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Datasets from "Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data"
<p>A collection of datasets on miRNA and lung cancer used in</p> <p>Berg, O.F.B.: Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data.<br> NTNU Open (2022)</p> <p> </p> <p>The datasets in this collection are processed and normalized from available raw datasets. The processing code that was used can be found on <a href="https://github.com/OleFredrik1/masterthesis">https://github.com/OleFredrik1/masterthesis</a>. The raw datasets are:</p> <p><strong>Asakura2020:</strong></p> <p>Asakura, K., Kadota, T., Matsuzaki, J., Yoshida, Y., Yamamoto, Y., Nakagawa, K., Takizawa, S., Aoki, Y., Nakamura, E., Miura, J., Sakamoto, H., Kato, K., Watanabe, S.-i., and Ochiya, T. (2020). A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy. <em>Communications Biology</em>, 3(1):1–9.</p> <p>Accession ID: GSE137140</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a></p> <p> </p> <p><strong>Bianchi2011:</strong></p> <p>Bianchi, F., Nicassio, F., Marzi, M., Belloni, E., Dall’Olio, V., Bernard, L., Pelosi, G., Maisonneuve, P., Veronesi, G., and Di Fiore, P. P. (2011). A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. <em>EMBO Molecular Medicine</em>, 3(8):495–503.</p> <p>Link: <a href="https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls">https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls</a></p> <p> </p> <p><strong>Chen2019:</strong></p> <p>Accession ID: GSE71661</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661</a></p> <p> </p> <p><strong>Duan2021:</strong></p> <p>Duan, X., Qiao, S., Li, D., Li, S., Zheng, Z., Wang, Q., and Zhu, X. (2021). Circulating miRNAs in Serum as Biomarkers for Early Diagnosis of Non-small Cell Lung Cancer. <em>Frontiers in Genetics</em>, 12:987.</p> <p>Accession ID: GSE137140</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a></p> <p> </p> <p><strong>Fehlmann2020:</strong></p> <p>Fehlmann, T., Kahraman, M., Ludwig, N., Backes, C., Galata, V., Keller, V., Geffers, L., Mercaldo, N., Hornung, D., Weis, T., Kayvanpour, E., Abu-Halima, M., Deuschle, C., Schulte, C., Suenkel, U., von Thaler, A.-K., Maetzler, W., Herr, C., Fähndrich, S., Vogelmeier, C., Guimaraes, P., Hecksteden, A., Meyer, T., Metzger, F., Diener, C., Deutscher, S., Abdul-Khaliq, H., Stehle, I., Haeusler, S., Meiser, A., Groesdonk, H. V., Volk, T., Lenhof, H.-P., Katus, H., Balling, R., Meder, B., Kruger, R., Huwer, H., Bals, R., Meese, E., and Keller, A. (2020). Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients. <em>JAMA oncology</em>, 6(5):714–723.</p> <p>Accession ID: E-MTAB-8026</p> <p>Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/</a></p> <p> </p> <p><strong>Halvorsen2016:</strong></p> <p>Halvorsen, A. R., Bjaanæs, M., LeBlanc, M., Holm, A. M., Bolstad, N., Rubio, L., Peñalver, J. C., Cervera, J., Mojarrieta, J. C., López-Guerrero, J. A., Brustugun, O. T., and Helland, Å. (2016). A unique set of 6 circulating microRNAs for early detection of non-small cell lung cancer. <em>Oncotarget</em>, 7(24):37250–37259.</p> <p>Accession ID: GSE70080</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080</a></p> <p> </p> <p><strong>Jin2017:</strong></p> <p>Jin, X., Chen, Y., Chen, H., Fei, S., Chen, D., Cai, X., Liu, L., Lin, B., Su, H., Zhao, L., Su, M., Pan, H., Shen, L., Xie, D., and Xie, C. (2017). Evaluation of Tumor-Derived Exosomal miRNA as Potential Diagnostic Biomarkers for Early-Stage Non–Small Cell Lung Cancer Using Next-Generation Sequencing. <em>Clinical Cancer Research</em>, 23(17):5311–5319.</p> <p>Link: <a href="https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as">https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as</a> (table s1)</p> <p> </p> <p><strong>Keller2009:</strong></p> <p>Keller, A., Leidinger, P., Borries, A., Wendschlag, A., Wucherpfennig, F., Scheffler, M., Huwer, H., Lenhof, H.-P., and Meese, E. (2009). miRNAs in lung cancer - Studying complex fingerprints in patient’s blood cells by microarray experiments. <em>BMC Cancer</em>, 9(1):353.</p> <p>Accession ID: GSE17681</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681</a></p> <p> </p> <p><strong>Keller2014:</strong></p> <p>Keller, A., Leidinger, P., Vogel, B., Backes, C., ElSharawy, A., Galata, V., Mueller, S. C., Marquart, S., Schrauder, M. G., Strick, R., Bauer, A., Wischhusen, J., Beier, M., Kohlhaas, J., Katus, H. A., Hoheisel, J., Franke, A., Meder, B., and Meese, E. (2014). miRNAs can be generally associated with human pathologies as exemplified for miR-144*. <em>BMC Medicine</em>, 12(1):224.</p> <p>Accession ID: GSE61741</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741</a></p> <p> </p> <p><strong>Keller2020:</strong></p> <p>Keller, A., Fehlmann, T., Backes, C., Kern, F., Gislefoss, R., Langseth, H., Rounge, T. B., Ludwig, N., and Meese, E. (2020). Competitive learning suggests circulating miRNA profiles for cancers decades prior to diagnosis. <em>RNA Biology</em>, 17(10):1416–1426.</p> <p>Link: <a href="https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945">https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945</a> (Supplemental Table 9)</p> <p> </p> <p><strong>Kryczka2021:</strong></p> <p>Kryczka, J., Migdalska-Sęk, M., Kordiak, J., Kiszałkiewicz, J. M., PastuszakLewandoska, D., Antczak, A., and Brzeziańska-Lasota, E. (2021). Serum Extracellular Vesicle-Derived miRNAs in Patients with Non-Small Cell Lung Cancer—Search for Non-Invasive Diagnostic Biomarkers. <em>Diagnostics</em>, 11(3):425.</p> <p>Link: <a href="https://www.mdpi.com/2075-4418/11/3/425/s1">https://www.mdpi.com/2075-4418/11/3/425/s1</a></p> <p> </p> <p><strong>Leidinger2011:</strong></p> <p>Leidinger, P., Keller, A., Borries, A., Huwer, H., Rohling, M., Huebers, J., Lenhof, H.-P., and Meese, E. (2011). Specific peripheral miRNA profiles for distinguishing lung cancer from COPD. <em>Lung Cancer</em>, 74(1):41–47.</p> <p>Accession ID: GSE24709</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709</a></p> <p> </p> <p><strong>Leidinger2014:</strong></p> <p>Leidinger, P., Backes, C., Dahmke, I. N., Galata, V., Huwer, H., Stehle, I., Bals, R., Keller, A., and Meese, E. (2014). What makes a blood cell based miRNA expression pattern disease specific? - A miRNome analysis of blood cell subsets in lung cancer patients and healthy controls. <em>Oncotarget</em>, 5(19):9484–9497.</p> <p>Accession ID: GSE55993</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993</a></p> <p> </p> <p><strong>Leidinger2016:</strong></p> <p>Leidinger, P., Brefort, T., Backes, C., Krapp, M., Galata, V., Beier, M., Kohlhaas, J., Huwer, H., Meese, E., and Keller, A. (2016). High-throughput qRT-PCR validation of blood microRNAs in non-small cell lung cancer. <em>Oncotarget</em>, 7(4):4611–4623.</p> <p>Link: <a href="https://www.oncotarget.com/article/6566/text/">https://www.oncotarget.com/article/6566/text/</a> (supplementary files)</p> <p> </p> <p><strong>Li2017:</strong></p> <p>Li, L.-L., Qu, L.-L., Fu, H.-J., Zheng, X.-F., Tang, C.-H., Li, X.-Y., Chen, J., Wang, W.-X., Yang, S.-X., Wang, L., Zhao, G.-H., Lv, P.-P., Zhang, M., Lei, Y.-Y., Qin, H.-F., Wang, H., Gao, H.-J., and Liu, X.-Q. (2017). Circulating microRNAs as novel biomarkers of ALK-positive non-small cell lung cancer and predictors of response to crizotinib therapy. <em>Oncotarget</em>, 8(28):45399–45414.</p> <p>Accession ID: GSE94536</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536</a></p> <p> </p> <p><strong>Marzi2016:</strong></p> <p>Marzi, M. J., Montani, F., Carletti, R. M., Dezi, F., Dama, E., Bonizzi, G., Sandri, M. T., Rampinelli, C., Bellomi, M., Maisonneuve, P., Spaggiari, L., Veronesi, G., Bianchi, F., Di Fiore, P. P., and Nicassio, F. (2016). Optimization and Standardization of Circulating MicroRNA Detection for Clinical Application: The miR-Test Case. <em>Clinical Chemistry</em>, 62(5):743–754</p> <p>Accession ID: GSE76462</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462</a></p> <p> </p> <p><strong>Nigita2018:</strong></p> <p>Nigita, G., Distefano, R., Veneziano, D., Romano, G., Rahman, M., Wang, K., Pass, H., Croce, C. M., Acunzo, M., and Nana-Sinkam, P. (2018). Tissue and exosomal miRNA editing in Non-Small Cell Lung Cancer. <em>Scientific Reports</em>, 8(1):10222.</p> <p>Accession ID: GSE114711</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711</a></p> <p> </p> <p><strong>Patnaik2012:</strong></p> <p>Patnaik, S. K., Yendamuri, S., Kannisto, E., Kucharczuk, J. C., Singhal, S., and Vachani, A. (2012). MicroRNA Expression Profiles of Whole Blood in Lung Adenocarcinoma. <em>PLOS ONE</em>, 7(9):e46045.</p> <p>Accession ID: GSE27486</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486</a></p> <p> </p> <p><strong>Patnaik2017:</strong></p> <p>Patnaik, S. K., Kannisto, E. D., Mallick, R., Vachani, A., and Yendamuri, S. (2017). Whole blood microRNA expression may not be useful for screening non-small cell lung cancer. <em>PLOS ONE</em>, 12(7):e0181926.</p> <p>Accession ID: GSE40738</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738</a></p> <p> </p> <p><strong>Qu2017:</strong></p> <p>Qu, L., Li, L., Zheng, X., Fu, H., Tang, C., Qin, H., Li, X., Wang, H., Li, J., Wang, W., Yang, S., Wang, L., Zhao, G., Lv, P., Lei, Y., Zhang, M., Gao, H., Song, S., and Liu, X. (2017). Circulating plasma microRNAs as potential markers to identify EGFR mutation status and to monitor epidermal growth factor receptor-tyrosine kinase inhibitor treatment in patients with advanced non-small cell lung cancer. <em>Oncotarget</em>, 8(28):45807–45824.</p> <p>Accession ID: GSE93300</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300</a></p> <p> </p> <p><strong>Reis2020:</strong></p> <p>Reis, P. P., Drigo, S. A., Carvalho, R. F., Lopez Lapa, R. M., Felix, T. F., Patel, D., Cheng, D., Pintilie, M., Liu, G., and Tsao, M.-S. (2020). Circulating miR-16-5p, miR-92a-3p, and miR-451a in Plasma from Lung Cancer Patients: Potential Application in Early Detection and a Regulatory Role in Tumorigenesis Pathways. <em>Cancers</em>, 12(8):2071.</p> <p>Accession ID: GSE152702</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702</a></p> <p> </p> <p><strong>Wozniak2015:</strong></p> <p>Wozniak, M. B., Scelo, G., Muller, D. C., Mukeria, A., Zaridze, D., and Brennan, P. (2015). Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer. <em>PLOS ONE</em>, 10(5):e0125026.</p> <p>Accession ID: GSE64591</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591</a></p> <p> </p> <p><strong>Yao2019:</strong></p> <p>Yao, B., Qu, S., Hu, R., Gao, W., Jin, S., Liu, M., and Zhao, Q. (2019). A panel of miRNAs derived from plasma extracellular vesicles as novel diagnostic biomarkers of lung adenocarcinoma. <em>FEBS Open Bio</em>, 9(12):2149–2158.</p> <p>Accession ID: GSE111803</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803</a></p> <p> </p> <p><strong>Zaporozhchenko2018:</strong></p> <p>Zaporozhchenko, I. A., Morozkin, E. S., Ponomaryova, A. A., Rykova, E. Y., Cherdyntseva, N. V., Zheravin, A. A., Pashkovskaya, O. A., Pokushalov, E. A., Vlassov, V. V., and Laktionov, P. P. (2018). Profiling of 179 miRNA Expression in Blood Plasma of Lung Cancer Patients and Cancer-Free Individuals. <em>Scientific Reports</em>, 8(1):6348.</p> <p>Accession ID: E-MTAB-6304</p> <p>Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/</a></p> <p> </p> <p>The Abdollahi2019 and Boeri2011 datasets are not included as I recived them by email and I did not recieve conformation that they were OK with me publishing the datasets.</p>
Intratumoural heterogeneity and immune modulation in lung adenocarcinoma of female smokers and never smokers
<p>Count matrix and associated meta data for single nucleus transcriptomics of healthy and tumour lung tissue from lung adenocarcinoma patients. Cohort includes young and elderly, female and male, smokers and never smokers.</p>
DATA: Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs
<p>This data set includes all the raw data collected for the following article: "Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs".</p>
Mice MR Images (lungs and pulmonary metastases)
<p>55 mice were imaged after injection on a 7T Bruker BioSpec system equipped with a gradient coil of 660 mT/m maximum strength and 110 μs rise time. 27 mice with Crispr/Cas9 il34 KO gene and 28 controls. Animals were imaged every week: from day 6 to day 32 post-implantation for control mice and from day 8 till their condition deteriorated (up to day 141 at most) for il34 mice. Two additional healthy mice were scanned 3 times, two times without repositioning and one time after waking them up in order to evaluate the reproducibility of the lungs’ segmentations.</p> <p>The balanced Steady State Free Precession (bSSFP) sequence was chosen, as it has previously been shown that high tumor contrast can be obtained in the brain and in the liver (<a href="https://doi.org/10.1002/jmri.21449">https://doi.org/10.1002/jmri.21449</a> ; <a href="https://doi.org/10.1002/jmri.22593">https://doi.org/10.1002/jmri.22593</a> ; <a href="https://doi.org/10.1002/jmri.24688">https://doi.org/10.1002/jmri.24688</a>). Combined with the Self-Gating (SG) method, it enables to delete echoes affected by motion and consequently obtain abdominal images without motion artifact (<a href="https://doi.org/10.1002/jmri.24688">https://doi.org/10.1002/jmri.24688</a>). </p> <p>Corresponding masks were manually drawn around the lungs on every slice of 185 3D images; masks were manually drawn around the pulmonary metastases on every slice of 62 3D images containing metastases. Both tasks were performed by two different investigators.</p> <p>Link to the code : https://github.com/cbib/DeepMeta</p>
Immunohistochemistry of Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control
<p>(A) Architecture of macaque TB lung granuloma, where lymphocytes and macrophages are present in distinct regions. Immunohistochemistry and confocal microscopy were performed on a granuloma from an animal at 11 weeks post-Mtb infection to visualize localization of CD11c+ macrophages (cyan), CD3+ T cells (yellow), and CD20+ B cells (magenta)</p> <p>(B) Detection of mast cells in a 10-week NHP granuloma using immunohistochemistry, staining for tryptase (green) and c-kit (CD117)(red).</p> <p>(C) Detection of mast cells in a human lung granuloma. Hematoxylin and eosin stain and immunohistochemistry with multinucleated giant cells (stars, (top left) and c-kit (CD117) staining (indicated by arrows, top and bottom right).</p>
Comparative genomics of human distal lung Streptococci
<p><strong>Comparative genomics of human lung streptococcal isolates</strong></p> <p>1. All analysis pipelines and scripts are on the GitHub page of Slipa Kanungo: <a href="https://github.com/slipa17/Whole-genome-sequencing-and-comparative-genomics-of-human-lung-streptococcal-isolates">https://github.com/slipa17/Whole-genome-sequencing-and-comparative-genomics-of-human-lung-streptococcal-isolates</a></p> <p>2. Additional analysis pipelines (especially Dataset S12) are on the GitHub page of Garance Sarton-Lohéac: <a href="https://github.com/gsartonl/Publication_Sarton-Loheac_2022">https://github.com/gsartonl/Publication_Sarton-Loheac_2022</a></p> <p>3. All raw data were uploaded to NCBI SRA BioProject <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1001255">PRJNA1001255</a></p> <p><strong>Supplementary Table bundle: for peer review purposes</strong></p> <p><strong>Supplementary Datasets</strong></p> <ul> <li><strong>Dataset S1_Lung_Streptococcus_genomes_metaQUAST: </strong>MetaQUAST (Quality Assessment Tool for Metagenome Assemblies) output including HTML and PDF reports, summary statistics including total contigs, assembly size, and N50. Coverage analysis assesses how well reference genomes are represented, contig length distribution plots visualize contig length ranges, mis-assembly analysis detects potential errors and graphical representations to visualize assemblies.</li> <li><strong>Dataset S2_Lung_streptococcus_isolate_genomes: </strong>Nucleotide FASTA files of six lung streptococcal isolates obtained that were obtained via whole genome sequencing.</li> <li><strong>Dataset S3_Lung_isolates_genome_annotation_prokka: </strong>Output folders after annotation of six lung streptococcal isolates with PROKKA. This includes protein FASTA, GenBank files and GFF annotations.</li> <li> <p><strong>Dataset S4_TYGS_dDDH_analysis: </strong>Contains results of TYGS analysis from DSMZ including downloadable reports. Outputs including taxonomic identification with genus, species, and strain details, a TYGS index for tracking genomes, genome quality assessment metrics, GBDP whole genome and 16S rRNA phylogenetic tree files, comparisons with reference type strains in the TYGS database with table.</p> </li> <li> <p><strong>Dataset S5_Reference_type_strains_TYGS_genomes: </strong>Nucleotide FASTA files of 47 closely related reference <em>Streptococcus</em> genomes listed by TYGS and downloaded from NCBI.</p> </li> <li> <p><strong>Dataset S6_Reference_type_strains_TYGS_proteins: </strong>Protein FASTA files of 47 closely related reference <em>Streptococcus</em> genomes listed by TYGS and downloaded from NCBI. </p> </li> <li> <p><strong>Dataset S7_Lung_streptococcus_isolate_proteins: </strong>Protein FASTA files of 6 six lung streptococcal isolates. </p> </li> <li> <p><strong>Dataset S8_OrthoFinder_core_genome: </strong>OrthoFinder is a bioinformatics tool that offers comprehensive outputs for orthology inference across multiple genomes. The output includes overall statistics, gene duplication information, orthologous genes, orthologous gene tree, single copy orthologous genes and STAG evolutionary trees.</p> </li> <li> <p><strong>Dataset S9_Pan-Strep_BLAST_db: </strong>BLAST database using the <code>makeblastdb</code> command of NCBI datasets command line tool. This is constructed using protein FASTA files of 47 closely related reference <em>Streptococcus</em> genomes listed by TYGS and downloaded from NCBI.</p> </li> <li><strong>Dataset S10_OrthoVenn_cluster_files: </strong>OrthoVenn is a web-based tool for orthologous gene comparison. Downloadable results include Venn diagrams depicting shared and unique orthologous clusters amongst species, tabular results detailing genes within each cluster and their annotations. Functional enrichment analysis for Gene Ontology terms and KEGG pathways are also provided. enhances biological insights.</li> <li> <p><strong>Dataset S11_COG_analysis: </strong>Results of COG analysis of six lung streptococcal isolates individually using eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) webtool. The output includes information on Clusters of Orthologous Groups (COGs) categorizing them into functional groups such as metabolism, information storage and processing, and cellular processes and signalling.</p> </li> <li> <p><strong>Dataset S12_CAZymes_lung_streptococci: </strong>Results of CAZyme analysis using a custom rule-based pipeline mostly based on dbCAN (Database for Carbohydrate-Active enZymes) provides information on the carbohydrate-active enzymes present in genomic datasets. The output includes the annotation of enzymes involved in the degradation, modification, or biosynthesis of carbohydrates: glycoside hydrolases (GH), glycosyltransferases (GT), carbohydrate-binding modules (CBM), Auxillary Activities (AA), Carbohydrates Esterases (CE) and Polysaccharide lyases (PL).</p> </li> <li> <p><strong>Dataset S13_pneumolysin_analysis: </strong>Results alignment and phylogeny of Pnuemolysin protein in <em>Streptococcus pneumoniae</em>, <em>Streptococcus pseudopneumoniae</em> and Streptococcus isolate P2E5 found by ABRIcate analysis. Visual plots by pyGenomeViz.</p> </li> <li> <p><strong>Dataset S14_capsule_analysis:</strong> Results from BLAST analysis of <em>Streptococcus pneumoniae </em>D39 capsular biosynthesis operon genes against the Pan-Strep (Dataset S10). Extracted of matching genes followed alignment and phylogeny. Visual plots by pyGenomeViz.</p> </li> <li> <p><strong>Dataset S15_Lung_isolate_HOMD_TYGS_comparison: </strong>Protein FASTA files of 47 closely related reference <em>Streptococcus</em> genomes listed by TYGS and downloaded from NCBI, 6 six lung streptococcal isolates and 47 streptococcal genomes from downloaded from human oral microbiome database (eHOMD).</p> </li> </ul>
Figure 2: Impedance by means of Bode-plot representation, symmetric (con- tinuous line) and the asymmetric (dashed line) tree.-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>Figure 2 shows the total impedance by means of its Bode plot, for the symmetric and the asymmetric tree, whereas the airway tubes are modelled by an R ¡ L ¡ C element in both representations.<br> It is signi¯cant to observe that in the frequency interval of clinical interest,<br> ! 2 [25; 300] rad/s, the two impedances tend to behave similarly. For the asymmetric case, we have a decrease of about -10dB/dec and a phase of ap-proximately ¡50o, resulting in a fractional order of n »=0:5. This observation suggests that a combined e®ect of more than one fractal order is present in the lungs and that it leads naturally to values closer to measured data in the low<br> frequency range.</p>
Figure 4: The estimated impedance within the measured frequency range for the symmetric (*) and the asymmetric (o) case against averaged data from healthy subjects-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>It is significant to observe that in the frequency interval of clinical interest,<br> ! 2 [25; 300] rad/s, the two impedances tend to behave similarly. For the<br> asymmetric case, we have a decrease of about -10dB/dec and a phase of ap-<br> proximately ¡50o, resulting in a fractional order of n »=</p> <p>This observation suggests that a combined efect of more than one fractal order is present in the<br> lungs and that it leads naturally to values closer to measured data in the low<br> frequency range. In other words, the symmetric tree representation does not<br> suffice to obtain a good fit between the model and the measured impedance<br> data. Another observation is that the constant-phase behavior is emphasized<br> at frequencies below those evaluated standardly in clinical practice, i.e. below<br> 5Hz. However, in the standard clinical range of frequencies for the forced oscil-<br> lation technique, namely 4-48Hz, both symmetric and asymmetric tree models<br> give similar results, as depicted in ¯gure 4</p>
Figure 3: Number of branches for each generation, in the asymmetric (TOP) and symmetric (BOTTOM) generation. Notice that the Y-axis is logarithmic.-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>Figure 3 shows the number of branches that are in one generation, for the symmetric and asymmetric<br> lung structure. Notice the diferent slope which characterizes the space-filling distribution.</p>
Figure 1: Asymmetric representation for the ¯rst four generations, in its elec- trical equivalent-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>For example, the average of the radius ratio<br> changes from 2¡0:1713 = 0:8881 to 0:8923 when only the ¯rst 16 generations are<br> taken into account, respectively to 0:8783 for the alveoli (generations 17-24)<br> [5]. This implies that the homothety factor changes, depending on the spatial<br> location within the tree. On the other hand, if we analyze the radius ratio from<br> generations 1 to 24 in steps of 4, we obtain an average of 0:8535, whereas if we<br> use steps of 2, we obtain an average homothety factor of 0:8623. These changes<br> might not seem signi¯cant, but one should recall that they are originated by<br> the symmetric geometry of the respiratory tree. However, when asymmetry<br> is considered, one deals with several homothety factors, i.e. as schematically<br> drawn in figure 1.</p>
Figure 1: Asymmetric representation for the ¯rst four generations, in its elec- trical equivalent-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>These changes<br> might not seem signi¯cant, but one should recall that they are originated by<br> the symmetric geometry of the respiratory tree. However, when asymmetry<br> is considered, one deals with several homothety factors, i.e. as schematically<br> drawn in ¯gure 1.</p>
Genome editing of LKB1 gene by CRISPR/Cas9 in lung cancer cells and evaluation of its role in metformin and cisplatin response.
<pre>LKB1 is an important upstream inhibitor of the mTOR/S6Ks pathway. Loss of LKB1 is often associated with cancer, including in A549 lung cancer cell line, boosting the transformation of pre-malignant neoplasic cells. Metformin, a natural compound derived from Galega officinalis, is mainly used as a treatment for diabetes mellitus type 2 (DM2), but recently, it has been associated to lower incidence of cancer. One of the main mechanisms is by activation of AMPK through different pathways, including LKB1 activation. Activation of AMPK inhibits the mTOR pathway, protein synthesis and thus cell growth. The combination of metformin and cisplatin, a main chemothepeutic drug for lung cancer, has shown to improve treatment of cancer cells to cisplatin and also to hinder the cisplatin associated resistance common in lung cancer. Here we aim to use the CRISPR/Cas9 technology to correct the mutation presented in A549 lung cancer cells and improve their response to metformin or to the combination between metformin and cisplatin. Besides, we will assess the mTOR signaling pathway status, as well as cell growth, proliferation, cell cycle and sensitivity to apoptosis induction by cisplatin. This project may be useful as a proof of principle that in the future therapies must consider the genomic background of cancer cells before administration of a specific anticancer drug.<br><br></pre>
Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy
<p>Data associated with Chen, Nieman, Spurrell et al "Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy" Nature Immunology, 2024. </p> <p>(1) Multiplex RNA scope. Processed cell-level data from 3 datasets, separated into manually selected "Stem Immunity" regions and "Tumor" regions, in separate files. </p> <p>(2) GeoMx. Unprocessed GeoMex data, with standard fields provided by Nanostring software. </p> <p>(3) MERFISH. Transcript-level information, including CellPose and Baysor segmentation. </p> <p>(4) Co-registered multispectral RNA and protein images. Processed cell-level data from 46 patients. Patient-level meta data information is available in supplementary table 1, tab B_Patient_Metadata. Fluorophore channel information is Figure 1a. </p> <p>Paper: <a title="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2" href="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2">https://www.nature.com/articles/s41590-024-01792-2</a></p>
Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provides both common and discordant biological insights
<p>The datasets generated by <em>Cellranger </em>for all 24 samples (.h5 format).<br><br></p>
Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer
<p>Wallen ZD, Ko H, Nesline MK, Hastings SB, Strickland KC, Previs RA, Zhang S, Pabla S, Conroy J, Jackson JB, Saini KS, Jensen TJ, Eisenberg M, Caveney B, Sathyan P, Severson EA, Ramkissoon SH. <strong>Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer.</strong> <em>Front Immunol.</em> 2024 Jun 21;15:1413956. doi: <a href="https://doi.org/10.3389/fimmu.2024.1413956">10.3389/fimmu.2024.1413956</a>. PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/38975340/">38975340</a>; PMCID: <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11224431/">PMC11224431</a>.</p> <p><strong>ABSTRACT</strong></p> <p>Younger patients with non-small cell lung cancer (NSCLC) (<50 years) represent a significant patient population with distinct clinicopathological features and enriched targetable genomic alterations compared to older patients. However, previous studies of younger NSCLC suffer from inconsistent findings, few studies have incorporated sex into their analyses, and studies targeting age-related differences in the tumor immune microenvironment are lacking. We performed a retrospective analysis of 8,230 patients with NSCLC, comparing genomic alterations and immunogenic markers of younger and older patients while also considering differences between male and female patients. We defined older patients as those ≥65 years and used a 5-year sliding threshold from <45 to <65 years to define various groups of younger patients. Additionally, in an independent cohort of patients with NSCLC, we use our observations to inform testing of the combinatorial effect of age and sex on survival of patients given immunotherapy with or without chemotherapy. We observed distinct genomic and immune microenvironment profiles for tumors of younger patients compared to tumors of older patients. Younger patient tumors were enriched in clinically relevant genomic alterations and had gene expression patterns indicative of reduced immune system activation, which was most evident when analyzing male patients. Further, we found younger male patients treated with immunotherapy alone had significantly worse survival compared to male patients ≥65 years, while the addition of chemotherapy reduced this disparity. Contrarily, we found younger female patients had significantly better survival compared to female patients ≥65 years when treated with immunotherapy plus chemotherapy, while treatment with immunotherapy alone resulted in similar outcomes. These results show the value of comprehensive genomic and immune profiling (CGIP) for informing clinical treatment of younger patients with NSCLC and provides support for broader coverage of CGIP for younger patients with advanced NSCLC.</p> <p><strong>DATA AVAILABILITY: </strong></p> <p>De-identified, individual-level patient data, genomic variants, and individual immune gene expression data used in the manuscript can be found in this repository (https://zenodo.org/record/11396552). An R markdown file with R code used to perform the analyses and generate figures is also provided in the repository along with the data. All versions of software used are provided in the Methods section of the manuscript. Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Data for immune gene expression signatures are not publicly available due to a non‑provisional patent filing covering the methods used to generate and analyze these data but are available from the corresponding author on reasonable request.</p>
Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma
Open the record for dataset details and reuse information.
Dataset: Pulmonx Corporation (LUNG) 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.
Figure 6 in The lungs of extinct and extant coelacanths: a morphological and histological review
Figure 6. – †Axelrodichthys araripensis from the Cretaceous of Brazil ("Josa collection"; specimen deposited at the MNHN, Paris). Sections of bony plates of the lung. A: Section of a bony plate showing the central artefactual fracture (black arrowheads), as on Latimeria plates (see Fig. 4A). B: Detail of a section of a bony plate showing the artefactual fracture (black arrowheads) and the Liesegang lines (black arrows) indi- cating a spheritic mineralization process. C: Section of a bony plate showing the histological organization with regu- lar collagenous layers and osteocytes with canalicles crossing the extracellular matrix. D: Horizontal ground section of a bony plate showing numerous star-shape osteocytes with cytoplasmic processes. Inset: detail of an osteocyte with its ramified canalicles. E: Ground section showing some large vascular cavities (white asterisks). F: Cross- ground section of a bony plate showing Liesegang lines (black arrowheads) and some mineralized spherules (black arrows). G: Horizontal ground section of a bony plate showing an osteocyte with its cytoplasmic processes (on the left, black arrow) and some mineralized spherules (on the right). Scale bars: A = 100 μm; B, C = 25 μm; D, F = 20 μm; inset D, E, G = 10 μm. Deposited at the Laboratoire de Paléontologie, MNHN, Paris.
Figure 7. – A-C in The lungs of extinct and extant coelacanths: a morphological and histological review
Figure 7. – A-C: †Axelrodichthys araripensis from the Cretaceous of Brazil ("Josa collection"; specimen deposited at the MNHN, Paris). SEM images of the bony plates of the lung. A: External view of bony plates of the lung. Succession of bony plates are visible on the right (white asterisk). B: Detail of the external surface of bony plates (see white asterisk of Fig. 7A). Each bumpy relief (white arrowheads) corresponds to a spheritic mineralization. C: Detail of a spheritic mineralized bulk. D: Detail of some mineralized spherules (arrowheads). An osteocyte is seen (white asterisk) with one osteocytic canalicle (white arrow). Scale bars: A = 200 μm; B, C = 20 μm; D = 5 μm. Deposited at the Laboratoire de Paléontologie, MNHN, Paris.
Figure 9 in The lungs of extinct and extant coelacanths: a morphological and histological review
Figure 9. – Protopterus dolloi. Drawing of the dorsal view of a reconstitution made from a series of frontal and transversal sections of the pharyngeal area of a larva. The pharynx (Ph) is massive and the lung (Lu) is coated by the mesentery (Me). BP: branchial pouchs, Br: brain, NC: nephrotic cavity, Oe: oesophagus, Op: opercular, Pn: pronephros, Vcl: left posterior vena cardinalis, Vcr: right posterior vena cardinalis, VE: vitellin endoblast. (Modified from Brien, 1964: fig. 7, p. 393).
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.