Volume-by-volume bioprinting of chondrocytes-alginate bioinks in high temperature thermoplastic scaffolds for cartilage regeneration.

IMPACT STATEMENT: 3D bioprinting represents a novel advance in the area of regenerative biomedicine and tissue engineering for the treatment of different pathologies, among which are those related to cartilage. Currently, the use of different thermoplastic polymers, such as PLA or PCL, for bioprinting processes presents an important limitation: the high temperatures that are required for extrusion affect the cell viability and the final characteristics of the construct. In this work, we present a novel bioprinting process called volume-by-volume (VbV) that allows us to preserve cell viability after bioprinting. This procedure allows cell injection at a safe thermoplastic temperature, and also allows the cells to be deposited in the desired areas of the construct, without the limitations caused by high temperatures. The VbV process could make it easier to bring 3D bioprinting into the clinic, allowing the generation of tissue constructs with polymers that are currently approved for clinical use.

Validation of suitable normalizers for miR expression patterns analysis covering tumour heterogeneity.

Oncogenic microRNAs (miRs) have emerged as diagnostic biomarkers and novel molecular targets for anti-cancer drug therapies. Real-time quantitative PCR (qPCR) is one of the most powerful techniques for analyzing miRs; however, the use of unsuitable normalizers might bias the results. Tumour heterogeneity makes even more difficult the selection of an adequate endogenous normalizer control. Here, we have evaluated five potential referenced small RNAs (U6, rRNA5s, SNORD44, SNORD24 and hsa-miR-24c-3p) using RedFinder algorisms to perform a stability expression analysis in i) normal colon cells, ii) colon and breast cancer cell lines and iii) cancer stem-like cell subpopulations. We identified SNORD44 as a suitable housekeeping gene for qPCR analysis comparing normal and cancer cells. However, this small nucleolar RNA was not a useful normalizer for cancer stem-like cell subpopulations versus subpopulations without stemness properties. In addition, we show for the first time that hsa-miR-24c-3p is the most stable normalizer for comparing these two subpopulations. Also, we have identified by bioinformatic and qPCR analysis, different miR expression patterns in colon cancer versus non tumour cells using the previously selected suitable normalizers. Our results emphasize the importance of select suitable normalizers to ensure the robustness and reliability of qPCR data for analyzing miR expression.