Wir danken Dr. Tanja Maritzen und Dr. Fabian Feutlinske (Leibniz-Institut für Molekulare Pharmakologie, Berlin, Deutschland), um mit uns ihre Version des Fuji ImageJ Clock Scan Plugins zu teilen und uns zu inspirieren, diese Version des Programms zu entwickeln. Wir danken Dr. Fritz Melchers (Abteilung für Lymphozytenentwicklung, Max-Planck-Institut für Infektionsbiologie) für seine freundliche Erlaubnis, die Bilder aus der Datenbank seiner Abteilung zum Zwecke der Prüfung und Verbesserung des Plugins zu nutzen. Unterstützung: Zentrum für Translationsneurowissenschaften; NIH bewilligen: P30-GM110702-03.

1. Software-Installation <ol><li> Installieren Sie die neuesten Versionen von gebündeltem Java und entweder ImageJ oder Fiji ImageJ wie auf den jeweiligen Webseiten empfohlen (siehe Materialtabelle für Links zu den entsprechenden Websites). Im folgenden Text werden beide Programme als &quot;ImageJ&quot; bezeichnet. </li><li> Kopiere die Plugin-Dateien &quot;Clock_Scan-1.0.1. Jar&quot; und &quot;Multi_Clock_Scan-1.0.1.jar&quot; mit dem in der Materialtabelle enthaltenen Link und füge sie in das ImageJ-Plugin-Verzeichnis ein. Alternativ verwenden Sie bitte die Option &quot;Plugins | Install Plugin&quot;, um diese Dateien zu installieren, nachdem sie auf der Festplatte des Computers gespeichert wurden. </li></ol> 2. Clock Scan Analyse <ol><li> Standard Clock Scan Plugin ( Abbildung 1 ): <ol><li> Verwenden Sie den Menübefehl ImageJ &quot;File | Open&quot;, um ein interessantes Bild zu öffnen. </li><li> Klicken Sie auf das Werkzeug &#39;Polygon&#39; oder &#39;segmentierte Zeilenauswahl&#39;Werkzeug, und zeichne dann auf das Bild, um den gesamten ROI oder ein Segment dieser Region zu skizzieren. Siehe Abbildung 1 A für ein Beispiel der Polygonauswahl (innere gestrichelte Kontur). HINWEIS: Andere Auswahlwerkzeuge, die in der Software (rechteckige, ovale und freihändige Zeilenauswahl) zur Verfügung stehen, können ebenfalls verwendet werden. </li><li> Wählen Sie &quot;Plugins | Clock Scan&quot; aus dem Menü, um das Standard-Takt-Scan-Protokoll-Popup-Fenster zu öffnen. Beachten Sie, dass dieser Befehl auch das ROI Manager-Fenster öffnet, wobei die Gliederung automatisch hinzugefügt wird. </li><li> Verwenden Sie das Plugin-Optionsfenster, um Folgendes auszuführen. <ol><li> Überprüfen und ändern Sie die X- und Y-Koordinaten des ROI-Zentrums (automatisch als Koordinaten des physikalischen Massenzentrums berechnet), indem Sie Bildlaufleisten verwenden oder die Werte in den entsprechenden Eingabefeldern ändern. Siehe Abbildung 1 B. </li><li> Je nachdem, wie viel von der Hintergrundregion außerhalb des Objekts shoSollte durch das Scannen abgedeckt werden, die Scan-Limits anpassen, indem Sie die Scroll-Leiste &quot;Scan-Limit&quot; verwenden. Siehe Abbildung 1 A. HINWEIS: Scan-Limit ist die Bruchzahl, die angibt, wie weit der Scan über die Grenze der Objekte hinausgehen sollte. Der Standardwert ist 1,20, was anzeigt, dass die Scanlänge 20% länger als der Objektradius in Richtung des Scans ist; Siehe Abbildung 1 A , äußere gestrichelte Linie). </li><li> Ändern Sie die Ausgabe des Plugins mit &quot;Real Radius&quot;, &quot;Subtrahieren Hintergrund&quot;, &quot;Polare Transformation&quot; und / oder die &quot;Plot mit Standardabweichung&quot; Kontrollkästchen. </li><li> Klicken Sie auf &quot;OK&quot;, um das Plugin auszuführen. Siehe Abbildung 1 C-H . HINWEIS: Beispiele für die Ausgabe des Protokolls mit &quot;Plot mit Standardabweichung&quot; und &quot;Polare Transformation&quot; oder &quot;Realradius&quot; und &quot;Polar TransfOrm &quot;-Optionen sind in Abbildung 1 C und 1D und Abbildung 1 E bzw. 1F dargestellt. Beachten Sie, dass die berechneten Standardabweichung (SD) die Veränderung zwischen einzelnen radialen Pixelintensitätsscans des Objekts darstellen. Beachten Sie auch die&quot; ROI-Auswahl &quot; Länge &quot;im Plugin-Fenster, die die Informationen über die ROI-Gliederungslänge anzeigt, die in Pixeln gemessen wird. </li></ol></li><li> In dem erzeugten &quot;Clock Scan Profile Plot&quot; verwenden Sie den Befehl &quot;List&quot;, um Werte anzuzeigen, die in zwei, X- und Y-Spalten von Daten für Graustufenbilder und in X- und vier Y-Spalten von Daten für RGB-Bilder dargestellt werden, von denen Y0, Y1, Y2 und Y3 werden mit integralen und individuellen (roten, grünen und blauen) Farbkanalpixelintensitätswerten gefüllt. </li></ol></li><li> Mehrere ROI Clock Scan Plugin - Arbeiten mit mehreren ROI ( <strongClass = &quot;xfig&quot;&gt; Abbildung 2 ): <ol><li> Öffnen Sie ein Bild mit mehreren ROI. </li><li> Öffnen Sie den ROI Manager, indem Sie auf &quot;Analysieren | Werkzeuge | ROI Manager&quot; klicken. </li><li> Sequenziell skizzieren (siehe Schritt 2.1.2) und füge jeden ROI dem ROI Manager hinzu, indem du im ROI Manager-Fenster auf &quot;Hinzufügen&quot; klickst. Tu das für alle ROIs im Bild. Verwenden Sie den Befehl &quot;Analysieren | Messen&quot;, wenn ROI-Metriken von Interesse sind. <ol><li> Siehe Abbildung 2 A für ein Beispiel für mehrere segmentierte Zeilenauswahl und Abbildung 2 E für ein Beispiel für mehrere Polygonauswahl. </li></ol></li><li> Wählen Sie im Menü &quot;Plugins&quot; die Option &quot;Multi Clock Scan&quot;, um das Popup-Fenster für die Protokolloptionen zu öffnen. </li><li> Verwenden Sie das Protokolloptionsfenster, um Folgendes auszuführen. <ol><li> Falls erforderlich, setzen Sie den Scan-Grenzwert nach Schritt 2.1.4.2 zurück; Der Standardwert ist 1,20. </li><li> Wenn nötig, wählen Sie das optIonen, um das mittlere Takt-Scan-Profil mit SD-Balken zu markieren, indem man die &quot;Plot mit Standardabweichung&quot; -Kasten überprüft. Siehe Abbildung 2 C und D. HINWEIS: Die berechneten SD-Werte stellen eine Variation zwischen integrierten Taktsuchprofilen verschiedener Objekte dar. Beachten Sie auch die Zeile im Plugin-Fenster mit Informationen über die &quot;Anzahl ausgewählter ROIs&quot;. </li><li> Klicken Sie auf &quot;OK&quot;, um das Protokoll auszuführen. </li></ol></li><li> In der generierten &quot;Clock Scan Profile Plot&quot; verwenden Sie den Befehl &quot;List&quot;, um die im Fenster &quot;Plot Values&quot; angezeigten Werte darzustellen. Siehe &quot;Multi Clock Scan Profile Plot&quot; Fenster Legende für Spaltenbezeichnung nach Farbkanal. </li><li> Beachten Sie, dass die ROIs nummeriert sind und ihre Taktsuchprofile für einen beliebigen Farbkanal in der gleichen Reihenfolge aufgetragen sind, in der die ROIs skizziert und dem &quot;ROI Manager&quot; hinzugefügt wurden. </li></ol></li><li> MulTip ROI Clock Scan Plugin - Arbeiten mit einem Bildstack ( Abbildung 3 ): <ol><li> Öffne einen Bildstapel von Interesse. </li><li> Öffnen Sie den ROI Manager, indem Sie auf &quot;Analysieren | Werkzeuge | ROI Manager&quot; klicken. </li><li> Planen Sie den ROI der Bilder im Stapel und fügen Sie ihn dem ROI-Manager hinzu, wie in den Schritten 2.1.2 und 2.2.3 beschrieben. Verwenden Sie den Befehl &quot;Analysieren | Messen&quot;, wenn die ROI-Metriken von Interesse sind. </li><li> Wählen Sie im Menü &quot;Plugins&quot; die Option &quot;Multi Clock Scan&quot;, um das Popup-Fenster für die Protokolloptionen zu öffnen. </li><li> Verwenden Sie das Protokolloptionsfenster, um Folgendes auszuführen. <ol><li> Setzen Sie die Scan-Grenze wie in Schritt 2.1.4.2 beschrieben zurück; Der Standardwert ist 1,20. </li><li> Wählen Sie die Option, um das mittlere Takt-Scan-Profil mit SD-Balken zu markieren, indem Sie das Feld &#39;Plot mit Standardabweichung&#39; markieren. HINWEIS: Die berechneten SD-Werte stellen eine Variation zwischen verschiedenen Instanzen des im Bild sta markierten Objekts darCk Beachten Sie auch die Zeile im Plugin-Fenster mit Informationen über die &quot;Anzahl der Bilder im Stack&quot;. </li><li> Klicken Sie auf &quot;OK&quot;, um das Protokoll auszuführen. </li></ol></li><li> Im Fenster &quot;Clock Scan Profile Plot&quot; klicken Sie auf &quot;List&quot;, um die im Fenster &quot;Plot Values&quot; angezeigten Werte darzustellen, wobei die Y-Spaltennummer die Bildposition innerhalb des Stacks darstellt - 1. </li></ol></li></ol>

<ol>
	<li>Dobretsov, M., Romanovsky, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term="Clock-scan"+protocol+for+image+analysis.">"Clock-scan" protocol for image analysis.</a> Am J Physiol Cell Physiol. 291, 869-879 (2006).</li><li>Feutlinske, F., Browarski, M., Ku, M. C., 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=Stonin1+mediates+endocytosis+of+the+proteoglycan+NG2+and+regulates+focal+adhesion+dynamics+and+cell+motility.">Stonin1 mediates endocytosis of the proteoglycan NG2 and regulates focal adhesion dynamics and cell motility.</a> Nat Commun. 6, 8535 (2015).</li><li>Schneider, C. A., Rasband, W. S., Eliceiri, K. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NIH+Image+to+ImageJ:+25+years+of+image+analysis.">NIH Image to ImageJ: 25 years of image analysis.</a> Nat Methods. 9, 671-675 (2012).</li><li>Schindelin, J., Arganda-Carreras, I., Frise, 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=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nat Methods. 9, 676-682 (2012).</li><li>Dobretsov, M., Hastings, S. L., Stimers, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-uniform+expression+of+alpha+subunit+isoforms+of+the+Na+/K++pump+in+rat+dorsal+root+ganglia+neurons.">Non-uniform expression of alpha subunit isoforms of the Na+/K+ pump in rat dorsal root ganglia neurons.</a> Brain Res. 821, 212-217 (1999).</li><li>Hayar, A., Gu, C., Al-Chaer, E. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+improved+method+for+patch+clamp+recording+and+calcium+imaging+of+neurons+in+the+intact+dorsal+root+ganglion+in+rats.">An improved method for patch clamp recording and calcium imaging of neurons in the intact dorsal root ganglion in rats.</a> J Neurosci Methods. 173, 74-82 (2008).</li><li>Dobretsov, M., Pierce, D., Light, K. E., Kockara, N. T., Kozhemyakin, M., Wight, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transgenic+mouse+model+to+selectively+identify+alpha3+Na,K-ATPase+expressing+cells+in+the+nervous+system.">Transgenic mouse model to selectively identify alpha3 Na,K-ATPase expressing cells in the nervous system.</a> Society for Neuroscience. , 1 (2015).</li><li>Lein, E. S., Hawrylycz, M. J., Ao, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+atlas+of+gene+expression+in+the+adult+mouse+brain.">Genome-wide atlas of gene expression in the adult mouse brain.</a> Nature. 445, 168-176 (2007).</li><li>Romanovsky, D., Mrak, R. E., Dobretsov, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-dependent+decline+in+density+of+human+nerve+and+spinal+ganglia+neurons+expressing+the+alpha3+isoform+of+Na/K-ATPase.">Age-dependent decline in density of human nerve and spinal ganglia neurons expressing the alpha3 isoform of Na/K-ATPase.</a> Neuroscience. 310, 342-353 (2015).</li><li>Campbell, J., Singh, D., Hollett, G., 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=Spatially+selective+photoconductive+stimulation+of+live+neurons.">Spatially selective photoconductive stimulation of live neurons.</a> Front Cell Neurosci. 8, 142 (2014).</li><li>Yuryev, M., Pellegrino, C., Jokinen, 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=In+vivo+Calcium+Imaging+of+Evoked+Calcium+Waves+in+the+Embryonic+Cortex.">In vivo Calcium Imaging of Evoked Calcium Waves in the Embryonic Cortex.</a> Front Cell Neurosci. 9, 500 (2015).</li><li>Qiao, M., Sanes, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+Method+for+Labeling+Electrically+Coupled+Cells:+Application+to+Retina.">Genetic Method for Labeling Electrically Coupled Cells: Application to Retina.</a> Front Mol Neurosci. 8, 81 (2015).</li></ol>

Die Autoren erklären, dass sie keine konkurrierenden finanziellen Interessen oder andere Interessenkonflikte haben.

Clock Scan Protocol: Das Clock Scan Protokoll ist ein schnelles und einfaches Werkzeug der Bildanalyse. Die Vorteile dieses Protokolls im Vergleich zu bestehenden gemeinsamen Ansätzen der Bildanalyse (wie z. B. lineare Pixelintensitätsscans oder Berechnung der mittleren Pixelintensität des ROI) wurden in den vorangegangenen Publikationen 1 , 9 ausführlich beschrieben. Kurz gesagt ermöglicht dieses Protokoll die Erzeugung von integralen radi...

Das Ziel dieser Arbeit ist es, ein Clock Scan-Protokoll zu präsentieren, das plattformfrei und frei verfügbar für jeden Forscher ist, der an dieser Art von Bildanalyse interessiert ist. Das Clock Scan-Protokoll wurde ursprünglich im Jahr 2006 entwickelt, mit dem Ziel, bestehende Methoden der Pixelintensitätsquantifizierung in konvex-förmigen Regionen von Interesse (ROI) zu verbessern, ein Verfahren, das eine bessere Integrationsfähigkeit und eine verbesserte räumliche Auflösung aufweist. Während der Erfassung sammelt das Protokoll sequentiell mehrere radiale Pixelintensitätsprofile, die vom ROI-Zentrum bis zu seinem Rand abgetastet werden, oder auf eine vorbestimmte Distanz außerhalb des ROI zum Zwecke der Messung der &quot;Hintergrund&quot; -Pixelintensität. Das Protokoll skaliert diese Profile nach dem Zellenradius, gemessen in Richtung des Scans. Somit ist der Abstand von der Mitte zur ROI-Grenze jedes einzelnen radialen Scans immer 100% der X-Skala. Schließlich übertrifft das Programm diese IndividuenAl-Profilen zu einem integralen radialen Pixel-Intensitäts-Profil. Wegen der Skalierung hängt das mittlere Pixel-Intensitätsprofil, das durch das &quot;Clock Scan&quot; -Protokoll erzeugt wird, weder von der ROI-Größe noch von vernünftigen Grenzen auf die ROI-Form ab. Diese Methode ermöglicht einen direkten Vergleich oder, falls erforderlich, Mittelwertbildung oder Subtraktion von Profilen unterschiedlicher ROIs. Das Protokoll erlaubt auch eine Korrektur der integralen Pixelintensitätsprofile, eines beliebigen Objekts für Hintergrundrauschen, durch eine einfache Subtraktion der durchschnittlichen Intensität von Pixeln, die sich außerhalb des Objekts befinden. Obwohl es nur in biologischen Proben getestet wurde, bietet unser Protokoll eine wertvolle Ergänzung zu anderen vorhandenen Bildanalyse-Werkzeugen, die in Studien von Bildern von physikalischen oder chemischen Prozessen verwendet werden, die um einen Ursprungsort herum angeordnet sind (wie die Diffusion von Substanzen aus einer Punktquelle ) 1 . Allerdings war die Hauptbeschränkung der ursprünglichen Bildanalyse Methode, dass das Protokoll dev warAls ein Visual Basic 6 (VB6) (Code, und daher war es plattformabhängig und schwer zu verteilen (erfordern VB6). Um dieses Problem zu lösen und um ähnliche jüngsten Bemühungen von anderen Forschern 2 beitreten, konvertierten wir die VB6 Clock Scan Programmcode in zwei Java-basierte Plugins, kompatibel mit den NIH-geförderten und frei verfügbaren Open-Source- und plattformunabhängigen Bildanalyseprogrammen, ImageJ 3 und Fiji ImageJ 4. Darüber hinaus haben diese Plugins nun mehrere neue Funktionen, die die Leistungsfähigkeit erweitern Des ursprünglichen Protokolls, um mehrere ROIs und Bildstapel zu verarbeiten Viele Bildanalyseanwendungen sind nicht benutzerfreundlich, was die statistische Auswertung mehrerer Objekte anbelangt und so werden oft nur repräsentative Daten angezeigt. Mit dem Multi Clock Scan ImageJ Plugin, Es ist möglich, die Analyse mehrerer Objekte gleichzeitig zu erleichtern. Eine robuste statistische Auswertung von Mikroskopiedaten,In Bezug auf die Signalintensitätsverteilung in einzelnen Zellen / Objekten ist nun mit dieser Plugin-Erweiterung möglich. Hier beschreiben wir die Clock Scan Plugins und zeigen Beispiele für ihre Anwendungen in der Bildanalyse.

<table border="0" cellpadding="0" cellspacing="0" width="896">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Computer</td>
			<td style="width:71px;">Any</td>
			<td style="width:289px;"></td>
			<td style="width:320px;">compatible with software listed below</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ImageJ or Fiji ImageJ</td>
			<td style="width:71px;">NIH</td>
			<td>https://imagej.nih.gov/ij/ or https://fiji.sc/</td>
			<td>bundled with Java 1.8 or higher</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Clock-scan plugins</td>
			<td style="width:71px;">freeware</td>
			<td>https://sourceforge.net/projects/clockscan/</td>
			<td>Clock_Scan-1.0.1 jar and Multi_Clock_Scan-1.0.1/ jar</td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:216px;">Origin 9.0</td>
			<td>OriginLab</td>
			<td style="width:289px;">Northampton, MA, USA</td>
			<td style="width:320px;">This program was used to generate some graphs of the original Clock Scan data. Any other graphic software can be used to perform this function</td>
		</tr>
	</tbody>
</table>

clock scan protocol for image analysis: imagej plugins

Das Taktabtastprotokoll für die Bildanalyse ist ein effizientes Werkzeug, um die durchschnittliche Pixelintensität innerhalb, an der Grenze und außerhalb (Hintergrund) eines geschlossenen oder segmentierten konvexen Bereichs von Interesse zu quantifizieren, was zur Erzeugung eines gemittelten integralen radialen Pixel- Intensitätsprofil. Dieses Protokoll wurde ursprünglich im Jahr 2006 als visuelles Basic 6-Skript entwickelt, aber als solches hatte es eine begrenzte Verteilung. Um dieses Problem zu lösen und ähnliche jüngsten Bemühungen von anderen zu verknüpfen, haben wir den ursprünglichen Taktsuchprotokollcode in zwei Java-basierte Plugins umgewandelt, die mit NIH-gesponserten und frei verfügbaren Bildanalyseprogrammen wie ImageJ oder Fiji ImageJ kompatibel sind. Darüber hinaus haben diese Plugins mehrere neue Funktionen, die den Umfang der Fähigkeiten des ursprünglichen Protokolls weiter ausbauen, wie etwa die Analyse mehrerer interessanter Bereiche und Bildstapel. Das letztere Merkmal des Programms ist besonders nützlich bei Anwendungen, bei denen es wichtig ist, Änderungen zu ermittelnZu Zeit und Ort. So kann die Taktsuchanalyse von Stapeln biologischer Bilder potentiell auf die Ausbreitung von Na + oder Ca ++ innerhalb einer einzelnen Zelle sowie auf die Analyse der Ausbreitungsaktivität ( z. B. Ca ++ - Wellen) in Populationen synaptisch angewendet werden Zusammengesetzte oder spaltübergreifende Zellen. Hier beschreiben wir diese neuen Uhren-Scan-Plugins und zeigen einige Beispiele für ihre Anwendungen in der Bildanalyse.

Die Bilder, die hier zur Veranschaulichung verwendet werden, stammen aus Datenbanken, die während unserer früheren Zelle und Gewebe biologischen Studien 5 , 6 , 7 und aus dem Allen Maus Gehirn Atlas 8 erstellt wurden . Beide Plugins wurden erfolgreich mit ImageJ 1.50i / Java 1.8.0_77, ImageJ 2.0.0-rc-44 / 1.50e / Java 1.8.9_66 und Fiji ImageJ 2.0.0-rc54 / 1.51g / Java 1...

Watch this Scientific Journal Video about Clock Scan Protocol for Image Analysis: ImageJ Plugins at JoVE.com

Clock Scan Protocol for Image Analysis: ImageJ Plugins

In diesem Beitrag werden zwei neue ImageJ-Plugins für die &quot;Clock Scan&quot; -Bildanalyse beschrieben. Diese Plugins erweitern die Funktionalität des ursprünglichen Visual Basic 6 Programms und stellen vor allem das Programm einer großen Forschungsgemeinschaft zur Verfügung, indem sie es mit dem ImageJ Free Image Analysis Softwarepaket bündeln.

Clock Scan Protocol für Bildanalyse: ImageJ Plugins

The clock scan protocol for image analysis is an efficient tool to quantify the average pixel intensity within, at the border, and outside (background) a ...

clock-scan-protocol-for-image-analysis-imagej-plugins

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

17.3K Aufrufe.  University of Arkansas for Medical Sciences.  In diesem Beitrag werden zwei neue ImageJ-Plugins für die "Clock Scan" -Bildanalyse beschrieben. Diese Plugins erweitern die Funktionalität des ursprünglichen Visual Basic 6 Programms und stellen vor allem das Programm einer großen Forschungsgemeinschaft zur Verfügung, indem sie es mit dem ImageJ Free Image Analysis Softwarepaket bündeln. 

Serie: Clock Scan Protocol for Image Analysis: ImageJ Plugins

Protocol for Mosquito Rearing (A. gambiae)

This protocol describes mosquito rearing in the insectary. The insectary rooms are maintained at 28&deg;C and ~80% humidity, with a 12 hr. day/night cycle. For this procedure, you'll need mosquito cages, 10% sterile sucrose solution, paper towels, beaker, whatman filter paper, glass feeders, human blood and serum, water bath, parafilm, distilled water, clean plastic trays, mosquito food (described below), mosquito net to cover the trays, vacuum, and a collection chamber to collect adults.

Protocol for Mosquito Rearing A. gambiae

This video illustrates the general techniques used to rear Anopheles gambiae in the laboratory. The methods for caring for laboratory mosquitoes are demonstrated through all stages of the organism's life cycle from larvae to pupae to blood-feeding adults.

Protokoll für Mosquito Aufzucht (A. gambiae)

This protocol describes mosquito rearing in the insectary. The insectary rooms are maintained at 28&deg;C and ~80% humidity, with a 12 hr. day/night ...

Forschung

Biologie

Neutrophil Isolation Protocol

Neutrophil polymorphonuclear granulocytes (PMN) are the most abundant leukocytes in humans and among the first cells to arrive on the site of inflammatory immune response. Due to their key role in inflammation, neutrophil functions such as locomotion, cytokine production, phagocytosis, and tumor cell combat are extensively studied. To characterize the specific functions of neutrophils, a clean, fast, and reliable method of separating them from other blood cells is desirable for in vitro studies, especially since neutrophils are short-lived and should be used within 2-4 hours of collection. Here, we demonstrate a standard density gradient separation method to isolate human neutrophils from whole blood using commercially available separation media that is a mixture of sodium metrizoate and Dextran 500. The procedure consists of layering whole blood over the density gradient medium, centrifugation, separation of neutrophil layer, and lysis of residual erythrocytes. Cells are then washed, counted, and resuspended in buffer to desired concentration. When performed correctly, this method has been shown to yield samples of >95% neutrophils with >95% viability.

Neutrophils are among the first cells to arrive on the site of inflammatory immune response, and their functions and mechanisms have been studied extensively in vitro. We demonstrate a standard density gradient separation method to isolate human neutrophils from whole blood using commercially available separation media.

Neutrophile Isolation Protocol

Neutrophil polymorphonuclear granulocytes (PMN) are the most abundant leukocytes in humans and among the first cells to arrive on the site of inflammatory ...

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

Eine Neuronale und Astrocyte Co-Kultur-Assay für High Content Analyse der Neurotoxizität

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.

Robotik und Dynamic Image Analysis für Studies of Gene Expression in pflanzlichen Geweben

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods ...

ReAsH/FlAsH Labeling and Image Analysis of Tetracysteine Sensor Proteins in Cells

Fluorescent proteins and dyes are essential tools for the study of protein trafficking, localization and function in cells. While fluorescent 
proteins such as green fluorescence protein (GFP) have been extensively used as fusion partners to proteins to track the properties of a protein of interest1, recent 
developments with smaller tags enable new functionalities of proteins to be examined in cells such as conformational change and protein-association 2, 3. One small 
tag system involves a tetracysteine motif (CCXXCC) genetically inserted into a target protein, which binds to biarsenical dyes, ReAsH (red fluorescent) and FlAsH 
(green fluorescent), with high specificity even in live cells 2. The TC/biarsenical dye system offers far less steric constraints to the host protein than 
fluorescent proteins which has enabled several new approaches to measure conformational change and protein-protein interactions 4-7. We recently developed 
a novel application of TC tags as sensors of oligomerization in cells expressing mutant huntingtin, which when mutated aggregates in neurons in Huntington 
disease 7. Huntingtin was tagged with two fluorescent dyes, one a fluorescent protein to track protein location, and the second a TC tag which only 
binds biarsenical dyes in monomers. Hence, changes in colocalization between protein and biarsenical dye reactivity enabled submicroscopic oligomer 
content to be spatially mapped within cells. Here, we describe how to label TC-tagged proteins fused to a fluorescent protein (Cherry, GFP or CFP) 
with FlAsH or ReAsH in live mammalian cells and how to quantify the two color fluorescence (Cherry/FlAsH, CFP/FlAsH or GFP/ReAsH combinations).

The biarsenical dyes FlAsH and ReAsH bind specifically to tetracysteine motifs in proteins and can selectively label proteins in live cells. Recently this labeling strategy has been used to develop sensors for different protein conformations or oligomeric states. We describe the labeling approach and methods to quantitatively analyze binding.

ReAsH / Flash Labeling und Bildanalyse von Tetracysteinmotiv Sensor Proteinen in Zellen

Fluorescent proteins and dyes are essential tools for the study of protein trafficking, localization and function in cells.  While fluorescent 
proteins ...

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the primary photosynthetic organ, and their size and shape vary developmentally and environmentally within a plant. For these reasons studies on leaf morphology require measurement of multiple parameters from numerous leaves, which is best done by semi-automated phenomics tools5,6. Canopy shade is an important environmental cue that affects plant architecture and life history; the suite of responses is collectively called the shade avoidance syndrome (SAS)7. Among SAS responses, shade induced leaf petiole elongation and changes in blade area are particularly useful as indices8. To date, leaf shape programs (e.g. SHAPE9, LAMINA10, LeafAnalyzer11, LEAFPROCESSOR12) can measure leaf outlines and categorize leaf shapes, but can not output petiole length. Lack of large-scale measurement systems of leaf petioles has inhibited phenomics approaches to SAS research. In this paper, we describe a newly developed ImageJ plugin, called LeafJ, which can rapidly measure petiole length and leaf blade parameters of the model plant Arabidopsis thaliana. For the occasional leaf that required manual correction of the petiole/leaf blade boundary we used a touch-screen tablet. Further, leaf cell shape and leaf cell numbers are important determinants of leaf size13. Separate from LeafJ we also present a protocol for using a touch-screen tablet for measuring cell shape, area, and size. Our leaf trait measurement system is not limited to shade-avoidance research and will accelerate leaf phenotyping of many mutants and screening plants by leaf phenotyping.

Demonstration of key methods for high throughput leaf measurements. These methods can be used to accelerate leaf phenotyping when studying many plant mutants or otherwise screening plants by leaf phenotype.

LeafJ: Ein ImageJ Plugin für Semi-automatisierte Blattform Measurement

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the ...

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ imaging data is time consuming and subject to bias. Automated signal analysis algorithms based on region of interest (ROI) detection have been implemented for one-dimensional line scan measurements, but there is no current algorithm which integrates optimized identification and analysis of ROIs in two-dimensional image sequences. Here an algorithm for rapid acquisition and analysis of ROIs in image sequences is described. It utilizes ellipses fit to noise filtered signals in order to determine optimal ROI placement, and computes Ca2+ signal parameters of amplitude, duration and spatial spread. This algorithm was implemented as a freely available plugin for ImageJ (NIH) software. Together with analysis scripts written for the open source statistical processing software R, this approach provides a high-capacity pipeline for performing quick statistical analysis of experimental output. The authors suggest that use of this analysis protocol will lead to a more complete and unbiased characterization of physiologic Ca2+ signaling.

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca2+ signals with minimal user input and bias.

Automatisierte Analyse von dynamischen Ca<sup&gt; 2 +</sup&gt; Signale in Bildsequenzen

Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ ...

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application &#8211; SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min&#160;on a minimally configured laptop, or around 1 min&#160;on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary &#8220;Manual Initialize&#8221; and &#8220;Hand Draw&#8221; tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.

We present a high-throughput image analysis software application to measure the size of three-dimensional tumor spheroids imaged with bright-field microscopy. This application provides a fast and effective way to examine the effects of therapeutic drugs on spheroids, which is beneficial for researchers who wish to use spheroids in drug screens.

Hochdurchsatz-Analyse von Bild Tumorsphäroide: Eine benutzerfreundliche Software-Anwendung, um die Größe der Sphäroide automatisch und präzise messen

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to ...

A Protocol for Lentiviral Transduction and Downstream Analysis of Intestinal Organoids

Intestinal crypt-villus structures termed organoids, can be kept in sustained culture three dimensionally when supplemented with the appropriate growth factors. Since organoids are highly similar to the original tissue in terms of homeostatic stem cell differentiation, cell polarity and presence of all terminally differentiated cell types known to the adult intestinal epithelium, they serve as an essential resource in experimental research on the epithelium. The possibility to express transgenes or interfering RNA using lentiviral or retroviral vectors in organoids has increased opportunities for functional analysis of the intestinal epithelium and intestinal stem cells, surpassing traditional mouse transgenics in speed and cost. In the current video protocol we show how to utilize transduction of small intestinal organoids with lentiviral vectors illustrated by use of doxycylin inducible transgenes, or IPTG inducible short hairpin RNA for overexpression or gene knockdown. Furthermore, considering organoid culture yields minute cell counts that may even be reduced by experimental treatment, we explain how to process organoids for downstream analysis aimed at quantitative RT-PCR, RNA-microarray and immunohistochemistry. Techniques that enable transgene expression and gene knock down in intestinal organoids contribute to the research potential that these intestinal epithelial structures hold, establishing organoid culture as a new standard in cell culture.

In this video protocol we give a step by step explanation of lentiviral transduction in organoids of primary intestinal epithelium and of processing and downstream analysis of these cultures by quantitative RT-PCR, RNA-microarray and immunohistochemistry.

Ein Protokoll für Lentivirale Transduktion und Downstream-Analyse der Darm Organoide

Intestinal crypt-villus structures termed organoids, can be kept in sustained culture three dimensionally when supplemented with the appropriate growth ...

Rapid Analysis of Circadian Phenotypes in Arabidopsis Protoplasts Transfected with a Luminescent Clock Reporter

The plant circadian clock allows the anticipation of daily changes to the environment. This anticipation aids the responses to temporally predictable biotic and abiotic stress. Conversely, disruption of circadian timekeeping severely compromises plant health and reduces agricultural crop yields. It is therefore imperative that we understand the intricate regulation of circadian rhythms in plants, including the factors that affect motion of the transcriptional clockwork itself.
Testing circadian defects in the model plant Arabidopsis thaliana (Arabidopsis) traditionally involves crossing specific mutant lines to a line rhythmically expressing firefly luciferase from a circadian clock gene promoter. This approach is laborious, time-consuming, and could be fruitless if a mutant has no circadian phenotype. The methodology presented here allows a rapid initial assessment of circadian phenotypes. Protoplasts derived from mutant and wild-type Arabidopsis are isolated, transfected with a rhythmically expressed luminescent reporter, and imaged under constant light conditions for 5 days. Luminescent traces will directly reveal whether the free-running period of mutant plants is different from wild-type plants. The advantage of the method is that any Arabidopsis line can efficiently be screened, without the need for generating a stably transgenic luminescent clock marker line in that mutant background.

The circadian clock regulates about a third of the Arabidopsis transcriptome, but the percentage of genes that feed back into timekeeping remains unknown. Here we visualize a method to rapidly assess circadian phenotypes in any mutant line of Arabidopsis using luminescent imaging of a circadian reporter transiently expressed in protoplasts.

Schnelle Analyse von Circadian Phänotypen in Arabidopsis Protoplasten Transfizierte mit einem lumineszenten Clock Reporter

The plant circadian clock allows the anticipation of daily changes to the environment. This anticipation aids the responses to temporally predictable ...