Biology subjects

Yardimci, G.

Publications and source records attributed to Yardimci, G..

5 recordsLinked to original sources

bioRxiv2025

Multiomic profiling of hypoxic glioblastoma stem cells reveals expansion of subpopulations with distinct epigenetic and CNV profiles

Glioblastoma is characterized by extensive intratumoral heterogeneity driven by dynamic mi-croenvironmental cues such as hypoxia. While transcriptional and epigenetic variability have been separately linked to hypoxia responses, the integrated impact of hypoxia on gene regulation and clonal architecture in glioblastoma stem cells (GSCs) remains poorly defined. We applied singlenucleus multi-omics--integrating RNA-seq and ATAC-seq--to patient-derived GSCs cultured under normoxic or hypoxic conditions. This enabled simultaneous profiling of gene expression and chromatin accessibility within the same cells. Transcription factor (TF) regulatory networks were inferred using Dictys, while RNA-chromatin dynamics were modeled with MultiVelo. Clonal structure and copy number variations (CNVs) were resolved at single-cell resolution using RIDDLER on snATAC-seq data. Hypoxia induced the emergence of four distinct GSC subpopulations with unique transcriptomic and epigenetic profiles enriched for mesenchymal, angiogenic, and proliferative signatures. Regulatory network modeling revealed novel hypoxia-associated TFs--SP2, CREM, and ETV3--that modulate downstream oncogenic pathways. Trajectory analysis uncovered hypoxia-driven reversals in RNA-chromatin coupling, revealing dysregulated future transcriptional states of key genes such as MMP16 and SVIL. CNV profiling identified 13 clonal substructures, with specific clones (e.g., 5, 6, 9) selectively enriched under hypoxia and harboring distinct chromosomal alterations. These results demonstrate coordinated remodeling of GSC gene regulation and clonal fitness in response to hypoxic stress. Our findings reveal that hypoxia drives concurrent epigenetic, transcriptomic, and clonal selection in glioblastoma stem cells. This integrated model of hypoxia-induced plasticity provides mechanistic insights into tumor adaptation and identifies novel regulators that may serve as targets for therapeutic intervention in the hypoxic niche of glioblastoma.

cancer biology↗

bioRxiv2024

Single-cell DNA methylation analysis tool Amethyst reveals distinct noncanonical methylation patterns in human glial cells

Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties in highly heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and expedites batch integration, doublet detection, dimensionality reduction, clustering, cell type annotation, differentially methylated region calling, and interpretation of results, facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell (PBMC) and human cortex data. We further leverage Amethyst on an atlas-scale brain dataset to describe a noncanonical methylation pattern in human astrocytes and oligodendrocytes, challenging the notion that this form of methylation is principally relevant to neurons in the brain. Tools such as Amethyst will increase accessibility to single-cell methylation data analysis, catalyzing research progress across diverse contexts.

neuroscience↗

bioRxiv2023

Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types

The human genome contains millions of candidate cis-regulatory elements (CREs) with cell-type-specific activities that shape both health and myriad disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these CREs. Here, we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of over 680,000 sequences, representing a nearly comprehensive set of all annotated CREs among three cell types (HepG2, K562, and WTC11), finding 41.7% to be functional. By testing sequences in both orientations, we find promoters to have significant strand orientation effects. We also observe that their 200 nucleotide cores function as non-cell-type-specific on switches providing similar expression levels to their associated gene. In contrast, enhancers have weaker orientation effects, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict CRE function with high accuracy and delineate regulatory motifs. Testing an additional lentiMPRA library encompassing 60,000 CREs in all three cell types, we further identified factors that determine cell-type specificity. Collectively, our work provides an exhaustive catalog of functional CREs in three widely used cell lines, and showcases how large-scale functional measurements can be used to dissect regulatory grammar.

genomics↗

bioRxiv2018

Unsupervised embedding of single-cell Hi-C data

Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell cycle axes. However, Hi-C data analysis requires methods that take into account the unique characteristics of this type of data. In this work, we explore whether methods that have been developed previously for the analysis of bulk Hi-C data can be applied to scHi-C data. In this work, we apply methods designed for analysis of bulk Hi-C data to scHi-C data in conjunction with unsupervised embedding. We find that one of these methods, HiCRep, when used in conjunction with multidimensional scaling (MDS), strongly outperforms three other methods, including a technique that has been used previously for scHi-C analysis. We also provide evidence that the HiCRep/MDS method is robust to extremely low per-cell sequencing depth, that this robustness is improved even further when high-coverage and low-coverage cells are projected together, and that the method can be used to jointly embed cells from multiple published datasets.

bioinformatics↗

bioRxiv2017

Measuring the reproducibility and quality of Hi-C data

Hi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study. Using real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established (e.g., ratio of intra to interchromosomal interactions) and novel (e.g., QuASAR-QC) measures to identify low quality experiments. In this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.

genomics↗