Biology subjects

Serin Harmanci, A.

Publications and source records attributed to Serin Harmanci, A..

11 recordsLinked to original sources

bioRxiv2026

H3 dopaminylation and CaMKII modulate diffuse midline glioma response to CDK9 inhibition

Aberrant neurotransmitter signaling and transcriptional dysregulation are hallmarks of gliomagenesis and represent potential therapeutic targets. Monoamine neurotransmitters such as dopamine and serotonin primarily activate GPCRs but can also function epigenetically as histone H3 modifications. Here, we uncover mechanisms of crosstalk between monoamine neurotransmitter signaling, H3 dopaminylation, and RNA polymerase II (Pol2) transcription in diffuse midline glioma (DMG). We find that co-treatment with Pol2-targeting CDK9 inhibitors (CDK9i) and FDA-approved neuropsychiatric drugs, including selective serotonin reuptake inhibitors (SSRIs), synergistically reduces DMG growth. Mechanistically, CDK9i+SSRI treatment alters H3 dopaminylation patterns and represses synaptic and neurodevelopmental gene transcription associated with CDK9i resistance. Further phospho-proteomic analyses show that CDK9i monotherapy activates pro-survival CaMKII signaling, which can be suppressed by co-treatment with neuromodulatory drugs. These studies establish roles for H3 dopaminylation and neurotransmitter signaling in DMG gene regulation and response to CDK9i, suggesting that monoamine neurotransmitter pathways may be exploited as a therapeutic strategy for DMG.

cancer biology↗

bioRxiv2026

SAGA/ATAC complexes sustain aberrant chromatin regulation and promote tumorigenesis in diffuse midline glioma

Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors characterized by chromatin and transcriptional dysregulation induced by H3K27M mutations, with a median survival of 11-15 months. We identified multiple components of the SAGA and ATAC chromatin regulatory complexes as DMG genetic dependencies and found that genetic or pharmacological inhibition of the SAGA/ATAC-associated chromatin reader SGF29 reduces DMG proliferation in vitro and prolongs survival in xenograft models. Small molecule inhibitors targeting SAGA/ATAC-associated histone acetylation, ubiquitination, and methylation similarly suppress DMG growth. Integrative chromatin and transcriptomic profiling reveals that disruption of SAGA/ATAC through SGF29 knockout remodels the DMG chromatin landscape, producing distinct alterations at metabolic genes (loss of H3K9ac) and at embryonic/neurodevelopmental genes (redistribution of H3K4me3 and H3K27me3) and accompanying transcriptional changes. We further show that inhibition of the SAGA/ATAC-associated KAT2A/2B histone acetyltransferases represses cholesterol metabolism gene expression and that combined KAT2A/2B and cholesterol synthesis inhibition synergistically suppresses DMG growth in vitro and in a xenograft model. Together, these findings establish a mechanistic link between SAGA/ATAC-dependent chromatin regulation and the transcriptional and metabolic dysregulation underlying DMG malignancy. SignificanceWe demonstrate that SAGA/ATAC-dependent chromatin regulation sustains malignant transcription controlling cholesterol metabolism, proliferation, and cell fate in DMG, suggesting that a SAGA/ATAC-regulated epigenome-metabolome axis may be targeted to treat DMG.

cancer biology↗

bioRxiv2024

Inferred Developmental Origins of Brain Tumors from Single Cell RNA-Sequencing Data

The reactivation of neurodevelopmental programs in cancer highlights parallel biological processes that occur in both normal development and brain tumors. Achieving a deeper understanding of how dysregulated developmental factors play a role in the progression of brain tumors is therefore crucial for identifying potential targets for therapeutic interventions. Single-cell RNA sequencing (scRNA-Seq) provides an opportunity to understand how developmental programs are dysregulated and reinitiated in brain tumors at single-cell resolution. Here, we introduce COORS (Cell Of ORigin like CellS), as a computational tool trained on developmental human brain single-cell datasets that enables annotation of "developmental-like" cell states in brain tumor cells. Applying COORS to various brain cancer datasets, including medulloblastoma (MB), glioma, and diffuse midline glioma (DMG), we identified developmental-like cells that represent putative cells of origin in these tumors. Our work adds to our cumulative understanding of brain tumor heterogeneity and helps pave the way for tailored treatment strategies.

bioinformatics↗

bioRxiv2024

Spiking GABAergic OPC tumor cells prolong survival in IDH1 mutant glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons, however, the electrophysiological properties endogenous to tumor cells remain obscure. To address this, we employed Patch-sequencing on human glioma specimens and found that one third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia by firing single, short action potentials. To define these hybrid cells (HCs) and discern if they are tumor in origin, we developed a computational tool, Single Cell Rule Association Mining (SCRAM), to annotate each cell individually. SCRAM revealed that HCs represent tumor and non-tumor cells that feature GABAergic neuron and oligodendrocyte precursor cell signatures. These studies are the first to characterize the combined electrophysiological and molecular properties of human glioma cells and describe a new cell type in human glioma with unique electrophysiological and transcriptomic properties that are likely also present in the non-tumor mammalian brain.

cancer biology↗

bioRxiv2024

Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data

Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies. Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ~8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. To integrate interpretable features from the bulk (n=78 samples) and single-cell profiling (~10K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models with RNA-inferred copy number variation (CNV) signals and the initial bulk model to create a meta-model, which exhibited the strongest performance in meningioma classification. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n=792 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (~200K cells) and bulk TCGA glioma data (n=711 samples). Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.

bioinformatics↗

bioRxiv2022

Identification of functional immune and neuronal tumor cells in glioma

Despite advances in molecular profiling, therapeutic development has been hindered by the inability to identify and target tumour-specific mechanisms without consequence to healthy tissue. Correspondingly, a computational framework capable of accurately distinguishing tumour from non-tumour cells has yet to be developed and cell annotation algorithms are unable to assign integrated genomic and transcriptional profiles to single cells on a cell-by-cell basis. To address these barriers, we developed the Single Cell Rule Association Mining (SCRAM) tool that integrates RNA-inferred genomic alterations with co-occurring cell type signatures for individual cells. Applying SCRAM to glioma, we identified tumour cell trajectories recapitulate temporally-restricted developmental paradigms and feature unique co-occurring identities. Specifically, we validated two previously unreported tumour cell populations with immune and neuronal signatures as hallmarks of human glioma subtypes. In vivo modeling revealed a rare immune-like tumour cell population resembling antigen presenting cells can direct CD8+ T cell responses. In parallel, Patch sequencing studies in human tumours confirmed that neuronal-like glioma cells fire action potentials and represent 40% of IDH1 mutant tumor cells. These studies identified new glioma cell types with functional properties similar to their non-tumour analogues and demonstrate the ability of SCRAM to identify these cell types in unprecedented detail.

cancer biology↗

bioRxiv2021

Single-cell RNA Sequencing Reveals Immunosuppressive Myeloid Cell Diversity and Restricted Cytotoxic Effector Cell Trafficking and Activation During Malignant Progression in Glioma

Low grade gliomas (LGG) account for about two-thirds of all glioma diagnoses in adolescents and young adults (AYA) and malignant progression of these patients leads to dismal outcomes. Recent studies have shown the importance of the dynamic tumor microenvironment in high-grade gliomas (HGG), yet its role is still poorly understood in low-grade glioma malignant progression. Here, we investigated the heterogeneity of the immune microenvironment using a platelet-derived growth factor (PDGF)-driven RCAS (replication-competent ASLV long terminal repeat with a splice acceptor) glioma model that recapitulates the malignant progression of low to high-grade glioma in humans and also provides a model system to characterize immune cell trafficking and evolution. To illuminate changes in the immune cell landscape during tumor progression, we performed single-cell RNA sequencing on immune cells isolated from animals bearing no tumor (NT), LGG and HGG, with a particular focus on the myeloid cell compartment, which is known to mediate glioma immunosuppression. LGGs demonstrated significantly increased infiltrating T cells, CD4 T cells, CD8 T cells, B cells, and natural killer cells in the tumor microenvironment, whereas HGGs significantly abrogated this infiltration. Our study identified two distinct macrophage clusters in the tumor microenvironment; one cluster appeared to be bone marrow-derived while another was defined by overexpression of Trem2, a marker of tumor associated macrophages. Our data demonstrates that these two distinct macrophage clusters show an immune-activated phenotype (Stat1, Tnf, Cxcl9 and Cxcl10) in LGG which evolves to an immunosuppressive state (Lgals3, Apoc1 and Id2) in HGG that restricts T cell recruitment and activation. We identified CD74 and macrophage migration inhibition factor (MIF) as potential targets for these distinct macrophage populations. Interestingly, these results were mirrored by our analysis of the TCGA dataset, which demonstrated a statistically significant association between CD74 overexpression and decreased overall survival in AYA patients with grade II gliomas. Targeting immunosuppressive myeloid cells and intra-tumoral macrophages within this therapeutic window may ameliorate mechanisms associated with immunosuppression before and during malignant progression.

cancer biology↗

bioRxiv2021

XCVATR: Detection and Characterization of Variant Impact on the Embeddings of Single -Cell and Bulk RNA-Sequencing Samples

Gene expression profiling via RNA-sequencing has become standard for measuring and analyzing the gene activity in bulk and at single cell level. Increasing sample sizes and cell counts provides substantial information about transcriptional architecture of samples. In addition to quantification of expression at cellular level, RNA-seq can be used for detecting of variants, including single nucleotide variants and small insertions/deletions and also large variants such as copy number variants. The joint analysis of variants with transcriptional state of cells or samples can provide insight about impact of mutations. To provide a comprehensive method to jointly analyze the genetic variants and cellular states, we introduce XCVATR, a method that can identify variants, detect local enrichment of expressed variants, within embedding of samples and cells. The embeddings provide information about cellular states among cells by defining a cell-cell distance metric. Unlike clustering algorithms, which depend on a cell-cell distance and use it to define clusters that explain cell clusters globally, XCVATR detects the local enrichment of expressed variants in the embedding space such that embedding can be computed using any type of measurement or method, for example by PCA or tSNE of the expression levels. XCVATR searches local patterns of association of each variant with the positions of cells in an embedding of the cells. XCVATR also visualizes the local clumps of small and large-scale variant calls in single cell and bulk RNA-sequencing datasets. We perform simulations and demonstrate that XCVATR can identify the enrichments of expressed variants. We also apply XCVATR on single cell and bulk RNA-seq datasets and demonstrate its utility.

bioinformatics↗

bioRxiv2021

scRegulocity: Detection of local RNA velocity patterns in embeddings of single cell RNA-Seq data

Single cell RNA-sequencing has revolutionized transcriptome analysis. ScRNA-seq provides a massive resource for studying biological phenomena at single cell level. One of the most important applications of scRNA-seq is the inference of dynamic cell states through modeling of transcriptional dynamics. Understanding the full transcriptional dynamics using the concept named RNA Velocity enables us to identify cell states, regimes of regulatory changes in cell states, and putative drivers within these states. We present scRegulocity that integrates RNA-velocity estimates with locality information from cell embedding coordinates. scRegulocity focuses on velocity switching patterns, local patterns where velocity of nearby cells change abruptly. These different transcriptional dynamics patterns can be indicative of transitioning cell states. scRegulocity annotates these patterns with genes and enriched pathways and also analyzes and visualizes the velocity switching patterns at the regulatory network level. scRegulocity also combines velocity estimation, pattern detection and visualization steps.

bioinformatics↗

bioRxiv2019

EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

The genomewide signal profiles from functional genomics experiments are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinct patterns along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in many epigenetic assays such as ChIP-Seq and bisulfite sequencing. Valleys mark locations of cis-regulatory elements such as enhancers. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements using epigenetic data. Nevertheless, the valleys are generally not reported by analysis pipelines. Here, we describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability, and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification and DNA methylation valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding, and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes.

bioinformatics↗

bioRxiv2018

CaSpER: Identification, visualization and integrative analysis of CNV events in multiscale resolution using single-cell or bulk RNA sequencing data

RNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNV). Here, we propose CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER performs smoothing of the genome-wide RNA sequencing signal profiles in different multiscale resolutions, identifying CNV events at different length scales. CaSpER also employs a novel methodology for generation of genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it in multiscale fashion for correction of CNV calls. The shift in allelic signal is used to quantify the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER uses Hidden Markov Models (HMM) to assign copy number states to regions. The multiscale nature of CaSpER enables comprehensive analysis of focal and large-scale CNVs and LOH segments. CaSpER performs well in accuracy compared to gold standard SNP genotyping arrays. In particular, analysis of single cell Glioblastoma (GBM) RNA sequencing data with CaSpER reveals novel mutually exclusive and co-occurring CNV sub-clones at different length scales. Moreover, CaSpER discovers gene expression signatures of CNV sub-clones, performs gene ontology (GO) enrichment analysis and identifies potential therapeutic targets for the sub-clones. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing data, especially in cancer research.

bioinformatics↗