Dataset Information

Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics.

ABSTRACT:

Background

The development of single-cell technologies yields large datasets of information as diverse and multimodal as transcriptomes, immunophenotypes, and spatial position from tissue sections in the so-called 'spatial transcriptomics'. Currently however, user-friendly, powerful, and free algorithmic tools for straightforward analysis of spatial transcriptomic datasets are scarce.

Results

Here, we introduce Single-Cell Spatial Explorer, an open-source software for multimodal exploration of spatial transcriptomics, examplified with 9 human and murine tissues datasets from 4 different technologies.

Conclusions

Single-Cell Spatial Explorer is a very powerful, versatile, and interoperable tool for spatial transcriptomics analysis.

SUBMITTER: Pont F

PROVIDER: S-EPMC9881287 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics.

Pont Frédéric F Cerapio Juan Pablo JP Gravelle Pauline P Ligat Laetitia L Valle Carine C Sarot Emeline E Perrier Marion M Lopez Frédéric F Laurent Camille C Fournié Jean Jacques JJ Tosolini Marie M

BMC bioinformatics 20230127 1

<h4>Background</h4>The development of single-cell technologies yields large datasets of information as diverse and multimodal as transcriptomes, immunophenotypes, and spatial position from tissue sections in the so-called 'spatial transcriptomics'. Currently however, user-friendly, powerful, and free algorithmic tools for straightforward analysis of spatial transcriptomic datasets are scarce.<h4>Results</h4>Here, we introduce Single-Cell Spatial Explorer, an open-source software for multimodal e ...[more]

PMID: 36707753

Similar Datasets

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.

Dataset Information

Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics.

Background

Results

Conclusions

Publications

Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets