Tandem RNA and Protein Extraction: A Platform for Maximizing the Use of Limited Ex Vivo Tissue Samples.
Аннотация
Human tissue samples represent the gold standard for obtaining clinically relevant and translatable insight into disease processes that in vitro systems cannot fully reproduce. However, patient-derived samples are often limited in size and availability, limiting the number of downstream assays that can be performed. To maximize the use of invaluable human samples, we present a protocol for the tandem extraction of high-quality RNA and protein from the same tissue section. This method has been optimized for 15-30 mg tissue sections, enabling more experimental conditions and technical replicates, while minimizing intrasample variability associated with heterogeneous tissues. This protocol also avoids potentially hazardous solvents present in phenol-chloroform-based methods such as TRIzol, providing a safer and more accessible workflow without compromising biomolecule integrity. This protocol was developed and validated using atherosclerotic plaque tissue from carotid endarterectomy, a very challenging tissue type to work with due to extensive calcification, necrosis, and limited surgical availability. We have also validated this method using mouse aortic tissue and cultured THP-1 cells, demonstrating its versatility across sample input types. As this protocol relies on standard column-based RNA extraction kits and commonly available reagents for protein precipitation and extraction, this methodology is widely accessible and easy to implement as a standard, streamlined workflow. Key features • Advances ex vivo models of human disease (e.g., atherosclerosis) by maximizing the use of patient-derived tissue samples. • Minimizes patient variability by generating directly comparable RNA and protein readouts. • Minimizes the number of patient samples required and maximizes the output from each sample. • Stabilizes RNA integrity from tissue without impacting the quality of proteins derived from the same sample.
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