Background The successful ex vivo expansion of T-cells in great numbers is the cornerstone of adoptive cell therapy. We aimed to achieve the most optimal T-cell expansion condition by comparing the expansion of T-cells at various seeding densities, IL-2 concentrations, and bead-to-cell ratios. we first expanded the peripheral blood mononuclear cells (PBMCs) of a healthy donor at a range of 20 to 500 IU/mL IL-2 concentrations, 125 × 103 to 1.5 × 106 cell/mL, and 1:10 to 10:1 B:C (Bead-to-cell) ratios and compared the results. We then expanded the PBMC of three healthy donors using the optimized conditions and examined the growth kinetics. On day 28, CD3, CD4, and CD8 expression of the cell populations were analyzed by flow cytometry. Results T-cells of the first donor showed greater expansion results in IL-2 concentrations higher than 50 IU/mL compared to 20 IU/mL (P = 0.02). A seeding density of 250 × 103 cell/mL was superior to higher or lower densities in expanding T-cells (P = 0.025). Also, we witnessed a direct correlation between the B:C ratio and T-cell expansion, in which, in 5:1 and 10:1 B:C ratios T-cell significantly expanded more than lower B:C ratios. The results of PBMC expansions of three healthy donors were similar in growth kinetics. In the optimized condition, 96–98% of the lymphocyte population expressed CD3. While the majority of these cells expressed CD8, the mean expression of CD4 in the donors was 19.3, 16.5, and 20.4%. Conclusions Our methodology demonstrates an optimized culture condition for the production of large quantities of polyclonal T-cells, which could be useful for future clinical and research studies.

The omics technologies provide an invaluable opportunity to employ a global view towards human diseases. However, the appropriate translation of big data to knowledge remains a major challenge. In this study, we have performed quality control assessments for 91 transcriptomics datasets deposited in gene expression omnibus database and also have evaluated the publications derived from these datasets. This survey shows that drawbacks in the analyses and reports of transcriptomics studies are more common than one may assume. This report is concluded with some suggestions for researchers and reviewers to enhance the minimal requirements for gene expression data generation, analysis and report.

High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data.

Olfactory receptors (ORs) which are mainly known as odor-sensors in the olfactory epithelium are shown to be expressed in several non-sensory tissues. Despite the specified role of some of these receptors in normal physiology of the kidney, little is known about their potential effect in renal disorders. In this study, using the holistic view of systems biology, it was determined that ORs are significantly changed during the progression of kidney fibrosis. For further validation, common differentially expressed ORs resulted from reanalysis of two time-course microarray datasets were selected for experimental evaluation in a validated murine model of unilateral ureteral obstruction (UUO). Transcriptional analysis by real-time quantitative polymerase chain reaction demonstrated considerable changes in the expression pattern of Olfr433, Olfr129, Olfr1393, Olfr161, and Olfr622 during the progression of kidney fibrosis. For localization of these ORs, single-cell RNA-sequencing datasets of normal and UUO mice were reanalyzed. Results showed that Olfr433 is highly expressed in macrophages in day-2 and 7 post-injury in UUO mice and not in normal subgroups. Besides, like previous findings, Olfr1393 was shown to be expressed prominently in the proximal tubular cells of the kidney. In conclusion, our combinatorial temporal approach to the underlying mechanisms of chronic kidney disease highlighted the potential role of ORs in progression of fibrosis. The expression of Olfr433 in the macrophages provides some clue about its relation to molecular mechanisms promoted in the fibrotic kidney. The proposed ORs in this study could be the subject of further functional assessments in the future.

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