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Next generation sequencing technologies enable the analysis of the transcriptomes of individual cells, providing a higher resolution of gene expression and function at the single cell level. Various single-cell data are continuously generated every year, covering fields from scientific research to clinical development. The fast-growing public datasets are collected by distinctive platforms, which are designed to facilitate biological discoveries, disease diagnosis, and new treatments. However, these platforms are hard to meet the urgency of having a unified data integration pipeline to improve comparability between datasets. Here we present SynEcoSys, an online multifunctional platform for single-cell transcriptomic data analysis, visualization, and exploration. SynEcoSys by Singleron Biotechnologies currently provides a massive collection of publicly available single-cell sequencing dataset, involving 46,326,175 cells from 731 datasets across multiple platforms and species. All datasets are generated with a strict and uniform data analysis pipeline and cell marker-based manual annotation, thus facilitating more comprehensive and reliable data mining. SynEcoSys is available at:https://www.synecosys.com.

This study aims to identify the active components and the mechanism of Jingqi Yukui Capsules(JQYK) in the treatment of gastric ulcer based on network pharmacology, and verify some key targets and signaling pathways through animal experiment. To be specific, first, the active components and targets of JQYK were retrieved from a Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine(BATMAN-TCM) and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP), and the targets of gastric ulcer from GeneCards and Online Mendelian Inheritance in Man(OMIM) with the search termquot;gastric ulcerquot;. The common targets of the two were the potential targets of the prescription for the treatment of the di-sease. Then, protein-protein interaction(PPI) network of key targets were constructed based on STRING and Cytoscape 3.7.2, followed by Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment by matescape database and pathway visualization by Omicshare. For the animal experiment, the improved method of Okabe was used to induce gastric ulcer in rats, and the model rats were classified into the model group, JQYK high-dose(JQYK-H), medium-dose(JQYK-M), and low-dose(JQYK-L) groups, Anweiyang Capsules(WYA) group, and Rabeprazole Sodium Enteric Capsules(RBPZ) group. Normal rats were included in the blank group. Rats in the blank group and model group were given distilled water and those in the administration groups received corresponding drugs. Then gastric ulcer healing in rats was observed. The changes of the gastric histomorphology in rats were evaluated based on hematoxylin-eosin(HE) staining, and the content of inducible nitric oxide synthase(iNOS) in rat gastric tissue was detected with Coomassie brilliant blue method. The mRNA and protein levels of some proteins in rat gastric tissue were determined by real-time quantitative polymerase chain reaction(RT-qPCR) and Western blot(WB) to further validate some key targets and signaling pathways. A total of 206 active components and 535 targets of JQYK, 1 305 targets of gastric ulcer, and 166 common targets of the disease and the drug were yielded. According to PPI analysis and KEGG pathway enrichment analysis, multiple key targets, such as interleukin-6(IL-6), tumor necrosis factor(TNF), mitogen-activated protein kinase 1(MAPK1), MAPK3, and MAPK14, as well as nuclear factor kappa-B(NF-κB) signaling pathway, IL-17 signaling pathway, and leukocyte transendothelial migration in the top 20 key signaling pathways were closely related to inflammation. The key protein p38 MAPK and NF-κB signaling pathway were selected for further verification by animal experiment. The gastric ulcer in the JQYK-H group recovered nearly to the level in the blank group, with significant decrease in the content of iNOS in rat gastric tissue and significant reduction in the mRNA and phosphorylation levels of p38 MAPK and the mRNA and protein levels of NF-κB p65 in rat gastric tissue. The results indicated that JQYK can inhibit the phosphorylation of the key protein p38 MAPK and the expression of NF-κB p65 in the NF-κB signaling pathway, thereby exerting the anti-inflammatory effect and effectively improving the quality of gastric ulcer healing in rats. Thus, the animal experiment result verifies some predictions of network pharmacology.

PURPOSE: To determine associations between genomic DNA methylation in testicular cells and azoospermia in human males. METHODS: This was a case-control study investigating the differences and conservations in DNA methylation, genome-wide DNA methylation, and bulk RNA-Seq for transcriptome profiling using testicular biopsy tissues from NOA and OA patients. Differential methylation and different conserved methylation regions associated with azoospermia were identified by comparing genomic DNA methylation of testicular seminiferous cells derived from NOA and OA patients. RESULTS: The genome methylation modification of testicular cells from NOA patients was disordered, and the reproductive-related gene expression was significantly different. CONCLUSION: Our findings not only provide valuable knowledge of human spermatogenesis but also paved the way for the identification of genes/proteins involved in male germ cell development. The approach presented in this report provides a powerful tool to identify responsible biomolecules, and/or cellular changes (e.g., epigenetic abnormality) that induce male reproductive dysfunction such as OA and NOA. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10815-020-01716-7) contains supplementary material, which is available to authorized users.

Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

Lung cancer is a highly heterogeneous disease. Cancer cells and cells within the tumor microenvironment together determine disease progression, as well as response to or escape from treatment. To map the cell type-specific transcriptome landscape of cancer cells and their tumor microenvironment in advanced non-small cell lung cancer (NSCLC), we analyze 42 tissue biopsy samples from stage III/IV NSCLC patients by single cell RNA sequencing and present the large scale, single cell resolution profiles of advanced NSCLCs. In addition to cell types described in previous single cell studies of early stage lung cancer, we are able to identify rare cell types in tumors such as follicular dendritic cells and T helper 17 cells. Tumors from different patients display large heterogeneity in cellular composition, chromosomal structure, developmental trajectory, intercellular signaling network and phenotype dominance. Our study also reveals a correlation of tumor heterogeneity with tumor associated neutrophils, which might help to shed light on their function in NSCLC., Comprehensive profiles of tumour and microenvironment are critical to understand heterogeneity in non-small cell lung cancer (NSCLC). Here, the authors profile 42 late-stage NSCLC patients with single-cell RNA-seq, revealing immune landscapes that are associated with cancer subtype or heterogeneity.

BackgroundDespite disappointing outcomes from immuno-monotherapy, studies reported that NSCLC patients with EGFR mutation may possibly benefit from combined immunotherapy. Whether the response to prior EGFR-TKI has association with the outcomes of subsequent immunotherapy remains unclear.Patients and MethodsAdvanced NSCLC patients with resistance to EGFR-TKIs and received ICI treatment from January 2016 to June 2019 were retrospectively analyzed. Single cell sequencing and flow cytometry were conducted to explore the difference of cell components in tumor microenvironments (TME). A 1:3 matched case–control study was conducted to compare the clinical effects of combined immunotherapy with standard chemotherapy as second-line treatment.ResultsFifty-eight patients treated with anti-PD-1/PD-L1 based immunotherapy behind EGFR-TKI treatment were enrolled. Correlation analysis showed TKI-PFS had a significantly negative association with corresponding IO-PFS (r = −0.35, p = 0.006). TKI-PFS cutoff 10 months had the most significant predictive function for posterior immunotherapy and was validated to be an independent predictor by uni- and multivariate analyses. Kaplan–Meier analysis showed that patients with TKI-PFS less than 10 months had significantly prolonged IO-PFS and higher ORR than those with long (median PFS, 15.1 vs 3.8 months; HR, 0.26, p = 0.0002; ORR, 31.8 versus 10%, p = 0.04). Single cell RNA-seq revealed that the cell components were varied among patients after treatment with EGFR-TKI. Patients with short TKI-PFS demonstrated a relatively higher proportion of CD8 effector cells and lower ratio of M2 like macrophage to M1 like macrophages, which was validated by flow cytometry. Case–control study demonstrated that combined immunotherapy achieved significantly longer PFS (HR, 0.51, 95% CI: 0.31–0.85, p = 0.02), longer OS (HR, 0.48, 95% CI: 0.26–0.89, p = 0.05) and higher ORR (33.3 vs 10.0%, p = 0.02) than traditional chemotherapy for patients with short TKI-PFS.ConclusionPatients with short TKI-PFS conferred better response to immunotherapy than those with long. The status of TME were different among those two populations. Combined ICI treatment could promisingly be a better choice than classical chemotherapy in second-line setting for patients with short TKI-PFS and no T790M mutation. Underlying mechanisms need to be further explored.

Cancer is one of the leading causes of death worldwide and well known for its complexity. Cancer cells within the same tumor or from different tumors are highly heterogeneous. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in how tumors progress and respond to different treatments. Recent advances in single cell technologies, especially massively parallel single cell sequencing, have made it possible to analyze cancer cells and cells in its tumor microenvironment in parallel with unprecedented high resolution. In this chapter, we will review recent developments in single cell sequencing technologies and their applications in cancer research. We will also explain how insights generated from single cell sequencing can be used to develop novel diagnostic and therapeutic approaches to conquer cancer.

ObjectivePrevious study indicated that bladder cells which express ACE2 were a potential infection route of 2019-nCov. This study observed some differences of bladder cell cluster and their ACE2 expression between OAB mice and healthy mice, indicating the change of infectious possibility and pathway under overactive bladder (OAB) circumstance.Material and methodPubic dataset acquisition was used to get ACE2 expression in normal human bladder and mice bladder (GSE129845). We built up over OAB model and studied the impact on cell typing and ACE2 expression. By way of using single-cell RNA sequencing (scRNA-seq) technique, bladder cell clustering and ACE2 expression in various cell types were measured respectively.ResultIn pubic database (healthy human and mice bladder), ACE2 expression in humans and mice is concentrated in bladder epithelial cells. The disappearance of umbrella cells, a component of bladder epithelial, was found in our OAB model. In the two mouse bladder samples, ACE2 expression of epithelial cells is 34.1%, also the highest of all cell types.ConclusionThe disappearance of umbrella cell may alternate the infection pathway of 2019-nCov and relate to the onset and progression of OAB.

A substantial number of male infertility is caused by azoospermia. However, the underlying etiology and the molecular basis remain largely unknown. Through single-cell (sc)RNA sequencing, we had analyzed testis biopsy samples from two patients with obstructive azoospermia (OA) and nonobstructive azoospermia (NOA). We found only somatic cells in the NOA samples and explored the transcriptional changes in Sertoli cells in response to a loss of interactions with germ cells. Moreover, we observed a germ cell population discrepancy between an OA (postvasectomy) patient and a healthy individual. We confirmed this observation in a secondary study with two datasets at GSM3526588 and GSE124263 for detailed analysis wherein the regulatory mechanisms at the transcriptional level were identified. These findings thus provide valuable information on human spermatogenesis, they also identified insightful information for further research on reproduction-related diseases.

SUMMARYTo explore whether the expression levels of viral-entry associated genes might contribute to the milder symptoms in children, we analyzed the expression of these genes in both children and adults’ lung tissues by single cell RNA sequencing (scRNA-seq) and immunohistochemistry (IHC). Both scRNA-seq and IHC analyses showed comparable levels of the key genes for SARS-CoV-2 entry in children and adults, includingACE2, TMPRSS2andFURIN, suggesting that instead of lower virus intrusion rate, other factors are more likely to be the key reasons for the milder symptoms of SARS-CoV-2 infected children.

Since December 2019, a novel coronavirus named 2019 coronavirus (2019-nCoV) has emerged in Wuhan of China and spread to several countries worldwide within just one month. Apart from fever and respiratory complications, acute kidney injury has been observed in some patients with 2019-nCoV. In a short period of time, angiotensin converting enzyme II (ACE2), have been proposed to serve as the receptor for the entry of 2019-nCoV, which is the same for severe acute respiratory syndrome coronavirus (SARS). To investigate the possible cause of kidney damage in 2019-nCoV patients, we used both published kidney and bladder cell atlas data and an independent unpublished kidney single cell RNA-Seq data generated in-house to evaluate ACE2 gene expressions in all cell types in healthy kidneys and bladders.Our results showed the enriched expression of all subtypes of proximal tubule cells of kidney and low but detectable levels of expression in bladder epithelial cells. These results indicated the urinary system is a potential route for 2019-nCoV infection, along with the respiratory system and digestion system. Our findings suggested the kidney abnormalities of SARS and 2019-nCoV patients may be due to proximal tubule cells damage and subsequent systematic inflammatory response induced kidney injury. Beyond that, laboratory tests of viruses and related indicators in urine may be needed in some special patients of 2019-nCoV.

A newly identified coronavirus, 2019-nCoV, has been posing significant threats to public health since December 2019. ACE2, the host cell receptor for severe acute respiratory syndrome coronavirus (SARS), has recently been demonstrated in mediating 2019-nCoV infection. Interestingly, besides the respiratory system, substantial proportion of SARS and 2019-nCoV patients showed signs of various degrees of liver damage, the mechanism and implication of which have not yet been determined. Here, we performed an unbiased evaluation of cell type specific expression of ACE2 in healthy liver tissues using single cell RNA-seq data of two independent cohorts, and identified specific expression in cholangiocytes. The results indicated that virus might directly bind to ACE2 positive cholangiocytes but not necessarily hepatocytes. This finding suggested the liver abnormalities of SARS and 2019-nCoV patients may not be due to hepatocyte damage, but cholangiocyte dysfunction and other causes such as drug induced and systemic inflammatory response induced liver injury. Our findings indicate that special care of liver dysfunction should be installed in treating 2019-nCoV patients during the hospitalization and shortly after cure.

Cancer is one of the leading causes of death worldwide and well known for its complexity. Cancer cells within the same tumor or from different tumors are highly heterogeneous. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in how tumors progress and respond to different treatments. Recent advances in single cell technologies, especially massively parallel single cell sequencing, have made it possible to analyze cancer cells and cells in its tumor microenvironment in parallel with unprecedented high resolution. In this chapter, we will review recent developments in single cell sequencing technologies and their applications in cancer research. We will also explain how insights generated from single cell sequencing can be used to develop novel diagnostic and therapeutic approaches to conquer cancer.

Purpose Past studies have paid extensive attention to investigate learning issues in individual, organizational and team contexts. Learning activities in the workplace, however, often occur in the interdisciplinary dyadic context. Hence, the purpose of this paper is to clarify what factors make interdisciplinary dyads lead to better learning effects. The authors attempted to clarify two major agendas: What knowledge factors (intellectual capitals) owned by the parties of the dyads can induce better learning effects? What contextual factors (learning tasks) can make better learning effects during the dyadic learning process? Design/methodology/approach To examine the previous agendas, the authors first conducted in-depth interviews and an exploratory survey so that a four-element dyadic intellectual capital (DIC) architecture was circumscribed: knowledge interdependency, expertise similarity, collaborative routines and mutual trust. Dyadic learning tasks were classified as exploitative and exploratory learning. The authors then sampled 248 respondents for the formal empirical survey to examine the relationship between DIC, dyadic learning tasks and knowledge sharing. Findings The statistical evidences confirmed the positive relationships between DIC and knowledge sharing, and the results also signified the previous relationships with a mediating effect from exploratory learning, while the effect of exploitative learning was not supported. Thus, only dyads (the two heads) with knowledge interdependency and mutual trust can make the exploratory task better than that of one individual (the one head). Originality/value This study provides a new insight into the learning issue with an interdisciplinary dyadic perspective to supplement the existing gap between academic efforts and learning practices in the workplace.

Stress-inducible phosphoprotein 1 (STI1) acts as a neuroprotective factor in the ischemic brain and its levels are increased following ischemia. Previous work has suggested that some of these STI1 actions in a stroke model depend on the recruitment of bone marrow-derived stem cells to improve outcomes after ischemic insult. However, STI1 can directly increase neuroprotective signaling in neurons by engaging with the cellular prion protein (PrPC) and activating 7 nicotinic acetylcholine receptors (alpha 7nAChR). Given that alpha 7nAChR activation has also been involved in neuroprotection in stroke, it is possible that STI1 can have direct actions on neurons to prevent deleterious consequences of ischemic insults. Here, we tested this hypothesis by exposing primary neuronal cultures to 1-h oxygen-glucose deprivation (OGD) and reperfusion and assessing signaling pathways activated by STI1/PrPC. Our results demonstrated that STI1 treatment significantly decreased apoptosis and cell death in mouse neurons submitted to OGD in a manner that was dependent on PrPC and alpha 7nAChR, but also on the activin A receptor 1 (ALK2), which has emerged as a signaling partner of STI1. Interestingly, pharmacological inhibition of the ALK2 receptor prevented neuroprotection by STI1, while activation of ALK2 receptors by bone morphogenetic protein 4 (BMP4) either before or after OGD was effective in decreasing neuronal death induced by ischemia. We conclude that PrPC/STI1 engagement and its subsequent downstream signaling cascades involving alpha 7nAChR as well as the ALK2 receptor may be activated in neurons by increased levels of STI1. This signaling pathway protects neurons from ischemic insults.

Background The molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. Results To identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation. We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Conclusions Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3853-9) contains supplementary material, which is available to authorized users.

Apoptosis is a feature of stroke and Alzheimer’s disease (AD), yet there is no accepted method to detect or follow apoptosis in the brain in vivo. We developed a bifunctional tracer [68Ga]Ga-TC3-OGDOTA containing a cell-penetrating peptide separated from fluorescent Oregon Green and 68Ga-bound labels by the caspase-3 recognition peptide DEVD. We hypothesized that this design would allow [68Ga]Ga-TC3-OGDOTA to accumulate in apoptotic cells. In vitro, Ga-TC3-OGDOTA labeled apoptotic neurons following exposure to camptothecin, oxygen-glucose deprivation, and β-amyloid oligomers. In vivo, PET showed accumulation of [68Ga]Ga-TC3-OGDOTA in the brain of mouse models of stroke or AD. Optical clearing revealed colocalization of [68Ga]Ga-TC3-OGDOTA and cleaved caspase-3 in brain cells. In stroke, [68Ga]Ga-TC3-OGDOTA accumulated in neurons in the penumbra area, whereas in AD mice [68Ga]Ga-TC3-OGDOTA was found in single cells in the forebrain and diffusely around amyloid plaques. In summary, this bifunctional tracer is selectively associated with apoptotic cells in vitro and in vivo in brain disease models and represents a novel tool for apoptosis detection that can be used in neurodegenerative diseases.

Chaperone networks are dysregulated with aging, but whether compromised Hsp70/Hsp90 chaperone function disturbs neuronal resilience is unknown. Stress-inducible phosphoprotein 1 (STI1; STIP1; HOP) is a co-chaperone that simultaneously interacts with Hsp70 and Hsp90, but whose function in vivo remains poorly understood. We combined in-depth analysis of chaperone genes in human datasets, analysis of a neuronal cell line lacking STI1 and of a mouse line with a hypomorphic Stip1 allele to investigate the requirement for STI1 in aging. Our experiments revealed that dysfunctional STI1 activity compromised Hsp70/Hsp90 chaperone network and neuronal resilience. The levels of a set of Hsp90 co-chaperones and client proteins were selectively affected by reduced levels of STI1, suggesting that their stability depends on functional Hsp70/Hsp90 machinery. Analysis of human databases revealed a subset of co-chaperones, including STI1, whose loss of function is incompatible with life in mammals, albeit they are not essential in yeast. Importantly, mice expressing a hypomorphic STI1 allele presented spontaneous age-dependent hippocampal neurodegeneration and reduced hippocampal volume, with consequent spatial memory deficit. We suggest that impaired STI1 function compromises Hsp70/Hsp90 chaperone activity in mammals and can by itself cause age-dependent hippocampal neurodegeneration in mice. Cover Image for this issue: doi: 10.1111/jnc.14749.

Inherent structure (IS) and geometry-based clustering methods are commonly used for analyzing molecular dynamics trajectories. ISs are obtained by minimizing the sampled conformations into local minima on potential/effective energy surface. The conformations that are minimized into the same energy basin belong to one cluster. We investigate the influence of the applications of these two methods of trajectory decomposition on our understanding of the thermodynamics and kinetics of alanine tetrapeptide. We find that at the microcluster level, the IS approach and root-mean-square deviation (RMSD)-based clustering method give totally different results. Depending on the local features of energy landscape, the conformations with close RMSDs can be minimized into different minima, while the conformations with large RMSDs could be minimized into the same basin. However, the relaxation timescales calculated based on the transition matrices built from the microclusters are similar. The discrepancy at the microcluster level leads to different macroclusters. Although the dynamic models established through both clustering methods are validated approximately Markovian, the IS approach seems to give a meaningful state space discretization at the macrocluster level in terms of conformational features and kinetics.

Complex traits such as crop performance and human diseases are controlled by multiple genetic loci, many of which have small effects and often go undetected by traditional quantitative trait locus (QTL) mapping. Recently, bulked segregant analysis with large F2 pools and genome-level markers (named extreme-QTL or X-QTL mapping) has been used to identify many QTL. To estimate parameters impacting QTL detection for X-QTL mapping, we simulated the effects of population size, marker density, and sequencing depth of markers on QTL detectability for traits with differing heritabilities. These simulations indicate that a high (>90%) chance of detecting QTL with at least 5% effect requires 5000× sequencing depth for a trait with heritability of 0.4−0.7. For most eukaryotic organisms, whole-genome sequencing at this depth is not economically feasible. Therefore, we tested and confirmed the feasibility of applying deep sequencing of target-enriched markers for X-QTL mapping. We used two traits in Arabidopsis thaliana with different heritabilities: seed size (H2 = 0.61) and seedling greening in response to salt (H2 = 0.94). We used a modified G test to identify QTL regions and developed a model-based statistical framework to resolve individual peaks by incorporating recombination rates. Multiple QTL were identified for both traits, including previously undiscovered QTL. We call our method target-enriched X-QTL (TEX-QTL) mapping; this mapping approach is not limited by the genome size or the availability of recombinant inbred populations and should be applicable to many organisms and traits.

Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine learning based algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. With an independent validation analysis, we demonstrate the models can correctly prioritize about 65% and 60% of Arabidopsis and rice causal genes when the top 20% ranked genes were considered. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including paralog copy number, being a transporter, being a transcription factor, and containing SNPs that cause premature stop codon. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.One sentence summaryWe systematically analyzed the genomic characteristics of causal genes in QTLs and developed a novel computational tool to prioritize causal genes.

Apoptosis is a feature of stroke and Alzheimer's disease (AD), yet there is no accepted method to detect or follow apoptosis in the brain in vivo. We developed a bifunctional tracer [68Ga]Ga-TC3-OGDOTA containing a cell-penetrating peptide separated from fluorescent Oregon Green and 68Ga-bound labels by the caspase-3 recognition peptide DEVD. We hypothesized that this design would allow [68Ga]Ga-TC3-OGDOTA to accumulate in apoptotic cells. In vitro, Ga-TC3-OGDOTA labeled apoptotic neurons following exposure to camptothecin, oxygen-glucose deprivation, and β-amyloid oligomers. In vivo, PET showed accumulation of [68Ga]Ga-TC3-OGDOTA in the brain of mouse models of stroke or AD. Optical clearing revealed colocalization of [68Ga]Ga-TC3-OGDOTA and cleaved caspase-3 in brain cells. In stroke, [68Ga]Ga-TC3-OGDOTA accumulated in neurons in the penumbra area, whereas in AD mice [68Ga]Ga-TC3-OGDOTA was found in single cells in the forebrain and diffusely around amyloid plaques. In summary, this bifunctional tracer is selectively associated with apoptotic cells in vitro and in vivo in brain disease models and represents a novel tool for apoptosis detection that can be used in neurodegenerative diseases.

Chaperone networks are dysregulated with aging and neurodegenerative disease, but whether compromised Hsp70/Hsp90 chaperone function directly contributes to neuronal degeneration is unknown. Stress-inducible phosphoprotein-1 (STI1; STIP1; HOP) is a co-chaperone that simultaneously interacts with Hsp70 and Hsp90, but whose functionin vivoremains poorly understood. To investigate the requirement of STI1-mediated regulation of the chaperone machinery in aging we combined analysis of a mouse line with a hypomorphicStip1allele, with a neuronal cell line lacking STI1 and in-depth analyses of chaperone genes in human datasets. Loss of STI1 function severely disturbed the Hsp70/Hsp90 machineryin vivo, and all client proteins tested and a subset of cochaperones presented decreased levels. Importantly, mice expressing a hypomorphic STI1 allele showed spontaneous age-dependent hippocampal neurodegeneration, with consequent spatial memory deficits. STI1 is a critical node for the chaperone network and it can contribute to age-dependent hippocampal neurodegeneration.

BackgroundWith the recent proliferation of single-cell RNA-Seq experiments, several methods have been developed for unsupervised analysis of the resulting datasets. These methods often rely on unintuitive hyperparameters and do not explicitly address the subjectivity associated with clustering.ResultsIn this work, we present DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets that addresses both the clustering interpretability and clustering subjectivity issues. DendroSplit offers a novel perspective on the single-cell RNA-Seq clustering problem motivated by the definition of “cell type,” allowing us to cluster using feature selection to uncover multiple levels of biologically meaningful populations in the data. We analyze several landmark single-cell datasets, demonstrating both the method’s efficacy and computational efficiency.ConclusionDendroSplit offers a clustering framework that is comparable to existing methods in terms of accuracy and speed but is novel in its emphasis on interpretabilty. We provide the full DendroSplit software package at https://github.com/jessemzhang/dendrosplit.

Aflatoxin B1 (AFB1) is a potent hepatocarcinogen that causes carcinogenesis in many animal species. In previous study, we found that isocitrate dehydrogenase 3α subunit (IDH3α) was upregulated in AFB1-induced carcinogenesis process. In this study, the sequences of IDH3α from various species were compared and the protein expression levels in different organs were examined, and the results showed that IDH3α was a widely distributed protein and shared highly conserved sequence in various species. In the same time, IDH3α was demonstrated to accumulate in a dose-dependent manner induced by AFB1 in cells, and was also up-regulated in the process of AFB1-induced liver lesion. Similar results were observed when H2O2 was used to replace AFB1. Over-expression of IDH3α increased the phosphorylation level of Akt (Protein kinase B) and neutralized the cellular toxicity induced by AFB1 or H2O2 and apoptosis induced by AFB1, while the reduced expression of IDH3α by siRNA decreased the phosphorylation, indicating that IDH3α played important roles in oxidative stress-induced PI3K/Akt pathway. Overall, the results suggested that AFB1 treatment could increase the expression of IDH3α, and the activated PI3K/Akt pathway by IDH3α eventually neutralized the apoptosis induced by AFB1.