Grandison, S

Kelemen, G

Leggett, RM

Morris, RJ

Moulton, V

English

Leggett, Richard M.

Grandison, Scott

Morris, Richard J.

Kelemen, Gabriella

Moulton, Vincent

University of East Anglia digital repository

Statistical analysis of the growth and morphologyof the filamentous microbe Streptomyces coelicolorRichard M. Leggett1∗, Scott Grandison1, Richard J. Morris2,Gabriella Kelemen3 and Vincent Moulton11 School of Computing Sciences, University of East Anglia2 Department of Computational and Systems Biology, John Innes Centre3 School of Biological Sciences, University of East Anglia1 IntroductionStreptomycetes are commonly occuring soil bacteria that grow through extension and branchingof hyphal filaments (Figure 1). This morphological growth pattern, which is rare for bacteriaand interesting in its own right, is thought to be connected to the onset of secondary metaboliteproduction. Streptomycetes are a rich source of biologically active secondary metabolites, someof which are potent antibiotics, growth promotors or anti-cancer agents (see Flardh and Buttner2009 for a full description of the organism’s life cycle). Streptomyces is therefore not only afascinating model system for morphological studies but also of high pharmaceutical importancefor its natural products.?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????Spore5μmFigure 1: Example image of S. coelicolor (wild-type strain) grown on solid media for 16 hours.Our research has focussed on understanding and modelling the early-stage growth and morphol-ogy of the model organism Streptomyces coelicolor. Part of this work has involved collectingthousands of microscope images and developing techniques to gather and analyse statistics fromthem. The data produced by this work is being used to study the differences between wild-typeand mutant strains, as well as informing and validating spatial growth models.2 Data gatheringSamples of S. coelicolor were grown on cellophane discs laid over solid media, an approachwhich encourages 2-dimensional growth. Preparation for microscopy involved methanol fixingand staining with a fluorescent wheat germ agglutinin (WGA) conjugate and propidium iodide107?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????(PI). The former binds to bacterial cell wall components and provides good contrast profiles ofthe organism’s shape; the latter binds to nucleic acids, allowing easy identification of spores.Fluorescence microscopy was carried out using a Zeiss AxioImager upright microscope with63x and 100x oil-based lenses. Channel images were saved as PNG files.Spores were innoculated and incubated for 6 to 18 hours and samples were taken at 2 hourintervals. Imaging this process thus provides a series of snapshots of spatial patterns fromwhich we aim to recover the morphological development of the growing bacteria.3 Image analysisWe have developed software to provide semi-automatic analysis of images of filamentous mi-crobes. This software has been used successfully for processing Streptomyces growth patternimages but it is of sufficient generality to be applicable for a wide range of similar image anal-ysis problems, for instance fungal growth, moss development, root hairs, veins, and nerves.10 00 00? ??1 = pixel must be set0 = pixel must be cleared? = pixel may be set or clear?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????(a) (b)Figure 2: Image processing: (a) The steps involved in processing images and extracting morphologicaldata. (b) Masks used for locating tip points.Each microscope image was loaded into the software and underwent an 8-stage process, asdepicted in Figure 2a. In the pre-processing stage, the image is cropped and a Gaussian filterapplied to smooth the image and remove noise (see, for example, Sonka et al. 1999). Thresholdsegmentation (see also Sonka et al. 1999) is used to find the organism’s shape and to removeimage background. This results in a black and white image with white pixels for bacteria andblack pixels for background. The image is then thinned to single pixel width by applying twocommon skeletonization algorithms in sequence - firstly that of Zhou et al. 1995, then that ofZhang and Suen 1984.Once this process has produced a skeleton image, algorithms are applied to identify tip pointsand branch points. For branch points, we perform a raster scan of the skeleton image, lookingfor any white pixel which has three or more neighbours in the 8-neighbourhood. Tip points areidentified by running a set of 3-by-3 pixel templates over the image which seek out characteristicpatterns (Figure 2b). The spore point has to be manually selected by the user, but this is easilyrecognised from the propidium iodide channel image.The skeleton is then divided up into segments - that is, a piece of connected filament boundedby any two key points (branch, spore or tip). A path following algorithm (Figure 3a) is ap-plied to build up segment information by tracing the skeleton path between points. Using thisinformation, the software calculates segment lengths with a standard arclength calculation andscaling factors provided with the microscope.108?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????Path following algorithm1 Mark all skeleton pixels as unvisited.2 Remove the next tip point from the list of tip points andset currentPosition = tipPoistion.3 Look in the 8-neighbourhood around currentPositionfor an unvisited pixel. If the pixel is a tip point or branchpoint, proceed to step 6.4 Mark pixel as visited and set currentPosition = pixel-Position.5 Repeat steps 3-5.6 If last pixel is a branch point, check if it needs to be re-classified by counting the number of unvisited pixelsin the 8-neighbourhood.7 Repeat steps 2-7 until no more tip points.(a)Filament classification algorithm1 Mark all segments as unvisited.2 Set startPoint = sporePoint and d = 0.3 Increment d.4 Build a list of all paths from startPoint to all tip points,travelling along only unvisited segments.5 Find longest path in list and mark each segment of itas visited and with depth of d6 For each branch point in the longest path, set start-Point = branchPoint and repeat steps 3-6.(b)Figure 3: Two algorithms implemented in the software: (a) Path following algorithm used for segmentidentification. (b) Filament classification algorithm used to determine the filament depth.4 Calculation of biometrics and statisticsWith all key points in an image identified (Figure 4a) and with lengths measured, the soft-ware calculates a range of biometrics and statistics. Basic biometrics include the total hyphallength, the number of tips, the area covered by the bacteria and the number of spore germ tubes.Histograms are generated of internal segment lengths (the lengths between branch points), dis-tances from tip to nearest branch point and a range of other metrics. Line graphs are plottedof mean values of metrics against time, while scatter plots are used to plot individual bacteriametrics against their total hyphal length (a property which is proportional to growth time).?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????Spore germ tubes are classified as primary filaments, their children as secondary filaments, theirgrandchildren as tertiary filaments. We use the term filament depth to describe this - a depthof 1 relates to a primary filament, 2 a secondary filament and so on. The relative amounts offilament at each depth is important, as it can be an indicator of the degree to which a bacteriumhas colonised its enviornment, as well as an indication of various morphological features.Because our images don’t show the development of the same bacterium over time, we cannotclassify filaments as they emerge; instead we assume (as is commonly understood) that all tipsextend at a constant, rate and apply the algorithm in Figure 3b to classify segmemts according todepth. We can also use the depth data to calculate a measure of hyphal balance. In Streptomyces,there are typically two germ tubes, the second emerging later than the first and extending in theopposite direction. Adding up the hyphal length on each side of the spore enables insights intothe symmetry of the organism.The positioning of branches within filaments is considered of biological significance. Our soft-ware looks through the list of tip points, identifying any where the distance to the nearest branchpoint is less than a threshold value (typically 1.5µm). This tip is classed as an emerging tip andthe nearest branch point provides the point of branching on the parent filament. This parentbranch point will be connected to a previous branch point which represents either its own birthfrom another filament, or the site of its previous child branch. New branches tend to form be-tween the tip and the previous branch, so the parent branch point is usually connected to a tippoint which represents the parent filament’s active tip. The distances from the parent’s activetip to the child branch site and from the child branch site to the previous branch point are usefulindicators of branching behaviour. We can use this information to plot a histogram showing thedistribution of new branch locations on the parent branch (Figure 4b).109?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????The software also seeks to identify apical branching. In wild-type S. coelicolor, new branchespredominantly form behind a tip, but in some fungi and mutants, the growing tip can split intotwo. Such cases are identified by looking for two emerging tips which share the same parentbranch point.SporeActive tipBranch pointPrimaryfilamentSecondaryfilament5μm(a)510152025102030405060708090100102030405060708090100% in bin  (Tip)    Position     (Branch)051015202510203040506070809010010203040506070809000  (Tip)    Position     (Branch)02468101 4 7101316192225281 4 710131619222528Wild-type% in binSegment length ( m)02468101 4 7101316192225281 4 710131619222528MutantSegment length ( m)(b)Figure 4: Example results: (a) The bacterium from Figure 1 after image processing and analysis. (b)Histograms for segment length and position of child branch for the wild-type and a mutant.5 DiscussionWe have presented the methodology underlying our automated filamentous growth image anal-ysis software tool. The main developments include algorithms for noise reduction, skeletoniza-?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????ysis software tool. The main developments include algorithms for noise reduction, skeletonization, tip and branch point identification, as well as routines to perform statistical analyses onthe derived data. Using the tool, we have begun to analyse a large collection of data from wild-type S. coelicolor and from a number of mutants which display markedly different branching(Figure 4). The data generated so far with our software is enabling us to highlight a number ofmorphological differences which had not previously been obvious and to provide quantitativeanalysis of anecdotal observations, with the goal of producing spatial models of growth. We arein the process of developing ideas about the biological mechanisms involved and, in so doing,hope to shed light on the function of the genes which are affected in the mutants.ReferencesFlardh, K. and Buttner, M. J. (2009). Streptomyces morphogenetics: dissecting differentiationin a filamentous bacterium. Nature Reviews Microbiology, 7: 36-49.Sonka, M., Hlavac, V. and Boyle, R. (1999). Image Processing, Analysis, and Machine Vision,PWS Publishing.Zhang, T. and Suen, C. (1984). A Fast Parallel Algorithm for Thinning Digital Patterns. Com-munications of the ACM, 27:236-239.Zhou, R., Quek, C. and Ng, G. (1995). A novel single-pass thinning algorithm and an effectiveset of performance criteria. Pattern Recognition Letters, 16:1267-1275.110?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????

Statistical analysis of the growth and morphology of the filamentous microbe Streptomyces coelicolor

Statistical analysis of the growth and morphology of the filamentous microbe Streptomyces coelicolor

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