Biology subjects

Zadeh, G.

Publications and source records attributed to Zadeh, G..

5 recordsLinked to original sources

bioRxiv2025

Generalized brain-state modeling with KenazLBM

The large-scale functional state of a human brain remains difficult to characterize, much less predict. Regardless, techniques have been engineered to electrically neuromodulate the brain to treat a subset of neurologic and psychiatric disorders with moderate efficacy. Accurate characterization of a brains instantaneous functional state has stymied the development of more effective neuromodulation paradigms. Advanced computational methods are required to address this gap and enable large-scale neuroscience. Here we define the concept of generalized brain-state modeling across humans as Large Brain-State Modeling (LBM) and present KenazLBM as the worlds first example. KenazLBM can instantaneously characterize the functional state of a persons brain with raw iEEG data, and predict future brain-states. KenazLBM was trained on over 17.9 billion unique multichannel tokens from people undergoing intracranial electroencephalography (iEEG) recordings, and has learned to interrelate brain-states between people into a common interpretable topology. Most importantly, the model generalizes to unseen subject data with significant recording channel heterogeneity from the training set. We offer KenazLBM as a first generalized brain-state model to serve as a new paradigm of basic neuroscience inquiry and potential translation into neuromodulation therapeutics.

neuroscience↗

bioRxiv2025

Deconvolution of Sample Identity in Single-Cell RNA Sequencing via Genome Imputation

BackgroundDroplet based single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring RNA abundance profiles at cell-specific resolution. Droplet-based barcoding technology allows sample multiplexing, thereby facilitating high-scale of single cell sequencing. The resulting processing complexity, sample contamination and the underlying chemistries can all contribute to cell mis-labelling and consequent spurious cell-to-sample assignment. Approaches for barcode-free de-multiplexing which leverage natural genetic variation have been developed, but generally require an external source of genotype information. ResultsWe propose a novel method to exploit genome imputation and clustering to assign cells to inferred donor groups in the absence of a priori genetic information. Using tumor-derived single-cell RNA-sequencing (scRNA-seq) data, our workflow successfully assigned individual cells to donor-of-origin with high concordance. ConclusionsThis imputation-clustering approach represents a quality-assessment and quality-control strategy for barcode-free single cell donor-origin deconvolution with the capacity to resolve cases of sample cross-contamination.

bioinformatics↗

bioRxiv2024

Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome.

Meningiomas, the most common intracranial tumor, though mostly benign can be recurrent and fatal. WHO grading does not always identify high risk meningioma and better characterizations of their aggressive biology is needed. To approach this problem, we combined 13 bulk RNA-Seq datasets to create a dimension-reduced reference landscape of 1298 meningiomas. Clinical and genomic metadata effectively correlated with landscape regions which led to the identification of meningioma subtypes with specific biological signatures. Time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape where nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcome. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

cancer biology↗

bioRxiv2022

Increased mRNA expression of CDKN2A is a transcriptomic marker of clinically aggressive meningiomas

BackgroundHomozygous loss of CDKN2A/B is a genetic alteration found in many cancer types including meningiomas, where it is associated with poor clinical outcome. It is now also a diagnostic criterion for grade 3 meningiomas in the 2021 WHO classification for central nervous system tumors. However, as in other cancers, the relationship between copy number loss of CDKN2A/B and expression of its gene product is unclear and may be either commensurate or paradoxical in nature. Therefore, we aimed to investigate the association of CDKN2A mRNA expression with clinical prognosis, WHO grade, and other molecular biomarkers in meningiomas such as DNA methylation, molecular group, and proteomics. MethodsWe used multidimensional molecular data of 490 meningioma samples from 4 independent cohorts to examine the relationship between mRNA expression of CDKN2A and copy number status, its correlation to clinical outcome, the transcriptomic pathways altered in differential CDKN2A expression, and its relationship with DNA methylation, and proteomics using an integrated molecular approach. ResultsMeningiomas without any copy number loss were dichotomized into high (CDKN2Ahigh) and low (CDKN2Alow) CDKN2A mRNA expression groups. Patients with CDKN2Ahigh meningiomas had poorer progression free survival (PFS) compared to those with CDKN2Alow meningiomas. CDKN2A mRNA expression was increased in more aggressive molecular groups, and in higher WHO grade meningiomas across all cohorts. CDKN2Ahigh meningiomas and meningiomas with CDKN2A copy number loss shared common up-regulated cell cycling pathways. CDK4 mRNA expression was increased in CDKN2Ahigh meningiomas and both p16 and CDK4 protein were more abundant in CDKN2Ahigh meningiomas. CDKN2Ahigh meningiomas were frequently hypermethylated at the gene body and UTR compared to CDKN2Alow meningiomas and found be more commonly Rb-deficient. ConclusionsAn intermediate level of CDKN2A mRNA expression appears to be optimal as significantly low (CDKN2A deleted) or high expression (CDKN2Ahigh) are associated with poorer outcomes clinically. Though CDK4 is elevated in CDKN2Ahigh meningiomas, Rb-deficiency may be more common in this group, leading to lack of response to CDK inhibitors.

cancer biology↗

bioRxiv2021

Molecular atlas of the human brain vasculature at the single-cell level

A broad range of brain pathologies critically relies on the vasculature, and cerebrovascular disease is a leading cause of death worldwide. However, the cellular and molecular architecture of the human brain vasculature remains poorly understood. Here, we performed single-cell RNA sequencing of 599,215 freshly isolated endothelial, perivascular and other tissue-derived cells from 47 fetuses and adult patients to construct a molecular atlas of the developing fetal, adult control and diseased human brain vasculature. We uncover extensive molecular heterogeneity of healthy fetal and adult human brains and across eight vascular-dependent CNS pathologies including brain tumors and brain vascular malformations. We identify alteration of arteriovenous differentiation and reactivated fetal as well as conserved dysregulated pathways in the diseased vasculature. Pathological endothelial cells display a loss of CNS-specific properties and reveal an upregulation of MHC class II molecules, indicating atypical features of CNS endothelial cells. Cell-cell interaction analyses predict numerous endothelial-to-perivascular cell ligand-receptor crosstalk including immune-related and angiogenic pathways, thereby unraveling a central role for the endothelium within brain neurovascular unit signaling networks. Our single-cell brain atlas provides insight into the molecular architecture and heterogeneity of the developing, adult/control and diseased human brain vasculature and serves as a powerful reference for future studies.

neuroscience↗