Vorrei ringraziare i miei colleghi a MilliporeSigma, Philip J. Morrissey e Sherree L. Friend, per il loro sostegno e la loro guida negli anni. Vorrei anche ringraziare Vidya Venkatachalam e Bryan R. Davidson per il loro aiuto con la funzionalità BDC3 nel software IDEAS , Ryan P. Jessup per contribuire a modificare questo manoscritto, Raymond Kong per aiuto nel giorno della ripresa e un ringraziamento speciale All&#39;artista trucco Cynthia Xamonthiene.

<ol>
	<li>Levine, B., Klionsky, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+by+self-digestion:+molecular+mechanisms+and+biological+functions+of+autophagy.">Development by self-digestion: molecular mechanisms and biological functions of autophagy.</a> Developmental Cell. 6, 463-477 (2004).</li><li>Zhang, X. J., Chen, S., Huang, K. X., Le, W. 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B., Lamark, T., &#216;vervatn, A., Harneshaug, I., Johansen, T., Bj&#248;rk&#248;y, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+reporter+cell+system+to+monitor+autophagy+based+on+p62/SQSTM1.">A reporter cell system to monitor autophagy based on p62/SQSTM1.</a> Autophagy. 6 (6), 784-793 (2010).</li><li>Demishtein, A., Porat, Z., Elazar, Z., Shvets, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applications+of+flow+cytometry+for+measurement+of+autophagy.">Applications of flow cytometry for measurement of autophagy.</a> Methods. 75, 87-95 (2015).</li><li>Mizushima, N., Yoshimori, T., Levine, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+in+mammalian+autophagy+research.">Methods in mammalian autophagy research.</a> Cell. 140 (3), 313-326 (2010).</li><li>Klionsky, D. 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=Guidelines+for+the+use+and+interpretation+of+assays+for+monitoring+autophagy.">Guidelines for the use and interpretation of assays for monitoring autophagy.</a> Autophagy. 8 (4), 445-544 (2012).</li><li>Arsov, I., 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=BAC-mediated+transgenic+expression+of+fluorescent+autophagic+protein+Beclin+1+reveals+a+role+for+Beclin+1+in+lymphocyte+development.">BAC-mediated transgenic expression of fluorescent autophagic protein Beclin 1 reveals a role for Beclin 1 in lymphocyte development.</a> Cell Death Differ. 15 (9), 1385-1395 (2008).</li><li>Maloyan, A., Sayegh, J., Osinska, H., Chua, B. H., Robbins, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Manipulation+of+death+pathways+in+desmin-related+cardiomyopathy.">Manipulation of death pathways in desmin-related cardiomyopathy.</a> Circ Res. 106 (9), 1524-1532 (2010).</li><li>Altman, B. 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=Autophagy+is+essential+to+suppress+cell+stress+and+to+allow+BCR-Abl-mediated+leukemogenesis.">Autophagy is essential to suppress cell stress and to allow BCR-Abl-mediated leukemogenesis.</a> Oncogene. 30 (16), 1855-1867 (2011).</li><li>Suarez, A. L., Kong, R., George, T., He, L., Yue, Z., van Dyk, L. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gammaherpesvirus+68+infection+of+endothelial+cells+requires+both+host+autophagy+genes+and+viral+oncogenes+for+optimal+survival+and+persistence.">Gammaherpesvirus 68 infection of endothelial cells requires both host autophagy genes and viral oncogenes for optimal survival and persistence.</a> J Virol. 85 (13), 6293-6308 (2011).</li><li>Tra, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autophagy+in+human+embryonic+stem+cells.">Autophagy in human embryonic stem cells.</a> PLoS One. 6 (11), (2011).</li><li>Yuan, 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=Bafilomycin+A1+targets+both+autophagy+and+apoptosis+pathways+in+pediatric+B-cell+acute+lymphoblastic+leukemia.">Bafilomycin A1 targets both autophagy and apoptosis pathways in pediatric B-cell acute lymphoblastic leukemia.</a> Haematologica. 100 (3), 345-356 (2015).</li><li>Leveque-El Mouttie, L., 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=Autophagy+is+required+for+stem+cell+mobilization+by+G-CSF.">Autophagy is required for stem cell mobilization by G-CSF.</a> Blood. 125 (19), 2933-2936 (2015).</li><li>Watson, A. 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=Autophagy+limits+proliferation+and+glycolytic+metabolism+in+acute+myeloid+leukemia.">Autophagy limits proliferation and glycolytic metabolism in acute myeloid leukemia.</a> Cell Death Discov. 1, 15008 (2015).</li><li>Stranks, A. 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=Autophagy+Controls+Acquisition+of+Aging+Features+in+Macrophages.">Autophagy Controls Acquisition of Aging Features in Macrophages.</a> J Innate Immun. 7 (4), 375-391 (2015).</li><li>Clarke, A. 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=Autophagy+is+activated+in+systemic+lupus+erythematosus+and+required+for+plasmablast+development.">Autophagy is activated in systemic lupus erythematosus and required for plasmablast development.</a> Ann Rheum Dis. 74 (5), 912-920 (2015).</li><li>Warnes, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometric+assays+for+the+study+of+autophagy.">Flow cytometric assays for the study of autophagy.</a> Methods. 82, 21-28 (2015).</li><li>Bj&#248;rk&#248;y, 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=p62/SQSTM1+forms+protein+aggregates+degraded+by+autophagy+and+has+a+protective+effect+on+huntingtin-induced+cell+death.">p62/SQSTM1 forms protein aggregates degraded by autophagy and has a protective effect on huntingtin-induced cell death.</a> J Cell Biol. 171 (4), 603-614 (2005).</li><li>Phadwal, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+method+for+autophagy+detection+in+primary+cells:+Impaired+levels+of+macroautophagy+in+immunosenescent+T+cells.">A novel method for autophagy detection in primary cells: Impaired levels of macroautophagy in immunosenescent T cells.</a> Autophagy. 8 (4), 677-689 (2012).</li><li>Rajan, R., Karbowniczek, M., Pugsley, H. R., Sabnani, M. K., Astrinidis, A., La-Beck, N. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+autophagosomes+and+autolysosomes+in+cells+using+imaging+flow+cytometry.">Quantifying autophagosomes and autolysosomes in cells using imaging flow cytometry.</a> Cytometry A. 87 (5), 451-458 (2015).</li><li>Kwok, A. 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=HspB8+mutation+causing+hereditary+distal+motor+neuropathy+impairs+lysosomal+delivery+of+autophagosomes.">HspB8 mutation causing hereditary distal motor neuropathy impairs lysosomal delivery of autophagosomes.</a> J Neurochem. 119 (6), 1155-1161 (2011).</li><li>Lopez-Herrera, 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=Deleterious+Mutations+in+LRBA+Are+Associated+with+a+Syndrome+of+Immune+Deficiency+and+Autoimmunity.">Deleterious Mutations in LRBA Are Associated with a Syndrome of Immune Deficiency and Autoimmunity.</a> Am J Hum Genet. 90 (6), 986-1001 (2012).</li><li>Pike, L. R., Phadwal, K., Simon, A. K., Harris, A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ATF4+orchestrates+a+program+of+BH3-only+protein+expression+in+severe+hypoxia.">ATF4 orchestrates a program of BH3-only protein expression in severe hypoxia.</a> Mol Biol Rep. 39 (12), 10811-10822 (2012).</li><li>Pike, L. 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=Transcriptional+up-regulation+of+ULK1+by+ATF4+contributes+to+cancer+cell+survival.">Transcriptional up-regulation of ULK1 by ATF4 contributes to cancer cell survival.</a> Biochem J. 449 (2), 389-400 (2013).</li><li>Pugsley, H. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+autophagy:+Measuring+LC3+puncta+and+autolysosome+formation+in+cells+using+multispectral+imaging+flow+cytometry.">Quantifying autophagy: Measuring LC3 puncta and autolysosome formation in cells using multispectral imaging flow cytometry.</a> Methods. 112, 147-156 (2017).</li><li>IDEAS. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Image Data Exploration and Analysis Software User's Manual Version 6.2. , (2015).</li></ol>

Sono impiegato da MilliporeSigma, il produttore dell&#39;immagine ImageStream di Amnis, che è stato utilizzato in questo studio.

Quando si esegue questa analisi, ci sono alcune cose da considerare. È importante avere campioni di controllo appropriati. Per questo esperimento sono stati utilizzati due diversi controlli: Controllo e Controllo + Chloroquine. Il campione di controllo è stato utilizzato per impostare la soglia per le regioni. Rappresenta il livello basale dell&#39;autofagia senza interrompere il degrado lisosomico ed è il controllo del campione di Starved. Tuttavia, il campione di controllo non è un controllo appropriato da confrontare con i risultati del campione di Starved + Chloroquine, in quanto il clorochina influisce sul campione di controllo. Pertanto, è stato necessario avere un campione Control + Chloroquine. Prima di effettuare qualsiasi analisi per l&#39;autofag, è importante analizzare / portare solo su singole celle che sono in messa a fuoco (ad esempio, gradiente RMS maggiore di 60), in quanto i risultati potrebbero essere influenzati se c&#39;è più di una cella o le immagini sono sfocato. È fondamentale che l&#39;autofagoEs e lisosomi sono al centro per il rilevamento corretto dei punti e l&#39;analisi BDC3. Le cellule apoptotiche devono essere rimosse prima dell&#39;analisi dell&#39;autofagia, in quanto possono risultare incline e non devono essere incluse. Ci dovrebbe essere una colorazione appropriata per tutti e tre i marcatori (ad es. LC3, p62 e LAMP1). Ad esempio, tutti e tre i marcatori sono intracellulari e devono avere un segnale punctato; Se il segnale non è punctato, o se è sulla superficie della cella, la colorazione non sarebbe appropriata e l&#39;esperimento / colorazione dovrebbe essere rifatta. Inoltre, affinché la BDC3 sia accurata, è fondamentale portare le celle che sono luminose per tutti e tre i marcatori fluorescenti di interesse. È necessario disporre di eventi positivi per impedire la misura di BDC3 su manufatti non specifici, artefatti di imaging e / o rumore. Ciò è cruciale, poiché BDC3 può potenzialmente amplificare gli artefatti che non sono veri segnali. Poiché BDC3 può essere eseguito solo su eventi luminosi positivi, molte celle potrebbero non essere incluse nell&#39;analisi finale; Questo è especVale per le celle di controllo che non hanno alcuna accumulazione di LC3, p62 e / o LAMP1. Pertanto, una limitazione di questo test è che BDC3 esamina solo le cellule positive per tutti e tre i marker, che potrebbero non essere appropriati per tutti gli esperimenti di autofagia. Come il BDS, che è stato precedentemente riportato 15 , 16 , 17 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , BDC3 da solo non tiene conto del numero di organismi di autofagia che co-localizzano, risultando in larga misura Di variabilità nel numero di autofosomi. Pertanto, l&#39;approccio bivariato presentato da Rajan et al. era impiegato. Guardando le trame bivariate, si potrebbe chiedere seLe cellule devono essere positive per LC3, p62 e LAMP1; Perché ci sono tante cellule che hanno 0 punti? Questo perché il segnale LC3 potrebbe essere abbastanza luminoso per la co-localizzazione, ma potrebbe non soddisfare la soglia impostata dalla maschera di picco (Peak (M11, Ch11-LC3-AF647, Bright, 4)) necessaria per essere considerata una individuare. Il protocollo qui presentato ha usato il MIFC per contare puncta LC3 e la co-localizzazione di tre marcatori autofagia per misurare il flusso autofagico. In condizioni basali (campione di controllo), il numero di autofagosomi era basso e alcune cellule sono state trovate con &quot;High Spots&quot;. Con l&#39;aggiunta di clorochina, che inibisce la fusione di autosfosi / lisosomi, si è verificato un aumento del numero di macchie LC3. Dal momento che il lisosoma non è in grado di abbattere l&#39;autofagogo e il p62, che risiede nell&#39;autofago, ciò porta ad un aumento della co-localizzazione dell&#39;accumulo di autolysosomi LC3, p62 e LAMP1. Questo effetto è stato amplificato sotto lo starvat nutrienteIone, che induce autofagia. Tuttavia, senza l&#39;aggiunta di clorochina, non vi è stato un aumento significativo del numero di autofosomi, probabilmente a causa di un aumento del tasso di turnover autofagico. Quando le cellule sono state affamate in presenza di clorochina, vi è stato un aumento della co-localizzazione e il numero di autofosomi, che supporta la conclusione che la fame aumenta il flusso autofagico. EBSS è noto per essere un potente induttore di autofagia. Pertanto, si prevede che l&#39;aumento sia grande. Se si utilizza un altro metodo, ad esempio l&#39;induzione di farmaci, per indurre l&#39;autofagia, la differenza tra i controlli e quelli trattati può essere più sottile. Questo particolare protocollo è stato progettato per misurare l&#39;autofagia nelle linee cellulari umane, ma il dosaggio potrebbe essere adattato ad altre specie passando ad anticorpi per quella particolare specie. Inoltre, il metodo di analisi potrebbe essere utilizzato per qualsiasi applicazione intracellulare che richiede la co-localizzazioneDi tre sonde / marcatori.

Macroautophagy, qui di seguito denominato autophagy, è un percorso catabolico in cui i componenti danneggiati o disfunzionali, le proteine ​​a lungo termine e gli organelli sono degradati attraverso il lisosoma e vengono riciclati 1 . L&#39;autofagia è un processo dinamico e multistep che coinvolge la formazione di un autofago; La fusione dell&#39;autofaggo con il lisosoma, formando l&#39;autolysosoma; E il degrado del contenuto dell&#39;autolysosoma 2 . Il marcatore biologico cruciale utilizzato per identificare l&#39;autofagia nei sistemi di mammiferi è la catena leggera 1A / 1B-light 3 (LC3) legata alla microtubula, che costituisce la membrana autofagosomica. Durante l&#39;autofagia, LC3-1 citosolico è coniugato alla fosfatidiletiletanolo per formare LC3-II; LC3-II è quindi incorporato nella membrana autofosfoma 3 . Un altro marcatore ampiamente utilizzato per il flusso autofagico è il sequestosoma 1 del recettore autofagico (SQSTM1, p62) che fisicamente liNks cargo autofagico alla membrana autofagica 4 , 5 . I metodi tradizionalmente utilizzati per misurare l&#39;autofagia sono blots occidentali e microscopia a fluorescenza 7 . Tuttavia, nessuno di questi metodi è considerato come &quot;standard d&#39;oro&quot; , 2 , 6 poiché ci sono sfide associate a entrambe queste tecniche. Le macchie occidentali danno solo una media perché usano un campione omogeneo, in modo che gli osservatori non riescano a vedere cosa succede nelle singole cellule. D&#39;altra parte, la microscopia a fluorescenza dà informazioni a livello di singola cellula, ma manca di capacità di trasmissione, rendendo difficile ottenere valutazioni oggettive e statisticamente rigorose. Negli ultimi anni la misurazione di LC3 e p62 mediante citometria a flusso di imaging multispectrale (MIFC, vedi tabella dei materiali ) sta guadagnando popuLeggerezza dovuta al suo potere quantitativo, alle capacità di trasmissione elevata, al potenziale di multiplexing e alla capacità di immagine di ogni cella. Uno dei metodi più utilizzati per misurare l&#39;autofagia utilizzando il MIFC, insieme al software di analisi companion (vedi tabella dei materiali ), è attraverso il conteggio a spot di LC3 puncta o autofagosomi 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 . Tuttavia, un aumento degli autofosomi non implica necessariamente un aumento dell&#39;autofagia, in quanto potrebbe anche rappresentare un blocco nel processo 2 . Il fatturato LC3-II è un parametro utile per misurare il flusso autofagico analizzando le cellule in presenza eAssenza di un inibitore della degradazione lisosomiale, come la clorochina. Il clorochina inibisce la fusione dell&#39;autofaggoma al lisosoma, consentendo così la quantitazione della formazione di autofaggo come misura del grado di autofagia arrestando il flusso autofagico prima che possa verificarsi il degrado lisosomico 18 . Inoltre, p62 è degradato principalmente da autofagia, e se la degradazione lisosomiale della autofagosoma e il suo contenuto è bloccato, un accumulo di p62 è previsto 17, 19. L&#39;autofagia è un processo multistep e la misurazione di LC3 o p62 da solo non fornisce un quadro completo di ciò che sta accadendo nelle cellule. Le pubblicazioni recenti hanno sottolineato la necessità di esaminare la formazione concorrente dell&#39;autolysosoma 2 , 17 , 20 , 21 . Il MIFC èunica in grado di misurare la formazione della autolysosome dal imaging del co-localizzazione del autofagosoma al lisosoma 15 - 17, 20, 21, 22, 23, 24, 25, 26. Inoltre, è stata esplorata la co-localizzazione di p62 e LC3 utilizzando MIFC per misurare il flusso autofagico 5 . Nel software di analisi di riferimento, la &quot;funzionalità&quot; che misura la co-localizzazione è chiamata &quot;Bright Detail Similarity R3&quot; (BDS) ed è stata specificamente progettata per confrontare i piccoli dettagli luminosi di due immagini. BDS è il coefficiente di correlazione Pearson trasformato dal log con i punti luminosi localizzati con un raggio di 3 pixel o meno; In altre parole, il BDS calcola il grado di oConfronta pixel da due diversi canali fluorescenti. I punti luminosi sono correlati ( cioè, la stessa posizione spaziale) o non correlati ( vale a dire differenti posizioni spaziali). Pertanto, il coefficiente di correlazione varia tra 0 (non correlato) e 1 (perfetta correlazione). Il coefficiente è log-trasformato per aumentare l&#39;intervallo tra zero e infinito 26 , 27 . BDS da solo potrebbe non essere sufficiente; Rajan et al. Ha scoperto che l&#39;utilizzo di solo BDS potrebbe portare a risultati falsi positivi o falsi negativi 21 . BDS valuta la co-localizzazione di due marcatori di autofagia, ma non prende in considerazione il numero di autofosomi. Per tenere conto del numero di autofosomi, Rajan et al. Incluso il punteggio a punti della LC3 puncta 21 . Rajan et al. Ha proposto di utilizzare un diagramma di dispersione bivariato di punti spot LC3 contro BDS. Utilizzando questa trama bivariata, due popolazioni sono andateE in primo luogo identificato: uno con le cellule che hanno un alto livello di LC3 e uno con le cellule che hanno un basso livello di LC3 macchie. La popolazione a punti LC3 è stata ulteriormente classificata in due popolazioni: le cellule con bassa co-localizzazione ( ossia l&#39; accumulo di autofosomi) e le cellule con elevata co-localizzazione ( ossia l&#39; accumulo di autolysosomi). Questa trama bivariata permette di distinguere tra cellule con pochi autofosomi e cellule con un accumulo di autofosomi e / o autolysosomi 21 . Fino ad ora la co-localizzazione simultanea di LC3, p62 e LAMP1 (marker lizosomico) non era possibile utilizzando un singolo &quot;tipo di funzionalità&quot; nel software di analisi del companion MIFC (vedi tabella dei materiali ). Tuttavia, una nuova funzionalità, recentemente introdotta nella versione 6.1, si chiama Bright Detail Colocalization 3 (BDC3). BDC3 confronta le immagini del dettaglio luminoso da ciascuna delle tre immagini (in questo caso, LC3, p62 e LAMP1) e quantifica la co-localizzazione delle tre sonde ( es. LC3, p62 e LAMP1). La funzione BDC3 calcola il coefficiente di correlazione di Pearson modificato per estendersi a tre immagini. Poiché le macchie luminose nelle tre immagini sono correlate o non correlate, il coefficiente di correlazione varia tra 0 (non correlato) e 1 (perfetta correlazione). Il coefficiente è log-trasformato per aumentare l&#39;intervallo dinamico tra 0 e infinito. Attivando la funzione BDS con la funzione BDC3, il metodo di analisi presentato da Rajan et al. Ora possono incorporare i tre indicatori più utilizzati per misurare l&#39;autofagia in un unico sistema contemporaneamente. La capacità di co-localizzare questi tre marcatori di autofagia in un singolo dosaggio potrebbe portare a nuovi approfondimenti sull&#39;induzione e la regolazione dell&#39;autofagia. Il seguente protocollo descrive i passi per indurre l&#39;autofagia nelle cellule Jurkat; Etichettare le cellule con gli anticorpi LC3, p62 e LAMP1; Acquisire dati su un multispCitometro di flusso ectrale di imaging; E analizzare i dati per valutare il flusso autofagico.

<table border="0" cellpadding="0" cellspacing="0" width="1012">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">15 mL centrifuge tube</td>
			<td style="width:244px;">Falcon</td>
			<td style="width:161px;">352096</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">50 mL centrifuge tube</td>
			<td>Falcon</td>
			<td>352070</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="121">
			<td height="121" style="height:121px;">AF647 labeled Donkey anti-Mouse - IgG&#160;</td>
			<td>Invitrogen</td>
			<td>A-31571</td>
			<td style="width:299px;">Donkey anti-Mouse IgG (H+L) Secondary Antibody, Alexa Fluor 647. Concntration 2mg/mL. https://www.thermofisher.com/antibody/product/Donkey-anti-Mouse-IgG-H-L-Secondary-Antibody-Polyclonal/A-31571</td>
		</tr>
		<tr height="62">
			<td height="62" style="height:62px;width:308px;">anti-human CD107a-PE (LAMP1)</td>
			<td>BioLegend</td>
			<td>328607</td>
			<td style="width:299px;">Clone H4A3. Concentration 400 &#181;g/mL. http://www.biolegend.com/pe-anti-human-cd107a-lamp-1-antibody-4967.html</td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;">anti-human CD45- AF488</td>
			<td>BioLegend</td>
			<td>304017</td>
			<td style="width:299px;">Clone HI30. http://www.biolegend.com/alexa-fluor-488-anti-human-cd45-antibody-2738.html</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;">anti-human CD45- PE&#160;</td>
			<td>BioLegend</td>
			<td>304008</td>
			<td style="width:299px;">Clone HI30.&#160; http://www.biolegend.com/pe-anti-human-cd45-antibody-708.html</td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;">anti-human CD45-AF647&#160;</td>
			<td>BioLegend</td>
			<td>304018</td>
			<td style="width:299px;">Clone HI30.&#160; http://www.biolegend.com/alexa-fluor-647-anti-human-cd45-antibody-2739.html</td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">Anti-LC3 (Human) mAb clone 4E12</td>
			<td>MBL International Corporation</td>
			<td>M152-3</td>
			<td style="width:299px;">Clone 4E+12.&#160; Concentration 2 mg/mL.&#160; https://www.mblintl.com/products/m152-3</td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;width:308px;">Anti-SQSTM1 / p62 antibody [EPR4844] - Autophagosome Marker (Alexa Fluor 488)</td>
			<td>abcam</td>
			<td>ab185015</td>
			<td style="width:299px;">Clone EPR4844.&#160; Concentration 0.5 mg/mL.&#160; http://www.abcam.com/sqstm1-p62-antibody-epr4844-autophagosome-marker-alexa-fluor-488-ab185015.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Chloroquine diphosphate Salt</td>
			<td>Sigma-Aldrich</td>
			<td>C6628-25G</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="101">
			<td height="101" style="height:101px;width:308px;">Cleanser - Coulter Clenz&#160;</td>
			<td>Beckman Coulter</td>
			<td>8546931</td>
			<td style="width:299px;">Fill container with 200 mL of Cleanser.&#160; https://www.beckmancoulter.com/wsrportal/page/itemDetails?itemNumber=8546931#2/10//0/25/1/0/asc/2/8546931///0/1//0/</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:308px;">DAPI Stain 5mg/mL</td>
			<td>MilliporeSigma</td>
			<td>508741</td>
			<td style="width:299px;">http://www.emdmillipore.com/US/en/product/DAPI-Stain---CAS-28718-90-3---Calbiochem,EMD_BIO-508741</td>
		</tr>
		<tr height="121">
			<td height="121" style="height:121px;width:308px;">Debubbler - 70% Isopropanol</td>
			<td>MilliporeSigma</td>
			<td>1.3704</td>
			<td style="width:299px;">Fill container with 200 mL of Debubbler.&#160; http://www.emdmillipore.com/US/en/product/2-Propanol-70%25-%28V%2FV%29-0.1-%C2%B5m-filtred,MDA_CHEM-137040?ReferrerURL=https%3A%2F%2Fwww.google.com%2F</td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">Dulbecco&#39;s Phosphate Buffered Saline 10X</td>
			<td>MilliporeSigma</td>
			<td>BSS-2010-B</td>
			<td style="width:299px;">&#160;Ca++Mg++ Free&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Dulbecco&#39;s Phosphate Buffered Saline 1X</td>
			<td>MilliporeSigma</td>
			<td>BSS-1006-B</td>
			<td style="width:299px;">PBS Ca++Mg++ Free&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Earle&#39;s Balanced Salt Solution 1X</td>
			<td>Gibco</td>
			<td>14155</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Fetal Bovine Serum</td>
			<td>HyClone</td>
			<td>SH30071.03</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="241">
			<td height="241" style="height:241px;width:308px;">Formaldehyde, 10%, methanol free, Ultra Pure</td>
			<td>Polysciences, Inc.</td>
			<td>04018</td>
			<td style="width:299px;">This is what is used for the 4% and 1% Formalin. CAUTION: Formalin/Formaldehyde toxic by inhalation and if swallowed.&#160; Irritating to the eyes, respiratory systems and skin.&#160; May cause sensitization by inhalation or skin contact. Risk of serious damage to eyes.&#160; Potential cancer hazard.&#160; http://www.polysciences.com/default/catalog-products/life-sciences/histology-microscopy/fixatives/formaldehydes/formaldehyde-10-methanol-free-pure/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Hanks&#39; Balanced Salt Solution 1X</td>
			<td>Gibco</td>
			<td>14175</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">Jurkat, Clone E6-1 (ATCC TIB-152)</td>
			<td>ATCC</td>
			<td>TIB-152</td>
			<td style="width:299px;">https://www.atcc.org/products/all/TIB-152.aspx</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">MEM Non-Essential Amino Acids 100X</td>
			<td>HyClone</td>
			<td>SH30238.01</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="201">
			<td height="201" style="height:201px;width:308px;">MIFC - ImageStreamX Mark II</td>
			<td>MilliporeSigma</td>
			<td>100220</td>
			<td style="width:299px;">A 2 camera ImageStreamX Mark II eqiped with the 405nm, 488nm, and 642nm lasers was used. http://www.emdmillipore.com/US/en/life-science-research/cell-analysis/amnis-imaging-flow-cytometers/imagestreamx-Mark-ii-imaging-flow-cytometer/VaSb.qB.QokAAAFLzRop.zHe,nav?cid=BI-XX-BDS-P-GOOG-FLOW-B325-0006</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:308px;">MIFC analysis software - IDEAS 6.2</td>
			<td>MilliporeSigma</td>
			<td>100220</td>
			<td style="width:299px;">The companion software to the MIFC (ImageStreamX MKII).&#160; IDEAS version 6.2</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:308px;">MIFC software - INSPIRE</td>
			<td>MilliporeSigma</td>
			<td>100220</td>
			<td style="width:299px;">This is the software that runs the MIFC (ImageStreamX MKII). INSPIRE version 200.1.388.0</td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">PE anti-human CD107a (LAMP-1) Antibody clone H4A3</td>
			<td>BioLegend</td>
			<td>328608</td>
			<td style="width:299px;">http://www.biolegend.com/pe-anti-human-cd107a-lamp-1-antibody-4967.html</td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:308px;">Penicllin/Streptomycin/Glutamine solution 100X</td>
			<td>HyClone</td>
			<td>SV30082.1</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:308px;">Rinse - Ultrapure water or deionized water</td>
			<td>NA</td>
			<td>NA</td>
			<td style="width:299px;">You can use any ultrapure water or deionized water.&#160; Fill container with 900 mL of Rinse.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">RPMI-1640 Medium 1X</td>
			<td>HyClone</td>
			<td>SH30027.01</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;width:308px;">Sheath - PBS</td>
			<td>MilliporeSigma</td>
			<td>BSS-1006-B</td>
			<td style="width:299px;">This is the same as Dulbecco&#39;s Phosphate Buffered Saline 1X&#160; Ca++MG++ free.&#160; Fill container with 900mL of Sheath.</td>
		</tr>
		<tr height="101">
			<td height="101" style="height:101px;width:308px;">Siliconized polypropylene microcentrifuge tubes</td>
			<td>Fisherbrand</td>
			<td>02-681-320</td>
			<td style="width:299px;">Fisherbran Siliconized Low-Retention Microcentrifuge Tubes 1.5 mL. https://www.fishersci.com/shop/products/fisherbrand-siliconized-low-retention-microcentrifuge-tubes-8/p-193936</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Sodium Pyruvate solution (100mM)</td>
			<td>HyClone</td>
			<td>SH30239.01</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="81">
			<td height="81" style="height:81px;width:308px;">Sterilizer - 0.4-0.7% Hypochlorite</td>
			<td>VWR</td>
			<td>JT9416-1</td>
			<td style="width:299px;">This is assentually 10% Clorox bleach that can be made by deluting Clorox bleach with water.&#160; Fill container with 200 mL of Sterilzer.</td>
		</tr>
		<tr height="121">
			<td height="121" style="height:121px;width:308px;">System Calibration Reagent - SpeedBead</td>
			<td>MilliporeSigma</td>
			<td>400041</td>
			<td style="width:299px;">Each tube holds ~10 mL.&#160; https://www.emdmillipore.com/US/en/life-science-research/cell-analysis/amnis-imaging-flow-cytometers/support-training/XDqb.qB.wQMAAAFLBDUp.zHu,nav&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">T75 flask</td>
			<td>Falcon</td>
			<td>353136</td>
			<td style="width:299px;"></td>
		</tr>
		<tr height="101">
			<td height="101" style="height:101px;width:308px;">TRITON X-100, PROTEIN GRADE Detergent, 10% Solution, Sterile-Filtered</td>
			<td>MilliporeSigma</td>
			<td>648463-50ML</td>
			<td style="width:299px;">http://www.emdmillipore.com/US/en/product/TRITON-X-100,-PROTEIN-GRADE-Detergent,-10%25-Solution,-Sterile-Filtered---CAS-9002-93-1---Calbiochem,EMD_BIO-648463</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:308px;">Water, Cell Culture Grade&#160;</td>
			<td>HyClone</td>
			<td>SH30529.03</td>
			<td style="width:299px;"></td>
		</tr>
	</tbody>
</table>

assessing autophagic flux by measuring lc3, p62, and lamp1 co-localization using multispectral imaging flow cytometry

L&#39;autofagia è un percorso catabolico in cui i componenti cellulari normali o disfunzionali che si accumulano durante la crescita e la differenziazione sono degradati attraverso il lisosoma e vengono riciclati. Durante l&#39;autofagia, la proteina LC3 citoplasmatica viene lipidizzata e reclutata alle membrane autofagosomali. L&#39;autofaggo quindi si fonde con il lisosoma per formare l&#39;autolysosoma, dove si verifica la rottura della vescicola di autofagosoma e il suo contenuto. La proteina associata con ubiquitina p62, che si lega a LC3, è anche usata per monitorare il flusso autofagico. Le cellule sottoposte a autofagia dovrebbero dimostrare la co-localizzazione di marcatori p62, LC3 e lisosomiali. La microscopia immunofluorescenza è stata usata per identificare visivamente LC3 puncta, p62 e / o lysosomi su base per cellula. Tuttavia, può essere difficile ottenere una valutazione oggettiva e statisticamente rigorosa. Per ovviare a questi problemi, è stata utilizzata citometria di flusso multispectrale di imaging e una caratteristica analitica che confronta la luminosità(LC3, p62 e lysosomal LAMP1) e quantifica la loro co-localizzazione in combinazione con il conteggio dei punti LC3 per misurare l&#39;autofagia in modo oggettivo, quantitativo e statisticamente robusto.

Questo metodo di analisi utilizza molteplici funzionalità per assaggiare il flusso autofagico. Per comprendere appieno la trama bivariata finale, bisogna prima studiare le caratteristiche di analisi individuale. Il conteggio degli autofosomi è un modo logico per misurare l&#39;autofagia; Tuttavia, la dimensione / forma / luminosità di LC3 puncta può variare drasticamente tra le celle. La variazione può rendere difficile il conteggio manuale di autofagozomi o l&#39;utilizzo di una funzione spot-count nel software di analisi. Pertanto, nessuna funzione di conteggio dei punti sarà perfetta a causa di questa grande variabilità in autofosomi. Tuttavia, una buona funzione di conteggio dei punti funzionerà sulla maggior parte delle celle 26 . La figura 3 mostra esempi di maschera spot di LC3 puncta nelle celle Jurkat usando differenti maschere spot. La funzione di conteggio spot selezionata per questo set di dati è Spot Count_Peak (M11, Ch11-LC3-AF647, Bright, 4) _4, il che significa che la funzione di conteggio spot conta i punti che il peMaschera AK individuata all&#39;interno del canale predefinito 11 maschera (M11) sul canale 11 (Ch11-LC3-AF647) con un rapporto di sfondo spot-to-cell 4 (Bright, 4). La Figura 4 mostra istogrammi di conteggio spot e immagini rappresentative per il conteggio spot medio per le cellule Control, Control + Chloroquine, Starved e Starved + Chloroquine Jurkat etichettate con anti-LC3-AF647. I conteggi spot di Controllo e Starving non sono significativamente diversi; Con l&#39;aggiunta di clorochina, c&#39;è una grande differenza nella media del Control + Chloroquine rispetto alla Starved + Chloroquine. La prossima funzionalità da indagare è BDC3. BDC3 è una misura della co-localizzazione di tre marcatori / sonde, in questo caso, LC3, p62 e LAMP1. La Figura 5A - 5D mostra gli istogrammi BDC3 per le cellule di Control, Control + Chloroquine, Starved e Starved + Chloroquine Jurkat etichettate con anti-p62-AF488, Anti-LAMP1-PE e anti-LC3-AF647. C&#39;è uno spostamento tra la media del Controllo e la media Starved, così come il Control + Chloroquine media alla media di Starved + Chloroquine. Tuttavia, guardando le immagini delle cellule dai punteggi medi di BDC3 nella figura 5E - 5H , c&#39;è una maggiore differenza tra i campioni che gli istogrammi possono portare a credere. Questo perché BDC3 non prende in considerazione il numero di organi di autofagia che co-localizzano, con conseguente grande variabilità del numero di autofosomi per lo stesso punteggio BDC3. Nella maggior parte dei casi, vi sono sovrapposizioni tra tutte le tre sonde perché, anche a livelli basali, p62, LAMP1 e LC3 dovrebbero in una certa misura co-localizzare o risiedere in regioni simili nelle cellule. In contrasto, un esempio di tre sonde che non dovrebbero essere co-localizzate sono il colorante nucleare anti-p62-AF488, anti-LC3-AF647 e DAPI per il campione Starved + Chloroquine, mostrato in Figura 6. &lt;/ P&gt; Quando si combinano la funzione di conteggio dei punti e la funzione BDC3, è evidente la presenza di diverse sottopopolazioni che migliorano la capacità di distinguere tra i diversi campioni / condizioni. La figura 7 mostra la trama bivariata di conteggio spot di LC3 rispetto a BDC3 p62 / LAMP1 / LC3 per i quattro campioni: Controllo, Controllo + Chloroquine, Fame e Starved + Chloroquine. Il campione di Controllo è stato utilizzato per impostare la strategia di gating per tre popolazioni: Low Spots, High Spots / Low BDC3 e High Spots / High BDC3. I campioni di controllo hanno dimostrato che più del 98% delle cellule avevano 1 o meno punti. Il limite tra le High Spots / Low BDC3 e High Spots / High BDC3 è stato impostato su un punteggio BDC3 di 1 perché più del 91% del campione di controllo ha ottenuto un punteggio BDC3 inferiore a 1. Un riepilogo dei risultati dei plotter bivariati È mostrato nella tabella 1 . age = &quot;1&quot;&gt; <img alt="Figura 1" class="xfigimg" src="/files/ftp_upload/55637/55637fig1.jpg" /> Figura 1: Impostazione strumento MIFC. Uno screenshot delle impostazioni dello strumento MIFC utilizzato per questo esperimento descritto nel passaggio 8 del protocollo. <a href="https://ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig1large.jpg" target="_blank">Clicca qui per visualizzare una versione più grande di questa figura.</a> <img alt="figura 2" class="xfigimg" src="/files/ftp_upload/55637/55637fig2.jpg" /> Figura 2: Analisi Software Gating Strategy. Uno screenshot dello schema di analisi del software di analisi, descritto nel passaggio 9 del protocollo. <a href="https://ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig2large.jpg" target="_blank">Clicca qui per visualizzare una versione più grande di questa figura.</a> Fig3.jpg &quot;/&gt; Figura 3: Maschera Spot LC3. Le immagini delle cellule Jurkat e le maschere utilizzate per creare la funzione di conteggio spot. Sono mostrati i picchi (M11, Ch11-LC3-AF647, Bright, 4), Peak (M11, Ch11-LC3-AF647, Peak (M11, Ch11-LC3-AF647, Bright 2) , Bright, 5), Spot (M11, Ch11-LC3-AF647, Bright, 5, 3, 1) e Spot (M11, Ch11-LC3-AF647, Bright, 6, 2, 1). La maschera che ha funzionato meglio per tutte le celle mostrate era Peak (M11, Ch11-LC3-AF647, Bright, 4). <a href="https://ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig3large.jpg" target="_blank">Clicca qui per visualizzare una versione più grande di questa figura.</a> <img alt="Figura 4" class="xfigimg" src="/files/ftp_upload/55637/55637fig4.jpg" /> Figura 4: istogrammi LC3 di conteggio dei punti. Utilizzando la funzione Spot Count_Peak (M11, Ch11-LC3-AF647, Bright, 4) _4 e gli istogrammi di conteggio spot LC3-AF647 per controllo ( A , azzurro), Control + ChloroquinE ( B , blu), Fame ( C , rosa) e Starved + Chloroquine ( D , rosso). I conteggi spot medi per Control, Control + Chloroquine, Starved e Starved + Chloroquine sono rispettivamente 0,07, 1,13, 0,10 e 2,77. Sono mostrati i controlli di controllo (punti E , 0), controllo + clorochina ( F , 1 spot), Starved ( G , 0 spot), e Starved + Chloroquine ( H , 3 spot). <a href="https://ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig4large.jpg" target="_blank">Clicca qui per visualizzare una versione più grande di questa figura.</a> <img alt="Figura 5" class="xfigimg" src="/files/ftp_upload/55637/55637fig5.jpg" /> Figura 5: istogrammi BDC3 di p62 / LAMP1 / LC3. Istogrammi BDC3 p62 / LAMP1 / LC3 perR Controllo ( A , azzurro), Control + Chloroquine ( B , blu), Fame ( C , rosa) e Starved + Chloroquine ( D , rosso). Il punteggio medio BDC3 per Controllo, Controllo + Chloroquine, Fame e Starved + Chloroquine è rispettivamente di 0,57, 0,82, 0,74 e 0,98. BF; P62-AF488 (verde); LAMP1-PE (giallo); LC3-AF647 (rosso); E un composito delle immagini p62-AF488, LAMP1-PE e LC3-AF647 di cellule rappresentative per il BDC3 medio sono mostrate per Control ( E ), Control + Chloroquine ( F ), Starved ( G ) e Starved + Chloroquine H ). <a href="https://ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig5large.jpg" target="_blank">Clicca qui per visualizzare una versione più grande di questa figura.</a> <img alt="Figura 6" class="xfigimg" src="/files/ftp_upload/55637/55637fig6.jpg" /> Figura 6: istogrammi BDC3 di p62 / LC3 / DAPI per il campione Starved + Chloroquine. ( A ) L&#39;istogramma BDC3 p62 / LC3 / DAPI per il campione Starved + Chloroquine è mostrato. Il punteggio medio BDC3 è 0,07. ( B ) BF; P62-AF488 (verde); LC3-AF647 (rosso); DAPI (blu); E un composito delle immagini p62-AF488, LC3-AF647 e DAPI delle cellule rappresentative per il BDC3 medio è mostrato per il campione Starved + Chloroquine. <a href="https://ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig6large.jpg" target="_blank">Clicca qui per visualizzare una versione più grande di questa figura.</a> <img alt="Figura 7" class="xfigimg" src="/files/ftp_upload/55637/55637fig7.jpg" /> Figura 7: Parti Bivariate di Count LC3 Spot Count nei confronti di BDC3 p62 / LAMP1 / LC3. ( A ) Parti bivariate di punti LC3 contro BDC3 p62 / LAMP1 / LC3 per le cellule di controllo, controllo + clorochina, fame e starved + Chloroquine Jurkat. ( B ) BF; P62-AF488 (verde); LAMP1-PE (giallo); LC3-AF647 (rosso); E un composito delle immagini p62-AF488, LAMP1-PE e LC3-AF647 provenienti da tre regioni ( cioè, punti bassi, alti punti / bassi BDC3 e High Spots / High BDC3) sono mostrati per il campione Starved + Chloroquine. <a href="https://ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55637/55637fig7large.jpg" target="_blank">Clicca qui per visualizzare una versione più grande di questa figura.</a> <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td></td><td> Cell Count </td><td> Bright Detail Colocalization 3 Medio </td><td> Punto di conteggio LC3 Mean </td><td> % Basso punto </td><td> % Alto punto / basso BDC3 </td><td> % Alto punto / alto BDC3 </td></tr><tr><td> Controllo </td><td> 439 </td><td> 0.57 </td><td> 0,07 </td><td> 98,2 </td><td> 1.8 </td><td> 0.0 </td></tr><tr><td> Control + Chloroquine </td><td> 1432 </td><td> 0.82 </td><td> 1.13 </td><td> 68,9 </td&gt; <td> 19.5 </td><td> 11.7 </td></tr><tr><td> Starved </td><td> 1204 </td><td> 0.74 </td><td> 0.10 </td><td> 98.4 </td><td> 0.6 </td><td> 1.0 </td></tr><tr><td> Starved + Chloroquine </td><td> 1811 </td><td> 0.98 </td><td> 2.77 </td><td> 32.1 </td><td> 36.9 </td><td> 31,0 </td></tr></tbody></table> Tabella 1: Sommario del conteggio di punti Jurkat LC3 contro BDC3 p62 / LAMP1 / LC3 Bivariate Plot

Watch this Scientific Journal Video about Assessing Autophagic Flux by Measuring LC3, p62, and LAMP1 Co-localization Using Multispectral Imaging Flow Cytometry at JoVE.com

Assessing Autophagic Flux by Measuring LC3, p62, and LAMP1 Co-localization Using Multispectral Imaging Flow Cytometry

Qui, la citometria a flusso di imaging multispectrale con una caratteristica analitica che confronta immagini luminose di dettagli di 3 marcatori autofag e quantifica la loro co-localizzazione, insieme al conteggio spot LC3, è stata utilizzata per misurare l&#39;autofagia in modo oggettivo, quantitativo e statisticamente robusto.

Valutare il flusso autofagico misurando LC3, p62 e LAMP1 Co-localizzazione usando la citometria del flusso di imaging multispettrale

Autophagy is a catabolic pathway in which normal or dysfunctional cellular components that accumulate during growth and differentiation are degraded via ...

assessing-autophagic-flux-measuring-lc3-p62-lamp1-co-localization

Nanyang Technological University

Research

JoVE Journal

Biology

31.0K Views.  MilliporeSigma.  Qui, la citometria a flusso di imaging multispectrale con una caratteristica analitica che confronta immagini luminose di dettagli di 3 marcatori autofag e quantifica la loro co-localizzazione, insieme al conteggio spot LC3, è stata utilizzata per misurare l'autofagia in modo oggettivo, quantitativo e statisticamente robusto. 

Video: Assessing Autophagic Flux by Measuring LC3, p62, and LAMP1 Co-localization Using Multispectral Imaging Flow Cytometry

IP-FCM:  Immunoprecipitation Detected by Flow Cytometry

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic principle extends that of sandwich ELISA, wherein the captured primary analyte can be detected together with other molecules physically associated within multiprotein complexes. The procedure involves covalent coupling of polystyrene latex microbeads with immunoprecipitating monoclonal antibodies (mAb) specific for a protein of interest, incubating these beads with cell lysates, probing captured protein complexes with fluorochrome-conjugated probes, and analyzing bead-associated fluorescence by flow cytometry. IP-FCM is extremely sensitive, allows analysis of proteins in their native (non-denatured) state, and is amenable to either semi-quantitative or quantitative analysis. As additional advantages, IP-FCM requires no genetic engineering or specialized equipment, other than a flow cytometer, and it can be readily adapted for high-throughput applications.

The IP-FCM method is presented, which allows a sensitive, robust, biochemical assessment of native protein-protein interactions, without requiring genetic engineering or large sample sizes.

IP-FCM: Immunoprecipitazione rilevato mediante citometria di flusso

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic ...

Ricerca

Biologia

Flow Cytometry Purification of Mouse Meiotic Cells

The heterogeneous nature of cell types in the testis and the absence of meiotic cell culture models have been significant hurdles to the study of the unique differentiation programs that are manifest during meiosis. Two principal methods have been developed to purify, to varying degrees, various meiotic fractions from both adult and immature animals: elutriation or Staput (sedimentation) using BSA and/or percoll gradients. Both of these methods rely on cell size and density to separate meiotic cells1-5. Overall, except for few cell populations6, these protocols fail to yield sufficient purity of the numerous meiotic cell populations that are necessary for detailed molecular analyses. Moreover, with such methods usually one type of meiotic cells can be purified at a given time, which adds an extra level of complexity regarding the reproducibility and homogeneity when comparing meiotic cell samples.
Here, we describe a refined method that allows one to easily visualize, identify, and purify meiotic cells, from germ cells to round spermatids, using FACS combined with Hoechst 33342 staining7,8. This method provides an overall snapshot of the entire meiotic process and allows one to highly purify viable cells from most stage of meiosis. These purified cells can then be analyzed in detail for molecular changes that accompany progression through meiosis, for example changes in gene expression9,10and the dynamics of nucleosome occupancy at hotspots of meiotic recombination11.

An efficient method to obtain highly purified viable meiotic fractions from mouse testis is described, which combines a refined cell dissociation protocol with fluorescent activated cell sorting (FACS). This method takes advantage of differences in the DNA content and nuclear density of discrete meiotic fractions.

Purificazione Citometria a flusso di cellule di topo Meiotic

The heterogeneous nature of cell types in the testis and the absence of meiotic cell culture models have been significant hurdles to the study of the ...

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

Precise control of the initiation and subsequent progression through the various phases of the cell cycle are of paramount importance in proliferating cells. Cell cycle division is an integral part of growth and reproduction and deregulation of key cell cycle components have been implicated in the precipitating events of carcinogenesis 1,2. Molecular agents in anti-cancer therapies frequently target biological pathways responsible for the regulation and coordination of cell cycle division 3. Although cell cycle kinetics tend to vary according to cell type, the distribution of cells amongst the four stages of the cell cycle is rather consistent within a particular cell line due to the consistent pattern of mitogen and growth factor expression. Genotoxic events and other cellular stressors can result in a temporary block of cell cycle progression, resulting in arrest or a temporary pause in a particular cell cycle phase to allow for instigation of the appropriate response mechanism. 
The ability to experimentally observe the behavior of a cell population with reference to their cell cycle progression stage is an important advance in cell biology. Common procedures such as mitotic shake off, differential centrifugation or flow cytometry-based sorting are used to isolate cells at specific stages of the cell cycle 4-6. These fractionated, cell cycle phase-enriched populations are then subjected to experimental treatments. Yield, purity and viability of the separated fractions can often be compromised using these physical separation methods. As well, the time lapse between separation of the cell populations and the start of experimental treatment, whereby the fractionated cells can progress from the selected cell cycle stage, can pose significant challenges in the successful implementation and interpretation of these experiments.
Other approaches to study cell cycle stages include the use of chemicals to synchronize cells. Treatment of cells with chemical inhibitors of key metabolic processes for each cell cycle stage are useful in blocking the progression of the cell cycle to the next stage. For example, the ribonucleotide reductase inhibitor hydroxyurea halts cells at the G1/S juncture by limiting the supply of deoxynucleotides, the building blocks of DNA. Other notable chemicals include treatment with aphidicolin, a polymerase alpha inhibitor for G1 arrest, treatment with colchicine and nocodazole, both of which interfere with mitotic spindle formation to halt cells in M phase and finally, treatment with the DNA chain terminator 5-fluorodeoxyridine to initiate S phase arrest 7-9. Treatment with these chemicals is an effective means of synchronizing an entire population of cells at a particular phase. With removal of the chemical, cells rejoin the cell cycle in unison. Treatment of the test agent following release from the cell cycle blocking chemical ensures that the drug response elicited is from a uniform, cell cycle stage-specific population. However, since many of the chemical synchronizers are known genotoxic compounds, teasing apart the participation of various response pathways (to the synchronizers vs. the test agents) is challenging. 
Here we describe a metabolic labeling method for following a subpopulation of actively cycling cells through their progression from the DNA replication phase, through to the division and separation of their daughter cells. Coupled with flow cytometry quantification, this protocol enables for measurement of kinetic progression of the cell cycle in the absence of either mechanically- or chemically- induced cellular stresses commonly associated with other cell cycle synchronization methodologies 10. In the following sections we will discuss the methodology, as well as some of its applications in biomedical research.

Tracking subtle changes in the progression and kinetics of cell cycle stages can be accomplished by use of a combination of metabolic labeling of nucleic acids with BrdU and total genomic DNA staining via Propidium Iodide. This method avoids the need of chemical synchronization of cycling cells, thereby preventing the introduction of non-specific DNA damage, which in turn affects cell cycle progression.

Cella di misura Kinetics progressione del ciclo con la marcatura metabolica e citometria a flusso

Precise control of the initiation and subsequent progression through the various phases of the cell cycle are of paramount importance in proliferating ...

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:
<ol>
	<li>stem and progenitor cell quiescence, proliferation and/or differentiation6-8</li>
	<li>antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and</li>
	<li>immune regulatory and effector cell function1,18-21.</li>
</ol>
Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.
The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:
<ol>
	<li>Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.</li>
	<li>Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.</li>
	<li>Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.</li>
</ol>
Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Successful use of cell tracking dyes to monitor immune cell function and proliferation involves several critical steps. We describe methods for: 1) obtaining bright, uniform, reproducible label-ing with membrane dyes; 2) selecting fluorochromes and data acquisition conditions; and 3) choosing a model to quantify cell proliferation based on dye dilution.

Ottimizzato Metodi di modellazione e colorazione proliferazione per il monitoraggio divisione cellulare utilizzando coloranti Tracking Cellulari

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of ...

Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Spermatozoa are male reproductive cells especially designed to reach, recognize and fuse with the egg. To perform these tasks, sperm cells must be prepared to face a constantly changing environment and to overcome several physical barriers. Being in essence transcriptionally and translationally silent, these motile cells rely profoundly on diverse signaling mechanisms to orient themselves and swim in a directed fashion, and to contend with challenging environmental conditions during their journey to find the egg. In particular, Ca2+-mediated signaling is pivotal for several sperm functions: activation of motility, capacitation (a complex process that prepares sperm for the acrosome reaction) and the acrosome reaction (an exocytotic event that allows sperm-egg fusion). The use of fluorescent dyes to track intracellular fluctuations of this ion is of remarkable importance due to their ease of application, sensitivity, and versatility of detection. Using one single dye-loading protocol we utilize four different fluorometric techniques to monitor sperm Ca2+ dynamics. Each technique provides distinct information that enables spatial and/or temporal resolution, generating data both at single cell and cell population levels.

Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Intracellular Ca2+ dynamics are very important in sperm physiology and Ca2+-sensitive fluorescent dyes constitute a versatile tool to study them. Population experiments (fluorometry and stopped flow fluorometry) and single cell experiments (flow cytometry and single cell imaging) are used to track spatio-temporal [Ca2+] changes in human sperm cells.

Misurare intracellulare Ca<sup&gt; 2 +</sup&gt; Modifiche di sperma umano utilizzando quattro tecniche: fluorometria convenzionale, si fermò fluorometria Flow, Citometria a flusso singolo e l&#39;imaging cellulare

Spermatozoa  are male reproductive cells especially designed to reach, recognize and fuse  with the egg. To perform these tasks, sperm cells must be ...

Identification of a Murine Erythroblast Subpopulation  Enriched in Enucleating Events by Multi-spectral Imaging Flow Cytometry

Erythropoiesis in mammals concludes with the dramatic process of enucleation that results in reticulocyte formation. The mechanism of enucleation has not yet been fully elucidated. A common problem encountered when studying the localization of key proteins and structures within enucleating erythroblasts by microscopy is the difficulty to observe a sufficient number of cells undergoing enucleation.&#160;We have developed a novel analysis protocol using multiparameter high-speed cell imaging in flow (Multi-Spectral Imaging Flow Cytometry), a method that combines immunofluorescent microscopy with flow cytometry, in order to identify efficiently a significant number of enucleating events, that allows to obtain measurements and perform statistical analysis.
We first describe here two in vitro erythropoiesis culture methods used in order to synchronize murine erythroblasts and increase the probability of capturing enucleation at the time of evaluation.&#160;Then, we describe in detail the staining of erythroblasts after fixation and permeabilization in order to study the localization of intracellular proteins or lipid rafts during enucleation by multi-spectral imaging flow cytometry.&#160;Along with size and DNA/Ter119 staining which are used to identify the orthochromatic erythroblasts, we utilize the parameters &#8220;aspect ratio&#8221; of a cell in the bright-field channel that aids in the recognition of elongated cells and &#8220;delta centroid XY Ter119/Draq5&#8221; that allows the identification of cellular events in which the center of Ter119 staining (nascent reticulocyte) is far apart from the center of Draq5 staining (nucleus undergoing extrusion), thus indicating a cell about to enucleate.&#160;The subset of the orthochromatic erythroblast population with high delta centroid and low aspect ratio is highly enriched in enucleating cells.

The present protocol describes a novel method of identifying a population of enucleating orthochromatic erythroblasts by multi-spectral imaging flow cytometry, providing a visualization of the erythroblast enucleation process.

Identificazione di un Murine eritroblasto sottopopolazione Enriched in enucleare Eventi da Multi-spettrale Imaging Citometria a Flusso

Erythropoiesis in mammals concludes with the dramatic process of enucleation that results in reticulocyte formation. The mechanism of enucleation has not ...

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) maintain the turnover of all mature blood cell lineages. The coordination of the complex signals leading to specific HSC fates relies upon the interaction between HSCs and the intricate bone marrow microenvironment, which is still poorly understood[1-2].
We describe how by combining a newly developed specimen holder for stable animal positioning with multi-step confocal and two-photon in vivo imaging techniques, it is possible to obtain high-resolution 3D stacks containing HSPCs and their surrounding niches and to monitor them over time through multi-point time-lapse imaging. High definition imaging allows detecting ex vivo labeled hematopoietic stem and progenitor cells (HSPCs) residing within the bone marrow. Moreover, multi-point time-lapse 3D imaging, obtained with faster acquisition settings, provides accurate information about HSPC movement and the reciprocal interactions between HSPCs and stroma cells.
Tracking of HSPCs in relation to GFP positive osteoblastic cells is shown as an exemplary application of this method. This technique can be utilized to track any appropriately labeled hematopoietic or stromal cell of interest within the mouse calvarium bone marrow space.

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

The nature of the interactions between hematopoietic stem and progenitor cells (HSPCs) and bone marrow niches is poorly understood. Custom hardware modifications and a multi-step acquisition protocol allow the use of two-photon and confocal microscopy to image ex vivo labeled HSPCs homed within bone marrow areas, tracking interactions and movement.

In Vivo inseguimento 4-dimensionale di emopoietiche staminali e progenitrici celle di topo adulto cranica di Midollo Osseo

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) ...

Cytosolic Calcium Measurements in Renal Epithelial Cells by Flow Cytometry

A variety of cellular processes, both physiological and pathophysiological, require or are governed by calcium, including exocytosis, mitochondrial function, cell death, cell metabolism and cell migration to name but a few. Cytosolic calcium is normally maintained at low nanomolar concentrations; rather it is found in high micromolar to millimolar concentrations in the endoplasmic reticulum, mitochondrial matrix and the extracellular compartment. Upon stimulation, a transient increase in cytosolic calcium serves to signal downstream events. Detecting changes in cytosolic calcium is normally performed using a live cell imaging set up with calcium binding dyes that exhibit either an increase in fluorescence intensity or a shift in the emission wavelength upon calcium binding. However, a live cell imaging set up is not freely accessible to all researchers. Alternative detection methods have been optimized for immunological cells with flow cytometry and for non-immunological adherent cells with a fluorescence microplate reader. Here, we describe an optimized, simple method for detecting changes in epithelial cells with flow cytometry using a single wavelength calcium binding dye. Adherent renal proximal tubule epithelial cells, which are normally difficult to load with dyes, were loaded with a fluorescent cell permeable calcium binding dye in the presence of probenecid, brought into suspension and calcium signals were monitored before and after addition of thapsigargin, tunicamycin and ionomycin.

Calcium is involved in numerous physiological and pathophysiological signaling pathways. Live cell imaging requires specialized equipment and can be time consuming. A quick, simple method using a flow cytometer to determine relative changes in cytosolic calcium in adherent epithelial cells brought into suspension was optimized.

Misure calcio citosolico nelle cellule epiteliali renali mediante citometria di flusso

A variety of cellular processes, both physiological and pathophysiological, require or are governed by calcium, including exocytosis, mitochondrial ...

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within tissues. Many semi-quantitative systems have been developed for scoring expression using immunohistochemistry, but inherent subjectivity limits reproducibility and accuracy of results. Furthermore, the investigation of spatially overlapping biomarkers such as nuclear transcription factors is difficult with current immunohistochemistry techniques. We have developed and optimized a system for simultaneous investigation of multiple proteins using high throughput methods of multiplexed immunohistochemistry and multispectral imaging. Multiplexed immunohistochemistry is performed by sequential application of primary antibodies with secondary antibodies conjugated to horseradish peroxidase or alkaline phosphatase. Different chromogens are used to detect each protein of interest. Stained slides are loaded into an automated slide scanner and a protocol is created for automated image acquisition. A spectral library is created by staining a set of slides with a single chromogen on each. A subset of representative stained images are imported into multispectral imaging software and an algorithm for distinguishing tissue type is created by defining tissue compartments on images. Subcellular compartments are segmented by using hematoxylin counterstain and adjusting the intrinsic algorithm. Thresholding is applied to determine positivity and protein co-localization. The final algorithm is then applied to the entire set of tissues. Resulting data allows the user to evaluate protein expression based on tissue type (ex. epithelia vs. stroma) and subcellular compartment (nucleus vs. cytoplasm vs. plasma membrane). Co-localization analysis allows for investigation of double-positive, double-negative, and single-positive cell types. Combining multispectral imaging with multiplexed immunohistochemistry and automated image acquisition is an objective, high-throughput method for investigation of biomarkers within tissues.

Immunohistochemistry is a powerful lab technique for evaluating protein localization and expression within tissues. Current semi-automated methods for quantitation introduce subjectivity and often create irreproducible results. Herein, we describe methods for multiplexed immunohistochemistry and objective quantitation of protein expression and co-localization using multispectral imaging.

La quantificazione di espressione della proteina e co-localizzazione Usando multiplex immunoistochimica di colorazione e multispettrale Imaging

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within ...

Measuring Endoreduplication by Flow Cytometry of Isolated Tuber Protoplasts

Endoreduplication, the replication of a cell's nuclear genome without subsequent cytokinesis, yields cells with increased DNA content and is associated with specialization, development and increase in cellular size. In plants, endoreduplication seems to facilitate the growth and expansion of certain tissues and organs. Among them is the tuber of potato (Solanum tuberosum), which undergoes considerable cellular expansion in fulfilling its function of carbohydrate storage. Thus, endoreduplication may play an important role in how tubers are able to accommodate this abundance of carbon. However, the cellular debris resulting from crude nuclear isolation methods of tubers, methods that can be used effectively with leaves, precludes the estimation of the tuber endoreduplication index (EI). This article presents a technique for assessing tuber endoreduplication through the isolation of protoplasts while demonstrating representative results obtained from different genotypes and compartmentalized tuber tissues. The major limitations of the protocol are the time and reagent costs required for sample preparation as well as relatively short lifespan of samples after lysis of protoplasts. While the protocol is sensitive to technical variation, it represents an improvement over traditional methods of nuclear isolation from these large specialized cells. Possibilities for improvements to the protocol such as recycling enzyme, the use of fixatives, and other alterations are proposed.

The protocol described herein is a method for measuring endoreduplication within tubers of potato (Solanum tuberosum). It includes plasmolysis and protoplast extraction steps to decrease the noise and debris in downstream flow cytometric analysis.

Endoreduplicazione tramite flusso Cytometry di tubero isolato protoplasti di misura

Endoreduplication, the replication of a cell's nuclear genome without subsequent cytokinesis, yields cells with increased DNA content and is associated ...