SUMA: a lightweight machine learning model-powered shared nearest neighbour-based clustering application interface for scRNA-Seq data.

Background/aim: Single-cell transcriptomics (scRNA-Seq) explores cellular diversity at the gene expression level. Due to the inherent sparsity and noise in scRNA-Seq data and the uncertainty on the types of sequenced cells, effective clustering and cell type annotation are essential. The graph-based clustering of scRNA-Seq data is a simple yet powerful approach that presents data as a "shared nearest neighbour" graph and clusters the cells using graph clustering algorithms. These algorithms are dependent on several user-defined parameters.Here we present SUMA, a lightweight tool that uses a random forest model to predict the optimum number of neighbours to obtain the optimum clustering results. Moreover, we integrated our method with other commonly used methods in an RShiny application. SUMA can be used in a local environment (https://github.com/hkarakurt8742/SUMA) or as a browser tool (https://hkarakurt.shinyapps.io/suma/). Materials and methods: Publicly available scRNA-Seq datasets and 3 different graph-based clustering algorithms were used to develop SUMA, and a large range for number of neighbours and variant genes was taken into consideration. The quality of clustering was assessed using the adjusted Rand index (ARI) and true labels of each dataset. The data were split into training and test datasets, and the model was built and optimised using Scikit-learn (Python) and randomForest (R) libraries. Results: The accuracy of our machine learning model was 0.96, while the AUC of the ROC curve was 0.98. The model indicated that the number of cells in scRNA-Seq data is the most important feature when deciding the number of neighbours. Conclusion: We developed and evaluated the SUMA model and implemented the method in the SUMAShiny app, which integrates SUMA with different clustering methods and enables nonbioinformatician users to cluster and visualise their scRNA data easily. The SUMAShiny app is available both for desktop and browser use.

In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways.

Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.

Integration of transcriptomic profile of SARS-CoV-2 infected normal human bronchial epithelial cells with metabolic and protein-protein interaction networks.

A novel coronavirus (SARS-CoV-2, formerly known as nCoV-2019) that causes an acute respiratory disease has emerged in Wuhan, China and spread globally in early 2020. On January the 30th, the World Health Organization (WHO) declared spread of this virus as an epidemic and a public health emergency. With its highly contagious characteristic and long incubation time, confinement of SARS-CoV-2 requires drastic lock-down measures to be taken and therefore early diagnosis is crucial. We analysed transcriptome of SARS-CoV-2 infected human lung epithelial cells, compared it with mock-infected cells, used network-based reporter metabolite approach and integrated the transcriptome data with protein-protein interaction network to elucidate the early cellular response. Significantly affected metabolites have the potential to be used in diagnostics while pathways of protein clusters have the potential to be used as targets for supportive or novel therapeutic approaches. Our results are in accordance with the literature on response of IL6 family of cytokines and their importance, in addition, we find that matrix metalloproteinase 2 (MMP2) and matrix metalloproteinase 9 (MMP9) with keratan sulfate synthesis pathway may play a key role in the infection. We hypothesize that MMP9 inhibitors have potential to prevent "cytokine storm" in severely affected patients.

FLASH Irradiation Spares Lung Progenitor Cells and Limits the Incidence of Radio-induced Senescence.

PURPOSE: One of the main limitations to anticancer radiotherapy lies in irreversible damage to healthy tissues located within the radiation field. "FLASH" irradiation at very high dose-rate is a new treatment modality that has been reported to specifically spare normal tissue from late radiation-induced toxicity in animal models and therefore could be a promising strategy to reduce treatment toxicity. EXPERIMENTAL DESIGN: Lung responses to FLASH irradiation were investigated by qPCR, single-cell RNA sequencing (sc-RNA-Seq), and histologic methods during the acute wound healing phase as well as at late stages using C57BL/6J wild-type and Terc-/- mice exposed to bilateral thorax irradiation as well as human lung cells grown in vitro. RESULTS: In vitro studies gave evidence of a reduced level of DNA damage and induced lethality at the advantage of FLASH. In mouse lung, sc-RNA-seq and the monitoring of proliferating cells revealed that FLASH minimized the induction of proinflammatory genes and reduced the proliferation of progenitor cells after injury. At late stages, FLASH-irradiated lungs presented less persistent DNA damage and senescent cells than after CONV exposure, suggesting a higher potential for lung regeneration with FLASH. Consistent with this hypothesis, the beneficial effect of FLASH was lost in Terc-/- mice harboring critically short telomeres and lack of telomerase activity. CONCLUSIONS: The results suggest that, compared with conventional radiotherapy, FLASH minimizes DNA damage in normal cells, spares lung progenitor cells from excessive damage, and reduces the risk of replicative senescence.