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DOI 10.3390/biomedicines11041079

Медицинская библиотека · Biomedicines · 2023

A Strategy for the Selection of RT-qPCR Reference Genes Based on Publicly Available Transcriptomic Datasets.

Nevone Alice, Lattarulo Francesca, Russo Monica, Panno Giada, Milani Paolo, Basset Marco, Avanzini Maria Antonietta, Merlini Giampaolo, Palladini Giovanni, Nuvolone Mario

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Аннотация

In the next-generation sequencing era, RT-qPCR is still widely employed to quantify levels of nucleic acids of interest due to its popularity, versatility, and limited costs. The measurement of transcriptional levels through RT-qPCR critically depends on reference genes used for normalization. Here, we devised a strategy to select appropriate reference genes for a specific clinical/experimental setting based on publicly available transcriptomic datasets and a pipeline for RT-qPCR assay design and validation. As a proof-of-principle, we applied this strategy to identify and validate reference genes for transcriptional studies of bone-marrow plasma cells from patients with AL amyloidosis. We performed a systematic review of published literature to compile a list of 163 candidate reference genes for RT-qPCR experiments employing human samples. Next, we interrogated the Gene Expression Omnibus to assess expression levels of these genes in published transcriptomic studies on bone-marrow plasma cells from patients with different plasma cell dyscrasias and identified the most stably expressed genes as candidate normalizing genes. Experimental validation on bone-marrow plasma cells showed the superiority of candidate reference genes identified through this strategy over commonly employed "housekeeping" genes. The strategy presented here may apply to other clinical and experimental settings for which publicly available transcriptomic datasets are available.

Для цитирования: Nevone Alice, Lattarulo Francesca, Russo Monica, Panno Giada, Milani Paolo, Basset Marco, Avanzini Maria Antonietta, Merlini Giampaolo, Palladini Giovanni, Nuvolone Mario. A Strategy for the Selection of RT-qPCR Reference Genes Based on Publicly Available Transcriptomic Datasets.. Biomedicines. 2023. DOI: 10.3390/biomedicines11041079.
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