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><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

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411 Views.  University of São Paulo. Deze methode kwantificeert de spatiotemporele dynamiek van gepolariseerde cellen langs de middellijn die fluorescerende reporter of kleurstof tot expressie brengt voor specifieke entiteiten van belang, zoals actine, ionen of blaasjesdynamiek. De versie van automatisering elimineert vooroordelen en maakt schaalbaarheid mogelijk, vooral voor taken die handmatig digitaal worden uitgevoerd en tijdrovend en subjectief kunnen worden. Open om te beginnen Jupyter Notebook en lees de time-lapse-bestan...

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

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and millisecond temporal resolution. These capabilities make single-molecule localization microscopy ideally suited to study molecular level biological functions in physiologically relevant environments. Here, we demonstrate an integrated protocol for both acquisition and processing/analysis of single-molecule tracking data to extract the different diffusive states a protein of interest may exhibit. This information can be used to quantify molecular complex formation in living cells. We provide a detailed description of a camera-based 3D single-molecule localization experiment, as well as the subsequent data processing steps that yield the trajectories of individual molecules. These trajectories are then analyzed using a numerical analysis framework to extract the prevalent diffusive states of the fluorescently labeled molecules and the relative abundance of these states. The analysis framework is based on stochastic simulations of intracellular Brownian diffusion trajectories that are spatially confined by an arbitrary cell geometry. Based on the simulated trajectories, raw single-molecule images are generated and analyzed in the same way as experimental images. In this way, experimental precision and accuracy limitations, which are difficult to calibrate experimentally, are explicitly incorporated into the analysis workflow. The diffusion coefficient and relative population fractions of the prevalent diffusive states are determined by fitting the distributions of experimental values using linear combinations of simulated distributions. We demonstrate the utility of our protocol by resolving the diffusive states of a protein that exhibits different diffusive states upon forming homo- and hetero-oligomeric complexes in the cytosol of a bacterial pathogen.

3D single-molecule localization microscopy is utilized to probe the spatial positions and motion trajectories of fluorescently labeled proteins in living bacterial cells. The experimental and data analysis protocol described herein determines the prevalent diffusive behaviors of cytosolic proteins based on pooled single-molecule trajectories.

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and ...

Biochemistry

Single-Cell Quantification of Protein Degradation Rates by Time-Lapse Fluorescence Microscopy in Adherent Cell Culture

Proteins are in a dynamic state of synthesis and degradation and their half-lives can be adjusted under various circumstances. However, most commonly used approaches to determine protein half-life are either limited to population averages from lysed cells or require the use of protein synthesis inhibitors. This protocol describes a method to measure protein half-lives in single living adherent cells, using SNAP-tag fusion proteins in combination with fluorescence time-lapse microscopy. Any protein of interest fused to a SNAP-tag can be covalently bound by a fluorescent, cell permeable dye that is coupled to a benzylguanine derivative, and the decay of the labeled protein population can be monitored after washout of the residual dye. Subsequent cell tracking and quantification of the integrated fluorescence intensity over time results in an exponential decay curve for each tracked cell, allowing for determining protein degradation rates in single cells by curve fitting. This method provides an estimate for the heterogeneity of half-lives in a population of cultured cells, which cannot easily be assessed by other methods. The approach presented here is applicable to any type of cultured adherent cells expressing a protein of interest fused to a SNAP-tag. Here we use mouse embryonic stem (ES) cells grown on E-cadherin-coated cell culture plates to illustrate how single cell degradation rates of proteins with a broad range of half-lives can be determined.

This protocol describes a method to determine protein half-lives in single living adherent cells, using pulse labeling and fluorescence time-lapse imaging of SNAP-tag fusion proteins.

Proteins are in a dynamic state of synthesis and degradation and their half-lives can be adjusted under various circumstances. However, most commonly used ...

Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking

Protein-DNA interactions are at the heart of many fundamental cellular processes. For example, DNA replication, transcription, repair, and chromosome organization are governed by DNA-binding proteins that recognize specific DNA structures or sequences. In vitro experiments have helped to generate detailed models for the function of many types of DNA-binding proteins, yet, the exact mechanisms of these processes and their organization in the complex environment of the living cell remain far less understood. We recently introduced a method for quantifying DNA-repair activities in live Escherichia coli cells using Photoactivated Localization Microscopy (PALM) combined with single-molecule tracking. Our general approach identifies individual DNA-binding events by the change in the mobility of a single protein upon association with the chromosome. The fraction of bound molecules provides a direct quantitative measure for the protein activity and abundance of substrates or binding sites at the single-cell level. Here, we describe the concept of the method and demonstrate sample preparation, data acquisition, and data analysis procedures.

Photoactivated localization microscopy (PALM) combined with single-molecule tracking allows direct observation and quantification of protein-DNA interactions in live Escherichia coli cells.

Protein-DNA interactions are at the heart of many fundamental cellular processes. For example, DNA replication, transcription, repair, and chromosome ...

Immunology and Infection

Rapid and Robust Analysis of Cellular and Molecular Polarization Induced by Chemokine Signaling

Cells respond to chemokine stimulation by losing their round shape in a process called polarization, and by altering the subcellular localization of many proteins. Classic imaging techniques have been used to study these phenomena. However, they required the manual acquisition of many cells followed by time consuming quantification of the morphology and the co-localization of the staining of tens of cells. Here, a rapid and powerful method is described to study these phenomena on samples consisting of several thousands of cells using an imaging flow cytometry technology that combines the advantages of a microscope with those of a cytometer. Using T lymphocytes stimulated with CCL19 and staining for MHC Class I molecules and filamentous actin, a gating strategy is presented to measure simultaneously the degree of shape alterations and the extent of co-localization of markers that are affected by CCL19 signaling. Moreover, this gating strategy allowed us to observe the segregation of filamentous actin (at the front) and phosphorylated Ezrin-Radixin-Moesin (phospho-ERM) proteins (at the rear) in polarized T cells after CXCL12 stimulation. This technique was also useful to observe the blocking effect on polarization of two different elements: inhibition of actin polymerization by a pharmacological inhibitor and expression of mutants of the Par6/atypical PKC signaling pathway. Thus, evidence is shown that this technique is useful to analyze both morphological alterations and protein redistributions.

Chemokine signaling elicits marked alterations of cellular morphology and some important redistributions of intracellular proteins. Here, a rapid and detailed protocol is provided to study these events.

Cells respond to chemokine stimulation by losing their round shape in a process called polarization, and by altering the subcellular localization of many ...

Monitoring Kinase and Phosphatase Activities Through the Cell Cycle by Ratiometric FRET

F&ouml;rster resonance energy transfer (FRET)-based reporters1 allow the assessment of endogenous kinase and phosphatase activities in living cells. Such probes typically consist of variants of CFP and YFP, intervened by a phosphorylatable sequence and a phospho-binding domain. Upon phosphorylation, the probe changes conformation, which results in a change of the distance or orientation between CFP and YFP, leading to a change in FRET efficiency (Fig 1). Several probes have been published during the last decade, monitoring the activity balance of multiple kinases and phosphatases, including reporters of PKA2, PKB3, PKC4, PKD5, ERK6, JNK7, Cdk18, Aurora B9 and Plk19. Given the modular design, additional probes are likely to emerge in the near future10. 
Progression through the cell cycle is affected by stress signaling pathways 11. Notably, the cell cycle is regulated differently during unperturbed growth compared to when cells are recovering from stress12.Time-lapse imaging of cells through the cell cycle therefore requires particular caution. This becomes a problem particularly when employing ratiometric imaging, since two images with a high signal to noise ratio are required to correctly interpret the results. Ratiometric FRET imaging of cell cycle dependent changes in kinase and phosphatase activities has predominately been restricted to sub-sections of the cell cycle8,9,13,14.
Here, we discuss a method to monitor FRET-based probes using ratiometric imaging throughout the human cell cycle. The method relies on equipment that is available to many researchers in life sciences and does not require expert knowledge of microscopy or image processing.

FRET-based reporters are increasingly used to monitor kinase and phosphatase activities in live cells. Here we describe a method on how to use FRET-based reporters to assess cell cycle-dependent changes in target phosphorylation.

F&ouml;rster resonance energy transfer (FRET)-based reporters1 allow the assessment of endogenous kinase and phosphatase activities in living cells. Such ...

Biology

Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

Single-molecule F&#246;rster resonance energy transfer (smFRET) is a versatile technique reporting on distances in the sub-nanometer to nanometer range. It has been used in a wide range of biophysical and molecular biological experiments, including the measurement of molecular forces, characterization of conformational dynamics of biomolecules, observation of intracellular colocalization of proteins, and determination of receptor-ligand interaction times. In a widefield microscopy configuration, experiments are typically performed using surface-immobilized probes. Here, a method combining single-molecule tracking with alternating excitation (ALEX) smFRET experiments is presented, permitting the acquisition of smFRET time traces of surface-bound, yet mobile probes in plasma membranes or glass-supported lipid bilayers. For the analysis of recorded data, an automated, open-source software collection was developed supporting (i) the localization of fluorescent signals, (ii) single-particle tracking, (iii) determination of FRET-related quantities including correction factors, (iv) stringent verification of smFRET traces, and (v) intuitive presentation of the results. The generated data can conveniently be used as input for further exploration via specialized software, e.g., for the assessment of the diffusional behavior of probes or the investigation of FRET transitions.

This article presents a method for spatiotemporal analysis of mobile, single-molecule F&#246;rster resonance energy transfer (smFRET)-based probes using widefield fluorescence microscopy. The newly developed software toolkit allows the determination of smFRET time traces of moving probes, including the correct FRET efficiency and the molecular positions, as functions of time.

Single-molecule F&#246;rster resonance energy transfer (smFRET) is a versatile technique reporting on distances in the sub-nanometer to nanometer range. ...

Bioengineering

Super-resolution Imaging of the Bacterial Division Machinery

Bacterial cell division requires the coordinated assembly of more than ten essential proteins at midcell1,2. Central to this process is the formation of a ring-like suprastructure (Z-ring) by the FtsZ protein at the division plan3,4. The Z-ring consists of multiple single-stranded FtsZ protofilaments, and understanding the arrangement of the protofilaments inside the Z-ring will provide insight into the mechanism of Z-ring assembly and its function as a force generator5,6. This information has remained elusive due to current limitations in conventional fluorescence microscopy and electron microscopy. Conventional fluorescence microscopy is unable to provide a high-resolution image of the Z-ring due to the diffraction limit of light (~200 nm). Electron cryotomographic imaging has detected scattered FtsZ protofilaments in small C. crescentus cells7, but is difficult to apply to larger cells such as E. coli or B. subtilis. Here we describe the application of a super-resolution fluorescence microscopy method, Photoactivated Localization Microscopy (PALM), to quantitatively characterize the structural organization of the E. coli Z-ring8.
	PALM imaging offers both high spatial resolution (~35 nm) and specific labeling to enable unambiguous identification of target proteins. We labeled FtsZ with the photoactivatable fluorescent protein mEos2, which switches from green fluorescence (excitation = 488 nm) to red fluorescence (excitation = 561 nm) upon activation at 405 nm9. During a PALM experiment, single FtsZ-mEos2 molecules are stochastically activated and the corresponding centroid positions of the single molecules are determined with &lt;20 nm precision. A super-resolution image of the Z-ring is then reconstructed by superimposing the centroid positions of all detected FtsZ-mEos2 molecules.
	Using this method, we found that the Z-ring has a fixed width of ~100 nm and is composed of a loose bundle of FtsZ protofilaments that overlap with each other in three dimensions. These data provide a springboard for further investigations of the cell cycle dependent changes of the Z-ring10 and can be applied to other proteins of interest.

We describe a super-resolution imaging method to probe the structural organization of the bacterial FtsZ-ring, an essential apparatus for cell division. This method is based on quantitative analyses of photoactivated localization microscopy (PALM) images and can be applied to other bacterial cytoskeletal proteins.

Bacterial cell division requires the coordinated  assembly of more than ten essential proteins at midcell1,2. Central  to this process is the formation of ...

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Fluorescence time-lapse microscopy has become a powerful tool in the study of many biological processes at the single-cell level. In particular, movies depicting the temporal dependence of gene expression provide insight into the dynamics of its regulation; however, there are many technical challenges to obtaining and analyzing fluorescence movies of single cells. We describe here a simple protocol using a commercially available microfluidic culture device to generate such data, and a MATLAB-based, graphical user interface (GUI) -based software package to quantify the fluorescence images. The software segments and tracks cells, enables the user to visually curate errors in the data, and automatically assigns lineage and division times. The GUI further analyzes the time series to produce whole cell traces as well as their first and second time derivatives. While the software was designed for S. cerevisiae, its modularity and versatility should allow it to serve as a platform for studying other cell types with few modifications.

We present a simple protocol to obtain fluorescence microscopy movies of growing yeast cells, and a GUI-based software package to extract single-cell time series data. The analysis includes automated lineage and division time assignment integrated with visual inspection and manual curation of tracked data.

Fluorescence time-lapse microscopy has become a powerful tool in the study of many biological processes at the single-cell level. In particular, movies ...

Internalization and Observation of Fluorescent Biomolecules in Living Microorganisms via Electroporation

The ability to study biomolecules in vivo is crucial for understanding their function in a biological context. One powerful approach involves fusing molecules of interest to fluorescent proteins such as GFP to study their expression, localization and function. However, GFP and its derivatives are significantly larger and less photostable than organic fluorophores generally used for in vitro experiments, and this can limit the scope of investigation. 
We recently introduced a straightforward, versatile and high-throughput method based on electroporation, allowing the internalization of biomolecules labeled with organic fluorophores into living microorganisms. Here we describe how to use electroporation to internalize labeled DNA fragments or proteins into Escherichia coli and Saccharomyces cerevisi&#230;, how to quantify the number of internalized molecules using fluorescence microscopy, and how to quantify the viability of electroporated cells. Data can be acquired at the single-cell or single-molecule level using fluorescence or FRET. The possibility of internalizing non-labeled molecules that trigger a physiological observable response in vivo is also presented. Finally, strategies of optimization of the protocol for specific biological systems are discussed.

Studies of biomolecules in vivo are crucial for understanding molecular function in a biological context. Here we describe a novel method allowing the internalization of fluorescent biomolecules, such as DNA or proteins, into living microorganisms. Analysis of in vivo data recorded by fluorescence microscopy is also presented and discussed.

The ability to study biomolecules in vivo is crucial for understanding their function in a biological context. One powerful approach involves fusing ...

High-resolution Time-lapse Imaging and Automated Analysis of Microtubule Dynamics in Living Human Umbilical Vein Endothelial Cells

The physiological process by which new vasculature forms from existing vasculature requires specific signaling events that trigger morphological changes within individual endothelial cells (ECs). These processes are critical for homeostatic maintenance such as wound healing, and are also crucial in promoting tumor growth and metastasis. EC morphology is defined by the organization of the cytoskeleton, a tightly regulated system of actin and microtubule (MT) dynamics that is known to control EC branching, polarity and directional migration, essential components of angiogenesis. To study MT dynamics, we used high-resolution fluorescence microscopy coupled with computational image analysis of fluorescently-labeled MT plus-ends to investigate MT growth dynamics and the regulation of EC branching morphology and directional migration. Time-lapse imaging of living Human Umbilical Vein Endothelial Cells (HUVECs) was performed following transfection with fluorescently-labeled MT End Binding protein 3 (EB3) and Mitotic Centromere Associated Kinesin (MCAK)-specific cDNA constructs to evaluate effects on MT dynamics. PlusTipTracker software was used to track EB3-labeled MT plus ends in order to measure MT growth speeds and MT growth lifetimes in time-lapse images. This methodology allows for the study of MT dynamics and the identification of how localized regulation of MT dynamics within sub-cellular regions contributes to the angiogenic processes of EC branching and migration.

Protocols for Human Umbilical Vein Endothelial Cell (HUVEC) culture, transient transfection of fluorescently-labeled markers of microtubule growth, live-cell imaging and automated analysis of interphase microtubule growth dynamics are detailed.

The physiological process by which new vasculature forms from existing vasculature requires specific signaling events that trigger morphological changes ...

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

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