Similar Datasets
Project description:To spatially contextualize and extend disease-associated cellular states, we generated Xenium spatial transcriptomics data from FFPE lung tissue of 38 participants spanning the COPD disease spectrum. Four tissue microarrays (TMA1–TMA4) were profiled using a custom 480-gene Xenium panel. These data capture the in situ organization of inflammatory, regenerative, and remodeling cell states and reveal spatially localized niches and patterns of cell–cell communications relevant to COPD pathology. This dataset provides high-resolution spatial context for characterizing microenvironmental structure and cell–cell interactions in COPD lung tissue.
2025-12-09 | GSE313006 | GEO
Project description:Chronic obstructive pulmonary disease (COPD) is clinically and molecularly heterogeneous. To investigate COPD heterogeneity, we performed single-nucleus RNA sequencing (snRNA-seq) on lung tissue from individuals with and without COPD spanning the full disease spectrum. We generated 1,516,727 high-quality cell transcriptomes from 146 lung lobes obtained from 141 subjects. The dataset captures broad shifts in cellular composition and disease-associated cell states in epithelial, stromal, and immune populations. These data define the cellular landscape of COPD heterogeneity, revealing molecular drivers and biomarkers that could inform therapeutic strategies.
2025-11-17 | GSE310058 | GEO
Project description:Chronic obstructive pulmonary disease (COPD) Metagenome
| PRJNA647843 | ENA
Project description:Single-nucleus and spatial transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD)
| PRJNA1378241 | ENA
Project description:Spatial transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [Xenium]
| PRJNA1378106 | ENA
Project description:Background: Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD, thus the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Methods: Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Results: Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, Random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. Conclusion: We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.
2022-10-17 | PXD035120 | Pride
Project description:Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood cells is a useful way to study disease pathobiology and may help elucidate biomarkers and molecular mechanisms of disease. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, some cell types and some indication. Accurate enumeration of the many component cell types that make up peripheral whole blood can be costly, however, and may further complicate the sample collection process. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. We present a freely-available, and open-source, multiresponse Gaussian model capable of accurately inferring the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles. The model was developed on a cohort of patients with chronic obstructive pulmonary disease (COPD) and tested in chronic heart failure patients.
2016-05-25 | GSE77344 | GEO
Project description:RationaleBased on surface brushings and bronchoalveolar lavage fluid, Hilty and coworkers demonstrated microbiomes in the human lung characteristic of asthma and chronic obstructive pulmonary disease (COPD), which have now been confirmed by others.ObjectivesTo extend these findings to human lung tissue samples.MethodsDNA from lung tissue samples was obtained from nonsmokers (n = 8); smokers without COPD (n = 8); patients with very severe COPD (Global Initiative for COPD [GOLD] 4) (n = 8); and patients with cystic fibrosis (CF) (n = 8). The latter served as a positive control, with sterile water as a negative control. All bacterial community analyses were based on polymerase chain reaction amplifying 16S rRNA gene fragments. Total bacterial populations were measured by quantitative polymerase chain reaction and bacterial community composition was assessed by terminal restriction fragment length polymorphism analysis and pyrotag sequencing.Measurement and main resultsTotal bacterial populations within lung tissue were small (20-1,252 bacterial cells per 1,000 human cells) but greater in all four sample groups versus the negative control group (P < 0.001). Terminal restriction fragment length polymorphism analysis and sequencing distinguished three distinct bacterial community compositions: one common to the nonsmoker and smoker groups, a second to the GOLD 4 group, and the third to the CF-positive control group. Pyrotag sequencing identified greater than 1,400 unique bacterial sequences and showed an increase in the Firmicutes phylum in GOLD 4 patients versus all other groups (P < 0.003) attributable to an increase in the Lactobacillus genus (P < 0.0007).ConclusionsThere is a detectable bacterial community within human lung tissue that changes in patients with very severe COPD.
| S-EPMC3359894 | biostudies-literature
Project description:Chronic Obstructive Pulmonary Disease (COPD) patient microbiome
| PRJEB46960 | ENA
Project description:Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.
| S-EPMC9569607 | biostudies-literature