To be able to predict antibiotic resistance in bacteria from fast label-free microscopic observations would benefit a broad range of applications in the biological and biomedical fields. Here, we demonstrate the utility of label-free Raman spectroscopy in monitoring the type of resistance and the mode of action of acquired resistance in a bacterial population of Escherichia coli, in the absence of antibiotics. Our findings are reproducible. Moreover, we identified spectral regions that best predicted the modes of action and explored whether the Raman signatures could be linked to the genetic basis of acquired resistance. Spectral peak intensities significantly correlated (False Discovery Rate, p < 0.05) with the gene expression of some genes contributing to antibiotic resistance genes. These results suggest that the acquisition of antibiotic resistance leads to broad metabolic effects reflected through Raman spectral signatures and gene expression changes, hinting at a possible relation between these two layers of complementary information.

Induced pluripotent stem cell (iPS) reprogramming allows to turn a differentiated somatic cell into a pluripotent cell. This process is accompanied by many changes in fundamental cell properties, such as energy production, cell-to-cell interactions, cytoskeletal organization, and others. Real-time quantitative polymerase chain reaction (RT-qPCR) can be used as a quantitative method of gene expression analysis to investigate iPS reprogramming but it requires a validation of reference genes for the accurate assessment of target genes’ expression. Currently, studies evaluating the performance of reference genes during iPS reprogramming are lacking. In this study we analysed the stability of 12 housekeeping genes during 20 days of iPS reprogramming of murine cells based on statistical analyses of RT-qPCR data using five different statistical algorithms. This study reports strong variations in housekeeping gene stability during the reprogramming process. Most stable genes were Atp5f1, Pgk1 and Gapdh, while the least stable genes were Rps18, Hprt, Tbp and Actb. The results were validated by a proof-of-point qPCR experiment with pluripotent markers Nanog, Rex1 and Oct4 normalized to the best and the worst reference gene identified by the analyses. Overall, this study and its implications are particularly relevant to investigations on the cell-state and pluripotency in iPS reprogramming.

Over the past decades many researchers have made major contributions towards the development of genetically encoded (GE) fluorescent sensors derived from fluorescent proteins. GE sensors are now used to study biological phenomena by facilitating the measurement of biochemical behaviors at various scales, ranging from single molecules to single cells or even whole animals. Here, we review the historical development of GE fluorescent sensors and report on their current status. We specifically focus on the development strategies of the GE sensors used for measuring pH, ion concentrations (e.g., chloride and calcium), redox indicators, membrane potential, temperature, pressure, and molecular crowding. We demonstrate that these fluroescent protein-based sensors have a shared history of concepts and development strategies, and we highlight the most original concepts used to date. We believe that the understanding and application of these various concepts will pave the road for the development of future GE sensors and lead to new breakthroughs in bioimaging.

Monitoring drug uptake, its metabolism, and response on the single-cell level is invaluable for sustaining drug discovery efforts. In this study, we show the possibility of accessing the information about the aforementioned processes at the single-cell level by monitoring the anticancer drug tamoxifen using live single-cell mass spectrometry (LSC−MS) and Raman spectroscopy. First, we explored whether Raman spectroscopy could be used as a label-free and nondestructive screening technique to identify and predict the drug response at the single-cell level. Then, a subset of the screened cells was isolated and analyzed by LSC−MS to measure tamoxifen and its metabolite, 4-Hydroxytamoxifen (4-OHT) in a highly selective, sensitive, and semiquantitative manner. Our results show the Raman spectral signature changed in response to tamoxifen treatment which allowed us to identify and predict the drug response. Tamoxifen and 4-OHT abundances quantified by LSC−MS suggested some heterogeneity among single-cells. A similar phenomenon was observed in the ratio of metabolized to unmetabolized tamoxifen across single-cells. Moreover, a correlation was found between tamoxifen and its metabolite, suggesting that the drug was up taken and metabolized by the cell. Finally, we found some potential correlations between Raman spectral intensities and tamoxifen abundance, or its metabolism, suggesting a possible relationship between the two signals. This study demonstrates for the first time the potential of using Raman spectroscopy and LSC−MS to investigate pharmacokinetics at the single-cell level.

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