Comparison between qPCR and RNAseq reveals challenges of quantifying HLA expression
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PROVIDER: PRJNA923098 | ENA |
REPOSITORIES: ENA
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Project description:Comparison between qPCR and RNAseq reveals challenges of quantifying HLA expression
| PRJNA923097 | ENA
Project description:Human leukocyte antigen (HLA) class I and II loci are essential elements of innate and acquired immunity. Their functions include antigen presentation to T cells leading to cellular and humoral immune responses, and modulation of NK cells. Their exceptional influence on disease outcome has now been made clear by genome-wide association studies. The exons encoding the peptide-binding groove have been the main focus for determining HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease outcome, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. To estimate HLA expression, immunogenetic studies traditionally rely on quantitative PCR (qPCR). Adoption of alternative high-throughput technologies such as RNA-seq has been hampered by technical issues due to the extreme polymorphism at HLA genes. Recently, however, multiple bioinformatic methods have been developed to accurately estimate HLA expression from RNA-seq data. This opens an exciting opportunity to quantify HLA expression in large datasets but also brings questions on whether RNA-seq results are comparable to those by qPCR. In this study, we analyze three classes of expression data for HLA class I genes for a matched set of individuals: (a) RNA-seq, (b) qPCR, and (c) cell surface HLA-C expression. We observed a moderate correlation between expression estimates from qPCR and RNA-seq for HLA-A, -B, and -C (0.2 ≤ rho ≤ 0.53). We discuss technical and biological factors which need to be accounted for when comparing quantifications for different molecular phenotypes or using different techniques.
| S-EPMC9883133 | biostudies-literature
Project description:Gene products from the highly variable major histocompatibility locus, including HLA, are essential for self-recognition and immune surveillance of malignancy. Following allogeneic hematopoietic cell transplantation (alloHCT), genetic and epigenetic alterations in HLA can drive disease recurrence, making precise HLA assessment critical for determination of future therapy. However, current methods lack the sensitivity to quantify HLA transcripts at the single cell level, limiting their clinical utility. We introduce scrHLA-typing, a novel method that accurately identifies and quantifies HLA transcripts in single cells using hybridization capture and long-read sequencing. When applied to samples from patients with post-transplant relapse, scrHLA-typing successfully detected allele-specific expression of MHC gene products at clinically actionable levels. By characterizing allele expression in residual leukemia cells, our assay identified differences in expression patterns among patients. This capability highlights scrHLA-typing’s potential to improve risk stratification and guide the selection of appropriate salvage therapies, enhancing personalized treatment strategies after post-transplant relapse.
2025-09-17 | GSE305306 | GEO
Project description:Quantifying HLA transcripts by genotype in chimeric mixtures at single-cell resolution [scrHLAtyping]
| PRJNA1305215 | ENA
Project description:Gene products from the highly variable major histocompatibility locus, including HLA, are essential for self-recognition and immune surveillance of malignancy. Following allogeneic hematopoietic cell transplantation (alloHCT), genetic and epigenetic alterations in HLA can drive disease recurrence, making precise HLA assessment critical for determination of future therapy. However, current methods lack the sensitivity to quantify HLA transcripts at the single cell level, limiting their clinical utility. We introduce scrHLA-typing, a novel method that accurately identifies and quantifies HLA transcripts in single cells using hybridization capture and long-read sequencing. When applied to samples from patients with post-transplant relapse, scrHLA-typing successfully detected allele-specific expression of MHC gene products at clinically actionable levels. By characterizing allele expression in residual leukemia cells, our assay identified differences in expression patterns among patients. This capability highlights scrHLA-typing’s potential to improve risk stratification and guide the selection of appropriate salvage therapies, enhancing personalized treatment strategies after post-transplant relapse.
2025-09-17 | GSE305305 | GEO
Project description:Gene products from the highly variable major histocompatibility locus, including HLA, are essential for self-recognition and immune surveillance of malignancy. Following allogeneic hematopoietic cell transplantation (alloHCT), genetic and epigenetic alterations in HLA can drive disease recurrence, making precise HLA assessment critical for determination of future therapy. However, current methods lack the sensitivity to quantify HLA transcripts at the single cell level, limiting their clinical utility. We introduce scrHLA-typing, a novel method that accurately identifies and quantifies HLA transcripts in single cells using hybridization capture and long-read sequencing. When applied to samples from patients with post-transplant relapse, scrHLA-typing successfully detected allele-specific expression of MHC gene products at clinically actionable levels. By characterizing allele expression in residual leukemia cells, our assay identified differences in expression patterns among patients. This capability highlights scrHLA-typing’s potential to improve risk stratification and guide the selection of appropriate salvage therapies, enhancing personalized treatment strategies after post-transplant relapse.
2025-09-17 | GSE305307 | GEO
Project description:Quantifying HLA transcripts by genotype in chimeric mixtures at single-cell resolution [10x_RNA_ADT-seq]
| PRJNA1305214 | ENA
Project description:Quantifying HLA transcripts by genotype in chimeric mixtures at single-cell resolution [scrHLAtyping_MAS-seq]
| PRJNA1305212 | ENA
Project description:LC-MS/MS-based identification of HLA-peptides is poised to provide a deep understanding of the rules underlying antigen presentation. However, a key obstacle limiting the utility of MS data is the ambiguity arising from the co-expression of multiple HLA alleles. Here, we introduce a strategy for profiling the HLA ligandome one allele at a time. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing a novel spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of novel binding motifs, and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a scalable strategy for systematically learning the rules of endogenous antigen presentation.
2017-02-21 | GSE93315 | GEO
Project description:Myeloid-derived suppressor cells (MDSC) is a heterogeneous population of cells that can negatively regulate T-cell function. As opposed to murine MDSC, which are characterized as Gr-1+CD11b+ cells, human MDSC are not so clearly defined due to lack of specific markers. Our lab has previously identified a new subset of MDSC as CD14+HLA-DR-neg/low cells from PBMC. CD14+HLA-DR-neg/low MDSC not only suppress proliferation and IFN-gamma secretion of autologous T cells, but also induce CD25+Foxp3+ regulatory T cells that are suppressive in vitro, whereas the counterpart CD14+HLA-DR-high monocytes don’t have the effect. In this study, we compare the immune-related gene expression between CD14+HLA-DR-neg/low MDSC and CD14+HLA-DR-high monocytes to better characterize the difference between these two populations and to find new potential specific marker for human MDSC. PBMC were isolated from fresh blood healthy donor by density centrifugation. CD14+ cells were isolated by AutoMACS CD14 microbeads using a AutoMACS (Miltenyi), and then stained with CD14 and HLA-DR antibodies. MDSC and monocytes control cells were sorted as CD14+ HLA-DR-neg/low and CD14+HLA-DR-high cells respectively. The sorted two populations were immediately frozen in liquid nitrogen and shipped to the company on dry ice for RNA isolation and further microarray.
2011-09-08 | E-GEOD-32001 | biostudies-arrayexpress