ReviewBiophysical reviews2022
Live-cell fluorescence spectral imaging as a data science challenge.
Review in Biophysical reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed, 20 citations in OpenAlex.
- DNA Origami and Their Application in Biosensors.Biosensors · 2026Review
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- Multi-signal regulation of the GSK-3β homolog Rim11 controls meiosis entry in budding yeast.The EMBO journal · 2024Article
- A Bright, Photostable, and Far-Red Dye That Enables Multicolor, Time-Lapse, and Super-Resolution Imaging of Acidic Organelles.ACS central science · 2024Article
- Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding.Advanced drug delivery reviews · 2023Review
- A Bright, Photostable Dye that Enables Multicolor, Time Lapse, and Super-Resolution Imaging of Acidic Organelles.bioRxiv : the preprint server for biology · 2023Article
- Super-multiplexing excitation spectral microscopy with multiple fluorescence bands.Biomedical optics express · 2022Article
- Editorial for 'Issue focus on 2nd Costa Rica biophysics symposium - March 11th-12th, 2021'.Biophysical reviews · 2022Article
- Biophysical Reviews: focusing on an issue.Biophysical reviews · 2022Article
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 2 countries.
Funding
Abstract
Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.
Indexed as
Identifiers
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.