Dataset Information

DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.

ABSTRACT: Although scRNA-seq is now ubiquitously adopted in studies of intratumor heterogeneity, detection of somatic mutations and inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that clusters single cells into genetically distinct subclones and reconstructs the phylogenetic tree relating the subclones. DENDRO utilizes transcribed point mutations and accounts for technical noise and expression stochasticity. We benchmark DENDRO and demonstrate its application on simulation data and real data from three cancer types. In particular, on a mouse melanoma model in response to immunotherapy, DENDRO delineates the role of neoantigens in treatment response.

SUBMITTER: Zhou Z

PROVIDER: S-EPMC6961311 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.

Zhou Zilu Z Xu Bihui B Minn Andy A Zhang Nancy R NR

Genome biology 20200114 1

Although scRNA-seq is now ubiquitously adopted in studies of intratumor heterogeneity, detection of somatic mutations and inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that clusters single cells into genetically distinct subclones and reconstructs the phylogenetic tree relating the subclones. DENDRO utilizes transcribed point mutations and accounts for technical noise and expression stochasticity. We benchmark DEND ...[more]

PMID: 31937348

Similar Datasets

Project description:MotivationSeveral recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (<0.05× per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing.ResultsWe developed Single Cell Data Tumor Clusterer (SECEDO, lat. 'to separate'), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7250 cells and eight tumor subclones from a single patient and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to five real single-cell sequencing datasets from a breast cancer patient, each consisting of ≈2000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03×, achieving an Adjusted Rand Index (ARI) score of ≈0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of ≈0, even after merging cells to create higher coverage data (factor 10 increase), and was only able to match SECEDOs performance when pooling data from all five datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants.Availability and implementationSECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo. Instructions to download the data and the evaluation code to reproduce the findings in this paper are available at: https://github.com/ratschlab/secedo-evaluation. The code and data of the submitted version are archived at: https://doi.org/10.5281/zenodo.6516955.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity. In this study, we adapted our algorithm for pathway dysregulation, Expression Variation Analysis (EVA), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVA has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. EVA was applied to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and cancer subtypes. Immune pathway heterogeneity of hematopoietic cell populations in breast tumors corresponded to the amount of diversity present in the T-cell repertoire of each individual. Cells from head and neck squamous cell carcinoma (HNSCC) primary tumors had significantly more heterogeneity across pathways than cells from metastases, consistent with a model of clonal outgrowth. Moreover, there were dramatic differences in pathway dysregulation across HNSCC basal primary tumors. Within the basal primary tumors, there was increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. These results demonstrate the broad utility of EVA to quantify intertumor and intratumor heterogeneity from scRNA-seq data without reliance on low-dimensional visualization. SIGNIFICANCE: This study presents a robust statistical algorithm for evaluating gene expression heterogeneity within pathways or gene sets in single-cell RNA-seq data.

Project description:The current paradigm of osteoblast fate is that the majority undergo apoptosis, while some further differentiate into osteocytes and others flatten and cover bone surfaces as bone lining cells. Osteoblasts have been described to exhibit heterogeneous expression of a variety of osteoblast markers at both transcriptional and protein levels. To explore further this heterogeneity and its biological significance, Venus-positive (Venus+) cells expressing the fluorescent protein Venus under the control of the 2.3-kb Col1a1 promoter were isolated from newborn mouse calvariae and subjected to single-cell RNA sequencing. Functional annotation of the genes expressed in 272 Venus+ single cells indicated that Venus+ cells are osteoblasts that can be categorized into four clusters. Of these, three clusters (clusters 1 to 3) exhibited similarities in their expression of osteoblast markers, while one (cluster 4) was distinctly different. We identified a total of 1920 cluster-specific genes and pseudotime ordering analyses based on established concepts and known markers showed that clusters 1 to 3 captured osteoblasts at different maturational stages. Analysis of gene co-expression networks showed that genes involved in protein synthesis and protein trafficking between endoplasmic reticulum (ER) and Golgi are active in these clusters. However, the cells in these clusters were also defined by extensive heterogeneity of gene expression, independently of maturational stage. Cells of cluster 4 expressed Cd34 and Cxcl12 with relatively lower levels of osteoblast markers, suggesting that this cell type differs from actively bone-forming osteoblasts and retain or reacquire progenitor properties. Based on expression and machine learning analyses of the transcriptomes of individual osteoblasts, we also identified genes that may be useful as new markers of osteoblast maturational stages. Taken together, our data show much more extensive heterogeneity of osteoblasts than previously documented, with gene profiles supporting diversity of osteoblast functional activities and developmental fates. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Dataset Information

DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.

Publications

DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets