The spatially resolved transcriptional profile of acute T cell-mediated rejection in a kidney allograft
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ABSTRACT:
PROVIDER: PRJNA1182237 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR31229274_1.fastq.gz | Fastqsanger.gz | |||
| SRR31229274_2.fastq.gz | Fastqsanger.gz | |||
| SRR31229275_1.fastq.gz | Fastqsanger.gz | |||
| SRR31229275_2.fastq.gz | Fastqsanger.gz | |||
| SRR31229276_1.fastq.gz | Fastqsanger.gz |
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Project description:To investigate the involvement of FCGR3A and FCGR2C signaling and NK cell activation during antibody-mediated rejection, we analyzed transcriptomic data obtained by bulk RNA sequencing of kidney allograft FFPE biopsies diagnosed with either antibody-mediated rejection, T-cell mediated rejection or normal biopsies.
2024-12-01 | GSE214703 | GEO
Project description:In this proof-of-concept study, spatial transcriptomics combined with public single-cell RNA sequencing data were used to explore the potential of this technology to study kidney allograft rejection. We aimed to map gene expression patterns within diverse pathological states by examining biopsies classified across non-rejection, T cell-mediated acute rejection, and interstitial fibrosis and tubular atrophy (IFTA). Our results revealed distinct immune cell signatures, including those of T and B lymphocytes, monocytes, mast cells, and plasma cells, and their spatial organization within the renal interstitium. We also mapped chemokine receptors and ligands to study immune-cell migration and recruitment. Finally, our analysis demonstrated differential spatial enrichment of transcription signatures associated with kidney allograft rejection across various biopsy regions. Interstitium regions displayed higher enrichment scores for rejection-associated gene expression patterns than did tubular areas, which had negative scores. This implies that these signatures are primarily driven by processes unfolding in the renal interstitium. Overall, this study highlights the value of spatial transcriptomics for revealing cellular heterogeneity and immune signatures in renal transplant biopsies, and demonstrates its potential for studying the molecular and cellular mechanisms associated with rejection. However, certain limitations must be borne in mind regarding the development and future applications of this technology.
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Project description:Acute renal allograft rejection is an important complication in kidney transplantation. Accurate diagnosis of rejection events is necessary for timely response and treatment. We illustrate the usefulness and biological relevance of selected multivariate approaches to detect rejection from genomic and proteomic signals. The data was used to study gene expression changes using whole genome microarray analysis of peripheral blood from subjects with acute rejection (n=20) and non-rejecting controls (n=20) to obtain insight into the molecular and biological causation of acute renal allograft rejection when combined with proteomics (iTRAQ) data for the same patients/time-points.
2014-04-26 | GSE46474 | GEO
Project description:The study comprises various components: Samples TD: We aims to screen out different gene expression profile in donor biopsies after revascularization , We aims to predict renal allograft dysfunction early after transplantation. Samples AR, ATN, Tx: We aim to screen out different gene expression profile in acute rejection on the kidney. We aim to screen out different gene expression profile in acute tubular necrosis on the kidney. Results from the various study components can help to diagnose renal allograft dysfunction with different causes by distinct gene expression profile. Keywords: acute rejection, acute tubular necrosis, donor biopsies, renal allograft dysfunction
2011-02-02 | GSE26578 | GEO
Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining “molecular Banff” signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set here comprises a small validation batch of renal allograft biopsies for clinical indications plus control normal kidney samples from patients at Hôpital Tenon, Paris (second batch) that complements the first batch of 60 samples. We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Keywords: disease state analysis 4 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to Banff'97 criteria and 2 normal kidney samples.
2009-02-24 | E-GEOD-9492 | biostudies-arrayexpress
Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining âmolecular Banffâ signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at Hôpital Tenon, Paris (February 2003 until September 2004) and few respective patients from Hôpital Bicêtre, Paris, Hôpital Pellegrin, Bordeaux, and Hôpital Dupuytren, Limoges, plus control normal kidney samples from Hôpital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Experiment Overall Design: 47 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to BANFF'97 criteria, and 13 normal kidney samples as controls.
2009-02-28 | E-GEOD-9489 | biostudies-arrayexpress
Project description:Rat corneal allograft rejection models were established to investigate the effects and mechanisms of resveratrol on corneal allograft rejection after corneal transplantation.
2023-07-10 | GSE236626 | GEO
Project description:The study comprises various components: Samples TD: We aims to screen out different gene expression profile in donor biopsies after revascularization , We aims to predict renal allograft dysfunction early after transplantation. Samples AR, ATN, Tx: We aim to screen out different gene expression profile in acute rejection on the kidney. We aim to screen out different gene expression profile in acute tubular necrosis on the kidney. Results from the various study components can help to diagnose renal allograft dysfunction with different causes by distinct gene expression profile. Keywords: acute rejection, acute tubular necrosis, donor biopsies, renal allograft dysfunction Samples AR1-AR17: This study has been accomplished with 17 patients of acute rejection on the kidney.Technical replicates: 2 replicates Samples ATN1-ATN5: This study has been accomplished with 5 patients of acute tubular necrosis on the kidney. Technical replicates: 2 replicates Samples Tx1-Tx14: This study has been accomplished with 14 patients of stable renal function on the kidney.Tecnical replicates:2 replicates(except Tx12) Samples TD1-TD12: This study has been accomplished with 12 patients of donor tissue with stable function early after transplantation on the kidney.Technical replicates: 2 replicates Samples TD13-TD21: This study has been accomplished with 9 patients of donor tissue with renal dysfunction early after transplantation on the kidney.Technical replicates: 2 replicates
2011-02-02 | E-GEOD-26578 | biostudies-arrayexpress
Project description:Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining M-bM-^@M-^\molecular BanffM-bM-^@M-^] signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at HM-CM-4pital Tenon, Paris (February 2003 until September 2004) and few respective patients from HM-CM-4pital BicM-CM-*tre, Paris, HM-CM-4pital Pellegrin, Bordeaux, and HM-CM-4pital Dupuytren, Limoges, plus control normal kidney samples from HM-CM-4pital Tenon, Paris, France (first batch). We used microarrays to identify different gene expression signatures of renal allograft biopsies that can classify them according to different types of allograft rejection or CAN. Keywords: disease state analysis Keywords: Expression profiling by array 16 renal allograft core biopsies for clinical indications with different histopathological diagnoses according to BANFF'97 criteria (additional samples associated with GSE9489)
2009-08-27 | E-GEOD-17861 | biostudies-arrayexpress
Project description:Acute allograft rejection is a leading cause for the failure of organ allotransplantation. Identifying the genes involved in the rejection process provides clues to study the mechanisms, and to provide specific gene targets for monitoring, predicting and preventing acute allograft rejection. Using a mice model of skin acute allograft rejection and SAGE method, we analyzed gene expression in the CD4+ T cells of the mice, the cell type known to play critical roles in acute allograft rejection. Our study identifies 402 SAGE tags significantly different from these from the control. From these SAGE tags, we identified 91 increasingly and 85 decreasingly expressed genes, and many genes have not been linked with acute allograft rejection before. Functional classification of these genes shows that apoptosis, transcription regulation, cell growth and maintenance and signal transduction are among the most frequently changed functional groups. Our study provides a genome-wide view for the genes involving acute allograft rejection in the CD4+ T cells, and indicates that acute allograft rejection involves multiple genes in different functional categories. The genes identified from the study provide candidates for further studying the mechanisms and for monitoring, predicting and preventing acute allograft rejection.
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