Impact of RNA integrity and blood sample storage conditions on the gene expression analysis

Shen, Yanting; Li, Rui; Tian, Fei; Chen, Zhenzhu; Lü, Na; Bai, Ya Mei; Ge, Qinyu; Zhang, Lu

doi:10.2147/ott.s158868

Cited by 41 publications

(36 citation statements)

References 17 publications

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“…Next-generation sequencing is typically performed on high quality RNA (RIN > 7), when possible [ 28 ]. However, clinical samples, particularly human neutrophils that contain high levels of endonucleases as part of the host defense response to bacteria, rarely produce RNA without any degree of degradation [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

et al. 2020

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Background Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. Methods Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. Results Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. Conclusions We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

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Section: Methodsmentioning

confidence: 99%

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

et al. 2020

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“…Next-generation sequencing is typically performed on high quality RNA (RIN>7), when possible. 28 However, clinical samples, particularly human neutrophils that contain high levels of endonucleases as part of the host defense response to bacteria, rarely produce RNA without any degree of degradation. 29 Between…”

Section: Differential Expression Analysis and Data Explorationmentioning

confidence: 99%

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Poppenberg

Tutino

Li³

et al. 2020

Preprint

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Background: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. Methods: Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n=94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n=40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. Results: Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC)=0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. Conclusions: We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

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“…In general, it is suggested that RNAs with RINs over 7 are preferred for downstream applications such as NGS. Against this general guideline, some publications have stated that RNAs with an RIN around 5 can also be used without causing any signi cant bias over high-quality RNA [13]. With reference to these thresholds of RIN 5-7, RNAs were selected for RNA sequencing using the Ion AmpliSeq platform (Thermo Fisher Scienti c).…”

Section: Rna Sequencing and Analysismentioning

confidence: 99%

Evaluating Alteration of Blood Transcriptome Affected by Storage Conditions Using RNA Sequencing

Koh¹,

Yu²,

Kim³

et al. 2020

Preprint

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BackgroundWhole blood is one of the most widely utilized human samples in biological research and is useful for analysing the mechanisms of diverse bio-molecular phenomena. However, owing to its fluidic properties, whole blood is relatively unstable in the frozen state compared to other biopsy samples. Because RNA is structurally unstable, sample damage can severely affect RNA quality, thereby reducing its usability. This study aimed to assess the quality of RNA prepared from blood stored at different temperatures and times prior to freezing, as well as the effect of freezer storage time. ResultsThe quality of the RNA derived from different blood samples was assessed by determining the RNA integrity number and RNA sequencing to identify genes (|fold-change (FC)| > 1.5, p-value < 0.05, false discovery rate (FDR) < 0.05) that were differentially expressed between the differently prepared RNA samples. We found that improper sample handling critically influenced both RNA quality and gene expression patterns. In particular, storing blood at room temperature over 12 h before freezing led to RNA degradation. Differential gene expression analysis revealed that expression of the CXCR1 gene was substantially reduced when using impaired RNA. ConclusionsThis study emphasizes the importance of proper sample management for obtaining reliable downstream application outcomes and suggests the CXCR1 gene as a candidate screening marker for RNA damage caused by improper sample handling.

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Impact of RNA integrity and blood sample storage conditions on the gene expression analysis

Cited by 41 publications

References 17 publications

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Evaluating Alteration of Blood Transcriptome Affected by Storage Conditions Using RNA Sequencing

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