Integrated analysis of multimodal single-cell data
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA690251 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity.
2021-01-15 | GSE164378 | GEO
Project description:Integrated Analysis of Multimodal Single-Cell Datasets for SARS-CoV-2 Vaccination
| PRJNA985376 | ENA
Project description:Integrated Analysis of Multimodal Single-Cell Datasets for SARS-CoV-2 Vaccination
| PRJNA985377 | ENA
Project description:The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
| S-EPMC8238499 | biostudies-literature
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity, but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (Single-Cell Transcriptomics Analysis and Multimodal Profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling single-cell genomics of hundreds to millions of cells at an unprecedented affordability. Immobilizing (‘stamping’) cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA, protein and H&E) profiling, while retaining cellular structure and morphology. Its flexible, ultra-high-throughput formats facilitate the analysis of single or multiple samples in the same experiment, enhancing experimental scalability and adaptability. We demonstrate STAMP's versatility across diverse experimental contexts, including the profiling of peripheral blood mononuclear cells (PBMCs), cell lines and stem cells. We also stamped cells and nuclei from dissociated tissues from mouse organs to simulate the generation of cell atlases. Accessibility was further enlarged by analyzing nuclei from archival formalin fixed and paraffin embedded (FFPE) tissue samples. Combining RNA and protein profiling, we applied STAMP for high-throughput immuno-phenotyping of millions of blood cells, providing multimodal insights into cellular heterogeneity. We highlight the capability of STAMP to identify ultra-rare cell populations, simulating clinical applications for detecting circulating tumor cells (CTCs). By capturing lineage dynamics during stem cell differentiation and subtle changes in in vitro activated PBMCs, we further showed its utility for large-scale perturbation studies. These results validate STAMP as a first-of-its-kind single-cell imaging analysis strategy. We present data for 10,962,092 high quality cells/nuclei and 6,030,429,954 high quality transcripts. By replacing sequencing with imaging, STAMP enables high-resolution cellular profiling that is more accessible, scalable, and cost-effective. STAMP has the potential to transform our ability to map biological diversity and dynamics, significantly advancing research and clinical applications.
2025-06-30 | GSE301112 | GEO
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity, but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (Single-Cell Transcriptomics Analysis and Multimodal Profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling single-cell genomics of hundreds to millions of cells at an unprecedented affordability. Immobilizing (‘stamping’) cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA, protein and H&E) profiling, while retaining cellular structure and morphology. Its flexible, ultra-high-throughput formats facilitate the analysis of single or multiple samples in the same experiment, enhancing experimental scalability and adaptability. We demonstrate STAMP's versatility across diverse experimental contexts, including the profiling of peripheral blood mononuclear cells (PBMCs), cell lines and stem cells. We also stamped cells and nuclei from dissociated tissues from mouse organs to simulate the generation of cell atlases. Accessibility was further enlarged by analyzing nuclei from archival formalin fixed and paraffin embedded (FFPE) tissue samples. Combining RNA and protein profiling, we applied STAMP for high-throughput immuno-phenotyping of millions of blood cells, providing multimodal insights into cellular heterogeneity. We highlight the capability of STAMP to identify ultra-rare cell populations, simulating clinical applications for detecting circulating tumor cells (CTCs). By capturing lineage dynamics during stem cell differentiation and subtle changes in in vitro activated PBMCs, we further showed its utility for large-scale perturbation studies. These results validate STAMP as a first-of-its-kind single-cell imaging analysis strategy. We present data for 10,962,092 high quality cells/nuclei and 6,030,429,954 high quality transcripts. By replacing sequencing with imaging, STAMP enables high-resolution cellular profiling that is more accessible, scalable, and cost-effective. STAMP has the potential to transform our ability to map biological diversity and dynamics, significantly advancing research and clinical applications.
2025-07-06 | GSE301544 | GEO
Project description:STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging
| PRJNA1228122 | ENA
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity, but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (Single-Cell Transcriptomics Analysis and Multimodal Profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling single-cell genomics of hundreds to millions of cells at an unprecedented affordability. Immobilizing (‘stamping’) cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA, protein and H&E) profiling, while retaining cellular structure and morphology. Its flexible, ultra-high-throughput formats facilitate the analysis of single or multiple samples in the same experiment, enhancing experimental scalability and adaptability. We demonstrate STAMP's versatility across diverse experimental contexts, including the profiling of peripheral blood mononuclear cells (PBMCs), cell lines and stem cells. We also stamped cells and nuclei from dissociated tissues from mouse organs to simulate the generation of cell atlases. Accessibility was further enlarged by analyzing nuclei from archival formalin fixed and paraffin embedded (FFPE) tissue samples. Combining RNA and protein profiling, we applied STAMP for high-throughput immuno-phenotyping of millions of blood cells, providing multimodal insights into cellular heterogeneity. We highlight the capability of STAMP to identify ultra-rare cell populations, simulating clinical applications for detecting circulating tumor cells (CTCs). By capturing lineage dynamics during stem cell differentiation and subtle changes in in vitro activated PBMCs, we further showed its utility for large-scale perturbation studies. These results validate STAMP as a first-of-its-kind single-cell imaging analysis strategy. We present data for 10,962,092 high quality cells/nuclei and 6,030,429,954 high quality transcripts. By replacing sequencing with imaging, STAMP enables high-resolution cellular profiling that is more accessible, scalable, and cost-effective. STAMP has the potential to transform our ability to map biological diversity and dynamics, significantly advancing research and clinical applications.
2025-07-03 | GSE301542 | GEO
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity, but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (Single-Cell Transcriptomics Analysis and Multimodal Profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling single-cell genomics of hundreds to millions of cells at an unprecedented affordability. Immobilizing (‘stamping’) cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA, protein and H&E) profiling, while retaining cellular structure and morphology. Its flexible, ultra-high-throughput formats facilitate the analysis of single or multiple samples in the same experiment, enhancing experimental scalability and adaptability. We demonstrate STAMP's versatility across diverse experimental contexts, including the profiling of peripheral blood mononuclear cells (PBMCs), cell lines and stem cells. We also stamped cells and nuclei from dissociated tissues from mouse organs to simulate the generation of cell atlases. Accessibility was further enlarged by analyzing nuclei from archival formalin fixed and paraffin embedded (FFPE) tissue samples. Combining RNA and protein profiling, we applied STAMP for high-throughput immuno-phenotyping of millions of blood cells, providing multimodal insights into cellular heterogeneity. We highlight the capability of STAMP to identify ultra-rare cell populations, simulating clinical applications for detecting circulating tumor cells (CTCs). By capturing lineage dynamics during stem cell differentiation and subtle changes in in vitro activated PBMCs, we further showed its utility for large-scale perturbation studies. These results validate STAMP as a first-of-its-kind single-cell imaging analysis strategy. We present data for 10,962,092 high quality cells/nuclei and 6,030,429,954 high quality transcripts. By replacing sequencing with imaging, STAMP enables high-resolution cellular profiling that is more accessible, scalable, and cost-effective. STAMP has the potential to transform our ability to map biological diversity and dynamics, significantly advancing research and clinical applications.
2025-06-23 | GSE290466 | GEO
Project description:Multimodal single cell profiling of bone fracture microenvironment
| PRJNA1143063 | ENA