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

By I. Tirosh, Andrew S. Venteicher, Christine Hebert, Leah E Escalante, Anoop P. Patel, K. Yizhak, Jonathan M. Fisher, Christopher Rodman, Christopher W. Mount, Mariella G. Filbin and 28 more. Nature, 2016

1,053 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/aa5b0ed1257980ed0b18dfb098cb1a6c982fbb0f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

In Oncology

Citations in the corpus

Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin (2020, methodology): “To calculate the cell cycle signature, we used our previously published cell cycle gene list (Tirosh et al., 2016) and summed up the normalized cell cycle gene counts per cell.”
A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation (2019, neutral): “…methods were used to pre-determine sample sizes but our sample sizes are similar or greater than those reported in previous publications (Tirosh et al 2016, Nature; Lake et al 2016, Science; Zhong et al, 2018, Nature) Data exclusions Two single-cell libraries were discarded due to high…”
CancerSEA: a cancer single-cell state atlas (2018, background): “found that these single cells exhibited widespread heterogeneity in stemness and differentiation, and revealed that a few cancer cells with high stemness may act as cancer stem cells to fuel the growth of cancer (12).”
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation (2018, methodology): “Gliomas Single Cell RNA expression (Tirosh et al., 2016) GEO: GSE70630 Malta et al.”
Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer (2017, methodology): “…and high quality cells based on the these two measures (data not shown), and we therefore conservatively excluded all cells with either fewer than 2,000 detected genes or an average housekeeping expression level (E) below 2.5, as done in previous studies (Patel et al., 2014; Tirosh et al., 2016a).”
Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution (2017, background): “Plotting the average expression of G1/S and G2/M genes (Tirosh et al., 2016) revealed an approximate circle (Figure S4D), presumably reflecting progression along the cell cycle.”
SCENIC: Single-cell regulatory network inference and clustering (2017, positive): “The comparison of batch effect removal methods was performed using the Oligodendroglioma dataset by Tirosh et al. 75 .”
Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. (2020, background): “In recent years, scRNA-seq appeared as a revolutionary technic for addressing intra-tumor cellular complexity (27,30,34-44).”
Reversed graph embedding resolves complex single-cell trajectories (2017, positive): “SLICER generated a branched trajectory in which the branch occurs within the erythroid population to bifurcate into either CMPs or GMPs. Monocle 2, Wishbone, and DPT produced orderings that were highly correlated with a “reference ordering”, constructed using a panel of markers similar to the approa”
Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming (2019, positive): “Identifying large chromosomal aberrations—We have previously developed methods to identify copy number variations (CNVs) in scRNA- seq data from tumor samples (Tirosh et al., 2016).”