|Home | About | Journals | Submit | Contact Us | Français|
This protocol and the accompanying software program called LEVER enable quantitative automated analysis of phase contrast time-lapse images of cultured neural stem cells. Images are captured at 5 min. intervals over a period of 5 to 15 days as the cells proliferate and differentiate. LEVER automatically segments, tracks and generates lineage trees of the stem cells from the image sequence. In addition to generating lineage trees capturing the population dynamics of clonal development, LEVER extracts quantitative phenotypic measurements of cell location, shape, movement, and size. When available, the system can include biomolecular markers imaged using fluorescence. It then displays the results to the user for highly efficient inspection and editing to correct any errors in the segmentation, tracking or lineaging. In order to enable high-throughput inspection, LEVER incorporates features for rapid identification of errors, and learning from user-supplied corrections to automatically identify and correct related errors.
Understanding how neural stem cells (NSCs) give rise to different types of progeny is a fundamental question in developmental biology. Lineage analysis is a key aspect of quantifying the patterns of development for stem cells. The importance of gathering lineage information is well-exemplified in the blood system where lineage analysis revealed that differentiated blood cells are derived from hematopoietic stem cells and identified factors that regulate the different types of blood cells generated information that was vital for design of blood disorder therapies1,2. In addition to lineage analysis, another key aspect of NSC behavior is the pattern of changes in the motion and morphology of an individual cell, patterns that have been shown to enable accurate prediction of the type of progeny an NSC will produce before the cell divides. In previous research we developed a software methodology called Algorithmic Information Theoretic Prediction and Discovery (AITPD)3,4. AITPD was applied to the segmentation and tracking results of NSC image sequence data where it discovered that NSCs with different cell fate outcomes exhibited visually subtle differences in the patterns of motion and morphology, and subsequently used those differences to accurately predict cell fate from the segmentation and tracking results.
There is a compelling and widespread need for computer automated tools to generate lineage trees from time-lapse images, and to produce quantitative phenotypic measurements. Due to the visual ambiguity inherent in image sequence data showing proliferating cells, such automated tools will inevitably make mistakes. Mistakes in the tracking or lineaging of NSCs can corrupt the ultimate statistical analysis. There is a pressing need for methods to automatically identify easily correct errors in the automated image analysis results. Here we present a software tool named LEVER (Lineage Editing and Validition) designed to quantify the lineage and the patterns of dynamic behaviors of NSC development. LEVER contains two integrated modules. The first performs automatic image segmentation (delineation), tracking and lineaging of the stem cells. The second module is an interactive software application that allows the user to inspect and edit the results of the automated image analysis. Integration of the two modules enables the automatic image analysis tools to learn from the manual edits, making the best possible use of human-provided corrections. In addition to generating lineage trees from the time lapse-image sequence data, LEVER accurately segments and tracks NSCs through the image sequence, extracting the segmentation and tracking information required to apply AITPD to wider problems relating to the analysis of NSC development. Figure 1 illustrates the LEVER process.
The automated segmentation, tracking and lineaging modules of LEVER are based originally on the NSC lineaging tools developed by Al Kofahi, et al.5. The segmentation and tracking was improved in Cohen, et al.4 to allow the tracking algorithm to automatically make corrections to segmentation errors. The tracking algorithm was also improved to incorporate multiple image frames when solving the data association problem between segmentation results and tracks.
This work improves further on the segmentation algorithm from these previous implementations by replacing the watershed transform that was used to separate touching cells with a thresholded morphological gradient, improving the ability to separate touching cells while reducing the over-segmentation associated with the watershed transform 6. This work also improves on these previous implementations by replacing the bipartite assignment step that was used to optimally assign existing tracks to future sequences of segmentations. The tracking algorithm used by the LEVER program uses an approach we term Multitemporal Association Tracking (MAT). MAT performs a minimum spanning tree optimization7 instead of using a bipartite graph matching approach to associate existing tracks with multiple frames of segmentation results. MAT eliminates the need to construct explicit probabilistic models of segmentation errors, instead using known stem cell behaviors to predict the most likely sequence of frames. Supplementary Table 1 shows a comparison between the MAT tracking approach and the bipartite matching tracker that was used in Cohen, et al.4. LEVER also exploits user supplied edits to automatically correct related segmentation errors. This inference-based approach to learning8 from user supplied edits was inspired by our prior work on retinal progenitor cells4, where tracking results were used to automatically correct segmentation errors.
In the LEVER program we have combined the MAT algorithm with the inspection and editing tools in order to optimally utilize manual edits for correcting the segmentation and tracking results. Importantly, each time the user performs an edit operation, MAT recalculates the segmentation and tracking for all cells affected by the manual edit throughout the image sequence. Figure 2 shows the LEVER user interface, with the image sequence data and automated segmentation and tracking results displayed in one window, and the lineage tree displayed in a second window. Edits and navigation of the spatiotemporal data can be done on either window. Figure 3 shows how a user can correct a segmentation error by simply specifying the correct number of cells in a region. Such corrections are then used to identify and correct related errors in subsequent image frames.
Here we describe a protocol that uses the LEVER computer program to automate lineaging for embryonic NSCs. To adapt the protocol for other cell types, the culture medium, growth factors, and coating of tissue culture dishes must be optimized. In addition, the division rate and proliferative potential of the cells must be considered. For highly proliferative cells that produce large clones, the initial plating density should be low to minimize clonal progeny overlapping and cells crawling on top of each other, which impedes tracking. In addition, cell motility is an important factor because cells can move out of the frame. If cells are highly motile, conditions that limit cell movement to within the field of view can be used, for example by etching small islands of appropriate adhesive surfaces9. Increasing the temporal resolution of imaging can make it easier to track highly motile cells. Depending on the cell cycle length and motility, the time of recording should be adjusted. For some cells, live imaging for example with specific promoter driven fluorescent tags, such as eGFP, can be acquired (e.g. once per hour). This is a useful technique that provides real-time information on phenotype, but is limited by cell sensitivity to exciting wavelengths. To elucidate the progeny phenotype at the end of clonal development, immunohistochemistry is performed using specific markers appropriate for the particular cell type being imaged. The LEVER program is applicable to any image sequence for which a human observer is able to specify the correct tracking and lineaging. Applications of LEVER are limited to cell culture densities at which it is possible for a human observer to validate and correct the automated results.
The segmentation algorithm described here is specific to phase contrast microscopy. Applications using a different imaging modality such as fluorescence microscopy will require a custom segmentation algorithm, or should investigate the use of either manual lineaging or the protocol developed by Murray et al. 10. Although the segmentation algorithm has been optimized for mouse embryonic NSCs, it is still possible to apply directly to other cell types. For example, in other current biological studies we have applied the segmentation algorithm to adult NSCs, and to hematopoietic stem cells. Because the adult NSCs tend to “clump” together, these image sequences require more manual editing to separate touching cells. Since the adult NSC clones being analyzed are smaller than embryonic clones, the overall editing time is similar. For the hematopoietic stem cells, because the cells are extremely motile we increased the temporal resolution of imaging to one frame per minute. This makes it easier for the tracker (and also for the human observer) to establish temporal correspondences between cells. The increased temporal resolution comes at a cost of decreased microscope throughput due to a reduction of the number of movies that can be captured simultaneously and also increases the data storage requirement and time required for application of the protocol. As a “rule of thumb”, temporal resolution should be chosen to be no less frequent than the cell radius divided by the maximum cellular velocity. The use of phase contrast rather than fluorescence imaging has eliminated issues related to phototoxicity for the cells imaged using this protocol, enabling long term high frequency image capture. At the maximum temporal resolution of imaging, the use of a deep red filter in the light path has allowed long term survivability under continuous illumination of a single field without impacting subsequent image analysis.
Experimental treatments and biological controls can be imaged from the same culture plate during the same period of image acquisition. Software controls consist of the sample data and results (Data Sets 1 and 2, which are available for download along with the LEVER executables on the webpage: https://pantherfile.uwm.edu/cohena/www/lever.html.). In adapting the protocol to other applications, users should compare their images to the sample images for cellular appearance and dynamics, as well as for contrast, illumination and background. Results can be compared to those obtained from the sample data.
The full diversity of potential applications for this protocol include the segmentation, tracking and lineaging of proliferating cells imaged using phase contrast microscopy. The software implementation of the protocol presented here has been applied to segmenting, tracking and lineaging 2-D phase contrast image sequences of adult and embryonic murine NSCs as well as to rat retinal progenitor cells and oligodendrocyte precursors4. The MAT tracking algorithm described here has also been applied to quantifying organelle transport deficiencies in Huntington’s disease11. The cell culture and staining steps of the protocol have been applied to the study of timing in cortical neurogenesis12. Our image analysis algorithms can adapt to normal variations in imaging conditions. In order to analyze other cell types imaged by other protocols, some modifications and adjustments to the segmentation algorithm may be required. With the exception of the segmentation algorithm, all of the LEVER program including the tracking, lineaging and editing user interface, can be applied to any type of stem or tumorigenic cell.
Recent research involving stem cell lineage analysis has used manual tracking to follow individual cells through the image sequence and establish parent-daughter cell relationships. In Gomes et al.13, thousands of rat retinal progenitor cells were tracked manually to reconstruct their lineages. While manual tracking to generate lineages allows the analysis of population dynamics related to the lineage tree, the approach is not only tedious but it also fails to capture important properties of shape and motion for the cells being analyzed. Additionally, manually generated lineages are challenging to validate as it can be difficult to establish correspondences between the lineage and the image data. In Eilken, et al.14, a software tool was developed that allows users to manually specify temporal correspondences between cells in every pair of image frames. This approach allows for the capture of attributes related to cell motion and shape, and also provides correspondences between the lineage tree and the image data. This protocol represents a significant reduction in the amount of labor required compared to manual segmentation approaches. The error rate for the automated image analysis portion of the protocol is approximately 1–2% (see Supplementary Table 1). In contrast, manual segmentation would require editing 100% of the image data.In order to enable high throughput analysis of large quantities of stem cell data necessary for analyzing large populations, automated tools are required.
A number of automated stem cell lineaging trackers have been developed. In particular, a system for automatically lineaging and editing the lineage for fluorescently image sequences of C. elegans embryos was described in previous a Nature Protocols publication10. The protocol here differs in that it is designed for analyzing phase contrast vertebrate NSCs rather than fluorescence images of C. elegans embryos. Additionally, the tracking approach described in that protocol used a nearest neighbor strategy to associate segmentation results with tracks. Nearest neighbors data association approaches in multitarget tracking are error-prone under high target and noise density. The MAT tracking approach utilized by LEVER solves the data association problem optimally across multiple image frames simultaneously, improving performance significantly under high target and noise density, as shown in Supplementary Table 1. Other sophisticated phase contrast vertebrate stem cell tracking programs have also been developed, but to our knowledge none are available under an open-source freeware model. Li et al. describe a tracking approach that achieves an error rate comparable to MAT but at the cost of significant implementation complexity 15. Chen et al.16 implemented segmentation and tracking for 3-D fluorescence image sequences of thymocytes that included a statistically guided edit-based validation system similar to the protocol described here, although the source code was not released. Our approach here is designed for phase contrast rather than fluorescence images and uses a more sophisticated tracking algorithm (MAT) compared to their bipartite assignment tracking algorithm. Finally, the protocol presented here is unique in that the segmentation, tracking and lineaging algorithms are tightly coupled with the editing application allowing the program to learn from user edits and automatically correct related errors. Here we are releasing both the automatic image analysis and the user interface portions of the LEVER program under an open-source license with the intention that future developments and improvements in stem cell segmentation and tracking can be integrated into the LEVER program in order to make those improvements available to the wider biological community using a consistent user interface.
Progenitor cells that are difficult to grow as adherent cultures, that grow on top of one another, are highly motile or are particularly photosensitive will be challenging. In addition, dense cultures are difficult to segment and follow, so this protocol is most suitable for cells that can be plated initially at lower density then proliferate to form a low to moderately dense culture.
One limitation of the current implementation of the LEVER computer program is that it requires a computer running the Windows operating system. We do provide all of the LEVER source code so that anyone with access to a C++ compiler and the MATLAB environment for a particular operating system should be able to use LEVER with that operating system. Our use of the GPL free software license will allow such a modified version of LEVER to be redistributed. The authors are not aware of any issues that would prevent LEVER from running on a platform other than Windows, but this has not been tested to date.
There are two additional limitations to the protocol presented here. First, the segmentation is specific to the visual appearance of the cells being segmented. For cells that are imaged by other imaging protocols, the segmentation algorithm may not work as well. Such image data may still be processed but will require additional manual editing subsequent to the automated image analysis. In order to mitigate this limitation, we have implemented the segmentation in MATLAB in the hopes that it will ease the creation of new segmentation algorithms for other types of stem cells. The second limitation is that the segmentation algorithm can take a long time to run on older computers. On our reference computer which contains a dual quad core Intel Xeon X5570 with 16 processing cores and 24 GB of RAM, segmentation fully exploits the parallel processing capabilities of the machine and typically takes 5 to 15 min.
Windows 7 operating system, dual quad core Xeon X5570 processors (16 cores total with hyperthreading), 24 GB RAM, 2 TB ESATA external hard drive for image data storage, dual monitors. All timing information in the procedure below is based on this reference system.
Windows operating system (XP or later) for running LEVER.exe. Approximately 2 GB free disk space is required for the executables and sample data.
Compiled Windows executables as well as all of the LEVER source code and sample image data and results (Data sets 1 and 2) can be downloaded from https://pantherfile.uwm.edu/cohena/www/lever.html. The software will be released under the GNU public license. Details of the license are described in the source code, but in essence it allows a user to use, modify and redistribute LEVER as long as the open source aspect is maintained.
Using aseptic technique in a biological safety cabinet, dilute 100mls of 10X PBS solution with 900mls of sterile water. This can be stored at room temperature for up to three years.
To 1L sterile water add 2.4g KCl, 0.2g NAOH, 0.6g NAH2PO4, 0.1g MgCL2, 2.2g Na-pyruvate, 1.0g glucose, and 36.4g sorbitol. pH to 7.3 with NAOH. This can be stored at room temperature for up to one month.
Prepare using aseptic technique in a biological safety cabinet. For every 10mls of medium use 9.5mls DMEM, add 100μl L-glutamine, 100μl Na-Pyruvate, 100μl N2 supplement, 200μl B27 supplement, 10μl FGF2. Sterile filter using a 50 ml conical filter system. This can be stored at 4°C overnight.
To 5mls of DMEM add 10 units papain and 15μl DNase. Sterile filter through a 0.22μm syringe attached to a 5ml syringe. Incubate at 37°C for 30 minutes and use within an hour.
In a flask add 450 ml sterile water and a magnetic stir bar. Place on a stirrer. Add 40 pellets KOH (the KOH adjusts the pH to dissolve the PIPES buffer). Add in order: 18.15g PIPES, 5.95g HEPES, 3.80g EGTA, 0.812g MgCl2.6H2O. Add pellets of KOH one at a time until all PIPES is dissolved, then bring up to 500mls with sterile water. This should be used in the same day to make 4% Phem Fix. Recipe follows.
In a certified fume hood heat 450mls water to 60–70°C on a heated stirrer, add 40g of paraformaldehyde while stirring with a magnetic stir bar. Turn off heat and allow to dissolve. Add 1–2 drops 10N NaOH (or 1 pellet) to clear the solution and cool to room temperature. This should be used in the same day to make 4% Phem Fix. Recipe follows.
Combine 500ml 2X Phem Buffer with 500ml Paraformaldehyde. Check pH with pH strips in fume hood and adjust pH to 7.4 with NaOH. Vacuum filter through filter paper. This can be aliqouted and stored at −20°C up to 6 months.
1X PBS with 0.1% Triton X-100 This can be stored at 4°C for 2 weeks.
1X PBS with 0.1% Triton X-100 plus 10% normal goat serum. This can be stored at 4 °C for up to one week.
At the completion of this protocol, a user can expect to have gone from time-lapse image sequence data showing the development of one or more vertebrate NSCs imaged using phase contrast microscopy to a fully corrected lineage tree. A log file containing a complete list of the edits, with time, date and the id of the user creating the edits is generated. A movie showing the lineage tree simultaneously with the image sequence data with segmentation and tracking overlaid throughout the image sequence can be generated. Lineage statistics for every cell in the movie including parent-daughter cell relationships, and cell lifetimes as well as cell velocity and size information can be exported to a spreadsheet program for further analysis. Thus, digitization of the image sequence by LEVER will enable in depth statistical analysis of parameters such as changes in cell cycle time, cell motility, cell phenotype and overall lineage structure.
Figure 4 shows an example of a LEVER generated lineage tree with phenotypic information for Data set 1 (left panel). This lineage required approximately 30 min. to fully correct. Figure 4 also shows an example plot generated from the exported data showing average velocity, size and cell cycle time for the each of the NSCs in the lineage tree arranged by increasing order of division number (right panel). The full segmentation, tracking and lineaging results can also be exported for more sensitive analysis using the Algorithmic Information Theoretic Prediction and Discovery software tools4. These software tools were previously applied to predicting cell fate of individual NSCs, but have not yet been applied to studying clonal properties of development because of the difficulty in obtaining fully corrected lineages. The protocol presented here will enable the comprehensive study of the temporal dynamics of cell cycle time and population growth, motion, morphology and associations between cells across large numbers of clones.
Steps 1–16: The image acquisition portion of the protocol typically requires 5 to 15 days for NSCs, but this could vary depending on the cell cycle time, proliferative potential and survival of the cell type under study. During this timeframe, the system can capture up to 100 movies simultaneously. Each movie typically contains a few clones.
Steps 17–18: installing the software takes 5–10 min.
Step 19: Obtaining segmentation, tracking and lineage results takes 5–15 min. Note that the time requirement will depend on the specific image sequence and on the computer used. The time requirement will also scale (nearly) linearly with the number of processor cores available. It is suggested that these steps be run on a dual quad core processor if possible.
Steps 20–22: Editing results for a single clone takes from 30 min. to four hours depending on the number of cells in the clone.
Steps 23–28: Exporting the results takes 5–15 min.
This sample timing information is for a 504 frame image sequence, with each image containing 1344 × 1024 pixels, an average of 46 cells per frame, and a maximum of 120 cells in a single frame.
Troubleshooting advice can be found in Table 1.
Comparison of the MAT tracking algorithm used by LEVER with the bipartite matching tracker originally developed in Al Kofahi, et al.5, and refined in Cohen, et al.4 Although automatic segmentation, tracking and lineaging algorithms continue to improve, even very low error rates translate to a significant number of editing operations in order to generate a fully corrected lineage tree.
This Excel spreadsheet contains the results of exporting cell metrics from LEVER for Data Set 1. The data was sorted by field “origin cell”. All cells whose origin cell was 243 (the lineage that we wish to analyze) were copied to the second worksheet labeled “clone 243”. These cells were then sorted by the “first frame” field. The cell cycle time, average speed and average area were then converted to appropriate units and plotted to produce panel B of Figure 4.
This video shows the corrected lineage corresponding to Supplementary Data Set 1. This video was generated directly from LEVER (protocol, Step 26).
This video shows a second corrected lineage. The image sequence data for this lineage was generated as described Step 1 of this protocol, but was not provided due to space restrictions on the web server. This video was also generated directly from the LEVER program. This lineage was used to create Figure 2.
This video shows an adult NSC lineage in the top frame and an embryonic NSC lineage below. The embryonic lineage is the same shown in Supplementary Video 2. Both lineages are zoomed to show cells on the lineage more closely. These movies were generated individually from the LEVER program. A separate MATLAB script then combined the two movies and added the white 25 μm scale bars.
This project was supported by the University of Wisconsin-Milwaukee, and by grant number NS033529 from the National Institutes of Health (NIH) and by New York State Department of Health (NYSDOH) Contract C024352 from the Empire State Stem Cell Fund and the Regenerative Research Foundation. This project was also supported by NIH grant number R01EB005157 and by Grant Number R01NS076709 from the National Institute Of Neurological Disorders And Stroke. The authors would like to thank Samareh Shahmohammadi, Ryan Ference, Aislyn DiRisio, Sarah Hardwick, Sarah M. Goderie, Erin McAuley, Raymond Futia and Claire Davenport for their help with cell culture and lineage validation.
AUTHOR CONTRIBUTIONSM.W., E.W., B.R. and A.R.C. designed and implemented the computational tools and algorithms, prepared the figures and wrote the manuscript. R. A. N. A. did cell preparation and maintenance, helped with staining and verified lineage trees. S.K.G., E.K. and S.T. cultured and imaged the NSCs, provided manual lineage reconstructions for comparison, edited the automated lineages, made suggestions on the design of the software tools from a biological perspective and helped prepare figures and write the manuscript.
COMPETING FINANCIAL INTERESTS
The authors declare that they have no competing financial interests.