Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma

Although human tumours are shaped by the genetic evolution of cancer cells, evidence also suggests that they display hierarchies related to developmental pathways and epigenetic programs in which cancer stem cells (CSCs) can drive tumour growth and give rise to differentiated progeny. Yet, unbiased evidence for CSCs in solid human malignancies remains elusive. Here we profile 4,347 single cells from six IDH1 or IDH2 mutant human oligodendrogliomas by RNA sequencing (RNA-seq) and reconstruct their developmental programs from genome-wide expression signatures. We infer that most cancer cells are differentiated along two specialized glial programs, whereas a rare subpopulation of cells is undifferentiated and associated with a neural stem cell expression program. Cells with expression signatures for proliferation are highly enriched in this rare subpopulation, consistent with a model in which CSCs are primarily responsible for fuelling the growth of oligodendroglioma in humans. Analysis of copy number variation (CNV) shows that distinct CNV sub-clones within tumours display similar cellular hierarchies, suggesting that the architecture of oligodendroglioma is primarily dictated by developmental programs. Subclonal point mutation analysis supports a similar model, although a full phylogenetic tree would be required to definitively determine the effect of genetic evolution on the inferred hierarchies. Our single-cell analyses provide insight into the cellular architecture of oligodendrogliomas at single-cell resolution and support the cancer stem cell model, with substantial implications for disease management

Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq

Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME).We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses.National Cancer Institute (U.S.) (Grant P30-CA14051

Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress

<div><p>From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single <i>Saccharomyces cerevisiae</i> cells treated with and without salt stress to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells appears to decouple expression of ribosomal protein genes from the environmental stress response in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results suggest that cells maintain a cellular filter against decoupled bursts of transcription factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.</p></div

RP transcripts show low variation in abundance across cells.

<p>The mean and variance of transcript read counts per mRNA length (“length-norm”) was plotted for each mRNA from unstressed (left) or stressed (right) cells. (A,C) highlight RP transcripts and (B,D) highlight iESR and RiBi transcript against all other mRNAs (grey points). Plots are zoomed to capture most points. iESR, induced-Environmental Stress Response; RiBi, ribosome biogenesis; RP, ribosomal protein.</p

Transcript detection rate correlates with functional class.

<p>The fraction of cells in which each mRNA was detected was plotted against the mean length-normalized read count for that transcript, calculated from cells in which the transcript was measured, in (A) unstressed or (B) stressed cells. Listed <i>p</i>-values and arrows (where significant) indicate if the detection rate was higher or lower than randomly sampled genes. Plots are zoomed in to show transcripts whose mean read count is below 1.0; most transcripts above this range are detected in all cells, not shown. iESR, induced-Environmental Stress Response; RiBi, ribosome biogenesis; RP, ribosomal protein.</p

The influence of cell-cycle phase on ESR activation.

<p>Cycling genes used for classification were identified by clustering the scRNA-seq data [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.ref054" target="_blank">54</a>] and then selecting clusters enriched for cell-cycle markers (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s003" target="_blank">S3 Table</a>, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#sec013" target="_blank">Methods</a>). (A) Cells (columns) were clustered based on the centroid expression pattern of genes within each group (rows) and manually classified into and sorted within designated groups (A, grey bins, <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s009" target="_blank">S9 Table</a>). Stressed and unstressed cells are annotated by the purple/orange vector (A, bottom row). (B) The percentage of cells in each cell-cycle phase. Cell phases are listed in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s005" target="_blank">S5 Table</a>. (C) Boxplots (without whiskers) of all iESR (red) or RP (blue) genes from cells in that phase. Significance was assessed by Welch <i>t</i> test on the pooled RP or iESR genes from cells within a given phase compared to all other cells; unstressed and stressed cells were analyzed separately. Note only one cell was classified as G1/S after stress. ESR, Environmental Stress Response; iESR, induced-Environmental Stress Response; RP, ribosomal protein; scRNA-seq, single-cell RNA sequencing.</p

Stress-activated regulators show both coordinated and decoupled nuclear localization.

<p>(A) Distribution of nuclear/cytoplasmic signal for paired factors in individual cells before and after NaCl treatment (average <i>n</i> = 676 cells per time point). Data from two biological replicates were very similar and combined (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s011" target="_blank">S11 Table</a>). (B) Median ratios from (A) plotted over time; the Msn2 plot combines measurements from all three strains. (C) Nuclear TF signals (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#sec013" target="_blank">Methods</a>) of Dot6-GFP (left) and Msn2-mCherry (right) expressed in the same cells over time, before stress and after NaCl addition at 81 min (arrows). Each row aligned across all plots represents a different cell, and each column represents a different time point. Red plots show traces of nuclear localization according to the key (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#sec013" target="_blank">Methods</a>), and corresponding grey-scale plots show quantitative measurements only for time points called as peaks. Colored boxes above the plots indicate 80 min before stress (grey box), 30 min after NaCl treatment (dark red box), and beyond 30 min after NaCl treatment (pink box). Data are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s012" target="_blank">S12 Table</a>. (D) Correlation between Dot6-GFP and Msn2-mCherry traces for each temporal phase, according to the key. (E) Representative traces from (C), where called peaks (colored according to key) are indicated with asterisks. TF, transcription factor.</p

Quantitative variation in ESR activation across cells.

<p>(A) Mean-centered log₂(read counts) for ESR gene groups before and after stress. Each row represents a transcript and each column is an individual cell, with expression values according to the key; white indicates no detected transcript. (B) The average mean-centered log₂ values for a given ESR gene group as measured in one cell was plotted against the average mean-centered log₂ values for a second ESR gene group as measured in the same cell. Correlations for unstressed (orange) and stressed (purple) cells are indicated on each plot. (C) Boxplots (without whiskers) of mean-centered log₂(read counts) of RP and iESR transcripts in individual cells, sorted by iESR-group median. Arrows indicate unstressed cells with unusually low RP transcript abundances (FDR < 0.05, see Quantitative variation in ESR expression) and asterisks indicate those cells that also had high median iESR log₂ values. ESR, Environmental Stress Response; FDR, false discovery rate; iESR, induced-Environmental Stress Response; RiBi, ribosome biogenesis; RP, ribosomal protein.</p