This work was supported by the New College of Interdisciplinary Arts and Sciences at Arizona State University, Applied Maths NV, and by the National Science Foundation (ROA Supplement to Award No. MCB0604300). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Attention: les bactéries non identifiés de ne importe quel environnement peuvent être pathogènes et doivent être manipulés avec prudence en utilisant des protocoles de biosécurité appropriées. Travailler avec des cultures vivantes doit être effectuée dans une armoire de biosécurité de classe II en utilisant de sécurité biologique de niveau 2 (BSL-2) procédures. Plus d&#39;informations sur BSL-2 procédures est disponible dans le manuel CDC / NIH intitulé «prévention des risques biotechnologiques en laboratoires microbiologiques et biomédicaux», pages 33-38. Le document est disponible en ligne à <a href="http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf">http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf</a> . Équipement de protection individuelle (EPI), y compris les sarraus de laboratoire / robes, des lunettes de sécurité et des gants en nitrile ou en latex, doit être porté. Pratiques et précautions microbiologiques standards doivent être respectées, et des déchets biologiques dangereux doivent être éliminés de façon appropriée. Bactéries utilisées dans cette démonstration ont été isolés à partir Kartchner,AZ, États-Unis, à partir de quatre environnements, y compris spéléothème sèche, la pierre de flux, spéléothème humide et stalactite goutte à goutte (tableau 1). Tous les isolats ont été identifiés par séquençage de l&#39;ADNr 16S et conservés à -80 ° C dans 25% de glycérol-R milieu 2B. Toutes les expériences ont été réalisées à température ambiante. Note: Nous vous recommandons d&#39;utiliser la même méthode de préparation d&#39;échantillon d&#39;acquérir des spectres de masse pour la construction de base de données et les spectres de masse d&#39;inconnues. Méthode de préparation des échantillons a été montré précédemment affecter la qualité du spectre et la reproductibilité 3. En utilisant une méthode de préparation d&#39;échantillon différent peut provoquer une mauvaise identification d&#39;inconnus, surtout quand résolution taxonomique plus élevé (par exemple, au niveau de la souche) est souhaitée. 1. dépôt sur la cible MALDI Attention: Plusieurs protocoles d&#39;obtenir des extraits de protéines nécessitent l&#39;utilisation d&#39;acides et de solvants organiques qui doivent être utilisé conformément aux guidElines et informations contenues dans leurs fiches respectives de données de sécurité des matériaux (MSDS). EPI approprié doit être porté et variera en fonction des type et le volume des produits chimiques utilisés (par exemple, sarraus de laboratoire / robes, des gants, des lunettes de sécurité, et la protection respiratoire doit être utilisée lorsque vous travaillez avec des quantités importantes de solvants toxiques et inflammables, tels que l&#39;acétonitrile, et des acides corrosifs, tels que les acides formique et trifluoroacétique). <ol><li> Dépôt de 1 pi d&#39;extrait de protéine ne contenant pas de cellules viables (obtenues à l&#39;aide de protocoles appropriés, décrits précédemment 11 à 13) sur une plaque de cible MALDI en acier inoxydable et le laisser sécher. Superposer l&#39;extrait protéinique séchée avec une solution de matrice ul (α-cyano-4-hydroxy-cinnamique solution d&#39;acide), et laisser sécher. </li><li> Pour chaque répétition biologique, repérer un nombre approprié de répétitions techniques (5-20 réplicats techniques). Ici, repérer 10 répétitions techniques pour chaque répétition biologique et trois répétitions biologiques were préparé. Note: Nous recommandons d&#39;utiliser un MALDI plaque cible en acier poli en utilisant la méthode de préparation des échantillons d&#39;extraction de protéines. Utilisation des plaques cibles de masse d&#39;acier peut provoquer une contamination accidentelle et la propagation des différents échantillons à l&#39;extérieur du puits d&#39;échantillon individuels. </li><li> Dépôt 1 ul norme d&#39;étalonnage sur la plaque cible et laisser sécher. Superposition avec une solution de matrice de pi et laisser sécher. </li><li> Dépôt solution de 2 ul de matrice sur la plaque cible en tant que témoin négatif. </li></ol> 2. Acquisition spectres de masse <ol><li> Utilisez un spectromètre de masse MALDI-TOF équipé d&#39;un laser d&#39;azote (λ = 337 nm) et exploité en utilisant un logiciel Bruker FlexControl. </li><li> Recueillir chaque spectre de masse en mode linéaire positive par l&#39;accumulation de 500 tirs laser par incréments de 100 tir. Régler la tension source d&#39;ions 1 à 20 kV; tension de la source d&#39;ions 2 18,15 kV; et la tension de la lentille à 9,05 kV. A noter que ces paramètres sont instrument spécific et pourrait nécessiter un ajustement sur d&#39;autres instruments pour obtenir des résultats optimaux. </li><li> Définissez la plage de masse-charge pour l&#39;évaluation automatisée du spectre 2-20 kDa par charge. Utiliser l&#39;algorithme de détection de pic centroïde. Réglez le seuil minimum de la résolution à 100 Da. Réglez le rapport signal sur bruit (S: N) seuil à 2. Réglez le seuil d&#39;intensité minimum à 100. </li></ol> 3. Base de données Construction <ol><li> la conception de base de données <ol><li> Créer une nouvelle base de données dans BioNumerics 7.1 utilisant le &quot;Nouvel assistant de base de données&quot;. </li><li> Créer un type d&#39;expérimentation du spectre, par exemple, Maldi, en utilisant les commandes dans le panneau &quot;Types de Experiment&quot;. </li><li> Créez les niveaux en utilisant le &quot;panneau de conception de base de données&quot;. Ajouter de nouveaux niveaux en utilisant le &quot;Niveau&gt; Ajouter nouveau niveau ...&quot; commande dans le menu &quot;Base de données&quot;. Ici, créer des &quot;espèces&quot; niveau &quot;, réplique biologique&quot; niveau &quot;et&quot; répétition technique &quot;niveau, respectivEly. </li></ol></li><li> Importation et prétraitement des spectres de masse brute <ol><li> Exporter les spectres de masse brute sous forme de fichiers .txt utilisant Flexanalysis en cliquant sur le &quot;Exporter&gt; Spectre de masse&quot; dans le menu &quot;Fichier&quot;. </li><li> Importez les spectres de masse brute (fichiers .txt) dans la base de données dans le niveau des répétitions techniques. </li><li> Prétraiter les spectres de masse brute. <ol><li> Importation et rééchantillonnage (en utilisant un algorithme d&#39;ajustement quadratique). </li><li> Effectuer une soustraction de ligne de base (avec un disque de roulement avec une taille de 50 points). </li><li> Calculer le bruit [transformation en ondelettes continue (CWT)], lisse (Kaiser Fenêtre avec une taille de fenêtre de 20 points et bêta de 10 points), et effectuer une seconde soustraction de base (laminage disque avec la taille de 200 points). </li><li> Détecter des pics [CWT avec un signal minimum sur bruit (S: N) 10]. </li></ol></li><li> Après prétraitement, mémoriser les motifs caractéristiques de chaque spectre de masse, tels que les listes de pointe contenant pictailles, des intensités de pointe, S: N, etc., dans la base de données. </li></ol></li><li> Création spectre de masse composite <ol><li> Créer spectres composite à partir des spectres prétraités à l&#39;aide de la commande &quot;Résumer ...&quot; dans le menu &quot;Analyse&quot;. Choisissez l&#39;option &quot;répétition biologique» comme niveau cible. </li><li> Ici, combiner les spectres de masse de 10 répétitions techniques de la même colonie pour obtenir un spectre de masse composite pour cette colonie, résultant en trois spectres de masse composite pour que isolat au niveau &quot;répliqué biologique&quot;. </li><li> Ici, résumer les trois spectres composite pour créer une spectre composite pour qui isolent au niveau «espèces». Remarque: Le spectre composite est la moyenne de point par point des répétitions techniques. Réplique d&#39;une similitude (de corrélation de Pearson) à la moyenne inférieure à 95% (réglage par défaut) sont exclus du composite. Les pics sur le spectre composite sont appelés seulement se ils sont présents dans 75% (réglage par défaut) des répétitions incluses. Pour les répétitions biologiques, ces paramètres étaient de 90 et 60%, respectivement. </li></ol></li></ol> Analyse 4. Spectre de masse de données <ol><li> Sélectionnez les entrées dans la base de données et de créer une comparaison en cliquant sur la commande &quot;Créer un nouveau rapport&quot; dans le panneau &quot;comparaisons&quot;. </li><li> Ici, utiliser les spectres de masse à la &quot;répétition technique&quot; et / ou les niveaux &quot;répétés biologique&quot; pour montrer des comparaisons et des analyses. </li><li> Analyse de cluster basé similarité et l&#39;analyse multidimensionnelle (MDS) <ol><li> Créer des groupes avec des couleurs. Sélectionnez les trois biologique spectre de masse composite et cliquez sur &quot;Créer un nouveau groupe de la sélection&quot; commande dans le menu &quot;Groupes&quot; pour créer un groupe pour l&#39;isolat correspondante. Désigner une couleur automatiquement utilisé pour ces trois spectres de masse. </li><li> Alternativement, définir des états de terrain avec des couleurs correspondantes en utilisant les commandes dans le &quot;Base de données entrées panel &quot;de telle sorte que tout groupement basée sur ce champ défini utilise la même couleur définie pour ce groupe. </li><li> Effectuer une analyse cluster. Cliquez sur la commande &quot;Calculer l&#39;analyse de cluster» dans le menu «Clustering». Sur la comparaison des paramètres Page 1, sélectionnez le «corrélation de Pearson&quot; et laissez les autres paramètres par défaut. À la page 2, sélectionnez &quot;UPGMA&quot;. Puis cliquez sur &quot;Terminer&quot;. </li><li> Obtenir un complot MDS utilisant la &quot;mise à l&#39;échelle multidimensionnelle ...&quot; commande dans le menu &quot;Statistiques&quot;. </li></ol></li><li> Appariement pic <ol><li> Cliquez sur le type de spectre &quot;Maldi&quot; dans le panneau &quot;expériences&quot;. Ensuite, sélectionnez &quot;Mise en page&gt; Voir l&#39;image&quot;. Spectra sont présentés comme des bandes de gel. </li><li> Effectuer une comparaison pic en utilisant la commande &quot;faire de la correspondance pic&quot; dans le menu &quot;Spectra&quot;. </li></ol></li><li> Identification des classes de pointe <ol><li> Effectuer l&#39;analyse en composantes principales (ACP). Mettez en surbrillance le &quot;Maldi &quot;type de l&#39;expérience dans le&quot; expérimentale &quot;panneau et utiliser le&quot; Analyse en Composantes Principales ... &quot;dans le menu&quot; statistique &quot;pour effectuer PCA. </li><li> Effectuer regroupement dans les deux sens. Cliquez sur le &quot;Statistiques&gt; minière Matrix&quot; ... dans la fenêtre &quot;Comparaison&quot;. L&#39;intensité des pics appariés aux classes de pointe est représenté en utilisant des couleurs différentes (carte de chaleur). </li></ol></li></ol> 5. Les bactéries d&#39;identification avec une base de données personnalisée <ol><li> Méthode fondée sur la similarité coefficient- <ol><li> Créer une comparaison et de générer un dendrogramme basé sur des spectres de masse au niveau &quot;répliqué technique&quot; tel que décrit à l&#39;étape 4.3.3. Enregistrez le dendrogramme de comparaison de similitude. </li><li> Sélectionnez un spectre de masse inconnue, et cliquez sur le &quot;Analyse&gt; Identifier entrées sélectionnées&quot;. La boîte de dialogue d&#39;identification apparaît. </li><li> Sélectionnez le &quot;Comparaison basée&quot; type de classificateur (ou un classif stockéeIER) et cliquez sur &quot;suivant&quot;. Sur la page suivante, choisissez le dendrogramme enregistré comme une comparaison de référence, puis cliquez sur &quot;Suivant&quot;. </li><li> Choisissez l&#39;option &quot;similitude de base&quot; comme une méthode d&#39;identification et puis cliquez sur &quot;Suivant&quot;. </li><li> Choisissez l&#39;option &quot;similitude maximale» comme méthode de notation. Tapez les valeurs de seuil appropriées et valeurs de différence minimum pour chaque paramètre, puis cliquez sur &quot;Suivant&quot;. </li><li> Une fois les calculs sont terminés, la fenêtre d&#39;identification apparaît. Dans le panneau &quot;Résultats&quot;, les membres de la base de données qui correspondent le mieux l&#39;inconnu sont répertoriés. </li><li> Enregistrez le projet d&#39;identification et valider l&#39;identification en utilisant le &quot;analyse de validation croisée&quot; dans le panneau &quot;projet d&#39;identification&quot;. </li></ol></li><li> Méthode basée sur les biomarqueurs potentiel <ol><li> Définir des classes de pointe. Dans la fenêtre «Matrix Mining&quot;, sélectionner des ensembles de pics des caractéristiques communes et de définir ces pics que spéciles classes fic de pointe (biomarqueurs potentiels) en utilisant &quot;Spectra&gt; Gérer les types de classe de pointe ...&quot; dans la fenêtre &quot;Comparaison&quot;. </li><li> Ici, définir des classes de pointe spécifiques pour chaque isolent pour tous les 15 isolats. </li><li> Sélectionner les spectres de masse d&#39;inconnues et faire correspondre les sommets de ces spectres pour les classes de pointe définis comme précédemment décrit. </li></ol></li></ol>

<ol>
	<li>Sandrin, T. R., Goldstein, J. E., Schumaker, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MALDI+TOF+MS+profiling+of+bacteria+at+the+strain+level:+A+review.">MALDI TOF MS profiling of bacteria at the strain level: A review.</a> Mass Spectrom Rev. 32 (3), 188-217 (2013).</li><li>Siegrist, T. 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=Discrimination+and+characterization+of+environmental+strains+of+Escherichia+coli+by+matrix-assisted+laser+desorption/ionization+time-of-flight+mass+spectrometry+(MALDI-TOF-MS).">Discrimination and characterization of environmental strains of Escherichia coli by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).</a> J Microbiol Meth. 68 (3), 554-562 (2007).</li><li>Goldstein, J. E., Zhang, L., Borror, C. M., Rago, J. V., Sandrin, T. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Culture+conditions+and+sample+preparation+methods+affect+spectrum+quality+and+reproducibility+during+profiling+of+Staphylococcus+aureus+with+matrix-assisted+laser+desorption/ionization+time-of-flight+mass+spectrometry.">Culture conditions and sample preparation methods affect spectrum quality and reproducibility during profiling of Staphylococcus aureus with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.</a> Lett Appl Microbiol. 57 (2), 144-150 (2013).</li><li>Benagli, 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=A+rapid+MALDI-TOF+MS+identification+database+at+genospecies+level+for+clinical+and+environmental+Aeromonas+strains.">A rapid MALDI-TOF MS identification database at genospecies level for clinical and environmental Aeromonas strains.</a> Plos One. 7 (10), (2012).</li><li>Sauer, S., Kliem, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+spectrometry+tools+for+the+classification+and+identification+of+bacteria.">Mass spectrometry tools for the classification and identification of bacteria.</a> Nat Rev Microbiol. 8 (1), 74-82 (2010).</li><li>Bohme, 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=SpectraBank:+An+open+access+tool+for+rapid+microbial+identification+by+MALDI-TOF+MS+fingerprinting.">SpectraBank: An open access tool for rapid microbial identification by MALDI-TOF MS fingerprinting.</a> Electrophoresis. 33 (14), 2138-2142 (2012).</li><li>Walker, J., Fox, A. J., Edwards-Jones, V., Gordon, D. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intact+cell+mass+spectrometry+(ICMS)+used+to+type+methicillin-resistant+Staphylococcus+aureus:+media+effects+and+inter-laboratory+reproducibility.">Intact cell mass spectrometry (ICMS) used to type methicillin-resistant Staphylococcus aureus: media effects and inter-laboratory reproducibility.</a> J Microbiol Meth. 48 (2-3), 117-126 (2002).</li><li>Ruelle, V., El Moualij, B., Zorzi, W., Ledent, P., De Pauw, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+identification+of+environmental+bacterial+strains+by+matrix-assisted+laser+desorption/ionization+time-of-flight+mass+spectrometry.">Rapid identification of environmental bacterial strains by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.</a> Rapid Commun Mass Sp. 18 (18), 2013-2019 (2004).</li><li>Sedo, O., Sedlacek, I., Zdrahal, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sample+Preparation+Methods+for+Maldi-MS+Profiling+of+Bacteria.">Sample Preparation Methods for Maldi-MS Profiling of Bacteria.</a> Mass Spectrom Rev. 30 (3), 417-434 (2011).</li><li>Swatkoski, S., Russell, S., Edwards, N., Fenselau, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+a+model+virus+using+residue-specific+chemical+cleavage+and+MALDI-TOF+mass+spectrometry.">Analysis of a model virus using residue-specific chemical cleavage and MALDI-TOF mass spectrometry.</a> Anal Chem. 79 (2), 654-658 (2007).</li><li>Freiwald, A., Sauer, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phylogenetic+classification+and+identification+of+bacteria+by+mass+spectrometry.">Phylogenetic classification and identification of bacteria by mass spectrometry.</a> Nat Protoc. 4 (5), 732-742 (2009).</li><li>Drevinek, M., Dresler, J., Klimentova, J., Pisa, L., Hubalek, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+sample+preparation+methods+for+MALDI-TOF+MS+identification+of+highly+dangerous+bacteria.">Evaluation of sample preparation methods for MALDI-TOF MS identification of highly dangerous bacteria.</a> Lett Appl Microbiol. 55 (1), 40-46 (2012).</li><li>Lasch, P., 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=MALDI-TOF+mass+spectrometry+compatible+inactivation+method+for+highly+pathogenic+microbial+cells+and+spores.">MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores.</a> Anal Chem. 80 (6), 2026-2034 (2008).</li><li>Usbeck, J. C., Kern, C. C., Vogel, R. F., Behr, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+experimental+and+modelling+parameters+for+the+differentiation+of+beverage+spoiling+yeasts+by+Matrix-Assisted-Laser-Desorption/Ionization+Time-of-Flight+Mass+Spectrometry+(MALDI-TOF+MS)+in+response+to+varying+growth+conditions.">Optimization of experimental and modelling parameters for the differentiation of beverage spoiling yeasts by Matrix-Assisted-Laser-Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) in response to varying growth conditions.</a> Food Microbiol. 36 (2), 379-387 (2013).</li><li>Del Chierico, F., 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=MALDI-TOF+MS+proteomic+phenotyping+of+filamentous+and+other+fungi+from+clinical+origin.">MALDI-TOF MS proteomic phenotyping of filamentous and other fungi from clinical origin.</a> J Proteomics. 75 (11), 3314-3330 (2012).</li><li>Vitale, R., Roine, E., Bamford, D. H., Corcelli, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+fingerprints+of+intact+viruses+by+MALDI-TOF/mass+spectrometry.">Lipid fingerprints of intact viruses by MALDI-TOF/mass spectrometry.</a> Bba-Mol Cell Biol L. 1831 (4), 872-879 (2013).</li><li>Zhang, L., Borror, C. M., Sandrin, T. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+designed+experiments+approach+to+optimization+of+automated+data+acquisition+during+characterization+of+bacteria+with+MALDI-TOF+mass+spectrometry.">A designed experiments approach to optimization of automated data acquisition during characterization of bacteria with MALDI-TOF mass spectrometry.</a> Plos One. 9 (3), (2014).</li><li>Christner, M., Rohde, H., Wolters, M., Sobottka, I., Wegscheider, K., Aepfelbacher, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+identification+of+bacteria+from+positive+blood+culture+bottles+by+use+of+matrix-assisted+laser+desorption-ionization+time+of+flight+mass+spectrometry+fingerprinting.">Rapid identification of bacteria from positive blood culture bottles by use of matrix-assisted laser desorption-ionization time of flight mass spectrometry fingerprinting.</a> J ClinMicrobiol. 48 (5), 1584-1591 (2010).</li></ol>

Authors Vranckx and Janssens are employees of Applied Maths NV, the manufacturer of data analysis software used in this video. Applied Maths NV provided select software modules highlighted in this video as well as a portion of the publication costs associated with this video.

Cette démonstration a montré des procédures détaillées de caractérisation et d&#39;identification des bactéries utilisant MALDI-TOF MS et une base de données personnalisée. En comparaison avec les méthodes moléculaires classiques, par exemple, les méthodes d&#39;empreintes digitales MS-séquençage de l&#39;ADNr 16S, MALDI-TOF de faciliter une identification plus rapide des diverses bactéries. En raison de sa robustesse, cette technique est largement utilisée pour caractériser les bactéries, les virus, l...

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been shown to be a rapid and reliable tool for identification of bacteria at the genus, species, and in some cases, strain levels1-4. MALDI-TOF MS ionizes biological molecules (typically proteins) that originate from cell surfaces, intracellular membranes, and ribosomes from bacterial whole cells or protein extracts1,5. The resulting peaks form characteristic patterns or &#8220;fingerprints&#8221; of the bacteria analyzed1. Identification of bacteria is based on these mass-to-charge &#8220;fingerprints&#8221;. 
Two of the most commonly used identification strategies are library-based and bioinformatics-based strategies1. Library-based approaches involve comparing the mass spectra of unknowns to previously collected mass spectra of known bacteria in databases/libraries for identification. Commercially available software, such as BioNumerics, Biotyper, and SARAMIS software packages, as well as open source software tools, such as SpectraBank6, are available to facilitate the comparison and quantification of similarity between mass spectra of unknowns and reference bacteria. Bioinformatics-based approaches usually rely on fully sequenced genomes of bacteria for identification. In contrast to library-based approaches which do not involve identification of the biological nature of particular peaks, bioinformatics-based approaches involve protein identification1. 
The majority of recent MALDI fingerprint-based studies have used library-based approaches to identify bacteria1. Library-based approaches require construction of databases and comparison of the similarity between mass spectra. Studies show that many experimental procedures, such as medium3,7, cultivation time8, sample preparation method3, and matrix used9, affect the mass spectra obtained. Furthermore, some closely-related species and strains generate spectra with only subtle differences. Thus, library-based approaches require rigorously standardized procedures to generate highly reproducible mass spectra between replicates. Minor variations in protocols may compromise the efficacy of identification, especially at the subspecies and strain levels1,3,10. However, neither manufacturer-provided reference databases nor reported custom databases include visually documented procedures for database construction and/or application of a data analysis pipeline. For this reason, the objective of this work was to develop, apply, and demonstrate a comprehensive and detailed procedure for library-based bacterial identification using MALDI-TOF MS. 
In this demonstration, mass spectra of 15 bacteria isolated from a karstic environment (Kartchner Cavern, AZ, USA) were collected and imported into software to construct a model database. Data processing and the analysis pipeline were detailed using the model database. Finally, mass spectra of blind-coded bacteria which were randomly selected from these 15 bacteria were collected again and compared to the reference spectra in the model database for identification. Results show that bacteria can be correctly identified either based on similarity coefficients or potential biomarkers/peak classes.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>&#945;-cyano-4-hydroxy-cinnamic acid</td>
			<td>ACROS Organics</td>
			<td align="right">163440050</td>
			<td>&#8805; 97%, CAS 28168-41-8</td>
		</tr>
		<tr>
			<td>MALDI calibration kit</td>
			<td>Aigma-Aldrich</td>
			<td>MSCAL1-1KT</td>
			<td>This kit also contains acetonitrile, trifluoroacetic acid , sinapinic acid, etc.</td>
		</tr>
		<tr>
			<td>MALDI target plate</td>
			<td>Bruker Daltonics</td>
			<td align="right">280800</td>
			<td>Polished Steel</td>
		</tr>
		<tr>
			<td>Bruker Microflex LRF MALDI-TOF mass spectrometer</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Bruker FlexControl software</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td>version 3.0</td>
		</tr>
		<tr>
			<td>Bruker FlexAnalysis software</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td>version 3.0</td>
		</tr>
		<tr>
			<td>Bionumerics software</td>
			<td>Applied Maths</td>
			<td></td>
			<td>version 7.1</td>
		</tr>
	</tbody>
</table>

use of maldi-tof mass spectrometry and a custom database to characterize bacteria indigenous to a unique cave environment (kartchner caverns, az, usa)

MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, strain levels. Commercially available and open source software tools have been developed to facilitate identification; however, no universal/standardized data analysis pipeline has been described in the literature. Here, we provide a comprehensive and detailed demonstration of bacterial identification procedures using a MALDI-TOF mass spectrometer. Mass spectra were collected from 15 diverse bacteria isolated from Kartchner Caverns, AZ, USA, and identified by 16S rDNA sequencing. Databases were constructed in BioNumerics 7.1. Follow-up analyses of mass spectra were performed, including cluster analyses, peak matching, and statistical analyses. Identification was performed using blind-coded samples randomly selected from these 15 bacteria. Two identification methods are presented: similarity coefficient-based and biomarker-based methods. Results show that both identification methods can identify the bacteria to the species level.

Les bases de données construites à cette manifestation avaient quatre niveaux, du plus haut au plus bas niveau, y compris &quot;tous les niveaux&quot;, &quot;répétition biologique&quot; &quot;Species&quot; et &quot;répétition technique&quot;, respectivement (figure 1A). La &quot;répétition technique&quot; niveau contenait tous les spectres prétraitée de répétitions techniques. La &quot;répétition biologique&quot; et les niveaux «espèce» contenaient le composite (résumé) spectres. &quo...

Watch this Scientific Journal Video about Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA at JoVE.c

Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA

This work details procedures for rapid identification of bacteria using MALDI-TOF MS. The identification procedures include spectrum acquisition, database construction, and follow up analyses. Two identification methods, similarity coefficient-based and biomarker-based methods, are presented.

L&#39;utilisation de MALDI-TOF spectrométrie de masse et une base de données personnalisée pour caractériser les bactéries indigènes à un environnement unique Cave (Kartchner, AZ, USA)

MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, ...

use-maldi-tof-mass-spectrometry-custom-database-to-characterize

Research

JoVE Journal

Biology

Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment (Kartchner Caverns, AZ, USA)

13.9K vues.  Arizona State University. This work details procedures for rapid identification of bacteria using MALDI-TOF MS. The identification procedures include spectrum acquisition, database construction, and follow up analyses. Two identification methods, similarity coefficient-based and biomarker-based methods, are presented. 

Vidéo: Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA

MALDI Sample Preparation: the Ultra Thin Layer Method

This video demonstrates the preparation of an ultra-thin matrix/analyte layer for analyzing peptides and proteins by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) 1,2. The ultra-thin layer method involves the production of a substrate layer of matrix crystals (alpha-cyano-4-hydroxycinnamic acid) on the sample plate, which serves as a seeding ground for subsequent crystallization of a matrix/analyte mixture. Advantages of the ultra-thin layer method over other sample deposition approaches (e.g. dried droplet) are that it provides (i) greater tolerance to impurities such as salts and detergents, (ii) better resolution, and (iii) higher spatial uniformity. This method is especially useful for the accurate mass determination of proteins. The protocol was initially developed and optimized for the analysis of membrane proteins and used to successfully analyze ion channels, metabolite transporters, and receptors, containing between 2 and 12 transmembrane domains 2. Since the original publication, it has also shown to be equally useful for the analysis of soluble proteins. Indeed, we have used it for a large number of proteins having a wide range of properties, including those with molecular masses as high as 380 kDa 3. It is currently our method of choice for the molecular mass analysis of all proteins. The described procedure consistently produces high-quality spectra, and it is sensitive, robust, and easy to implement.

This video demonstrates the preparation of an ultra-thin matrix/analyte layer for analyzing peptides and proteins by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS).

Préparation des échantillons MALDI: la méthode de la couche ultra mince

This video demonstrates the preparation of an ultra-thin matrix/analyte layer for analyzing peptides and proteins by Matrix-Assisted Laser Desorption ...

Recherche

Biologie

Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in quantitation, are associated with a high cost, long lead time, and are fraught with drawbacks (e.g. heterophilic antibodies, autoantibody interference, 'hook-effect').1 An alternative technique is affinity enrichment of peptides coupled with quantitative mass spectrometry, commonly referred to as SISCAPA (Stable Isotope Standards and Capture by Anti-Peptide Antibodies).2 In this technique, affinity enrichment of peptides with stable isotope dilution and detection by selected/multiple reaction monitoring mass spectrometry (SRM/MRM-MS) provides quantitative measurement of peptides as surrogates for their respective proteins. SRM/MRM-MS is well established for accurate quantitation of small molecules 3, 4 and more recently has been adapted to measure the concentrations of proteins in plasma and cell lysates.5-7 To achieve quantitation of proteins, these larger molecules are digested to component peptides using an enzyme such as trypsin. One or more selected peptides whose sequence is unique to the target protein in that species (i.e. "proteotypic" peptides) are then enriched from the sample using anti-peptide antibodies and measured as quantitative stoichiometric surrogates for protein concentration in the sample. Hence, coupled to stable isotope dilution (SID) methods (i.e. a spiked-in stable isotope labeled peptide standard), SRM/MRM can be used to measure concentrations of proteotypic peptides as surrogates for quantification of proteins in complex biological matrices. The assays have several advantages compared to traditional immunoassays. The reagents are relatively less expensive to generate, the specificity for the analyte is excellent, the assays can be highly multiplexed, enrichment can be performed from neat plasma (no depletion required), and the technique is amenable to a wide array of proteins or modifications of interest.8-13 In this video we demonstrate the basic protocol as adapted to a magnetic bead platform.

Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA) couples affinity enrichment of peptides with stable isotope dilution mass spectrometry (MRM-MS) to provide quantitative measurement of peptides as surrogates for their respective proteins. Here we describe the protocol using magnetic particles in a partially automated format.

La quantification des protéines à l&#39;aide d&#39;enrichissement d&#39;immunoaffinité peptide couplé à la spectrométrie de masse

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in ...

Amide Hydrogen/Deuterium Exchange &amp; MALDI-TOF Mass Spectrometry Analysis of Pak2 Activation

Amide hydrogen/deuterium exchange (H/D exchange) coupled with mass spectrometry has been widely used to analyze the interface of protein-protein interactions, protein conformational changes, protein dynamics and protein-ligand interactions. H/D exchange on the backbone amide positions has been utilized to measure the deuteration rates of the micro-regions in a protein by mass spectrometry1,2,3. The resolution of this method depends on pepsin digestion of the deuterated protein of interest into peptides that normally range from 3-20 residues. Although the resolution of H/D exchange measured by mass spectrometry is lower than the single residue resolution measured by the Heteronuclear Single Quantum Coherence (HSQC) method of NMR, the mass spectrometry measurement in H/D exchange is not restricted by the size of the protein4. H/D exchange is carried out in an aqueous solution which maintains protein conformation. We provide a method that utilizes the MALDI-TOF for detection2, instead of a HPLC/ESI (electrospray ionization)-MS system5,6. The MALDI-TOF provides accurate mass intensity data for the peptides of the digested protein, in this case protein kinase Pak2 (also called &gamma;-Pak). Proteolysis of Pak 2 is carried out in an offline pepsin digestion. This alternative method, when the user does not have access to a HPLC and pepsin column connected to mass spectrometry, or when the pepsin column on HPLC does not result in an optimal digestion map, for example, the heavily disulfide-bonded secreted Phospholipase A2 (sPLA2). Utilizing this method, we successfully monitored changes in the deuteration level during activation of Pak2 by caspase 3 cleavage and autophosphorylation7,8,9.

MALDI-TOF mass spectrometry was successfully utilized to monitor the amide hydrogen/deuterium exchange in protein kinase Pak2 activation.

Amide hydrogène / deutérium Analyse Bourse &amp; MALDI-TOF spectrométrie de masse de PAK2 activation

Amide hydrogen/deuterium exchange (H/D exchange) coupled with mass spectrometry has been widely used to analyze the interface of protein-protein ...

A Fluorescence-based Exonuclease Assay to Characterize DmWRNexo, Orthologue of Human Progeroid WRN Exonuclease, and Its Application to Other Nucleases

WRN exonuclease is involved in resolving DNA damage that occurs either during DNA replication or following exposure to endogenous or exogenous genotoxins. It is likely to play a role in preventing accumulation of recombinogenic intermediates that would otherwise accumulate at transiently stalled replication forks, consistent with a hyper-recombinant phenotype of cells lacking WRN. In humans, the exonuclease domain comprises an N-terminal portion of a much larger protein that also possesses helicase activity, together with additional sites important for DNA and protein interaction. By contrast, in Drosophila, the exonuclease activity of WRN (DmWRNexo) is encoded by a distinct genetic locus from the presumptive helicase, allowing biochemical (and genetic) dissection of the role of the exonuclease activity in genome stability mechanisms. Here, we demonstrate a fluorescent method to determine WRN exonuclease activity using purified recombinant DmWRNexo and end-labeled fluorescent oligonucleotides. This system allows greater reproducibility than radioactive assays as the substrate oligonucleotides remain stable for months, and provides a safer and relatively rapid method for detailed analysis of nuclease activity, permitting determination of nuclease polarity, processivity, and substrate preferences.

Exonucleases play critical roles in ensuring genome stability. Loss of WRN exonuclease function results in premature aging. Studying substrates and other requirements of the nuclease in vitro can help elucidate its role in vivo. Here we demonstrate a rapid and reproducible fluorescence-based assay to measure its nuclease activity.

Un Exonuclease analyse basée sur la fluorescence pour caractériser DmWRNexo, orthologue humain progéroïde WRN Exonuclease, et son application à d&#39;autres nucléases

WRN exonuclease is involved in resolving DNA damage that occurs either during DNA replication or following exposure to endogenous or exogenous genotoxins. ...

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis (diguanylate cyclases) and degradation (phosphodiesterases) of the second messenger c-di-GMP. In contrast, little information is available regarding the molecular mechanisms and cellular components through which this signaling molecule regulates a diverse range of cellular processes. Most of the known effector proteins belong to the PilZ family or are degenerated diguanylate cyclases or phosphodiesterases that have given up on catalysis and have adopted effector function. Thus, to better define the cellular c-di-GMP network in a wide range of bacteria experimental methods are required to identify and validate novel effectors for which reliable in silico predictions fail.
We have recently developed a novel Capture Compound Mass Spectrometry (CCMS) based technology as a powerful tool to biochemically identify and characterize c-di-GMP binding proteins. This technique has previously been reported to be applicable to a wide range of organisms1. Here we give a detailed description of the protocol that we utilize to probe such signaling components. As an example, we use Pseudomonas aeruginosa, an opportunistic pathogen in which c-di-GMP plays a critical role in virulence and biofilm control. CCMS identified 74% (38/51) of the known or predicted components of the c-di-GMP network. This study explains the CCMS procedure in detail, and establishes it as a powerful and versatile tool to identify novel components involved in small molecule signaling.

The ubiquitous second messenger c-di-GMP controls growth and behavior of many bacteria. We have developed a novel Capture Compound Mass Spectrometry based technology to biochemically identify and characterize c-di-GMP binding proteins in virtually any bacterial species.

Captation composé spectrométrie de masse - un outil puissant pour identifier de nouveaux c-di-GMP protéines effectrices

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis ...

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and product development. Stress-strain relationships are typically obtained from mechanical testing data to enable comparative assessment among samples and in some cases identification of constitutive mechanical properties. However, errors may be introduced through the use of average strain measures, as significant heterogeneity in the strain field may result from geometrical non-uniformity of the sample and stress concentrations induced by mounting/gripping of soft tissues within the test system. When strain field heterogeneity is significant, accurate assessment of the sample mechanical response requires measurement of local strains. This study demonstrates a novel biomechanical testing protocol for calculating local surface strains using a mechanical testing device coupled with a high resolution camera and a digital image correlation technique. A series of sample surface images are acquired and then analyzed to quantify the local surface strain of a vascular tissue specimen subjected to ramped uniaxial loading. This approach can improve accuracy in experimental vascular biomechanics and has potential for broader use among other native soft tissues, engineered soft tissues, and soft hydrogel/polymeric materials. In the video, we demonstrate how to set up the system components and perform a complete experiment on native vascular tissue.

We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.

Utilisation de corrélation d&#39;images à caractériser les souches locales sur des échantillons de tissus vasculaires

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and ...

Identification of MyoD Interactome Using Tandem Affinity Purification Coupled to Mass Spectrometry

Skeletal muscle terminal differentiation starts with the commitment of pluripotent mesodermal precursor cells to myoblasts. These cells have still the ability to proliferate or they can differentiate and fuse into multinucleated myotubes, which maturate further to form myofibers. Skeletal muscle terminal differentiation is orchestrated by the coordinated action of various transcription factors, in particular the members of the Muscle Regulatory Factors or MRFs (MyoD, Myogenin, Myf5, and MRF4), also called the myogenic bHLH transcription factors family. These factors cooperate with chromatin-remodeling complexes within elaborate transcriptional regulatory network to achieve skeletal myogenesis. In this, MyoD is considered the master myogenic transcription factor in triggering muscle terminal differentiation. This notion is strengthened by the ability of MyoD to convert non-muscle cells into skeletal muscle cells. Here we describe an approach used to identify MyoD protein partners in an exhaustive manner in order to elucidate the different factors involved in skeletal muscle terminal differentiation. The long-term aim is to understand the epigenetic mechanisms involved in the regulation of skeletal muscle genes, i.e., MyoD targets. MyoD partners are identified by using Tandem Affinity Purification (TAP-Tag) from a heterologous system coupled to mass spectrometry (MS) characterization, followed by validation of the role of relevant partners during skeletal muscle terminal differentiation. Aberrant forms of myogenic factors, or their aberrant regulation, are associated with a number of muscle disorders: congenital myasthenia, myotonic dystrophy, rhabdomyosarcoma and defects in muscle regeneration. As such, myogenic factors provide a pool of potential therapeutic targets in muscle disorders, both with regard to mechanisms that cause disease itself and regenerative mechanisms that can improve disease treatment. Thus, the detailed understanding of the intermolecular interactions and the genetic programs controlled by the myogenic factors is essential for the rational design of efficient therapies.

MyoD is a myogenic transcription factor with a strong capacity to induce myogenic transdifferentiation of many fully differentiated non-muscle cell lines. The epigenetic mechanisms involved in this transdifferentiation are largely unknown. Here we describe a double-affinity purification method followed by mass spectrometry to exhaustively characterize MyoD partners.

Identification de MyoD Interactome Utilisation Tandem Affinity Purification couplée à la spectrométrie de masse

Skeletal muscle terminal differentiation starts with the commitment of pluripotent mesodermal precursor cells to myoblasts. These cells have still the ...

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone function is mediated by extensive post-translational modification by a myriad of nuclear proteins. These modifications are critical for nuclear integrity as they regulate chromatin structure and recruit enzymes involved in gene regulation, DNA repair and chromosome condensation. Even though a large part of the scientific community adopts antibody-based techniques to characterize histone PTM abundance, these approaches are low throughput and biased against hypermodified proteins, as the epitope might be obstructed by nearby modifications. This protocol describes the use of nano liquid chromatography (nLC) and mass spectrometry (MS) for accurate quantification of histone modifications. This method is designed to characterize a large variety of histone PTMs and the relative abundance of several histone variants within single analyses. In this protocol, histones are derivatized with propionic anhydride followed by digestion with trypsin to generate peptides of 5 - 20 aa in length. After digestion, the newly exposed N-termini of the histone peptides are derivatized to improve chromatographic retention during nLC-MS. This method allows for the relative quantification of histone PTMs spanning four orders of magnitude.

This protocol outlines a fully integrated workflow for characterizing histone post-translational modifications using mass spectrometry (MS). The workflow includes histone purification from cell cultures or tissues, histone derivatization and digestion, MS analysis using nano-flow liquid chromatography and instructions for data analysis. The protocol is designed for completion within 2 - 3 days.

Flux de travail complet pour l&#39;analyse des histones modifications post-traductionnelles utilisant la spectrométrie de masse Bottom-up: De Histone Extraction à l&#39;analyse des données

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone ...

At-Risk Butterfly Captive Propagation Programs to Enhance Life History Knowledge and Effective Ex Situ Conservation Techniques

Improving knowledge of ex situ best practices for at-risk butterflies is important for generating successful conservation and recovery program outcomes. Research on such captive populations can also yield valuable data to address key information gaps about the behavior, life history, and ecology of the target taxa. We describe a protocol for captive propagation of the federally endangered Cyclargus thomasi bethunebakeri that can be used as a model for other at-risk butterfly ex situ programs, especially those in the family Lycaenidae. We further provide a simple and straightforward protocol for recording various life history metrics that can be useful for informing ex situ methodologies as well as adapted for laboratory studies of other lepidoptera.

Here, we present protocols for 1) the laboratory captive propagation of the federally endangered Miami blue butterfly (Cyclargus thomasi bethunebakeri), and 2) assessing basic life history information such as immature development time and number of larval stadia. Both methods can be adapted for use with other ex situ conservation programs.

Programmes de propagation des papillons captifs à risque pour améliorer les connaissances sur l’histoire de la vie et les techniques efficaces de conservation Ex Situ

Improving knowledge of ex situ best practices for at-risk butterflies is important for generating successful conservation and recovery program outcomes. ...

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A)

Studying the structure and the dynamics of kinetochores and centromeres is important in understanding chromosomal instability (CIN) and cancer progression. How the chromosomal location and function of a centromere (i.e., centromere identity) are determined and participate in accurate chromosome segregation is a fundamental question. CENP-A is proposed to be the non-DNA indicator (epigenetic mark) of centromere identity, and CENP-A ubiquitylation is required for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability.
Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-CENP-A K124R mutant suggesting that ubiquitylation at a different lysine is induced because of the EYFP tagging in the CENP-A K124R mutant protein. Lysine 306 (K306) ubiquitylation in EYFP-CENP-A K124R was successfully identified, which corresponds to lysine 56 (K56) in CENP-A through mass spectrometry analysis. A caveat is discussed in the use of GFP/EYFP or the tagging of high molecular weight protein as a tool to analyze the function of a protein. Current technical limit is also discussed for the detection of ubiquitylated bands, identification of site-specific ubiquitylation(s), and visualization of ubiquitylation in living cells or a specific single cell during the whole cell cycle.
The method of mass spectrometry analysis presented here can be applied to human CENP-A protein with different tags and other centromere-kinetochore proteins. These combinatory methods consisting of several assays/analyses could be recommended for researchers who are interested in identifying functional roles of ubiquitylation.

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A EYFP-CENP-A

CENP-A ubiquitylation is an important requirement for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability. Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A) protein.