Breaking out of the cycle: Including quiescence in cell cycle classification.

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The ccAFv2 classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state, and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat (https://github.com/plaisier-lab/ccafv2_R) or a PyPI package integrated with scanpy (https://pypi.org/project/ccAFv2/). We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.

Systematic integration of protein-affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile.

Somatic mutations occur as random genetic changes in genes through protein-affecting mutations (PAMs), gene fusions, or copy number alterations (CNAs). Mutations of different types can have a similar phenotypic effect (i.e., allelic heterogeneity) and should be integrated into a unified gene mutation profile. We developed OncoMerge to fill this niche of integrating somatic mutations to capture allelic heterogeneity, assign a function to mutations, and overcome known obstacles in cancer genetics. Application of OncoMerge to TCGA Pan-Cancer Atlas increased detection of somatically mutated genes and improved the prediction of the somatic mutation role as either activating or loss of function. Using integrated somatic mutation matrices increased the power to infer gene regulatory networks and uncovered the enrichment of switch-like feedback motifs and delay-inducing feedforward loops. These studies demonstrate that OncoMerge efficiently integrates PAMs, fusions, and CNAs and strengthens downstream analyses linking somatic mutations to cancer phenotypes.

Molecular subtypes of ALS are associated with differences in patient prognosis.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease with poorly understood clinical heterogeneity, underscored by significant differences in patient age at onset, symptom progression, therapeutic response, disease duration, and comorbidity presentation. We perform a patient stratification analysis to better understand the variability in ALS pathology, utilizing postmortem frontal and motor cortex transcriptomes derived from 208 patients. Building on the emerging role of transposable element (TE) expression in ALS, we consider locus-specific TEs as distinct molecular features during stratification. Here, we identify three unique molecular subtypes in this ALS cohort, with significant differences in patient survival. These results suggest independent disease mechanisms drive some of the clinical heterogeneity in ALS.

Investigating the Interactions of Glioma Stem Cells in the Perivascular Niche at Single-Cell Resolution using a Microfluidic Tumor Microenvironment Model.

The perivascular niche (PVN) is a glioblastoma tumor microenvironment (TME) that serves as a safe haven for glioma stem cells (GSCs), and acts as a reservoir that inevitably leads to tumor recurrence. Understanding cellular interactions in the PVN that drive GSC treatment resistance and stemness is crucial to develop lasting therapies for glioblastoma. The limitations of in vivo models and in vitro assays have led to critical knowledge gaps regarding the influence of various cell types in the PVN on GSCs behavior. This study developed an organotypic triculture microfluidic model as a means to recapitulate the PVN and study its impact on GSCs. This triculture platform, comprised of endothelial cells (ECs), astrocytes, and GSCs, is used to investigate GSC invasion, proliferation and stemness. Both ECs and astrocytes significantly increased invasiveness of GSCs. This study futher identified 15 ligand-receptor pairs using single-cell RNAseq with putative chemotactic mechanisms of GSCs, where the receptor is up-regulated in GSCs and the diffusible ligand is expressed in either astrocytes or ECs. Notably, the ligand-receptor pair SAA1-FPR1 is demonstrated to be involved in chemotactic invasion of GSCs toward PVN. The novel triculture platform presented herein can be used for therapeutic development and discovery of molecular mechanisms driving GSC biology.

Neural G0: a quiescent-like state found in neuroepithelial-derived cells and glioma.

Single-cell RNA sequencing has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA-seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From these data, we constructed a cell cycle classifier that identifies traditional cell cycle phases and a putative quiescent-like state in neuroepithelial-derived cell types during mammalian neurogenesis and in gliomas. The Neural G0 markers are enriched with quiescent NSC genes and other neurodevelopmental markers found in non-dividing neural progenitors. Putative glioblastoma stem-like cells were significantly enriched in the Neural G0 cell population. Neural G0 cell populations and gene expression are significantly associated with less aggressive tumors and extended patient survival for gliomas. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down-regulation of quiescence-associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent-like state found in neuroepithelial-derived cells and gliomas.