Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively. Availability and implementation https://github.com/ratschlab/scim. Supplementary information Supplementary data are available at Bioinformatics online.

Recent technological advances allow profiling of tumor samples to an unparalleled level with respect to molecular and spatial composition as well as treatment response. We describe a prospective, observational clinical study performed within the Tumor Profiler (TuPro) Consortium that aims to show the extent to which such comprehensive information leads to advanced mechanistic insights of a patient's tumor, enables prognostic and predictive biomarker discovery, and has the potential to support clinical decision making. For this study of melanoma, ovarian carcinoma, and acute myeloid leukemia tumors, in addition to the emerging standard diagnostic approaches of targeted NGS panel sequencing and digital pathology, we perform extensive characterization using the following exploratory technologies: single-cell genomics and transcriptomics, proteotyping, CyTOF, imaging CyTOF, pharmacoscopy, and 4i drug response profiling (4i DRP). In this work, we outline the aims of the TuPro study and present preliminary results on the feasibility of using these technologies in clinical practice showcasing the power of an integrative multi-modal and functional approach for understanding a tumor's underlying biology and for clinical decision support.

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BackgroundThe NMDA receptor antagonist ketamine was found to act as a fast-acting antidepressant. The effects of single treatment were reported to persist for days to weeks, even in otherwise treatment-refractory cases. Identification of the mechanisms underlying ketamine’s antidepressant action may permit development of novel drugs, with similar clinical properties but lacking psychotomimetic, sedative and other side effects.MethodsWe applied whole-genome microarray profiling to analyze detailed time-course (1, 2, 4 and 8 h) of transcriptome alterations in the striatum and hippocampus following acute administration of ketamine, memantine and phencyclidine in C57BL/6 J mice. The transcriptional effects of ketamine were further analyzed using next-generation sequencing and quantitative PCR. Gene expression alterations induced by the NMDA antagonists were compared to the molecular profiles of psychotropic drugs: antidepressants, antipsychotics, anxiolytics, psychostimulants and opioids.ResultsWe identified 52 transcripts (e.g. Dusp1, Per1 and Fkbp5) with altered expression (FDR < 1 %) in response to treatment with NMDA receptor antagonists. Functional links that connect expression of the regulated genes to the MAPK, IL-6 and insulin signaling pathways were indicated. Moreover, ketamine-regulated expression of specific gene isoforms was detected (e.g. Tsc22d3, Sgk1 and Hif3a). The comparison with other psychotropic drugs revealed that the molecular effects of ketamine are most similar to memantine and phencyclidine. Clustering based on expression profiles placed the NMDA antagonists among fluoxetine, tianeptine, as well as opioids and ethanol.ConclusionsThe identified patterns of gene expression alteration in the brain provided novel molecular classification of ketamine. The transcriptional profile of ketamine reflects its multi-target pharmacological nature. The results reveal similarities between the effects of ketamine and monoaminergic antidepressants that may explain the mechanisms of its rapid antidepressant action.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2713-3) contains supplementary material, which is available to authorized users.

BackgroundIncreased permeability of the intestinal wall and intestinal dysbiosis may contribute to chronic systemic inflammation, one of the causes of accelerated atherosclerosis and cardiovascular morbidity and mortality burden in patients with chronic kidney disease. The aim of this study was to evaluate the association between markers of intestinal permeability and inflammation in haemodialysis (HD) patients.MethodsPlasma concentration of zonulin, haptoglobin, TNFα, IL6, d-lactates and bacterial lipopolysaccharides (LPS) was assessed in blood samples obtained after overnight fast before midweek morning HD session in 150 stable, prevalent HD patients. Daily intake of energy and macronutrients was assessed on the basis of a food frequency questionnaire.ResultsSerum hsCRP level was increased in over 70% of patients. Plasma levels of zonulin [11.6 (10.9–12.3) vs 6.8 (5.8–7.8) ng/mL], IL6 [6.2 (1.0–10.3) vs 1.3 (1.0–2.0) pg/mL] and TNFα [5.9 (2.9–11.8) vs 1.6 (1.3–1.8) pg/mL], but not LPS and d-lactates were significantly higher in HD than in healthy controls. d-lactates and LPS levels were weakly associated with IL6 (R = 0.175; p = 0.03, and R = 0.241; p = 0.003). There was a borderline correlation between plasma zonulin and serum hsCRP (R = 0.159; p = 0.07), but not with IL6, LPS and d-lactates. In multiple regression, both serum CRP and plasma IL6 variability were explained by LPS (β = 0.143; p = 0.08 and β = 0.171; p = 0.04, respectively), only.ConclusionThe weak association between plasma d-lactate, LPS and IL6 levels indicates that intestinal flora overgrowth or increased intestinal permeability contributes very slightly to the chronic inflammation development in HD patients.

BackgroundThe mechanisms of steroids actions in the brain mainly involve the binding and nuclear translocation of specific cytoplasmic receptors. These receptors can act as transcription factors and regulate gene expression. However, steroid-dependent transcriptional regulation in different types of neural cells is not yet fully understood. The aim of this study was to evaluate and compare transcriptional alterations induced by various steroid receptor agonists in primary cultures of astrocytes and neurons from mouse brain.ResultsWe utilized whole-genome microarrays (Illumina Mouse WG-6) and quantitative PCR analyses to measure mRNA abundance levels. To stimulate gene expression we treated neuronal and astroglial cultures with dexamethasone (100 nM), aldosterone (200 nM), progesterone (200 nM), 5α-dihydrotestosterone (200 nM) and β-Estradiol (200 nM) for 4 h. Neurons were found to exhibit higher levels of expression of mineralocorticoid receptor, progesterone receptor and estrogen receptor 2 than astrocytes. However, higher mRNA level of glucocorticoid receptor mRNA was observed in astrocytes. We identified 956 genes regulated by steroids. In astrocytes we found 381 genes altered by dexamethasone and 19 altered by aldosterone. Functional classification of the regulated genes indicated their putative involvement in multiple aspects of cell metabolism (up-regulated Slc2a1, Pdk4 and Slc45a3) and the inflammatory response (down-regulated Ccl3, Il1b and Tnf). Progesterone, dihydrotestosterone and estradiol did not change gene expression in astrocytes. We found no significant changes in gene expression in neurons.ConclusionsThe obtained results indicate that glial cells might be the primary targets of transcriptional action of steroids in the central nervous system. Substantial changes in gene expression driven by the glucocorticoid receptor imply an important role for the hypothalamic–pituitary–adrenal axis in the hormone-dependent regulation of brain physiology. This is an in vitro study. Hence, the model may not accurately reflect all the effects of steroids on gene expression in neurons in vivo.Electronic supplementary materialThe online version of this article (doi:10.1186/s12868-017-0352-5) contains supplementary material, which is available to authorized users.

Chronic exposure to opioids induces adaptations in brain function that lead to the formation of the behavioral and physiological symptoms of drug dependence and addiction. Animal models commonly used to test these symptoms typically last less than two weeks, which is presumably too short to observe the alterations in the brain that accompany drug addiction. Here, we analyzed the phenotypic and molecular effects of nearly lifelong morphine or saccharin intake in C57BL/6J mice. We used multiple paradigms to evaluate the symptoms of compulsive drug intake: a progressive ratio schedule, intermittent access and a schedule involving a risk of punishment were programmed into an automated IntelliCage system. Gene expression profiles were evaluated in the striatum using whole-genome microarrays and further validated using quantitative polymerase chain reaction in the striatum and the prefrontal cortex. Mice voluntary self-administering morphine showed addiction-related behavioral pattern that included: higher motivation to work for a drug reward, increased reward seeking and increased craving. The analysis of molecular changes revealed a tolerance effect in the transcriptional response to morphine injection (20 mg/kg, ip), as well as some long-lasting alterations in gene expression profiles between the analyzed groups of animals. Interestingly, among the morphine-drinking animals, certain transcriptional profiles were found to be associated with alterations in behavior. In conclusion, our model represents a novel approach for investigating the behavioral and molecular mechanisms underlying opioid addiction. Prolonged morphine intake caused adaptive processes in the brain that manifested as altered behavior and transcriptional sensitivity to opioids.

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