Similar Datasets
Project description:We provide 5' and/or 3' single cell and single nuclei RNA sequencing data for matched comparisons of profiling from fresh (single cells) and frozen (single nuclei) samples of a non small cell lung cancer sample, a cutaneous melanoma sample , and a primary uveal melanoma sample. We also provide matched samples processed with published dissociation protocols (*TST and *CST) for two samples as comparison. Next, we profiled three sequential biopsies (one pre-treatment and two on-treatment) from the KEYNOTE-001 trial by 5' single-nuclei RNA sequencing from frozen specimens. For these three datapoints we provide matched SlideSeq V2 spatial transcriptomic data. Finally, we provide 5' single nuclei RNA-sequencing data for 7 liver-metastatic uveal melanoma samples profiled at pre, on, and post treatment timepoints with MEK-inhibitor selumetinib. For all samples profiled with 5' capture we also provide TCR receptor information. This study provides a framework for the application of single nuclei RNA-sequencing on clinical grade specimens, and demonstrates the feasibility of this approach to dissect the biology of archival specimens or rare diseases.
2022-10-19 | GSE216055 | GEO
Project description:We provide 5' and/or 3' single cell and single nuclei RNA sequencing data for matched comparisons of profiling from fresh (single cells) and frozen (single nuclei) samples of a non small cell lung cancer sample, a cutaneous melanoma sample , and a primary uveal melanoma sample. We also provide matched samples processed with published dissociation protocols (*TST and *CST) for two samples as comparison. Next, we profiled three sequential biopsies (one pre-treatment and two on-treatment) from the KEYNOTE-001 trial by 5' single-nuclei RNA sequencing from frozen specimens. For these three datapoints we provide matched SlideSeq V2 spatial transcriptomic data. Finally, we provide 5' single nuclei RNA-sequencing data for 7 liver-metastatic uveal melanoma samples profiled at pre, on, and post treatment timepoints with MEK-inhibitor selumetinib. For all samples profiled with 5' capture we also provide TCR receptor information. This study provides a framework for the application of single nuclei RNA-sequencing on clinical grade specimens, and demonstrates the feasibility of this approach to dissect the biology of archival specimens or rare diseases.
2022-10-19 | GSE216069 | GEO
Project description:BACKGROUND: Whole exome and genome sequencing (WES/WGS) is now routinely offered as a clinical test by a growing number of laboratories. As part of the test design process each laboratory must determine the performance characteristics of the platform, test and informatics pipeline. This report documents one such characterization of WES/WGS. METHODS: Whole exome and whole genome sequencing was performed on multiple technical replicates of five reference samples using the Illumina HiSeq 2000/2500. The sequencing data was processed with a GATK-based genome analysis pipeline to evaluate: intra-run, inter-run, inter-mode, inter-machine and inter-library consistency, concordance with orthogonal technologies (microarray, Sanger) and sensitivity and accuracy relative to known variant sets. RESULTS: Concordance to high-density microarrays consistently exceeds 97% (and typically exceeds 99%) and concordance between sequencing replicates also exceeds 97%, with no observable differences between different flow cells, runs, machines or modes. Sensitivity relative to high-density microarray variants exceeds 95%. In a detailed study of a 129 kb region, sensitivity was lower with some validated single-base insertions and deletions "not called". Different variants are "not called" in each replicate: of all variants identified in WES data from the NA12878 reference sample 74% of indels and 89% of SNVs were called in all seven replicates, in NA12878 WGS 52% of indels and 88% of SNVs were called in all six replicates. Key sources of non-uniformity are variance in depth of coverage, artifactual variants resulting from repetitive regions and larger structural variants. CONCLUSION: We report a comprehensive performance characterization of WES/WGS that will be relevant to offering laboratories, consumers of genome sequencing and others interested in the analytical validity of this technology.
| S-EPMC4022392 | biostudies-literature
Project description:We report dynamics of X-chromosome upregulation (XCU) along X-chromosome inactivation (XCI) in mESCs as they differentiate into EpiSCs. F1 hybrid C57BL6/J × CAST/EiJ male and female mESCs were grown in serum/LIF conditions were differentiated using Fgf2 and Activin A for 1, 2, 4 and 7 days to induce random XCI in female cells. Multi-modal single-cell sequencing was performed using scATAC on nuclei and Smart-seq3 to assay chromatin accessibility and poly-A+ RNA expression, respectively. Allelic resolution is achieved using strain-specific SNPs in the data. We reveal dynamic balancing of X alleles as cells undergo XCI to compensate dosage imbalances between sexes as well as between X and autosomes. Furthermore, we reveal that female naïve mESCs with two active X chromosomes lack XCU on both alleles which has major implications for reprogramming studies. Finally, we estimate allelic transcriptional burst kinetics from the data and find that progressively increased burst frequencies underlies the XCU process.
2022-02-14 | E-MTAB-10714 | biostudies-arrayexpress
Project description:Latent knowledge can be extracted from the electronic notes that are recorded during patient encounters with the health system. Using these clinical notes to decipher a patient's underlying comorbidites, symptom burdens, and treatment courses is an ongoing challenge. Latent topic model as an efficient Bayesian method can be used to model each patient's clinical notes as "documents" and the words in the notes as "tokens". However, standard latent topic models assume that all of the notes follow the same topic distribution, regardless of the type of note or the domain expertise of the author (such as doctors or nurses). We propose a novel application of latent topic modeling, using multi-note topic model (MNTM) to jointly infer distinct topic distributions of notes of different types. We applied our model to clinical notes from the MIMIC-III dataset to infer distinct topic distributions over the physician and nursing note types. Based on manual assessments made by clinicians, we observed a significant improvement in topic interpretability using MNTM modeling over the baseline single-note topic models that ignore the note types. Moreover, our MNTM model led to a significantly higher prediction accuracy for prolonged mechanical ventilation and mortality using only the first 48 hours of patient data. By correlating the patients' topic mixture with hospital mortality and prolonged mechanical ventilation, we identified several diagnostic topics that are associated with poor outcomes. Because of its elegant and intuitive formation, we envision a broad application of our approach in mining multi-modality text-based healthcare information that goes beyond clinical notes. Code available at https://github.com/li-lab-mcgill/heterogeneous_ehr.
| S-EPMC8031429 | biostudies-literature
Project description:To acquire a deeper understanding of malignant melanoma (MM), it is essential to study the proteome of patient tissues. In particular, phosphoproteomics of MM has become of significant importance because of the central role that phosphorylation plays in the development of MM. Investigating clinical samples, however, is an extremely challenging task as there is usually only very limited quantities of material available to perform targeted enrichment approaches. Here, an automated phosphopeptide enrichment protocol using the AssayMap Bravo platform was applied to MM tissues and assessed for performance. The strategy proved to be highly-sensitive, less prone to variability, less laborious than existing techniques and adequate for starting quantities at the microgram level. An Fe(III)-NTA-IMAC-based enrichment workflow was applied to a dilution series of MM tissue lysates. The workflow was efficient in terms of sensitivity, reproducibility and phosphosite localization; and from only 12.5 ?g of sample, more than 1,000 phosphopeptides were identified. In addition, from 60 ?g of protein material the number of identified phosphoproteins from individual MM samples was comparable to previous reports that used extensive fractionation methods. Our data set included key pathways that are involved in MM progression; such as MAPK, melanocyte development and integrin signaling. Moreover, tissue-specific immunological proteins were identified, that have not been previously observed in the proteome of MM-derived cell lines. In conclusion, this workflow is suitable to study large cohorts of clinical samples that demand automatic and careful handling.
| S-EPMC6287822 | biostudies-literature
Project description:Next-generation sequencing (NGS) has emerged as a powerful technique for the detection of genetic variants in the clinical laboratory. NGS can be performed using DNA from FFPE tissue, but it is unknown whether such specimens are truly equivalent to unfixed tissue for NGS applications. To address this question, we performed hybridization-capture enrichment and multiplexed Illumina NGS for 27 cancer-related genes using DNA from 16 paired fresh-frozen and routine FFPE lung adenocarcinoma specimens and conducted extensive comparisons between the sequence data from each sample type. This analysis revealed small but detectable differences between FFPE and frozen samples. Compared with frozen samples, NGS data from FFPE samples had smaller library insert sizes, greater coverage variability, and an increase in C to T transitions that was most pronounced at CpG dinucleotides, suggesting interplay between DNA methylation and formalin-induced changes; however, the error rate, library complexity, enrichment performance, and coverage statistics were not significantly different. Comparison of base calls between paired samples demonstrated concordances of >99.99%, with 96.8% agreement in the single-nucleotide variants detected and >98% accuracy of NGS data when compared with genotypes from an orthogonal single-nucleotide polymorphism array platform. This study demonstrates that routine processing of FFPE samples has a detectable but negligible effect on NGS data and that these samples can be a reliable substrate for clinical NGS testing.
| S-EPMC4912568 | biostudies-literature
Project description:BACKGROUND:DNA methylation plays an important role in multiple biological processes that are closely related to human health. The study of DNA methylation can provide an insight into the mechanism behind human health and can also have a positive effect on the assessment of human health status. However, the available sequencing technology is limited by incomplete CpG coverage. Therefore, it is crucial to discover an efficient and convenient method capable of distinguishing between the states of CpG sites. Previous studies focused on identifying methylation states of the CpG sites in single cell, which only evaluated sequence information or structural information. RESULTS:In this paper, we propose a novel model, LightCpG, which combines the positional features with the sequence and structural features to provide information on the CpG sites at two stages. Next, we used the LightGBM model for training of the CpG site identification, and further utilized sample extraction and merged features to reduce the training time. Our results indicate that our method achieves outstanding performance in recognition of DNA methylation. The average AUC values of our method using the 25 human hepatocellular carcinoma cells (HCC) cell datasets and six human heptoplastoma-derived (HepG2) cell datasets were 0.9616 and 0.9213, respectively. Moreover, the average training times for our method on the HCC and HepG2 datasets were 8.3 and 5.06 s, respectively. Furthermore, the computational complexity of our model was much lower compared with other available methods that detect methylation states of the CpG sites. CONCLUSIONS:In summary, LightCpG is an accurate model for identifying the DNA methylation status of CpG sites in single cells. Furthermore, three types of feature extraction methods and two strategies used in LightCpG are helpful for other prediction problems.
| S-EPMC6480911 | biostudies-literature
Project description:BACKGROUND:Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. RESULTS:We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. CONCLUSION:A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.
| S-EPMC6923906 | biostudies-literature
Project description:Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.
| S-EPMC7212577 | biostudies-literature