depletion-mouse-cells-from-human-tumor-xenografts-significantly

Depletion of Mouse Cells from Human Tumor Xenografts Significantly Improves Downstream Analysis of Target Cells

The use of in vitro cell line models for cancer research has been a useful tool. However, it has been shown that these models fail to reliably mimic ...

Miltenyi Biotec GmbH

Aim of this study was to investigate the anticancer properties of the new lanthanum compound [tris(1,10-phenanthroline)lanthanum(III)]trithiocyanate (KP772; FFC24). In vitro, growth inhibition by KP772 was comparable for >60 tumour cell models with IC50 values generally in the low microM range. KP772 induced tumour cell apoptosis indicated by chromatin condensation, caspase substrate cleavage and mitochondrial membrane depolarisation. DNA is unlikely to represent the primary molecular target of KP772, as no significant interaction or damage of DNA was detectable both in vitro and in living cells. Moreover, we found no evidence for induction of radical species. In contrast, KP772 potently inhibited DNA synthesis paralleled by a massive block of cell cycle in G0/G1 phase and a selective decrease of cyclin B1. Although treatment with KP772 induced expression of p53 and p21Waf1, transfection of wild-type p53 into knock-out cells only marginally enhanced the cytostatic activity of KP772. In vivo, the anticancer activity of KP772 against human DLD-1 colon carcinoma xenografts was comparable to that of cisplatin and methotrexate at doses not causing significant adverse effects. With regard to toxicity, the LD50 and no-observed-adverse-effect levels (NOAEL) of KP772 in Sprague-Dawley rats were 21.6 and 7.5 mg/kg, in outbred albino mice 62 and 10 mg/kg, respectively. In summary, KP772 exerts anticancer activity via potent induction of cell cycle arrest and/or apoptosis and has promising in vivo anticancer activity against a human colon cancer xenograft. Together, these data suggest further development of KP772 as a new anticancer metal-drug.

Anticancer activity of the lanthanum compound [tris(1,10-phenanthroline)lanthanum(III)]trithiocyanate (KP772; FFC24).

Microrna (Mirna) &#x662F;&#x4E00;&#x79CD;&#x5C0F; RNAs &#x7EA6; 21-23-&#x6838;&#x82F7;&#x9178;&#x957F;&#x5DF2;&#x8BC1;&#x660E;&#xFF0C;&#x5728;&#x8BB8;&#x591A;&#x4E0D;&#x540C;&#x7684;&#x7EC6;&#x80DE;&#x3001; &#x53D1;&#x5C55;&#x548C;&#x751F;&#x7406;&#x8FC7;&#x7A0B;&#x4E2D;&#x53D1;&#x6325;&#x7740;&#x91CD;&#x8981;&#x7684;&#x4F5C;&#x7528;&#x3002;&#x56E0;&#x6B64;&#xFF0C;&#x8BB8;&#x591A;&#x805A;&#x5408;&#x9176;&#x94FE;&#x53CD;&#x5E94;-&#x3001; &#x6D4B;&#x5E8F;&#x6216;&#x57FA;&#x4E8E;&#x6742;&#x4EA4;&#x7684;&#x65B9;&#x6CD5;&#xFF0C;&#x4EE5;&#x8BC6;&#x522B;&#x548C;&#x91CF;&#x5316; Mirna&#x3002;&#x5176;&#x957F;&#x5EA6;&#x77ED;&#x5BFC;&#x81F4;&#x7684;&#x7194;&#x5316;&#x6E29;&#x5EA6;&#x7684;&#x9AD8;&#x52A8;&#x6001;&#x8303;&#x56F4;&#x548C;&#x56E0;&#x6B64;&#x963B;&#x788D;&#x9009;&#x62E9;&#x9002;&#x5F53;&#x7684;&#x68C0;&#x6D4B;&#x63A2;&#x9488;&#x6216;&#x4F18;&#x5316; PCR &#x5F15;&#x7269;&#x3002;&#x5C3D;&#x7BA1;&#x7C73;&#x5C14;&#x82AF;&#x7247;&#x5141;&#x8BB8;&#x6240;&#x6709;&#x5DF2;&#x77E5; Mirna &#x5927;&#x89C4;&#x6A21;&#x5E76;&#x884C;&#x548C;&#x51C6;&#x786E;&#x76F8;&#x5BF9;&#x6D4B;&#x91CF;&#xFF0C;&#x4ED6;&#x4EEC;&#x5230;&#x76EE;&#x524D;&#x4E3A;&#x6B62;&#x5DF2;&#x7ECF;&#x4E0D;&#x90A3;&#x4E48;&#x6709;&#x7528;&#x7684;&#x7EDD;&#x5BF9;&#x5B9A;&#x91CF;&#x6D4B;&#x5B9A;&#x3002;&#x5728;&#x8FD9;&#x91CC;&#xFF0C;&#x6211;&#x4EEC;&#x76EE;&#x524D;&#x5168;&#x7403;&#x548C;&#x7EDD;&#x5BF9;&#x91CF;&#x5316;&#x7684; Mirna &#x57FA;&#x4E8E;&#x82AF;&#x7247;&#x7684;&#x65B9;&#x6CD5;&#x3002;&#x8BE5;&#x65B9;&#x6CD5;&#x4F9D;&#x8D56;&#x4E8E;&#x5E76;&#x884C;&#x6742;&#x4EA4;&#x7684;&#x5174;&#x8DA3;&#x4E0E; Cy5 &#x548C; 954 &#x5408;&#x6210; Mirna &#x82AF;&#x7247;&#x5E7B;&#x706F;&#x7247;&#x5305;&#x542B;&#x6240;&#x6709;&#x4EBA;&#x7C7B;&#x63A2;&#x6D4B;&#x5668;&#x3001; &#x9F20;&#x6807;&#x3001; &#x9F20;&#x3001; &#x75C5;&#x6BD2;&#x6027; Mirna (miRBase 12.0) &#x4E0A;&#x90FD;&#x6807;&#x6709; Cy3 &#x7684;&#x7B49;&#x6469;&#x5C14;&#x6D53;&#x5EA6;&#x666E;&#x904D;&#x5F15;&#x7528;&#x6807;&#x8BB0;&#x7684;&#x793A;&#x4F8B;&#x3002;&#x6BCF;&#x4E2A;&#x5355;&#x4E00;&#x7684;&#x7C73;&#x5C14;&#x88AB;&#x91CF;&#x5316;&#x5C31;&#x53D6;&#x6D88;&#x504F;&#x89C1;&#x4E0E;&#x76F8;&#x5173;&#x7684;&#x5E8F;&#x5217;&#x3001; &#x6DFB;&#x52A0;&#x6807;&#x7B7E;&#xFF0C;&#x6216;&#x6742;&#x4EA4;&#x7684;&#x901A;&#x7528;&#x53C2;&#x8003;&#x6587;&#x732E;&#x3002;&#x6211;&#x4EEC;&#x901A;&#x8FC7;&#x5404;&#x79CD;&#x56FE;&#x6587;&#x573A;&#x4E2D;&#x5B9E;&#x9A8C;&#x8BC1;&#x660E;&#x65B9;&#x6CD5;&#x7684;&#x51C6;&#x786E;&#x6027;&#x3002;&#x6B64;&#x5916;&#xFF0C;&#x6211;&#x4EEC;&#x91CF;&#x5316;&#x7C73;&#x5C14;&#x62F7;&#x8D1D;&#x6570;&#x5728;&#x809D;&#x810F;&#x6837;&#x672C;&#x548C; CD34(+)/CD133(-) &#x9020;&#x8840;&#x7956;&#x7EC6;&#x80DE;&#x3002;

&#x7EDD;&#x5BF9;&#x91CF;&#x5316;&#x7684; Microrna &#x901A;&#x8FC7;&#x4F7F;&#x7528;&#x901A;&#x7528;&#x7684;&#x5F15;&#x7528;&#x3002;

MicroRNAs (miRNAs) are a species of small RNAs approximately 21-23-nucleotides long that have been shown to play an important role in many different cellular, developmental, and physiological processes. Accordingly, numerous PCR-, sequencing-, or hybridization-based methods have been established to identify and quantify miRNAs. Their short length results in a high dynamic range of melting temperatures and therefore impedes a proper selection of detection probes or optimized PCR primers. While miRNA microarrays allow for massive parallel and accurate relative measurement of all known miRNAs, they have so far been less useful as an assay for absolute quantification. Here, we present a microarray-based approach for global and absolute quantification of miRNAs. The method relies on the parallel hybridization of the sample of interest labeled with Cy5 and a universal reference of 954 synthetic miRNAs in equimolar concentrations that are labeled with Cy3 on a microarray slide containing probes for all human, mouse, rat, and viral miRNAs (miRBase 12.0). Each single miRNA is quantified with respect to the universal reference canceling biases related to sequence, labeling, or hybridization. We demonstrate the accuracy of the method by various spike-in experiments. Furthermore, we quantified miRNA copy numbers in liver samples and CD34(+)/CD133(-) hematopoietic progenitor cells.

Absolute quantification of microRNAs by using a universal reference.

Les microARN (miARN) sont des esp&#xE8;ces de petits ARN d&#x26;#39;environ 21-23-nucl&#xE9;otides de long qui ont &#xE9;t&#xE9; montr&#xE9;s &#xE0; jouer un r&#xF4;le important dans de nombreux cellulaires, les processus de d&#xE9;veloppement, et physiologique. En cons&#xE9;quence, de nombreuses m&#xE9;thodes de PCR-, le s&#xE9;quen&#xE7;age-, ou d&#x26;#39;hybridation &#xE0; base ont &#xE9;t&#xE9; mis en place pour identifier et quantifier les miARN. Leurs r&#xE9;sultats de courte longueur dans une plage dynamique &#xE9;lev&#xE9;e des temp&#xE9;ratures de fusion et donc emp&#xEA;che une s&#xE9;lection correcte des sondes de d&#xE9;tection ou optimis&#xE9;s amorces de PCR. Bien puces miRNA permettre massif mesure relative parall&#xE8;le et pr&#xE9;cise de tous les miARN connus, ils ont jusqu&#x26;#39;&#xE0; pr&#xE9;sent &#xE9;t&#xE9; moins utile que d&#x26;#39;un dosage pour la quantification absolue. Ici, nous pr&#xE9;sentons une approche bas&#xE9; sur un micror&#xE9;seau pour la quantification globale et absolue des miARN. La m&#xE9;thode repose sur l&#x26;#39;hybridation parall&#xE8;le de l&#x26;#39;&#xE9;chantillon d&#x26;#39;int&#xE9;r&#xEA;t marqu&#xE9; avec Cy5 et une r&#xE9;f&#xE9;rence universelle de 954 miARN synth&#xE9;tiques dans des concentrations &#xE9;quimolaires qui sont marqu&#xE9;s en Cy3 sur une lame de microarray contenant des sondes pour tous les &#xEA;tres humains, la souris, le rat et miARN viraux (miRBase 12,0). Chaque miARN seul est quantifi&#xE9; &#xE0; l&#x26;#39;&#xE9;gard des pr&#xE9;jug&#xE9;s universels suppresseur de r&#xE9;f&#xE9;rence relatifs &#xE0; la s&#xE9;quence, l&#x26;#39;&#xE9;tiquetage, ou d&#x26;#39;hybridation. Nous d&#xE9;montrons la pr&#xE9;cision de la m&#xE9;thode par diverses pointes dans les exp&#xE9;riences. En outre, nous avons quantifi&#xE9; le nombre de copies miRNA dans des &#xE9;chantillons de foie et CD34 (+) / CD133 (-) des cellules souches h&#xE9;matopo&#xEF;&#xE9;tiques.

Quantification absolue de microARN en utilisant une r&#xE9;f&#xE9;rence universelle

Microrna (Mirna) &#x8868;&#x660E;&#xFF0C;&#x5728;&#x9020;&#x8840;&#x529F;&#x80FD;&#x4E2D;&#x53D1;&#x6325;&#x7740;&#x91CD;&#x8981;&#x7684;&#x4F5C;&#x7528;&#x3002;&#x9610;&#x660E; Mirna &#x9020;&#x8840;&#x529F;&#x80FD;&#x7684;&#x65E9;&#x671F;&#x6B65;&#x9AA4;&#x4E2D;&#x7684;&#x4F5C;&#x7528;&#xFF0C;&#x6211;&#x4EEC;&#x76F4;&#x63A5;&#x6BD4;&#x8F83;&#x6350;&#x52A9;&#x5339;&#x914D;&#x9AA8;&#x9AD3;&#x7684;&#x7C73;&#x5C14;&#x548C; mRNA &#x6C34;&#x5E73;&#x4E0A;&#x66F4;&#x6709;&#x533A;&#x522B; CD34(+) CD133(-) &#x548C; CD34(-) CD133(-) &#x7EC6;&#x80DE;&#x7684; CD133(+) &#x5355;&#x5143;&#x683C;&#x3002;&#x4F7F;&#x7528;&#x5B9A;&#x91CF;&#x7684;&#x5168;&#x57FA;&#x56E0;&#x7EC4;&#x7C73;&#x5C14;&#x82AF;&#x7247;&#x548C;&#x57FA;&#x4E8E;&#x5E8F;&#x5217;&#x7684;&#x5206;&#x6790;&#xFF0C;&#x6211;&#x4EEC;&#x53D1;&#x73B0; 109 (CD133(+)) &#x548C; 216 (CD34(-) CD133(-)) &#x4E4B;&#x95F4; Mirna &#x8868;&#x793A;&#x3002;&#x91CF;&#x5316;&#x900F;&#x9732;&#x6700;&#x9AD8; 25 &#x8868;&#x793A; Mirna &#x5360;&#x603B;&#x7C73;&#x5C14;&#x6C60;&#x7684; 73%&#x3002;&#x548C;&#x5E73;&#x53F7;&#x7A7A;&#x95F4;&#x7AD9;-142-3 p &#x662F;&#x6700;&#x9AD8;&#x7684;&#x8868;&#x793A;&#x7684;&#x7C73;&#x5C14;&#x4E0E;&#x6700;&#x591A; 2000 &#x4EFD;&#x6BCF;&#x4E2D; CD34(+) CD133(-) &#x5355;&#x5143;&#x683C;&#x7684;&#x5355;&#x5143;&#x683C;&#x3002;&#x5341;&#x516B;&#x4E2A; Mirna &#x663E;&#x8457;&#x5DEE;&#x5F02;&#x8868;&#x793A;&#x4E86; CD133(+) &#x548C; CD34(+) CD133(-) &#x7684;&#x5355;&#x5143;&#x683C;&#x4E4B;&#x95F4;&#x3002;&#x6211;&#x4EEC;&#x901A;&#x8FC7;&#x68C0;&#x67E5;&#x7684; Mirna &#x5171;&#x8868;&#x8FBE;&#x5206;&#x6790;&#x5176;&#x751F;&#x7269;&#x5B66;&#x4F5C;&#x7528;&#x548C;&#x5176; bioinformatically &#x9884;&#x8A00; mRNA &#x76EE;&#x6807;&#x548C;&#x57FA;&#x4E8E;&#x8367;&#x5149;&#x7D20;&#x9176;&#x7684;&#x8BB0;&#x8005;&#x68C0;&#x5B9A;&#x6CD5;&#x3002;&#x6211;&#x4EEC;&#x63D0;&#x4F9B; CD133 &#x7684;&#x548C;&#x5E73;&#x53F7;&#x7A7A;&#x95F4;&#x7AD9;-142-3 p&#xFF0C;&#x4EE5;&#x53CA;&#x539F;&#x808C;&#x7403;&#x86CB;&#x767D; 1 &#x548C;&#x50BB;&#x6C14;&#x540C;&#x6E90; 5 &#x7684;&#x548C;&#x5E73;&#x53F7;&#x7A7A;&#x95F4;&#x7AD9; 29a &#x76F4;&#x63A5;&#x76D1;&#x7BA1;&#x7684;&#x7B2C;&#x4E00;&#x4E2A;&#x8BC1;&#x636E;&#x3002;CD133(+) &#x7EC6;&#x80DE; Mirna &#x8868;&#x8FBE;&#x8868;&#x660E;&#x548C;&#x5E73;&#x53F7;&#x7A7A;&#x95F4;&#x7AD9;-142-3 p &#x4EA7;&#x751F;&#x5BF9;&#x603B;&#x7684;&#x96C6;&#x843D;&#x5F62;&#x6210;&#x80FD;&#x529B;&#x7684;&#x8D1F;&#x9762;&#x5F71;&#x54CD;&#x3002;&#x6700;&#x540E;&#xFF0C;CD133(+) &#x548C; CD34(+) CD133(-) &#x5355;&#x5143;&#x683C;&#x4E4B;&#x95F4;&#x5DEE;&#x5F02;&#x8868;&#x8FBE; Mirna &#x53C2;&#x4E0E;&#x5206;&#x5316;&#x7684;&#x6291;&#x5236;&#x4F5C;&#x7528;&#x3001; &#x7EC6;&#x80DE;&#x51CB;&#x4EA1;&#x548C;&#x7EC6;&#x80DE;&#x9AA8;&#x67B6;&#x91CD;&#x6784;&#x7684;&#x9884;&#x9632;&#x3002;&#x8FD9;&#x4E9B;&#x7ED3;&#x679C;&#x662F;&#x9AD8;&#x5EA6;&#x76F8;&#x5173;&#x7684;&#x5E72;&#x7EC6;&#x80DE;&#x7597;&#x6CD5;&#x5728; CD133(+) &#x7EC6;&#x80DE;&#x548C;&#x9996;&#x6B21;&#x5212;&#x5B9A; CD133(+) &#x7EC6;&#x80DE;&#x7684;&#x5E72;&#x7EC6;&#x80DE;&#x7279;&#x6027;&#x5982;&#x4F55;&#x5B9A;&#x4E49;&#x7684;&#x7279;&#x5B9A; Mirna &#x7684;&#x8868;&#x8FBE;&#x3002;

&#x8054;&#x5408;&#x7684;&#x8868;&#x5F81;&#x7684; microRNA &#x548C; mRNA &#x6982;&#x51B5;&#x5206;&#x754C;&#x7EBF; CD133 + &#x548C; /cd34 + &#x9020;&#x8840;&#x5E72;&#x7956;&#x7EC6;&#x80DE;&#x7684;&#x65E9;&#x671F;&#x5206;&#x5316;&#x7684;&#x9014;&#x5F84;&#x3002;

MicroRNAs (miRNAs) have been shown to play an important role in hematopoiesis. To elucidate the role of miRNAs in the early steps of hematopoiesis, we directly compared donor-matched CD133(+) cells with the more differentiated CD34(+) CD133(-) and CD34(-) CD133(-) cells from bone marrow on the miRNA and mRNA level. Using quantitative whole genome miRNA microarray and sequencing-based profiling, we found that between 109 (CD133(+) ) and 216 (CD34(-) CD133(-) ) miRNAs were expressed. Quantification revealed that the 25 highest expressed miRNAs accounted for 73% of the total miRNA pool. miR-142-3p was the highest expressed miRNA with up to 2,000 copies per cell in CD34(+) CD133(-) cells. Eighteen miRNAs were significantly differentially expressed between CD133(+) and CD34(+) CD133(-) cells. We analyzed their biological role by examining the coexpression of miRNAs and its bioinformatically predicted mRNA targets and luciferase-based reporter assays. We provide the first evidence for a direct regulation of CD133 by miR-142-3p as well as tropomyosin 1 and frizzled homolog 5 by miR-29a. Overexpression of miRNAs in CD133(+) cells demonstrated that miR-142-3p has a negative influence on the overall colony-forming ability. In conclusion, the miRNAs expressed differentially between the CD133(+) and CD34(+) CD133(-) cells are involved in inhibition of differentiation, prevention of apoptosis, and cytoskeletal remodeling. These results are highly relevant for stem cell-based therapies with CD133(+) cells and delineate for the first time how the stem cell character of CD133(+) cells is defined by the expression of specific miRNAs.

Combined characterization of microRNA and mRNA profiles delineates early differentiation pathways of CD133+ and CD34+ hematopoietic stem and progenitor cells.

Les microARN (miARN) ont &#xE9;t&#xE9; montr&#xE9; &#xE0; jouer un r&#xF4;le important dans l&#x26;#39;h&#xE9;matopo&#xEF;&#xE8;se. Pour &#xE9;lucider le r&#xF4;le des miARN dans les &#xE9;tapes pr&#xE9;coces de l&#x26;#39;h&#xE9;matopo&#xEF;&#xE8;se, nous compar&#xE9; directement des bailleurs de fonds appari&#xE9;s CD133 (+) des cellules avec le CD34 plus diff&#xE9;renci&#xE9;e (+) CD133 (-) et CD34 (-) CD133 (-) des cellules de la moelle osseuse sur le niveau miARN et ARNm. Utilisation de puces &#xE0; ADN quantitative l&#x26;#39;ensemble du g&#xE9;nome et le s&#xE9;quen&#xE7;age miARN bas&#xE9; sur le profilage, nous avons constat&#xE9; qu&#x26;#39;entre 109 (CD133 (+)) et 216 (CD34 (-) CD133 (-)) miARN ont &#xE9;t&#xE9; exprim&#xE9;es. Quantification a r&#xE9;v&#xE9;l&#xE9; que les 25 miARN exprim&#xE9;s plus haut ont compt&#xE9; pour 73% de la piscine miARN totale. miR-142-3p a &#xE9;t&#xE9; le plus &#xE9;lev&#xE9; miARN exprim&#xE9;s avec un maximum de 2.000 copies par cellule dans CD34 (+) CD133 (-) des cellules. Dix-huit miARN ont &#xE9;t&#xE9; significativement diff&#xE9;rentiellement exprim&#xE9;s entre CD133 (+) et CD34 (+) CD133 (-) des cellules. Nous avons analys&#xE9; leur r&#xF4;le biologique en examinant la co-expression des miARN et ses cibles d&#x26;#39;ARNm bioinformatique pr&#xE9;dites et des dosages &#xE0; base de rapporteur de la lucif&#xE9;rase. Nous fournissons la premi&#xE8;re preuve d&#x26;#39;une r&#xE9;gulation directe de CD133 par miR-142-3p, ainsi que la tropomyosine 1 et 5 par frizzled homolog miR-29a. La surexpression de miARN dans CD133 (+) des cellules d&#xE9;montr&#xE9; que miR-142-3p a une influence n&#xE9;gative sur l&#x26;#39;ensemble des colonies de la capacit&#xE9; de formation. En conclusion, les miARN exprim&#xE9;s de mani&#xE8;re diff&#xE9;rentielle entre le CD133 (+) et CD34 (+) CD133 (-) les cellules sont impliqu&#xE9;es dans l&#x26;#39;inhibition de la diff&#xE9;renciation, la pr&#xE9;vention de l&#x26;#39;apoptose et le remodelage du cytosquelette. Ces r&#xE9;sultats sont tr&#xE8;s pertinents pour souches de cellules-th&#xE9;rapies avec CD133 (+) des cellules et &#xE0; d&#xE9;limiter pour la premi&#xE8;re fois la fa&#xE7;on dont le caract&#xE8;re de cellules souches de CD133 (+) des cellules est d&#xE9;finie par l&#x26;#39;expression d&#x26;#39;miRNAs sp&#xE9;cifiques.

Caract&#xE9;risation combin&#xE9;e des microARN et profils ARNm d&#xE9;limite voies de diff&#xE9;renciation pr&#xE9;coce des CD133 + et CD34 + souches h&#xE9;matopo&#xEF;&#xE9;tiques et des cellules prog&#xE9;nitrices

MicroRNAs (MiRNAs) nachweislich in Blutbildung eine wichtige Rolle. Um die Rolle der MiRNAs in den fr&uuml;hen Schritten der Blutbildung zu erhellen, verglichen wir direkt passende Spender CD133(+) Zellen mit den differenzierteren CD34(+) CD133(-) und CD34(-) CD133(-) Zellen aus dem Knochenmark auf der MiRNA und mRNA-Ebene. Mit quantitativen gesamten Genoms MiRNA Microarray und Sequenzierung basierende Profilerstellung, fanden wir, dass zwischen 109 (CD133(+)) und 216 (CD34(-) CD133(-)) MiRNAs ausgedr&uuml;ckt wurden. Quantifizierung ergab, dass die h&ouml;chsten 25 MiRNAs entfielen 73 % des gesamten MiRNA Pools ausgedr&uuml;ckt. MiR-142-3 p war die h&ouml;chste exprimierten MiRNA mit bis zu 2.000 Kopien pro Zelle in CD34(+) CD133(-) Zellen. Achtzehn MiRNAs wurden zwischen CD133(+) und CD34(+) CD133(-) Zellen signifikant differentiell ge&auml;u&szlig;ert. Wir analysierten ihre biologische Rolle durch Untersuchen der Coexpression von MiRNAs und seine Bioinformatically vorausgesagt mRNA Ziele und auf der Grundlage der Luciferase Reporter-Assays. Wir bieten die erste Beweise f&uuml;r eine direkte Regulierung CD133 von MiR-142-3 p sowie Tropomyosin 1 und frizzled Homolog 5 von MiR-29a. Eine &Uuml;berexpression von MiRNAs in CD133(+) Zellen gezeigt, der MiR-142-3-p hat einen negativen Einfluss auf die gesamte kolonienbildende F&auml;higkeit. Abschlie&szlig;end sind die MiRNAs differenziell ausgedr&uuml;ckt zwischen den CD133(+) und CD34(+) CD133(-) Zellen beteiligt Hemmung der Differenzierung, Pr&auml;vention von Apoptosis und das Zytoskelett umgestaltet. Diese Ergebnisse sind besonders relevant f&uuml;r Stammzell-basierte Therapien mit CD133(+) Zellen und Abgrenzung zum ersten Mal wie der Stammzell-Charakter der CD133(+) Zellen durch den Ausdruck der spezifischen MiRNAs definiert ist.

Kombinierte Charakterisierung der MicroRNA und mRNA-profile schildert fr&uuml;he Differenzierung Wege von CD133 + und CD34 + h&auml;matopoetische Stamm- und Vorl&auml;uferzellen Zellen.

MicroRNA (miRNAs) hanno dimostrato di giocare un ruolo importante nell&#39;ematopoiesi. Per chiarire il ruolo dei miRNAs nei primi passi dell&#39;emopoiesi, abbiamo confrontato direttamente matched da donatore cellule CD133(+) con le cellule pi&ugrave; differenziate CD34(+) CD133(-) e CD34(-) CD133(-) dal midollo osseo al livello di miRNA e mRNA. Utilizzando quantitativi intero genoma miRNA microarray e analisi basata sul sequenziamento, abbiamo riscontrato che tra 109 (CD133(+)) e 216 (CD34(-) CD133(-)) sono state espresse miRNAs. Quantificazione ha rivelato che i 25 pi&ugrave; alto espresso miRNAs rappresentato il 73% del pool totale miRNA. miR-142-3 p era il pi&ugrave; alto miRNA espressa con fino a 2.000 copie per ogni cella in cellule CD34(+) CD133(-). Diciotto miRNAs erano significativamente differenzialmente espressi tra CD133(+) e CD34(+) CD133(-) cellule. Abbiamo analizzato il loro ruolo biologico esaminando il coexpression di miRNAs e sua bioinformatically predetto mRNA target e reporter luciferasi-based assays. Forniamo la prima prova per un regolamento diretto di CD133 di miR-142-3 p cos&igrave; come omologo 1 e frizzled tropomiosina 5 da miR-29a. Sovraespressione di miRNAs in cellule CD133(+) dimostrato che miR-142-3 p ha un&#39;influenza negativa sulla capacit&agrave; complessiva formanti. In conclusione, i miRNAs differenzialmente espressi tra le cellule CD133(+) e CD34(+) CD133(-) sono coinvolti nell&#39;inibizione della differenziazione, la prevenzione dell&#39;apoptosi e rimodellamento del citoscheletro. Questi risultati sono estremamente rilevanti per terapie a base di cellule staminali con cellule CD133(+) e delineano per la prima volta come il personaggio di cellule staminali di cellule CD133(+) &egrave; definito dall&#39;espressione di miRNAs specifici.

Combinato caratterizzazione dei profili di microRNA e mRNA delinea primi percorsi di differenziazione delle CD133 + e CD34 + cellule ematopoietiche staminali e progenitrici.

&#x30DE;&#x30A4;&#x30AF;&#x30ED;RNA&#xFF08;miRNA&#xFF09;&#x306F;&#x3001;&#x9020;&#x8840;&#x306B;&#x304A;&#x3044;&#x3066;&#x91CD;&#x8981;&#x306A;&#x5F79;&#x5272;&#x3092;&#x679C;&#x305F;&#x3059;&#x3053;&#x3068;&#x304C;&#x793A;&#x3055;&#x308C;&#x3066;&#x3044;&#x308B;&#x3002; &#xFF08; - &#xFF09;&#x9020;&#x8840;&#x306E;&#x521D;&#x671F;&#x6BB5;&#x968E;&#x306B;&#x304A;&#x3051;&#x308B;miRNA&#x306E;&#x5F79;&#x5272;&#x3092;&#x89E3;&#x660E;&#x3059;&#x308B;&#x305F;&#x3081;&#x306B;&#x3001;&#x6211;&#x3005;&#x304C;&#x76F4;&#x63A5;&#x30C9;&#x30CA;&#x30FC;&#x3092;&#x30DE;&#x30C3;&#x30C1;&#x3055;&#x305B;&#x305F;CD133&#xFF08;+&#xFF09;&#x3088;&#x308A;&#x5206;&#x5316;&#x3057;&#x305F;CD34&#xFF08;+&#xFF09;CD133&#x306E;&#x7D30;&#x80DE;&#x3068;&#x6BD4;&#x8F03;&#x3057;&#x3066;CD34&#x3092;&#xFF08; - &#xFF09;CD133&#xFF08; - &#xFF09;&#x306E;&#x9AA8;&#x9AC4;&#x7531;&#x6765;&#x306E;&#x7D30;&#x80DE;miRNA&#x3068;mRNA&#x30EC;&#x30D9;&#x30EB;&#x3002;&#x5B9A;&#x91CF;&#x7684;&#x306A;&#x5168;&#x30B2;&#x30CE;&#x30E0;miRNA&#x306E;&#x30DE;&#x30A4;&#x30AF;&#x30ED;&#x30A2;&#x30EC;&#x30A4;&#x304A;&#x3088;&#x3073;&#x30B7;&#x30FC;&#x30B1;&#x30F3;&#x30B7;&#x30F3;&#x30B0;&#x30D9;&#x30FC;&#x30B9;&#x306E;&#x30D7;&#x30ED;&#x30D5;&#x30A1;&#x30A4;&#x30EA;&#x30F3;&#x30B0;&#x3092;&#x4F7F;&#x7528;&#x3057;&#x3066;&#x3001;&#x6211;&#x3005;&#x304C;&#x767A;&#x898B;&#x3057;&#x305F;109&#xFF08;CD133&#xFF08;+&#xFF09;&#xFF09;&#x3068;216&#x306E;&#x9593;&#x306B;&#xFF08;CD34&#xFF08; - &#xFF09;CD133&#xFF08; - &#xFF09;&#xFF09;miRNA&#x304C;&#x767A;&#x73FE;&#x3057;&#x305F;&#x3002;&#x5B9A;&#x91CF;&#x5316;&#x306F;25&#x6700;&#x9AD8;&#x306B;&#x767A;&#x73FE;miRNA&#x306F;&#x30C8;&#x30FC;&#x30BF;&#x30EB;miRNA&#x306E;&#x30D7;&#x30FC;&#x30EB;&#x306E;73&#xFF05;&#x3092;&#x5360;&#x3081;&#x3066;&#x3044;&#x308B;&#x3053;&#x3068;&#x3092;&#x660E;&#x3089;&#x304B;&#x306B;&#x3057;&#x305F;&#x3002; &#xFF08; - &#xFF09;&#x7D30;&#x80DE;&#x306E;miR-142-3P&#x306F;&#x3001;CD34&#x306E;&#x7D30;&#x80DE;&#x3042;&#x305F;&#x308A;2000&#x679A;&#x307E;&#x3067;&#x3067;&#x6700;&#x9AD8;&#x306E;&#x767A;&#x73FE;&#x3057;&#x305F;miRNA&#xFF08;+&#xFF09;CD133&#x3067;&#x3042;&#x3063;&#x305F;&#x3002; &#xFF08; - &#xFF09;&#x7D30;&#x80DE;&#x516B;miRNA&#x306F;&#x304B;&#x306A;&#x308A;&#x5DEE;CD133&#xFF08;+&#xFF09;&#x3068;CD34&#xFF08;+&#xFF09;CD133&#x3068;&#x306E;&#x9593;&#x3067;&#x767A;&#x73FE;&#x3055;&#x305B;&#x305F;&#x3002;&#x79C1;&#x305F;&#x3061;&#x306F;&#x3001;miRNA&#x3068;&#x305D;&#x306E;bioinformatically&#x4E88;&#x6E2C;&#x6A19;&#x7684;mRNA&#x3068;&#x30EB;&#x30B7;&#x30D5;&#x30A7;&#x30E9;&#x30FC;&#x30BC;&#x30D9;&#x30FC;&#x30B9;&#x306E;&#x30EC;&#x30DD;&#x30FC;&#x30BF;&#x30FC;&#x30A2;&#x30C3;&#x30BB;&#x30A4;&#x306E;&#x5171;&#x767A;&#x73FE;&#x3092;&#x8ABF;&#x3079;&#x308B;&#x3053;&#x3068;&#x306B;&#x3088;&#x3063;&#x3066;&#x3001;&#x305D;&#x308C;&#x3089;&#x306E;&#x751F;&#x7269;&#x5B66;&#x7684;&#x5F79;&#x5272;&#x3092;&#x5206;&#x6790;&#x3057;&#x305F;&#x3002;&#x6211;&#x3005;&#x306F;&#x3001;miR-29A&#x306B;&#x3088;&#x308B;&#x76F4;&#x63A5;&#x306E;miR-142-3P&#x306B;&#x3088;&#x308B;CD133&#x306E;&#x898F;&#x5236;&#x3060;&#x3051;&#x3067;&#x306A;&#x304F;&#x3001;&#x30C8;&#x30ED;&#x30DD;&#x30DF;&#x30AA;&#x30B7;&#x30F3;1&#x3068;&#x30D5;&#x30EA;&#x30C3;&#x30C4;&#x30EB;&#x30C9;&#x30DB;&#x30E2;&#x30ED;&#x30B0;5&#x306E;&#x6700;&#x521D;&#x306E;&#x8A3C;&#x62E0;&#x3092;&#x63D0;&#x4F9B;&#x3057;&#x3066;&#x3044;&#x307E;&#x3059;&#x3002; CD133&#xFF08;+&#xFF09;&#x7D30;&#x80DE;&#x306B;&#x304A;&#x3051;&#x308B;miRNA&#x306E;&#x904E;&#x5270;&#x767A;&#x73FE;&#x306F;&#x3001;miR-142-3P&#x306F;&#x3001;&#x5168;&#x4F53;&#x7684;&#x306A;&#x30B3;&#x30ED;&#x30CB;&#x30FC;&#x5F62;&#x6210;&#x80FD;&#x306B;&#x8CA0;&#x306E;&#x5F71;&#x97FF;&#x3092;&#x6301;&#x3063;&#x3066;&#x3044;&#x308B;&#x3053;&#x3068;&#x3092;&#x5B9F;&#x8A3C;&#x3057;&#x305F;&#x3002; &#xFF08; - &#xFF09;&#x7D30;&#x80DE;&#x306F;&#x5206;&#x5316;&#x306E;&#x6291;&#x5236;&#x3001;&#x30A2;&#x30DD;&#x30C8;&#x30FC;&#x30B7;&#x30B9;&#x306E;&#x9632;&#x6B62;&#x3001;&#x7D30;&#x80DE;&#x9AA8;&#x683C;&#x306E;&#x30EA;&#x30E2;&#x30C7;&#x30EA;&#x30F3;&#x30B0;&#x306B;&#x95A2;&#x4E0E;&#x3057;&#x3066;&#x3044;&#x308B;&#x7D50;&#x8AD6;&#x3067;&#x306F;&#x3001;miRNA&#x306F;&#x3001;CD133&#xFF08;+&#xFF09;&#x3068;CD34&#xFF08;+&#xFF09;CD133&#x3068;&#x306E;&#x9593;&#x3067;&#x793A;&#x5DEE;&#x7684;&#x306B;&#x767A;&#x73FE;&#x3002;&#x3053;&#x308C;&#x3089;&#x306E;&#x7D50;&#x679C;&#x306F;&#x3001;CD133&#xFF08;+&#xFF09;&#x7D30;&#x80DE;&#x3068;&#x5E79;&#x7D30;&#x80DE;&#x30D9;&#x30FC;&#x30B9;&#x306E;&#x6CBB;&#x7642;&#x306E;&#x305F;&#x3081;&#x306B;&#x975E;&#x5E38;&#x306B;&#x95A2;&#x9023;&#x3057;&#x3066;&#x3044;&#x308B;&#x3068;CD133&#xFF08;+&#xFF09;&#x7D30;&#x80DE;&#x306E;&#x5E79;&#x7D30;&#x80DE;&#x306E;&#x30AD;&#x30E3;&#x30E9;&#x30AF;&#x30BF;&#x306F;&#x3001;&#x7279;&#x5B9A;&#x306E;miRNA&#x306E;&#x767A;&#x73FE;&#x306B;&#x3088;&#x3063;&#x3066;&#x5B9A;&#x7FA9;&#x3055;&#x308C;&#x308B;&#x65B9;&#x6CD5;&#x3092;&#x521D;&#x3081;&#x3066;&#x63CF;&#x304F;&#x3002;

&#x30DE;&#x30A4;&#x30AF;&#x30ED;RNA&#x3068;mRNA&#x306E;&#x30D7;&#x30ED;&#x30D5;&#x30A1;&#x30A4;&#x30EB;&#x3092;&#x7D44;&#x307F;&#x5408;&#x308F;&#x305B;&#x305F;&#x30AD;&#x30E3;&#x30E9;&#x306F;&#x3001;CD133 +&#x304A;&#x3088;&#x3073;CD34 +&#x9020;&#x8840;&#x5E79;&#x7D30;&#x80DE;&#x3084;&#x524D;&#x99C6;&#x7D30;&#x80DE;&#x306E;&#x521D;&#x671F;&#x5206;&#x5316;&#x7D4C;&#x8DEF;&#x306E;&#x8F2A;&#x90ED;&#x3092;&#x63CF;&#x304F;

MicroRNAs (miRNAs) &#xC870;&#xC5D0; &#xC911;&#xC694; &#xD55C; &#xC5ED;&#xD560;&#xC744; &#xBCF4;&#xC5EC;&#xC654;&#xB2E4;. &#xC870;&#xC758; &#xCD08;&#xAE30; &#xB2E8;&#xACC4;&#xC5D0;&#xC11C; Mirnas&#xC758; &#xC5ED;&#xD560;&#xC744; &#xBA85;&#xB8CC; &#xD558; &#xC6B0;&#xB9AC;&#xB294; &#xC9C1;&#xC811; &#xACE8; miRNA &#xBC0F; mRNA &#xC218;&#xC900;&#xC5D0;&#xC11C; &#xB354; &#xCC28;&#xBCC4;&#xD654; &#xB41C; CD34(+) CD133(-) &#xBC0F; CD34(-) CD133(-) &#xC140;&#xACFC; &#xC77C;&#xCE58; &#xD558;&#xB294; &#xAE30;&#xC99D;&#xC790; CD133(+) &#xC140; &#xBE44;&#xAD50;. &#xC591;&#xC801; &#xC804;&#xCCB4; &#xAC8C;&#xB188; miRNA microarray &#xBC0F; &#xC2DC;&#xD000;&#xC2F1; &#xAE30;&#xBC18; &#xD504;&#xB85C; &#xD30C;&#xC77C;&#xB9C1;&#xC744; &#xC0AC;&#xC6A9; &#xD558; &#xC5EC;, &#xC6B0;&#xB9AC;&#xB294; 109 (CD133(+))&#xC640; 216 (CD34(-) CD133(-)) &#xC0AC;&#xC774;&#xC758; miRNAs &#xD45C;&#xD604; &#xD588;&#xB2E4; &#xBC1C;&#xACAC;. &#xBD80; &#xB7C9; &#xCD5C;&#xACE0; 25 &#xD45C;&#xD604; miRNAs &#xCD1D; miRNA &#xD480;&#xC758; 73%&#xB97C; &#xCC28;&#xC9C0; &#xD558; &#xACE0; &#xBC1D;&#xD614;&#xB2E4;. &#xBBF8;&#xB974;-142-3 p&#xB294; &#xCD5C;&#xB300; CD34(+) CD133(-) &#xC140;&#xC5D0; &#xC140; &#xB2F9; 2, 000 &#xC0AC;&#xBCF8;&#xC73C;&#xB85C; &#xAC00;&#xC7A5; &#xB192;&#xC740; &#xD45C;&#xD604;&#xB41C; &#xBBF8;&#xB974;. 18 MiRNAs CD133(+) &#xBC0F; CD34(+) CD133(-) &#xC140; &#xC0AC;&#xC774;&#xC758; &#xCC28;&#xB3D9; &#xD45C;&#xD604; &#xD06C;&#xAC8C; &#xD588;&#xB2E4;. &#xC6B0;&#xB9AC; Mirnas&#xC758; coexpression&#xB97C; &#xAC80;&#xC0AC; &#xD558; &#xC5EC; &#xADF8;&#xB4E4;&#xC758; &#xC0DD;&#xBB3C; &#xD559;&#xC801; &#xC5ED;&#xD560;&#xC744; &#xBD84;&#xC11D; &#xD558; &#xACE0; &#xADF8; bioinformatically &#xC608;&#xCE21; mRNA &#xB300;&#xC0C1;&#xACFC; &#xAE30;&#xC790; &#xB8E8;&#xC2DC;&#xD398;&#xB77C;&#xC544;&#xC81C; &#xAE30;&#xBC18; &#xBD84;&#xC11D; &#xC2E4;&#xD5D8;. &#xC6B0;&#xB9AC; &#xBBF8;&#xB974;-142-3 p tropomyosin 1 &#xBC0F; frizzled &#xCCB4; 5 &#xBBF8;&#xB974; 29a&#xC5D0; &#xC758;&#xD574; &#xC758;&#xD574; cd133&#xC758; &#xC9C1;&#xC811; &#xADDC;&#xC81C;&#xC5D0; &#xB300; &#xD55C; &#xCCAB; &#xBC88;&#xC9F8; &#xC99D;&#xAC70;&#xB97C; &#xC81C;&#xACF5;&#xD569;&#xB2C8;&#xB2E4;. CD133(+) &#xC140;&#xC5D0; Mirnas&#xC758; overexpression &#xADF8; &#xBBF8;&#xB974;-142-3 p&#xB294; &#xC804;&#xCCB4; &#xC2DD;&#xBBFC;&#xC9C0;&#xB97C; &#xD615;&#xC131; &#xB2A5;&#xB825;&#xC5D0; &#xBD80;&#xC815;&#xC801;&#xC778; &#xC601;&#xD5A5;&#xC744; &#xC124;&#xBA85; &#xD588;&#xB2E4;. &#xB05D;&#xC73C;&#xB85C;, CD133(+) &#xBC0F; CD34(+) CD133(-) &#xC140; &#xC0AC;&#xC774;&#xC758; &#xCC28;&#xB3D9; &#xD45C;&#xD604; miRNAs &#xBD84;&#xD654;&#xC758; &#xC5B5;&#xC81C;, apoptosis, &#xBC0F; cytoskeletal &#xB9AC; &#xBAA8;&#xB378;&#xB9C1;&#xC758; &#xC608;&#xBC29;&#xC5D0; &#xAD00;&#xB828; &#xB41C;. &#xC774;&#xB7EC;&#xD55C; &#xACB0;&#xACFC; CD133(+) &#xC138;&#xD3EC;&#xC640; &#xC904;&#xAE30; &#xC138;&#xD3EC; &#xAE30;&#xBC18; &#xCE58;&#xB8CC;&#xBC95;&#xC5D0; &#xB300; &#xD55C; &#xAD00;&#xB828;&#xC131;&#xC774; &#xB192;&#xC740; &#xC788;&#xC73C;&#xBA70; &#xCC98;&#xC74C;&#xC73C;&#xB85C; &#xC5B4;&#xB5BB;&#xAC8C; CD133(+) &#xC140;&#xC758; &#xC904;&#xAE30; &#xC138;&#xD3EC; &#xBB38;&#xC790; &#xD2B9;&#xC815; Mirnas&#xC758; &#xC2DD;&#xC5D0; &#xC758;&#xD574; &#xC815;&#xC758; &#xB429;&#xB2C8;&#xB2E4;.

Mrna&#xACFC; microRNA &#xD504;&#xB85C; &#xD30C;&#xC77C;&#xC758; &#xACB0;&#xD569; &#xB41C; &#xD2B9;&#xC131;&#xD654; CD133 +&#xC640; CD34 + &#xC870; &#xD608; &#xC904;&#xAE30;&#xC640; &#xC870;&#xC0C1; &#xC138;&#xD3EC;&#xC758; &#xCD08;&#xAE30; &#xBD84;&#xD654; &#xACBD;&#xB85C; &#xAD6C;&#xBD84; &#xD569;&#xB2C8;&#xB2E4;.

MicroRNAs (miRNAs) se ha demostrado que juegan un papel importante en la hematopoyesis. Para dilucidar el papel de los miRNAs en las primeras etapas de la hematopoyesis, que compar&#xF3; directamente por los donantes corresponde CD133 (+) las c&#xE9;lulas m&#xE1;s diferenciadas con el CD34 (+) CD133 (-) y CD34 (-) CD133 (-) las c&#xE9;lulas de la m&#xE9;dula &#xF3;sea en el nivel de miRNA y del mRNA. Usando cuantitativa de microarrays toda miARN perfiles de secuenciaci&#xF3;n del genoma y la base, se encontr&#xF3; que entre los 109 (CD133 (+)) y 216 (CD34 (-) CD133 (-)) miRNAs se expresaron. La cuantificaci&#xF3;n revel&#xF3; que los 25 miRNAs expresados &#x200B;&#x200B;m&#xE1;s alto el 73% de la cartera total de miRNA. MIR-142-3P fue el m&#xE1;s alto miARN expresado con un m&#xE1;ximo de 2.000 copias por c&#xE9;lula de CD34 (+) CD133 (-) las c&#xE9;lulas. Dieciocho fueron significativamente miRNAs expresados &#x200B;&#x200B;diferencialmente entre CD133 (+) y CD34 (+) CD133 (-) de las c&#xE9;lulas. Se analiz&#xF3; su funci&#xF3;n biol&#xF3;gica mediante el examen de la co-expresi&#xF3;n de miRNAs y sus metas previstos bioinformatically mRNA de luciferasa y basados &#x200B;&#x200B;en ensayos de reportero. Nos proporcionan la primera evidencia de una regulaci&#xF3;n directa de CD133 por el MIR-142-3p, as&#xED; como la tropomiosina 1 y 5 hom&#xF3;logo rizado por el MIR-29a. La sobre-expresi&#xF3;n de miRNAs en CD133 (+) c&#xE9;lulas demostrado que el miR-142-3P tiene una influencia negativa en el total de la capacidad de formaci&#xF3;n de colonias. En conclusi&#xF3;n, los miRNAs expresados &#x200B;&#x200B;diferencialmente entre el CD133 (+) y CD34 (+) CD133 (-) las c&#xE9;lulas est&#xE1;n involucradas en la inhibici&#xF3;n de la diferenciaci&#xF3;n, la prevenci&#xF3;n de la apoptosis, y la remodelaci&#xF3;n del citoesqueleto. Estos resultados son de gran relevancia para madre terapias basadas en c&#xE9;lulas con CD133 (+) las c&#xE9;lulas y delinear por primera vez c&#xF3;mo el personaje de c&#xE9;lulas madre de CD133 (+) las c&#xE9;lulas se define por la expresi&#xF3;n de miRNAs espec&#xED;ficos.

Caracterizaci&#xF3;n combinado de microARN y los perfiles de mRNA de delinea v&#xED;as de diferenciaci&#xF3;n temprana de CD133 + CD34 + madre hematopoy&#xE9;ticas y c&#xE9;lulas progenitoras

Olfactory bulb (OB) neurogenesis generates neurons that use GABA or dopamine as their neurotransmitters throughout life. Regionalized stem cell populations in the periventricular zone (PVZ) of the lateral ventricles (LVs) have been shown to be at the basis of neuronal diversity in the system. For example dopaminergic neurons arise predominantly from neural stem cells (NSCs) residing in the dorsal PVZ and depend on the expression of the transcription factors Pax6 and Dlx2 for their specification. In addition, Dlx2 is required for neurogenesis in general. Using targeted in vivo electroporation combined with immuno-fluorescence imaging and microarray analysis, we provide here detailed spatial and temporal expression data with cellular resolution in this system. We find that all along the neurogenic process Pax6 expression remains restricted to the dorsal PVZ, whereas nearly all neuroblasts express Dlx2, including those of the dorsal lineage, which are switched on for Dlx2 when they enter the rostral migratory stream (RMS). These data allow to explain and precise the functions of these two genes in postnatal OB neurogenesis.

Dynamic expression of the pro-dopaminergic transcription factors Pax6 and Dlx2 during postnatal olfactory bulb neurogenesis.

We performed next generation sequencing- and microarray-based gene expression profiling of CD44(+)/CD24(-)/CD45(-) breast CSCs (cancer stem cells) isolated from primary ERα-positive breast cancer. By combining semi-automated dissociation of human tumor tissue, magnetic cell sorting and cDNA amplification less than 500 CSCs were required for transcriptome analyses. Besides overexpressing genes involved in maintenance of stemness, the CSCs showed higher levels of genes that drive the PI3K pathway, including EGFR, HB-EGF, PDGFRA/B, PDGF, MET, PIK3CA, PIK3R1 and PIK3R2. This suggests that, in CSCs of ERα-positive breast cancer, the PI3K pathway which is involved in endocrine resistance is hyperactive.

Highly sensitive profiling of CD44+/CD24- breast cancer stem cells by combining global mRNA amplification and next generation sequencing: evidence for a hyperactive PI3K pathway.

Promiscuous expression of numerous tissue-restricted self-antigens (TRAs) in medullary thymic epithelial cells (mTECs) is essential to safeguard self-tolerance. A distinct feature of promiscuous gene expression is its mosaic pattern (i.e., at a given time, each self-antigen is expressed only in 1-3% of mTECs). How this mosaic pattern is generated at the single-cell level is currently not understood. Here, we show that subsets of human mTECs expressing a particular TRA coexpress distinct sets of genes. We identified three coexpression groups comprising overlapping and complementary gene sets, which preferentially mapped to certain chromosomes and intrachromosomal gene clusters. Coexpressed gene loci tended to colocalize to the same nuclear subdomain. The TRA subsets aligned along progressive differentiation stages within the mature mTEC subset and, in vitro, interconverted along this sequence. Our data suggest that single mTECs shift through distinct gene pools, thus scanning a sizeable fraction of the overall repertoire of promiscuously expressed self-antigens. These findings have implications for the temporal and spatial (re)presentation of self-antigens in the medulla in the context of tolerance induction.

Overlapping gene coexpression patterns in human medullary thymic epithelial cells generate self-antigen diversity.

A novel approach to locate, identify and refine positions and whole areas of cell structures based on elemental contents measured by X-ray fluorescence microscopy is introduced. It is shown that, by initializing with only a handful of prototypical cell regions, this approach can obtain consistent identification of whole cells, even when cells are overlapping, without training by explicit annotation. It is robust both to different measurements on the same sample and to different initializations. This effort provides a versatile framework to identify targeted cellular structures from datasets too complex for manual analysis, like most X-ray fluorescence microscopy data. Possible future extensions are also discussed.

Unsupervised cell identification on multidimensional X-ray fluorescence datasets.

X-Ray absorption spectromicroscopy provides rich information on the chemical organization of materials down to the nanoscale. However, interpretation of this information in studies of "natural" materials such as biological or environmental science specimens can be complicated by the complex mixtures of spectroscopically complicated materials present. We describe here the shortcomings that sometimes arise in previously-employed approaches such as cluster analysis, and we present a new approach based on non-negative matrix approximation (NNMA) analysis with both sparseness and cluster-similarity regularizations. In a preliminary study of the large-scale biochemical organization of human spermatozoa, NNMA analysis delivers results that nicely show the major features of spermatozoa with no physically erroneous negative weightings or thicknesses in the calculated image.

Non-negative matrix analysis for effective feature extraction in X-ray spectromicroscopy.

Knee arthroscopy knee is gold standard in diagnosis and simultaneous treatment of knee disorders. But most patients undergo magnetic resonance imaging (MRI) before arthroscopy, although MRI results are not always consistent with arthroscopic findings. This raises the question in which suspected diagnoses MRI really has influence on diagnosis and consecutive surgical therapy.

Indications requiring preoperative magnetic resonance imaging before knee arthroscopy.

De novo formation of beige/brite adipocytes from progenitor cells contributes to the thermogenic adaptation of adipose tissue and holds great potential for the therapeutic remodeling of fat as a treatment for obesity. Despite the recent identification of several factors regulating browning of white fat, there is a lack of physiological cell models for the mechanistic investigation of progenitor-mediated beige/brite differentiation. We have previously revealed prostacyclin (PGI2) as one of the few known endogenous extracellular mediators promoting de novo beige/brite formation by relaying β-adrenergic stimulation to the progenitor level. Here, we present a cell model based on murine primary progenitor cells defined by markers previously shown to be relevant for in vivo browning, including a simplified isolation procedure. We demonstrate the specific and broad induction of thermogenic gene expression by PGI2 signaling in the absence of lineage conversion, and reveal the previously unidentified nuclear relocalization of the Ucp1 gene locus in association with transcriptional activation. By profiling the time course of the progenitor response, we show that PGI2 signaling promoted progenitor cell activation through cell cycle and adhesion pathways prior to metabolic maturation toward an oxidative cell phenotype. Our results highlight the importance of core progenitor activation pathways for the recruitment of thermogenic cells and provide a resource for further mechanistic investigation.

Transcriptional Pathways in cPGI2-Induced Adipocyte Progenitor Activation for Browning.

Modulation of gene expression is a useful tool to study the biology of haematopoietic stem and progenitor cells (HSPCs) and might also be instrumental to expand these cells for therapeutic approaches. Most of the studies so far have employed stable gene modification by viral vectors that are burdensome when translating protocols into clinical settings. Our study aimed at exploring new ways to transiently modify HSPC gene expression using non-integrating, RNA-based molecules. First, we tested different methods to deliver these molecules into HSPCs. The delivery of siRNAs with chemical transfection methods such as lipofection or cationic polymers did not lead to target knockdown, although we observed more than 90% fluorescent cells using a fluorochrome-coupled siRNA. Confocal microscopic analysis revealed that despite extensive washing, siRNA stuck to or in the cell surface, thereby mimicking a transfection event. In contrast, electroporation resulted in efficient, siRNA-mediated protein knockdown. For transient overexpression of proteins, we used optimised mRNA molecules with modified 5'- and 3'-UTRs. Electroporation of mRNA encoding GFP resulted in fast, efficient and persistent protein expression for at least seven days. Our data provide a broad-ranging comparison of transfection methods for hard-to-transfect cells and offer new opportunities for DNA-free, non-integrating gene modulation in HSPCs.

RNA-based, transient modulation of gene expression in human haematopoietic stem and progenitor cells.

The surface protein composition of extracellular vesicles (EVs) is related to the originating cell and may play a role in vesicle function. Knowledge of the protein content of individual EVs is still limited because of the technical challenges to analyse small vesicles. Here, we introduce a novel multiplex bead-based platform to investigate up to 39 different surface markers in one sample. The combination of capture antibody beads with fluorescently labelled detection antibodies allows the analysis of EVs that carry surface markers recognized by both antibodies. This new method enables an easy screening of surface markers on populations of EVs. By combining different capture and detection antibodies, additional information on relative expression levels and potential vesicle subpopulations is gained. We also established a protocol to visualize individual EVs by stimulated emission depletion (STED) microscopy. Thereby, markers on single EVs can be detected by fluorophore-conjugated antibodies. We used the multiplex platform and STED microscopy to show for the first time that NK cell-derived EVs and platelet-derived EVs are devoid of CD9 or CD81, respectively, and that EVs isolated from activated B cells comprise different EV subpopulations. We speculate that, according to our STED data, tetraspanins might not be homogenously distributed but may mostly appear as clusters on EV subpopulations. Finally, we demonstrate that EV mixtures can be separated by magnetic beads and analysed subsequently with the multiplex platform. Both the multiplex bead-based platform and STED microscopy revealed subpopulations of EVs that have been indistinguishable by most analysis tools used so far. We expect that an in-depth view on EV heterogeneity will contribute to our understanding of different EVs and functions.

A novel multiplex bead-based platform highlights the diversity of extracellular vesicles.

In x-ray spectromicroscopy, a set of images can be acquired across an absorption edge to reveal chemical speciation. We previously described the use of non-negative matrix approximation methods for improved classification and analysis of these types of data. We present here an approach to find appropriate values of regularization parameters for this optimization approach.

Non-negative matrix analysis in x-ray spectromicroscopy: choosing regularizers.

Antiretroviral therapy (ART) efficiently suppresses HIV replication but immune activation and low CD4 T cell counts often persist. The underlying mechanism of this ART-resistant pathogenesis is not clear. We observed that levels of plasma extracellular vesicles (pEV) are strongly elevated in HIV infection and do not decline during ART. Surprisingly, these vesicles contained the viral accessory proteins Nef and Vpu, which are assumed to be not expressed under efficient ART, as well as pro-inflammatory effectors, including activated ADAM17. HIV pEV were characterized by the presence of activated αvβ3 and absence of CD81 and Tsg101. Correlating with immune activation, peripheral monocytes ingested large amounts of pEV, giving rise to an increased population of CD1c(+) CD14(+) cells that secreted inflammatory cytokines. Importantly, the pro-inflammatory content, particularly ADAM17 activity, correlated with low T cell counts. Preliminary evidence suggested that HIV pEV derived from peripheral mononuclear cells and from an unknown myeloid cell population. In summary we propose an important role of pro-inflammatory pEV in chronic HIV infection due to ongoing viral Nef activity.

Article	Year
Anticancer activity of the lanthanum compound [tris(1,10-phenanthroline)lanthanum(III)]trithiocyanate (KP772; FFC24). Biochemical pharmacology\| PubMed ID: 16343446	2006
Absolute quantification of microRNAs by using a universal reference. RNA (New York, N.Y.)\| PubMed ID: 19861428	2009
Combined characterization of microRNA and mRNA profiles delineates early differentiation pathways of CD133+ and CD34+ hematopoietic stem and progenitor cells. Stem cells (Dayton, Ohio)\| PubMed ID: 21394831	2011
Dynamic expression of the pro-dopaminergic transcription factors Pax6 and Dlx2 during postnatal olfactory bulb neurogenesis. Frontiers in cellular neuroscience\| PubMed ID: 22371698	2012
Highly sensitive profiling of CD44+/CD24- breast cancer stem cells by combining global mRNA amplification and next generation sequencing: evidence for a hyperactive PI3K pathway. Cancer letters\| PubMed ID: 22771536	2012
Overlapping gene coexpression patterns in human medullary thymic epithelial cells generate self-antigen diversity. Proceedings of the National Academy of Sciences of the United States of America\| PubMed ID: 23980163	2013
Unsupervised cell identification on multidimensional X-ray fluorescence datasets. Journal of synchrotron radiation\| PubMed ID: 24763647	2014
Non-negative matrix analysis for effective feature extraction in X-ray spectromicroscopy. Faraday discussions\| PubMed ID: 25415133	2014
Indications requiring preoperative magnetic resonance imaging before knee arthroscopy. Archives of medical science : AMS\| PubMed ID: 25624852	2014
Transcriptional Pathways in cPGI2-Induced Adipocyte Progenitor Activation for Browning. Frontiers in endocrinology\| PubMed ID: 26347713	2015
RNA-based, transient modulation of gene expression in human haematopoietic stem and progenitor cells. Scientific reports\| PubMed ID: 26599627	2015
A novel multiplex bead-based platform highlights the diversity of extracellular vesicles. Journal of extracellular vesicles\| PubMed ID: 26901056	2016
Non-negative matrix analysis in x-ray spectromicroscopy: choosing regularizers. AIP conference proceedings\| PubMed ID: 27041779	2016
HIV-Nef and ADAM17-Containing Plasma Extracellular Vesicles Induce and Correlate with Immune Pathogenesis in Chronic HIV Infection. EBioMedicine\| PubMed ID: 27211553	2016

Stefan Wild

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