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Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anticancer drug response.

ABSTRACT: The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anticancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here, we used single-cell RNA-seq to examine transcriptomic profiles of cancer cells treated with paclitaxel, celecoxib and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that cellular mRNA abundance shows dynamic regulation after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription across single cells. Interestingly, the drug combination resulted in transcriptional amplification and hyperactivation of mitochondrial oxidative phosphorylation pathway linking to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammation-related genes activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response and emphasizes the importance of adding spike-in molecules to study gene expression regulation using single-cell RNA-seq.

SUBMITTER: Wang X

PROVIDER: S-EPMC8210883 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response

Project description:The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anti-cancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here we used single-cell RNA sequencing (scRNA-seq) to examine transcriptomic profiles of cancer cells treated with chemotherapy drug paclitaxel, anti-inflammatory drug celecoxib, and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that global mRNA abundance shows dynamic regulation across single cells after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification, and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription at variable levels across single cells. Interestingly, the combination of the two drugs resulted in transcriptional amplification and hyperactivation of the mitochondrial oxidative phosphorylation pathway, which is linked to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammatory genes that was activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across human cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response. The results also emphasize the importance of adding spike-in molecules to study gene expression regulation using scRNA-seq experiments.

2021-05-25 | GSE162256 | GEO

Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response

Project description:Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response

| PRJNA681205 | ENA

Normalization of human RNA-seq experiments using chimpanzee RNA as a spike-in standard.

Project description:Normalization of human RNA-seq experiments employing chimpanzee RNA as a spike-in standard is reported. Human and chimpanzee RNAs exhibit single nucleotide variations (SNVs) in average 210-bp intervals. Spike-in chimpanzee RNA would behave the same as the human counterparts during the whole NGS procedures owing to the high sequence similarity. After discrimination of species origins of the NGS reads based on SNVs, the chimpanzee reads were used to read-by-read normalize biases and variations of human reads. By this approach, as many as 10,119 transcripts were simultaneously normalized for the entire NGS procedures leading to accurate and reproducible quantification of differential gene expression. In addition, incomparable data sets from different in-process degradations or from different library preparation methods were made well comparable by the normalization. Based on these results, we expect that the normalization approaches using near neighbor genomes as internal standards could be employed as a standard protocol, which will improve both accuracy and comparability of NGS results across different sample batches, laboratories and NGS platforms.

| S-EPMC4995400 | biostudies-literature

Novel small RNA spike-in oligonucleotides enable absolute normalization of small RNA-Seq data.

Project description:Normalization of high-throughput small RNA sequencing (sRNA-Seq) data is required to compare sRNA levels across different samples. Commonly used relative normalization approaches can cause erroneous conclusions due to fluctuating small RNA populations between tissues. We developed a set of sRNA spike-in oligonucleotides (sRNA spike-ins) that enable absolute normalization of sRNA-Seq data across independent experiments, as well as the genome-wide estimation of sRNA:mRNA stoichiometries when used together with mRNA spike-in oligonucleotides.

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ChIPseqSpikeInFree: a ChIP-seq normalization approach to reveal global changes in histone modifications without spike-in.

Project description:MotivationThe traditional reads per million normalization method is inappropriate for the evaluation of ChIP-seq data when treatments or mutations have global effects. Changes in global levels of histone modifications can be detected with exogenous reference spike-in controls. However, most ChIP-seq studies overlook the normalization that must be corrected with spike-in. A method that retrospectively renormalizes datasets without spike-in is lacking.ResultsChIPseqSpikeInFree is a novel ChIP-seq normalization method to effectively determine scaling factors for samples across various conditions and treatments, which does not rely on exogenous spike-in chromatin or peak detection to reveal global changes in histone modification occupancy. Application of ChIPseqSpikeInFree on five datasets demonstrates that this in silico approach reveals a similar magnitude of global changes as the spike-in method does.Availability and implementationSt. Jude Cloud (https://pecan.stjude.cloud/permalink/spikefree) and St. Jude Github ( https://github.com/stjude/ChIPseqSpikeInFree).Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC7523640 | biostudies-literature

Assessment of Single Cell RNA-Seq Normalization Methods.

Project description:We have assessed the performance of seven normalization methods for single cell RNA-seq using data generated from dilution of RNA samples. Our analyses showed that methods considering spike-in External RNA Control Consortium (ERCC) RNA molecules significantly outperformed those not considering ERCCs. This work provides a guidance of selecting normalization methods to remove technical noise in single cell RNA-seq data.

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GC-content normalization for RNA-Seq data.

Project description:BackgroundTranscriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.ResultsWe focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and p-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq.ConclusionsOur within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.

| S-EPMC3315510 | biostudies-literature

"The Wild West of Spike-in Normalization": Benchmarking the ChIP-seq spike-in normalization method

Project description:Spike-in normalization is a powerful approach to assess global changes in data obtained from genomic mapping of DNA-associated proteins by methods such as ChIP-sequencing (ChIP-seq) or CUT&RUN. While multiple spike-in methods provide detailed documentation, the implementation of these approaches often omit critical quality control steps and veer from the established procedures. Here, we show that proper application of spike-in normalization can increase quantification accuracy across a spectrum of conditions. Next, we outline how misuse of spike-in approaches can create erroneous biological interpretations and provide guidelines to minimize pitfalls when applying this approach to normalize data from protein-DNA interaction results.

2024-08-05 | GSE273915 | GEO

An Integrated Approach for RNA-seq Data Normalization.

Project description:BACKGROUND:DNA copy number alteration is common in many cancers. Studies have shown that insertion or deletion of DNA sequences can directly alter gene expression, and significant correlation exists between DNA copy number and gene expression. Data normalization is a critical step in the analysis of gene expression generated by RNA-seq technology. Successful normalization reduces/removes unwanted nonbiological variations in the data, while keeping meaningful information intact. However, as far as we know, no attempt has been made to adjust for the variation due to DNA copy number changes in RNA-seq data normalization. RESULTS:In this article, we propose an integrated approach for RNA-seq data normalization. Comparisons show that the proposed normalization can improve power for downstream differentially expressed gene detection and generate more biologically meaningful results in gene profiling. In addition, our findings show that due to the effects of copy number changes, some housekeeping genes are not always suitable internal controls for studying gene expression. CONCLUSIONS:Using information from DNA copy number, integrated approach is successful in reducing noises due to both biological and nonbiological causes in RNA-seq data, thus increasing the accuracy of gene profiling.

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SCnorm: robust normalization of single-cell RNA-seq data.

Project description:The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.

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