S100A4 mRNA-protein relationship uncovered by measurement noise reduction
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA615229 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11425266_1.fastq.gz | Fastqsanger.gz | |||
| SRR11425266_2.fastq.gz | Fastqsanger.gz | |||
| SRR11425267_1.fastq.gz | Fastqsanger.gz | |||
| SRR11425267_2.fastq.gz | Fastqsanger.gz | |||
| SRR11425268_1.fastq.gz | Fastqsanger.gz |
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Project description:Intrinsic biological fluctuation and/or measurement error can obscure the association of gene expression patterns between RNA and protein levels. Appropriate normalisation of reverse-transcription quantitative PCR (RT-qPCR) data can reduce technical noise in transcript measurement, thus uncovering such relationships. The accuracy of gene expression measurement is often challenged in the context of cancer due to its genetic instability and ‘splicing weakness’. Here we sequenced the poly(A) cancer transcriptome of canine osteosarcoma using mRNA-Seq. Expressed sequences were resolved at the level of two consecutive exons to enable the design of exon-border spanning RT-qPCR assays and ranked for stability based on the coefficient of variation (CV). Using the same template type for RT-qPCR validation, i.e. poly(A) RNA, avoided skewing of stability assessment by circular RNAs (circRNAs) and/or rRNA deregulation. The strength of the relationship between mRNA expression of the tumour marker S100A4 and its proportion score of quantitative immunochemistry (qIHC) was introduced as an experimental read-out to fine-tune the normalisation choice. Together with the essential logit transformation of qIHC scores, this approach reduced the noise of measurement as demonstrated by uncovering a highly significant, strong association between mRNA and protein expressions of S100A4 (Spearman's coefficient r = 0.72 (p = 0.006)).
2020-03-26 | GSE147550 | GEO
Project description:Intrinsic biological fluctuation and/or measurement error can obscure the association of gene expression patterns between RNA and protein levels. Appropriate normalization of reverse-transcription quantitative PCR (RT-qPCR) data can reduce technical noise in transcript measurement, thus uncovering such relationships. The accuracy of gene expression measurement is often challenged in the context of cancer due to the genetic instability and "splicing weakness" involved. Here, we sequenced the poly(A) cancer transcriptome of canine osteosarcoma using mRNA-Seq. Expressed sequences were resolved at the level of two consecutive exons to enable the design of exon-border spanning RT-qPCR assays and ranked for stability based on the coefficient of variation (CV). Using the same template type for RT-qPCR validation, i.e. poly(A) RNA, avoided skewing of stability assessment by circular RNAs (circRNAs) and/or rRNA deregulation. The strength of the relationship between mRNA expression of the tumour marker S100A4 and its proportion score of quantitative immunohistochemistry (qIHC) was introduced as an experimental readout to fine-tune the normalization choice. Together with the essential logit transformation of qIHC scores, this approach reduced the noise of measurement as demonstrated by uncovering a highly significant, strong association between mRNA and protein expressions of S100A4 (Spearman's coefficient ρ = 0.72 (p = 0.006)). KEY MESSAGES: • RNA-seq identifies stable pairs of consecutive exons in a heterogeneous tumour. • Poly(A) RNA templates for RT-qPCR avoid bias from circRNA and rRNA deregulation. • HNRNPL is stably expressed across various cancer tissues and osteosarcoma. • Logit transformed qIHC score better associates with mRNA amount. • Quantification of minor S100A4 mRNA species requires poly(A) RNA templates and dPCR.
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Project description:Analysis of S100A4+ stromal cells at the gene expression level in physiological setting versus metastatic setting. Total RNA was isolated from FACS-sorted S100A4+ stromal cells from normal lung of S100A4-GFP transgenic mice compared to FACS-sorted S100A4+ stromal cells from metastatic lung of 4T1 tumor-bearing S100A4-GFP transgenic mice.
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Project description:Progressive loss of effector functions, especially IFN-γ secreting capability, in effector memory CD8+ T (CD8+ TEM) cells plays a causal role in asthma worsening. However, the mechanisms of CD8+ TEM cell dysfunction remain elusive. Here, we report that S100A4 drives CD8+ TEM cell dysfunction, impairing their protective memory response and promoting asthma worsening in OVA-induced asthma model. We find that allergic CD8+ TEM cells contain two subsets based on S100A4 expression. S100A4+ subsets exhibit dysfunctional effector phenotypes with increased proliferative capability, whereas S100A4- subsets retain effector function but are more inclined to apoptosis, giving rise a dysfunctional CD8+ TEM cell pool. Mechanistically, S100A4 upregulation of mitochondrial metabolism results in a decrease of acetyl-CoA levels, which impair the transcription of effector genes, especially ifn-γ, facilitating cell survival, tolerance and memory potential. Our findings thus reveal general insights into how S100A4 reprograms CD8+ TEM cells into dysfunctional and less protective phenotypes to promote asthma worsening.
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Project description:To identify gene products expressed in S100A4-producing cells in Peyer's patches, we used transgenic mice expressing enhanced green fluorescent protein (EGFP) under the control of the mouse S100A4 promoter (S100A4-GFP mice) and analyzed gene expression profiles using microarray. As a result, we found that S100A4-producing cells expressed many genes characteristic of LysoMac/DCs and ILC3s.
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Project description:Whole lens transcriptome profiling by RNA-seq analysis was performed to investigate the role of S100A4 in lens architecture and function. These studies yielded the intriguing discovery that absence of S100A4 in the lens results in robust induction of gene expression of S100A5, an olfactory sensory neuron specific protein, and of genes encoding various photoreceptor and Müller glial cell proteins
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Project description:Analysis of S100A4+ stromal cells at the gene expression level in physiological setting versus metastatic setting.
2011-08-30 | GSE31711 | GEO
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Project description:S100A4 is a known metastasis-promoting factor, rich in the tumor microenvironment. To clarify how extracellular S100A4 execute its pro-metastatic function, we analyzed gene expression in melanoma cells stimulated with recombinant protein rS100A4 and compared it to the expression in control non-stimulated cells. Two melanoma cell lines representing two distinct phenotypes – invasive (Melmet 1) and non-invasive/proliferative (Melmet 5) – were included in the study. The response at the gene expression level was much stronger in Melmet 5 than in Melmet 1. Melmet 5 down-regulated genes associated with melanocytic differentiation, indicating that Melmet 5 cells gained dedifferentiated, more invasive phenotype after stimulation with rS100A4. The observed changes in the expression of metabolism associated genes suggested the occurrence of metabolic reprogramming. We conclude that extracellular S100A4 stimulate the transition to the invasive phenotype in poorly-invasive melanoma cells, and that this transition is associated with metabolic reprogrammingve
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