MULTIMODAL MICROSCOPY IMAGE ALIGNMENT USING SPATIAL AND SHAPE INFORMATION AND A BRANCH-AND-BOUND ALGORITHM.

Multimodal microscopy experiments that image the same population of cells under different experimental conditions have become a widely used approach in systems and molecular neuroscience. The main obstacle is to align the different imaging modalities to obtain complementary information about the observed cell population (e.g., gene expression and calcium signal). Traditional image registration methods perform poorly when only a small subset of cells are present in both images, as is common in multimodal experiments. We cast multimodal microscopy alignment as a cell subset matching problem. To solve this non-convex problem, we introduce an efficient and globally optimal branch-and-bound algorithm to find subsets of point clouds that are in rotational alignment with each other. In addition, we use complementary information about cell shape and location to compute the matching likelihood of cell pairs in two imaging modalities to further prune the optimization search tree. Finally, we use the maximal set of cells in rigid rotational alignment to seed image deformation fields to obtain a final registration result. Our framework performs better than the state-of-the-art histology alignment approaches regarding matching quality and is faster than manual alignment, providing a viable solution to improve the throughput of multimodal microscopy experiments.

Blind demixing methods for recovering dense neuronal morphology from barcode imaging data.

Cellular barcoding methods offer the exciting possibility of 'infinite-pseudocolor' anatomical reconstruction-i.e., assigning each neuron its own random unique barcoded 'pseudocolor,' and then using these pseudocolors to trace the microanatomy of each neuron. Here we use simulations, based on densely-reconstructed electron microscopy microanatomy, with signal structure matched to real barcoding data, to quantify the feasibility of this procedure. We develop a new blind demixing approach to recover the barcodes that label each neuron, and validate this method on real data with known barcodes. We also develop a neural network which uses the recovered barcodes to reconstruct the neuronal morphology from the observed fluorescence imaging data, 'connecting the dots' between discontiguous barcode amplicon signals. We find that accurate recovery should be feasible, provided that the barcode signal density is sufficiently high. This study suggests the possibility of mapping the morphology and projection pattern of many individual neurons simultaneously, at high resolution and at large scale, via conventional light microscopy.

Deep-Red and Near-Infrared Iridium Complexes with Fine-Tuned Emission Colors by Adjusting Trifluoromethyl Substitution on Cyclometalated Ligands Combined with Matched Ancillary Ligands for Highly Efficient Phosphorescent Organic Light-Emitting Diodes.

Six novel Ir(C^N)2(L^X)-type heteroleptic iridium complexes with deep-red and near-infrared region (NIR)-emitting coverage were constructed through the cross matching of various cyclometalating (C^N) and ancillary (LX) ligands. Here, three novel C^N ligands were designed by introducing the electron-withdrawing group CF3 on the ortho (o-), meta (m-), and para (p-) positions of the phenyl ring in the 1-phenylisoquinoline (piq) group, which were combined with two electron-rich LX ligands (dipba and dipg), respectively, leading to subsequent iridium complexes with gradually changing emission colors from deep red (≈660 nm) to NIR (≈700 nm). Moreover, a series of phosphorescent organic light-emitting diodes (PhOLEDs) were fabricated by employing these phosphors as dopant emitters with two doping concentrations, 5% and 10%, respectively. They exhibited efficient electroluminescence (EL) with significantly high EQE values: >15.0% for deep red light0 (λmax = 664 nm) and >4.0% for NIR cases (λmax = 704 nm) at a high luminance level of 100 cd m-2. This work not only provides a promising approach for finely tuning the emission color of red phosphors via the easily accessible molecular design strategy, but also enables the establishment of an effective method for enriching phosphorescent-emitting molecules for practical applications, especially in the deep-red and near-infrared region (NIR).

BARcode DEmixing through Non-negative Spatial Regression (BarDensr).

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.

Complexity and graded regulation of neuronal cell-type-specific alternative splicing revealed by single-cell RNA sequencing.

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.

Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.

BACKGROUND: A 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. RESULTS: Standard 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. CONCLUSIONS: This 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.

Gelsemine alleviates both neuropathic pain and sleep disturbance in partial sciatic nerve ligation mice.

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.

Excretion of berberine and its metabolites in oral administration in rats.

Berberine (BBR) has been confirmed to show extensive bioactivities for the treatments of diabetes and hypercholesterolemia in clinic. However, there are few pharmacokinetic studies to elucidate the excretions of BBR and its metabolites. Our research studied the excretions of BBR and its metabolites in rats after oral administration (200 mg/kg). Metabolites in bile, urine, and feces were detected by liquid chromatography coupled to ion trap time-of-flight mass spectrometry; meanwhile, a validated liquid chromatography coupled with tandem mass spectrometry method was developed for their quantifications. Sixteen metabolites, including 10 Phase I and six Phase II metabolites were identified and clarified after dosing in vivo. Total recovered rate of BBR was 22.83% (19.07% of prototype and 3.76% of its metabolites) with 9.2 × 10(-6) % in bile (24 h), 0.0939% in urine (48 h), and 22.74% in feces (48 h), respectively. 83% of BBR was excreted as thalifendine (M1) from bile, whereas thalifendine (M1) and berberrubine (M2) were the major metabolites occupying 78% of urine excretion. Most of BBR and its metabolites were found in feces containing 84% of prototype. In summary, we provided excretion profiles of BBR and its metabolites after oral administration in rats in vivo.

Bee venom phospholipase A2-induced phasic contractions in mouse rectum: independent roles of eicosanoid and gap junction proteins and their loss in experimental colitis.

Various events including digestion and inflammation are regulated by secreted phospholipase A2 (sPLA2) in gastrointestinal tissues, however, the role of sPLA2 on contractile activity has not been elucidated. We investigated the effect of bee venom PLA2 (bvPLA2), which is homologous to the central domain of group III sPLA2, on contractile activity in mouse rectum. The longitudinal preparations of rectum showed rhythmic phasic contractions (RPCs) with varied amplitude and high frequency. Treatment with bvPLA2 at 1 µg/ml increased amplitudes of RPCs without marked changes in frequency and basal tone. RPCs by bvPLA2 were affected neither by atropine nor by inhibition of nitric oxide synthase, and partly inhibited by dual inhibition of the cyclooxygenase and lipoxygenase pathways. Pretreatment of bvPLA2 with dithiothreitol, which inhibits the enzyme activity, partly reduced bvPLA2-induced RPCs, and arachidonic acid-increased RPCs were completely abolished by cyclooxygenase/lipoxygenase inhibition. Phasic contractions have been shown to be regulated by gap junction and to be decreased in gastrointestinal tissues with experimental colitis. Treatment with inhibitors of gap junction proteins, 50 µM 18ß-glycyrrhetinic acid and 100 µM carbenoxolone, partly and almost completely reduced bvPLA2-induced RPCs without and with the cyclooxygenase/lipoxygenase inhibitors, respectively, but not arachidonic acid-induced RPCs. In rectum from mouse having colitis, where total levels and modified forms of connexin43 increased, bvPLA2-induced RPCs were markedly decreased. Our results suggest that both arachidonic acid metabolism and gap junction proteins independently regulated the sPLA2-induced RPCs in mouse rectum. An increased expression and/or modification of connexin43 may influence sPLA2-induced RPCs in rectum with colitis.