Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response
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PROVIDER: PRJNA681205 | ENA |
REPOSITORIES: ENA
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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
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.
2017-07-19 | GSE98553 | GEO
Project description:This study aims to assess the reliability of spike-in normalization for analyzing single-cell RNA sequencing data. This is done by performing mixture experiments where two different sets of spike-in RNA (ERCC and SIRV) are added separately to a single mouse cell (416B or trophoblast stem cells (TSCs)), followed by generation of sequencing libraries using a modified version of the Smart-seq2 protocol. The aim is to measure the variance of the log-ratio of the total counts between the two spike-in sets. This will quantify how precisely the spike-in RNA was added to each well. As a control, addition was also performed with a premixed solution of both spike-ins, to quantify the variability in the log-ratios due to the experimental protocol. The same data can also be used to measure the well-to-well variability in the differences in behaviour between the two spike-in sets. The data contain four batches of libraries (block), using different batches of cells that were processed and sequenced separately. Each batch contains libraries with all three types of spike-in addition (ERCC+SIRV, SIRV+ERCC or Premixed). The 416B cells also contain a CBFB-MYH11 oncogene, which is expressed in half of the cells (Induced) and silent in the other half (Control).
2017-05-31 | E-MTAB-5522 | biostudies-arrayexpress
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2014-10-30 | E-GEOD-60104 | biostudies-arrayexpress
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