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Neuropeptide S (NPS) is a regulatory peptide expressed by limited number of neurons in the brainstem. The simultaneous anxiolytic and arousal-promoting effect of NPS suggests an involvement in mood control and vigilance, making the NPS-NPS receptor system an interesting potential drug target. Here we examined, in detail, the distribution of NPS-immunoreactive (IR) fiber arborizations in brain regions of rat known to be involved in the regulation of sleep and arousal. Such nerve terminals were frequently apposed to GABAergic/galaninergic neurons in the ventro-lateral preoptic area (VLPO) and to tyrosine hydroxylase-IR neurons in all hypothalamic/thalamic dopamine cell groups. Then we applied the single platform-on-water (mainly REM) sleep deprivation method to study the functional role of NPS in the regulation of arousal. Of the three pontine NPS cell clusters, the NPS transcript levels were increased only in the peri-coerulear group in sleep-deprived animals, but not in stress controls. The density of NPS-IR fibers was significantly decreased in the median preoptic nucleus-VLPO region after the sleep deprivation, while radioimmunoassay and mass spectrometry measurements showed a parallel increase of NPS in the anterior hypothalamus. The expression of the NPS receptor was, however, not altered in the VLPO-region. The present results suggest a selective activation of one of the three NPS-expressing neuron clusters as well as release of NPS in distinct forebrain regions after sleep deprivation. Taken together, our results emphasize a role of the peri-coerulear cluster in the modulation of arousal, and the importance of preoptic area for the action of NPS on arousal and sleep.

Modern techniques like automated microscopy and spectroscopy now make it possible to study quantitatively, across multiple phenotypic and molecular parameters, how cell populations are affected by different treatments and/or environmental disturbances. As the technology development at the instrument level often is ahead of the data analytical tools and the scientific questions, there is a large and growing need for computational algorithms enabling desired data analysis. These algorithms must have capacity to extract and process quantitative dynamic information about how the cell population is affected by different stimuli with the final goal to transform this information into development of new powerful therapeutic strategies. In particular, there is a great need for automated systems that can facilitate the analysis of massive data streams for label-free methods such as phase contrast microscopy (PCM) imaging and spectroscopy (NMR). Therefore, in this thesis, algorithms for quantitative high-throughput phenotypic and systemic profiling of in vitro growing cell populations via label-free microscopy and spectroscopy are developed and evaluated. First a two-dimensional filter approach for high-throughput screening for drugs inducing autophagy and apoptosis from phase contrast time-lapse microscopy images is studied. Then new methods and applications are presented for label-free extraction and comparison of time-evolving morphological features in phase-contrast time-lapse microscopy images recorded from in vitro growing cell populations. Finally, the use of dynamic morphology and NMR/MS spectra for implementation of a reference database of drug induced changes, analogous to the outstanding mRNA gene expression based Connectivity Map database, is explored. In conclusion, relatively simple computational methods are useful for extraction of very valuable biological and pharmacological information from time-lapse microscopy images and NMR spectroscopy data offering great potential for biomedical applications in general and cancer pharmacology in particular.

Drug induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances as well as to support drug repurposing, i.e. identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the “Connectivity Map” (CMap). The potential of similar exercises at the metabolite level is, however, still largely unexplored. Only recently the first high throughput metabolomic assay pilot study was published, involving screening of metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here we report results from a separately developed metabolic profiling assay, which leverages 1H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of most tested drugs according to their respective pharmacological class. A more advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (three metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this kind of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.

Automated phase-contrast video microscopy now makes it feasible to monitor a high-throughput (HT) screening experiment in a 384-well microtiter plate format by collecting one time-lapse video per well. Being a very cost-effective and label-free monitoring method, its potential as an alternative to cell viability assays was evaluated. Three simple morphology feature extraction and comparison algorithms were developed and implemented for analysis of differentially time-evolving morphologies (DTEMs) monitored in phase-contrast microscopy videos. The most promising layout, pixel histogram hierarchy comparison (PHHC), was able to detect several compounds that did not induce any significant change in cell viability, but made the cell population appear as spheroidal cell aggregates. According to recent reports, all these compounds seem to be involved in inhibition of platelet-derived growth factor receptor (PDGFR) signaling. Thus, automated quantification of DTEM (AQDTEM) holds strong promise as an alternative or complement to viability assays in HT in vitro screening of chemical compounds.

Label free time-lapse video microscopy based monitoring of time evolving cell population morphology has potential to offer a simple and cost effective method for identity control of cell lines. Such morphology monitoring also has potential to offer discovery of chemically induced differential changes between pairs of cell lines of interest, for example where one in a pair of cell lines is normal/sensitive and the other malignant/resistant. A new simple algorithm, pixel histogram hierarchy comparison (PHHC), for comparison of time evolving morphologies (TEM) in phase contrast time-lapse microscopy movies was applied to a set of 10 different cell lines and three different iso-genic colon cancer cell line pairs, each pair being genetically identical except for a single mutation. PHHC quantifies differences in morphology by comparing pixel histogram intensities at six different resolutions. Unsupervised clustering and machine learning based classification methods were found to accurately identify cell lines, including their respective iso-genic variants, through time-evolving morphology. Using this experimental setting, drugs with differential activity in iso-genic cell line pairs were likewise identified. Thus, this is a cost effective and expedient alternative to conventional molecular profiling techniques and might be useful as part of the quality control in research incorporating cell line models, e.g. in any cell/tumor biology or toxicology project involving drug/agent differential activity in pairs of cell line models.