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Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"
<p>Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing.</p> <p>The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab.</p> <p>This work is available in medRxiv and has been submitted</p>
3DNIV/3DNIV: A Novel Dual Non-Invasive Ventilator Continuous Positive Airway Pressure Non-Aerosolization Circuit for Emergency Use in the COVID-19 Pandemic
<p>The COVID19 pandemic is a public health emergency of unprecedented scale. The surge in clinical cases of patients with severe respiratory illness has overwhelmed the traditional capacity of healthcare systems worldwide. Continuous Positive Airway Pressure (CPAP) delivered through Non-Invasive Ventilation (NIV) has been shown to be useful in caring for patients with COVID19. In particular patients with early stage milder acute hypoxemic respiratory failure can benefit from NIV CPAP therapy, though there is an acknowledged risk of COVID19 aerosolization with traditional circuit use. Furthermore, given the surge in clinical care demand, there is an acute global shortage of ventilators, including NIV devices and therefore innovative methods are needed to increase NIV capacity and ameliorate infectious aerosolization. This work outlines an emergency use modified dual NIV CPAP Circuit that uses a 3D printed splitter designed to work with traditional international NIV CPAP tubing standards and a 3D printed respiratory face mask knuckle to allow for distal expiratory breath exhalation through a viral filter rather than through an open to air proximal valve, which is the traditional NIV CPAP configuration. We expect that this work will increase global NIV CPAP capacity and ameliorate aerosolization of COVID19 in patients undergoing therapy in an emergency scenario.</p>
Immuno-proteomic profiling reveals aberrant immune cell regulation in the airways of individuals with ongoing post-COVID-19 respiratory disease
<p><span><span><span><span><span><span><span><span><span><span><span>Some patients hospitalized with acute COVID-19 suffer respiratory symptoms that persist for many months. We delineated the immune-proteomic landscape in the airway and peripheral blood of healthy controls and post-COVID-19 patients 3 to 6 months after hospital discharge. Post-COVID-19 patients showed abnormal airway (but not plasma) proteomes, with elevated concentration of proteins associated with apoptosis, tissue repair and epithelial injury versus healthy individuals. Increased numbers of cytotoxic lymphocytes were observed in individuals with greater airway dysfunction, while increased B cell numbers and altered monocyte subsets were associated with more widespread lung abnormalities. 1 year follow-up of some post-COVID-19 patients indicated that these abnormalities resolved over time. In summary, COVID-19 causes a prolonged change to the airway immune landscape in those with persistent lung disease, with evidence of cell death and tissue repair linked to ongoing activation of cytotoxic T cells. </span></span></span></span></span></span></span></span></span></span></span></p>
The Upper-Airway Microbiome as a Biomarker of Asthma Exacerbations despite Inhaled Corticosteroid Treatment
<p>The response to inhaled corticosteroids (ICS) in asthma is affected by the interplay of several factors. Among these, the role of the upper-airway microbiome has been scarcely investigated. We aimed to evaluate the association between the salivary, pharyngeal, and nasal microbiome with asthma exacerbations despite ICS use. Samples from 250 asthma patients from the Genomics and Metagenomics of Asthma Severity (GEMAS) study treated with ICS were analyzed. Controls/cases were defined by the absence/presence of asthma exacerbations in the past six months despite being treated with ICS. The bacterial microbiota was profiled by sequencing the V3-V4 region of the 16S rRNA gene. Differences between groups were assessed by PERMANOVA and regression models adjusted for potential confounders. A false discovery rate (FDR) of 5% was used to correct for multiple comparisons. Classification models of asthma exacerbations despite ICS treatment were built with machine learning approaches based on clinical, genetic, and microbiome data. In nasal and saliva samples, cases had lower bacterial diversity (Richness, Shannon, and Faith indexes) than controls (0.007≤p≤0.037). Asthma exacerbations accounted for 8-9% of the interindividual variation of the salivary and nasal microbiomes (0.003≤p≤0.046). Three, four, and eleven bacterial genera from the salivary, pharyngeal, and nasal microbiomes were differentially abundant between groups (4.09x10<sup>-12</sup>≤FDR≤0.047). Integrating clinical, genetic, and microbiome data showed good discrimination for the development of asthma exacerbations despite ICS use (AUC<sub>training</sub>:0.82 and AUC<sub>validation</sub>:0.77). The diversity and composition of the upper-airway microbiome are associated with asthma exacerbations despite ICS treatment. The salivary microbiome has a potential application as a biomarker of asthma exacerbations despite ICS use.</p> <p>The summary results of the association between bacterial genera and asthma exacerbations despite ICS use are published in this repository. Analyses were conducted using the DESeq2 package adjusting for potential confounders (see more details in the manuscript). The submitted summary tables contains the following columns:</p> <ul> <li>Kingdom: Taxonomic classification at Kingdom level</li> <li>Phylum: Taxonomic classification at Phylum level</li> <li>Class: Taxonomic classification at Class level</li> <li>Order: Taxonomic classification at Order level</li> <li>Family: Taxonomic classification at Family level</li> <li>Genus: Taxonomic classification at Genus level</li> <li>log2FC: log<sub>2</sub>(fold-change). Positive values indicate higher abundance in cases and negative values in controls</li> <li>SE: standard error of log<sub>2</sub>(fold-change)</li> <li>P.Value: p-value of the regression model</li> <li>FDR: adjusted p-value by false discovery rate (Benjamin-Hochberg method)</li> </ul>
Dataset: JetBlue Airways Corporation (JBLU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset TrainBatch1 for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)
<p>Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)</p> <p>This is the TrainBatch1. </p> <p>We provide the casename correspondence information in the TrainBatch1_CaseInfo.csv file. It is convinient for researchers to locate the original cases in LIDC-IDRI with our provided full airway annotation for other research purposes.</p> <p>If you use this dataset in your research, you must cite the papers in the <strong>References </strong>below !!!</p>
Grandmaternal allergen sensitization reprograms epigenetic and airway responses to allergen in second generation offspring
<p><strong>SUPPLEMENTARY FIGURE LEGEND</strong></p> <p><strong>Supplemental Figure 1. Grandmaternal allergen sensitization during pregnancy regulates differentially methylated cytosines in airway epithelium of second-generation offspring at baseline and after allergen sensitization</strong>. Grandmaternal HDM exposure resulted in 1,390 DMCs in airway epithelium of F2 offspring at baseline (F0 HDM•F2 Base vs F0 Veh•F2 Base). F2 HDM sensitization alone resulted in 1,467 DMCs (F0Veh•F2Sens vs F0 Veh•F2 Base). In contrast, HDM sensitization in F2 mice from HDM-exposed founders resulted in 3,756 total DMCs (F0 HDM•F2 Sens vs F0 HDM•F2 Base). N=8 total for each group (n=4 males and n=4 females).</p> <p> </p> <p><strong>Supplemental Figure 2. Gene ontology analysis of DMCs in airway epithelium of second-generation offspring</strong>. Gene ontology analysis of DMCs in airway epithelium of F2 mice identified enriched biological processes (individual small circles), which were then grouped based on functional annotation using Cytoscape Enrichment Map (larger, labeled, black circles). Each small circle represents an individual biological process and size is proportional to number of involved genes, while connecting edges represent shared genes between individual biological processes. (A) Gene ontology analysis showing enriched biological processes due to grandmaternal HDM exposure in F2 offspring at baseline (F0 HDM•F2 Base versus F0 VEH•F2 Base). (B) Gene ontology analysis showing effects of HDM sensitization alone on enriched biological processes in F2 offspring from vehicle-exposed F0 mice (F0 Veh•F2 Sens vs F0 VEH•F2 Base). (C) Gene ontology analysis showing potentiating effects of F0 grandmaternal HDM exposure on enriched biological processes after HDM sensitization in F2 mice (F0 HDM•F2 Sens vs F0 HDM•F2 Base). N=8 total for each group (n=4 males and n=4 females).</p> <p> </p> <p><strong>Supplementary Figure 3. Differential methylation is linked to altered gene expression in airway epithelium of second-generation offspring. </strong>A subset of F2 airway epithelial genes identified during differentially-methylated pathway analyses were analyzed by real-time RT-PCR to assess for expression. Grandmaternal HDM exposure significantly increased Smad3 expression at baseline and potentiated its response after F2 HDM sensitization. Paxillin expression was also increased after HDM sensitization compared to non-sensitized F2 descendants from HDM exposed founders, although this difference did not meet statistical significance (<em>p</em> = 0.06). No differences were observed between Ets3, Cdh3, or Cacna1d expression between groups. *<em>p</em> < 0.05. Delta CT values were analyzed with two-way ANOVA and Tukey’s post-test. N=8 total for each group (n=4 males “open circles” and n=4 females “closed circles”), except for F0 HDM•F2 Base, n=5.</p> <p> </p> <p><strong>Supplementary Figure 4. Grandmaternal allergen exposure during pregnancy regulates vagal ganglia cytosine methylation in second-generation offspring at baseline and after allergen sensitization</strong>. Grandmaternal HDM sensitization during pregnancy resulted in 901 DMCs in F2 mice at baseline (F0 HDM•F2 Base vs F0 Veh•F2 Base). F2 HDM sensitization alone resulted in 1,467 DMCs (F0Veh•F2Sens vs F0 Veh•F2 Base). In contrast, HDM sensitization in F2 mice from HDM-sensitized F0 grandmaternal founders resulted in 1,787 total DMCs (F0 HDM•F2 Sens vs F0 HDM•F2 Base). N=8 total for each group (n=4 males and n=4 females).</p> <p> </p> <p><strong>Supplementary Figure 5. Grandmaternal allergen sensitization during pregnancy modifies epigenetic signatures in second-generation offspring in a tissue-specific manner. </strong>Only rarely were differentially methylated genes shared between vagal ganglia neurons and airway epithelium in all groups. Specifically, neurons and epithelium in non-sensitized F2 mice from grandmaternal HDM-sensitized F0 founders shared only 15 genes in common (light blue circles). Neurons and epithelium in HDM-sensitized F2 mice from Vehicle-exposed grandmaternal founders shared only 9 genes in common (grey circles). Neurons and epithelium in HDM-sensitized F2 mice from HDM-sensitized grandmaternal F0 founders shared only 41 genes in common. N=8 total for each group (n=4 males and n=4 females).</p> <p> </p> <p><strong>Supplementary Figure 6. Gene ontology analysis of biological process enrichment in vagal ganglia from second-generation offspring</strong>. Gene ontology analysis of DMCs in vagal ganglia of F2 mice identified enriched biological processes (individual small circles), which were then grouped based on functional annotation using Cytoscape Enrichment Map (larger, labeled, black circles). Each small circle represents an individual biological process and size is proportional to number of involved genes, while connecting edges represent shared genes between individual biological processes. (A) Gene ontology analysis showing effects of HDM sensitization on biological process enrichment in F2 offspring from vehicle-exposed F0 mice (F0 Veh•F2 Sens vs F0 VEH•F2 Base). (B) Gene ontology analysis showing potentiating effects of F0 grandmaternal HDM sensitization on biological process enrichment in F2 mice after HDM sensitization (F0 HDM•F2 Sens vs F0 HDM•F2 Base). N=8 total for each group (n=4 males and n=4 females).</p> <p> </p> <p><strong>Supplemental Figure 7. Quantification of airway sensory nerve density using 3D confocal microscopy.</strong> Airway epithelial substance P-positive sensory nerves were analyzed in whole mount specimens using immunofluorescence and 3-dimensional confocal microscopy. Total nerve length and the number of branch points were quantified from nerve models generated based on substance P-positive voxels. Top) Representative images of substance P-expressing nerves (purple, top left) and nerve modeling (white filaments, top right). Bottom) Nerve length (left) and the number of branch points (right) were similar between untreated F2 mice from HDM exposed grandmothers and vehicle exposed grandmothers. N=6 both groups (n=3 males “open circles” and n=3 females “closed circles”).</p> <p> </p> <p><strong>SUPPLEMENTAL TABLES</strong></p> <p><strong>Supplemental Table S1.</strong> Pathways identified by DMC analysis that were not present on DMR analysis in airway epithelium of second-generation F2 mice. All DMR-associated pathways were also identified by DMC analysis, as indicated in Table 1 in main text.</p> <p> </p> <p><strong>Supplemental Table S2. </strong>Over-represented transcription factor binding sites in DMRs of airway epithelium and vagal ganglia from F2 mice before and after allergen sensitization.</p> <p> </p> <p><strong>Supplemental Table S3. </strong>Pathway identified by DMC analysis in vagal ganglia of second-generation F2 mice. All DMR-associated pathways were also identified by DMC analysis, as indicated in Table 3 in main text.</p> <p> </p> <p><strong>SUPPLEMENTARY DATA FILES</strong></p> <p><strong>Lebold&Cook_DMC_Gene List.xlsx. </strong></p> <p><strong>Lebold&Cook_DMR Gene List.xlsx.</strong></p> <p>Differentially methylated cytosines (DMC) and regions (DMR) source data files.</p>
Assessment of Continuous Positive Airway Pressure Therapy in OSA and ILD
ClinicalTrials.gov study NCT03901534. IPD Sharing: YES. Countries: 1. Publications: 3.
Novel by Upper Airway Respiratory Muscle Training to Treat OSA in Chronic SCI
ClinicalTrials.gov study NCT03664765. IPD Sharing: YES. Countries: 1. Publications: 0.
The Effect of Dupilumab on Lung Inflammation and Related Changes in Airway Volumes Detectable by Functional Respiratory Imaging in Patients With Moderate-severe Asthma
ClinicalTrials.gov study NCT04400318. IPD Sharing: YES. Countries: 13. Publications: 2.
Aggravated Airway Inflammation: Research on Biological Treatment (Mepolizumab)
ClinicalTrials.gov study NCT04823585. IPD Sharing: NO. Countries: 1. Publications: 1.
Sleep Apnea in TIA/Stroke: Reducing Cardiovascular Risk With Positive Airway Pressure
ClinicalTrials.gov study NCT01446913. IPD Sharing: YES. Countries: 1. Publications: 2.
Immuno-proteomic profiling reveals aberrant immune cell regulation in the airways of individuals with ongoing post-COVID-19 respiratory disease
Open the record for dataset details and reuse information.
Data from: Live imaging of SARS-CoV-2 infected airway epithelium cultures
Open the record for dataset details and reuse information.
Dataset for publication "Spectral graph theory efficiently characterises ventilation heterogeneity in airway networks".
<p>This directory contains files for the tree networks used in the manuscript "Spectral graph theory efficiently characterises ventilation heterogeneity in airway networks" by C Whitfield et al. Publication details to follow.</p> <p>Each folder contains the network as labelled in the paper in two formats:<br> - .vtk format<br> - plain text format where it is split into 3 files with suffixes .branches .nodes and .termnodes<br> - The .nodes file has 4 columns, the first is the node index and the other 3 are the (x,y,z) node coordinates in mm<br> - The .branches file has 4 columns (ignoring extra info in following columns), which are the edge index, node in index, node out index and radius (mm)<br> - The .termnodes file contains a list of node indices corresponding to terminal nodes of the tree.</p> <p>Each folder also contains the CT centerline data (identified by the suffix _CT) in .vtk format.</p>
Dataset for airway managed by emergency physicians or anaesthesiologists in trauma patients: A retrospective cohort analysis of outcomes
<p>This data is generated for a registry-based unmatched cohort study. We identified trauma patients requiring intubation during resuscitation in the emergency department of Queen Elizabeth Hospital, Hong Kong. Then extracted relevant trauma related parameters and outcome data from the trauma registry of the hospital and patients’ health records.<br> The hospital trauma registry captures any patients who met trauma resuscitation team activation criteria, who were triaged as critical or emergency in the ED, who died (excluding death prior to arrival to ED) and were admitted to intensive care units (ICU)/high dependency units (HDU). Injury and outcome data were prospectively collected and entered into the registry.</p>
The airway microbiota and exacerbations of COPD
<p><span><span><span><span><span><span><span><span><span><span><span>Aim: To investigate whether the compositionality of the lower airway microbiota predicts later exacerbation risk in persons with COPD in a cohort study.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Materials and methods: We collected lower airways microbiota samples by bronchoalveolar lavage and protected specimen brushes, and oral wash samples from 122 participants with COPD. Bacterial DNA was extracted from all samples, before we sequenced the V3-V4 region of the 16S RNA gene. The frequency of moderate and severe COPD exacerbations was surveyed in telephone interviews and in a follow-up visit. Compositional taxonomy and alpha and beta diversity were compared between participants with and without later exacerbations. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Results: The four most abundant phyla were <i>Firmicutes</i>, <i>Bacteroidetes</i>, <i>Proteobacteria</i> and <i>Fusobacteria</i> in both groups, and the four most abundant genera were <i>Streptococcus</i>, <i>Veillonella</i>, <i>Prevotella</i> and <i>Gemella</i>. The relative abundances of different taxa showed a large variation between samples and individuals, and no statistically significant difference of either compositional taxonomy, or alpha or beta diversity could be found between participants with and without COPD exacerbations within follow-up. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Conclusion: The findings from the current study, indicate that individual differences in the lower airway microbiota in persons with COPD far outweigh group differences between frequent and non-frequent COPD exacerbators, and that the compositionality of the microbiota is so complex as to present large challenges for use as a biomarker of later exacerbations. </span></span></span></span></span></span></span></span></span></span></span></p>
Dataset for "Developmental progression of the nasopharyngeal microbiota during childhood and association with the lower airway microbiota"
<p>Data files and R Markdown Notebooks used in "Developmental progression of the nasopharyngeal microbiota during childhood and association with the lower airway microbiota".</p><p>AOJ16S_decontam.Rmd performs decontamination of low abundance sequencing data.</p><p>AOJ16S_exploratory_analysis.Rmd analyzes and produces figures used in the manuscript.</p>
scRNA-seq dataset "A novel in vitro tubular model to recapitulate features of distal airways: the bronchioid"
<p>We provide a .Rds file of an annotated Seurat Object of scRNA-seq data of two bronchioid models derived from distinct donors after 21days of culture using 10x genomics 3' v3 chemistry. Raw data was processed using CellRanger v7.1.0. Cells were filtered based on detected UMIs (>2000) and fraction of mitochondrial counts (<10%).<br>Metadata annotations contain:<br>- Patient -> patient information for every cell (patient1 or patient2)<br>- nCount_RNA -> UMI counts per cell<br>- nFeature_RNA -> genes detected per cell<br>- percent.mt -> mitochondrial count fraction per cell<br>- seurat_clusters -> unsupervised clustering results using Louvain algorithm with resolution = 0.5<br>- Manual.Annotation -> Cell types annotated based on marker gene expression<br>- Celltypist.prediction -> Cell types predicted with CellTypist Python package<br>- Celltypist.prediction.ari -> Cell types predicted with CellTypist Python package, with harmonized names for comparison with manual annotation</p>
Deprez et al. - 2020 - A Single-Cell Atlas of the Human Healthy Airways
<p>AnnData object of the single-cell atlas</p> <p> </p> <p>Rationale: The respiratory tract constitutes an elaborate line of defense that is based on a unique cellular ecosystem. Objectives: We aimed to investigate cell population distributions and transcriptional changes along the airways by using single-cell RNA profiling. Methods: We have explored the cellular heterogeneity of the human airway epithelium in 10 healthy living volunteers by single-cell RNA profiling. A total of 77,969 cells were collected at 35 distinct locations, from the nose to the 12th division of the airway tree. Measurements and Main Results: The resulting atlas is composed of a high percentage of epithelial cells (89.1%) but also immune (6.2%) and stromal (4.7%) cells with distinct cellular proportions in different regions of the airways. It reveals differential gene expression between identical cell types (suprabasal, secretory, and multiciliated cells) from the nose (MUC4, PI3, SIX3) and tracheobronchial (SCGB1A1, TFF3) airways. By contrast, cell-type–specific gene expression is stable across all tracheobronchial samples. Our atlas improves the description of ionocytes, pulmonary neuroendocrine cells, and brush cells and identifies a related population of NREPpositive cells. We also report the association of KRT13 with dividing cells that are reminiscent of previously described mouse “hillock” cells and with squamous cells expressing SCEL and SPRR1A/B. Conclusions: Robust characterization of a single-cell cohort in healthy airways establishes a valuable resource for future investigations. The precise description of the continuum existing from the nasal epithelium to successive divisions of the airways and the stable gene expression profile of these regions better defines conditions under which relevant tracheobronchial proxies of human respiratory diseases can be developed. Keywords: single-cell RNAseq; epithelium; nose; trachea; bronchus</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.