ArticleNature protocols2019
Real-time visualization of mutations and their fitness effects in single bacteria.
Article in Nature protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Phenotypic heterogeneity optimizes trade-offs during adaptive deployment of the type VI secretion system.PLoS biology · 2026Article
- A Single-Cell Interrogation System from Scratch: Microfluidics and Deep Learning.The journal of physical chemistry. B · 2024Article
- Microbial single-cell applications under anoxic conditions.Applied and environmental microbiology · 2024Review
- Single-Cell Microfluidics: A Primer for Microbiologists.The journal of physical chemistry. B · 2024Review
- Near-infrared co-illumination of fluorescent proteins reduces photobleaching and phototoxicity.Nature biotechnology · 2024Article
- Real-time monitoring of replication errors' fate reveals the origin and dynamics of spontaneous mutations.Nature communications · 2024Article
- Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications.Frontiers in bioengineering and biotechnology · 2022Review
- Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability.Evolutionary applications · 2021Review
- Bacterial Vivisection: How Fluorescence-Based Imaging Techniques Shed a Light on the Inner Workings of Bacteria.Microbiology and molecular biology reviews : MMBR · 2020Review
- Review
- High-throughput detection and tracking of cells and intracellular spots in mother machine experiments.Nature protocols · 2019Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutations are the driving force of evolution and the source of important pathologies. The characterization of the dynamics and effects of mutations on fitness is therefore central to our understanding of evolution and to human health. This protocol describes how to implement two methods that we recently developed: mutation visualization (MV) and microfluidic mutation accumulation (µMA), which allow the occurrence of mutations created by DNA replication errors (MV) and the evolution of cell fitness during MA (µMA) to be followed directly in individual cells of Escherichia coli. MV provides a quantitative characterization of the dynamics of mutation occurrences, and µMA allows precise estimation of the distribution of fitness effects (DFEs) of mutations. Both methods use microfluidics and time-lapse microscopy, and a fluorescent mismatch repair (MMR) MutL protein is used as a marker for nascent mutations. Here, we present a single protocol describing how to implement the MV and µMA methods, including detailed procedures for microfluidic setup installation, data acquisition and data analysis and interpretation. Using this procedure, the microfluidic setup installation can be completed within 1 d, and automated data acquisition takes 2-4 d.
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Identifiers
31554956What 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.