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| Status |
Public on Dec 03, 2023 |
| Title |
Discovering Molecular Signatures in ATMR: Single-cell RNA Sequencing Analysis of Human Blood and Tissue Spatial Transcriptomics [Spatial Transcriptomics] |
| Organism |
Homo sapiens |
| Experiment type |
Other
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| Summary |
Background. Acute T cell-mediated rejection (ATMR) is a significant challenge in kidney allograft. Despite this, high-resolution transcriptomic analysis of both the targeted tissue and systemic blood is lacking. To address this void, we embarked on a study employing single-cell RNA sequencing (scRNA-seq) on peripheral blood mononuclear cells (PBMCs), coupled with spatial transcriptomics analysis on graft biopsy specimens, to unveil the intricacies of ATMR. Methods. We collected paired pre- and post-operative blood samples from six kidney transplant patients. Among these, two patients exhibited a lack of rejection, while four patients bore biopsy-proven ATMR. By scrutinizing tissue samples from protocol biopsies taken two weeks post-operation in comparison to zero-time biopsies. Moreover, our investigation delved into peripheral blood samples, where we detected an expanded cell population within PBMCs, presumed to harbor numerous alloreactive T cells. This CD8+ effector memory (CD8 Tem) cell population was subsequently examined for DEGs between the two patient groups. Results. Our comprehensive approach, which combined scRNA-seq and spatial transcriptomics results, unveiled specific genes such as LTB, GZMK, STAT1, PSME2, UBE2L6, and GBP5, which showcased heightened expression levels among ATMR patients. Network analysis underscored the interconnection of STAT1 with the other identified genes, suggesting its plausible role in the rejection process. Conclusions. Our findings demonstrate CD8+ STAT1+ hold notable significance in the context of ATMR. This subpopulation undergoes clonal expansion and retains a memory phenotype, there by contributing to a heightened and swifter inflammatory response within the graft.
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| Overall design |
Human PBMC samples were used for single cell RNA sequencing, human kidney biopsy tissue samples were used for spatial transcriptomics
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| Contributor(s) |
Song J, Kang M, Jang Y, Kim Y, Kim H, Yang S |
| Citation missing |
Has this study been published? Please login to update or notify GEO. |
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| Submission date |
Nov 28, 2023 |
| Last update date |
Dec 04, 2023 |
| Contact name |
Minji Kang |
| E-mail(s) |
minzi@snu.ac.kr
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| Organization name |
Seoul National University graduate school
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| Department |
biomedical sciences
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| Street address |
103 daehak-ro
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| City |
Seoul |
| ZIP/Postal code |
03080 |
| Country |
South Korea |
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| Platforms (1) |
| GPL24676 |
Illumina NovaSeq 6000 (Homo sapiens) |
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| Samples (24)
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| GSM7920090 |
spatial transcriptomics, ROI4 |
| GSM7920091 |
spatial transcriptomics, ROI5 |
| GSM7920092 |
spatial transcriptomics, ROI6 |
| GSM7920093 |
spatial transcriptomics, ROI7 |
| GSM7920094 |
spatial transcriptomics, ROI8 |
| GSM7920095 |
spatial transcriptomics, ROI9 |
| GSM7920096 |
spatial transcriptomics, ROI10 |
| GSM7920097 |
spatial transcriptomics, ROI11 |
| GSM7920098 |
spatial transcriptomics, ROI12 |
| GSM7920099 |
spatial transcriptomics, ROI13 |
| GSM7920100 |
spatial transcriptomics, ROI14 |
| GSM7920101 |
spatial transcriptomics, ROI15 |
| GSM7920102 |
spatial transcriptomics, ROI16 |
| GSM7920103 |
spatial transcriptomics, ROI17 |
| GSM7920104 |
spatial transcriptomics, ROI18 |
| GSM7920105 |
spatial transcriptomics, ROI19 |
| GSM7920106 |
spatial transcriptomics, ROI20 |
| GSM7920107 |
spatial transcriptomics, ROI21 |
| GSM7920108 |
spatial transcriptomics, ROI22 |
| GSM7920109 |
spatial transcriptomics, ROI23 |
| GSM7920110 |
spatial transcriptomics, ROI24 |
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| This SubSeries is part of SuperSeries: |
| GSE248789 |
Discovering Molecular Signatures in ATMR: Single-cell RNA Sequencing Analysis of Human Blood and Tissue Spatial Transcriptomics |
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| Relations |
| BioProject |
PRJNA1046018 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE248787_RAW.tar |
1.4 Mb |
(http)(custom) |
TAR (of DCC) |
SRA Run Selector |
| Raw data are available in SRA |
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