D.K.V and A.R thank the Manipal Academy of Higher Education for the TMA Pai fellowship. We thank the Science and Engineering Research Board of India for SERB-SRG (#SRG/2021/001315), as well as the Indian Council of Medical Research of India (#5/4-5/Ad-hoc/Neuro/216/2020-NCD-I) and the Intramural fund of Manipal Academy of Higher Education, Manipal, India. We thank JNCASR&#39;s (Jawaharlal Nehru Centre for Advanced Scientific Research, India) confocal facility and B. Suma for the confocal microscopy in JNCASR.

NOTE: SH-SY5Y cells cultured in DMEM/F-12 media were differentiated with 10 &#181;M retinoic acid for 7 days and treated with 1 &#181;M oA&#946; oligomers for 2 h at 37 &#176;C (5% CO2). After treatment, the cells were fixed with Karnovsky&#39;s fixative solution and double-immunostained with phospho-PAK1 (Thr423)/PAK2 (Thr402) antibody and F-actin-binding phalloidin. Later, confocal z-stack images were taken using a confocal laser scanning microscope. The TNTs were quantified by manual counting and distinguished from other neurites/cell protrusions by constructing 3D volume view images and identifying the structures from their characteristic of hovering between two cells without touching the substratum (Figure 1).
1. Cell culture and differentiation
<ol>
	<li>Culture the SH-SY5Y neuronal cells in DMEM/F12 (Ham&#39;s) media 1:1 with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin-neomycin mixture (PSN). Seed the cells in a 35 mm imaging dish with a 14 mm well made up of a #1.5 coverslip at the center of the dish attached to the bottom. Seed the cells on the imaging dish at a concentration of 12,000 cells/cm2 and perform the experiments at 60%-70% confluency.</li>
	<li>Partially differentiate the cells with 10 &#181;M retinoic acid (RA) from a 100 mM stock solution (5 mg of RA in 15 mL of dimethyl sulfoxide [DMSO]). Then, incubate the cells for 7 days at 37 &#176;C (5% CO2) for differentiation with media change every 2 days. 
	NOTE: Be careful about maintaining the confluency (60%-70%) of the cells (both undifferentiated and partially differentiated cells). Cell density can influence the formation of TNTs between the cells.</li>
</ol>
2. Preparation of oligomers of amyloid-&#946;1-42 (oA&#946;) to treat neuronal cells
<ol>
	<li>Dissolve A&#946;1-42 (1 mg) in 200 &#181;L of 1,1,1,3,3,3-hexafluoro-2-propanol and divide the solution into 20 aliquots, each containing 0.05 mg of peptides. Lyophilize and store the aliquots at &#8722;20 &#176;C for later use.</li>
	<li>Prepare the stock solution (5 mM) of the lyophilized A&#946;1-42 in DMSO by adding 2.2 &#181;L of DMSO to 0.05 mg of lyophilized peptide. To carefully dissolve the peptides, vortex the solution and sonicate for 2 min in a water bath sonicator.</li>
	<li>Dilute the stock solution to a concentration of 100 &#181;M in DMEM, pH 7.4, and vortex to convert the peptides to monomers, followed by incubation at 4 &#176;C for 24 h to obtain oligomers of A&#946;1-42 (oA&#946;)12,21,22.</li>
	<li>Characterize oA&#946; before the experiments as reported previously21,22 by transmission electron microscopy imaging.</li>
	<li>Treat the partially differentiated SH-SY5Y cells with 1 &#181;M oA&#946;. Remove the medium before the treatments, and add fresh FBS-free DMEM. After the medium change, add previously prepared oA&#946; (100 &#181;M) to the medium to a final concentration of 1 &#181;M. Incubate the cells with 1 &#181;M oA&#946; for 2 h at 37 &#176;C (5% CO2). Include a negative control in the form of untreated cells.</li>
</ol>
3. Immunostaining of F-actin and activated-PAK1 for the characterization of TNTs
<ol>
	<li>Perform differential immunostaining by using phospho-PAK1 (Thr423)/PAK2 (Thr402) antibody and F-actin-binding phallotoxin phalloidin.</li>
	<li>Wash the control and oA&#946;-treated cells with 1x phosphate-buffered saline (PBS) for 2 x 2 min before fixation. Prepare the Karnovsky&#39;s fixative solution by using 2% formalin fixative and 2.5% glutaraldehyde dissolved in 0.1 M phosphate buffer, pH 7.2. Then, fix the cells in the imaging dish by adding Karnovsky&#39;s fixative solution (just enough to cover the cells) for 45 min at room temperature.</li>
	<li>Wash the fixed cells 2 x 2 min using incubation buffer. Prepare the incubation buffer (20x) by dissolving 0.1 g of saponin in 5 mL of FBS and dilute to 1x by adding 95 mL of 1x PBS.</li>
	<li>After fixation, add the first antibody against phopho-PAK1 at a dilution of 1:250 in the incubation buffer and incubate overnight at 4 &#176;C in a dark moist chamber.</li>
	<li>The next day, after 24 h of incubation, wash the cells 2 x 2 min with the incubation buffer; then, add secondary antibody conjugated to Alexa Fluor 488 (1:1,000 dilution) and Phalloidin 555 (1:1,000 dilution). Incubate the cells in the dark moist chamber for 1.5 h at room temperature.</li>
	<li>After the incubation, wash the cells 2 x 2 min with incubation buffer. Stain the nucleus by adding 4&#39;,6-diamidino-2-phenylindole (DAPI) in a 1:2,000 dilution and incubate for 5 min to 10 min at room temperature in the dark.</li>
	<li>Prepare DABCO mounting medium using 25 mg of DABCO in 90% glycerol and 10% 1x PBS. To dissolve properly, adjust the pH to 8.6 using spectrophotometric grade, dilute HCl (diluted 1:20 with water) and keep the solution on a rocker for mixing.</li>
	<li>As an anti-bleaching agent, add the DABCO mounting medium directly onto the imaging dish containing the coverslip at the bottom. Wait for at least 1-2 h and directly proceed to perform confocal imaging. 
	NOTE: No adhesives are required to fix the coverslips.</li>
</ol>
4. Immunostaining of F-actin and &#946;-III tubulin to distinguish TNTs from neurites
<ol>
	<li>Perform differential immunostaining with &#946;-III tubulin antibody and F-actin-binding phallotoxin phalloidin.</li>
	<li>Wash the oA&#946;-treated cells 2x with 1x PBS before adding Karnovsky&#39;s fixative solution. Incubate the cells for 45 min at room temperature and wash the cells 2 x 2 min with incubation buffer.</li>
	<li>After fixation, add the antibody against &#946;-III tubulin (TUBb3) at a dilution of 1:250 in the incubation buffer and incubate at 4 &#176;C overnight in the dark moist chamber.</li>
	<li>The next day, wash the cells with incubation buffer (2 x 2 min), and add secondary antibody conjugated to Alexa Fluor 488 (1:1,000 dilution) and Phalloidin 555 (1:1,000 dilution) together to the same dish. Then, incubate the cells for 1.5 h at room temperature in the dark moist chamber.</li>
	<li>Wash the cells 2x with incubation buffer, and add nuclear-staining DAPI in a 1:2,000 dilution for 5-10 min at room temperature in the dark.</li>
	<li>Add anti-bleaching agent DABCO in mounting medium as mentioned above and wait for 2 h before imaging.</li>
</ol>
5. Imaging with confocal microscopy
<ol>
	<li>To identify TNTs, capture z-stack images of immunostained cells using a confocal laser scanning microscope. Take the images using 40x/1.40 Oil DIC objective with DAPI, fluorescein isothiocyanate (FITC), and tetramethylrhodamine (TRITC) filter sets.</li>
	<li>First, select the channels by clicking the required lasers sequentially in the windows Track 1, Track 2, and Track 3 in the confocal software. Click the option T-PMT below the Track 1 window to take the differential interference contrast (DIC) images with fluorescence channels. 
	NOTE: DIC images are captured by a different detector labeled as T-PMT.</li>
	<li>Select the acquisition tab of the software, click on the Z-stack tab, and wait for a window to open. Then, click live scan to focus the cells at the bottom of the dish. Select that focused image as the first stack. Then, focus up to see the topmost part of the cell and select that as the last stack. Stop live scanning and click the number next to the optimal tab to fix the step size of the stacks. Step size determines the number of slices and the intervals based on the thickness of the cells. 
	NOTE: Step size will be selected based on Nyquist sampling to take enough slices and ensure there are no gaps between the two stacks. Nyquist sampling is determined on the basis of the objective and the wavelengths of the lights23.</li>
	<li>Take sequential images of three channels of DAPI, FITC, and TRITC with 405 nm, 488 nm, and 561 nm lasers and capture with a pixel dwell time of 1.02 &#181;s. Capture images in the DIC channel with fluorescence channels to observe the cell boundary.</li>
	<li>Capture a stack of images with the dimensions of x: 224.92 &#181;m&#160;and y: 224.92 &#181;m, with each pixel of 220 nm2 size and z-scaling of 415 nm.</li>
	<li>Take images capturing several z-stacks (15-22 stacks) from the bottom to the top of the cells. Acquire at least 10 images from the random field of the culture dish to get a total of ~200-300 cells.</li>
	<li>Analyze the captured images offline to identify the TNTs by 3D volume view analysis</li>
</ol>
6. Analysis of confocal z-stack images to identify and quantify TNTs
<ol>
	<li>Open the confocal images saved in .czi data format in Fiji software for analysis.</li>
	<li>Select the Hyperstack option to see each z-stack and channel of the image. Look for the hyperstacks of the z-stacks and channels, which open in a single window as shown in Figure 2. Scroll the channel (indicated by red and green arrows) and z-stack (indicated by blue arrows) scroll bars to select the exact stack of a particular channel of interest. 
	NOTE: In fixed cells, as the hovering TNTs or membrane conduits stay above the surface, the structures are not visible in the lower parts of the z-stacks. However, in fixed cells, neurites lie on the surface and are detectable in the lower parts of the z-stacks (z = 0 to 2). See Figure 2 for the identification steps.</li>
	<li>As shown in Figure 2, select the F-actin-stained channel (indicated by red arrows) first by scrolling the channel bar. Then, manually scroll the z-stacks (indicated by blue arrows) to see each stack one by one. Identify the F-actin-stained structures that seem to be connecting cells, are visible in the lower parts of the z-stacks, and are close to the surface of the imaging dish (z = 2) as neurites (indicated by white arrows). 
	NOTE: The majority of the neurites are easy to identify as they appear as extended protrusions (indicated by pink arrows).</li>
	<li>Identify the TNTs, the F-actin-positive, hovering cell-to-cell conduits, by scrolling the z-stacks toward the top (from z = 4 in Figure 2; indicated by yellow arrows). Look for neurites near the lower parts of the z-stacks toward the surface and observe that they start disappearing with the scrolling of z-stacks toward the top (at z = 6 in Figure 2; neurites are not clearly visible).</li>
	<li>Identify phospho-PAK1-positive TNTs similarly as F-actin-positive TNTs by analyzing the hovering nature of the conduits from the z-stacks. As phospho-PAK1 staining is weaker than F-actin staining, look for phopho-PAK1-stained TNTs at z = 4 (faintly visible)and at z = 6 (prominent).</li>
	<li>Further, observe the DIC images to verify that the F-actin- and phospho-PAK1-stained TNT structures are membrane conduits between cells (Figure 3). In addition, merge F-actin (red) and phospho-PAK1 (green) channels to verify that the identified TNTs are F-actin and phospho-PAK1 co-stained structures (Figure 3).</li>
	<li>To quantify the TNTs, count the total cell numbers and the identified TNTs manually and represent the numbers as percentages.</li>
	<li>To distinguish F-actin-positive TNTs and &#946;-III tubulin (TUBb3)-positive, TNT-like hovering conduits from z-stack images, merge F-actin (red) and TUBb3 (green) channels (Figure 4). Then, analyze the z-stacks of the merged images.
	<ol>
		<li>Look for exclusively F-actin-stained TNTs that are faintly visible at z = 3 and prominent at z = 6 and z = 9 (yellow arrows). Similarly, identify F-actin and &#946;-III tubulin (TUBb3) double-positive, TNT-like hovering conduits at z = 6 and z = 9 (cyan arrows). Identify other F-actin- and &#946;-III tubulin-stained, non-hovering protrusions from the lower parts of the z-stacks (white arrows).</li>
	</ol>
	</li>
	<li>Measure the diameter of the TNTs using the line tool in Fiji. Verify the scale of measurement by clicking Analyze | Set Scale so that &#34;distance in pixels&#34; is set automatically from the .czi images. Measure the diameters of the TNTs at the xy-plane. 
	NOTE: Most of the diameters are between 1 pixel and 4 pixels (i.e., 220-880 nm); each pixel is 220 nm. See Figure 5 for a summary of the protocol for the analysis of z-stack confocal images.</li>
</ol>
7. 3D reconstruction of z-stack images to characterize TNTs
<ol>
	<li>Use the volume viewer plugin in Fiji, which allows 3D reslicing and threshold-enabled 3D visualization (Figure 6).</li>
	<li>Split the z-stack images into individual channels. Then, crop the single-channel (F-actin channel) z-stack images to use 3D reconstruction view to highlight one or two TNTs or neurites at a time. Enable the volume view plugin to visualize xy, yz, and xz views.</li>
	<li>Pin a single TNT or neurite in the xy plane (white arrows represent neurites in Figure 6A; yellow arrows represent TNTs in Figure 6B) and mark xz (red) and yz (green) axis cross-sections. Observe the neurites at the bottom of the xz plane in the xz and yz planes (white arrows, Figure 6A) and the TNTs in the upper z-stacks (yellow arrows, Figure 6B).</li>
	<li>Select the individual TNT or neurite to reconstruct the 3D volume view in the xz-plane (Figure 6C, D). In the 3D reconstruction, observe the neurites at the bottom of the z-plane (white arrows, Figure 6C) and the TNTs appearing as hovering structures connecting two cells without touching the bottom z-plane (yellow arrows, Figure 6D).</li>
</ol>

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The authors have no conflict of interest to disclose.

Several researchers in the past 2 decades have been trying to understand and characterize the structure of TNTs18. The lack of specific markers hinders progress, and there is an increasing demand for a convenient, standardized method that can be used to identify, characterize, and quantify TNTs. TNTs are defined as F-actin-based membrane conduits that hover between two cells. Studies have shown that &#946;-tubulin-positive, close-ended, developing neurites hover between two distant cells and resem...

Tunneling nanotubes (TNTs) are F-actin-based, primarily open-ended membrane conduits and play a vital role in the intercellular transfer of cargo and organelles1. The unique characteristic of TNTs is that they connect neighboring cells without any contact with the substratum; they are over 10-300 &#181;m in length and their diameters vary between 50 nm to 1 &#181;m2,3. TNTs are transient structures, and their lifetime lasts between a few minutes to several hours. TNTs were first demonstrated in PC12 neuronal cells1; later, numerous studies showed their existence in several cell types in vitro and in vivo4,5. Several studies have revealed the pathological significance of TNTs in various disease models, such as neurodegenerative diseases, cancer, and viral infections6,7,8.
The structural heterogeneities of TNTs have been demonstrated by several studies in various cellular systems9. The differences are based on cytoskeleton composition, the mechanism of formation, and the cargo types transferred10. Primarily, the open-ended, F-actin-positive membrane continuity that hovers between two neighboring cells and transfers organelles is considered to consist of TNTs11. However, the lack of clarity or diversity observed in the formation of TNTs adds to the difficulty in developing TNT-specific markers. Thus, it is difficult to identify TNT structures by conventional detection methods and distinguish membrane nanotubes in terms of open-ended and close-ended protrusions12. However, the characteristic of TNTs to hover as F-actin membrane protrusions between two cells is relatively easier and more feasible to identify using conventional imaging techniques. Other actin-based cellular protrusions such as filopodia and dorsal filopodia cannot hover between two distant cells, particularly when cells are fixed. Of note, close-ended, electrically coupled, developing neurites are often termed TNT-like structures13.
It is known that F-actin plays an important role in TNT formation, and several studies have shown that the F-actin inhibitor cytochalasin D inhibits the formation of TNTs14,15. In contrast, inhibitors of microtubules do not have any effect on TNT formation16. The last 2 decades have seen several reports on the significant role that TNTs play in the spread of pathology and tumor resistance and therapy17. Therefore, there is a never-ending demand for better techniques for TNT characterization.
The lack of specific markers of TNTs and the diversity in morphology and cytoskeletal composition make it difficult to develop a unique method of characterization. Some studies have used automated image detection and TNT quantification techniques18,19. However, there are several advantages of the present 3D volume manual analysis method over automatic image analysis for the detection and quantification of TNTs. Often, trained human eyes can spot these hovering nano-structures more easily than an automated image detection method. Moreover, automatic detection methods could be difficult to implement in laboratories lacking algorithm expertise. The present method could be widely adopted by researchers due to its precision and reproducibility.
In a recent study, we showed that oA&#946; promotes the biogenesis of TNTs in neuronal cells via a PAK1-mediated, actin-dependent, endocytosis mechanism12. oA&#946;-induced TNTs also express activated PAK1 (or phospho-PAK1). We developed a 3D volume view image reconstruction method to distinguish oA&#946;-induced, F-actin- and phospho-PAK1-immunostained TNTs. &#946;-III tubulin-positive, developing neurites often resemble TNT-like hovering structures20. Hence, we further distinguished F-actin-based TNTs from &#946;-III tubulin-positive neurites and other TNT-like protrusions. 3D volume view images have been used to identify TNTs on the basis of their characteristics of hovering over the substratum and staying connected between two neighboring cells. This paper describes the identification and detection of actin-containing membrane conduits or TNTs using confocal z-stack images and, finally, manual quantification of the identified structures from 3D volume view reconstruction images. The presented method cannot distinguish open-ended proper TNTs from close-ended TNT-like structures; this method helps identify TNTs in in vitro 2D cell culture on a flat substratum. However, the method is easy to implement and reproduce and can be widely used for the precise quantification of only actin-based TNTs and to distinguish them from neurites and &#946;-tubulin positive TNT-like structures.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>35 mm dish with 14 mm well size made of #1.5 cover slip</td>
			<td>Cellvis</td>
			<td>D35-14-1.5-N</td>
			<td>Imaging dish used to seed cells for staining experiments</td>
		</tr>
		<tr>
			<td>A&#946; (1-42) 1 mg</td>
			<td>AnaSpec</td>
			<td>#64129</td>
			<td>Oligomers of amyloid beta to treat the cells</td>
		</tr>
		<tr>
			<td>Alexa flour 488 Goat Anti-rabbit IgG (H+L)</td>
			<td>Invitrogen</td>
			<td>A11070</td>
			<td>Secondary antibody for phospho-PAK1</td>
		</tr>
		<tr>
			<td>Biological Safety Cabinet</td>
			<td>Thermo Scientific (MSC Advantage)</td>
			<td>51025411</td>
			<td>Provide aspetic conditions duirng cell culture</td>
		</tr>
		<tr>
			<td>CO2 Incubator</td>
			<td>Thermo Electron Corporation (Heraeus Hera Cell 240)</td>
			<td>51026556</td>
			<td>For growing cells at or near body temperature</td>
		</tr>
		<tr>
			<td>Confocal Laser Scanning Microscope</td>
			<td>ZEISS (Carl Zeiss)</td>
			<td>LSM 880</td>
			<td>Able to generate three-dimensional images of large specimen at super-resolution</td>
		</tr>
		<tr>
			<td>DABCO [1,4-Diazobicyclo-(2,2,2) octane]</td>
			<td>Merck</td>
			<td>8034560100</td>
			<td>Anti-bleaching reagent</td>
		</tr>
		<tr>
			<td>DAPI (4&#8242;,6-diamidino-2-phenylindole)</td>
			<td>Sigma</td>
			<td>D9542-1MG</td>
			<td>Neuclear stainer</td>
		</tr>
		<tr>
			<td>DMEM media</td>
			<td>Gibco</td>
			<td>11965092</td>
			<td>Used for the preparation of 100uM of A&#946; (1-42)</td>
		</tr>
		<tr>
			<td>&#160;DMEM/F12 (1:1 mixture of DMEM and Ham&#8217;s F12)</td>
			<td>Gibco</td>
			<td>12500062</td>
			<td>Culture media for SH-SY5Y</td>
		</tr>
		<tr>
			<td>DMSO (Dimethyl sulphide) Cell culture grade</td>
			<td>Cryopur</td>
			<td>CP-100</td>
			<td>Cell culture grade used as dissolving agent for Retinoic acid</td>
		</tr>
		<tr>
			<td>DMSO (Dimethyl sulphide) Molecular grade</td>
			<td>Himedia</td>
			<td>MB058</td>
			<td>Used as one of the dissolving agent for the lyophilized A&#946; (1-42)</td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum</td>
			<td>Gibco</td>
			<td>16000044</td>
			<td>&#160;Major supplement for Culture media (US origin)</td>
		</tr>
		<tr>
			<td>Formalin Fixative (Neutral buffered 10%)</td>
			<td>Sigma Aldrich</td>
			<td>HT5014-120ML</td>
			<td>Component in the Karnovsky&#39;s fixative solution</td>
		</tr>
		<tr>
			<td>Glutaraldehyde (Grade I, 25% in H2O)</td>
			<td>Sigma</td>
			<td>G5882</td>
			<td>Component in the Karnovsky&#39;s fixative solution</td>
		</tr>
		<tr>
			<td>HFIP (1,1,1,3,3,3-hexafluoro-2-propanol ) solution</td>
			<td>TCI</td>
			<td>H024</td>
			<td>Used to dissolve A&#946; (1-42) 1 mg</td>
		</tr>
		<tr>
			<td>Image Processing/ Analysis Software: FIJI (ImageJ)</td>
			<td>National Institute of Health (NIH)</td>
			<td></td>
			<td>Used to process/analyze the images and to differentiate the TNTs from neurites using its plugin named &#34;volume viewer&#34;.</td>
		</tr>
		<tr>
			<td>Lyophilizer</td>
			<td>Christ, Alpha</td>
			<td>2.4 LDplus</td>
			<td>0.05 mg aliquots of A&#946; (1-42) can be stored in -20 &#176;C after lyophilization only</td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin-Neomycin Mixture</td>
			<td>Thermo fisher Scientific</td>
			<td>15640055</td>
			<td>Antibiotic mixture</td>
		</tr>
		<tr>
			<td>Phalloidin-iFlor 555</td>
			<td>Abcam</td>
			<td>ab176756</td>
			<td>F-actin binding stain</td>
		</tr>
		<tr>
			<td>Phospho-PAK1 (Thr423) /PAK2 (Thr402) [Rabbit]</td>
			<td>CST</td>
			<td>#2601</td>
			<td>Primary antibody</td>
		</tr>
		<tr>
			<td>Polyclonal Antibody to Tubulin Beta 3 (TUBb3)</td>
			<td>Cloud clone</td>
			<td>PAE711Hu01</td>
			<td>Primary antibody</td>
		</tr>
		<tr>
			<td>Retinoic acid</td>
			<td>Sigma-Aldrich</td>
			<td>R2625-50MG</td>
			<td>Differentiating reagent</td>
		</tr>
		<tr>
			<td>Saponin</td>
			<td>Merck</td>
			<td>8047-15-2</td>
			<td>Detergent used in the Incubation buffer in immunostaining</td>
		</tr>
		<tr>
			<td>Water bath sonicator (Quart, Drain valve Heater)</td>
			<td>Ultrasonic Cleaner</td>
			<td>3.0 L/3.2</td>
			<td>Sonicator used to dissolve A&#946; (1-42) stock, after DMSO adding to it during the preparation of 100 &#181;M A&#946; (1-42)</td>
		</tr>
		<tr>
			<td>ZEN Microscopy software</td>
			<td>ZEISS (Carl Zeiss)</td>
			<td></td>
			<td>Imaging software to acquire confocal microscopy images with smart automation</td>
		</tr>
	</tbody>
</table>

detection and quantification of tunneling nanotubes using 3d volume view images

Recent discoveries have revealed that cells perform direct, long-range, intercellular transfer via nano-scale, actin-membrane conduits, namely &#34;tunneling nanotubes&#34; (TNTs). TNTs are defined as open-ended, lipid bilayer-encircled membrane extensions that mediate continuity between neighboring cells of diameters ranging between 50 nm and 1 &#181;m. TNTs were demonstrated initially in neuronal cells, but successive studies have revealed the existence of TNTs in several cell types and diseases, such as neurodegenerative diseases, viral infections, and cancer. Several studies have referred to close-ended, electrically coupled membrane nanostructures between neighboring cells as TNTs or TNT-like structures.
The elucidation of ultrastructure in terms of membrane continuity at the endpoint is technically challenging. In addition, studies on cell-cell communication are challenging in terms of the characterization of TNTs using conventional methods due to the lack of specific markers. TNTs are primarily defined as F-actin-based, open-ended membrane protrusions. However, one major limitation is that F-actin is present in all types of protrusions, making it challenging to differentiate TNTs from other protrusions. One of the notable characteristics of F-actin-based TNTs is that these structures hover between two cells without touching the substratum. Therefore, distinct F-actin-stained TNTs can conveniently be distinguished from other protrusions such as filopodia and neurites based on their hovering between cells.
We have recently shown that the internalization of oligomeric amyloid-&#946;1-42 (oA&#946;) via actin-dependent endocytosis stimulates activated p21-activated kinase-1 (PAK1), which mediates the formation of F-actin-containing TNTs coexpressed with phospho-PAK1 between SH-SY5Y neuronal cells. This protocol outlines a 3D volume analysis method to identify and characterize TNTs from the captured z-stack images of F-actin- and phospho-PAK1-immunostained membrane protrusions in oA&#946;-treated neuronal cells. Further, TNTs are distinguished from developing neurites and neuronal outgrowths based on F-actin- and &#946;-III tubulin-immunostained membrane conduits.

Here, we identify and characterize oA&#946;-induced TNTs in SH-SY5Y neuronal cells by constructing 3D volume views from confocal z-stack images (Figure 1). The cells were double-immunostained with F-actin and phospho-PAK1. Confocal z-stack images of immunostained cells were analyzed to identify TNTs (Figure 2). Further, DIC images were analyzed to verify that the F-actin- and phospho-PAK1-stained TNT structures were membrane conduits between cells (<strong class...

Watch this Scientific Journal Video about Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images at JoVE.com

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Tunneling nanotubes (TNTs) are primarily open-ended F-actin membrane nanotubes that connect neighboring cells, facilitating intercellular communication. The notable characteristic that distinguishes TNTs from other cell protrusions is the hovering nature of the nanotubes between cells. Here, we characterize TNTs by constructing a 3D volume view of confocal z-stack images.

Recent discoveries have revealed that cells perform direct, long-range, intercellular transfer via nano-scale, actin-membrane conduits, namely ...

detection-quantification-tunneling-nanotubes-using-3d-volume-view

Research

JoVE Journal

Biology

2.6K Views.  Manipal Academy of Higher Education, India. Tunneling nanotubes (TNTs) are primarily open-ended F-actin membrane nanotubes that connect neighboring cells, facilitating intercellular communication. The notable characteristic that distinguishes TNTs from other cell protrusions is the hovering nature of the nanotubes between cells. Here, we characterize TNTs by constructing a 3D volume view of confocal z-stack images. 

Video: Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Concentration Determination of Nucleic Acids and Proteins Using the Micro-volume Bio-spec Nano Spectrophotometer

Nucleic Acid quantitation procedures have advanced significantly in the last three decades. More and more, molecular biologists require consistent small-volume analysis of nucleic acid samples for their experiments. The BioSpec-nano provides a potential solution to the problems of inaccurate, non-reproducible results, inherent in current DNA quantitation methods, via specialized optics and a sensitive PDA detector. The BioSpec-nano also has automated functionality such that mounting, measurement, and cleaning are done by the instrument, thereby eliminating tedious, repetitive, and inconsistent placement of the fiber optic element and manual cleaning.
In this study, data is presented on the quantification of DNA and protein, as well as on measurement reproducibility and accuracy. Automated sample contact and rapid scanning allows measurement in three seconds, resulting in excellent throughput. Data analysis is carried out using the built-in features of the software. The formula used for calculating DNA concentration is:
Sample Concentration = DF &middot; (OD260-OD320)&middot; NACF (1)
Where DF = sample dilution factor and NACF = nucleic acid concentration factor.
The Nucleic Acid concentration factor is set in accordance with the analyte selected1.
Protein concentration results can be expressed as &mu;g/ mL or as moles/L by entering e280 and molecular weight values respectively. When residue values for Tyr, Trp and Cysteine (S-S bond) are entered in the e280Calc tab, the extinction coefficient values are calculated as e280 = 5500 x (Trp residues) + 1490 x (Tyr residues) + 125 x (cysteine S-S bond). The e280 value is used by the software for concentration calculation.
In addition to concentration determination of nucleic acids and protein, the BioSpec-nano can be used as an ultra micro-volume spectrophotometer for many other analytes or as a standard spectrophotometer using 5 mm pathlength cells.

This communication presents data on the accuracy and reproducibility of the BioSpec-nano UV-VIS spectrophotometer for dsDNA and protein quantitation. Even with ultra-small volumes (1 to 2 L), reproducibility is excellent, while the automated wiping function improves throughput and results in minimal carryover for more precise results.

Nucleic Acid quantitation procedures have advanced significantly in the last three decades.  More and more, molecular biologists require consistent ...

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.
Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized ...

Cardiac Pressure-Volume Loop Analysis Using Conductance Catheters in Mice

Cardiac pressure-volume loop analysis is the &#8220;gold-standard&#8221; in the assessment of load-dependent and load-independent measures of ventricular systolic and diastolic function. Measures of ventricular contractility and compliance are obtained through examination of cardiac response to changes in afterload and preload. These techniques were originally developed nearly three decades ago to measure cardiac function in large mammals and humans. The application of these analyses to small mammals, such as mice, has been accomplished through the optimization of microsurgical techniques and creation of conductance catheters. Conductance catheters allow for estimation of the blood pool by exploiting the relationship between electrical conductance and volume. When properly performed, these techniques allow for testing of cardiac function in genetic mutant mouse models or in drug treatment studies. The accuracy and precision of these studies are dependent on careful attention to the calibration of instruments, systematic conduct of hemodynamic measurements and data analyses. We will review the methods of conducting pressure-volume loop experiments using a conductance catheter in mice.

Cardiac pressure-volume loop analysis is the most comprehensive way to measure cardiac function in the intact heart. We describe a technique to perform and analyze cardiac pressure volume loops, using conductance catheters.

Cardiac pressure-volume loop analysis is the &#8220;gold-standard&#8221; in the assessment of load-dependent and load-independent measures of ventricular ...

Colorimetric Detection of Bacteria Using Litmus Test

There are increasing demands for simple but still effective methods that can be used to detect specific pathogens for point-of-care or field applications. Such methods need to be user-friendly and produce reliable results that can be easily interpreted by both specialists and non-professionals. The litmus test for pH is simple, quick, and effective as it reports the pH of a test sample via a simple color change. We have developed an approach to take advantage of the litmus test for bacterial detection. The method exploits a bacterium-specific RNA-cleaving DNAzyme to achieve two functions: recognizing a bacterium of interest and providing a mechanism to control the activity of urease. Through the use of magnetic beads immobilized with a DNAzyme-urease conjugate, the presence of bacteria in a test sample is relayed to the release of urease from beads to solution. The released urease is transferred to a test solution to hydrolyze urea into ammonia, resulting in an increase of pH that can be visualized using the classic litmus test.

We describe a protocol for colorimetric detection of E. coli using a modified litmus test that takes advantage of an RNA-cleaving DNAzyme, urease, and magnetic beads.

There are increasing demands for simple but still effective methods that can be used to detect specific pathogens for point-of-care or field applications. ...

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis algorithms which can be used effectively to count various biological particles. When counting large numbers of cell samples, the hemocytometer presents a bottleneck with regards to time. Likewise, counting membranes from migration/invasion assays with the ImageJ plugin Cell Counter, although accurate, is exceptionally labor intensive, subjective, and infamous for causing wrist pain. To address this need, we developed two plugins within ImageJ for the sole task of automated hemocytometer (or known volume) and migration/invasion cell counting. Both plugins rely on the ability to acquire high quality micrographs with minimal background. They are easy to use and optimized for quick counting and analysis of large sample sizes with built-in analysis tools to help calibration of counts. By combining the core principles of Cell Counter with an automated counting algorithm and post-counting analysis, this greatly increases the ease with which migration assays can be processed without any loss of accuracy.

This paper describes the quantification of hemocytometer and migration/invasion micrographs through two new open-source ImageJ plugins Cell Concentration Calculator and migration assay Counter. Furthermore, it describes image acquisition and calibration protocols as well as discusses in detail the input requirements of the plugins.

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis ...

Pulling Membrane Nanotubes from Giant Unilamellar Vesicles

The reshaping of the cell membrane is an integral part of many cellular phenomena, such as endocytosis, trafficking, the formation of filopodia, etc. Many different proteins associate with curved membranes because of their ability to sense or induce membrane curvature. Typically, these processes involve a multitude of proteins making them too complex to study quantitatively in the cell. We describe a protocol to reconstitute a curved membrane in vitro, mimicking a curved cellular structure, such as the endocytic neck. A giant unilamellar vesicle (GUV) is used as a model of a cell membrane, whose internal pressure and surface tension are controlled with micropipette aspiration. Applying a point pulling force on the GUV using optical tweezers creates a nanotube of high curvature connected to a flat membrane. This method has traditionally been used to measure the fundamental mechanical properties of lipid membranes, such as bending rigidity. In recent years, it has been expanded to study how proteins interact with membrane curvature and the way they affect the shape and the mechanics of membranes. A system combining micromanipulation, microinjection, optical tweezers, and confocal microscopy allows measurement of membrane curvature, membrane tension, and the surface density of proteins, concurrently. From these measurements, many important mechanical and morphological properties of the protein-membrane system can be inferred. In addition, we lay out a protocol of creating GUVs in the presence of physiological salt concentration, and a method of quantifying the surface density of proteins on the membrane from fluorescence intensities of labeled proteins and lipids.

Many proteins in the cell sense and induce membrane curvature. We describe a method to pull membrane nanotubes from lipid vesicles to study the interaction of proteins or any curvature-active molecule with curved membranes in vitro.

The reshaping of the cell membrane is an integral part of many cellular phenomena, such as endocytosis, trafficking, the formation of filopodia, etc. Many ...

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of interest. When considering the analysis of complex biological systems, the segmentation of three-dimensional image data is a time consuming and labor intensive step. With the increased availability of many imaging modalities and with automated data collection schemes, this poses an increased challenge for the modern experimental biologist to move from data to knowledge. This publication describes the use of SuRVoS Workbench, a program designed to address these issues by providing methods to semi-automatically segment complex biological volumetric data. Three datasets of differing magnification and imaging modalities are presented here, each highlighting different strategies of segmenting with SuRVoS. Phase contrast X-ray tomography (microCT) of the fruiting body of a plant is used to demonstrate segmentation using model training, cryo electron tomography (cryoET) of human platelets is used to demonstrate segmentation using super- and megavoxels, and cryo soft X-ray tomography (cryoSXT) of a mammalian cell line is used to demonstrate the label splitting tools. Strategies and parameters for each datatype are also presented. By blending a selection of semi-automatic processes into a single interactive tool, SuRVoS provides several benefits. Overall time to segment volumetric data is reduced by a factor of five when compared to manual segmentation, a mainstay in many image processing fields. This is a significant savings when full manual segmentation can take weeks of effort. Additionally, subjectivity is addressed through the use of computationally identified boundaries, and splitting complex collections of objects by their calculated properties rather than on a case-by-case basis.

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of ...

Two-Step Reverse Transcription Droplet Digital PCR Protocols for SARS-CoV-2 Detection and Quantification

Diagnosis of the ongoing SARS-CoV-2 pandemic is a priority for all countries across the globe. Currently, reverse transcription quantitative PCR (RT-qPCR) is the gold standard for SARS-CoV-2 diagnosis as no permanent solution is available. However effective this technique may be, research has emerged showing its limitations in detection and diagnosis especially when it comes to low abundant targets. In contrast, droplet digital PCR (ddPCR), a recent emerging technology with superior advantages over qPCR, has been shown to overcome the challenges of RT-qPCR in diagnosis of SARS-CoV-2 from low abundant target samples. Prospectively, in this article, the capabilities of RT-ddPCR are further expanded by showing steps on how to develop simplex, duplex, triplex probe mix, and quadruplex assays using a two-color detection system. Using primers and probes targeting specific sites of the SARS-CoV-2 genome (N, ORF1ab, RPP30, and RBD2), the development of these assays is shown to be possible. Additionally, step by step detailed protocols, notes, and suggestions on how to improve the assays workflow and analyze data are provided. Adapting this workflow in future works will ensure that the maximum number of targets can be sensitively detected in a small sample significantly improving on cost and sample throughput.

This work summarizes steps on developing different assays for SARS-CoV-2 detection using a two color ddPCR system. The steps are elaborate and notes have been included on how to improve the assays and experiment performance. These assays may be used for multiple SARS-CoV-2 RT-ddPCR applications.

Diagnosis of the ongoing SARS-CoV-2 pandemic is a priority for all countries across the globe. Currently, reverse transcription quantitative PCR (RT-qPCR) ...

Quantification of Cellular Densities and Antigenic Properties using Magnetic Levitation

The described method was developed based on the principles of magnetic levitation, which separates cells and particles based on their density and magnetic properties. Density is a cell type identifying property, directly related to its metabolic rate, differentiation, and activation status. Magnetic levitation allows a one-step approach to successfully separate, image and characterize circulating blood cells, and to detect anemia, sickle cell disease, and circulating tumor cells based on density and magnetic properties. This approach is also amenable to detecting soluble antigens present in a solution by using sets of low- and high-density beads coated with capture and detection antibodies, respectively. If the antigen is present in solution, it will bridge the two sets&#160;of beads, generating a new bead-bead complex, which will levitate in between the rows of antibody-coated beads. Increased concentration of the target antigen in solution will generate a larger number of bead-bead complexes when compared to lower concentrations of antigen, thus allowing for quantitative measurements of the target antigen. Magnetic levitation is advantageous to other methods due to its decreased sample preparation time and lack of dependance on classical readout methods. The image generated is easily captured and analyzed using a standard microscope or mobile device, such as a smartphone or a tablet.

This paper describes a magnetic levitation-based method that can specifically detect the presence of antigens, either soluble or membrane-bound, by quantifying changes in the levitation height of capture beads with fixed densities.

The described method was developed based on the principles of magnetic levitation, which separates cells and particles based on their density and magnetic ...

Quantification of Interbacterial Competition using Single-Cell Fluorescence Imaging

Interbacterial competition can directly impact the structure and function of microbiomes. This work describes a fluorescence microscopy approach that can be used to visualize and quantify competitive interactions between different bacterial strains at the single-cell level. The protocol described here provides methods for advanced approaches in slide preparation on both upright and inverted epifluorescence microscopes, live-cell and time-lapse imaging techniques, and quantitative image analysis using the open-source software FIJI. The approach in this manuscript outlines the quantification of competitive interactions between symbiotic Vibrio fischeri populations by measuring the change in area over time for two coincubated strains that are expressing different fluorescent proteins from stable plasmids. Alternative methods are described for optimizing this protocol in bacterial model systems that require different growth conditions. Although the assay described here uses conditions optimized for V. fischeri, this approach is highly reproducible and can easily be adapted to study competition among culturable isolates from diverse microbiomes.

This manuscript describes a method for using single-cell fluorescence microscopy to visualize and quantify bacterial competition in coculture.

Interbacterial competition can directly impact the structure and function of microbiomes. This work describes a fluorescence microscopy approach that can ...