Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data

AbstractWe present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

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HGG-26. SINGLE-CELL RNA-SEQ OF PEDIATRIC HIGH-GRADE GLIOMAS IDENTIFIES COMMON ONCOGENIC PROCESSES AMONG DISTINCT TUMOR HISTOLOGIES

Neuro-Oncology ◽

10.1093/neuonc/noab090.090 ◽

2021 ◽

Vol 23 (Supplement_1) ◽

pp. i22-i22

Author(s):

John DeSisto ◽

Andrew Donson ◽

Rui Fu ◽

Bridget Sanford ◽

Kent Riemondy ◽

...

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Treatment Modalities ◽

Patient Specific ◽

Cell Populations ◽

High Grade ◽

Methylation Data ◽

Rna Seq

Abstract Background Pediatric high-grade glioma (PHGG) is a deadly childhood brain tumor that responds poorly to treatment. PHGG comprises two major subtypes: cortical tumors with wild-type H3K27 and diffuse midline gliomas (DMG) that occur in the midline and have characteristic H3K27M mutations. Cortical PHGG is heterogeneous with multiple molecular subtypes. In order to identify underlying commonalities in cortical PHGG that might lead to better treatment modalities, we performed molecular profiling, including single-cell RNA-Seq (scRNA-Seq), on PHGG samples from Children’s Hospital Colorado. Methods Nineteen cortical PHGG tumor samples, one DMG and one normal margin sample obtained at biopsy were disaggregated to isolate viable cells. Fifteen were glioblastomas (GBM), including five with epithelioid and/or giant cell features and five radiation-induced glioblastomas (RIG). There were also four non-GBM PHGG. We performed scRNA-Seq using 10X Genomics v.3 library preparation to enable capture of infiltrating immune cells. We also performed bulk RNA-Seq and DNA methylation profiling. Results After eliminating patient-specific and cell-cycle effects, RIG, epithelioid GBM, and other GBM each formed identifiable subgroups in bulk RNA-Seq and scRNA-Seq datasets. In the scRNA-Seq data, clusters with cells from multiple tumor samples included a PDGFRA-positive population expressing oligodendrocyte progenitor markers, astrocytic, mesenchymal and stemlike populations, macrophage/monocyte immune cells, and a smaller T-cell population. Analyses of DNA methylation data showed PDGFRA and CDK4 amplification and CDKN2A deletion are common alterations among PHGG. Inferred copy number variation analysis of the single-cell data confirmed that individual tumors include populations that both include and lack the molecular alterations identified in the methylation data. RNA velocity studies to define tumor cells of origin and further analyses of the immune cell populations are underway. Conclusions Single-cell analysis of PHGG confirms a large degree of tumor heterogeneity but also shows that PHGG have stemlike, mesenchymal and immune cell populations with common characteristics.

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Optimal Transport improves cell-cell similarity inference in single-cell omics data

10.1101/2021.03.19.436159 ◽

2021 ◽

Author(s):

Geert-Jan Huizing ◽

Gabriel Peyré ◽

Laura Cantini

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Optimal Transport ◽

State Of The Art ◽

Cell Types ◽

Unsupervised Clustering ◽

Methylation Data ◽

Omics Data ◽

Similarity Metric ◽

Cell Cell

AbstractThe recent advent of high-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity is typically achieved through unsupervised clustering, which crucially relies on a similarity metric.We here propose the use of Optimal Transport (OT) as a cell-cell similarity metric for single-cell omics data. OT defines distances to compare, in a geometrically faithful way, high-dimensional data represented as probability distributions. It is thus expected to better capture complex relationships between features and produce a performance improvement over state-of-the-art metrics. To speed up computations and cope with the high-dimensionality of single-cell data, we consider the entropic regularization of the classical OT distance. We then extensively benchmark OT against state-of-the-art metrics over thirteen independent datasets, including simulated, scRNA-seq, scATAC-seq and single-cell DNA methylation data. First, we test the ability of the metrics to detect the similarity between cells belonging to the same groups (e.g. cell types, cell lines of origin). Then, we apply unsupervised clustering and test the quality of the resulting clusters.In our in-depth evaluation, OT is found to improve cell-cell similarity inference and cell clustering in all simulated and real scRNA-seq data, while its performances are comparable with Pearson correlation in scATAC-seq and single-cell DNA methylation data. All our analyses are reproducible through the OT-scOmics Jupyter notebook available at https://github.com/ComputationalSystemsBiology/OT-scOmics.

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Accurate prediction of single-cell DNA methylation states using deep learning

10.1101/055715 ◽

2016 ◽

Cited By ~ 8

Author(s):

Christof Angermueller ◽

Heather J. Lee ◽

Wolf Reik ◽

Oliver Stegle

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Deep Neural Networks ◽

Single Cells ◽

Cell Types ◽

Computational Approach ◽

Methylation Data ◽

Sequence Composition ◽

Technological Advances ◽

Genome Wide

AbstractRecent technological advances have enabled assaying DNA methylation at single-cell resolution. Current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. Here, we report DeepCpG, a computational approach based on deep neural networks to predict DNA methylation states from DNA sequence and incomplete methylation profiles in single cells. We evaluated DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols, finding that DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the parameters of our model can be interpreted, thereby providing insights into the effect of sequence composition on methylation variability.

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Faculty Opinions recommendation of Tracing dynamic changes of DNA methylation at single-cell resolution.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725809241.793539778 ◽

2017 ◽

Author(s):

Christopher Gregg

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Dynamic Changes

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EPEN-21. IMPAIRED NEURONAL-GLIAL FATE SPECIFICATION IN PEDIATRIC EPENDYMOMA REVEALED BY SINGLE-CELL RNA-SEQ

Neuro-Oncology ◽

10.1093/neuonc/noaa222.158 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii311-iii312

Author(s):

Bernhard Englinger ◽

Johannes Gojo ◽

Li Jiang ◽

Jens M Hübner ◽

McKenzie L Shaw ◽

...

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Regulatory Networks ◽

Target Genes ◽

Target Identification ◽

Rna Seq ◽

Cell Models ◽

Pediatric Ependymoma ◽

Glial Fate ◽

Anatomic Locations

Abstract Ependymoma represents a heterogeneous disease affecting the entire neuraxis. Extensive molecular profiling efforts have identified molecular ependymoma subgroups based on DNA methylation. However, the intratumoral heterogeneity and developmental origins of these groups are only partially understood, and effective treatments are still lacking for about 50% of patients with high-risk tumors. We interrogated the cellular architecture of ependymoma using single cell/nucleus RNA-sequencing to analyze 24 tumor specimens across major molecular subgroups and anatomic locations. We additionally analyzed ten patient-derived ependymoma cell models and two patient-derived xenografts (PDXs). Interestingly, we identified an analogous cellular hierarchy across all ependymoma groups, originating from undifferentiated neural stem cell-like populations towards different degrees of impaired differentiation states comprising neuronal precursor-like, astro-glial-like, and ependymal-like tumor cells. While prognostically favorable ependymoma groups predominantly harbored differentiated cell populations, aggressive groups were enriched for undifferentiated subpopulations. Projection of transcriptomic signatures onto an independent bulk RNA-seq cohort stratified patient survival even within known molecular groups, thus refining the prognostic power of DNA methylation-based profiling. Furthermore, we identified novel potentially druggable targets including IGF- and FGF-signaling within poorly prognostic transcriptional programs. Ependymoma-derived cell models/PDXs widely recapitulated the transcriptional programs identified within fresh tumors and are leveraged to validate identified target genes in functional follow-up analyses. Taken together, our analyses reveal a developmental hierarchy and transcriptomic context underlying the biologically and clinically distinct behavior of ependymoma groups. The newly characterized cellular states and underlying regulatory networks could serve as basis for future therapeutic target identification and reveal biomarkers for clinical trials.

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EMeth: An EM algorithm for cell type decomposition based on DNA methylation data

Scientific Reports ◽

10.1038/s41598-021-84864-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hanyu Zhang ◽

Ruoyi Cai ◽

James Dai ◽

Wei Sun

Keyword(s):

Dna Methylation ◽

Tumor Cells ◽

T Regulatory Cells ◽

Simulated Data ◽

Cell Types ◽

Computational Method ◽

Methylation Data ◽

Cell Type ◽

A Cell ◽

Type Decomposition

AbstractWe introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

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