Name: amebas: automatic midline extraction and background subtraction of ratiometric fluorescence time-lapses of polarized single cells
Uploaded: 2023-06-23T12:30:00
Description: Watch this Scientific Journal Video about AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells at JoVE.com

The authors are grateful to FAPESP grants 2015/22308-2, 2019/23343-7, 2019/26129-6, 2020/06744-5, 2021/05363-0, CNPq, NIH R01 grant GM131043 and the NSF grants MCB1714993, MCB1930165 for financial support. Root hair data were produced with the infrastructure and under the supervision of Prof. Andrea Bassi and Prof. Alex Costa.

1. Interactive notebook protocol
The Jupyter notebook can be used directly on the web using Google Colab at https://colab.research.google.com/github/badain/amebas/blob/main/AMEBAS_Colab.ipynb, where the instructions below were based. Alternatively, the Jupyter notebook is available at https://github.com/badain/amebas, where it can be downloaded and configured to run locally in Jupyter (Anaconda can provide an easy and cross-platform installation process). A complete test data ca...

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The novel method presented here is a potent tool to streamline and automate the analysis of fluorescence microscopy image stacks of polarized cells. Current methods described in the literature, such as ImageJ Kymograph plugins, require manual tracing of the midline of the polarized cell of interest, a task that is not only time consuming but also prone to human errors. Since the definition of the midline in this pipeline is supported by a numerical method18,19 th...

Cell polarity is a fundamental biological process in which the concerted action of a collection of spatially concentrated molecules and structures culminates in the establishment of specialized morphological subcellular domains1. Cell division, growth and migration rely on such polarity sites, while its loss has been associated with cancer in epithelial tissue-related disorders2.
Apically growing cells are a dramatic example of polarity, where the polarity site at the tip typically reorients to extracellular cues3. These include developing neurites, fungal hyphae, root hairs, and pollen tubes, where multiple cellular processes show pronounced differences from the tip of the cell toward the shank. In pollen tubes, in particular, actin polymerization, vesicle trafficking, and ionic concentrations are markedly polarized, showing tip-focused gradients4. Pollen tubes are the male gametophytes of flowering plants and are responsible for delivering the sperm cells to the ovule by growing exclusively at the apex of the cell at one of the fastest growth rates known for a single cell. The tip-focused gradients of ions such as calcium5 (Ca2+) and protons6 (H+) play a major role in sustaining pollen tube growth, which is essential to accomplish its main biological function that culminates in a double fertilization5,6. Thus, quantitative methods to analyze the spatiotemporal dynamics along the midline of apically growing cells are essential to investigate the cellular and molecular mechanisms underlying polarized growth7,8,9. Researchers often use kymographs, i.e., a matrix that represents the pixel intensities of the cell&#39;s midline (e.g., columns) through time (e.g., rows), which allows visualizing cell growth and migration in the diagonal (Figure 1). Despite their usefulness, kymographs are frequently extracted by manually tracing the midline, being prone to biases and human errors while also being rather laborious. This calls for an automated method of midline extraction that is the first feature of the pipeline introduced herein named AMEBaS: Automatic Midline Extraction and Background Subtraction of ratiometric fluorescence time lapses of polarized single cells.
In terms of experimental procedures, quantitative imaging of ions/molecules/species of interest in single cells can be achieved with genetically encoded fluorescent probes10. Among the ever-expanding choices, ratiometric probes are one of the most accurate since they emit different fluorescence wavelengths when bound/unbound to the molecules of interest11. This allows for correction of the spatial heterogeneity in the intracellular concentration of the probe by using the ratio of two channels with their channel specific background subtracted. However, estimating the background threshold for each channel and time point can be a complex task since it often varies in space due to effects like shading, where the corners of the image have luminosity variation relative to the center, and in time due to fading of the fluorophore (photobleaching)12. Although there are multiple possible methods, this manuscript proposes determining the background intensity automatically using the segmentation threshold obtained with the Isodata algorithm13, which is then smoothed across frames through polynomial regression as a standard. Spatial components stemming from fluorescence heterogeneity unrelated to the target cell removed in12, however, were ignored by this method. Automatic thresholding can be performed by several methods, but the Isodata algorithm produced the best results empirically. Thus, automatic background value subtraction and ratiometric calculation are the second main feature of AMEBaS (Figure 1), which, taken together, receives as input a stack of dual-channel fluorescence microscopy images, estimates the cell&#39;s midline and the channel-specific background, and outputs kymographs of both channels and their ratio (main output #1) after background subtraction, smoothing, and outlier removal, together with a stack of ratiometric images (main output #2).
AMEBaS was tested with fluorescence time lapses of growing Arabidopsis pollen tubes obtained under a microscope, either with Ca2+ (CaMeleon)8 or pH (pHluorin)6 ratiometric sensors expressed under the pollen-specific LAT52 promoter. Images from each channel were taken every 4 s coupled to an inverted microscope, a front-illuminated camera (2560 pixels &#215; 2160 pixels, pixel size 6.45 &#956;m), a fluorescence illuminator, and a water immersion objective lens 63x, 1.2NA. Filters settings used for CaMeleon were: excitation 426-450 nm (CFP) and 505-515 nm (YFP), emission 458-487 nm (CFP) and 520-550 nm (YFP), while for pHluorin, excitation 318-390 nm (DAPI) and 428-475 nm (FITC), emission 435-448 nm (DAPI) and 523-536 nm (FITC). A complete data set was added for testing at Zenodo (DOI: 10.5281/zenodo.7975350)14.
In addition, the pipeline was tested with root hair data, where imaging was performed with a light sheet microscope (SPIM) as previously described15,16 with Arabidopsis root hairs expressing the genetically encoded Ca2+ reporter NES-YC3.6 under the control of the UBQ10 promoter17. The home-made LabView software that controlled the camera acquisition, sample translation and shutter of the light sheet microscope permitted the observation of the two cpVenus and CFP channels, but also the visualization of their ratio in real time. Every ratio image of the time-lapse represented a maximum intensity projection (MIP) between the cpVenus and CFP fluorescent channels images obtained from 15 slices of the sample spaced 3 &#956;m apart. The time-lapse cpVenus/CFP ratio of MIPs was saved and directly used for the AMEBaS analysis.
Although this pipeline can work with multiple types of growing and migrating cells, it was specifically designed to analyze growing cells that grow exclusively at the tip, such as pollen tubes, root hairs, and fungal hyphae, where there is a correspondence of the non-growing cytoplasmic regions between frames. When such a correspondence is not present, the user should choose the complete_skeletonization option in step&#160;1.3.1.1 (see the Discussion section for more details).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64857/64857fig01.jpg" /> 
Figure 1: An overview of the pipeline workflow. The AMEBaS pipeline analyses and processes microscopic time lapses in three main steps: Single-Cell Segmentation, Midline Tracing, and Kymograph Generation. <a href="https://www.jove.com/files/ftp_upload/64857/64857fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Github</td>
			<td>Github</td>
			<td></td>
			<td>https://github.com/badain/amebas</td>
		</tr>
		<tr>
			<td>Google Colab</td>
			<td>Google</td>
			<td></td>
			<td>https://colab.research.google.com/github/badain/amebas/blob/main/AMEBAS_Colab.ipynb</td>
		</tr>
	</tbody>
</table>

amebas: automatic midline extraction and background subtraction of ratiometric fluorescence time-lapses of polarized single cells

Cell polarity is a macroscopic phenomenon established by a collection of spatially concentrated molecules and structures that culminate in the emergence of specialized domains at the subcellular level. It is associated with developing asymmetric morphological structures that underlie key biological functions such as cell division, growth, and migration. In addition, the disruption of cell polarity has been linked to tissue-related disorders such as cancer and gastric dysplasia.
Current methods to evaluate the spatiotemporal dynamics of fluorescent reporters in individual polarized cells often involve manual steps to trace a midline along the cells&#39; major axis, which is time consuming and prone to strong biases. Furthermore, although ratiometric analysis can correct the uneven distribution of reporter molecules using two fluorescence channels, background subtraction techniques are frequently arbitrary and lack statistical support.
This manuscript introduces a novel computational pipeline to automate and quantify the spatiotemporal behavior of single cells using a model of cell polarity: pollen tube/root hair growth and cytosolic ion dynamics. A three-step algorithm was developed to process ratiometric images and extract a quantitative representation of intracellular dynamics and growth. The first step segments the cell from the background, producing a binary mask through a thresholding technique in the pixel intensity space. The second step traces a path through the midline of the cell through a skeletonization operation. Finally, the third step provides the processed data as a ratiometric timelapse and yields a ratiometric kymograph (i.e., a 1D spatial profile through time). Data from ratiometric images acquired with genetically encoded fluorescent reporters from growing pollen tubes were used to benchmark the method. This pipeline allows for faster, less biased, and more accurate representation of the spatiotemporal dynamics along the midline of polarized cells, thus advancing the quantitative toolkit available to investigate cell polarity. The AMEBaS Python source code is available at:&#160;https://github.com/badain/amebas.git

The AMEBaS pipeline automates the extraction of midline dynamics of polarized single cells from fluorescence microscopy image stacks, making it less time consuming and less prone to human errors. The method quantifies these time lapses by generating kymographs and ratiometric image stacks (Figure 1) in growing single cells. It can be adjusted to work on migrating single cells, but further experiments are necessary. AMEBaS is implemented in Python as an interactive Jupyter Notebook (described...

Watch this Scientific Journal Video about AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells at JoVE.com

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Current methods for analyzing the intracellular dynamics of polarized single cells are often manual and lack standardization. This manuscript introduces a novel image analysis pipeline for automating midline extraction of single polarized cells and quantifying spatiotemporal behavior from time lapses in a user-friendly online interface.

Cell polarity is a macroscopic phenomenon established by a collection of spatially concentrated molecules and structures that culminate in the emergence ...

This method quantifies the spatiotemporal dynamics of polarized cells along its midline expressing fluorescent reporter or dye for specific entity of interest such as actin, ions, or vesicle dynamics. The version of automation eliminates biases and permits scalability, especially for tasks that are digitally done manually and can become time-consuming and subjective. To begin, open Jupyter Notebook and read the time-lapse files, either by accessing the Google CoLab interactive notebook homepage, or downloading and opening the AMEBaS_local IP YNB from GitHub.To prepare the directory setup for the input and output data using the local version, place the fluorescence time-lapse as a TIF or DV file inside a folder named data located in the program's root folder. Create an out folder to receive the generated data, then run the setup code block. If using the notebook on Google CoLab, execute the setup code block to automatically generate data and out folders, then run the file input code block to read the time-lapse data by clicking the play button.If utilizing Google CoLab, upload the time-lapse file to the data folder by clicking the choose file button. Next, choose to generate additional outputs for each step by setting the verbose parameter to true or false. To detect the main cell and segment from the background, run the single cell segmentation code block by clicking the play button to automatically separate the cell of interest from the background.Adjust the sigma value in the sigma variable to fine tune the smoothness of the segmentation mask. The default value is 2.0. Now set the variable estimate to false for storing the threshold estimated directly from the ISO data, or true for smoothing it across neighboring frames using local polynomial regression.Adjust the n_points variable to fine tune the function with the default value being 40. To trace the midline along the cell extension, run the cell midline tracing code block by clicking the play button to skeletonize the cell using Lee's method and extend the tip of the last skeleton through linear extrapolation. Next, select the tracing option for the midline by adjusting the complete_skeletonization argument to trace either on the last frame or once per frame.Adjust the interpolation_fraction variable to set the fraction of points in the skeleton for interpolation during extrapolation. The default value here is 0.25. Next, modify the extrapolation_length variable to determine the length of the midline extrapolation.The default value is minus one, which extends the skeleton to the nearest edge. Now run the first data visualization code block by clicking the play button to automatically generate kymographs for both channels. Choose the Gaussian kernel size for smoothing by adjusting the kymograph_kernel variable.To display the intensities of nonextended skeletons properly, a custom color map is needed for their capped kymographs. Adjust the shift_fraction variable to choose the fraction percentage of intensities that will be designated as the background color, black. Then run the second data visualization code block by clicking the play button to generate a ratio metric kymograph and time-lapse automatically.Adjust the switch_ratio variable to change the order of channels used as the numerator and denominator during ratio calculations with the default being false. Check if the ratio time lapse requires additional smoothing with a median filter pass by adjusting the smooth_ratio variable. By default, this is set to false.Next, choose to either remove or keep outliers resulting from low signal in the denominator channel by adjusting the reject_outliers variable. Outliers are marked as values 1.5 times the interquartile range above the third quartile. Finally, choose if the background and the ratio metric output needs to be exported by adjusting the variable background_ratio.The default is false to replace the background with zeros. AMEBaS tested on datasets like pollen tubes expressing the calcium reporter cameleon, pollen tubes expressing the pH indicator florin, root hairs expressing the calcium reporter NESYC 3.6 worked successfully despite differences in the growth direction, imaging techniques, fluorescent reporters, and cell types. Remember to execute the set of block so you can install the required packages.Additionally, it's crucial to adjust the sigma values specifically for your images so you can get more accurate results. The resulting kymographs then can be used to analyze the spatiotemporal dynamics of the solve interest. Additionally, you can calculate it finding method, growth and time series to analyze the right solution.

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