ArticlePloS one2016
Multiplexed Spectral Imaging of 120 Different Fluorescent Labels.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 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.
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Who cites it
54 citing papers in PubMed.
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- Social lives of bacteria as revealed through CLASI-FISH.Essays in biochemistry · 2026Review
- Spatial mapping of microbial communities by an integrated automation platform of sequential FISH.Cell reports methods · 2026Article
- BEEP Learning: Multi-View Image Decomposition for Massively Multiplexed Biological Fluorescence Microscopy.bioRxiv : the preprint server for biology · 2026Article
- Applications and development of in situ nucleic acid visualization techniques.Frontiers in bioengineering and biotechnology · 2026Review
- Examining Oral Microbial Communities at the Gingival Interface During Health and Inflammatory Diseases.Advances in experimental medicine and biology · 2026Review
- Shining Light on Oral Biofilm Fluorescence In Situ Hybridization (FISH): Probing the Accuracy of In Situ Biogeography Studies.Molecular oral microbiology · 2025Article
- Live-cell omics with Raman spectroscopy.Microscopy (Oxford, England) · 2025Review
- Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics.JACS Au · 2025Review
- CoSiDeX: A hyperspectral fluorescent protein resource for highly multiplexed imaging.bioRxiv : the preprint server for biology · 2025Article
- A framework of multi-view machine learning for biological spectral unmixing of fluorophores with overlapping excitation and emission spectra.Briefings in bioinformatics · 2024Article
- A Framework of Multi-View Machine Learning for Biological Spectral Unmixing of Fluorophores with Overlapping Excitation and Emission Spectra.bioRxiv : the preprint server for biology · 2024Article
- HCR spectral imaging: 10-plex, quantitative, high-resolution RNA and protein imaging in highly autofluorescent samples.Development (Cambridge, England) · 2024Article
- FluoMALDI Microscopy: Matrix Co-Crystallization Simultaneously Enhances Fluorescence and MALDI Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Engineering a tunable micropattern-array assay to sort single extracellular vesicles and particles to detect RNA and protein in situ.Journal of extracellular vesicles · 2023Article
- Imagining the future of optical microscopy: everything, everywhere, all at once.Communications biology · 2023Review
- AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging.Biomedical optics express · 2023Article
- Optical projection tomography of fluorescent microscopic specimens using lateral translation of tube lens.Optics letters · 2023Article
- Spatial profiling of microbial communities by sequential FISH with error-robust encoding.Nature communications · 2023Article
- Illuminating the oral microbiome and its host interactions: tools and approaches for molecular ecological studies.FEMS microbiology reviews · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The number of fluorescent labels that can unambiguously be distinguished in a single image when acquired through band pass filters is severely limited by the spectral overlap of available fluorophores. The recent development of spectral microscopy and the application of linear unmixing algorithms to spectrally recorded image data have allowed simultaneous imaging of fluorophores with highly overlapping spectra. However, the number of distinguishable fluorophores is still limited by the unavoidable decrease in signal to noise ratio when fluorescence signals are fractionated over multiple wavelength bins. Here we present a spectral image analysis algorithm to greatly expand the number of distinguishable objects labeled with binary combinations of fluorophores. Our algorithm utilizes a priori knowledge about labeled specimens and imposes a binary label constraint on the unmixing solution. We have applied our labeling and analysis strategy to identify microbes labeled by fluorescence in situ hybridization and here demonstrate the ability to distinguish 120 differently labeled microbes in a single image.
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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.