Transkriptionale Analyse und Charakterisierung von ex vivo expandierten T-Zellen

Adoptive immunotherapy with ex vivo expanded T cells has become a promising tool for the treatment of opportunistic infections, viral pathogens, and a variety of cancers. The challenge for these therapies is the production of large numbers of biologically active cells. Therefore, an understanding of the effects of culture parameters or modalities, such as oxygen tension and autologous plasma, or expansion of T-cell subsets, is essential for optimizing expansion and improving biological activity and efficacy. Low oxygen tension (5% pO2) and autologous plasma have been shown to profoundly increase the ex vivo expansion of T cells in 15-day experiments (median ratios: 1.8-fold, low oxygen; 5.4-fold, plasma). The addition of autologous plasma improved viability, which was not affected in low oxygen cultures. Microarray analysis revealed that reduced growth was caused by cellular stress responses, which included oxidative stress responsive genes, DNA repair, apoptosis and cytoskeleton genes. Furthermore, microarray analysis suggested that cultures at 5% pO2 and cultures supplemented with plasma may have increased immune functionality, as revealed by upregulation of genes encoding granzymes (5% pO2, plasma) and immunoglobulins/ integrins (plasma). These results were confirmed by Q-RT-PCR, protein-level analyses and functional assays, which proved that microarray analysis is a valid tool to investigate primary patient samples. In addition, temporal analysis across conditions and patient samples showed a correlation between expression patterns and specific proliferation rates of the individual samples, which might be used to predict culture outcomes or help design intervention measures to rescue poorly expanding samples. Furthermore, a distinct gene expression pattern was uncovered that correlated well with viability differences between plasma-supplemented and serum-free cultures. Lipid metabolism genes, which were highly over-represented in this cluster, suggested a potential deficit of lipids in serum-free cultures. CD4(+) T cells have been shown to greatly support the ex vivo expansion of CD8(+). As some clinical expansion protocols require the expansion of either CD4(+) or CD8(+) T cells, a better understanding of T-cell subset expansion is paramount to developing more efficient protocols. Thus, microarray analysis was conducted on CD4(+) and CD8(+) T-cell subsets during the early activation period (0h-72h). As a result, many genes encoding co-stimulatory molecules, cytokines and proteins with unknown function were differentially expressed on CD4(+) and CD8(+) T cells. Of paramount importance were proteins that have been involved with growth support and differentiation, e.g. CD40L on CD4(+) and 4-1BB on CD8(+) cells. These data can be used to replace co-culture experiments by adding soluble factors that, e.g., engage receptors on a specific T-cell subset. Furthermore, these data could also be used to define general activation ‘signatures’ of T-cell subsets, which may serve as databases to test alternative activation protocols. In conclusion, microarray analysis was successfully applied to monitor and characterize T cells from primary patient samples during ex vivo expansion. Supplementary assays, such as Q-RT-PCR, protein-level analysis and functional assays supported microarray analysis, proved its validity, and helped to formulate hypothesis that may improve clinical protocols. In addition, microarray data could be correlated to phenotypic data, which is an important feature to classify primary patient samples and predict culture outcomes.Patienten-spezifische Immunotherapie mit ex vivo expandierten T-Zellen hat sich zu einer erfolgversprechenden Methode zur Behandlung von Virusinfektionen und Krebserkrankungen entwickelt. Die Herausforderung besteht allerdings in der Produktion einer grossen Zahl von funktionellen, biologisch wirksamen Zellen. Deshalb ist das Verständnis von Zellkulturparametern oder experimenteller Gestaltung, z. B. Sauerstoffpartialdruck und Zugabe von patienteneigenem Plasma, oder die Expansion von T-Zell Subpopulationen, essentiell for die Optimierung von Zellexpansion und Verbesserung der biologischen Aktivität und Effizienz. Niedriger Sauerstoffpartialdruck (5% pO2) und patienteneigenes Plasma haben die ex vivo Zellexpansion in 15-tägigen Experimenten erheblich gesteigert (Median Verhältnis: 1.8-fach, niedriger Sauerstoffpartialdruck; 5.4-fach, Plasma). Die Zugabe von patienteneigenem Plasma verbesserte die Lebensfähigkeit, die allerdings unter niedrigem Sauerstoffpartialdruck nicht verändert war. Mikroarray Analysen ergaben, dass das verringerte Wachstum durch eine zelluläre Stress-Antwort verursacht wurde, die Gene aus den folgenden Familien umfasste: oxidativer Stress, DNA Reparatur, Apoptose, Zytoskelett. Weiterhin legten Mikroarray Analysen nahe, dass Kulturen in niedrigem Sauerstoffpartialdruck und nach Zugabe von Plasma bessere Immunfunktionalität besitzen, was durch die Hochregulierung von Genen, die Granzyme (in 5% pO2 und Plasma-Kulturen) und Immunoglobuline/ Integrine (in Plasma-Kulturen), kodieren. Diese Resultate wurden bestätigt durch Q-RT-PCR, Protein Analysen und funktionelle Assays, was zeigte, dass Mikroarray Analyse eine zulässige Methode darstellt, um primäre Proben von Patienten zu untersuchen. Zusätzlich zeigten temporäre Analysen über mehrere Kultur-Konditionen und Patienten-Proben eine Korrelation zwischen Expressionsmustern und spezifischen Proliferationsraten der einzelnen Patienten-Proben, was genutzt werden könnte, um Kulturausgänge vorherzusagen oder Eingriffe während der Kultur zu ermöglichen, z. B. um wenig expandierende Kulturen zu unterstützen. Weiterhin wurde ein auffälliges Expressionsmuster entdeckt, das gut mit den Lebensfähigkeitsunterschieden zwischen Plasma-haltigen und Plasma-freien Kulturen korrelierte. Fettstoffwechsel-Gene, die als überrepräsentiert in diesem Expressionsmuster identifiziert worden waren, legten mögliche Mängel in Plasma-freien Kulturen nahe. Für CD4(+) T-Zellen wurde gezeigt, dass sie die ex vivo Expansion von CD8(+) erheblich unterstützen. Da einige klinische Protokolle die Expansion von CD4(+) oder CD8(+) T-Zellen erfordern, ist ein besseres Verständnis der T-Zell Subpopulationen entscheidend fuer die Entwicklung von effizienteren Protokollen. Aus diesem Grund wurden Mikroarray Analysen mit CD4(+) und CD8(+) T-Zell Subpopulationen während des frühen Aktivierungszeitraumes durchgeführt (0h-72h). Im Ergebnis waren viele Gene, die co-stimulierende Moleküle, Zytokine und Proteine mit unbekannter Funktion kodieren, differenziell exprimiert in CD4(+) und CD8(+) T-Zellen. Besonders wichtig waren Proteine, die mit Wachstumsunterstützung und Differenzierung in Zusammenhang stehen, z. B. CD40L in CD4(+) und 4-1BB in CD8(+) Zellen. Diese Daten könnten benutzt werden, um co-Kultivierungsexperimente zu ersetzen, z. B. durch die Zugabe von löslichen Faktoren, die mit Rezeptoren auf T-Zell Subpopulationen interagieren. Weiterhin könnten diese Daten auch benutzt werden, um generelle Aktivierungsmuster von T-Zell Subpopulationen zu definieren, die anschliessend als Datenbank fuer die Testung von weiteren Aktivierungsprotokollen genutzt werden könnten. Schlussfolgernd sei herausgestellt, dass Mikroarray Analyse erfolgreich eingesetzt wurde, um primäre T-Zellen von Patienten während der ex vivo Expansion zu charakterisieren. Zusätzliche Assays, wie z. B. Q-RT-PCR, Protein-Level Analysen und funktionelle Assays unterstützten die Mikroarray Analyse, zeigten ihre Gültigkeit, and unterstützten die Formulierung von Hypothesen zur Verbesserung von klinischen Protokollen. Zusätzlich wurden Mikroarray Analysen mit phänotypischen Daten korreliert, was eine wichtige Eigenschaft darstellt, um Patienten-Proben zu klassifizieren und Kulturausgänge hervorzusagen

Comparative analysis of transcriptional profiling of CD3 , CD4 and CD8 T cells identifies novel immune response players in T-Cell activation-7

0 hour, is denoted by different color (green: downregulation, red: upregulation) at each timepoint in the sequence of 4, 10, 48 and 96 hours. (B) Supernatant ELISA analysis of IFNG secretion in three independent CD3+ T-cell experiments, E3–E5. CD3+ T cells were selected, stimulated (by anti-CD3/anti-CD28 antibodies) and the supernatants were harvested at the indicated timepoints of culture.<p><b>Copyright information:</b></p><p>Taken from "Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD8+ T cells identifies novel immune response players in T-Cell activation"</p><p>http://www.biomedcentral.com/1471-2164/9/225</p><p>BMC Genomics 2008;9():225-225.</p><p>Published online 16 May 2008</p><p>PMCID:PMC2396644.</p><p></p

Comparative analysis of transcriptional profiling of CD3 , CD4 and CD8 T cells identifies novel immune response players in T-Cell activation-2

Roups (with mostly upregulated genes and with mostly downregulated genes) according to their distinct expression patterns based on hierarchical clustering using the Euclidian distance metric. Color denotes degree of differential expression compared to 0 hour (saturated red = 3-fold up-regulation, saturated green = 3-fold down-regulation, black = unchanged, gray = no data available). Clusters (I-VI) of genes with different expression patterns among the three populations were noted on the side. Expression data shown are averages from three independent biological experiments for each T-cell population.<p><b>Copyright information:</b></p><p>Taken from "Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD8+ T cells identifies novel immune response players in T-Cell activation"</p><p>http://www.biomedcentral.com/1471-2164/9/225</p><p>BMC Genomics 2008;9():225-225.</p><p>Published online 16 May 2008</p><p>PMCID:PMC2396644.</p><p></p

Comparative analysis of transcriptional profiling of CD3 , CD4 and CD8 T cells identifies novel immune response players in T-Cell activation-6

Lture. Data from three independent CD3+ T-cell experiments, E3–E5, are shown.<p><b>Copyright information:</b></p><p>Taken from "Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD8+ T cells identifies novel immune response players in T-Cell activation"</p><p>http://www.biomedcentral.com/1471-2164/9/225</p><p>BMC Genomics 2008;9():225-225.</p><p>Published online 16 May 2008</p><p>PMCID:PMC2396644.</p><p></p

Comparative analysis of transcriptional profiling of CD3 , CD4 and CD8 T cells identifies novel immune response players in T-Cell activation-10

Nd 1258 significant genes in the CD8+ population.<p><b>Copyright information:</b></p><p>Taken from "Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD8+ T cells identifies novel immune response players in T-Cell activation"</p><p>http://www.biomedcentral.com/1471-2164/9/225</p><p>BMC Genomics 2008;9():225-225.</p><p>Published online 16 May 2008</p><p>PMCID:PMC2396644.</p><p></p

Comparative analysis of transcriptional profiling of CD3 , CD4 and CD8 T cells identifies novel immune response players in T-Cell activation-0

Of healthy donors and activated with anti-CD3/anti-CD28 antibodies. T-cell expansion as assessed by cell numbers; The percentage of the viable T cells as determined by flow cytometry; The percentage of the viable cells expressing CD69; The percentage of the viable cells expressing CD25. Data from 6 independent experiments (CD3+ T-cell experiments, E1–E3, and CD4+ and CD8+ T-cell experiments, E7–E9) using cells from 6 different healthy donors are shown.<p><b>Copyright information:</b></p><p>Taken from "Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD8+ T cells identifies novel immune response players in T-Cell activation"</p><p>http://www.biomedcentral.com/1471-2164/9/225</p><p>BMC Genomics 2008;9():225-225.</p><p>Published online 16 May 2008</p><p>PMCID:PMC2396644.</p><p></p

Comparative analysis of transcriptional profiling of CD3 , CD4 and CD8 T cells identifies novel immune response players in T-Cell activation-5

Nscription in CD3+ T cells, compared to 0 hour, is denoted by different color (green: downregulation; red: upregulation) at each timepoint in the sequence of 4, 10, 48 and 96 hours.<p><b>Copyright information:</b></p><p>Taken from "Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD8+ T cells identifies novel immune response players in T-Cell activation"</p><p>http://www.biomedcentral.com/1471-2164/9/225</p><p>BMC Genomics 2008;9():225-225.</p><p>Published online 16 May 2008</p><p>PMCID:PMC2396644.</p><p></p

Comparative analysis of transcriptional profiling of CD3 , CD4 and CD8 T cells identifies novel immune response players in T-Cell activation-8

0 hour (saturated red = 3-fold up-regulation, saturated green = 3-fold down-regulation, black = unchanged, gray = no data available). Expression data shown are averages from three independent biological experiments for each T-cell population.Intracellular protein expression profiles of GZMB in the CD4+ and CD8+ subsets. CD4+ T cells and CD8+ T cells were selected, stimulated (by anti-CD3/anti-CD28 antibodies), cultured separately and harvested at the indicated timepoints of culture to analyze the protein expression via by flow cytometric assays. Data from four independent CD4+ and CD8+ T-cell experiments, E8–E11, are shown.<p><b>Copyright information:</b></p><p>Taken from "Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD8+ T cells identifies novel immune response players in T-Cell activation"</p><p>http://www.biomedcentral.com/1471-2164/9/225</p><p>BMC Genomics 2008;9():225-225.</p><p>Published online 16 May 2008</p><p>PMCID:PMC2396644.</p><p></p