Resumen:
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[EN] T-cell receptor (TCR) analysis is relevant for the study of immune system diseases. The expression of TCRs is usually measured with targeted sequencing approaches where TCR genes are selectively amplified. However, ...[+]
[EN] T-cell receptor (TCR) analysis is relevant for the study of immune system diseases. The expression of TCRs is usually measured with targeted sequencing approaches where TCR genes are selectively amplified. However, many non-targeted RNA-seq experiments also contain reads of TCR genes, which could be leveraged for TCR expression analysis while reducing sample requirements and costs. Moreover, a step-by-step pipeline for the processing of transcriptome RNA-seq reads to deliver immune repertoire data is missing, and these types of analyses are usually not included in RNA-seq studies of immunological conditions. This represents a missed opportunity for complementing them with the analysis of the immune repertoire.
We present a Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data. We used a case study where TCR repertoire profiles were recovered from bulk RNA-seq of isolated CD4 T cells from control patients, cirrhotic patients without and with Minimal Hepatic Encephalopathy (MHE). MHE is a neuropsychiatric syndrome, mediated by peripheral inflammation, that may affect cirrhotic patients. After the recovery of 498-1,114 distinct TCR beta chains per patient, repertoire analysis of patients resulted in few public clones, high diversity and elevated within-repertoire sequence similarity, independently of immune status. Additionally, TCRs associated with celiac disease and inflammatory bowel disease were significantly overrepresented in MHE patient repertoires. The provided computational pipeline functions as a resource to facilitate TCR profiling from RNA-seq data boosting immunophenotype analyses of immunological diseases.
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Agradecimientos:
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We acknowledge generous support by The Leona M. and Harry
B. Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO WorldLeading Research Community (to VG), UiO:LifeScience Convergence
Environment Immunolingo (to VG), EU ...[+]
We acknowledge generous support by The Leona M. and Harry
B. Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO WorldLeading Research Community (to VG), UiO:LifeScience Convergence
Environment Immunolingo (to VG), EU Horizon 2020 iReceptorplus
(#825821) (to VG), a Research Council of Norway FRIPRO project
(#300740, to VG), a Research Council of Norway IKTPLUSS project
(#311341, to VG), a Norwegian Cancer Society Grant (#215817, to
VG). This work was also supported in part by Fundación Ramón Areces (to CM), the Ministerio de Ciencia e Innovación Spain (SAF2017-
82917-R and PID2020-113388RB-I00 to VF; FIS PI18/00150 to CM),
Consellería Educación Generalitat Valenciana (PROMETEOII/2018/051
to VF), Ministerio de Economía y Competitividad (BIO2015-71658-R to
AC), Centro de Investigación Príncipe Felipe (Ayudas para proyectos de
investigación intergrupos to TR) and co-funded with European Regional
Development Funds (ERDF to VF, CM, AC).
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