Nascent transcript and single-cell RNA-seq analysis defines the mechanism of action of the LSD1 inhibitor INCB059872 in myeloid leukemia

Johnston, Gretchen; Ramsey, Haley E.; Liu, Qi; Wang, Jing; Stengel, Kristy R.; Sampathi, Shilpa; Acharya, Pankaj; Arrate, Maria Pia; Stubbs, Matthew C.; Burn, Timothy C.; Savona, Michael R.; Hiebert, Scott W.

doi:10.1016/j.gene.2020.144758

Cited by 19 publications

(14 citation statements)

References 54 publications

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“…We have explored a variety of SCReadCounts applications on over 300,000 single cells from six different studies on normal and tumor human samples, including adipose tissue, adrenal neuroblastoma, acute myeloid leukemia, non-small lung cancer, prostate carcinoma, and the MCF7 cell line derived from breast adenocarcinoma [ 3 – 6 , 25 – 27 ]. Here we demonstrate three different SCReadCounts applications on 59,884 cells derived from seven neuroblastoma samples [ 3 ]: (1) estimation of cell level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell level allele expression from biallelic positions as called in the pooled scRNA-seq data, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest.…”

Section: Resultsmentioning

confidence: 99%

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

et al. 2021

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Background Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. Results To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available (https://github.com/HorvathLab/NGS) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. Conclusions SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

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Section: Resultsmentioning

confidence: 99%

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

et al. 2021

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“…We have explored a variety of SCReadCounts applications on over 300,000 single cells from 6 different studies on normal and tumor human samples, including adipose tissue, adrenal neuroblastoma, acute myeloid leukemia, non-small lung cancer, prostate carcinoma, and the MCF7 cell line derived from breast adenocarcinoma [12,[24][25][26][27][28][29][30]. Here we demonstrate three different SCReadCounts applications on 59,884 cells derived from seven neuroblastoma samples [26]: (1) estimation of cell level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell level allele expression from biallelic positions as called in the pooled scRNA-seq data, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest.…”

Section: Resultsmentioning

confidence: 99%

SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

Prashant

Alomran

Chen

et al. 2020

Preprint

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SummarySCReadCounts is a method for a cell-level estimation of the sequencing read counts bearing a particular nucleotide at genomic positions of interest from barcoded scRNA-seq alignments. SCReadCounts generates an array of outputs, including cell-SNV matrices with the absolute variant-harboring read counts, as well as cell-SNV matrices with expressed Variant Allele Fraction (VAFRNA); we demonstrate its application to estimate cell level expression of somatic mutations and RNA-editing on cancer datasets. SCReadCounts is benchmarked against GATK and Samtools and is freely available as a 64-bit self-contained binary distribution (Linux), along with MacOS and Python installation.Availabilityhttps://github.com/HorvathLab/NGS/tree/master/SCReadCountsSupplementary InformationSCReadCounts_Supplementary_Data.zip

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“…To also evaluate TRAWLING on scRNASeq data we used the data presented in Johnston et al. (2020) (GEO accession GSM4317810).…”

Section: Methodsmentioning

confidence: 99%

“…In addition, it allows the aggregation of read counts based on the donor and acceptor splice motifs. As proof of concept, we evaluated TRAWLING on three different transcriptome datasets: whole transcriptome sequencing (Shuai et al (2019)), single cell RNA sequencing (Johnston et al (2020)) and Digital RNA with pertUrbation of Genes (Li J et al, (2021)).…”

Section: Introductionmentioning

confidence: 99%

TRAWLING: a Transcriptome Reference Aware of spLIciNG events

Nanni

Reyes

et al. 2021

Preprint

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Alternative splicing is critical for human gene expression regulation and plays an important role in multiple human diseases. In this context, RNA sequencing has emerged as powerful approach to detect alternative splicing events. In parallel, fast alignment-free methods have emerged as a viable alternative to quantify gene and transcript level abundance from RNAseq data. However, the ability to detect differential splicing events is dependent on the annotation of the transcript reference provided by the user. Here, we introduce a new reference transcriptome aware of splicing events, TRAWLING, which simplifies the detection of aberrant splicing events in a fast and simple way. In addition, we evaluate the performances and the benefits of aligning transcriptome data to TRAWLING using three different RNA sequencing datasets: whole transcriptome sequencing, single cell RNA sequencing and Digital RNA with pertUrbation of Genes. Collectively, our comprehensive evaluation underlines the value of using TRAWLING in transcriptomic data analysis.

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Nascent transcript and single-cell RNA-seq analysis defines the mechanism of action of the LSD1 inhibitor INCB059872 in myeloid leukemia

Cited by 19 publications

References 54 publications

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

TRAWLING: a Transcriptome Reference Aware of spLIciNG events

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