preparation-and-applications-of-organotypic-thymic-slice-cultures

Preparation and Applications of Organotypic Thymic Slice Cultures

Thymic selection proceeds in a unique and highly organized thymic microenvironment resulting in the generation of a functional, self-tolerant T cell ...

Hôpital Maisonneuve-Rosemont

Université de Montréal

alphabeta and gammadelta T cells originate from a common, multipotential precursor population in the thymus, but the molecular mechanisms regulating this lineage-fate decision are unknown. We have identified Sox13 as a gammadelta-specific gene in the immune system. Using Sox13 transgenic mice, we showed that this transcription factor promotes gammadelta T cell development while opposing alphabeta T cell differentiation. Conversely, mice deficient in Sox13 expression exhibited impaired development of gammadelta T cells but not alphabeta T cells. One mechanism of SOX13 function is the inhibition of signaling by the developmentally important Wnt/T cell factor (TCF) pathway. Our data thus reveal a dominant pathway regulating the developmental fate of these two lineages of T lymphocytes.

Regulation of gammadelta versus alphabeta T lymphocyte differentiation by the transcription factor SOX13.

&#x86CB;&#x767D;&#x8D28;&#x5728;&#x7EC6;&#x80DE;&#x5185;&#x7684;&#x8FD0;&#x52A8;&#x53EF;&#x4EE5;&#x63D0;&#x4F9B;&#x52A8;&#x6001;&#x7684;&#x7EC6;&#x80DE;&#x4FE1;&#x53F7;&#x8F6C;&#x5BFC;&#x4E0E;&#x7EC6;&#x80DE;&#x6781;&#x6027;&#x7684;&#x8FF9;&#x8C61;&#xFF0C;&#x4F46;&#x9700;&#x8981;&#x65B9;&#x6CD5;&#x8DDF;&#x8E2A;&#x548C;&#x91CF;&#x5316;&#x5728;&#x7EC4;&#x7EC7;&#x73AF;&#x5883;&#x4E2D;&#x7684;&#x4E9A;&#x7EC6;&#x80DE;&#x86CB;&#x767D;&#x8D28;&#x8FD0;&#x52A8;&#x3002;&#x5728;&#x8FD9;&#x91CC;&#x6211;&#x4EEC;&#x4ECB;&#x7ECD;&#x534A;&#x81EA;&#x52A8;&#x7684;&#x505A;&#x6CD5;&#x6765;&#x91CF;&#x5316;&#x4E9A;&#x7EC6;&#x80DE;&#x86CB;&#x767D;&#x8D28;&#x5B9A;&#x4F4D;&#x4E3A;&#x6570;&#x4EE5;&#x767E;&#x8BA1;&#x7684;&#x8FC1;&#x79FB;&#x7EC6;&#x80DE;&#x5185;&#x5B8C;&#x597D; retrovirally &#x4F7F;&#x7528;&#x7684;&#x6D3B;&#x7EC4;&#x7EC7;&#x8367;&#x5149;&#x878D;&#x5408;&#x86CB;&#x767D;&#x548C;&#x5EF6;&#x65F6;&#x53CC;&#x5149;&#x5B50;&#x663E;&#x5FAE;&#x955C;&#x7684;&#x5B8C;&#x597D;&#x80F8;&#x817A;&#x5C0F;&#x53F6;&#x8868;&#x793A;&#x3002;&#x6211;&#x4EEC;&#x5DF2;&#x9A8C;&#x8BC1;&#x7684;&#x65B9;&#x6CD5;&#xFF0C;&#x5229;&#x7528;&#x7EFF;&#x8272;&#x8367;&#x5149;&#x86CB;&#x767D;-PKC&zeta;&#xFF0C;&#x7EC6;&#x80DE;&#x6781;&#x6027;&#x7684;&#x6807;&#x8BB0;&#x548C; LAT-&#x7EFF;&#x8272;&#x8367;&#x5149;&#x86CB;&#x767D;&#x6807;&#x8BB0;&#x7684; T &#x7EC6;&#x80DE;&#x53D7;&#x4F53;&#x4FE1;&#x53F7;&#xFF0C;&#x5E76;&#x5177;&#x6709;&#x76F8;&#x5173;&#x7684;&#x8FD9;&#x4E9B;&#x86CB;&#x767D;&#x8D28;&#x4E0D;&#x5BF9;&#x79F0;&#x5206;&#x5E03;&#x7684;&#x65B9;&#x5411;&#x548C;&#x7EC6;&#x80DE;&#x8FC1;&#x79FB;&#x7684;&#x901F;&#x5EA6;&#x3002;&#x8FD9;&#x4E9B;&#x65B9;&#x6CD5;&#x53EF;&#x4EE5;&#x5BB9;&#x6613;&#x5730;&#x9002;&#x5E94;&#x5176;&#x4ED6;&#x8367;&#x5149;&#x878D;&#x5408;&#x86CB;&#x767D;&#x3001; &#x7EC4;&#x7EC7;&#x548C;&#x751F;&#x7269;&#x95EE;&#x9898;&#x3002;

&#x91CF;&#x5316;&#x8FC1;&#x79FB;&#x5728;&#x7EC4;&#x7EC7;&#x5185;&#x7684;&#x7EC6;&#x80DE;&#x4E2D;&#x7684;&#x8367;&#x5149;&#x878D;&#x5408;&#x86CB;&#x767D;&#x7684;&#x4E9A;&#x7EC6;&#x80DE;&#x7684;&#x5206;&#x5E03;&#x3002;

The movement of proteins within cells can provide dynamic indications of cell signaling and cell polarity, but methods are needed to track and quantify subcellular protein movement within tissue environments. Here we present a semiautomated approach to quantify subcellular protein location for hundreds of migrating cells within intact living tissue using retrovirally expressed fluorescent fusion proteins and time-lapse two-photon microscopy of intact thymic lobes. We have validated the method using GFP-PKCÎ¶, a marker for cell polarity, and LAT-GFP, a marker for T-cell receptor signaling, and have related the asymmetric distribution of these proteins to the direction and speed of cell migration. These approaches could be readily adapted to other fluorescent fusion proteins, tissues and biological questions.

Quantifying subcellular distribution of fluorescent fusion proteins in cells migrating within tissues.

Le mouvement des prot&#xE9;ines dans les cellules peuvent fournir des indications dynamiques de signalisation cellulaire et de la polarit&#xE9; cellulaire, mais les m&#xE9;thodes sont n&#xE9;cessaires pour suivre et de quantifier le mouvement des prot&#xE9;ines subcellulaire dans des environnements de tissus. Ici, nous pr&#xE9;sentons une approche semi-automatique de quantifier la prot&#xE9;ine emplacement subcellulaire des centaines de cellules qui migrent dans le tissu vivant intact en utilisant un retrovirus exprim&#xE9;es prot&#xE9;ines de fusion fluorescentes et time-lapse microscopie &#xE0; deux photons des lobes thymiques intactes. Nous avons valid&#xE9; la m&#xE9;thode utilisant la GFP-PKC&#x3B6;, un marqueur de la polarit&#xE9; cellulaire, et LAT-GFP, un marqueur de signalisation du r&#xE9;cepteur des cellules T, et ont reli&#xE9; la distribution asym&#xE9;trique de ces prot&#xE9;ines &#xE0; la direction et la vitesse de la migration cellulaire. Ces approches pourraient &#xEA;tre facilement adapt&#xE9; &#xE0; d&#x26;#39;autres prot&#xE9;ines de fusion fluorescentes, des tissus et des questions biologiques.

Quantifier la distribution subcellulaire des prot&#xE9;ines de fusion fluorescentes dans les cellules migrent &#xE0; l&#x26;#39;int&#xE9;rieur des tissus

Die Bewegung der Proteine innerhalb der Zellen kann dynamische Angaben von Zelle signalisieren und Zellpolarit&auml;t, aber Methoden sind notwendig, um zu verfolgen und subzellulare Protein Bewegung im Gewebe-Umgebungen zu quantifizieren. Hier pr&auml;sentieren wir Ihnen einen semiautomated Ansatz zur Quantifizierung der subzellul&auml;ren Protein Lage f&uuml;r Hunderte von Migration intakt innerhalb von Zellen fluoreszierende Schmelzverfahrensproteine und Zeitraffer zwei-Photonen-Mikroskopie intakt Thymom Lappen lebendes Gewebe mit retrovirally ausgedr&uuml;ckt. Wir haben die Methode mit der GFP-PKC&zeta;, ein Marker f&uuml;r die Zellpolarit&auml;t, und LAT-GFP, ein Marker f&uuml;r die T-Zell-Rezeptor Signalgebung, validiert und haben mit die asymmetrische Verteilung dieser Proteine, die Richtung und Geschwindigkeit der Zellmigration Bezug. Diese Ans&auml;tze k&ouml;nnten andere fluoreszierende Fusionsproteine, Gewebe und biologische Fragen leicht angepasst werden.

Quantifizierung der subzellul&auml;ren Verteilung von fluoreszierenden Schmelzverfahrensproteine in den Zellen im Gewebe migrieren.

Il movimento delle proteine all&#39;interno delle cellule pu&ograve; fornire indicazioni dinamiche di segnalazione delle cellule e la polarit&agrave; delle cellule, ma i metodi sono necessari per monitorare e quantificare il movimento sottocellulare della proteina all&#39;interno di ambienti di tessuto. Qui di seguito presentiamo un approccio semiautomatico per quantificare la posizione sottocellulare della proteina per centinaia di migrazione di cellule all&#39;interno intatto tessuto vivente usando retrovirally espresso proteine di fusione fluorescenti e time-lapse microscopia del due-fotone di lobi timici intatti. Ci hanno convalidato il metodo usando GFP-PKC&zeta;, un marker per la polarit&agrave; delle cellule e LAT-GFP, un marker per T-cell receptor signaling ed hanno correlato la distribuzione asimmetrica di queste proteine per la direzione e la velocit&agrave; di migrazione cellulare. Questi approcci potrebbero essere facilmente adattati ad altre proteine di fusione fluorescenti, tessuti e questioni biologiche.

Quantificare la distribuzione subcellulare delle proteine di fusione fluorescenti in cellule di migrare all&#39;interno dei tessuti.

&#x7D30;&#x80DE;&#x5185;&#x306E;&#x30BF;&#x30F3;&#x30D1;&#x30AF;&#x8CEA;&#x306E;&#x52D5;&#x304D;&#x306F;&#x3001;&#x7D30;&#x80DE;&#x30B7;&#x30B0;&#x30CA;&#x30EB;&#x4F1D;&#x9054;&#x3068;&#x7D30;&#x80DE;&#x6975;&#x6027;&#x306E;&#x52D5;&#x7684;&#x306A;&#x8868;&#x793A;&#x3092;&#x63D0;&#x4F9B;&#x3059;&#x308B;&#x3053;&#x3068;&#x304C;&#x3067;&#x304D;&#x307E;&#x3059;&#x304C;&#x3001;&#x65B9;&#x6CD5;&#x306F;&#x3001;&#x7D44;&#x7E54;&#x306E;&#x74B0;&#x5883;&#x5185;&#x3067;&#x306E;&#x7D30;&#x80DE;&#x5185;&#x30BF;&#x30F3;&#x30D1;&#x30AF;&#x8CEA;&#x306E;&#x52D5;&#x304D;&#x3092;&#x8FFD;&#x8DE1;&#x3057;&#x3001;&#x5B9A;&#x91CF;&#x5316;&#x3059;&#x308B;&#x305F;&#x3081;&#x306B;&#x5FC5;&#x8981;&#x3068;&#x3055;&#x308C;&#x3066;&#x3044;&#x308B;&#x3002;&#x3053;&#x3053;&#x3067;&#x306F;&#x3001;&#x30EC;&#x30C8;&#x30ED;&#x30A6;&#x30A4;&#x30EB;&#x30B9;&#x767A;&#x73FE;&#x3059;&#x308B;&#x86CD;&#x5149;&#x878D;&#x5408;&#x86CB;&#x767D;&#x8CEA;&#x3068;&#x6642;&#x9593;&#x7D4C;&#x904E;&#x3092;&#x305D;&#x306E;&#x307E;&#x307E;&#x80F8;&#x817A;&#x8449;&#x306E;&#x4E8C;&#x5149;&#x5B50;&#x9855;&#x5FAE;&#x93E1;&#x3092;&#x4F7F;&#x7528;&#x3057;&#x3066;&#x7121;&#x50B7;&#x306E;&#x751F;&#x4F53;&#x7D44;&#x7E54;&#x5185;&#x306E;&#x7D30;&#x80DE;&#x306B;&#x79FB;&#x884C;&#x3059;&#x308B;&#x4F55;&#x767E;&#x3082;&#x306E;&#x7D30;&#x80DE;&#x5185;&#x86CB;&#x767D;&#x8CEA;&#x306E;&#x4F4D;&#x7F6E;&#x3092;&#x5B9A;&#x91CF;&#x5316;&#x3059;&#x308B;&#x305F;&#x3081;&#x306E;&#x534A;&#x81EA;&#x52D5;&#x624B;&#x6CD5;&#x3092;&#x63D0;&#x6848;&#x3059;&#x308B;&#x3002;&#x6211;&#x3005;&#x306F;&#x3001;&#x7D30;&#x80DE;&#x6975;&#x6027;&#x306E;&#x305F;&#x3081;&#x306B;&#x3001;GFP-PKC&#x3B6;&#x3001;&#x30DE;&#x30FC;&#x30AB;&#x30FC;&#x3001;&#x304A;&#x3088;&#x3073;LAT-GFP&#x3001;T&#x7D30;&#x80DE;&#x53D7;&#x5BB9;&#x4F53;&#x30B7;&#x30B0;&#x30CA;&#x30EB;&#x4F1D;&#x9054;&#x306E;&#x30DE;&#x30FC;&#x30AB;&#x30FC;&#x3092;&#x4F7F;&#x7528;&#x3057;&#x3066;&#x30E1;&#x30BD;&#x30C3;&#x30C9;&#x3092;&#x691C;&#x8A3C;&#x3057;&#x3066;&#x304A;&#x308A;&#x3001;&#x7D30;&#x80DE;&#x79FB;&#x52D5;&#x306E;&#x65B9;&#x5411;&#x3068;&#x901F;&#x5EA6;&#x306B;&#x3053;&#x308C;&#x3089;&#x306E;&#x30BF;&#x30F3;&#x30D1;&#x30AF;&#x8CEA;&#x306E;&#x975E;&#x5BFE;&#x79F0;&#x5206;&#x5E03;&#x306B;&#x95A2;&#x9023;&#x3057;&#x3066;&#x3044;&#x307E;&#x3059;&#x3002;&#x3053;&#x308C;&#x3089;&#x306E;&#x30A2;&#x30D7;&#x30ED;&#x30FC;&#x30C1;&#x306F;&#x3001;&#x5BB9;&#x6613;&#x306B;&#x4ED6;&#x306E;&#x86CD;&#x5149;&#x878D;&#x5408;&#x30BF;&#x30F3;&#x30D1;&#x30AF;&#x8CEA;&#x306F;&#x3001;&#x7D44;&#x7E54;&#x3084;&#x751F;&#x7269;&#x5B66;&#x7684;&#x306A;&#x8CEA;&#x554F;&#x306B;&#x9069;&#x5408;&#x3055;&#x305B;&#x308B;&#x3053;&#x3068;&#x304C;&#x3067;&#x304D;&#x307E;&#x3057;&#x305F;&#x3002;

&#x7D30;&#x80DE;&#x306E;&#x86CD;&#x5149;&#x878D;&#x5408;&#x30BF;&#x30F3;&#x30D1;&#x30AF;&#x8CEA;&#x306E;&#x7D30;&#x80DE;&#x5185;&#x5206;&#x5E03;&#x3092;&#x5B9A;&#x91CF;&#x5316;&#x3059;&#x308B;&#x7D44;&#x7E54;&#x5185;&#x306E;&#x79FB;&#x884C;

&#xC138;&#xD3EC; &#xB0B4; &#xB2E8;&#xBC31;&#xC9C8; &#xC774;&#xB3D9; &#xC138;&#xD3EC; &#xC2E0;&#xD638; &#xBC0F; &#xC140; &#xADF9;&#xC131;&#xC758; &#xB3D9;&#xC801; &#xD45C;&#xC2DC;&#xB97C; &#xC81C;&#xACF5;&#xD560; &#xC218; &#xC788;&#xC9C0;&#xB9CC; &#xBA54;&#xC11C;&#xB4DC; &#xCD94;&#xC801; &#xBC0F; &#xB2E8;&#xBC31;&#xC9C8; subcellular &#xC6B4;&#xB3D9; &#xC870;&#xC9C1; &#xD658;&#xACBD; &#xB0B4;&#xC5D0;&#xC11C; &#xACC4;&#xB7C9; &#xD558;&#xB294; &#xB370; &#xD544;&#xC694;&#xD55C;. &#xC5EC;&#xAE30;&#xC5D0; &#xC6B0;&#xB9AC;&#xAC00; &#xD604;&#xC7AC; &#xB9C8;&#xC774;&#xADF8;&#xB808;&#xC774;&#xC158; &#xC218;&#xBC31; &#xADF8;&#xB300;&#xB85C; &#xB0B4; &#xC138;&#xD3EC;&#xB97C; &#xC704;&#xD55C; &#xB2E8;&#xBC31;&#xC9C8; subcellular &#xC704;&#xCE58; &#xCC99;&#xB3C4;&#xB97C; semiautomated &#xBC29;&#xC2DD; retrovirally&#xB97C; &#xC0AC;&#xC6A9; &#xD558; &#xC5EC; &#xC870;&#xC9C1; &#xC0DD;&#xD65C; &#xD45C;&#xD604; &#xD615;&#xAD11; &#xC735;&#xD569; &#xB2E8;&#xBC31;&#xC9C8; &#xBC0F; &#xADF8;&#xB300;&#xB85C; thymic &#xB3CC;&#xCD9C;&#xBD80;&#xC758; &#xC2DC;&#xAC04; &#xACBD;&#xACFC; 2 &#xAD11;&#xC790; &#xD604;&#xBBF8;&#xACBD;. GFP-PKC&zeta;, &#xC138;&#xD3EC; &#xADF9;&#xC131; &#xD45C;&#xC2DD; &#xACE0; &#xC704;&#xB3C4;-GFP, T &#xC138;&#xD3EC; &#xC218;&#xC6A9; &#xCCB4; &#xC2E0;&#xD638;&#xC5D0; &#xB300; &#xD55C; &#xB9C8;&#xCEE4;&#xB97C; &#xC0AC;&#xC6A9; &#xD558;&#xB294; &#xBC29;&#xBC95;&#xC758; &#xC720;&#xD6A8;&#xC131;&#xC744; &#xAC80;&#xC0AC; &#xD574;&#xC57C; &#xD558; &#xACE0; &#xBC29;&#xD5A5;&#xACFC; &#xC140; &#xB9C8;&#xC774;&#xADF8;&#xB808;&#xC774;&#xC158; &#xC18D;&#xB3C4;&#xC774; &#xB2E8;&#xBC31;&#xC9C8;&#xC758; &#xBE44;&#xB300;&#xCE6D; &#xBD84;&#xD3EC; &#xAD00;&#xB828;. &#xC774;&#xB7EC;&#xD55C; &#xC811;&#xADFC; &#xBC29;&#xC2DD;&#xC744; &#xB2E4;&#xB978; &#xD615;&#xAD11; &#xC735;&#xD569; &#xB2E8;&#xBC31;&#xC9C8;, &#xC870;&#xC9C1; &#xBC0F; &#xC0DD;&#xBB3C; &#xD559;&#xC801; &#xC9C8;&#xBB38;&#xC744; &#xC27D;&#xAC8C; &#xC801;&#xC6A9;&#xD560; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;.

&#xC870;&#xC9C1; &#xB0B4;&#xC5D0;&#xC11C; &#xB9C8;&#xC774;&#xADF8;&#xB808;&#xC774;&#xC158;&#xC744; &#xC140;&#xC5D0; &#xD615;&#xAD11; &#xC735;&#xD574; &#xB2E8;&#xBC31;&#xC9C8; subcellular &#xBD84;&#xD3EC; &#xCE21;&#xC815;.

El movimiento de las prote&#xED;nas dentro de las c&#xE9;lulas puede proporcionar indicaciones din&#xE1;micas de se&#xF1;alizaci&#xF3;n celular y la polaridad celular, pero los m&#xE9;todos son necesarios para rastrear y cuantificar el movimiento subcelular de prote&#xED;nas en los entornos de los tejidos. Aqu&#xED; presentamos un enfoque semiautom&#xE1;tico para cuantificar la ubicaci&#xF3;n subcelular de prote&#xED;nas de cientos de c&#xE9;lulas que migran dentro de los tejidos vivos intactos utilizando prote&#xED;nas de fusi&#xF3;n expresadas retroviralmente fluorescentes y time-lapse microscop&#xED;a de dos fotones de la intactos los l&#xF3;bulos t&#xED;micos. Hemos validado el m&#xE9;todo que utiliza las buenas pr&#xE1;cticas agrarias PKC&#x3B6;, un marcador de la polaridad celular, y LAT-GFP, un marcador para la se&#xF1;alizaci&#xF3;n del receptor de c&#xE9;lulas T, y se han relacionado con la distribuci&#xF3;n asim&#xE9;trica de estas prote&#xED;nas a la direcci&#xF3;n y la velocidad de la migraci&#xF3;n celular. Estos enfoques podr&#xED;a ser f&#xE1;cilmente adaptado a otras prote&#xED;nas de fusi&#xF3;n fluorescentes, tejidos biol&#xF3;gicos y preguntas.

La cuantificaci&#xF3;n de la distribuci&#xF3;n subcelular de las prote&#xED;nas de fusi&#xF3;n fluorescentes en las c&#xE9;lulas que migran dentro de los tejidos

Two-photon microscopy is a powerful method for visualizing biological processes as they occur in their native environment in real time. The immune system uniquely benefits from this technology as most of its constituent cells are highly motile and interact extensively with each other and with the environment. Two-photon microscopy has provided many novel insights into the dynamics of the development and function of the immune system that could not have been deduced by other methods and has become an indispensible tool in the arsenal of immunologists. In this unit, we provide several protocols for preparation of various organs for imaging by two-photon microscopy that are intended to introduce the new user to some basic aspects of this method.

Two-photon imaging of the immune system.

The ordered migration of thymocytes from the cortex to the medulla is critical for the appropriate selection of the mature T cell repertoire. Most studies of thymocyte migration rely on mouse models, but we know relatively little about how human thymocytes find their appropriate anatomical niches within the thymus. Moreover, the signals that retain CD4+CD8+ double-positive (DP) thymocytes in the cortex and prevent them from entering the medulla prior to positive selection have not been identified in mice or humans. Here, we examined the intrathymic migration of human thymocytes in both mouse and human thymic stroma and found that human thymocyte subsets localized appropriately to the cortex on mouse thymic stroma and that MHC-dependent interactions between human thymocytes and mouse stroma could maintain the activation and motility of DP cells. We also showed that CXCR4 was required to retain human DP thymocytes in the cortex, whereas CCR7 promoted migration of mature human thymocytes to the medulla. Thus, 2 opposing chemokine gradients control the migration of thymocytes from the cortex to the medulla. These findings point to significant interspecies conservation in thymocyte-stroma interactions and provide the first evidence that chemokines not only attract mature thymocytes to the medulla, but also play an active role in retaining DP thymocytes in the cortex prior to positive selection.

Opposing chemokine gradients control human thymocyte migration in situ.

The recognition by the T cell receptor (TCR) of self-peptides presented by the major histocompatibility complex (MHC) on antigen-presenting cells, such as dendritic cells and thymic epithelial cells, controls T cell fate in the thymus, with weak TCR signals inducing survival (positive selection) and stronger signals inducing death (negative selection). In vitro studies indicate that peptide ligands that induce positive selection stimulate a low, but sustained, pattern of TCR signaling; however, the temporal pattern of TCR signaling in MHC class I-restricted thymocytes (thymocytes that are presented with peptides by MHC class I) in the thymus, under conditions that support positive selection, is unknown. We addressed this question by examining intracellular Ca(2+) dynamics and migratory changes in thymocytes undergoing positive and negative selection in thymic slices. Brief, serial signaling events that were separated by migratory periods and low cytosolic Ca(2+) concentrations correlated with the positive selection of MHC class I-restricted thymocytes, whereas sustained Ca(2+) signaling and the arrest of thymocytes were associated with negative selection. Low-avidity peptides and the presentation of peptides by cortical thymic epithelial cells, rather than dendritic cells, failed to induce strong migratory arrest of thymocytes, which led to transient TCR signaling. Thus, we provide a comparison of positive and negative selection signals in situ and suggest that the absence of strong stop signals distinguishes between positive and negative selection.

Distinct temporal patterns of T cell receptor signaling during positive versus negative selection in situ.

Specialized microenvironments within the thymus are comprised of unique cell types with distinct roles in directing the development of a diverse, functional, and self-tolerant T cell repertoire. As they differentiate, thymocytes transit through a number of developmental intermediates that are associated with unique localization and migration patterns. For example, during one particular developmental transition, immature thymocytes more than double in speed as they become mature T cells that are among the fastest cells in the body. This transition is associated with dramatic changes in the expression of chemokine receptors and their antagonists, cell adhesion molecules, and cytoskeletal components to direct the maturing thymocyte population from the cortex to medulla. Here we discuss the dynamic changes in behavior that occur throughout thymocyte development, and provide an overview of the cell-intrinsic and extrinsic mechanisms that regulate human thymocyte migration.

Tracking migration during human T cell development.

The catalytic activity of Zap70 is crucial for T cell antigen receptor (TCR) signaling, but the quantitative and temporal requirements for its function in thymocyte development are not known. Using a chemical-genetic system to selectively and reversibly inhibit Zap70 catalytic activity in a model of synchronized thymic selection, we showed that CD4(+)CD8(+) thymocytes integrate multiple, transient, Zap70-dependent signals over more than 36 h to reach a cumulative threshold for positive selection, whereas 1 h of signaling was sufficient for negative selection. Titration of Zap70 activity resulted in graded reductions in positive and negative selection but did not decrease the cumulative TCR signals integrated by positively selected OT-I cells, which revealed heterogeneity, even among CD4(+)CD8(+) thymocytes expressing identical TCRs undergoing positive selection.

Quantitative and temporal requirements revealed for Zap70 catalytic activity during T cell development.

Positive selection of CD8 T cells in the thymus is thought to be a multistep process lasting 3-4 d; however, the discrete steps involved are poorly understood. Here, we examine phenotypic changes, calcium signaling, and intrathymic migration in a synchronized cohort of MHC class I-specific thymocytes undergoing positive selection in situ. Transient elevations in intracellular calcium concentration ([Ca(2+)]i) and migratory pauses occurred throughout the first 24 h of positive selection, becoming progressively briefer and accompanied by a gradual shift in basal [Ca(2+)]i over time. Changes in chemokine-receptor expression and relocalization from the cortex to medulla occurred between 12 and 24 h after the initial encounter with positive-selecting ligands, a time frame at which the majority of thymocytes retain CD4 and CD8 expression and still require T-cell receptor (TCR) signaling to efficiently complete positive selection. Our results identify distinct phases in the positive selection of MHC class I-specific thymocytes that are distinguished by their TCR-signaling pattern and intrathymic location and provide a framework for understanding the multistep process of positive selection in the thymus.

Distinct phases in the positive selection of CD8+ T cells distinguished by intrathymic migration and T-cell receptor signaling patterns.

The classic anti-viral cytokine interferon (IFN)-β can be induced during parasitic infection, but relatively little is know about the cell types and signaling pathways involved. Here we show that inflammatory monocytes (IMs), but not neutrophils, produce IFN-β in response to T. gondii infection. This difference correlated with the mode of parasite entry into host cells, with phagocytic uptake predominating in IMs and active invasion predominating in neutrophils. We also show that expression of IFN-β requires phagocytic uptake of the parasite by IMs, and signaling through Toll-like receptors (TLRs) and MyD88. Finally, we show that IMs are major producers of IFN-β in mesenteric lymph nodes following in vivo oral infection of mice, and mice lacking the receptor for type I IFN-1 show higher parasite loads and reduced survival. Our data reveal a TLR and internalization-dependent pathway in IMs for IFN-β induction to a non-viral pathogen.

Internalization and TLR-dependent type I interferon production by monocytes in response to Toxoplasma gondii.

Negative selection is one of the primary mechanisms that render T cells tolerant to self. Thymic dendritic cells play an important role in negative selection, in line with their ability to induce migratory arrest and sustained TCR signals. Thymocytes themselves display self-peptide/MHC class I complexes, and although there is evidence that they can support clonal deletion, it is not clear whether they do so directly via stable cell-cell contacts and sustained TCR signals. In this study, we show that murine thymocytes can support surprisingly efficient negative selection of Ag-specific thymocytes. Furthermore, we observe that agonist-dependent thymocyte-thymocyte interactions occurred as stable, motile conjugates led by the peptide-presenting thymocyte and in which the trailing peptide-specific thymocyte exhibited persistent elevations in intracellular calcium concentration. These data confirm that self-Ag presentation by thymocytes is an additional mechanism to ensure T cell tolerance and further strengthen the correlation between stable cellular contacts, sustained TCR signals, and efficient negative selection.

Stable interactions and sustained TCR signaling characterize thymocyte-thymocyte interactions that support negative selection.

Humanized mice represent an important model to study the development and function of the human immune system. While it is known that mouse thymic stromal cells can support human T-cell development, the extent of interspecies cross-talk and the degree to which these systems recapitulate normal human T-cell development remain unclear. To address these questions, we compared conventional and non-conventional T-cell development in a neonatal chimera humanized mouse model with that seen in human fetal and neonatal thymus samples, and also examined the impact of a human HLA-A2 transgene expressed by the mouse stroma. Given that dynamic migration and cell-cell interactions are essential for T-cell differentiation, we also studied the intrathymic migration pattern of human thymocytes developing in a murine thymic environment. We found that both conventional T-cell development and intra-thymic migration patterns in humanized mice closely resemble human thymopoiesis. Additionally, we show that developing human thymocytes engage in short, serial interactions with other human hematopoietic-derived cells. However, non-conventional T-cell differentiation in humanized mice differed from both fetal and neonatal human thymopoiesis, including a marked deficiency of Foxp3(+) T-cell development. These data suggest that although the murine thymic microenvironment can support a number of aspects of human T-cell development, important differences remain, and additional human-specific factors may be required.

Conserved and divergent aspects of human T-cell development and migration in humanized mice.

Recently, tissue slices have been adapted to study both mouse and human T cell development. Thymic slices combine and complement the strengths of existing organotypic culture systems to study thymocyte differentiation. Specifically, the thymic slice system allows for high throughput experiments and the ability to introduce homogenous developmental intermediate populations into an environment with a well-established cortex and medulla. These qualities make thymic slices a highly versatile and technically accessible model to study thymocyte development. Here we describe methods to prepare, embed, and slice thymic lobes to study T cell development in situ.

Heather J. Melichar

Centre de Recherche

Hôpital Maisonneuve-Rosemont

Preparation and Applications of Organotypic Thymic Slice Cultures

Heather J. Melichar

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Centre de Recherche

Hôpital Maisonneuve-Rosemont

Preparation and Applications of Organotypic Thymic Slice Cultures

Centre de Recherche