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 can be found at Zenodo (https://doi.org/10.5281/zenodo.7975350), containing single and dual channel data of Arabidopsis pollen tubes expressing either pH or Ca&#8203;2+ reporters14. The pipeline has been divided into parts, where every step can be executed by clicking on the play button after setting the user-specific options. The necessary files for this study are available in AMEBaS- main zip folder (Supplementary Coding File 1).
<ol>
	<li>Open Jupyter notebook and read the time-lapse files.
	<ol>
		<li>Navigate to the homepage of the interactive notebook in Google Colab referred above or download and open the AMEBaS_Local.ipynb notebook from GitHub.</li>
		<li>Prepare the directory setup for the input and output data:
		<ol>
			<li>If using the local version, place the fluorescence time-lapse as a TIFF file or DV file inside a folder named data that must be in the root folder of the program. A folder named out must be created to receive the generated data. Then run the&#160;Setup&#160;code block.</li>
			<li>If using the notebook on Google Colab, run the Setup code block to automatically generate the data and out folders.</li>
		</ol>
		</li>
		<li>Run the File Input code block to read the time lapse data by clicking the play button. If using the Google Colab version of the notebook, click on the Choose File&#160;button to directly upload the time lapse file to the data folder. 
		&#8203;NOTE: The number of channels will be automatically detected based on the dimension of the image.</li>
		<li>Choose if additional outputs of each step will be generated by setting the &#39;verbose&#39; parameter to True or False.</li>
	</ol>
	</li>
	<li>Detect the main cell and segment from the background (Figure 2).
	<ol>
		<li>Run the Single Cell Segmentation code block to automatically separate the cell of interest from the background by clicking the play button. 
		NOTE: Median and Gaussian filters will be applied as a preprocessing step to remove unwanted noise before segmenting the foreground from the background by Isodata thresholding and isolating the region of the largest area to remove undesired artifacts.
		<ol>
			<li>Adjust the sigma value used by the Gaussian in the &#39;sigma&#39; variable to fine-tune the smoothness of the segmentation mask. The default value is 2.0.</li>
			<li>Set the variable estimate to False to store the threshold estimated from Isodata directly or to True to smooth it across neighboring frames using local polynomial regression (LOESS). Fine-tune its function by changing the n_points variable. The default value is 40.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Trace midline along the cell extension (Figure 3).
	<ol>
		<li>Run the Cell Midline Tracing code block by clicking the play button to automatically skeletonize the cell using Lee&#39;s method18 and extend the tip of the last skeleton through linear extrapolation.
		<ol>
			<li>Choose to trace the midline only on the last frame or once per frame by adjusting the complete_skeletonization argument. 
			NOTE: When all frames are skeletonized, extrapolation is skipped.</li>
			<li>Set the fraction of points in the skeleton to be interpolated during extrapolation by adjusting the interpolation_fraction variable. The default value is 0.25.</li>
			<li>Choose the length of the midline extrapolation by changing the variable extrapolation_length. The default is -1, which extends the skeleton up to the nearest edge.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Generate kymographs for each channel ( Figure 4).
	<ol>
		<li>Run the first Data Visualization code block by clicking the play button to automatically generate kymographs for both channels.
		<ol>
			<li>Choose the size of the Gaussian kernel used for smoothing by adjusting the variable kymograph_kernel. 
			NOTE: It corresponds to the size of the neighborhood (in pixels) over which the pixel intensities are averaged. The default is 3 pixels x 3 pixels.</li>
			<li>Nonextended skeletons generate capped Kymographs that must use a custom colormap to properly display their intensities. Choose the fraction percentage of the intensities that will be assigned to the background color, black, adjusting the shift_fraction variable. The default value is 0.7.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Calculate the ratio between channels (Figure 5).
	<ol>
		<li>Run the second Data Visualization code block by clicking the play button to automatically generate a ratiometric kymograph and a ratiometric timelapse (Figure 6). 
		NOTE: This step is only available when using dual-channel time lapses. The background intensity threshold stored in step 1.2.1.2 is subtracted from each channel.
		<ol>
			<li>Adjust the switch_ratio variable to switch the order of channels used as the numerator and denominator during ratio calculations. The default is False.</li>
			<li>Choose if the ratio timelapse must be further smoothed with a median filter pass by adjusting the smooth_ratio variable. The default is False.</li>
			<li>Choose whether to remove outliers produced by the low signal of the denominator channel by manipulating the reject_outliers variable. Defaults to True and defines outliers as values 1.5 times the interquartile range above the third quartile (where 75% of the values lie).</li>
			<li>Choose whether the background in the ratiometric output must be exported by adjusting the variable background_ratio. The default is False, which replaces it with zeros.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64857/64857fig02.jpg" /> 
Figure 2: Single cell segmentation step. Image processing techniques such as filtering, thresholding, and area labeling are used to isolate the signal of interest (step 1.2). This particular data had the following values for Lowest Intensity: 2556, Median: 3441, and Highest Intensity: 32125. <a href="https://www.jove.com/files/ftp_upload/64857/64857fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64857/64857fig03.jpg" /> 
Figure 3:&#160;Midline tracing overview- The midline of the single cell is obtained by calculating its skeleton (white). The tip (magenta) is extrapolated linearly from the last points at the end of the skeleton (step 1.3). In this composition both the midline and its tip are superposed over the original cell. <a href="https://www.jove.com/files/ftp_upload/64857/64857fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/64857/64857fig04.jpg" /> 
Figure 4: Timelapse&#39;s kymographs- Comparison of the kymographs for each channel generated with &#39;complete_skeletonization&#39; turned off (step 1.4). The vertical axis describes the progression of time, and the horizontal axis plots the average intensity of the extrapolated midline path followed by a single cell. For this particular data, the colormap represents the following values for Channel 1 Lowest Intensity: 2886, Median: 3167, Highest Intensity: 21021. Channel 2 Lowest Intensity: 3030, Median: 3400, Highest Intensity: 29688. <a href="https://www.jove.com/files/ftp_upload/64857/64857fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/64857/64857fig05.jpg" /> 
Figure 5: Background threshold smoothing- The background segmentation threshold is estimated via the isodata algorithm and then smoothed via local polynomial regression (step 1.5). <a href="https://www.jove.com/files/ftp_upload/64857/64857fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/64857/64857fig06.jpg" /> 
Figure 6: Ratiometric results- (A) Comparison between the last frame of the ratiometric timelapse and the segmented original first channel. (B) Kymograph generated from the ratiometric timelapse (step 1.5). <a href="https://www.jove.com/files/ftp_upload/64857/64857fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Batch mode protocol
<ol>
	<li>Download and place the file pipeline.py from the AMEBaS GitHub repository in the same directory as the data.</li>
	<li>Type in the file location on the command line after the program file.</li>
	<li>Include --v as a positional argument to show the internal steps of the pipeline, if desired.</li>
	<li>Include --s to choose in the sigma value used in the Gaussian Filter preprocessing step in preparation for cell segmentation. The default is 2.</li>
	<li>Include --a to trace the midline for each frame of the time lapse. By default, the pipeline uses only the last frame.</li>
	<li>Include --f to choose the fraction [0,1] of the skeleton to be used in the interpolation. The default value is 0.25.</li>
	<li>Include -e to choose the length in pixels of the extrapolated skeleton. The default is -1, which extends the skeleton up to the nearest edge.</li>
	<li>Include --sf to choose the fraction of the color range that will be shifted to the background in non-extrapolated kymographs. The default value is 0.7.</li>
	<li>Include --k to determine the size of the kernel used in the kymograph Gaussian filtering. The default is 3.</li>
	<li>Include --eb to estimate global background threshold intensity via LOESS polynomial regression of the frame-specific background threshold intensities.
	<ol>
		<li>Customize the number of points used in LOESS smoothing of the background threshold values by modifying the parameter --n. The default value is 40.</li>
	</ol>
	</li>
	<li>Switch the channels used as the numerator and denominator during ratio calculations, including -r or --switch_ratio, if the timelapse has two channels. By default, the second channel is the numerator and the first is the denominator.</li>
	<li>Choose if the ratio timelapse must be further smoothed with a Median Filter pass with the --sm argument. The default is False.</li>
	<li>Include -o to reject pixels with abnormal intensities during ratiometric time-lapse generation.</li>
	<li>Choose if the background in the ratiometric output must be exported using the argument --b. The default is False, which replaces it with zeros.</li>
	<li>Press enter to run. The output will be generated in the same directory as the program file.</li>
</ol>

<ol>
	<li>Drubin, D. G., Nelson, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Origins+of+cell+polarity.">Origins of cell polarity.</a> Cell. 84 (3), 335-344 (1996).</li><li>Wodarz, A., N&#228;thke, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+polarity+in+development+and+cancer.">Cell polarity in development and cancer.</a> Nature Cell Biology. 9 (9), 1016-1024 (2007).</li><li>Palanivelu, R., Preuss, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pollen+tube+targeting+and+axon+guidance:+parallels+in+tip+growth+mechanisms.">Pollen tube targeting and axon guidance: parallels in tip growth mechanisms.</a> Trends in Cell Biology. 10 (12), 517-524 (2000).</li><li>Portes, M. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Pollen Tube Oscillator: Integrating Biophysics and Biochemistry into Cellular Growth and Morphogenesis. Rhythms in Plants: Dynamic Responses in a Dynamic Environment. , (2015).</li><li>Wudick, M. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CORNICHON+sorting+and+regulation+of+GLR+channels+underlie+pollen+tube+Ca2++homeostasis.">CORNICHON sorting and regulation of GLR channels underlie pollen tube Ca2+ homeostasis.</a> Science. 360 (6388), 533-536 (2018).</li><li>Hoffmann, R. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Plasma+membrane+H+-ATPases+sustain+pollen+tube+growth+and+fertilization.">Plasma membrane H+-ATPases sustain pollen tube growth and fertilization.</a> Nature Communications. 11 (1), 1-15 (2020).</li><li>Michard, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glutamate+receptor-like+genes+form+Ca2++channels+in+pollen+tubes+and+are+regulated+by+pistil+D-serine.">Glutamate receptor-like genes form Ca2+ channels in pollen tubes and are regulated by pistil D-serine.</a> Science. 332 (6028), 434-437 (2011).</li><li>Damineli, D. S., Portes, M. T., Feij&#243;, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oscillatory+signatures+underlie+growth+regimes+in+Arabidopsis+pollen+tubes:+computational+methods+to+estimate+tip+location,+periodicity,+and+synchronization+in+growing+cells.">Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity, and synchronization in growing cells.</a> Journal of Experimental Botany. 68 (12), 3267-3281 (2017).</li><li>Li, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+optimized+genetically+encoded+dual+reporter+for+simultaneous+ratio+imaging+of+Ca2++and+H++reveals+new+insights+into+ion+signaling+in+plants.">An optimized genetically encoded dual reporter for simultaneous ratio imaging of Ca2+ and H+ reveals new insights into ion signaling in plants.</a> New Phytologist. 230 (6), 2292-2310 (2021).</li><li>Sadoine, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Designs,+applications,+and+limitations+of+genetically+encoded+fluorescent+sensors+to+explore+plant+biology.">Designs, applications, and limitations of genetically encoded fluorescent sensors to explore plant biology.</a> Plant Physiology. 187 (2), 485-503 (2021).</li><li>Grenzi, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Illuminating+the+hidden+world+of+calcium+ions+in+plants+with+a+universe+of+indicators.">Illuminating the hidden world of calcium ions in plants with a universe of indicators.</a> Plant Physiology. 187 (2), 550-571 (2021).</li><li>Munglani, G., Vogler, H., Grossniklaus, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+and+flexible+processing+of+large+FRET+image+stacks+using+the+FRET-IBRA+toolkit.">Fast and flexible processing of large FRET image stacks using the FRET-IBRA toolkit.</a> PLoS Computational Biology. 18 (4), 1009242 (2022).</li><li>Ridler, T., Calvard, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Picture+thresholding+using+an+iterative+selection+method.">Picture thresholding using an iterative selection method.</a> IEEE Transactions on Systems, Man, and Cybernetics. 8 (8), 630-632 (1978).</li><li>Portes, M. T., Feij&#243;, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Growing Arabidopsis pollen tubes expressing genetically encoded reporters for calcium and pH. , (2023).</li><li>Candeo, A., Doccula, F. G., Valentini, G., Bassi, A., Costa, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Light+sheet+fluorescence+microscopy+quantifies+calcium+oscillations+in+root+hairs+of+Arabidopsis+thaliana.">Light sheet fluorescence microscopy quantifies calcium oscillations in root hairs of Arabidopsis thaliana.</a> Plant &amp; Cell Physiology. 58 (7), 1161-1172 (2017).</li><li>Romano Armada, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vivo+light+sheet+fluorescence+microscopy+of+calcium+oscillations+in+Arabidopsis+thaliana.">In vivo light sheet fluorescence microscopy of calcium oscillations in Arabidopsis thaliana.</a> Methods in Molecular Biology. 1925, 87-101 (2019).</li><li>Krebs, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FRET-based+genetically+encoded+sensors+allow+high-resolution+live+cell+imaging+of+Ca2++dynamics.">FRET-based genetically encoded sensors allow high-resolution live cell imaging of Ca2+ dynamics.</a> The Plant Journal. 69 (1), 181-192 (2012).</li><li>Lee, T. -. C., Kashyap, R. L., Chu, C. -. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Building+skeleton+models+via+3-D+medial+surface+axis+thinning+algorithms.">Building skeleton models via 3-D medial surface axis thinning algorithms.</a> CVGIP: Graphical Models and Image Processing. 56 (6), 462-478 (1994).</li><li>Nunez-Iglesias, J., Blanch, A. J., Looker, O., Dixon, M. W., Tilley, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+Python+library+to+analyse+skeleton+images+confirms+malaria+parasite+remodelling+of+the+red+blood+cell+membrane+skeleton.">A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton.</a> PeerJ. 6, 4312 (2018).</li></ol>

The authors of this manuscript declare no competing financial interests or other conflicts of interest.

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 ...

amebas-automatic-midline-extraction-background-subtraction

Research

Education

JoVE Core

JoVE Journal

Electrical Engineering

Developmental Biology

Unit 1

Sampling

SCIENTISTS IN ACTION

375 Views.  University of São Paulo. 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.

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

Isolation and Characterization of Single Cells from Zebrafish Embryos

The zebrafish (Danio rerio) is a powerful model organism to study vertebrate development. Though many aspects of zebrafish embryonic development have been described at the morphological level, little is known about the molecular basis of cellular changes that occur as the organism develops. With recent advancements in microfluidics and multiplexing technologies, it is now possible to characterize gene expression in single cells. This allows for investigation of heterogeneity between individual cells of specific cell populations to identify and classify cell subtypes, characterize intermediate states that occur during cell differentiation, and explore differential cellular responses to stimuli. This study describes a protocol to isolate viable, single cells from zebrafish embryos for high throughput multiplexing assays. This method may be rapidly applied to any zebrafish embryonic cell type with fluorescent markers. An extension of this method may also be used in combination with high throughput sequencing technologies to fully characterize the transcriptome of single cells. As proof of principle, the relative abundance of cardiac differentiation markers was assessed in isolated, single cells derived from nkx2.5 positive cardiac progenitors. By evaluation of gene expression at the single cell level and at a single time point, the data support a model in which cardiac progenitors coexist with differentiating progeny. The method and work flow described here is broadly applicable to the zebrafish research community, requiring only a labeled transgenic fish line and access to microfluidics technologies.

This protocol describes a method for isolating single cells from zebrafish embryos, enriching for cells of interest, capturing zebrafish cells in microfluidic based single cell multiplex systems, and assessing gene expression from single cells.

The zebrafish (Danio rerio) is a powerful model organism to study vertebrate development. Though many aspects of zebrafish embryonic development have been ...

Identification of Critical Conditions for Immunostaining in the Pea Aphid Embryos: Increasing Tissue Permeability and Decreasing Background Staining

The pea aphid Acyrthosiphon pisum, with a sequenced genome and abundant phenotypic plasticity, has become an emerging model for genomic and developmental studies. Like other aphids, A. pisum propagate rapidly via parthenogenetic viviparous reproduction, where the embryos develop within egg chambers in an assembly-line fashion in the ovariole. Previously we have established a robust platform of whole-mount in situ hybridization allowing detection of mRNA expression in the aphid embryos. For analyzing the expression of protein, though, established protocols for immunostaining the ovarioles of asexual viviparous aphids did not produce satisfactory results. Here we report conditions optimized for increasing tissue permeability and decreasing background staining, both of which were problems when applying established approaches. Optimizations include: (1) incubation of proteinase K (1 &#181;g/ml, 10 min), which was found essential for antibody penetration in mid- and late-stage aphid embryos; (2) replacement of normal goat serum/bovine serum albumin with a blocking reagent supplied by a Digoxigenin (DIG)-based buffer set and (3) application of methanol rather hydrogen peroxide (H2O2) for bleaching endogenous peroxidase; which significantly reduced the background staining in the aphid tissues. These critical conditions optimized for immunostaining will allow effective detection of gene products in the embryos of A. pisum and other aphids.

A protocol of whole-mount immunostaining on aphid embryos is presented, in which critical conditions for decreasing background staining and increasing tissue permeability are addressed. These conditions are specifically developed for effective detection of protein expression in the embryonic tissues of aphids.

The pea aphid Acyrthosiphon pisum, with a sequenced genome and abundant phenotypic plasticity, has become an emerging model for genomic and developmental ...

The C. elegans Intestine As a Model for Intercellular Lumen Morphogenesis and In Vivo Polarized Membrane Biogenesis at the Single-cell Level: Labeling by Antibody Staining, RNAi Loss-of-function Analysis and Imaging

Multicellular tubes, fundamental units of all internal organs, are composed of polarized epithelial or endothelial cells, with apical membranes lining the lumen and basolateral membranes contacting each other and/or the extracellular matrix. How this distinctive membrane asymmetry is established and maintained during organ morphogenesis is still an unresolved question of cell biology. This protocol describes the C. elegans intestine as a&#160;model for the analysis of polarized membrane biogenesis during tube morphogenesis, with emphasis on apical membrane and lumen biogenesis. The C. elegans twenty-cell single-layered intestinal epithelium is arranged into a simple bilaterally symmetrical tube, permitting analysis on a single-cell level. Membrane polarization occurs concomitantly with polarized cell division and migration during early embryogenesis, but de novo polarized membrane biogenesis continues throughout larval growth, when cells no longer proliferate and move. The latter setting allows one to separate subcellular changes that simultaneously mediate these different polarizing processes, difficult to distinguish in most polarity models. Apical-, basolateral membrane-, junctional-, cytoskeletal- and endomembrane components can be labeled and tracked throughout development by GFP fusion proteins, or assessed by in situ antibody staining. Together with the organism&#39;s genetic versatility, the C. elegans intestine thus provides a unique in vivo model for the visual, developmental, and molecular genetic analysis of polarized membrane and tube biogenesis. The specific methods (all standard) described here include how to: label intestinal subcellular components by antibody staining; analyze genes involved in polarized membrane biogenesis by loss-of-function studies adapted to the typically essential tubulogenesis genes; assess polarity defects during different developmental stages; interpret phenotypes by epifluorescence, differential interference contrast (DIC) and confocal microscopy; quantify visual defects. This protocol can be adapted to analyze any of the often highly conserved molecules involved in epithelial polarity, membrane biogenesis, tube and lumen morphogenesis.

The C. elegans Intestine As a Model for Intercellular Lumen Morphogenesis and In Vivo Polarized Membrane Biogenesis at the Single-cell Level: Labeling by Antibody Staining, RNAi Loss-of-function Analy

The transparent C. elegans intestine can serve as an "in vivo tissue chamber" for studying apicobasal membrane and lumen biogenesis at the single-cell and subcellular level during multicellular tubulogenesis. This protocol describes how to combine standard labeling, loss-of-function genetic/RNAi and microscopic approaches to dissect these processes on a molecular level.

Multicellular tubes, fundamental units of all internal organs, are composed of polarized epithelial or endothelial cells, with apical membranes lining the ...

Horizontal Whole Mount: A Novel Processing and Imaging Protocol for Thick, Three-dimensional Tissue Cross-sections of Skin

Processing a tissue of interest to generate a microscopic image that supports a scientific argument can be challenging. The acquisition of high-quality microscopic images is not entirely dependent upon the quality of the microscope, but also upon the methods of tissue processing, which often involve multiple critical actions or steps. Furthermore, mesenchymal cell types in the skin and other tissues represent a new challenge for tissue preparation and imaging. Here, we present a complete process, from tissue harvest to microscopy. Our technique, called &#34;horizontal whole mount,&#34; is one that novices can quickly become proficient in and that allows for antigen preservation and detection in 60-300 &#181;m-thick sections cut with a cryostat. Sections of this thickness provide enhanced visualization of tissue microarchitecture in a three-dimensional environment. In addition, the protocol preserves mesenchymal cells in a manner that enhances image quality when compared to standard cryostat or paraffin sections, thereby increasing the efficacy and reliability of immunostaining. We believe that this protocol will benefit all laboratories that visualize skin, and possibly other tissues and organs.

This work presents a novel processing and imaging protocol for thick, three-dimensional tissue cross-section analysis that enables the full exploitation of confocal imaging modalities. This protocol preserves antigenicity and represents a robust system to analyze skin histology and potentially other tissue types.

Processing a tissue of interest to generate a microscopic image that supports a scientific argument can be challenging. The acquisition of high-quality ...

Single-cell Photoconversion in Living Intact Zebrafish

Animal and plant tissue is composed of distinct populations of cells. These cells interact over time to build and maintain the tissue and can cause disease when disrupted. Scientists have developed clever techniques to investigate characteristics and natural dynamics of these cells within intact tissue by expressing fluorescent proteins in subsets of cells. However, at times, experiments require more selected visualization of cells within the tissue, sometimes at the single-cell or population-of-cells manner. To achieve this and visualize single cells within a population of cells, scientists have utilized single-cell photoconversion of fluorescent proteins. To demonstrate this technique, we show here how to direct UV light to an Eos-expressing cell of interest in an intact, living zebrafish. We then image those photoconverted Eos+ cells 24 h later to determine how they changed in the tissue. We describe two techniques: single cell photoconversion and photoconversions of populations of cell. These techniques can be used to visualize cell-cell interactions, cell-fate and differentiation, and cell migrations, making it a technique that is applicable in numerous biological questions.

Here, we present a protocol to show how cell photoconversion is achieved through UV exposure to specific areas expressing the fluorescent protein, Eos, in living animals.

Animal and plant tissue is composed of distinct populations of cells. These cells interact over time to build and maintain the tissue and can cause ...

Computational Analysis of the Caenorhabditis elegans Germline to Study the Distribution of Nuclei, Proteins, and the Cytoskeleton

The Caenorhabditis elegans (C. elegans) germline is used to study several biologically important processes including stem cell development, apoptosis, and chromosome dynamics. While the germline is an excellent model, the analysis is often two dimensional due to the time and labor required for three-dimensional analysis. Major readouts in such studies are the number/position of nuclei and protein distribution within the germline. Here, we present a method to perform automated analysis of the germline using confocal microscopy and computational approaches to determine the number and position of nuclei in each region of the germline. Our method also analyzes germline protein distribution that enables the three-dimensional examination of protein expression in different genetic backgrounds. Further, our study shows variations in cytoskeletal architecture in distinct regions of the germline that may accommodate specific spatial developmental requirements. Finally, our method enables automated counting of the sperm in the spermatheca of each germline. Taken together, our method enables rapid and reproducible phenotypic analysis of the C. elegans germline.

Computational Analysis of the Caenorhabditis elegans Germline to Study the Distribution of Nuclei, Proteins, and the Cytoskeleton

We present an automated method for three-dimensional reconstruction of the Caenorhabditis elegans germline. Our method determines the number and position of each nucleus within the germline and analyses germline protein distribution and cytoskeletal structure.

The Caenorhabditis elegans (C. elegans) germline is used to study several biologically important processes including stem cell development, apoptosis, and ...

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells

Pancreatic endocrine cells, which are clustered in islets, regulate blood glucose stability and energy metabolism. The distinct cell types in islets, including insulin-secreting &#946; cells, are differentiated from common endocrine progenitors during the embryonic stage. Immature endocrine cells expand via cell proliferation and mature during a long postnatal developmental period. However, the mechanisms underlying these processes are not clearly defined. Single-cell RNA-sequencing is a promising approach for the characterization of distinct cell populations and tracing cell lineage differentiation pathways. Here, we describe a method for the single-cell RNA-sequencing of isolated pancreatic &#946; cells from embryonic, neonatal and postnatal pancreases.

We describe a method for the isolation of endocrine cells from embryonic, neonatal and postnatal pancreases followed by single-cell RNA sequencing. This method allows analyses of pancreatic endocrine lineage development, cell heterogeneity and transcriptomic dynamics.

Pancreatic endocrine cells, which are clustered in islets, regulate blood glucose stability and energy metabolism. The distinct cell types in islets, ...

In Vitro Generation of Somite Derivatives from Human Induced Pluripotent Stem Cells

In response to signals such as WNTs, bone morphogenetic proteins (BMPs), and sonic hedgehog (SHH) secreted from surrounding tissues, somites (SMs) give rise to multiple cell types, including the myotome (MYO), sclerotome (SCL), dermatome (D), and syndetome (SYN), which in turn develop into skeletal muscle, axial skeleton, dorsal dermis, and axial tendon/ligament, respectively. Therefore, the generation of SMs and their derivatives from human induced pluripotent stem cells (iPSCs) is critical to obtain pluripotent stem cells (PSCs) for application in regenerative medicine and for disease research in the field of orthopedic surgery. Although the induction protocols for MYO and SCL from PSCs have been previously reported by several researchers, no study has yet demonstrated the induction of SYN and D from iPSCs. Therefore, efficient induction of fully competent SMs remains a major challenge. Here, we recapitulate human SM patterning with human iPSCs in vitro by mimicking the signaling environment during chick/mouse SM development, and report on methods of systematic induction of SM derivatives (MYO, SCL, D, and SYN) from human iPSCs under chemically defined conditions through the presomitic mesoderm (PSM) and SM states. Knowledge regarding chick/mouse&#160;SM development was successfully applied to the induction of SMs with human iPSCs. This method could be a novel tool for studying human somitogenesis and patterning without the use of embryos and for cell-based therapy and disease modeling.

We present here a protocol for the differentiation of human induced pluripotent stem cells into each somite derivative (myotome, sclerotome, dermatome, and syndetome) in chemically defined conditions, which has applications in future disease modeling and cell-based therapies in orthopedic surgery.

In response to signals such as WNTs, bone morphogenetic proteins (BMPs), and sonic hedgehog (SHH) secreted from surrounding tissues, somites (SMs) give ...

Efficient Neural Differentiation using Single-Cell Culture of Human Embryonic Stem Cells

In vitro differentiation of human embryonic stem cells (hESCs) has transformed the ability to study human development on both biological and molecular levels and provided cells for use in regenerative applications. Standard approaches for hESC culture using colony type culture to maintain undifferentiated hESCs and embryoid body (EB) and rosette formation for differentiation into different germ layers are inefficient and time-consuming. Presented here is a single-cell culture method using hESCs instead of a colony-type culture. This method allows maintenance of the characteristic features of undifferentiated hESCs, including expression of hESC markers at levels comparable to colony type hESCs. In addition, the protocol presents an efficient method for neural progenitor cell (NPC) generation from single-cell type hESCs that produces NPCs within 1 week. These cells highly express several NPC marker genes and can differentiate into various neural cell types, including dopaminergic neurons and astrocytes. This single-cell culture system for hESCs will be useful in investigating the molecular mechanisms of these processes, studies of certain diseases, and drug discovery screens.

Presented here is a protocol for the generation of a single-cell culture of human embryonic stem cells and their subsequent differentiation into neural progenitor cells. The protocol is simple, robust, scalable, and suitable for drug screening and regenerative medicine applications.

In vitro differentiation of human embryonic stem cells (hESCs) has transformed the ability to study human development on both biological and molecular ...

Preparation of Small RNA Libraries for Sequencing from Early Mouse Embryos

MicroRNAs (miRNAs) are important for the complex regulation of cell fate decisions and developmental timing. In vivo studies of the contribution of miRNAs during early development are technically challenging due to the limiting cell number. Moreover, many approaches require a miRNA of interest to be defined in assays such as northern blotting, microarray, and qPCR. Therefore, the expression of many miRNAs and their isoforms have not been studied during early development. Here, we demonstrate a protocol for small RNA sequencing of sorted cells from early mouse embryos to enable relatively unbiased profiling of miRNAs in early populations of neural crest cells. We overcome the challenges of low cell input and size selection during library preparation using amplification and gel-based purification. We identify embryonic age as a variable accounting for variation between replicates and stage-matched mouse embryos must be used to accurately profile miRNAs in biological replicates. Our results suggest that this method can be broadly applied to profile the expression of miRNAs from other lineages of cells. In summary, this protocol can be used to study how miRNAs regulate developmental programs in different cell lineages of the early mouse embryo.

We describe a technique for profiling microRNAs in early mouse embryos. This protocol overcomes the challenge of low cell input and small RNA enrichment. This assay can be used to analyze changes in miRNA expression over time in different cell lineages of the early mouse embryo.

MicroRNAs (miRNAs) are important for the complex regulation of cell fate decisions and developmental timing. In vivo studies of the contribution of miRNAs ...