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Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development.

ABSTRACT: In preparation to create a clinical assay that predicts 1-year survival status of advanced heart failure (AdHF) patients before surgical/interventional therapies and to select the appropriate clinical assay platform for the future assay, we compared the properties of next generation sequencing (NGS) used in the gene discovery phase to the NanoString platform used in the clinical assay development phase. In 25 AdHF patients in a tertiary academic medical center from 2015 to 2016, PBMC samples were collected and aliquoted for NGS RNA whole transcriptome sequencing and compared to 770 genes represented on NanoString's PanCancer IO 360 Gene Expression research panel. Prior to statistical analysis, NanoString and NGS expression values were log transformed. We computed Pearson correlation coefficients for each sample, comparing gene expression values between NanoString and NGS across the set of matched genes and for each of the matched genes across the set of samples. Genes were grouped by average NGS expression, and the NanoString-NGS correlation for each group was computed. Out of 770 genes from the NanoString panel, 734 overlapped between both platforms and showed high intrasample correlation. Within an individual sample, there was an expression-level dependent correlation between both platforms. The low- vs. intermediate/high-expression groups showed NGS average correlation 0.21 vs. 0.58-0.68, respectively, and NanoString average correlation 0.07-0.34 vs. 0.59-0.70, respectively. NanoString demonstrated high reproducibility (R 2 > 0.99 for 100 ng input), sensitivity (probe counts between 100 and 500 detected and quantified), and robustness (similar gene signature scores across different RNA input concentrations, cartridges, and outcomes). Data from NGS and NanoString were highly correlated. These platforms play a meaningful, complementary role in the biomarker development process.

SUBMITTER: Bondar G

PROVIDER: S-EPMC6974694 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development.

Bondar Galyna G Xu Wenjie W Elashoff David D Li Xinmin X Faure-Kumar Emmanuelle E Bao Tra-Mi TM Grogan Tristan T Moose Jim J Deng Mario C MC

Journal of biological methods 20200103 1

In preparation to create a clinical assay that predicts 1-year survival status of advanced heart failure (AdHF) patients before surgical/interventional therapies and to select the appropriate clinical assay platform for the future assay, we compared the properties of next generation sequencing (NGS) used in the gene discovery phase to the NanoString platform used in the clinical assay development phase. In 25 AdHF patients in a tertiary academic medical center from 2015 to 2016, PBMC samples wer ...[more]

PMID: 31976350

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Project description:BackgroundSarcoidosis is a granulomatous systemic disease of unknown etiology. Mononuclear cells such as macrophages or lymphocytes in lung tissue and hilar or mediastinal lymph nodes have been recognized to play an essential role in granuloma formation in pulmonary sarcoidosis. Peripheral blood mononuclear cells (PBMCs) consist of several immunocompetent cells and have been shown to play a mechanistic role in the pathogenesis of sarcoidosis. However, the genetic modifications that occur in bulk PBMCs of sarcoidosis remain to be elucidated.PurposeThis study aimed to explore the pathobiological markers of sarcoidosis in PBMCs by comparing the transcriptional signature of PBMCs from patients with pulmonary sarcoidosis with those of healthy controls by RNA sequencing.MethodsPBMC samples were collected from subjects with pulmonary sarcoidosis with no steroid/immunosuppressant drugs (n = 8) and healthy controls (n = 11) from August 2020 to April 2021, and RNA sequencing was performed with the PBMC samples.ResultsPrincipal component analysis using RNA sequencing datasets comparing pulmonary sarcoidosis with healthy controls revealed that the two groups appeared to be differentiated, in which 270 differentially expressed genes were found in PBMCs between sarcoidosis and healthy controls. Enrichment analysis for gene ontology suggested that some biological processes related to the pathobiology of sarcoidosis, such as cellular response to interleukin (IL)-1 and IFN-γ, regulation of IL-6 production, IL-8 secretion, regulation of mononuclear cell migration, and response to lipopolysaccharide, were involved. Enrichment analysis of the KEGG pathway indicated the involvement of tumor necrosis factor (TNF), toll-like receptor signaling, IL-17 signaling pathways, phagosomes, and ribosomes. Most of the genes involved in TNF and IL-17 signaling pathways and phagosomes were upregulated, while most of the ribosome-related genes were downregulated.ConclusionThe present study demonstrated that bulk gene expression patterns in PBMCs were different between patients with pulmonary sarcoidosis and healthy controls. The changes in the gene expression pattern of PBMCs could reflect the existence of sarcoidosis lesions and influence granuloma formation in sarcoidosis. These new findings are important to strengthen our understanding of the etiology and pathobiology of sarcoidosis and indicate a potential therapeutic target for sarcoidosis.

Project description:Background: Sepsis, as a clinical emergency, usually causes multiorgan dysfunction and can lead to high mortality. Establishment of specific and sensitive biomarkers for early diagnosis is critical to identify patients who would benefit from targeted therapy. In this study, we investigated this syndrome by analyzing the transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with sepsis and identified sepsis-specific biomarkers. Methods: In this study, a total of 87 patients with sepsis and 40 healthy controls from a prospective multicenter cohort were enrolled. Samples from 44 subjects (24 patients with sepsis and 20 healthy controls) were sequenced and the remaining patients were included in the validation group. Using high-throughput sequencing, a gene expression profile of PBMCs from patients with sepsis was generated to elucidate the pathophysiology of sepsis and identify sepsis-specific biomarkers. Results: Principal component analysis (PCA) and unsupervised hierarchical cluster analysis showed that patients with sepsis separated from healthy controls. A total of 1639 differentially expressed genes (DEGs) were identified (|log2 fold change|>2, adjusted P value <0.05) between these two groups, with 1278 (78.0%) upregulated and 361 (22.0%) downregulated in patients with sepsis. Gene Ontology (GO) analysis of the upregulated DEGs identified 194 GO terms that were clustered into 27 groups, and analysis of the downregulated DEGs identified 20 GO terms that were clustered into 4 groups. Four unique genes were identified that could be predictive of patients with sepsis. External validation of the four genes using quantitative real-time polymerase chain reaction (qRT-PCR) was consistent with the results of mRNA sequencing, revealing their potential in sepsis diagnosis. Conclusions: The transcriptome characteristics of PBMCs, which were significantly altered in sepsis patients, provide new insights into sepsis pathogenesis. The four identified gene expression changes differentiated patients with sepsis from healthy subjects, which could serve as a convenient tool contributing to sepsis diagnosis.

Project description:BackgroundHeart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.MethodsWe collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP.ResultsResults indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.ConclusionGEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.

Dataset Information

Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development.

Publications

Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development.

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