-CT exchange data format and provide examples of biological conclusions that could be drawn thanks to the availability of such datasets.

  PublisherOA PDF

• Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets

  Cell Reports Medicine · 2024-04-26 · 22 citations

  articleOpen accessSenior author

  Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.

  PublisherDOI

Recent grants

• 3-Dimensional genomic architecture in innate lymphoid cells and allergic inflammation

  NIH · $3.2M · 2022–2027

• Critical role of TCF-1 on the epigenetic identity of memory T cells

  NIH · $2.3M · 2019–2024

• Super-enhancer structure defines a signature of inflammatory bowel disease (IBD)

  NIH · $266k · 2015–2017

• Dissecting transcriptomics and epigenomic signatures of immune cells in type 1 diabetes

  NIH · $2.0M · 2020–2026

• Single-cell dissection of chromatin architecture mechanisms connecting pathologic instability and transcriptional silencing

  NIH · $3.1M · 2020–2025

Frequent coauthors

• John J. O’Shea

  National Institute of Arthritis and Musculoskeletal and Skin Diseases

  80 shared

• Yuka Kanno

  National Institute of Arthritis and Musculoskeletal and Skin Diseases

  80 shared

• Robert B. Faryabi

  California University of Pennsylvania

  51 shared

• Kiyoshi Hirahara

  51 shared

• Arian Laurence

  University College London

  42 shared

• Maria Fasolino

  California University of Pennsylvania

  41 shared

• Naomi Goldman

  California University of Pennsylvania

  37 shared

• Hong‐Wei Sun

  35 shared

Labs

• Vahedi LaboratoryPI