ArticleNature communications2015
Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 357 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.
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Who cites it
357 citing papers in PubMed, 593 citations in OpenAlex.
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- Direct bacterial transcript visualization illuminates spatiotemporal environmental adaptation ofScience advances · 2026Article
- Deciphering lung cancer at high resolution: a narrative review of applications of single-cell and spatial transcriptomics sequencing.Translational lung cancer research · 2026Review
- Spatium: A Protein Language Foundation Model for Spatial Proteomics.bioRxiv : the preprint server for biology · 2026Article
- Cyclic immunofluorescence platform using photocleavable linkers for direct antibody labeling enables cancer phenotyping.bioRxiv : the preprint server for biology · 2026Article
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- Dissecting human organ development using spatial technologies.Developmental cell · 2026Review
- High-throughput, multiplex immunofluorescence for the computer-automated immunophenotyping of Mycosis Fungoides.JID innovations : skin science from molecules to population health · 2026Article
- A Versatile Multiplexed Immunofluorescence Strategy for Efficient, Host-Independent, and Scalable Spatial Protein Profiling.Small methods · 2026Article
- Advances in spatial omics for the analysis of prostate cancer.Analytical and bioanalytical chemistry · 2026Review
- Multiplexed video hyperspectral microendoscopy reveals tumor-immune dynamics in vivo.Research square · 2026Article
- Cross-Modality Alignment of Spatial Transcriptomics, Multiplexed Imaging, and Histology with PHARAOH.Research square · 2026Article
- Modeling development of tertiary lymphoid structures in pulmonary tuberculosis by 3D profiling and trajectory analysis.bioRxiv : the preprint server for biology · 2026Article
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
297 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications.
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What OpenQuestion holds
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.