Name: detection and quantification of tunneling nanotubes using 3d volume view images
Uploaded: 2022-08-31T14:00:00
Description: Watch this Scientific Journal Video about Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images at JoVE.com

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 distinguis...

<ol>
	<li>Rustom, A., Saffrich, R., Markovic, I., Walther, P., Gerdes, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nanotubular+highways+for+intercellular+organelle+transport.">Nanotubular highways for intercellular organelle transport.</a> Science. 303 (5660), 1007-1010 (2004).</li><li>Desir, S., 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=Chemotherapy-induced+tunneling+nanotubes+mediate+intercellular+drug+efflux+in+pancreatic+cancer.">Chemotherapy-induced tunneling nanotubes mediate intercellular drug efflux in pancreatic cancer.</a> Scientific Reports. 8, 9484 (2018).</li><li>Cordero Cervantes, D., Zurzolo, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peering+into+tunneling+nanotubes-The+path+forward.">Peering into tunneling nanotubes-The path forward.</a> The EMBO Journal. 40 (8), 105789 (2021).</li><li>Dieriks, B. V., 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=&#945;-synuclein+transfer+through+tunneling+nanotubes+occurs+in+SH-SY5Y+cells+and+primary+brain+pericytes+from+Parkinson's+disease+patients.">&#945;-synuclein transfer through tunneling nanotubes occurs in SH-SY5Y cells and primary brain pericytes from Parkinson's disease patients.</a> Scientific Reports. 7, 42984 (2017).</li><li>Chen, J., Cao, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Astrocyte-to-neuron+transportation+of+enhanced+green+fluorescent+protein+in+cerebral+cortex+requires+F-actin+dependent+tunneling+nanotubes.">Astrocyte-to-neuron transportation of enhanced green fluorescent protein in cerebral cortex requires F-actin dependent tunneling nanotubes.</a> Scientific Reports. 11, 16798 (2021).</li><li>Mittal, R., 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=Cell+communication+by+tunneling+nanotubes:+Implications+in+disease+and+therapeutic+applications.">Cell communication by tunneling nanotubes: Implications in disease and therapeutic applications.</a> Journal of Cellular Physiology. 234 (2), 1130-1146 (2019).</li><li>Polak, R., de Rooij, B., Pieters, R., den Boer, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=B-cell+precursor+acute+lymphoblastic+leukemia+cells+use+tunneling+nanotubes+to+orchestrate+their+microenvironment.">B-cell precursor acute lymphoblastic leukemia cells use tunneling nanotubes to orchestrate their microenvironment.</a> Blood. 126 (21), 2404-2414 (2015).</li><li>Panasiuk, M., Rychlowski, M., Derewonko, N., Bienkowska-Szewczyk, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tunneling+nanotubes+as+a+novel+route+of+cell-to-cell+spread+of+herpesviruses.">Tunneling nanotubes as a novel route of cell-to-cell spread of herpesviruses.</a> Journal of Virology. 92 (10), 00090 (2018).</li><li>Austefjord, M. W., Gerdes, H. H., Wang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tunneling+nanotubes:+Diversity+in+morphology+and+structure.">Tunneling nanotubes: Diversity in morphology and structure.</a> Communicative &amp; Integrative Biology. 7 (1), 27934 (2014).</li><li>Han, X., Wang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opportunities+and+challenges+in+tunneling+nanotubes+research:+How+far+from+clinical+application.">Opportunities and challenges in tunneling nanotubes research: How far from clinical application.</a> International Journal of Molecular Sciences. 22 (5), 2306 (2021).</li><li>Zurzolo, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tunneling+nanotubes:+Reshaping+connectivity.">Tunneling nanotubes: Reshaping connectivity.</a> Current Opinion in Cell Biology. 71, 139-147 (2021).</li><li>Dilna, A., 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=Amyloid-beta+induced+membrane+damage+instigates+tunneling+nanotube-like+conduits+by+p21-activated+kinase+dependent+actin+remodulation.">Amyloid-beta induced membrane damage instigates tunneling nanotube-like conduits by p21-activated kinase dependent actin remodulation.</a> Biochimica et Biophysica Acta (BBA)- Molecular Basis of Disease. 1867 (12), 166246 (2021).</li><li>Chang, 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=Formation+of+cellular+close-ended+tunneling+nanotubes+through+mechanical+deformation.">Formation of cellular close-ended tunneling nanotubes through mechanical deformation.</a> Science Advances. 8 (13), (2022).</li><li>Bukoreshtliev, N. V., 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=Selective+block+of+tunneling+nanotube+(TNT)+formation+inhibits+intercellular+organelle+transfer+between+PC12+cells.">Selective block of tunneling nanotube (TNT) formation inhibits intercellular organelle transfer between PC12 cells.</a> FEBS Letters. 583 (9), 1481-1488 (2009).</li><li>Zhang, J., 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=Direct+observation+of+tunneling+nanotubes+within+human+mesenchymal+stem+cell+spheroids.">Direct observation of tunneling nanotubes within human mesenchymal stem cell spheroids.</a> The Journal of Physical Chemistry B. 122 (43), 9920-9926 (2018).</li><li>Hanna, S. J., 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=The+role+of+Rho-GTPases+and+actin+polymerization+during+Macrophage+Tunneling+Nanotube+Biogenesis.">The role of Rho-GTPases and actin polymerization during Macrophage Tunneling Nanotube Biogenesis.</a> Scientific Reports. 7, 8547 (2017).</li><li>Raghavan, A., Rao, P., Neuzil, J., Pountney, D. L., Nath, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oxidative+stress+and+Rho+GTPases+in+the+biogenesis+of+tunnelling+nanotubes:+Implications+in+disease+and+therapy.">Oxidative stress and Rho GTPases in the biogenesis of tunnelling nanotubes: Implications in disease and therapy.</a> Cellular and Molecular Life Sciences. 79, 36 (2021).</li><li>Abounit, S., Delage, E., Zurzolo, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+and+characterization+of+tunneling+nanotubes+for+intercellular+trafficking.">Identification and characterization of tunneling nanotubes for intercellular trafficking.</a> Current Protocols in Cell Biology. 67, 11-21 (2015).</li><li>Hodneland, 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=Automated+detection+of+tunneling+nanotubes+in+3D+images.">Automated detection of tunneling nanotubes in 3D images.</a> Cytometry A. 69 (9), 961-972 (2006).</li><li>Wang, X., Bukoreshtliev, N. V., Gerdes, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Developing+neurons+form+transient+nanotubes+facilitating+electrical+coupling+and+calcium+signaling+with+distant+astrocytes.">Developing neurons form transient nanotubes facilitating electrical coupling and calcium signaling with distant astrocytes.</a> PLoS One. 7 (10), 47429 (2012).</li><li>Nath, S., 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=Spreading+of+neurodegenerative+pathology+via+neuron-to-neuron+transmission+of+beta-amyloid.">Spreading of neurodegenerative pathology via neuron-to-neuron transmission of beta-amyloid.</a> Journal of Neuroscience. 32 (26), 8767-8777 (2012).</li><li>Domert, J., 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=Spreading+of+amyloid-beta+peptides+via+neuritic+cell-to-cell+transfer+is+dependent+on+insufficient+cellular+clearance.">Spreading of amyloid-beta peptides via neuritic cell-to-cell transfer is dependent on insufficient cellular clearance.</a> Neurobiology of Disease. 65, 82-92 (2014).</li><li>Pawley, J. B., Pawley, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Points,+Pixels,+and+Gray+Levels:+Digitizing+Image+Data.">Points, Pixels, and Gray Levels: Digitizing Image Data.</a> Handbook Of Biological Confocal Microscopy. , 59-79 (2006).</li><li>Pontes, B., 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=Structure+and+elastic+properties+of+tunneling+nanotubes.">Structure and elastic properties of tunneling nanotubes.</a> European Biophysics Journal. 37 (2), 121-129 (2008).</li><li>Haimovich, G., Gerst, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+mRNA+transfer+between+mammalian+cells+in+coculture+by+single-molecule+fluorescent+in+situ+hybridization+(smFISH).">Detection of mRNA transfer between mammalian cells in coculture by single-molecule fluorescent in situ hybridization (smFISH).</a> Methods in Molecular Biology. 2038, 109-129 (2019).</li><li>Janardhan, K. S., Jensen, H., Clayton, N. P., Herbert, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immunohistochemistry+in+investigative+and+toxicologic+pathology.">Immunohistochemistry in investigative and toxicologic pathology.</a> Toxicologic Pathology. 46 (5), 488-510 (2018).</li><li>Qin, Y., 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=The+combination+of+paraformaldehyde+and+glutaraldehyde+is+a+potential+fixative+for+mitochondria.">The combination of paraformaldehyde and glutaraldehyde is a potential fixative for mitochondria.</a> Biomolecules. 11 (5), 711 (2021).</li><li>Graham, R. C., Karnovsky, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+histochemical+demonstration+of+monoamine+oxidase+activity+by+coupled+peroxidatic+oxidation.">The histochemical demonstration of monoamine oxidase activity by coupled peroxidatic oxidation.</a> Journal of Histochemistry &amp; Cytochemistry. 13 (7), 604-605 (1965).</li></ol>

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 ...

The lack of tunneling nanotubes specific markers restrict progress in the field and there is an increasing demand for a method to identify, characterize, and quantify tunneling nanotubes. Automatic detection methods are difficult to implement in without the expertise to develop an algorithm and/or change the existing algorithm, but our manual method is easy to implement with better precision. Long-range intercellular transfer via tunneling nanotubes is implemented in several ways in various disease models, including neurodegenerative pathology, viral spreading, and cancer.Thus, studies on tunneling nanotubes to understand their role in pathogenesis are important. It is possible the tunneling nanotubes might break during the staining process. Avoid using cover slips and mounting on the slides, using glass bottom imaging dishes instead.Modified fixation protocol helps to keep tunneling nanotubes intact. Demonstrating the procedure will be Deepak KV, a PhD student from the laboratory of Sangeeta NAF. To begin, wash the control and oligomeric A-beta treated cells twice with PBS for two minutes before fixation.Prepare Karnovsky's fixative solution by using 2%formalin fixative and 2.5%glutaraldehyde dissolved in 0.1 molar phosphate buffer of pH 7.2. Then fix the cells in the imaging dish by adding Karnovsky's fixative solution for 45 minutes at room temperature. Prepare the incubation buffer by dissolving 0.1 gram of saponin in five milliliters of FBS and diluted by adding 95 milliliters of PBS.Then wash the fixed cells twice using incubation buffer for two minutes. After fixation, add the first antibody against phospho-PAK1, add a dilution of one to 250 in the incubation buffer and incubate overnight at four degrees Celsius in a dark, moist chamber. The next day after 24 hours of incubation, wash the cells twice with the incubation buffer for two minutes.Then add secondary antibody conjugated to Alexa Fluor 488 and Phalloidin 555. Incubate the cells in the dark, moist chamber for 1.5 hours at room temperature. After the incubation, wash the cells twice with incubation buffer for two minutes.Stain the nucleus by adding DAPI in a one to 2000 dilution and incubate for five to 10 minutes at room temperature in the dark. Prepare DABCO mounting medium using 25 milligrams of DABCO in 90%glycerol and 10%PBS. To dissolve properly, adjust the pH to 8.6 using spectrophotometric grade dilute hydrochloric acid and keep the solution on a rocker for mixing.As an anti-bleaching agent, add the DABCO mounting medium directly onto the imaging dish containing the cover slip at the bottom. Wait for at least one to two hours and directly proceed to perform confocal imaging. To identify tunneling nanotubes, capture Z-stack images of immunostained cells using a confocal laser-scanning microscope by first selecting the channels and clicking the required lasers sequentially in the windows track one, track two, and track three in the confocal software.Click the option TPMT below the track one window to take the differential interference contrast images with fluorescence channels. 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, and 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 which determines the number of slices and the intervals based on the thickness of the cells. Take sequential images of three channels of DAPI, FITC, and TRITC with 405 nanometer, 488 nanometer, and 561 nanometer lasers and capture with a pixel dwell time of 1.02 microsecond.Capture images in the DIC channel with fluorescence channels to observe the cell boundary. Open the confocal images saved in czi data format in Fiji software for analysis. Select the Hyperstack option to see each Z-stack and channel of the image.Scroll the channel and Z-stack scroll bars to select the exact stack of a particular channel of interest. Then select the F-actin stained channel first by scrolling the channel bar and manually scroll the Z-stacks to see each stack one by one. Identify the F-actin stained structures that seem to be connecting cells that are visible in the lower parts of the Z-stacks and are close to the surface of the imaging dish with Z equal to two as neurites.Identify the tunneling nanotubes, the F-actin positive hovering cell to cell conduits by scrolling the Z-stacks toward the top from Z equals to four. 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 equal to six. Identify phospho-PAK1 positive tunneling nanotubes similarly as F-actin positive tunneling nanotubes by analyzing the hovering nature of the conduits from the Z-stacks.As phospho-PAK1 staining is weaker than F-actin staining, look for phospho-PAK1 stained tunneling nanotubes at Z equal to four and at Z equal to six. Further, observe the DIC images to verify that the F-actin and phospho-PAK1 stained tunneling nanotube structures are membrane conduits between cells. In addition, merge F-actin and phospho-PAK1 channels to verify that the identified tunneling nanotubes are F-actin and phospho-PAK1 co-stained structures.To quantify the tunneling nanotubes, count the total cell numbers and the identified tunneling nanotubes manually and represent the numbers as percentages. To distinguish F-actin and beta III tubulin positive tunneling nanotubes like hovering conduits from Z-stack images, merge F-actin and beta III tubulin channels, then analyze the Z-stacks of the merged images. Look for exclusively F-actin stained tunneling nanotubes that are faintly visible at Z equal to three and prominent at Z equal to six and Z equal to nine.Similarly, identify F-actin and beta III tubulin. double-positive tunneling nanotubes like hovering conduits at Z equal to six and Z equal to nine. Identify other F-actin and beta III tubulin stained non-hovering protrusions from the lower parts of the Z-stacks.Measure the diameter of the tunneling nanotubes using the Line tool in Fiji. Verify the scale of measurement by clicking Analyze and then set scale so that distance in pixels is set automatically from the czi images. Measure the diameters of the tunneling nanotubes at the XY plane.Using the Volume Viewer plugin in Fiji which allows 3D re-slicing and threshold-enabled 3D visualization, split the Z-stack images into individual channels. Then crop the single channel Z-stack images to use 3D reconstruction view to highlight one or two tunneling nanotubes or neurites at a time. Pin a single tunneling nanotubes or neurite in the XY plane and mark XZ and YZ access cross sections.Observe the neurites at the bottom of the XZ plane, in the XZ and YZ planes, and the tunneling nanotubes in the upper Z-stacks. Select the individual tunneling nanotubes or neurite to reconstruct the 3D volume view in the XZ plane. In the 3D reconstruction, observe the neurites at the bottom of the Z-plane and the tunneling nanotubes appearing as hovering structures connecting two cells without touching the bottom Z-plane.Confocal Z-stack images of immunostained cells with F-actin and phospho-PAK1 were analyzed to identify tunneling nanotubes. Further, DIC images were analyzed to verify that the F-actin and phospho-PAK1 stained tunneling nanotubes structures were membrane conduits between cells. The cells were double immunostained with F-actin and beta III tubulin and the tunneling nanotube-like F-actin and the tunneling nanotube-like F-actin and beta III tubulin double positive membrane conduits were distinguished from only F-actin positive tunneling nanotubes.The tunneling nanotubes co-expressed with phospho-PAK1 and F-actin were distinguished from other neurites'cell protrusions by constructing 3D volume view images based on their characteristic of hovering between two cells without touching the substratum. Usage of right fixative agent is important as imperfect fixation of the sample may lead to rapid proteolytic degradation of the target proteins and a reduction in the specific immunoreactivity.

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

Research

JoVE Journal

Biology

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 ...