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Can we use 10x Single Cell 3’ RNAseq to detect SVs and CNVs?

Posted By: katherinehuang, on Sep 28, 2018 at 11:27 AM

I was asked to use our 10x single cell gene expression data to study genome variants, such as CNVs and SVs, which have been done by using SMART-seq data in publications as follows. Would like to know if these methods are applicable to 10x Single Cell 3’ RNAseq? And has anyone tried the same thing here? If they are not applicable, why?

 

Science. 2017 Mar 31;355(6332). pii: eaai8478. doi: 10.1126/science.aai8478. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Venteicher AS1,2,3, Tirosh I4, Hebert C1,2, Yizhak K1,2, Neftel C1,2,5, Filbin MG1,2,6, Hovestadt V1,2, Escalante LE1,2, Shaw ML1,2, Rodman C2, Gillespie SM1, Dionne D2, Luo CC1, Ravichandran H1, Mylvaganam R1, Mount C7, Onozato ML1, Nahed BV3, Wakimoto H3, Curry WT3, Iafrate AJ1, Rivera MN1,2, Frosch MP1, Golub TR2,6,8, Brastianos PK9, Getz G1,2, Patel AP3, Monje M7, Cahill DP3, Rozenblatt-Rosen O2, Louis DN1, Bernstein BE1,2, Regev A4,8,10, Suvà ML11,2. Abstract: 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.

PMID: 28360267

 

 

Blood. 2017 Dec 21;130(25):2762-2773. doi: 10.1182/blood-2017-08-803353. Epub 2017 Oct 13. Single-cell RNA-seq reveals a distinct transcriptome signature of aneuploid hematopoietic cells. Zhao X1,2, Gao S1, Wu Z1,2, Kajigaya S1, Feng X1, Liu Q1,2, Townsley DM1, Cooper J1, Chen J3, Keyvanfar K1, Fernandez Ibanez MDP1, Wang X4, Young NS1.Absract: Cancer cells frequently exhibit chromosomal abnormalities. Specific cytogenetic aberrations often are predictors of outcome, especially in hematologic neoplasms, such as monosomy 7 in myeloid malignancies. The functional consequences of aneuploidy at the cellular level are difficult to assess because of a lack of convenient markers to distinguish abnormal from diploid cells. We performed single-cell RNA sequencing (scRNA-seq) to studyhematopoietic stem and progenitor cells from the bone marrow of 4 healthy donors and 5 patients with bone marrow failure and chromosome gain or loss. In total, transcriptome sequences were obtained from 391 control cells and 588 cells from patients. We characterized normal hematopoiesis as binary differentiation from stem cells to erythroid and myeloid-lymphoid pathways. Aneuploid cells were distinguished from diploid cells in patient samples by computational analyses of read fractions and gene expression of individual chromosomes. We confirmed assignment of aneuploidy to individual cells quantitatively, by copy-number variation, and qualitatively, by loss of heterozygosity. When we projected patients' single cells onto the map of normal hematopoiesis, diverse patterns were observed, broadly reflecting clinical phenotypes. Patients' monosomy 7 cells showed downregulation of genes involved in immune response and DNA damage checkpoint and apoptosis pathways, which may contribute to the clonal expansion of monosomy 7 cellswith accumulated gene mutations. scRNA-seq is a powerful technique through which to infer the functional consequences of chromosome gain and loss and explore gene targets for directed therapy.

Comment in Single-cell dissection of monosomy 7 syndromes. [Blood. 2017]

 

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Re: Can we use 10x Single Cell 3’ RNAseq to detect SVs and CNVs?

Posted By: agottscho, on Oct 2, 2018 at 1:04 PM

Dear Katherine,

 

This is Andrew with 10x Genomics support. I'd be happy to address your questions about studying SVs and CNVs with single cell 3' gene expression data.

 

Please note that SV and CNV calling are not part of our supported applications for the 3' gene expression solution. The 3' solution captures transcripts with a poly A tail and is measuring gene expression from that end of the transcript only, so coverage is sparse over the genome at large. The 3' solution is more focused on identifying cell types based on gene expression and differential expression analysis between clusters of cells.

 

You may be interested in a tool called vartrix to evaluate either somatic variants or variants contained within a copy number variant (CNV) event: "VarTrix is a software tool for extracting single cell variant information from 10x Genomics single cell data. VarTrix will take a set of previously defined variant calls and use that to identify those variants in the single cell data. VarTrix does not perform variant calling. VarTrix is useful for evaluating heterogeneity within a sample, which means that the types of variants that will be useful are either somatic or contained within a copy number variant (CNV) event." But this tool is not officially supported. If you have any comments, please submit a GitHub issue: https://github.com/10XGenomics/vartrix 

 

However, we do offer two other solutions to address SV and CNV calling specifically.

 

Our genome-exome solution uses linked reads to detect structural variants, but please note that this is done by isolating long gDNA molecules, not single cells: https://support.10xgenomics.com/genome-exome 

 

By contrast, our new CNV solution is a single-cell assay based on gDNA: https://support.10xgenomics.com/single-cell-dna 

 

 

Hope this helps, if you have any further questions please contact support@10xgenomics.com.

 

Best,

 

Andrew Gottscho, PhD

Software Field Operations Engineer, 10x Genomics

andrew.gottscho@10xgenomics.com