Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity

Jonasson, Emma; Andersson, Lisa; Dolatabadi, Soheila; Ghannoum, Salim; Åman, Pierre; Ståhlberg, Anders

doi:10.3390/cells9030759

Cited by 8 publications

(11 citation statements)

References 47 publications

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“…Levels of mRNA were quantified by a Smart‐seq2‐based approach as described before (Picelli et al , 2014; Jonasson et al , 2020). Briefly, RNA was purified from 10 × 10 6 cultured cells using the NucleoSpin RNA kit (Macherey‐Nagel) and eluted in 60 μl water.…”

Section: Methodsmentioning

confidence: 99%

hnRNP R negatively regulates transcription by modulating the association of P‐TEFb with 7SK and BRD4

Deng

Antor

et al. 2022

EMBO Reports

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The P‐TEFb complex promotes transcription elongation by releasing paused RNA polymerase II. P‐TEFb itself is known to be inactivated through binding to the non‐coding RNA 7SK but there is only limited information about mechanisms regulating their association. Here, we show that cells deficient in the RNA‐binding protein hnRNP R, a known 7SK interactor, exhibit increased transcription due to phosphorylation of RNA polymerase II. Intriguingly, loss of hnRNP R promotes the release of P‐TEFb from 7SK, accompanied by enhanced hnRNP A1 binding to 7SK. Additionally, we found that hnRNP R interacts with BRD4, and that hnRNP R depletion increases BRD4 binding to the P‐TEFb component CDK9. Finally, CDK9 is stabilized upon loss of hnRNP R and its association with Cyclin K is enhanced. Together, our results indicate that hnRNP R negatively regulates transcription by modulating the activity and stability of the P‐TEFb complex, exemplifying the multimodal regulation of P‐TEFb by an RNA‐binding protein.

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Section: Methodsmentioning

confidence: 99%

hnRNP R negatively regulates transcription by modulating the association of P‐TEFb with 7SK and BRD4

Deng

Antor

et al. 2022

EMBO Reports

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“…Efforts have also been made to measure total mRNA levels in single cells, for instance using global reverse transcription followed by quantitative PCR (Jonasson et al, 2020). A different approach, which relies on an additional step during library preparation, is the inclusion of external RNA spike-in molecules, as suggested by the External RNA Control Consortium (ERCC) (Lichun et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition

et al. 2022

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Motivation As complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of ‘pseudo-bulk’ data, generated by aggregating single-cell RNA-seq expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. Results We developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modeling of cell-type-specific mRNA bias using experimentally derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. SimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. Availability and implementation SimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license. Supplementary information Supplementary data are available at Bioinformatics online.

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“…To confirm conclusions solely made on the basis of scRNA-seq, it is common practice to validate expression data by scRT-qPCR [26] primarily to control the elevated amount of technical noise and thus dropouts. Several scRT-qPCR workflows have been described [27][28][29][30][31] as well as a few scRT-ddPCR workflows [32][33][34]. The majority of these workflows use fluorescence-activated cell sorting (FACS) for single-cell isolation [28,29,31,32,35], while other studies use microfluidic devices [33,34], micromanipulators [27] or manual cell picking [30].…”

mentioning

confidence: 99%

“…Thus, DE analysis from scRNA-seq must be independently confirmed by single-cell PCR [25]. Several scRT-qPCR workflows have been described [26][27][28][29][30] as well as a few scRT-ddPCR workflows [31][32][33]. The majority of these workflows use fluorescence-activated cell sorting (FACS) for single-cell isolation [27,28,30,31,34], while other studies use microfluidic devices [32,33], micromanipulators [26] or manual cell picking [29].…”

mentioning

confidence: 99%

Validation of scRNA-seq by scRT-ddPCR using the example ofErbB2in MCF7 cells

Lange

Groß

Jeney

et al. 2022

Preprint

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Single-cell RNA sequencing (scRNA-seq) can unmask transcriptional heterogeneity facilitating the detection of rare subpopulations at unprecedented resolution. In response to challenges related to coverage and quantity of transcriptome analysis, the lack of unbiased and quantitative validation methods hampers further improvements. Digital PCR (dPCR) represents such a method as the inherent partitioning could enhance detection by increasing the effective concentration of nucleic acids. Thus, we have developed a novel, integrated workflow combining down-scaled, single-cell Smart-seq2 and absolute quantitative, single-cell digital PCR. Our workflow reduces biological and technical variability by analyzing single cells from the same population using identical steps for liquid and single-cell handling. Using two breast cancer cell lines, MCF7 and BT-474, we validated the performance of our down-scaled Smart-seq2 by comparative clustering with published data and our scRT-ddPCR by comparison of absolute gene mRNA counts to bulk methods. We observed significant differences in signal distributions of low-abundant ErbB2 in MCF7 between scRNA-seq and scRT-ddPCR. The differences are mirrored in the calculated fold changes. We assume that low-abundant transcripts suffer from dropouts in scRNA-seq while high abundant transcripts (such as ACTB or ErbB2 in BT-474 cells) suffer from minimal dropouts in the down-scaled Smart-seq2. In scRT-ddPCR however, the inherent partitioning of the reaction volume increases the effective concentration of mRNAs and thus improves sensitivity. As a conclusion, our workflow combines transcriptome-wide gene expression profiling by scRNA-seq with the precise and reliable determination of absolute gene mRNA per cell counts and fold changes by scRT-ddPCR. This can correct biased conclusions made solely on the basis of scRNA-seq. We think this workflow is a valuable addition to the single-cell transcriptomic research toolbox and could even become a new standard in fold change validation because of its reliability, ease of use and increased sensitivity.

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Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity

Cited by 8 publications

References 47 publications

hnRNP R negatively regulates transcription by modulating the association of P‐TEFb with 7SK and BRD4

hnRNP R negatively regulates transcription by modulating the association of P‐TEFb with 7SK and BRD4

SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition

Validation of scRNA-seq by scRT-ddPCR using the example ofErbB2in MCF7 cells

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