BackgroundA fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data.ResultsStandard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other.ConclusionsThis study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2217-z) contains supplementary material, which is available to authorized users.
The enormous cellular diversity in the mammalian brain, which is highly prototypical and organized in a hierarchical manner, is dictated by cell-type–specific gene-regulatory programs at the molecular level. Although prevalent in the brain, the contribution of alternative splicing (AS) to the molecular diversity across neuronal cell types is just starting to emerge. Here, we systematically investigated AS regulation across over 100 transcriptomically defined neuronal types of the adult mouse cortex using deep single-cell RNA-sequencing data. We found distinct splicing programs between glutamatergic and GABAergic neurons and between subclasses within each neuronal class. These programs consist of overlapping sets of alternative exons showing differential splicing at multiple hierarchical levels. Using an integrative approach, our analysis suggests that RNA-binding proteins (RBPs) Celf1/2, Mbnl2, and Khdrbs3 are preferentially expressed and more active in glutamatergic neurons, while Elavl2 and Qk are preferentially expressed and more active in GABAergic neurons. Importantly, these and additional RBPs also contribute to differential splicing between neuronal subclasses at multiple hierarchical levels, and some RBPs contribute to splicing dynamics that do not conform to the hierarchical structure defined by the transcriptional profiles. Thus, our results suggest graded regulation of AS across neuronal cell types, which may provide a molecular mechanism to specify neuronal identity and function that are orthogonal to established classifications based on transcriptional regulation.
Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform.
Aim:Gelsemine, an alkaloid from the Chinese herb Gelsemium elegans (Gardn & Champ) Benth., is effective in mitigating chronic pain in rats. In the present study we investigated whether the alkaloid improved sleep disturbance, the most common comorbid symptoms of chronic pain, in a mouse model of neuropathic pain.Methods:Mice were subjected to partial sciatic nerve ligation (PSNL). After the mice were injected with gelsemine or pregabalin (the positive control) intraperitoneally, mechanical allodynia and thermal hyperalgesia were assessed, and electroencephalogram (EEG)/electromyogram (EMG) recording was performed. Motor performance of the mice was assessed using rota-rod test. c-Fos expression in the brain was analyzed with immunohistochemical staining.Results:In PSNL mice, gelsemine (2 and 4 mg/kg) increased the mechanical threshold for 4 h and prolonged the thermal latencies for 3 h. Furthermore, gelsemine (4 mg/kg, administered at 6:30 AM) increased non-rapid eye movement (non-REM, NREM) sleep, decreased wakefulness, but did not affect REM sleep during the first 3 h in PSNL mice. Sleep architecture analysis showed that gelsemine decreased the mean duration of wakefulness and increased the total number of episodes of NREM sleep during the first 3 h after the dosing. Gelsemine (4 mg/kg) did not impair motor coordination in PSNL mice. Immunohistochemical study showed that PSNL increased c-Fos expression in the neurons of the anterior cingulate cortex, and gelsemine (4 mg/kg) decreased c-Fos expression by 58%. Gelsemine (4 mg/kg, administered at either 6:30 AM or 8:30 PM) did not produce hypnotic effect in normal mice. Pregabalin produced similar antinociceptive and hypnotic effects, but impaired motor coordination in PSNL mice.Conclusion:Gelsemine is an effective agent for treatment of both neuropathic pain and sleep disturbance in PSNL mice; anterior cingulate cortex might play a role in the hypnotic effects of gelsemine.
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