Spatially resolved single-cell deconvolution of bulk transcriptomes using Bulk2Space
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA794115 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17424595_1.fastq.gz | Fastqsanger.gz | |||
| SRR17424595_2.fastq.gz | Fastqsanger.gz | |||
| SRR21948572_1.fastq.gz | Fastqsanger.gz | |||
| SRR21948572_2.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 4 of 4 |
Similar Datasets
Project description:We developed a spatially resolved RNA-seq protocol based on laser capture microdissection (LCM) and spatial barcoding strategy termed Spatial-seq. Here, we provide the demo transcriptomic data derived from mouse hypothalamus and isocortex regions for applications of our newly proposed spatial deconvolution algorithm termed Bulk2Space. We want to reveal the spatial heterogeneity and cellular diversity within the bulk brain region.
2022-01-09 | GSE192999 | GEO
Project description:Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. To address these limitations, we generated spatially resolved transcriptome maps of human IPF and bleomycin-induced mouse lung fibrosis.
2024-05-21 | GSE267904 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220607 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220605 | GEO
Project description:The recent development of spatial omics enables single-cell profiling of the transcriptome and the 3D organization of the genome in a spatially resolved manner. A spatial epigenomics method would expand the repertoire of spatial omics tools and accelerate our understanding of the spatial regulation of cellular processes and tissue functions. Here, we developed an imaging approach for spatially resolved profiling of epigenetic modifications in single cells
2022-12-16 | GSE191069 | GEO
Project description:Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.
2024-01-12 | PXD044157 | Pride
Project description:Understanding the spatial distribution of T cells is pivotal to decrypting immune dysfunction in cancer. Current spatially resolved transcriptomics fall short in directly annotating T cell receptors (TCRs), limiting the comprehension of anti-cancer immunity. We introduce a novel technology, Spatially Resolved T Cell Receptor Sequencing (SPTCR-seq), integrating target enrichment and long-read sequencing for highly sensitive TCR sequencing. This approach yields an on-target rate of ~85%, and via a bespoke computational pipeline, it provides meticulous spatial mapping, error correction, and UMI refinement. SPTCR-seq outperforms PCR-based methods, offering superior reconstruction of the complete TCR architecture, inclusive of V, D, J regions and the vital complementarity-determining region 3 (CDR3). Applying SPTCR-seq, we reveal local T cell diversity, clonal expansion, and transcriptional evolution across spatially distinct niches in glioblastoma, identifying critical involvement of NK and B cells in spatial T cell adaptation. SPTCR-seq, by bridging spatially resolved omics and TCR sequencing, stands as a robust tool for exploring T cell dysfunction in cancers and beyond.
2023-07-25 | GSE238071 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220651 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220606 | GEO
Project description:Spatially resolved transcriptomics of high-grade serous ovarian carcinoma
| PRJNA784634 | ENA