Project description:Detection of SARS-CoV-2 using RT–PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT–PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.
Project description:Nasal swab specimens were collected from children who presented to the emergency department with an acute exacerbation of asthma or wheeze. Samples were also collected from control subjects. Convalescent/quiescent samples were collected from children who were followed-up at least 6 weeks after an acute exacerbation of asthma or wheeze. Gene expression was profiled on microarrays.
Project description:To elucidate key pathways in the host transcriptome of patients infected with SARS-CoV-2, we used RNA sequencing (RNA Seq) to analyze nasopharyngeal (NP) swab and whole blood (WB) samples from 333 COVID-19 patients and controls, including patients with other viral and bacterial infections. Analyses of differentially expressed genes (DEGs) and pathways was performed relative to other infections (e.g. influenza, other seasonal coronaviruses, bacterial sepsis) in both NP swabs and WB. Comparative COVID-19 host responses between NP swabs and WB were examined. Both hospitalized patients and outpatients exhibited upregulation of interferon-associated pathways, although heightened and more robust inflammatory and immune responses were observed in hospitalized patients with more clinically severe disease. A two-layer machine learning-based classifier, run on an independent test set of NP swab samples, was able to discriminate between COVID-19 and non-COVID-19 infectious or non-infectious acute respiratory illness using complete (>1,000 genes), medium (<100) and small (<20) gene biomarker panels with 85.1%-86.5% accuracy, respectively. These findings demonstrate that SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for differential diagnosis of COVID-19 disease.
Project description:Detection of SARS-CoV-2 using RT-PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT-PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.
Project description:The nasal and bronchial epithelium are unified parts of the respiratory tract that are affected in the monogenic disorder cystic fibrosis (CF). Recent studies have uncovered that nasal and bronchial tissues exhibit intrinsic variability, including differences in mucociliary cell composition and expression of unique transcriptional regulatory proteins which relate to germ layer origin. In the present study, we explored transcriptomic differences between cultured nasal and bronchial epithelial cells from people with CF. Comparison of air-liquid interface-differentiated epithelial cells from subjects with CF revealed distinct mucociliary differentiation states of nasal and bronchial cultures. Moreover, using RNA sequencing we identified cell type-specific signature transcription factors in differentiated nasal and bronchial epithelial cells.
Project description:Assessment of host gene expression is an emerging tool for the diagnosis of human infections. We compared nasal and blood samples for evaluation of the host transcriptomic response in children with acute respiratory syncytial virus (RSV), symptomatic and asymptomatic picornavirus (PV) infection, and virus-negative asymptomatic controls (Ctrls). RNA was extracted from nasal and blood samples and analyzed by microarray. Despite generally lower quality of nasal RNA, the number of genes detected in each sample type was equivalent. Nasal gene expression signal derived mainly from epithelial cells but also included a leukocyte contribution that was higher in samples from symptomatic children. The number of genes with increased expression in virus-infected children was comparable in nasal and blood samples, while nasal samples also had large numbers of genes with decreased expression, including many genes associated with ciliary function and assembly. Compared to symptomatic children, those with asymptomatic PV had fewer genes with increased or decreased expression in both sample types. Genes with increased expression in comparisons of symptomatic children versus Ctrls included genes associated with components of innate immunity and apoptosis. Children with RSV but not PV also had increased expression of genes related to the cell cycle. Using nested leave-one-pair-out cross-validation and supervised principal components analysis, we defined sets of genes whose expression patterns accurately classified subjects, with high area-under-the-curve values in receiver operating characteristic analysis. Our results support use of nasal samples to augment pathogen-based tests to diagnose viral respiratory infection.