ArticleNature communications2023
Machine learning-based motion tracking reveals an inverse correlation between adhesivity and surface motility of the leptospirosis spirochete.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 10 citations in OpenAlex.
- A periplasmic layer formed by an outer membrane lipoprotein governs the cell-envelope integrity and stiffness of Leptospira interrogans.Communications biology · 2026Article
- Leptospira spp. Toolbox for Chemotaxis Assay.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine Learning-Based Quantification of Motility Parameters in Individual Leptospira Cells.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine Learning-Based, Label-Free Motion Tracking of Leptospira spp. on Cultured Cells.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Measurement of the Cell-Body Rotation of Leptospira.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Structure, function, and biophysics of bacterial motility and the flagellar motor-IUPAB2024 session commentary.Biophysical reviews · 2024Article
- Frontiers of microbial movement research.Biophysics and physicobiology · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Bacterial motility is often a crucial virulence factor for pathogenic species. A common approach to study bacterial motility is fluorescent labeling, which allows detection of individual bacterial cells in a population or in host tissues. However, the use of fluorescent labeling can be hampered by protein expression stability and/or interference with bacterial physiology. Here, we apply machine learning to microscopic image analysis for label-free motion tracking of the zoonotic bacterium Leptospira interrogans on cultured animal cells. We use various leptospiral strains isolated from a human patient or animals, as well as mutant strains. Strains associated with severe disease, and mutant strains lacking outer membrane proteins (OMPs), tend to display fast mobility and reduced adherence on cultured kidney cells. Our method does not require fluorescent labeling or genetic manipulation, and thus could be applied to study motility of many other bacterial species.
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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 Socratic graph.