SINC(I)   25518
INSTITUTO DE INVESTIGACION EN SEÑALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Deconvolution of rat Pancreatic islet RNA-seq to uncouple islet cell composition effect from INGAP peptide treatment
Autor/es:
CAROLINA ROMÁN; LUIS FLORES; SANTIAGO RODRIGUEZ-SEGUÍ; ANA CAROLINA HEIDENREICH; JUAN GAGLIARDINO; LEANDRO EZEQUIEL DI PERSIA; AGUSTÍN ROMERO; BÁRBARA MAIZTEGUI; DIEGO HUMBERTO MILONE
Lugar:
Virtual
Reunión:
Congreso; 1st Latin America Congres of women in Bioinformatics and Data Science; 2020
Institución organizadora:
Women in Bioinformatics and Data Science LA
Resumen:
Diabetes Mellitus is a disease characterized by the loss or reduction of the pancreatic Beta cell mass, with the consequent impairment of insulin production. A pentadecapeptide derived from the Islet Neogenesis Associated Protein (INGAP-PP) has been previously shown to increase the Beta cell mass and insulin secretion in normal and diabetic animals.In this work, we performed cuadruplicated RNA-seq assays on rat pancreatic islets treated in vitro with INGAP-PP to gain insights into the mechanisms modulated by the peptide treatment. Sequence read alignment was performed using HiSAT, followed by Stringtie for de novo gene annotation, transcript expression quantification and normalization across samples. Pancreatic islets are heterogeneous tissue, composed of at least fourteen cell types including exocrine, endocrine, vascular and immune cells, among others. As well, the INGAP-PP can induce transcriptional changes on several of these cell types.Indeed, a standard RNA-seq analysis pipeline did not allow a clear discrimination of the INGAP-PP treatment transcriptional effects. By combining our bulk RNA-seq with gene expression profiles obtained from islet single cell RNA-seq data, different levels of expression were tracked in samples (gene subsets associated with specific islet cell components in control RNA-seq). This finding led us to develop bioinformatics tools to model and deconvolve the original islet cell composition in control samples. Deconvolution process is implemented through an iterative algorithm with factorization of non-negative matrices. Therefore, the proportion of each cell type of the samples can be inferred, allowing a better determination of the differential expression. Our ongoing work aims at using these tools to uncouple the islet cell composition effect from the INGAP-PP treatment, ultimately allowing for a more precise analysis of the signaling pathways modulated by the peptide treatment.