Following Embryonic Stem Cells, Their Differentiated Progeny, and Cell-State Changes During iPS Reprogramming by Raman Spectroscopy.

Monitoring cell-state transition in pluripotent cells is invaluable for application and basic research. In this study, we demonstrate the pertinence of noninvasive, label-free Raman spectroscopy to monitor and characterize the cell-state transition of mouse stem cells undergoing reprogramming. Using an isogenic cell line of mouse stem cells, reprogramming from neuronal cells was performed, and we showcase a comparative analysis of living single-cell spectral data of the original stem cells, their neuronal progenitors, and reprogrammed cells. Neural network, regression models, and ratiometric analyses were used to discriminate the cell states and extract several important biomarkers specific to differentiation or reprogramming. Our results indicated that the Raman spectrum allowed us to build a low-dimensional space allowing us to monitor and characterize the dynamics of cell-state transition at a single-cell level, scattered in heterogeneous populations. The ability of monitoring pluripotency by Raman spectroscopy and distinguishing differences between ES and reprogrammed cells is also discussed.

Pairwise efficiency: a new mathematical approach to qPCR data analysis increases the precision of the calibration curve assay.

BACKGROUND: The real-time quantitative polymerase chain reaction (qPCR) is routinely used for quantification of nucleic acids and is considered the gold standard in the field of relative nucleic acid measurements. The efficiency of the qPCR reaction is one of the most important parameters in data analysis in qPCR experiments. The Minimum Information for publication of Quantitative real-time PCR Experiments (MIQE) guidelines recommends the calibration curve as the method of choice for estimation of qPCR efficiency. The precision of this method has been reported to be between SD = 0.007 (three replicates) and SD = 0.022 (no replicates). RESULTS: In this article, we present a novel approach to the analysis of qPCR data which has been obtained by running a dilution series. Unlike previously developed methods, our method, Pairwise Efficiency, involves a new formula that describes pairwise relationships between data points on separate amplification curves and thus enables extensive statistics. The comparison of Pairwise Efficiency with the calibration curve by Monte Carlo simulation shows the two-folds improvement in the precision of estimations of efficiency and gene expression ratios on the same dataset. CONCLUSIONS: The Pairwise Efficiency nearly doubles the precision in qPCR efficiency determinations compared to standard calibration curve method. This paper demonstrates that applications of combinatorial treatment of data provide the improvement of the determination.

Single-Cell Screening of Tamoxifen Abundance and Effect Using Mass Spectrometry and Raman-Spectroscopy.

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.

Enhancement of K-strategy evolution in histidine utilization using a container with compartments.

Evolutionary strategies in growth improvement can be classified into r- or K-strategies. The former strategy corresponds to an evolutionary increase in growth rate, whereas the latter corresponds to an increase in the maximum amount of organisms or carrying capacity. What determines the strategies to be adopted during evolution? Spatial structures that compartmentalize the population into small patches are key to inducing the K-strategy. Interestingly, previous evolution experiments using Escherichia coli in a glucose-limited batch culture showed that carrying capacity could improve evolutionally even in the absence of spatial structures. However, it is unclear if the lack of spatial structures can direct evolution toward high carrying capacity for utilization of other resources. To address this question, we established a simplified evolution experiment using histidine-requiring E. coli grown under histidine limitation in a container with compartments. We confirmed the importance of spatial structures in K-strategy evolution in histidine utilization. Whole genome sequencing of the K-adapted strains showed functional variety of the mutated genes during the fitness-increasing period. These results validate the importance of spatial structures and imply that restriction of K-strategy evolution on a sort of nutrients is attributable to a paucity of appropriate selection rather than a paucity of causal mutation.

Evaluation and prediction of salt effects on pig muscle by deep UV and machine learning.

The salting process for meat transformation is a crucial step in conventional industry. Recent developments in label-free spectrometry techniques combined with machine learning hold great promise for high-precision salt processing. In this study, we applied UV fluorescence to characterize salting treatments in pig's Teres major muscle and predict NaCl concentrations. t-SNE analyses based on spectral measurements revealed clear differences between NaCl-free and salted treatments. However, salt treatments were not clearly identified. We then highlighted and exploited a variability seen in the emission spectra at the wavelengths 300, 318, and 360 nm, which reflected structural or compositional changes. Using this information, predictive models could accurately identify the five salted treatments with a high specificity and sensitivity or predict salt concentrations. This study paves the way toward the possibility for industrials to precisely adjust NaCl concentrations with precision during processing.

The Effects of Postmortem Time on Muscle Trout Biochemical Composition and Structure.

Fish industry operators have to process fish that arrive at various postmortem times. Postmortem time constrains processing and impacts product quality, safety, and economic value. The objective identification of biomarkers is desirable to predict the postmortem day of aging and this requires a comprehensive longitudinal characterisation of postmortem aging. We analysed the postmortem aging process in trout over a 15-day window. Quantitative physicochemical measurements (pH, colour, texture, aw, proteolysis, and myofibrillar protein solubility) performed on the same fish over time revealed the levels of protein denaturation, solubility, and pH, among other parameters, change very little when assessed by conventional chemical methods. Histological analyses were performed on thin sections and revealed fibre ruptures after 7 days of storage on ice. Ultrastructures were observed by transmission electronic microscopy (TEM) and revealed that sarcomere disorganisation occurred more often after 7 days of storage. Label-free FTIR micro-spectroscopy combined with a SVM model accurately predicted the postmortem time. Spectra-based PC-DA models also enable the identification of biomarkers corresponding to Day 7 and Day 15 postmortem. This study provides insights on postmortem aging and raises prospects for the rapid assessment of trout's freshness status from label-free imaging.

Discriminating cell line specific features of antibiotic-resistant strains of Escherichia coli from Raman spectra via machine learning analysis.

While Raman spectroscopy can provide label-free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain-specific (and function-specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model.

Effect of dry salt versus brine injection plus dry salt on the physicochemical characteristics of smoked salmon after filleting.

Smoked fish fillets are pre-salted as a food conservation and quality preservation measure. Here we investigated biochemical and sensory aspects of smoked salmon fillets. Left-side salmon fillets were dry-salted while the right-side fillets underwent a mixed salting method consisting of an injection of saturated brine followed by surface application of dry salt. After 6 h of salting, all the fillets were smoked. At each step of the process, quality was evaluated using instrumental measurements (pH, color, texture, water content, salt content, aw), and lipid distribution was visualized by MRI. Mixed-salted fillets had a higher salt content than dry-salted fillets and variability in salt distribution was dependent on the salting process. However, these variations had no effect on pH, color or texture, which showed similar values regardless of salting method. Fatty areas had a lower salt content due to slower diffusion of aqueous salt solutions through them. Mixed salting speeds up the salting of the muscle without significantly affecting the quality traits of the salmon fillet.

Analysis of the stability of 70 housekeeping genes during iPS reprogramming.

Studies on induced pluripotent stem (iPS) cells highly rely on the investigation of their gene expression which requires normalization by housekeeping genes. Whether the housekeeping genes are stable during the iPS reprogramming, a transition of cell state known to be associated with profound changes, has been overlooked. In this study we analyzed the expression patterns of the most comprehensive list to date of housekeeping genes during iPS reprogramming of a mouse neural stem cell line N31. Our results show that housekeeping genes' expression fluctuates significantly during the iPS reprogramming. Clustering analysis shows that ribosomal genes' expression is rising, while the expression of cell-specific genes, such as vimentin (Vim) or elastin (Eln), is decreasing. To ensure the robustness of the obtained data, we performed a correlative analysis of the genes. Overall, all 70 genes analyzed changed the expression more than two-fold during the reprogramming. The scale of this analysis, that takes into account 70 previously known and newly suggested genes, allowed us to choose the most stable of all genes. We highlight the fact of fluctuation of housekeeping genes during iPS reprogramming, and propose that, to ensure robustness of qPCR experiments in iPS cells, housekeeping genes should be used together in combination, and with a prior testing in a specific line used in each study. We suggest that the longest splice variants of Rpl13a, Rplp1 and Rps18 can be used as a starting point for such initial testing as the most stable candidates.

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects.

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in drug discovery studies. This can be achieved by accurately measuring the plethora of cellular interactions between a cell and drug at the single-cell level (i.e., drug uptake, metabolism, and effect). This paper describes a single-cell Raman spectroscopy and mass spectrometry (MS) platform to monitor metabolic changes of cells in response to drugs. Using this platform, metabolic changes in response to the drug can be measured by Raman spectroscopy, while the drug and its metabolite can be quantified using mass spectrometry in the same cell. The results suggest that it is possible to access information about drug uptake, metabolism, and response at a single-cell level.

Validation of Common Housekeeping Genes as Reference for qPCR Gene Expression Analysis During iPS Reprogramming Process.

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.

Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli.

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.

Single cell analysis reveals a biophysical aspect of collective cell-state transition in embryonic stem cell differentiation.

In the stem cell research field, the molecular regulatory network used to define cellular states has been extensively studied, however, the general driving force guiding the collective state dynamics remains to be identified from biophysical aspects. Here we monitored the time-development of the cell-state transition at the single-cell and colony levels, simultaneously, during the early differentiation process in mouse embryonic stem cells. Our quantitative analyses revealed that cellular heterogeneity was a result of spontaneous fluctuation of cellular state and cell-cell cooperativity. We considered that the cell state is like a ball fluctuating on a potential landscape, and found that the cooperativity affects the fluctuation. Importantly, the cooperativity temporarily decreased and increased in the intermediate state of cell differentiation, leading to cell-state transition in unison. This process can be explained using the mathematical equation of flashing-ratchet behaviour, which suggests that a general mechanism is driving the collective decision-making of stem cells.

Cell type discrimination based on image features of molecular component distribution.

Machine learning-based cell classifiers use cell images to automate cell-type discrimination, which is increasingly becoming beneficial in biological studies and biomedical applications. Brightfield or fluorescence images are generally employed as the classifier input variables. We propose to use Raman spectral images and a method to extract features from these spatial patterns and explore the value of this information for cell discrimination. Raman images provide information regarding distribution of chemical compounds of the considered biological entity. Since each spectral wavelength can be used to reconstruct the distribution of a given compound, spectral images provide multiple channels of information, each representing a different pattern, in contrast to brightfield and fluorescence images. Using a dataset of single living cells, we demonstrate that the spatial information can be ranked by a Fisher discriminant score, and that the top-ranked features can accurately classify cell types. This method is compared with the conventional Raman spectral analysis. We also propose to combine the information from whole spectral analyses and selected spatial features and show that this yields higher classification accuracy. This method provides the basis for a novel and systematic analysis of cell-type investigation using Raman spectral imaging, which may benefit several studies and biomedical applications.

Raman spectroscopy as a tool for ecology and evolution.

Scientists are always on the lookout for new modalities of information which could reveal new biological features that are useful for deciphering the complexity of biological systems. Here, we introduce Raman spectroscopy as a prime candidate for ecology and evolution. To encourage the integration of this microscopy technique in the field of ecology and evolution, it is crucial to discuss first how Raman spectroscopy fits within the conceptual, technical and pragmatic considerations of ecology and evolution. In this paper, we show that the spectral information holds reliable indicators of intra- and interspecies variations, which can be related to the environment, selective pressures and fitness. Moreover, we show how the technical and pragmatic aspects of this modality (non-destructive, non-labelling, speed, relative low cost, etc.) enable it to be combined with more conventional methodologies. With this paper, we hope to open new avenues of research and extend the scope of available methodologies used in ecology and evolution.

Design and development of genetically encoded fluorescent sensors to monitor intracellular chemical and physical parameters.

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.

Genetic diversity of oxytetracycline-resistant bacteria and tet(M) genes in two major coastal areas of South Korea.

This study aimed to analyse the prevalence and genetic diversity of the tet(M) resistance gene as well as the species composition of oxytetracycline-resistant bacteria in coastal areas of South Korea. Both culturable and non-culturable bacterial communities were sampled in 2010 and 2011 within two coastal areas (Wando and Geoje). tet(M) and 16S rRNA gene sequences were obtained by PCR and sequencing and were used for phylogenetic analyses. Quantitative PCR (qPCR) was performed to determine the prevalence of tet(M) in sampled areas. Phylogenetic analyses revealed high heterogeneity of tet(M) sequences between Wando and Geoje areas. Sequences found in Wando were highly similar to each other and were similar to sequences found in Japanese aquaculture sites. A larger diversity of tet(M) sequences was obtained from natural assemblage in Geoje. qPCR showed a high prevalence of tet(M) in Wando's aquaculture sites (up to 10-2gene copies per 16S rRNA copy) and for industrial sites in Geoje (up to 10-3gene copies per 16S rRNA copy). 16 rRNA-based identification showed that Vibrio sp. and Photobacterium sp. were the most representative species in Wando, whereas in Geoje a broader range of species was found, with several isolates identified as pathogenic Acinetobacter, Aeromonas and Klebsiella. Altogether, these results showed a high prevalence of tet(M) in both sites, and different origins of contamination were identified. Because several pathogenic strains of oxytetracycline-resistant bacteria were reported for the first time, these results strongly warrant further analyses and actions to prevent contamination events by antibiotics in South Korea.

Physiological changes of a green alga (Micractinium sp.) involved in an early-stage of association with Tetrahymena thermophila during 5-year microcosm culture.

Endosymbioses between phototrophic algae and heterotrophic organisms are an important symbiotic association in that this association connects photo- and heterotrophic metabolism, and therefore, affects energy/matter pathways and cycling in the ecosystem. However, little is known about the early processes of evolution of an endosymbiotic association between previously non-associated organisms. In previous studies, we analyzed an early process of the evolution of an endosymbiotic association between an alga and a ciliate by using a long-term culture of an experimental model ecosystem (CET microcosm) composed of a green alga (Micractinium sp.), a bacterium (Escherichia coli), and a ciliate (Tetrahymena thermophila). The results revealed that an algal type, isolated from 5-year cultures of the microcosm, prolonged the longevity of the ancestral and derived clones of T. thermophila in the absence of bacteria, suggesting that a cooperative algal phenotype that benefited the ciliate had evolved in the microcosm. Here, we investigated the physiological changes of the derived Micractinium clones that benefited Tetrahymena, focusing on the release of carbohydrates by and abundance of photopigments in the ancestral and 2 derived algal clones (SC10-2 and SC9-1) isolated from inside Tetrahymena cells. Analyses using HPLC revealed that the algal isolates released glycerol and sucrose at higher concentrations per cell and also contained higher levels of photopigments per cell at pH 7.2, in comparison with the ancestral strain. These phenotypic characters were considered responsible for the increased longevity of Tetrahymena cells, and thus supported the cooperator alga hypothesis.