FiloDetect: automatic detection of filopodia from fluorescence microscopy images
© Nilufar et al.; licensee BioMed Central Ltd. 2013
Received: 30 December 2012
Accepted: 11 July 2013
Published: 23 July 2013
Filopodia are small cellular projections that help cells to move through and sense their environment. Filopodia play crucial roles in processes such as development and wound-healing. Also, increases in filopodia number or size are characteristic of many invasive cancers and are correlated with increased rates of metastasis in mouse experiments. Thus, one possible route to developing anti-metastatic therapies is to target factors that influence the filopodia system. Filopodia can be detected by eye using confocal fluorescence microscopy, and they can be manually annotated in images to quantify filopodia parameters. Although this approach is accurate, it is slow, tedious and not entirely objective. Manual detection is a significant barrier to the discovery and quantification of new factors that influence the filopodia system.
Here, we present FiloDetect, an automated tool for detecting, counting and measuring the length of filopodia in fluorescence microscopy images. The method first segments the cell from the background, using a modified triangle threshold method, and then extracts the filopodia using a series of morphological operations. We verified the accuracy of FiloDetect on Rat2 and B16F1 cell images from three different labs, showing that per-cell filopodia counts and length estimates are highly correlated with the manual annotations. We then used FiloDetect to assess the role of a lipid kinase on filopodia production in breast cancer cells. Experimental results show that PI4KIII β expression leads to an increase in filopodia number and length, suggesting that PI4KIII β is involved in driving filopodia production.
FiloDetect provides accurate and objective quantification of filopodia in microscopy images, and will enable large scale comparative studies to assess the effects of different genetic and chemical perturbations on filopodia production in different cell types, including cancer cell lines.
Although there are no automated tools for filopodia detection on cancer cell images, there is considerable work on the closely related problem of tracing neurites in images of neurons. Neurites are any cellular extension of a neuron. Usually, the term refers to axons and dendrites, though it is sometimes used with filopodia. There are sophisticated algorithms for tracing neurites in images, and good public software packages are available [6–12]. The general neurite tracing problem differs in some details from the filopodia detection problem we study. Neurites can have complex branching structures, and it is commonly required to trace them in congested images with multiple cells and many visually crossing neurites. We focus on single-cell images. In these images, filopdia do not branch or cross so extensively as some neurites—although it is not unusual for longer filopodia to cross over other ones, and detection of these filopodia is challenging. Neurites such as axons and dendrites are significantly larger than filopodia, especially in comparison with the cell body. The filopodia we wish to detect can be little more than a pixel wide. Moreover, unless global context is taken into account, other cytoskeletal features within the cell can be confused with filopodia, and the bases of the filopodia, where they enter the cell body, have a considerably heterogeneous appearance.
Our recent work suggests that filopodia sizes in non-neural cells are lognormally distributed . The few, longest filopodia are not representative of the majority of the filopodia distribution, and thus we must detect all or nearly all filopodia to accurately assess the length distribution. Thus, new algorithms that can accurately detect and quantify filopodia in non-neural cells are greatly needed, as this will allow more rigorous and thorough study of the relationships between filopodia characteristics and the factors that control them.
In this paper, we propose FiloDetect, a fully automated method to detect filopodia from the cell body and measure filopodia length. The approach is inspired by neurite detection methods, including NeuriteQuant and fTracker, but designed in such a way as to avoid the problems they have with our kind of images. We employ intensity-based thresholding and a combination of morphological operations to detect the filopodia. The algorithm is implemented in Matlab and is publicly available at http://www.perkinslab.ca/Software.html. We validated FiloDetect on the non-transformed rodent cell line Rat2 and mouse melanoma cell line B16F1. The Rat2 images used to test the algorithm have been previously manually annotated for filopodia length and number , allowing us to assess the accuracy. The B16F1 images were annotated newly for this study.
We then used FiloDetect on a novel dataset, to determine whether expression of the lipid kinase, PI4KIII β, impacts filopodia production in breast cancer cells. We were interested in this question because of several lines of evidence implicating a role for PI4KIII β in breast cancer and filopodia production: it is activated by eEF1A2 (eukaryotic elongation factor 1 alpha 2) , which is amplified in approximately two-thirds of breast tumours [15, 16]; it was recently identified as a putative breast cancer driver gene, in a large-scale copy number and gene expression analysis of 2000 breast tumours ; and ectopic expression of PI4KIII β in fibroblast cells increases filopodia number and length [5, 14]. Thus, we hypothesized that PI4KIII β may drive filopodia formation in breast cancer cells, potentially enhancing their invasivenes. Our analysis shows this is indeed the case, with PI4KIII β involved in both increasing the filopodia length and number in the breast cancer cells.
Experiments were carried out on three datasets from three different cell lines. Rat2 rodent fibroblasts, B16F1 mouse melanoma cells, and BT549 human breast ductal carcinoma cells.
This dataset consists of a subset of 38 single Rat2, rodent fibroblast, cell images taken from . The details of fixation and imaging of these cells can be found in that publication. In this work, all filopodia at least 0.4 microns long were manually annotated, yielding the total number of filopodia on each cell, as well as the lengths and positions of those filopodia (Figure 1(c)). The subset of Rat2 cells studied in this paper were not genetically altered or chemically stimulated. Out of these 38 cells, 12 images were used in the training phase for the development of the automatic detection method and the remaining 26 images were used to test the method.
This dataset consists of images of B16F1 mouse melanoma cells, and was used for additional validation of FiloDetect, without any further tuning of parameters. We used five images provided by Dr. J. Schober  and seven images provided by Dr. T. Svitkina [19, 20]. We call these two groups of images the Schober and Svitkina datasets respectively. We manually annotated these images for filopodia, as described previously .
This data set consists of images of BT549, human breast cancer cells, that have been manipulated to express the protein phosphatidylinositol 4-kinase III beta (PI4KIII β). The BT549 cells ectopically expressing PI4KIII β were generated using the pLXSN retroviral system as described by . Human PI4KIII β cDNA was cloned into the pLXSN retroviral expression vector (Clontech). Polyclonal pools of BT549 cells stably expressing PI4KIII β were selected with 0.4 mg/ml G418. Cells selected to contain the empty pLXSN vector (EV) were also isolated and used as a control. For filopodia imaging, the cells were seeded onto coverslips in 6-well plates (1×105 cells/well), and allowed to adhere for 24hrs. Cells were then fixed in 3.7% paraformaldehyde, permeabilized with 0.1% Triton X-100, blocked with 1% BSA and stained with Phalloidin-546 (Invitrogen). Following staining, cells were mounted on slides using fluorescence mounting media (Dako). All images were acquired with a 100X NA 1.4 oil immersion objective (Olympus) at 1 airy U on a laser-scanning confocal microscope (IX80, Olympus) with Olympus Fluoview FV1000 software. From each group, empty vector control (EV) and PI4KIII β expressing (PI4K β), 5 images were used in the training phase to fine-tune parameters and 30 images were used in the testing phase.
Step 1: Cell segmentation
Cell body selection There can be substantial noise in images and debris in culture due to cell culturing, fixing and/or imaging conditions. Collectively, these factors result in a variety of objects of different sizes appearing in the thresholded image. Therefore we must select the primary cell from the image. To do this, we use an eight-connected neighborhood to define individual objects. This assigns all ON or white pixels touching vertically, horizontally or diagonally to the same object. The areas of all of the objects present in the image are calculated, and the object with largest area is preserved and considered as the main cell body. All other pixels are set to OFF or zero.
Step 2: Filopodia detection
After obtaining an initial segmented image, a series of morphological operations is applied to detect the filopodia. Morphology, originally defined as operations on sets, is applied to process images based on shapes .
Splitting the filopodia from the cell body To split the filopodia from the main cell body, we begin by applying the morphological opening operation. Opening consists of an erosion step (in which a pixel remains ON only if all pixels in its neighborhood are ON), followed by a dilation step (in which a pixel is turned ON if any pixel in its neighborhood is ON). The opening operation tends to remove small protrusions from the periphery of a larger object. In this case, the fragments removed from the cell body are considered candidate filopodia. However, it is unclear what size of neighborhood is ideal for detecting filopodia. To address this problem, we initially take the neighborhood of a pixel P to be all those pixels whose centers are ≤ 0.5 microns from the center of pixel P. We chose this threshold because the filopodia in our images generally had a width of ≤ 0.4 microns, and thus are eliminated by the opening operation. We further filter objects that are not sufficiently filament-like, by fitting an ellipse to the pixels in the object and discarding objects whose major axis in less than 1.5 times as long as the minor axis. This removes cellular protrusions too thick to be considered single filopodia. We use the remaining objects to get a more precise, cell-specific estimate of filopodia width, by calculating their average minor axis length L. We then apply the opening operation again to the original image using a structuring element of radius L, generating a revised set of candidate filopodia. Finally, we filter this set to remove objects less than 0.4 microns long. The same criterion was used in the  study, on the grounds that human annotators could not always agree on whether such small objects represented filopodia or not.
Step 3: Length estimation of the filopodia
The split filopodia are morphologically thinned into one pixel connected lines and the lengths of the filopodia are calculated by the area of each thinned filopodium. In this way, combined filopodia are length equivalent to the total length of all filopodia in the combined group. In the Rat2 cells images, the majority of the combined filopodia represent fused or bifurcating filopodia, which share a common base, and are not due to crossing over events. We have considered these fused filopodia as one object and have calculated the length of the fused filopodia using the method detailed above. In the manual count, combined filopodia were also considered as a single object, as they share the same base .
Results and discussion
Next we applied FiloDetect to assess whether increased PI4KIII β expression leads to enhanced filopodia number or length in BT549 breast cancer cells. Here we calculated the length and number of single and combined filopodia separately in response to the fact that filopodia are relatively long in BT549 cells, with many filopodia crossing events.
In this paper, we proposed FiloDetect to automate the quantification of filopodia, making more reliable and reproducible the task of quantifying filopodia from static microscopy images. The proposed FiloDetect system was evaluated on Rat2 fibroblast and B16F1 mouse melanoma cell images, manually annotated for filopodia number and length. A comparative analysis of the results shows the good performance of FiloDetect, in both number and length determination. This method was then applied to measure the effect of PI4KIII β’s expression on filopodia production in BT549 breast cancer cells. We found that PI4KIII β expression leads to an increase in filopodia number and length, suggesting that PI4KIII β is involved in driving filopodia production in the cell. When overexpressed, PI4KIII β may promote cancer cell metastasis, as filopodia are a characteristic of invasive cells.
Although FiloDetect compared favorably to manual annotations and was accurate enough to carry out the PI4KIII β analysis, further improvements may be possible. In Costantino’s work on detecting filopodia on neural growth cones  they found that segmentation based on edge detection was superior to intensity based thresholding–although both are options in their software. In pilot studies, we did not find an advantage to edge detection. However this might be true for other image sets. Adaptive intensity thresholding methods, where the threshold varies for different parts of the image, or methods that combine intensity and edge information might also yield improvements. Because the filopodia are comparatively small objects in typical images, and because it can be difficult for morphological analysis to correct for errors in segmentation, high quality segmentation is key to our approach. A completely different approach would be to forego segmentation and use a tracing-based approach to delineate filopodia. In the neurite detection literature, tracing-based approaches are generally considered to be the most accurate, although their computational burden is higher than that of morphology-based approaches.
Another area for possible improvement is in the untangling of combined filopodia. Following the policy of our previous manual annotations, we have not attempted untangling. However, some combined filopodia are truly physically joined, whereas others are really separate but overlap visually. By analyzing joined structure in more detail, it may be possible to discriminate between these cases. We have conducted preliminary analysis of 3D image stacks, to see if they might be informative in this regard. However, segmenting the cell is much more difficult in this case, because each layer of the stack contains differing and only partial information on where the cell boundaries are.
Filopodia are just one of many cytoskeletal features that are biologically relevant and that we might want to quantify automatically from images. For instance, it would be of interest in the study of cytoskeleton remodelling to be able to automatically define and measure the relative size/cellular proportion of a cell’s lamellipodium, which defines the flat and broad cellular protrusion containing a meshwork of branched F-actin found at the leading edge . In addition, it would be useful to develop an algorithm that is able to quantify the number/proportion of stress fibers, contractile acto-myosin structures, which span the length of a cell, and are involved in adhesion and motility . Robust and automated quantification of the size of the lamellipodium and the number of stress fibers in a cell under genetic and chemical perturbations, along with the measure of filopodial protrusions would allow a broader study of events of cytoskeletal rearrangement. Also, it would be interesting to see if our algorithm to measure filopodia number and length could be applied in a live cell imaging context, allowing real-time actin dynamic remodelling events to be studied quantitatively.
Availability and requirements
Algorithms were implemented in Matlab2009. The FiloDetect system and some sample cell images are available at http://www.perkinslab.ca/Software.html. There is no restrictions on non-commercial use of this software.
1 Here we define, MAE where M i = M1,M2,⋯M N are the manual counts and F i = F1,F2,⋯F N are the FiloDetect counts for N different cells.
We thank Dr. J. Schober and Dr. T. Svitkina for the B16F1 cell images we used to validate our system. This work was supported in part by a Government of Ontario Ministry of Economic Development and Innovation (MEDI) grant to TJP, an NSERC Discovery grant to TJP, a MITACS Elevate fellowship to SN, a CIHR Doctoral Research Award to AAM and an NSERC Discovery grant to JML.
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