STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging [CosMx Spatial]
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PROVIDER: PRJNA1228127 | ENA |
REPOSITORIES: ENA
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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: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.
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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.
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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:STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging
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Project description:STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging [Xenium]
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Project description:Spatial omics are increasingly "fused" with classical biomedical imaging modalities to complement them with spatially-resolved molecular profiling, enabling deeper understanding of the biochemical basis of health and diesease. Before the multimodal information can be fused, all contributing imaging data must be spatially aligned through a process called coregistration, which remains a challenge. Here we present a multimodal coregistration framework with great genericity and accuracy for spatial omics data. Specifically, an original dimension reduction algorithm "PseudoRep" is used to bridge cross-modal gaps: PseudoRep can generate a single-channel representation of the highly multiplexed molecular image from spatial omics and ensure that the resulting representation appears "pseudo-unimodal" to the image from the other modality. Then, a Deep Learning-based transformation algorithm "DeepReg" is adopted to accurately model nonlinear tissue deformations due to anatomical variations or sample manipulations. Our method demonstrated its versatility by coregistering mass spectrometry imaging-mediated spatial metabolomics data with diverse modalities (magnetic resonance imaging, brain atlas, microscopy, and spatial transcriptomics) and reduced 38\%\textemdash69\% of coregistration errors compared to existing methods. Notably, we open-source a toolbox to implement both the coregistration and its downstream tasks (co-expression analysis, anatomical annotation, and pansharpening), which is expected to greatly facilitate the democratization of multimodal fusion studies in the spatial omics community.
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