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Laboratory automation technologies have revolutionized biomedical research. However, the availability of automation solutions at the single-cell level remains scarce, primarily owing to the inherent challenges of handling cells with such small dimensions in a precise, biocompatible manner. Here, we present a single-cell-level laboratory automation solution that configures various experiments onto standardized, microscale test-tube matrices via our precise ultrasonic liquid sample ejection technology, known as PULSE. PULSE enables the transformation of titer plates into microdroplet arrays by printing nanodrops and single cells acoustically in a programmable, scalable, and biocompatible manner. Unlike pipetting robots, PULSE enables researchers to conduct biological experiments using single cells as anchoring points (e.g., 1 cell vs. 1000 cells per "tube"), achieving higher resolution and potentially more relevant data for modeling and downstream analyses. We demonstrate the ability of PULSE to perform biofabrication, precision gating, and deterministic array barcoding via preallocated droplet-addressable primers. Single cells can be gently printed at a speed range of 5–20 cell⋅s−1 with an accuracy of 90.5–97.7%, which can then adhere to the substrate and grow for up to 72 h while preserving cell integrity. In the deterministic barcoding experiment, 95.6% barcoding accuracy and 2.7% barcode hopping were observed by comparing the phenotypic data with known genotypic data from two types of single cells. Our PULSE platform allows for precise and dynamic analyses by automating experiments at the single-cell level, offering researchers a powerful tool in biomedical research. [ABSTRACT FROM AUTHOR]

Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. We present multiDGD, a scalable deep generative model providing a probabilistic framework to learn shared representations of transcriptome and chromatin accessibility. It shows outstanding performance on data reconstruction without feature selection. We demonstrate on several data sets from human and mouse that multiDGD learns well-clustered joint representations. We further find that probabilistic modeling of sample covariates enables post-hoc data integration without the need for fine-tuning. Additionally, we show that multiDGD can detect statistical associations between genes and regulatory regions conditioned on the learned representations. multiDGD is available as an scverse-compatible package on GitHub. Understanding single-cell multi-omics data requires powerful solutions. Here, authors present a data-efficient machine learning approach for paired data. It enables integration from unseen covariates and can link distal regulatory elements to promoters, presenting a computational version of HiC. [ABSTRACT FROM AUTHOR]

Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers. Patient-derived xenografts (PDX) could contribute to understanding how colorectal cancer (CRC) responds to targeted therapies like cetuximab. Here, the authors characterise the response to cetuximab in 231 CRC PDXs using multiomics and develop an integrative ensemble classifier - CeSta - to predict sensitivity to cetuximab. [ABSTRACT FROM AUTHOR]

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas. Single-cell, multiomic datasets are integrated using dimensionality reduction, imputation and batch correction. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD's efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression. Identifying rare cells is essential for advancing our understanding of complex biological systems and disease mechanisms. Here, authors propose scCAD, a method that combines cluster decomposition and anomaly detection to effectively identify rare cell types across diverse biological scenarios. [ABSTRACT FROM AUTHOR]

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials. Multi-omics data are readily integrated using quartet reference materials. [ABSTRACT FROM AUTHOR]

Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section. MaxFuse enables data integration between modalities even when features are weakly correlated. [ABSTRACT FROM AUTHOR]

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings. A new RNA reference set improves detection of differential expression in clinical settings. [ABSTRACT FROM AUTHOR]

The Human Cell Atlas is yielding detailed maps of human tissues and systems throughout life, along with methods to handle single-cell data. Four scientists reflect on how the project is transforming our understanding of human biology.Scientists reflect on insights from the Human Cell Atlas. [ABSTRACT FROM AUTHOR]

Despite growing concern in the social innovation (SI) literature about the tackling of grand challenges, our understanding of the role of multinational enterprises (MNEs) remains in its infancy. This article examines foreign MNE subsidiaries' SI investments focusing on United Nations' Sustainable Development Goals (UNSDGs) in host countries. Using financial data from large, listed subsidiaries of foreign MNEs operating in India, along with hand-collected data from firms' disclosures of corporate social responsibility (CSR) activity for five years starting in 2015, we utilise the externalities framework propounded by Montiel et al. (2021). This neatly translates the 17 UNSDGS into actionable goals to examine the efforts of foreign MNE subsidiaries in increasing positive externalities as opposed to reducing negative externalities via SI-related investment in host countries. The study also evaluates the effects of the local embeddedness of the foreign MNE subsidiaries on SI investment. We find that MNE subsidiaries tend to favour increasing positive externalities as compared to reducing negative externalities through their SI investments. Also, older subsidiaries tend to prioritize greater investments in SI projects related to reducing negative externalities and subsidiaries with higher MNE ownership tend to reduce investments in SI projects related to increasing positive externalities. We discuss possible interpretations of the exploratory results using the institutional logics perspective and conclude with implications for policy and future research. [ABSTRACT FROM AUTHOR]

Background: Internet addiction is increasing among adolescents worldwide. There is a lack of research assessing internet addiction and factors contributing to it among adolescents in Jeddah city. The current study aimed to assess the rate of internet addiction among adolescents in Jeddah, Saudi Arabia, and the potential factors associated with it. Methods: A cross-sectional online survey, hosted by SurveyMonkey, was used to capture data on internet use from 462 adolescents aged 12–18 years between March and May 2022. Young's Internet Addiction Scale was used to assess the degree of internet addiction as perceived by adolescents. Multiple linear regression analysis was used to identify possible predictors of internet addiction among adolescents in Jeddah. Results: The mean age of the participants was 15.5 ± 1.9 years. The majority were females (75%) from public schools (63%), spent an average of six hours on the internet daily, owned smartphones (98%), accessed the internet via a smartphone (94%), and used the internet for socializing (82%). Internet addiction mean score was 39.20 ± 15.20 out of 100. More than two-thirds of the participants had mild to moderate levels of internet addiction (68%). Significant predictors contributing to internet addiction were using the internet for socialization and playing online games. The more hours spent on the internet daily, the more the internet addiction was (p ≤ 0.05). Conclusions: The internet addiction rate is high among school adolescents in Jeddah. The majority of high school adolescents had mild to moderate levels of internet addiction. Interventional multidisciplinary programs are needed to mitigate the factors that influence internet addiction. [ABSTRACT FROM AUTHOR]

Recent advancements for simultaneously profiling multi-omics modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data and substantial costs. Although computational methods have been proposed to translate single-cell data across modalities, broad applications of the methods still remain impeded by formidable challenges. Here, we propose scButterfly, a versatile single-cell cross-modality translation method based on dual-aligned variational autoencoders and data augmentation schemes. With comprehensive experiments on multiple datasets, we provide compelling evidence of scButterfly's superiority over baseline methods in preserving cellular heterogeneity while translating datasets of various contexts and in revealing cell type-specific biological insights. Besides, we demonstrate the extensive applications of scButterfly for integrative multi-omics analysis of single-modality data, data enhancement of poor-quality single-cell multi-omics, and automatic cell type annotation of scATAC-seq data. Moreover, scButterfly can be generalized to unpaired data training, perturbation-response analysis, and consecutive translation. Technical limitations of simultaneously multi-omics profiling lead to highly noisy multi-modal data and substantial costs. Here, authors proposed a versatile framework and data augmentation schemes, capable of single-cell cross-modality translation and multiple extensive applications. [ABSTRACT FROM AUTHOR]

Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components' interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues' composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition. [ABSTRACT FROM AUTHOR]

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools. The challenge of simulating multiomic single-cell data is addressed by a probabilistic model. [ABSTRACT FROM AUTHOR]

Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference. Mosaic data integration is improved by capturing features that do not overlap between datasets. [ABSTRACT FROM AUTHOR]

Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighter's design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets. Identification and classification of cells in multiplexed microscopy remain challenging. Here, the authors propose CellSighter, which uses neural networks to perform cell classification directly on multiplexed images, thus leveraging the spatial expression characteristics of proteins. [ABSTRACT FROM AUTHOR]

Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities. Multimodal biological data is challenging to analyze. Here, the authors develop UnitedNet, an explainable deep neural network for analyzing single-cell multimodal biological data and estimating relationships between gene expression and other modalities with cell-type specificity. [ABSTRACT FROM AUTHOR]

Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes. Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. Here the authors benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches and suggest several high-performance methods under different conditions based on simulation and real data analyses. [ABSTRACT FROM AUTHOR]

Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications. An evaluation of normalization methods shows that it is possible to combine microarray and RNA-seq data for machine learning applications. [ABSTRACT FROM AUTHOR]

The repressor element 1 silencing transcription factor (REST) has been proposed to function as a transcription factor to silence gene transcription by binding to repressor element 1 (RE1), a highly conserved DNA motif. The functions of REST in various tumors have been studied, but its role and correlation with immune cell infiltration remains uncertain in gliomas. REST expression was analyzed in datasets of The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) and validated by the Gene Expression Omnibus and Human Protein Atlas databases. The clinical prognosis of REST was evaluated by clinical survival data of TCGA cohort and validated by Chinese Glioma Genome Atlas cohort. MicroRNAs (miRNAs) contributing to REST overexpression in glioma were identified by a combination of a series of in silico analyses, including expression analysis, correlation analysis, and survival analysis. The correlations between immune cell infiltration level and REST expression were analyzed by TIMER2 and GEPIA2 tools. Enrichment analysis of REST was performed using STRING and Metascape tools. The expression and function of predicted upstream miRNAs at REST and their association with glioma malignancy and migration were also confirmed in glioma cell lines. REST was highly expressed and associated with poorer overall survival and disease-specific survival in glioma and some other tumors. MiR-105-5p and miR-9-5p were identified as the most potential upstream miRNAs of REST in glioma patient cohort and experiments in vitro. REST expression was positively correlated with infiltration of immune cells and the expression of immune checkpoints such as PD1/PD-L1 and CTLA-4 in glioma. Furthermore, histone deacetylase 1 (HDAC1) was a potential REST-related gene in glioma. Enrichment analysis of REST found chromatin organization and histone modification were the most significant enriched terms, and Hedgehog-Gli pathway might be involved in the effect of REST on the pathogenesis of glioma. Our study suggests REST to be an oncogenic gene and the biomarker of poor prognosis in glioma. High REST expression might affect the tumor microenvironment of glioma. More basic experiments and large clinical trials aimed at the carcinogenetic study of REST in glioma will be needed in the future. [ABSTRACT FROM AUTHOR]

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis. Single-cell multi-omics and deep learning could lead to the inference of biological networks across specific cell types. Here, the authors develop DeepMAPS, a deep learning, graph-based approach for cell-type specific network inference from single-cell multi-omics data that is tested on healthy and tumour tissue datasets. [ABSTRACT FROM AUTHOR]

Global alignment of complex pseudotime trajectories between different single-cell RNA-seq datasets is challenging, as existing tools mainly focus on linear alignment of single-cell trajectories. Here we present CAPITAL (comparative analysis of pseudotime trajectory inference with tree alignment), a method for comparing single-cell trajectories with tree alignment whereby branching trajectories can be automatically compared. Computational tests on synthetic datasets and authentic bone marrow cells datasets indicate that CAPITAL has achieved accurate and robust alignments of trajectory trees, revealing various gene expression dynamics including gene–gene correlation conservation between different species. Global alignment of complex cell state trajectories between single-cell datasets remains challenging. Here, the authors present a computational method called CAPITAL to compare branching trajectories, and demonstrate that this method achieves accurate and robust alignments. [ABSTRACT FROM AUTHOR]

Purpose: The epidermal growth factor receptor (EGFR) represents a top therapeutic target in the treatment of non-small cell lung cancer. EGFR expression is intricately modulated by receptor endocytosis, during which EGFR ubiquitylation and deubiquitylation play fundamental roles to govern receptor fate. This study aims to uncover novel aspects of the endocytic regulation of EGFR trafficking by deubiquitylases. Methods: The expression and ubiquitylation of EGFR in non-small cell lung cancer cells treated with deubiquitylase inhibitors were assessed by immunoblotting, immunoprecipitation and mass spectrometry analyses. The intracellular EGFR distribution was investigated using immunofluorescence and confocal microscopy assays, and colocalizations with endocytic compartments were examined using GFP-tagged Rab proteins as markers. The influence of the proteasomal deubiquitylase inhibitor b-AP15 on EGF- and HSP90 inhibitor-induced EGFR downregulation was evaluated by immunoblotting. The anticancer effects of b-AP15 were assessed by cell proliferation, colony formation and flow cytometry assays, as well as xenograft animal models. Results: We found that b-AP15 caused a dramatically enhanced ubiquitylation of EGFR in lung cancer cells. Treatment with b-AP15 decreased cell surface EGFR levels and accumulated EGFR on recycling endosomes marked with Rab4A and Rab11A. b-AP15 effectively repressed EGF- and HSP90 inhibitor-induced EGFR degradation. Lung cancer cells exposed to b-AP15 showed markedly reduced cell propagation and significantly increased cell apoptosis. Furthermore, b-AP15 effectively inhibited tumor xenograft growth in nude mice. Conclusion: Proteasomal USP14 and UCHL5 act collectively to promote cell surface recovery of EGFR. Inhibition of proteasomal deubiquitylase activity induces increased EGFR ubiquitylation and retention on recycling endosomes. The USP14 and UCHL5 dual inhibitor b-AP15 elicits potent tumor-suppressive effects to deter cell proliferation and induce apoptotic cell death in lung cancer. [ABSTRACT FROM AUTHOR]

Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE. Different single-cell data modalities are integrated at atlas-scale by modeling regulatory interactions. [ABSTRACT FROM AUTHOR]

The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities. [ABSTRACT FROM AUTHOR]

Diagonal integration of multimodal single-cell data emerges as a trending topic. However, empowering diagonal methods for novel biological discoveries requires bridging huge gaps. Here, we comment on potential risks and future directions of diagonal integration for multimodal single-cell data. [ABSTRACT FROM AUTHOR]

Cannabis sativa (C. sativa) is commonly chemically classified based on its Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) content ratios. However, the plant contains nearly 150 additional cannabinoids, referred to as minor cannabinoids. Minor cannabinoids are gaining interest for improved plant and product characterization, e.g., for medical use, and bioanalytical questions in the medico-legal field. This study describes the development and validation of an analytical method for the elucidation of minor cannabinoid fingerprints, employing liquid chromatography coupled to high-resolution mass spectrometry. The method was used to characterize inflorescences from 18 different varieties of C. sativa, which were cultivated under the same standardized conditions. Complementing the targeted detection of 15 cannabinoids, untargeted metabolomics employing in silico assisted data analysis was used to detect additional plant ingredients with focus on cannabinoids. Principal component analysis (PCA) was used to evaluate differences between varieties. The overall purpose of this study was to examine the ability of targeted and non-targeted metabolomics using the mentioned techniques to distinguish cannabis varieties from each other by their minor cannabinoid fingerprint. Quantitative determination of targeted cannabinoids already gave valuable information on cannabinoid fingerprints as well as inter- and intra-variety variability of cannabinoid contents. The untargeted workflow led to the detection of 19 additional compounds. PCA of the targeted and untargeted datasets revealed further subgroups extending commonly applied phenotype classification systems of cannabis. This study presents an analytical method for the comprehensive characterization of C. sativa varieties. [ABSTRACT FROM AUTHOR]

Single-cell multi-omics (scMulti-omics) has brought transformative insights into immuno-oncology, demonstrating success in describing novel immune subsets and defining important regulators of antitumor immunity. Here, we give examples of how scMulti-omics has been used in specific tumor studies and discuss how this may develop in the future. [ABSTRACT FROM AUTHOR]

One of the most substantial and established environmental risk factors for neurological and psychiatric disorders is stress exposure, whose detrimental consequences hinge on several variables including time. In this regard the gestational period is known to present an intrinsic vulnerability to environmental insults and thus stressful events during pregnancy can lead to severe consequences on the offspring's brain development with long-term repercussions throughout adulthood. On this basis, we investigated the long-lasting impact of prenatal stress exposure on the susceptibility to the experimental autoimmune encephalomyelitis (EAE), a well-established murine model of multiple sclerosis. Although stress is considered a triggering factor for this chronic, progressive, autoimmune disease, little is known about the underlying mechanisms. To this end, EAE was induced by immunization with MOG35-55/CFA and pertussis toxin administration in adult female C57BL/6 mice born from control or stressed dams exposed to restraint stress during the last days of gestation. Our results demonstrate that gestational stress induces a marked increase in the severity of EAE symptoms in adulthood. Further, we highlight an altered maturation of oligodendrocytes in the spinal cord of prenatally stressed EAE mice, as indicated by the higher levels of GPR17, a marker of immature oligodendrocyte precursor cells. These behavioral and molecular alterations are paralleled by changes in the expression and signaling of the neurotrophin BDNF, an important mediator of neural plasticity that may contribute to stress-induced impaired remyelination. Since several already marketed drugs are able to modulate BDNF levels, these results pave the way to the possibility of repositioning these drugs in multiple sclerosis. [ABSTRACT FROM AUTHOR]

The present study aimed to modulate the oxidative status-mediated polarity of the oocytes for sex-specific sperm fertilization to generate desired sex embryos. In vitro embryos were produced at different oxidative status, varying O2 concentrations, and without/with l-carnitine in maturation and culture media. The majority of the embryos produced at high oxidative stress were males whereas; low oxidative status favoured female embryos production. Low O2 doubled the proportion of female embryos (10.59 vs 21.95%); however, l-carnitine supplementation in media increased approximately seven-folds of the female embryos (12.26 vs. 77.62%) production. Oocytes matured at high oxidative status were in the repolarized state favouring positively charged Y sperm fertilization to produce significantly more male embryos. Low oxidative status favoured negatively charged X sperm fertilization to the oocytes in the depolarized state to produce more female embryos. Intracellular ROS was significantly low in female embryos than in males; however, female embryos were more stressful than males. The study concluded that the oxidative status-mediated alteration in pH of the medium to modulate the intracellular positive ions is the main critical factor to influence the sex of embryos through sex-specific sperms fertilization to the oocytes as per their polarity. [ABSTRACT FROM AUTHOR]

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics. Regev, Theis and colleagues outline the challenges and concepts of the analysis of spatial transcriptomics data. [ABSTRACT FROM AUTHOR]

Single-cell genomic technologies provide an unprecedented opportunity to define molecular cell types in a data-driven fashion, but present unique data integration challenges. Many analyses require "mosaic integration", including both features shared across datasets and features exclusive to a single experiment. Previous computational integration approaches require that the input matrices share the same number of either genes or cells, and thus can use only shared features. To address this limitation, we derive a nonnegative matrix factorization algorithm for integrating single-cell datasets containing both shared and unshared features. The key advance is incorporating an additional metagene matrix that allows unshared features to inform the factorization. We demonstrate that incorporating unshared features significantly improves integration of single-cell RNA-seq, spatial transcriptomic, SNARE-seq, and cross-species datasets. We have incorporated the UINMF algorithm into the open-source LIGER R package (https://github.com/welch-lab/liger). Single-cell genomic technologies present unique data integration challenges. Here the authors introduce an integrative nonnegative matrix factorization algorithm that incorporates features unshared between datasets when performing dataset integrations, improving integration results for spatial transcriptomic, cross-modality, and cross-species data. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing data can unveil the molecular diversity of cell types. Cell type atlases of the mouse spinal cord have been published in recent years but have not been integrated together. Here, we generate an atlas of spinal cell types based on single-cell transcriptomic data, unifying the available datasets into a common reference framework. We report a hierarchical structure of postnatal cell type relationships, with location providing the highest level of organization, then neurotransmitter status, family, and finally, dozens of refined populations. We validate a combinatorial marker code for each neuronal cell type and map their spatial distributions in the adult spinal cord. We also show complex lineage relationships among postnatal cell types. Additionally, we develop an open-source cell type classifier, SeqSeek, to facilitate the standardization of cell type identification. This work provides an integrated view of spinal cell types, their gene expression signatures, and their molecular organization. Single-cell profiling has led to the identification of diverse cell types. Here, the authors generate a harmonized cell atlas of the mouse post-natal spinal cord. They also provide spatial analysis of the distribution of the identified cell types and an open-source cell type classifier. [ABSTRACT FROM AUTHOR]

Vigabatrin is the drug of choice in resistant epilepsy and infantile spasms. Ataxia, tremors, and abnormal gait have been frequently reported following its use indicating cerebellar involvement. This study aimed, for the first time, to investigate the involvement of necroptosis and apoptosis in the VG-induced cerebellar cell loss and the possible protective role of combined omega-3 and vitamin B12 supplementation. Fifty Sprague-Dawley adult male rats (160-200 g) were divided into equal five groups: the control group received normal saline, VG200 and VG400 groups received VG (200 mg or 400 mg/kg, respectively), VG200 + OB and VG400 + OB groups received combined VG (200 mg or 400 mg/kg, respectively), vitamin B12 (1 mg/kg), and omega-3 (1 g/kg). All medications were given daily by gavage for four weeks. Histopathological changes were examined in H&E and luxol fast blue (LFB) stained sections. Immunohistochemical staining for caspase-3 and receptor-interacting serine/threonine-protein kinase-1 (RIPK1) as well as quantitative real-time polymerase chain reaction (qRT-PCR) for myelin basic protein (MBP), caspase-3, and receptor-interacting serine/threonine-protein kinase-3 (RIPK3) genes were performed. VG caused a decrease in the granular layer thickness and Purkinje cell number, vacuolations, demyelination, suppression of MBP gene expression, and induction of caspases-3, RIPK1, and RIPK3 in a dose-related manner. Combined supplementation with B12 and omega-3 improved the cerebellar histology, increased MBP, and decreased apoptotic and necroptotic markers. In conclusion, VG-induced neuronal cell loss is dose-dependent and related to both apoptosis and necroptosis. This could either be ameliorated (in low-dose VG) or reduced (in high-dose VG) by combined supplementation with B12 and omega-3. [ABSTRACT FROM AUTHOR]

Purpose of Review: The use of cannabinoids has increased since legalization of recreational and medical use in the USA. It is likely that many orthopaedic patients consume cannabinoid products during the traumatic or perioperative period. The purpose of this study was to investigate the pre-clinical data evaluating the mechanism of action of cannabidiol (CBD) and Δ9-Tetrahydrocannabinol (Δ9-THC) and to evaluate the current clinical data on the use of cannabinoids in musculoskeletal illness. Recent Findings: Recent pre-clinical studies have demonstrated that cannabinoid use and the endocannabinoid system (ECS) has an important role in bone healing and bone homeostasis. There is data that suggests that the use of cannabidiol (CBD) may increase bone healing, whereas the use of Δ9-Tetrahydrocannabinol (Δ9-THC), the major psychoactive ingredient in marijuana, likely inhibits bone metabolism and repair. The clinical implications and consumption of marijuana by orthopaedic patients have not been thoroughly evaluated. Studies have demonstrated concern for negative cardiovascular and psychiatric effects caused by marijuana use, but have not yet elucidated outcomes in the orthopaedic literature. Summary: With the recent increase in advertising of CBD products and legalization of marijuana, it is likely that many orthopaedic patients are consuming cannabinoid products. The clinical implications and consumption of these products are unclear. We need more robust and well-designed clinical studies prior to making further recommendations to our patients on the consumption of these products. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies show an increasing trend of hospitalization for acute diverticulitis (AD), but data regarding the trend in hospitalization for complicated AD in Italy are scarce. The aim of this study was to analyze the Italian trend in hospitalization for complicated AD, from 2008 to 2015. Methods: Using the Italian Hospital Information System, we identified all patients with complicated colonic AD as a discharge diagnosis. Age- and sex-specific rates for AD as well as type of hospital admission (emergency/elective), type of complication (peritonitis, obstruction, bleeding, abscess, fistula, perforation, sepsis) and type of treatment (medical/surgical), were analyzed. Results: A total of 41,622 patients with a discharge diagnosis of complicated AD were identified. Over the study period the admission rate grew from 8.8 to 11.8 per 100,000 inhabitants. The hospitalization rate was highest for patients ≥ 70 years, but the increase in the admission rate was higher among patients aged ≤ 60 years. There were more males in the group < 60 years and more females in the group ≥ 60 years old. The rate of emergency admissions associated with surgery showed a significant mean annual increase (+ 3.9% per year) in the rate of emergency admissions associated with surgery, whereas elective admissions for surgery remained stable. Peritonitis was the most frequent complication (35.5%). The rate of surgery increased in AD complicated by peritonitis (+ 5.1% per year), abscess (+ 5.8% per year) and decreased for obstruction (− 1.8% per year). Conclusions: From 2008 to 2015, we documented an increasing rate of hospitalization for complicated AD, especially for younger patients, with an increase in surgery for peritonitis and abscess. Further studies are needed to clearly assess the risk factors for complications and risk of surgery. [ABSTRACT FROM AUTHOR]

In the present paper, the spectral meshless radial point interpolation (SMRPI) technique is applied to the solution of pattern formation in nonlinear reaction diffusion systems. Firstly, we obtain a time discrete scheme by approximating the time derivative via a finite difference formula, then we use the SMRPI approach to approximate the spatial derivatives. This method is based on a combination of meshless methods and spectral collocation techniques. The point interpolation method with the help of radial basis functions is used to construct shape functions which act as basis functions in the frame of SMRPI. In the current work, to eliminate the nonlinearity, a simple predictor–corrector (P–C) scheme is performed. The effect of parameters and conditions are studied by considering the well-known Schnakenberg model. [ABSTRACT FROM AUTHOR]

The customer satisfaction, an important concept in the field of general marketing and in the management of firms, is the measure of the degree of customer satisfaction. More precisely, the customer satisfaction provides an assessment of the discrepancy between the perceived performance by the customer (i.e. all the sensations and impressions after use of a product or the fruition of a service) and the expectations (expectations and desires) of the same customer but also represents the company's ability to anticipate and to manage the expectations of the customer, to satisfy its needs with competence and responsibility. The company resources, in fact, must be organized taking account of the demands of customers who represent a precious commodity for the company, even if their value does not appear in the financial statements, the fulcrum around which revolves the whole system business. To have satisfied customers is, therefore, fundamental for a company and, therefore, the customer satisfaction can be defined as a touchstone for the management of customer relationships, as a key to create the competitive advantage as of a company. In this article, the authors present some of the main processes and detection systems required for a scrupulous and reliable process of measuring of the customer satisfaction or better they present a mathematical framework to determine and to improve the customer satisfaction and to demonstrate how it has a great impact on corporate performance. In particular, the authors propose a model of structural equations with latent variables which is the most rigorous methodology currently available for the evaluations of the customer satisfaction and they hope that the paper, which at first sight has little in common with mathematics, is a very useful for stimulating research at the interface of mathematics and marketing and that it builds a link that can become beneficial for both disciplines involved. [ABSTRACT FROM AUTHOR]

A rapidly growing collection of software tools is helping researchers to analyse multiple huge ‘-omics’ data sets. [ABSTRACT FROM AUTHOR]

Appropriate representation is one of the keys to successful face recognition technologies. Actual facial appearance sometimes differs dramatically because of variations in pose, illumination, expression, and occlusion. However, existing face representation methods remain insufficiently powerful and robust. Hence, we propose a new feature extraction approach for face representation based on heat kernel volume and local binary patterns. Multi-scale heat kernel faces are created in our proposed framework. We then reformulate these multi-scale heat kernel faces as three-dimensional volume. Furthermore, we generate multi-orientation planes from the heat kernel volume, which reflects orientation co-occurrence statistics among different heat kernel faces. Finally, we apply local binary pattern (LBP) analysis on the multi-orientation planes of the heat kernel volume to capture the microstructure and macrostructure of face appearance. Hence, we obtain the heat kernel volume based local binary pattern on multi-orientation planes (HKV-LBP-MOP) descriptor. The proposed method is successfully be paralleled. We applied the method to face recognition and obtain the performance of 99.28 and 87.82% on ORL and Yale databases respectively. Experimental results on the show that the proposed algorithm significantly outperforms other well-known approaches in terms of recognition rate. [ABSTRACT FROM AUTHOR]

Infrared imaging has the advantage of all-weather working ability. Due to the limitation of the hardware and the high cost, the resolution of infrared image (IR) is very low. To improve the resolution of IR images, this paper exploits super-resolution (SR) method for IR images. A new SR framework by using random forests is proposed in this paper. Existing methods adopts single regression model for SR. However, which single regression model tends to overfit training data, and would lead to a poor performance. Furthermore, the existing methods are not suitable for real-time system due to the heavy time consuming. To resolve this problem, an ensemble regression model, i.e. random forests rather than single regression model is adopted in this paper. In addition, to achieve better results multi-regression models rather than a single regression model are trained on the clustered training data. Moreover, the features used in many SR methods cannot extract features on diagonal orientation. To resolve this problem, we adopt a second order derivative filter, which can extract features on diagonal orientation. The experimental results demonstrate the availability of the proposed method. [ABSTRACT FROM AUTHOR]

Advanced Encryption Standard (AES) is an effective cryptography algorithm for providing the better data communication since it guaranties high security. The Galois/Counter Mode (AES-GCM) has been integrated in various security constrained applications because it provides both authentication and confidentiality. AES algorithm helps to provide data confidentiality while authentication is provided by a universal GHASH function. Since most of existing GCM architectures concentrated on power and area reduction but an compact and efficient hardware architecture should also be considered. In this paper, high-performance architecture for GCM is proposed and its implementation is described. In order to achieve higher operating frequency and throughput, pipelined S-boxes are used in AES algorithm. For a GCM realization of AES, a high-speed, high-throughput, parallel architecture is proposed. Experimental results proves that the performance of the proposed work is around 17% higher than the existing architecture with 3 Gb/s throughput using TSMC 45-nm CMOS technology. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies in Western countries have documented an increase of hospitalizations for acute diverticulitis (AD) but Italian evidence is scarce. The aim of the present study was to analyse the trend in hospitalization for AD, including in-hospital mortality, in Italy from 2008 to 2015.Methods: Through the Italian Hospital Information System of the National Health System, we identified diverticulitis of the colon as a discharge diagnosis. Age- and gender-specific rates of hospitalization for AD were assessed.Results: 174,436 hospitalizations were identified with an increasing rate in 2008-2015 from 39 to 48 per 100,000 inhabitants (p < 0.001). The rate of hospitalization was higher for women, but the increasing trend over time was even more pronounced among men (mean increase per year 3.9% and 2.1% among men and women, respectively) (p < 0.001). The increased rate of hospitalization was accounted for by patients less than 70 years old, especially those under 60. In contrast, the hospitalization rate for older patients (age ≥ 70 years) was higher but remained unchanged during the study period. The number of patients with one hospital admissions was significantly higher than the number of patients with at least two hospitalizations (p < 0.001) and both groups showed a significant and comparable increase year by year. The overall in-hospital mortality rate increased from 1.2 to 1.5% (p = 0.017). More specifically, the increase was observed in patients at their first hospitalization [from 1 to 1.4% (mean increase per year of 3%, p = 0.003)]. An increase in mortality was most evident among women (from 1.4 to 1.8% p = 0.025) and in older patients [age 70-79 years from 1.2 to 1.7% (p = 0.034), ≥ 80 years from 2.9 to 4% (p = 0.001)].Conclusions: In Italy, between 2008 and 2015, the rate of hospitalization for AD has been constantly increasing due to the hospitalization of younger individuals, especially men. There was a significant increase of in-hospital mortality especially among women, elderly and during the first hospitalization. These findings suggest the need for increased awareness and clinical skills in the management of this common condition. [ABSTRACT FROM AUTHOR]

We deal with the following scheduling problem: an infinite number of tasks must be scheduled for processing on a finite number of heterogeneous machines, such as all tasks are sent to execution with a minimum delay. The tasks have causal dependencies and are generated in the context of biomedical applications, and produce results relevant for the medical domain, such as diagnosis support or drug dose adjust measures. The proposed scheduling model had a starting point in two known bounded number of processors algorithms: Modified Critical Path and Highest Level First With Estimated Times. Several steps were added to the original implementation along with a merge stage in order to combine the results obtained for each of the previously scheduled tasks. Regarding the implementation, a simulator was used to analyze and design the scheduling algorithms. [ABSTRACT FROM AUTHOR]