Biology subjects

Ketela, T.

Publications and source records attributed to Ketela, T..

4 recordsLinked to original sources

bioRxiv2023

Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two age-related and fatal neurodegenerative disorders that lie on a shared disease spectrum. While both disorders involve complex interactions between neuronal and glial cells, the specific cell-type alterations and their contributions to disease pathophysiology remain incompletely understood. Here, we applied single-nucleus RNA sequencing of the orbitofrontal cortex, a region affected in ALS-FTLD, to map cell-type specific transcriptional signatures in C9orf72-related ALS (with and without FTLD) and sporadic ALS cases. Our findings reveal disease- and cell-type-specific transcriptional changes, with neurons exhibiting the most pronounced alterations, primarily affecting mitochondrial function, protein homeostasis, and chromatin remodeling. A comparison with independent datasets from different cortical regions of C9orf72 and sporadic ALS cases showed concordance in several pathways, with neuronal STMN2 and NEFL showing consistent up-regulation between brain regions and disease subtypes. We also interrogated alternative polyadenylation (APA) as an additional layer of transcriptional regulation, demonstrating that APA events are not correlated with identified gene expression changes. To interpret these events, we developed APA-Net, a deep learning model that integrates transcript sequences with RNA-binding protein expression profiles, revealing cell type-specific patterns of APA regulation. Our atlas illuminates cell type-specific pathomechanisms of ALS/FTLD, providing a valuable resource for further investigation.

neuroscience↗

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↗

bioRxiv2021

Genome-wide in vivo screen of circulating tumor cells identifies SLIT2 as a regulator of metastasis

Circulating tumor cells (CTCs) break free from primary tumors and travel through the bloodstream and lymphatic system to seed metastatic tumors, which are the major cause of death from cancer. The identification of the major genetic factors that enhance production and persistence of CTCs in the bloodstream at a whole genome level would enable more comprehensive molecular mechanisms of metastasis to be elucidated and the identification of novel therapeutic targets, but this remains a challenging task due to the heterogeneity and extreme rarity of CTCs. Here, we describe the first in vivo genome-wide CRISPR KO screen using CTCs directly isolated from a mouse xenograft. This screen elucidated SLIT2 - a gene encoding a secreted protein acting as a cellular migration cue - as the most significantly represented gene knockout in the CTC population. SLIT2 knockout cells are highly metastatic with hypermigratory and mesenchymal phenotype. Reduced expression of SLIT2 is observed in human tumors, indicating its role as a negative modulator of tumor progression and metastasis.

cancer biology↗

bioRxiv2019

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and that some dedicated methods are available only as web servers with limited cell type gene expression signatures. In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Our results show that, in general, all four methods show a high performance in the task as evaluated by Receiver Operating Characteristic curve analysis (average AUC = 0.94, sd = 0.036), whereas Precision-Recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

bioinformatics↗