Single-Cell RNAseq of Childhood Ependymoma Reveals Distinct Neoplastic Cell Subpopulations that Impact Etiology, Molecular Classification and Outcome

Abstract Ependymoma (EPN) is a brain tumor commonly presenting in childhood that remains fatal in the majority of children. Intra-tumoral cellular heterogeneity in bulk-tumor samples significantly confounds our understanding of EPN biology, impeding development of effective therapy. We therefore used single-cell RNA sequencing to catalog cellular heterogeneity of 26 childhood EPN, predominantly from ST-RELA, PFA1 and PFA2 subgroups. ST-RELA and PFA subgroups clustered separately, with ST-RELA clustering largely according to individual sample-of-origin. PFA1 and PFA2 subgroup EPNs cells were intermixed and revealed 4 major subpopulations – 2 with characteristics of ependymal differentiation (transporter and ciliated phenotype subpopulations), an undifferentiated subpopulation and a mesenchymal phenotype. Pseudotime analysis showed the undifferentiated progenitor subpopulation either differentiating into ependymal differentiation subpopulations or transitioning into the mesenchymal subpopulation. Histological analysis revealed that undifferentiated and mesenchymal subpopulations cells colocalized to perinecrotic/perivascular zones, the putative ependymoma stem cell niche. Deconvolution of PFA bulk transcriptome data showed that undifferentiated and mesenchymal subpopulations were associated with a poor prognosis; whereas the ciliated ependymal cell-differentiated subpopulation was associated with a good prognosis. In conflict with current distinct classification paradigms, the ratio of mesenchymal and ciliated subpopulations determined bulk-tumor subgroups assignment to PFA1 and PFA2 respectively. This atlas of EPN cellular heterogeneity provides an important advance in our understanding of EPN biology, identifying high-risk associated subpopulations for therapeutic targeting.

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Single-Cell RNA Sequencing of Childhood Ependymoma Reveals Neoplastic Cell Subpopulations That Impact Molecular Classification and Etiology

Cell Reports ◽

10.1016/j.celrep.2020.108023 ◽

2020 ◽

Vol 32 (6) ◽

pp. 108023 ◽

Cited By ~ 1

Author(s):

Austin E. Gillen ◽

Kent A. Riemondy ◽

Vladimir Amani ◽

Andrea M. Griesinger ◽

Ahmed Gilani ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Molecular Classification ◽

Neoplastic Cell ◽

Single Cell Rna Sequencing ◽

Cell Subpopulations

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Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining

npj Systems Biology and Applications ◽

10.1038/s41540-020-00162-6 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Luca Alessandri ◽

Francesca Cordero ◽

Marco Beccuti ◽

Nicola Licheri ◽

Maddalena Arigoni ◽

...

Keyword(s):

Quality Control ◽

Single Cell ◽

Medical Personnel ◽

Cellular Heterogeneity ◽

Biological Information ◽

Cell Clusters ◽

The Neural Network ◽

Rnaseq Data ◽

Functional Features ◽

Cell Subpopulations

AbstractSingle-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

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PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

BMC Bioinformatics ◽

10.1186/s12859-021-04277-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Marilisa Montemurro ◽

Elena Grassi ◽

Carmelo Gabriele Pizzino ◽

Andrea Bertotti ◽

Elisa Ficarra ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Single Cell ◽

Tumor Heterogeneity ◽

Third Party ◽

Sequencing Technology ◽

Reduction Methods ◽

Visualization Tools ◽

Cell Subpopulations ◽

Multiple Samples ◽

Analytical Approaches

Abstract Background Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. Results We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. Conclusions PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.

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EPCO-06. AGE- AND REGION-SPECIFIC MULTI-OMIC CHARACTERIZATION OF H3-K27M MUTANT DIFFUSE MIDLINE GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noab196.005 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi2-vi2

Author(s):

Ilon Liu ◽

Jiang Li ◽

Daeun Jeong ◽

Olivia A Hack ◽

McKenzie Shaw ◽

...

Keyword(s):

Single Cell ◽

Immune Cell ◽

Age Groups ◽

Mesenchymal Cell ◽

Cell Compartments ◽

Histone 3 ◽

Age At Surgery ◽

Single Nucleus ◽

Pre Treatment ◽

Cell Subpopulations

Abstract Diffuse midline gliomas driven by lysine27-to-methionine mutations in histone 3 (H3-K27M DMGs) are among the most fatal brain tumors. Molecular studies including single cell RNA-sequencing (scRNA-seq) of pediatric and predominantly pontine H3-K27M DMGs have shown that the H3-K27M oncohistone keeps glioma cells locked in a stem-like oligodendrocyte precursor cell (OPC) state that is capable of self-renewal and tumor-initiation. However, a comprehensive dissection of the cellular architecture of H3-K27M DMGs across different midline regions and age groups is required to better understand the cell-intrinsic and contextual regulation of H3-K27M DMG cell identities. In particular, the more recently described group of adult H3-K27M DMGs remains understudied. Here, we have collected and characterized 45 H3-K27M mutant patient tumors, spanning pontine (n=26), thalamic (n=17), and spinal (n=2) locations. Median age at surgery was 12 (2-68) years, encompassing 21 early childhood (0-10 years), 12 adolescent (11-20 years), and 12 adult (≥ 21 years) tumors. The majority of samples were obtained pre-treatment (n=28), as opposed to post-treatment or at autopsy (n=17). We profiled all 45 tumors by single cell/single nucleus RNA-seq and selected tumors were further characterized by the single cell assay for transposase-accessible chromatin (scATAC-seq). Our integrated analyses highlight the predominance of transcriptionally and epigenetically defined OPC-like tumor cells as the main cell population of H3-K27M DMGs across all age groups and locations. We further identify distinct age- and location-specific OPC-like cell subpopulations. Comparison of pediatric and adult tumors further demonstrates a significant increase of mesenchymal cell states in adult H3-K27M DMGs, which we link to differences in glioma-associated immune cell compartments between age groups. Together, this study sheds light on the effects of age- and region-dependent microenvironments in shaping cellular identities in H3-K27M DMGs.

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Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

Physiological Genomics ◽

10.1152/physiolgenomics.00065.2020 ◽

2020 ◽

Vol 52 (10) ◽

pp. 468-477

Author(s):

Alexander C. Zambon ◽

Tom Hsu ◽

Seunghee Erin Kim ◽

Miranda Klinck ◽

Jennifer Stowe ◽

...

Keyword(s):

Cell Cycle ◽

Single Cell ◽

Single Cell Analysis ◽

Imaging System ◽

Single Cells ◽

Cell Types ◽

Cell Analysis ◽

Mcf7 Cells ◽

Genomic Studies ◽

Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

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Single‐Cell Immune Profiling in Coronary Artery Disease: The Role of State‐of‐the‐Art Immunophenotyping With Mass Cytometry in the Diagnosis of Atherosclerosis

Journal of the American Heart Association ◽

10.1161/jaha.120.017759 ◽

2020 ◽

Vol 9 (24) ◽

Author(s):

Katharine A. Kott ◽

Stephen T. Vernon ◽

Thomas Hansen ◽

Macha de Dreu ◽

Souvik K. Das ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Single Cell ◽

Immune Cell ◽

Mass Cytometry ◽

Artery Disease ◽

Immune Cell Subpopulations ◽

Cell Subpopulations ◽

Serological Biomarkers

Abstract Coronary artery disease remains the leading cause of death globally and is a major burden to every health system in the world. There have been significant improvements in risk modification, treatments, and mortality; however, our ability to detect asymptomatic disease for early intervention remains limited. Recent discoveries regarding the inflammatory nature of atherosclerosis have prompted investigation into new methods of diagnosis and treatment of coronary artery disease. This article reviews some of the highlights of the important developments in cardioimmunology and summarizes the clinical evidence linking the immune system and atherosclerosis. It provides an overview of the major serological biomarkers that have been associated with atherosclerosis, noting the limitations of these markers attributable to low specificity, and then contrasts these serological markers with the circulating immune cell subtypes that have been found to be altered in coronary artery disease. This review then outlines the technique of mass cytometry and its ability to provide high‐dimensional single‐cell data and explores how this high‐resolution quantification of specific immune cell subpopulations may assist in the diagnosis of early atherosclerosis in combination with other complimentary techniques such as single‐cell RNA sequencing. We propose that this improved specificity has the potential to transform the detection of coronary artery disease in its early phases, facilitating targeted preventative approaches in the precision medicine era.

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Single-cell mass cytometry reveals cross-talk between inflammation-dampening and inflammation-amplifying cells in osteoarthritic cartilage

Science Advances ◽

10.1126/sciadv.aay5352 ◽

2020 ◽

Vol 6 (11) ◽

pp. eaay5352 ◽

Cited By ~ 4

Author(s):

Fiorella Carla Grandi ◽

Reema Baskar ◽

Piera Smeriglio ◽

Shravani Murkherjee ◽

Pier Francesco Indelli ◽

...

Keyword(s):

Single Cell ◽

Interleukin 1 ◽

Cell Mass ◽

Tumor Necrosis Factor Receptor ◽

Mass Cytometry ◽

Therapeutic Approaches ◽

Osteoarthritic Cartilage ◽

Regenerative Cells ◽

Cell Subpopulations ◽

D Cells

Aging or injury leads to degradation of the cartilage matrix and the development of osteoarthritis (OA). Because of a paucity of single-cell studies of OA cartilage, little is known about the interpatient variability in its cellular composition and, more importantly, about the cell subpopulations that drive the disease. Here, we profiled healthy and OA cartilage samples using mass cytometry to establish a single-cell atlas, revealing distinct chondrocyte progenitor and inflammation-modulating subpopulations. These rare populations include an inflammation-amplifying (Inf-A) population, marked by interleukin-1 receptor 1 and tumor necrosis factor receptor II, whose inhibition decreased inflammation, and an inflammation-dampening (Inf-D) population, marked by CD24, which is resistant to inflammation. We devised a pharmacological strategy targeting Inf-A and Inf-D cells that significantly decreased inflammation in OA chondrocytes. Using our atlas, we stratified patients with OA in three groups that are distinguished by the relative proportions of inflammatory to regenerative cells, making it possible to devise precision therapeutic approaches.

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Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML

Blood ◽

10.1182/blood-2016-07-728873 ◽

2017 ◽

Vol 129 (17) ◽

pp. 2384-2394 ◽

Cited By ~ 51

Author(s):

Rebecca Warfvinge ◽

Linda Geironson ◽

Mikael N. E. Sommarin ◽

Stefan Lang ◽

Christine Karlsson ◽

...

Keyword(s):

Gene Expression ◽

Stem Cell ◽

Single Cell ◽

Gene Expression Analysis ◽

Therapy Response ◽

Cell Heterogeneity ◽

Key Points ◽

Cell Gene Expression ◽

Cell Subpopulations ◽

Cell Gene

Key Points Single-cell gene expression analysis reveals CML stem cell heterogeneity and changes imposed by TKI therapy. A subpopulation with primitive, quiescent signature and increased survival to therapy can be high-purity captured as CD45RA−cKIT−CD26+.

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