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# CountmRNA.jl Our gene noise modulation project captured a series of 3D live cell fluorescence time-lapse image using spin-disk confocal. We aim to count mRNA number inside each nuclei during gene expression. Thanks to many other researches' work, we apply and adjust algorithm to split each trajectory, extract nuclei, then recognize mRNA. This algorithm features on morphology processing. There are our algorithm in julia code. You are free to take anything to help your study! Even you don't care algorithm, `julia2ims.jl` and `tiffxml.jl` might save your day if you work with imaris/imagej. Red fluorescence prefixed with NLS are expressed to label nuclei, while mRNA are more brilliant. We use Andor spin-disk microscope, 60x TIRF objective, Hamamatsus Ocra Fusion 4(roi 1900x1300). Each position capture 20 z slices and about 110 time points(~ 18 hours). ## Files | NAME | DESCRIPTION | |-------------------|---------------------------------------------------------- | splitcell.jl | Use Laplace of Gaussian(LoG) filter extract nuclei from raw 3d image | | lineage.jl | Use connected component to find each track and use watershed to split | | segmentation3d.jl | Use Otsu's method to threshold 3d nuclei | | normalization3d.jl| Normalize minimal and mean intensity of nuclei | | julia2ims.jl | Useful functions to load and save imaris 5 file | | tiffxml.jl | Useful functions to save tiff with OME-TIFF info | | CountmRNA.jl | Completed pipline to extract and track nuclei | | deprecations.jl | Deprecated functons collections | | ims2info.ipynb | Extract Imaris data to matlab .mat files | | notebook/xxx.ipynp| Debug files respond to each julia function, you can ignore them | ## Algorithm Algorithm is special to our data feature and analysis target, which try to low compute complexity but not for general case. In our strategy, we first project 4D image(XYZT) into 3D image(XYT) to simplify problem, then search connected components in projected image to locate each cell/nuclei trajectory and border. At the end, benefiting from known trajectory and border, segmentation can be limited in local. We apply common Otsu's method at each marked space in original data, which separates reliable 3D nuclei out of cytoplasm background in spite of cell intensity variation and photobleach. Firstly, extract binary nuclei mask from raw image. We apply medium filter(size=5) and LoG filter(gamma=40) on z-projected image. Secondly, we search connected component as cell trajectories in 3D image(XYT). Because cells move slow(large overlap between frame) and occupy large area(under 60X objective), using connected component do work correctly. More, comparing finding closest neighbour by cell centroid, its direct and completed logic help to handle cell come/leave from image border. Each detected connected components are assigned unique id. By filtering connected component length in time dimension, short trajectories are removed. Then possible collisions are detected by scanning connected component number among each time slice for each trajectory, then they are split using a distance transform and resign new unique id. These trajectories are the seeds to divide each cell in whole image. Thirdly, we use above trajectories as seeds/markers to perform watershed at each z-projected image. As a result, each detected cell occupy unique area without overlap in whole image, which called land mask. (Note: border mark don't equal to nuclei edge) Fourthly, backing to original 3D image(XYZ), we extend each 2D land mask to 3D and apply Otsu's method to separate nuclei. Otsu's method requires enough background and object information in image, this is why we keep both nuclei and cytoplasm inside land mask. We use a 512x512x20 box to mask land mask again, then Otsu's method is applied to original 3D image defined by final mask. Due to cell intensity variation and photobleach, threshold is calculated for each nuclei and time point independently. Fifthly, normalize nuclei intensity for imaris analysis. Because imaris is not flexible to set variable parameter for search RNA spot, we normalize minimal intensity and mean intensity of nuclei to fixed value. ## Count mRNA spot with imaris Before we have these code, we tried to use Imaris for all these work, but Imaris allocated huge memory for calculation and failed to handle photobleach and cell intensity variaction. So we decide to simplify question: extract cell using own code and just let imaris count mRNA spot. More, my limited experience and time also suggest me to use imaris at this time. Before load into imaris, I still need to remove some failed segmentation result by hand. Just use Imaris' spot model, the creation Parameters are: ``` [Algorithm] Enable Region Of Interest = false Enable Region Growing = false Enable Tracking = false Enable Region Growing = false Enable Shortest Distance = false [Source Channel] Source Channel Index = 1 Estimated XY Diameter = 0.540 um Estimated Z Diameter = 1.50 um Background Subtraction = false [Classify Spots] # NOTE: The value are slight different among expriments "Intensity Center Ch=1 Img=1" above 2000 or "Intensity Mean Ch=1 Img=1" above 1850 or 1950 or 1800 ``` ## Known Issue and Todo 1. fail to separate some collisions: may require fully watershed instead of just distance transform 2. some dark line occur in middle of nuclei: may some biology feature 3. fill hole inside neclei ## Licence MIT License Copyright (c) 2020 H.F.
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