Comparison of RT-qPCR and Nanostring in the measurement of blood interferon response for the diagnosis of type I interferonopathies

Pescarmona, Rémi; Belot, Alexandre; Villard, Marine; Besson, Laurie; Lopez, Jonathan; Mosnier, Isabelle; Mathieu, Alban; Lombard, Christine; Garnier, L.; Frachette, Cécile; Walzer, Thierry; Viel, Sébastien

doi:10.1016/j.cyto.2018.10.023

Cited by 71 publications

(54 citation statements)

References 39 publications

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“…First, we measured mRNA-based IFN scores by both qPCR and Nanostring technology. We observed a strong correlation between IFN scores measured by the two methods, thus confirming previous reports that Nanostring is a valid alternative to qPCR for easy quantification of mRNA-based IFN scores 21 22…”

Section: Discussionsupporting

confidence: 88%

Protein and DNA methylation-based scores as surrogate markers for interferon system activation in patients with primary Sjögren’s syndrome

et al. 2020

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ObjectiveStandard assessment of interferon (IFN) system activity in systemic rheumatic diseases depends on the availability of RNA samples. In this study, we describe and evaluate alternative methods using plasma, serum and DNA samples, exemplified in the IFN-driven disease primary Sjögren’s syndrome (pSS).MethodsPatients with pSS seropositive or negative for anti-SSA/SSB and controls were included. Protein-based IFN (pIFN) scores were calculated from levels of PD-1, CXCL9 and CXCL10. DNA methylation-based (DNAm) IFN scores were calculated from DNAm levels at RSAD2, IFIT1 and IFI44L. Scores were compared with mRNA-based IFN scores measured by quantitative PCR (qPCR), Nanostring or RNA sequencing (RNAseq).ResultsmRNA-based IFN scores displayed strong correlations between B cells and monocytes (r=0.93 and 0.95, p<0.0001) and between qPCR and Nanostring measurements (r=0.92 and 0.92, p<0.0001). The pIFN score in plasma and serum was higher in patients compared with controls (p<0.0001) and correlated well with mRNA-based IFN scores (r=0.62–0.79, p<0.0001), as well as with each other (r=0.94, p<0.0001). Concordance of classification as ‘high’ or ‘low’ IFN signature between the pIFN score and mRNA-based IFN scores ranged from 79.5% to 88.6%, and the pIFN score was effective at classifying patients and controls (area under the curve, AUC=0.89–0.93, p<0.0001). The DNAm IFN score showed strong correlation to the RNAseq IFN score (r=0.84, p<0.0001) and performed well in classifying patients and controls (AUC=0.96, p<0.0001).ConclusionsWe describe novel methods of assessing IFN system activity in plasma, serum or DNA samples, which may prove particularly valuable in studies where RNA samples are not available.

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Section: Discussionsupporting

confidence: 88%

Protein and DNA methylation-based scores as surrogate markers for interferon system activation in patients with primary Sjögren’s syndrome

et al. 2020

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“…Because the use of this simple test might represent a gold standard for the evaluation of various immune diseases, it could also be used to monitor patients treated with other drugs targeting the type I IFN pathway. 6 Finally, the monitoring of target cells could be also a solution to predict clinical efficacy of JAK inhibitors. As JAK inhibitors mainly target B and T lymphocytes, it has been proposed to monitor JAK phosphorylation in vivo after administration.…”

Section: The Nightmare Monitoring Of Jakinhibsmentioning

confidence: 99%

“…3 Unlike the A1/G12 antibodies used in our study, the R5 antibody misestimates clinically meaningful gluten content because it does not detect some of the most immunogenic gluten peptides. [4][5][6] Thus, reliance on currently used assays for gluten detection in food…”

Section: Conflicts Of Interestmentioning

confidence: 99%

The Nightmare Monitoring of JAKinhibs

Paul¹,

Roblin

2020

Gastroenterology

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“…This score has been used for example to asses type I IFN activity in pediatric patients with type I interferonopathies, systemic lupus erythematosus, dermatomyositis and systemic juvenile idiopathic arthritis (2). Both qPCR and Nanostring technology have similar sensitivity and reproducibility for IS determination (3). The use of different whole blood RNA collection systems on the IS have not been evaluated however despite evidence of method-dependent changes in gene expression (4).…”

Section: Comparison Of Paxgene and Tempus Whole Blood Rna Collection mentioning

confidence: 99%

Thu0511 comparison of Paxgene and Tempus Whole Blood Rna Collection and Isolation Systems for the Quantification of Type I Interferon-Stimulated Gene Expression

Lamot

Niemietz

Brown

2019

Poster Presentations

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Background:Type I interferons (IFN) have important roles in many pediatric and adult rheumatic diseases and are a new therapeutic target for which several “anti-interferon (anti-IFN)” treatments are currently in use or in development. Since the direct detection of these proteins in biological samples has proved challenging, indirect methods are often used to infer the presence of type I IFN. Most commonly this involves quantification of the relative expression of interferon-stimulated genes (ISGs) that are used to calculate an interferon score (IS) (1). This score has been used for example to asses type I IFN activity in pediatric patients with type I interferonopathies, systemic lupus erythematosus, dermatomyositis and systemic juvenile idiopathic arthritis (2). Both qPCR and Nanostring technology have similar sensitivity and reproducibility for IS determination (3). The use of different whole blood RNA collection systems on the IS have not been evaluated however despite evidence of method-dependent changes in gene expression (4).Objectives:The aim of the study was to compare expression of six common ISGs (IFI27, IFI44L, IFIT1, ISIG15, RSAD2, SIGLEC1) and the corresponding IS in RNA derived from two commonly used whole blood RNA collection systems (PAXgene and Tempus).Methods:Whole blood was collected from ten healthy individuals (median age 25.5 years) in sodium heparin tubes and incubated without or with recombinant human interferon alpha 2b (rhIFNα, 2 IU/ml, 4 hrs, 37C°, 5% CO2). Next, samples were divided between PAXgene (PreAnalytiX, Becton Dickinson) and Tempus (Applied Biosystems) tubes and RNA was isolated according to the manufacturer’s protocols. cDNA was synthesized (∼500ng input RNA; qScript cDNA synthesis kit) and ISG expression measured on a QuantStudio 6 Real-Time PCR instrument using a TaqMan Fast Advanced Assay. For each ISG, expression was normalized against the geometric mean of two housekeeping genes (18s rRNA and HPRT1) and calculated using the formula 2-ΔCt. Relative gene expression is reported as the normalized expression of each ISG divided by the median of normalized expression of the same ISG in unstimulated samples. The median relative expression of all six ISGs was used to calculate the IFN score for each sample.Results:There was no statistically significant difference in the normalized expression of any of the six ISGs in either the rhIFNα-stimulated or unstimulated samples derived from PAXgene or Tempus tubes. The greatest difference in mean normalized expression in both unstimulated and stimulated samples was observed for ISG15 (difference in mean normalized expression was 0.0034 and 0.11, respectively). Overall there was a strong correlation of the IFN score between PAXgene and Tempus tubes for both the unstimulated (R2 = 0.9117, p<0.0001) and and rhIFNα-stimulated samples (R2 = 0.8529, p=0.0001).Conclusion:Despite reported differences in gene expression patterns associated with samples collected in PAXgene versus Tempus tubes, our results demonstrate that 6-gene interferon scores ...

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Comparison of RT-qPCR and Nanostring in the measurement of blood interferon response for the diagnosis of type I interferonopathies

Cited by 71 publications

References 39 publications

Protein and DNA methylation-based scores as surrogate markers for interferon system activation in patients with primary Sjögren’s syndrome

Protein and DNA methylation-based scores as surrogate markers for interferon system activation in patients with primary Sjögren’s syndrome

The Nightmare Monitoring of JAKinhibs

Thu0511 comparison of Paxgene and Tempus Whole Blood Rna Collection and Isolation Systems for the Quantification of Type I Interferon-Stimulated Gene Expression

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