Biology subjects

Muskovic, W.

Publications and source records attributed to Muskovic, W..

4 recordsLinked to original sources

bioRxiv2024

Adult-type diffuse gliomas share recurring cell states driven by a common astrocyte-like glioma stem cell population

Adult-type diffuse gliomas are a family of aggressive brain tumours with few effective treatments. Their complex cellular makeup adds to the challenge of finding successful therapies. This intratumoural heterogeneity is fuelled by a subpopulation of glioma stem-like cells (GSCs) that drive tumour growth and resistance to standard treatments. Previous research focused on the three glioma types (astrocytoma, oligodendroglioma, glioblastoma) individually revealed malignant cells mimic the transcriptional profiles of normal brain cell types. Whether these diverse cellular states stem from a shared biological origin is unknown. Here, we show through single-cell RNA sequencing of 40 glioma tumours that all gliomas are described by seven recurring cell states. We also identify a shared astrocyte-like GSC population. Our unique method of identifying GSCs, based on reconstructed tumour phylogenies, repositions astrocyte-like cells at the apex of a differentiation hierarchy in glioma. Our findings indicate the transcriptional heterogeneity observed in gliomas stems from a GSC population recapitulating lineages of healthy adult neural stem cells. These results suggest a shared lineage drives the intratumoural heterogeneity observed in adult-type diffuse gliomas. We anticipate that a deeper understanding of the molecular mechanisms maintaining the GSC state will provide a new framework for future therapeutic development and research into glioma cell biology. HighlightsO_LIRecurring cell states are shared across adult-type diffuse gliomas C_LIO_LIReconstructed tumour phylogenies identify an astrocyte-like glioma stem cell population C_LIO_LITumour subclones are segregated non-randomly across cell states C_LI

cancer biology↗

bioRxiv2024

Protein Translation Dysregulation and Immune Cell Evasion Define Metastatic Clones in HPV-related Cancer of the Oropharynx

Head and neck cancers, representing the seventh most common malignancy globally, have seen a shift in causative factors from traditional smoking and alcohol use to human papillomavirus (HPV) infection, now accounting for up to 80% of oropharyngeal cancers. We identify the cellular and clonal mechanisms underlying immune avoidance and metastasis by analysing single-cell and spatial genomic data from primary and metastatic cancers. We first map the clonal evolution of malignant cells based on the accumulation of mutations. We identify metastasising clones based on mutational similarity scores between cells in the primary and lymph node metastasis. Genomic analysis of metastasising and non-metastasising clones identified virally mediated protein translation relief (P=4.24x10-24) pathway underlying metastatic expansion. We show that in metastatic clones, this process is driven through upregulation of transition-initiating factors, EIF4E (P=1.5x10-13) and EIFG1 (P<2.22x10-16), and suppression of regulatory kinases EIF4EBP1 (P=2.1x10), EIF2AK2 (P<2.22x10-16), and EIF2S1 (P<2.22x10-16). We subsequently identify that metastatic clones have a corresponding downregulation of the JAK/STAT pathway and immunoproteasome genes PSMB8 (P<2.22x10- 16) and PSMB9 (P<2.22x10-16), suggesting these clones escape immune surveillance through decreased INF inflammatory response and antigen presentation. We validate these results using spatial RNA-seq data, where metastatic cancer clones show decreased cell-to-cell interactions with CD4 T-effector memory cells (CD4TEM) (P=0.0077), CD8 T-exhausted cells (CD8Ex) (P=0.0191), and innate lymphoid cells (ILC) (P=0.04). Finally, we demonstrate that the upregulation of cap-independent translational drives cell proliferation in metastatic clones through the expression of translation initiation factors (EIF4G1: P<2.22x10-16). Our results provide evidence of the mechanisms by which virally induced cancer clones lead to advanced disease and poor prognosis in patients.

cancer biology↗

bioRxiv2021

High temporal resolution RNA-seq time course data reveals mammalian lncRNA activationmirrors neighbouring protein-coding genes

BackgroundThe advent of next-generation sequencing revealed extensive transcription beyond protein-coding genes, identifying tens of thousands of long non-coding RNAs (lncRNAs). Selected functional examples raised the possibility that lncRNAs, as a class, may maintain broad regulatory roles. Compellingly, lncRNA expression is strongly linked with adjacent protein-coding gene expression, suggesting a potential cis-regulatory function. Evidence for these regulatory roles may be obtained through careful examination of the precise timing of lncRNA expression relative to adjacent protein-coding genes. ResultsWhere causal cis-regulatory relationships exist, lncRNA activation is expected to precede changes in adjacent target gene expression. Using an RNA-seq time course of uniquely high temporal resolution, we profiled the expression dynamics of several thousand lncRNAs and protein-coding genes in synchronized, transitioning human cells. Our findings reveal lncRNAs are expressed synchronously with adjacent protein-coding genes. Analysis of lipopolysaccharide-activated mouse dendritic cells revealed the same temporal relationship observed in transitioning human cells. ConclusionOur findings suggest broad-scale cis-regulatory roles for lncRNAs are not common. The strong association between lncRNAs and adjacent genes may instead indicate an origin as transcriptional by-products from active protein-coding gene promoters and enhancers.

bioinformatics↗

bioRxiv2021

DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data

Advances in droplet-based single cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free "ambient" RNA predominately caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell-containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells - so called empty droplets. Additional to the challenge of identifying empty drops, there are currently no methods available to detect droplets containing damaged cells, which comprise of partially lysed cells - the original source of the ambient RNA. Here we describe DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. We demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. We have implemented DropletQC as an R package, which can be easily integrated into existing single cell analysis workflows.

genomics↗