ArticleCell2025
STAMP: Single-cell transcriptomics analysis and multimodal profiling through imaging.
Article in Cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Modeling Tau-Mediated Pathology in Monkey Brain Slices.Biology · 2026Article
- Sequencing-free spatial profiling of post-transcriptional regulation in fresh tissues using nanoneedle arrays.Nature biomedical engineering · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets.Nature biotechnology · 2026Article
- Mass Spectrometry Imaging Combined With Single-Cell Transcriptional Profiling Reveals the Multidimensional Spatial Distributions and Biosynthetic Pathways of Medicinal Components in Andrographis paniculata.Plant biotechnology journal · 2026Article
- SpaHE-Infil: A spatial heterogeneity framework for decoding TME infiltration from H&E-stained slides.iScience · 2026Article
- Single-cell technologies drive mechanistic insights and therapeutic translation in skeletal system research.iScience · 2026Review
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex.bioRxiv : the preprint server for biology · 2026Article
- Single-cell omics in tumor lymph node metastasis: mechanisms and therapeutic implications.Molecular cancer · 2026Review
- Single-Cell Profiling: Any Scale, Any Size, All at Once.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Isolation and typing techniques for circulating tumor cells.Biomarker research · 2026Review
- Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-Multi.Cell genomics · 2026Article
- The Five-Decade Journey of Small Cell Lung Cancer.Cancer communications (London, England) · 2026Review
- Single-cell transcriptomic insights into the intrinsic cardiac nervous system: diversity, development, and neuro-cardiac interactions.Frontiers in genetics · 2026Review
- Democratizing the spatial view: STAMP technology from an analytical perspective.EXO : beyond the cell · 2026Article
- Imaging-based multimodal profiling of single cells with STAMP.Nature reviews. Genetics · 2025Article
- Spatial transcriptomics: a bibliometric analysis with large language model on English literatures.Briefings in bioinformatics · 2025Article
- Primate amnion development.Development (Cambridge, England) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
Abstract
Single-cell RNA sequencing 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 cost-efficient single-cell genomics of millions of cells. Immobilizing (stamping) cells in suspension onto imaging slides, STAMP supports multimodal (RNA, protein, and H&E) profiling, while retaining cellular structure and morphology. We demonstrate STAMP's versatility by profiling peripheral blood mononuclear cells, cell lines, and stem cells. We highlight the capability of STAMP to identify ultra-rare cell populations, simulate clinical applications, and show its utility for large-scale perturbation studies. In total, we present data for 10,962,092 high-quality cells/nuclei and 6,030,429,954 transcripts. STAMP makes high-resolution cellular profiling more accessible, scalable, and affordable.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.