Automated cell tracking using StarDist and TrackMate [version 1; peer review: awaiting peer review]

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images

Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images. </div

An integrated assessment of nitrogen source, transformation and fate within an intensive dairy system to inform management change

From an environmental perspective optimised dairy systems, which follow current regulations, still have low nitrogen (N) use efficiency, high N surplus (kg N ha-1) and enable ad-hoc delivery of direct and indirect reactive N losses to water and the atmosphere. The objective of the present study was to divide an intensive dairy farm into N attenuation capacity areas based on this ad-hoc delivery. Historical and current spatial and temporal multi-level datasets (stable isotope and dissolved gas) were combined and interpreted. Results showed that the farm had four distinct attenuation areas: high N attenuation: characterised by ammonium-N (NH4+-N) below 0.23 mg NH4+-N l-1 and nitrate (NO3--N) below 5.65 mg NO3--N l-1 in surface, drainage and groundwater, located on imperfectly to moderately-well drained soils with high denitrification potential and low nitrous oxide (N2O) emissions (av. 0.0032 mg N2O-N l-1); moderate N attenuation: characterised by low NO3--N concentration in drainage water but high N2O production (0.0317 mg N2O-N l-1) and denitrification potential lower than group 1 (av. δ15N-NO3-: 16.4‰, av. δ18O-NO3-: 9.2‰), on well to moderately drained soils; low N attenuation—area 1: characterised by high NO3--N (av. 6.90 mg NO3--N l-1) in drainage water from well to moderately-well drained soils, with low denitrification potential (av. δ15N-NO3-: 9.5‰, av. δ18O-NO3-: 5.9‰) and high N2O emissions (0.0319 mg N2O l-1); and low N attenuation—area 2: characterised by high NH4+-N (av. 3.93 mg NH4+-N l-1 and high N2O emissions (av. 0.0521 mg N2O l-1) from well to imperfectly drained soil. N loads on site should be moved away from low attenuation areas and emissions to air and water should be assessed

Cervical epithelial damage promotes Ureaplasma parvum ascending infection, intrauterine inflammation and preterm birth induction in mice

Around 40% of preterm births are attributed to ascending intrauterine infection, and Ureaplasma parvum (UP) is commonly isolated in these cases. Here we present a mouse model of ascending UP infection that resembles human disease, using vaginal inoculation combined with mild cervical injury induced by a common spermicide (Nonoxynol-9, as a surrogate for any mechanism of cervical epithelial damage). We measure bacterial load in a non-invasive manner using a luciferase-expressing UP strain, and post-mortem by qPCR and bacterial titration. Cervical exposure to Nonoxynol-9, 24 h pre-inoculation, facilitates intrauterine UP infection, upregulates pro-inflammatory cytokines, and increases preterm birth rates from 13 to 28%. Our results highlight the crucial role of the cervical epithelium as a barrier against ascending infection. In addition, we expect the mouse model will facilitate further research on the potential links between UP infection and preterm birth

Automated cell tracking using StarDist and TrackMate [version 2; peer review: 3 approved]

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images

An integrated assessment of nitrogen source, transformation and fate within an intensive dairy system to inform management change

From an environmental perspective optimised dairy systems, which follow current regulations, still have low nitrogen (N) use efficiency, high N surplus (kg N ha¯¹) and enable ad-hoc delivery of direct and indirect reactive N losses to water and the atmosphere. The objective of the present study was to divide an intensive dairy farm into N attenuation capacity areas based on this ad-hoc delivery. Historical and current spatial and temporal multi-level data-sets (stable isotope and dissolved gas) were combined and interpreted. Results showed that the farm had four distinct attenuation areas: high N attenuation: characterised by ammonium-N (NH₄⁺-N) below 0.23 mg NH₄⁺-N l¯¹ and nitrate (NO₃¯-N) below 5.65 mg NO₃-N l¯¹ in surface, drainage and groundwater, located on imperfectly to moderately-well drained soils with high denitrification potential and low nitrous oxide (N₂O) emissions (av. 0.0032 mg N₂O-N l¯¹); moderate N attenuation: characterised by low NO₃¯-N concentration in drainage water but high N₂O production (0.0317 mg N₂O-N l¯¹) and denitrification potential lower than group 1 (av. δ¹⁵N-NO3-: 16.4, av. δ¹⁸O-NO₃-: 9.2), on well to moderately drained soils; low N attenuation—area 1: characterised by high NO₃-¯N (av. 6.90 mg NO₃¯-N l¯¹) in drainage water from well to moderately-well drained soils, with low denitrification potential (av. δ¹⁵N-NO₃-: 9.5, av. δ¹⁸O-NO₃-: 5.9) and high N₂O emissions (0.0319 mg N₂O l-1); and low N attenuation—area 2: characterised by high NH₄⁺-N (av. 3.93 mg NH4+-N l¯¹ and high N₂O emissions (av. 0.0521 mg N₂O l¯¹) from well to imperfectly drained soil. N loads on site should be moved away from low attenuation areas and emissions to air and water should be assessed

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic