Archive tissues are the most available source of human tissues useful for molecular analysis in translational research. The main issues for those specimens are the modification and degradation of biomolecules, namely proteins, DNA, and RNA. In the last decade, several high-throughput analytical methods have been applied to archive tissues. Although histological tissues are fixed in neutral-buffered formalin nowadays, in the recent past, Bouin\'s solution was also used in tissue processing. The present study aims to investigate the feasibility of nCounter Nanostring hybridization in quantifying mRNA in highly degraded samples, such as Bouin\'s fixed and paraffin-embedded (BFPE) tissues, in comparison to the standard formalin-fixed and paraffin-embedded (FFPE) tissues as a source of RNA. A total of 16 paraffin-embedded tissue blocks from eight patients were analyzed (8 were FFPE and 8 were BEPE). Nanostring technology was applied to 300 ng of each RNA sample, whereas 360 ng of the same templates were retrotranscribed and submitted to qPCR and ddPCR. Our results show that the Nanostring technology outperforms the reference methods (ddPCR and qPCR) in detecting target mRNA in FFPE and BFPE samples. However, even Nanostring technology does not escape the limitation imposed by the degradation of the RNA templates, which could lead to misleading conclusions on the gene expression level.

BACKGROUND: Meibomian gland dysfunction (MGD) is one of the most common conditions in ophthalmic practice and the most frequent cause of evaporative dry eye disease (DED). However, the immune mechanisms leading to this pathology are not fully understood and the diagnostic tests available are limited. Here, we used the nCounter technology to analyze immune gene expression in DED-MGD that can be used for developing diagnostic signatures for DED.
METHODS: Conjunctival cell samples were obtained by aspiration from patients with DED-MGD (n = 27) and asymptomatic controls (n = 22). RNA was purified, converted to cDNA, preamplified and analyzed using the Gene Expression Human Immune V2 panel (NanoString), which includes 579 target and 15 housekeeping genes. A machine learning (ML) algorithm was applied to design a signature associated with DED-MGD.
RESULTS: Forty-five immune genes were found upregulated in DED-MGD vs. controls, involved in eight signaling pathways, IFN I/II, MHC class I/II, immunometabolism, B cell receptor, T Cell receptor, and T helper-17 (Th-17) differentiation. Additionally, statistically significant correlations were found between 31 genes and clinical characteristics of the disease such as lid margin or tear osmolarity (Pearson\'s r < 0.05). ML analysis using a recursive feature elimination (RFE) algorithm selected a 4-gene mRNA signature that discriminated DED-MGD from control samples with an area under the ROC curve (AUC ROC) of 0.86 and an accuracy of 77.5%.
CONCLUSIONS: Multiplexed mRNA analysis of conjunctival cells can be used to analyze immune gene expression patterns in patients with DED-MGD and to generate diagnostic signatures.

BACKGROUND: This communication reports the identification of a new panel of transcriptional changes in inflammation-associated genes observed in response to ionising radiation received by radiotherapy patients.
METHODS: Peripheral blood samples were taken with ethical approval and informed consent from a total of 20 patients undergoing external beam radiotherapy for breast, lung, gastrointestinal or genitourinary tumours. Nanostring nCounter analysis of transcriptional changes was carried out in samples prior and 24 h post-delivery of the 1st radiotherapy fraction, just prior to the 5th or 6th fraction, and just before the last fraction.
RESULTS: Statistical analysis with BRB-ArrayTools, GLM MANOVA and nSolver, revealed a radiation responsive panel of genes which varied by patient group (type of cancer) and with time since exposure (as an analogue for dose received), which may be useful as a biomarker of radiation response.
CONCLUSIONS: Further validation in a wider group of patients is ongoing, together with work towards a full understanding of patient specific responses in support of personalised approaches to radiation medicine.