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Sample preparation method considerations for integrated transcriptomic and proteomic analysis of tumors
Author(s) -
Bhat Anupama Rajan,
Gupta Manoj Kumar,
Krithivasan Priya,
Dhas Kunal,
Nair Jayalakshmi,
Reddy Ram Bhupal,
Sudheendra Holalugunda Vittalamurthy,
Chavan Sandip,
Vardhan Harsha,
Darsi Sujatha,
Balakrishnan Lavanya,
Katragadda Shanmukh,
Kekatpure Vikram,
Suresh Amritha,
Tata Pramila,
Panda Binay,
Kuriakose Moni A.,
Sirdeshmukh Ravi
Publication year - 2017
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201600100
Subject(s) - rna extraction , rna , transcriptome , proteome , computational biology , biology , messenger rna , extraction (chemistry) , microbiology and biotechnology , bioinformatics , chromatography , gene expression , chemistry , biochemistry , gene
Sample processing protocols that enable compatible recovery of differentially expressed transcripts and proteins are necessary for integration of the multiomics data applied in the analysis of tumors. In this pilot study, we compared two different isolation methods for extracting RNA and protein from laryngopharyngeal tumor tissues and the corresponding adjacent normal sections. In Method 1, RNA and protein were isolated from a single tissue section sequentially and in Method 2, the extraction was carried out using two different sections and two independent and parallel protocols for RNA and protein. RNA and protein from both methods were subjected to RNA‐seq and iTRAQ‐based LC‐MS/MS analysis, respectively. Analysis of data revealed that a higher number of differentially expressed transcripts and proteins were concordant in their regulation trends in Method 1 as compared to Method 2. Cross‐method comparison of concordant entities revealed that RNA and protein extraction from the same tissue section (Method 1) recovered more concordant entities that are missed in the other extraction method (Method 2) indicating heterogeneity in distribution of these entities in different tissue sections. Method 1 could thus be the method of choice for integrated analysis of transcriptome and proteome data.