Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0844-5) contains supplementary material, which is available to authorized users.

Plasma levels of high density lipoprotein cholesterol (HDL-C) are inversely proportional to the incidence of cardiovascular disease. Recent applications of modern proteomic technologies have identified upward of 50 distinct proteins associated with HDL particles with many of these newly discovered proteins implicating HDL in nonlipid transport processes including complement activation, acute phase response and innate immunity. However, almost all MS-based proteomic studies on HDL to date have utilized density gradient ultracentrifugation techniques for HDL isolation prior to analysis. These involve high shear forces and salt concentrations that can disrupt HDL protein interactions and alter particle function. Here, we used high-resolution size exclusion chromatography to fractionate normal human plasma to 17 phospholipid-containing subfractions. Then, using a phospholipid binding resin, we identified proteins that associate with lipoproteins of various sizes by electrospray ionization mass spectrometry. We identified 14 new phospholipid-associated proteins that migrate with traditionally defined HDL, several of which further support roles for HDL in complement regulation and protease inhibition. The increased fractionation inherent to this method allowed us to visualize HDL protein distribution across particle size with unprecedented resolution. The observed heterogeneity across subfractions suggests the presence of HDL particle subpopulations each with distinct protein components that may prove to impart distinct physiological functions.

A remarkable feature of development is its reproducibility, the ability to correct embryo-to-embryo variations and instruct precise patterning. In Drosophila, embryonic patterning along the anterior-posterior axis is controlled by the morphogen gradient Bicoid (Bcd). In this report, we describe quantitative studies of the native Bcd gradient and its target Hunchback (Hb). We show that the native Bcd gradient is highly reproducible and is itself scaled with embryo length. While a precise Bcd gradient is necessary for precise Hb expression, it still has positional errors greater than Hb expression. We describe analyses further probing mechanisms for Bcd gradient scaling and correction of its residual positional errors. Our results suggest a simple model of a robust Bcd gradient sufficient to achieve scaled and precise activation of its targets. The robustness of this gradient is conferred by its intrinsic properties of "self-correcting" the inevitable input variations to achieve a precise and reproducible output.

Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs.

BackgroundAtherosclerosis constitutes the leading contributor to morbidity and mortality in cardiovascular and cerebrovascular diseases. Lipid deposition and inflammatory response are the crucial triggers for the development of atherosclerosis. Recently, microRNAs (miRNAs) have drawn more attention due to their prominent function on inflammatory process and lipid accumulation in cardiovascular and cerebrovascular disease. Here, we investigated the involvement of miR-21 in lipopolysaccharide (LPS)-induced lipid accumulation and inflammatory response in macrophages.MethodsAfter stimulation with the indicated times and doses of LPS, miR-21 mRNA levels were analyzed by Quantitative real-time PCR. Following transfection with miR-21 or anti-miR-21 inhibitor, lipid deposition and foam cell formation was detected by high-performance liquid chromatography (HPLC) and Oil-red O staining. Furthermore, the inflammatory cytokines interleukin 6 (IL-6) and interleukin 10 (IL-10) were evaluated by Enzyme-linked immunosorbent assay (ELISA) assay. The underlying molecular mechanism was also investigated.ResultsIn this study, LPS induced miR-21 expression in macrophages in a time- and dose-dependent manner. Further analysis confirmed that overexpression of miR-21 by transfection with miR-21 mimics notably attenuated lipid accumulation and lipid-laden foam cell formation in LPS-stimulated macrophages, which was reversely up-regulated when silencing miR-21 expression via anti-miR-21 inhibitor transfection, indicating a reverse regulator of miR-21 in LPS-induced foam cell formation. Further mechanism assays suggested that miR-21 regulated lipid accumulation by Toll-like receptor 4 (TLR4) and nuclear factor-κB (NF-κB) pathway as pretreatment with anti-TLR4 antibody or a specific inhibitor of NF-κB (PDTC) strikingly dampened miR-21 silence-induced lipid deposition. Additionally, overexpression of miR-21 significantly abrogated the inflammatory cytokines secretion of IL-6 and increased IL-10 levels, the corresponding changes were also observed when silencing miR-21 expression, which was impeded by preconditioning with TLR4 antibody or PDTC.ConclusionsTaken together, these results corroborated that miR-21 could negatively regulate LPS-induced lipid accumulation and inflammatory responses in macrophages by the TLR4-NF-κB pathway. Accordingly, our research will provide a prominent insight into how miR-21 reversely abrogates bacterial infection-induced pathological processes of atherosclerosis, indicating a promising therapeutic prospect for the prevention and treatment of atherosclerosis by miR-21 overexpression.

The distribution of circulating lipoprotein particles affects the risk for cardiovascular disease (CVD) in humans. Lipoproteins are historically defined by their density, with low-density lipoproteins positively and high-density lipoproteins (HDLs) negatively associated with CVD risk in large populations. However, these broad definitions tend to obscure the remarkable heterogeneity within each class. Evidence indicates that each class is composed of physically (size, density, charge) and compositionally (protein and lipid) distinct subclasses exhibiting unique functionalities and differing effects on disease. HDLs in particular contain upward of 85 proteins of widely varying function that are differentially distributed across a broad range of particle diameters. We hypothesized that the plasma lipoproteins, particularly HDL, represent a continuum of phospholipid platforms that facilitate specific protein-protein interactions. To test this idea, we separated normal human plasma using three techniques that exploit different lipoprotein physicochemical properties (gel filtration chromatography, ionic exchange chromatography, and preparative isoelectric focusing). We then tracked the co-separation of 76 lipid-associated proteins via mass spectrometry and applied a summed correlation analysis to identify protein pairs that may co- Lipoproteins are circulating emulsions of protein and lipid that play important roles, both positive and negative, in cardiovascular disease (CVD).1 Historically defined by their density as separated by ultracentrifugation, the major lipoprotein classes include the neutral lipid ester-rich very low-density and low-density lipoproteins (VLDLs and LDLs, respectively), which function to transport triglyceride and cholesterol from the liver to the peripheral tissues. Significant epidemiological evidence, in vitro studies, animal experiments, and human clinical trials have shown that high-LDL cholesterol is a bona fide causative factor in CVD (1). In contrast, protein-and phospholipid-rich high-density lipoproteins (HDLs) are thought to mediate the reverse transport of cholesterol from the periphery to the liver for catabolism and to perform anti-oxidative and anti-inflammatory functions (reviewed in Refs. 2 and 3). A host of human epidemiology and animal studies indicate that HDLs are atheroprotective (4). However, recent clinical trials of therapeutics that generically raise HDL, at least as measured by its cholesterol levels, have failed to confer the expected CVD protections (5-7).Although these traditional density-centric definitions have been used for nearly 40 years, accumulating evidence indicates that they are not particularly reflective of lipoprotein compositional and functional complexity. With respect to most physical traits (size, charge, lipid content, protein content, etc.), one can demonstrate significant heterogeneity within each density class. This suggests that particle subspecies exist with unique functions and effects on disease. For example, LDL can be resolved into large,...

Coronavirus disease 2019 (COVID‐19) has widely spread all over the world and the numbers of patients and deaths are increasing. According to the epidemiology, virology, and clinical practice, there are varying degrees of changes in patients, involving the human body structure and function and the activity and participation. Based on the World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF) and its biopsychosocial model of functioning, we use the WHO Family of International Classifications (WHO‐FICs) framework to form an expert consensus on the COVID‐19 rehabilitation program, focusing on the diagnosis and evaluation of disease and functioning, and service delivery of rehabilitation, and to establish a standard rehabilitation framework, terminology system, and evaluation and intervention systems based the WHO‐FICs.

17Single-cell transcriptomic profiling enables the unprecedented interrogation of gene 18 expression heterogeneity in rare cell populations that would otherwise be obscured in 19bulk RNA sequencing experiments. The stochastic nature of transcription is revealed in 20 the bimodality of single-cell transcriptomic data, a feature shared across single-cell 21 expression platforms. There is, however, a paucity of computational tools that take 22 103 treatment groups are summarized with pairs of regression coefficients whose sampling 104 distributions are available through bootstrap or asymptotic expressions, enabling us to perform 105 complementary differential gene expression and gene set enrichment analyses (GSEA). We use 106 an empirical Bayesian framework to regularize model parameters, which helps improve 107 inference for genes with sparse expression, much like what has been done for bulk gene 108 expression 14 . Our GSEA approach accounts for gene-gene correlations, which is important for 109proper control of type I errors 15 . This GSEA framework is particularly useful for synthesizing 110 observed gene-level differences into statements about pathways or modules. Finally, our model 111yields single cell residuals that can be manipulated to interrogate cellular heterogeneity and 112 gene-gene correlations across cells and conditions. We have named our approach MAST for 113Model-based Analysis of Single-cell Transcriptomics. 114 115 We illustrate the method on two data sets. We first apply our approach to an experiment 116 comparing primary human non-stimulated and cytokine-activated Mucosal-Associated Invariant 117 T (MAIT) cells. MAST identifies novel expression signatures of activation, and the single-cell 118 residuals produced by the model highlights a population of MAIT cells showing partial activation 119 but no induction of effector function. We then illustrate the application of MAST to a previously-120 published complex experiment studying temporal changes in murine bone marrow-derived 121 dendritic cells subjected to LPS stimulation. We both recapitulate the findings of the original 122 publication and describe additional coordinated gene expression changes at the single-cell level 123 across time in LPS stimulated mDC cells. 124 125Results 126 127MAST can account for variation in the cellular detection rate. As discussed previously and 128 as shown on Figure 1 by principal component analysis (PCA), the cellular detection rate (CDR, 129 see Methods for exact definition), is an important source of variability. It is highly correlated with 130 the second principal component (PC, Pearson's rho=0.76 grouped, 0.91 stimulated, 0.97 non-131 stimulated) in the MAIT dataset and the first PC (rho=0.92 grouped, 0.97 non-stimulated, 0.92 132 LPS, 0.89 PAM, 0.92 PIC) in the mDC dataset. We observe larger CDR variability within 133 analysis. Nature methods 1-5 (2014).