Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning
Understanding pioneer bacterial adhesion is essential to appreciate bacterial colonization and consider appropriate control strategies. This bacterial entrapment at the wall is known to be controlled by many physical, chemical or biological factors, including hydrodynamic conditions. However, due to...
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Elsevier
2024-12-01
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| Series: | Biofilm |
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| author | Lucie Klopffer Nicolas Louvet Simon Becker Jérémy Fix Cédric Pradalier Laurence Mathieu |
| author_facet | Lucie Klopffer Nicolas Louvet Simon Becker Jérémy Fix Cédric Pradalier Laurence Mathieu |
| author_sort | Lucie Klopffer |
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| description | Understanding pioneer bacterial adhesion is essential to appreciate bacterial colonization and consider appropriate control strategies. This bacterial entrapment at the wall is known to be controlled by many physical, chemical or biological factors, including hydrodynamic conditions. However, due to the nature of early bacterial adhesion, i.e. a short and dynamic process with low biomass involved, such investigations are challenging. In this context, our study aimed to evaluate the effect of wall shear rate on the early bacterial adhesion dynamics. Firstly, at the population scale by assessing bacterial colonization kinetics and the mechanisms responsible for wall transfer under shear rates using a time-lapse approach. Secondly, at the individual scale, by implementing an automated image processing method based on deep learning to track each individual pioneer bacterium on the wall. Bacterial adhesion experiments are performed on a model bacterium (Shewanella oneidensis MR-1) at different shear rates (0 to1250 s−1) in a microfluidic system mounted under a microscope equipped with a CCD camera. Image processing was performed using a trained neural network (YOLOv8), which allowed information extraction, i.e. bacterial wall residence time and orientation for each adhered bacterium during pioneer colonization (14 min). Collected from over 20,000 bacteria, our results showed that adhered bacteria had a very short residence time at the wall, with over 70 % remaining less than 1 min. Shear rates had a non-proportional effect on pioneer colonization with a bell-shape profile suggesting that intermediate shear rates improved both bacterial wall residence time as well as colonization rate and level. This lack of proportionality highlights the dual effect of wall shear rate on early bacterial colonization; initially increasing it improves bacterial colonization up to a threshold, beyond which it leads to higher bacterial wall detachment. The present study provides quantitative data on the individual dynamics of just adhered bacteria within a population when exposed to different rates of wall shear. |
| format | Article |
| id | doaj-art-2092b6a1fee14527a58f9a7ad3ea1b76 |
| institution | Kabale University |
| issn | 2590-2075 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| series | Biofilm |
| spelling | doaj-art-2092b6a1fee14527a58f9a7ad3ea1b762024-12-12T05:22:50ZengElsevierBiofilm2590-20752024-12-018100240Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learningLucie Klopffer0Nicolas Louvet1Simon Becker2Jérémy Fix3Cédric Pradalier4Laurence Mathieu5Université de Lorraine, CNRS, LCPME, F-54000, Nancy, France; Université de Lorraine, CNRS, LEMTA, F-54000, Nancy, FranceUniversité de Lorraine, CNRS, LEMTA, F-54000, Nancy, France; Corresponding author. Nicolas Louvet, LEMTA, 2 Avenue de la Forêt de Haye, BP 90161, F-54505, Vandoeuvre-lès-Nancy, FranceUniversité de Lorraine, CNRS, LEMTA, F-54000, Nancy, FranceUnviversité de Lorraine, CNRS, Centrale Supélec, F-57070, Metz, FranceGeorgiaTech Europe, IRL 2958, F-57070, Metz, FranceEPHE, PSL, UMR CNRS 7564, LCPME, F-54000, Nancy, France; Corresponding author. Laurence Mathieu, LCPME, Campus Brabois Santé, Bâtiment AB, 9 Avenue de la Forêt de Haye, BP 20199, F-54505, Vandoeuvre-lès-Nancy, FranceUnderstanding pioneer bacterial adhesion is essential to appreciate bacterial colonization and consider appropriate control strategies. This bacterial entrapment at the wall is known to be controlled by many physical, chemical or biological factors, including hydrodynamic conditions. However, due to the nature of early bacterial adhesion, i.e. a short and dynamic process with low biomass involved, such investigations are challenging. In this context, our study aimed to evaluate the effect of wall shear rate on the early bacterial adhesion dynamics. Firstly, at the population scale by assessing bacterial colonization kinetics and the mechanisms responsible for wall transfer under shear rates using a time-lapse approach. Secondly, at the individual scale, by implementing an automated image processing method based on deep learning to track each individual pioneer bacterium on the wall. Bacterial adhesion experiments are performed on a model bacterium (Shewanella oneidensis MR-1) at different shear rates (0 to1250 s−1) in a microfluidic system mounted under a microscope equipped with a CCD camera. Image processing was performed using a trained neural network (YOLOv8), which allowed information extraction, i.e. bacterial wall residence time and orientation for each adhered bacterium during pioneer colonization (14 min). Collected from over 20,000 bacteria, our results showed that adhered bacteria had a very short residence time at the wall, with over 70 % remaining less than 1 min. Shear rates had a non-proportional effect on pioneer colonization with a bell-shape profile suggesting that intermediate shear rates improved both bacterial wall residence time as well as colonization rate and level. This lack of proportionality highlights the dual effect of wall shear rate on early bacterial colonization; initially increasing it improves bacterial colonization up to a threshold, beyond which it leads to higher bacterial wall detachment. The present study provides quantitative data on the individual dynamics of just adhered bacteria within a population when exposed to different rates of wall shear.http://www.sciencedirect.com/science/article/pii/S2590207524000650Early bacterial adhesion dynamicsWall shear rateDeep learningMicrofluidic systemShewanella oneidensis |
| spellingShingle | Lucie Klopffer Nicolas Louvet Simon Becker Jérémy Fix Cédric Pradalier Laurence Mathieu Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning Biofilm Early bacterial adhesion dynamics Wall shear rate Deep learning Microfluidic system Shewanella oneidensis |
| title | Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning |
| title_full | Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning |
| title_fullStr | Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning |
| title_full_unstemmed | Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning |
| title_short | Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning |
| title_sort | effect of shear rate on early shewanella oneidensis adhesion dynamics monitored by deep learning |
| topic | Early bacterial adhesion dynamics Wall shear rate Deep learning Microfluidic system Shewanella oneidensis |
| url | http://www.sciencedirect.com/science/article/pii/S2590207524000650 |
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