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Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet processing in one pot for trace samples) platform for small cell population proteomics analysis. NanoPOTS enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive liquid chromatography-MS, nanoPOTS allows identification of ~1500 to ~3000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins are consistently identified from as few as 10 cells. Furthermore, we demonstrate quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues. There is a great need of developing highly sensitive mass spectrometry-based proteomics analysis for small cell populations. Here, the authors establish a robotically controlled chip-based nanodroplet processing platform and demonstrate its ability to profile the proteome from 10–100 mammalian cells.

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

The accelerating tide of antimicrobial resistance (AMR) is a major worldwide policy concern. Like climate change, the incentives for individual decision-makers do not take into account the costs to society at large. AMR represents an impending “tragedy of the commons,” and there is an immediate need for collective action to prevent future harm. Roope et al. review the issues associated with AMR from an economics perspective and draw parallels with climate change. A major stumbling block for both challenges is to build consensus about the best way forward when faced with many uncertainties and inequities. Science , this issue p. eaau4679 As antibiotic consumption grows, bacteria are becoming increasingly resistant to treatment. Antibiotic resistance undermines much of modern health care, which relies on access to effective antibiotics to prevent and treat infections associated with routine medical procedures. The resulting challenges have much in common with those posed by climate change, which economists have responded to with research that has informed and shaped public policy. Drawing on economic concepts such as externalities and the principal–agent relationship, we suggest how economics can help to solve the challenges arising from increasing resistance to antibiotics. We discuss solutions to the key economic issues, from incentivizing the development of effective new antibiotics to improving antibiotic stewardship through financial mechanisms and regulation.

Photosynthetic microbes are of emerging interest as production organisms in biotechnology because they can grow autotrophically using sunlight, an abundant energy source and CO 2 , a greenhouse gas. Important traits for such microbes are fast growth and amenability to genetic manipulation. Here we describe Synechococcus elongatus UTEX 2973, a unicellular cyanobacterium capable of rapid autotrophic growth, comparable to heterotrophic industrial hosts such as yeast. Synechococcus UTEX 2973 can be readily transformed for facile generation of desired knockout and knock-in mutations. Genome sequencing coupled with global proteomics studies revealed that Synechococcus UTEX 2973 is a close relative of the widely studied cyanobacterium Synechococcus elongatus PCC 7942, an organism that grows more than two times slower. A small number of nucleotide changes are the only significant differences between the genomes of these two cyanobacterial strains. Thus, our study has unraveled genetic determinants necessary for rapid growth of cyanobacterial strains of significant industrial potential.

Global quantification of protein abundances in single cells could provide direct information on cellular phenotypes and complement transcriptomics measurements. However, single-cell proteomics is still immature and confronts many technical challenges. Herein we describe a nested nanoPOTS (N2) chip to improve protein recovery, operation robustness, and processing throughput for isobaric-labeling-based scProteomics workflow. The N2 chip reduces reaction volume to <30 nL and increases capacity to >240 single cells on a single microchip. The tandem mass tag (TMT) pooling step is simplified by adding a microliter droplet on the nested nanowells to combine labeled single-cell samples. In the analysis of ~100 individual cells from three different cell lines, we demonstrate that the N2 chip-based scProteomics platform can robustly quantify ~1500 proteins and reveal membrane protein markers. Our analyses also reveal low protein abundance variations, suggesting the single-cell proteome profiles are highly stable for the cells cultured under identical conditions. Single-cell proteomics is an emerging technology but protein coverage, throughput and quantitation accuracy are often still insufficient. Here, the authors develop a nested nanowell chip that improves protein recovery, throughput and robustness of isobaric labeling-based quantitative single-cell proteomics.

Understanding how ligand binding influences protein flexibility is important, especially in rational drug design. Protein flexibility upon ligand binding is analyzed herein using 305 proteins with 2369 crystal structures with ligands (holo) and 1679 without (apo). Each protein has at least two apo and two holo structures for analysis. The inherent variation in structures with and without ligands is first established as a baseline. This baseline is then compared to the change in conformation in going from the apo to holo states to probe induced flexibility. The inherent backbone flexibility across the apo structures is roughly the same as the variation across holo structures. The induced backbone flexibility across apo-holo pairs is larger than that of the apo or holo states, but the increase in RMSD is less than 0.5 Å. Analysis of χ1 angles revealed a distinctly different pattern with significant influences seen for ligand binding on side-chain conformations in the binding site. Within the apo and holo states themselves, the variation of the χ1 angles is the same. However, the data combining both apo and holo states show significant displacements. Upon ligand binding, χ1 angles are frequently pushed to new orientations outside the range seen in the apo states. Influences on binding-site variation could not be easily attributed to features such as ligand size or x-ray structure resolution. By combining these findings, we find that most binding site flexibility is compatible with the common practice in flexible docking, where backbones are kept rigid and side chains are allowed some degree of flexibility.

Outer membrane vesicles (OMVs) produced by Gram-negative bacteria have key roles in cell envelope homeostasis, secretion, interbacterial communication, and pathogenesis. The facultative intracellular pathogen Salmonella Typhimurium increases OMV production inside the acidic vacuoles of host cells by changing expression of its outer membrane proteins and modifying the composition of lipid A. However, the molecular mechanisms that translate pH changes into OMV production are not completely understood. Here, we show that the outer membrane protein PagC promotes OMV production through pH-dependent interactions between its extracellular loops and surrounding lipopolysaccharide (LPS). Structural comparisons and mutational studies indicate that a pH-responsive amino acid motif in PagC extracellular loops, containing PagC-specific histidine residues, is crucial for OMV formation. Molecular dynamics simulations suggest that protonation of histidine residues leads to changes in the structure and flexibility of PagC extracellular loops and their interactions with the surrounding LPS, altering membrane curvature. Consistent with that hypothesis, mimicking acidic pH by mutating those histidine residues to lysine increases OMV production. Thus, our findings reveal a mechanism for sensing and responding to environmental pH and for control of membrane dynamics by outer membrane proteins. The pathogen Salmonella Typhimurium increases production of outer membrane vesicles (OMVs) inside acidic vacuoles of host cells, but the mechanisms are unclear. Here, Dehinwal et al. show that acidic pH induces conformational changes in an outer membrane protein that affect its interaction with membrane lipids, thus modulating OMV formation.

Glycosylation modifies the physicochemical properties and protein binding functions of glycoconjugates. These modifications are biosynthesized in the endoplasmic reticulum and Golgi apparatus by a series of enzymatic transformations that are under complex control. As a result, mature glycans on a given site are heterogeneous mixtures of glycoforms. This gives rise to a spectrum of adhesive properties that strongly influences interactions with binding partners and resultant biological effects. In order to understand the roles glycosylation plays in normal and disease processes, efficient structural analysis tools are necessary. In the field of glycomics, liquid chromatography/mass spectrometry (LC/MS) is used to profile the glycans present in a given sample. This technology enables comparison of glycan compositions and abundances among different biological samples, i.e. normal versus disease, normal versus mutant, etc. Manual analysis of the glycan profiling LC/MS data is extremely time-consuming and efficient software tools are needed to eliminate this bottleneck. In this work, we have developed a tool to computationally model LC/MS data to enable efficient profiling of glycans. Using LC/MS data deconvoluted by Decon2LS/DeconTools, we built a list of unique neutral masses corresponding to candidate glycan compositions summarized over their various charge states, adducts and range of elution times. Our work aims to provide confident identification of true compounds in complex data sets that are not amenable to manual interpretation. This capability is an essential part of glycomics work flows. We demonstrate this tool, GlycReSoft, using an LC/MS dataset on tissue derived heparan sulfate oligosaccharides. The software, code and a test data set are publically archived under an open source license.

Beverages, especially sugar-sweetened beverages (SSBs), have been increasingly subject to policies aimed at reducing their consumption as part of measures to tackle obesity. However, precision targeting of policies is difficult as information on what types of consumers they might affect, and to what degree, is missing. We fill this gap by creating a typology of beverage consumers in Great Britain (GB) based on observed beverage purchasing behaviour to determine what distinct types of beverage consumers exist, and what their socio-demographic (household) characteristics, dietary behaviours, and weight status are. We used cross-sectional latent class analysis to characterise patterns of beverage purchases. We used data from the 2016 GB Kantar Fast-Moving Consumer Goods (FMCG) panel, a large representative household purchase panel of food and beverages brought home, and restricted our analyses to consumers who purchase beverages regularly (i.e., >52 l per household member annually) (n = 8,675). Six categories of beverages were used to classify households into latent classes: SSBs; diet beverages; fruit juices and milk-based beverages; beer and cider; wine; and bottled water. Multinomial logistic regression and linear regression were used to relate class membership to household characteristics, self-reported weight status, and other dietary behaviours, derived from GB Kantar FMCG. Seven latent classes were identified, characterised primarily by higher purchases of 1 or 2 categories of beverages: 'SSB' (18% of the sample; median SSB volume = 49.4 l/household member/year; median diet beverage volume = 38.0 l), 'Diet' (16%; median diet beverage volume = 94.4 l), 'Fruit & Milk' (6%; median fruit juice/milk-based beverage volume = 30.0 l), 'Beer & Cider' (7%; median beer and cider volume = 36.3 l; median diet beverage volume = 55.6 l), 'Wine' (18%; median wine volume = 25.5 l; median diet beverage volume = 34.3 l), 'Water' (4%; median water volume = 46.9 l), and 'Diverse' (30%; diversity of purchases, including median SSB volume = 22.4 l). Income was positively associated with being classified in the Diverse class, whereas low social grade was more likely for households in the classes SSB, Diet, and Beer & Cider. Obesity (BMI > 30 kg/m2) was more prevalent in the class Diet (41.2%, 95% CI 37.7%-44.7%) despite households obtaining little energy from beverages in that class (17.9 kcal/household member/day, 95% CI 16.2-19.7). Overweight/obesity (BMI > 25 kg/m2) was above average in the class SSB (66.8%, 95% CI 63.7%-69.9%). When looking at all groceries, households from the class SSB had higher total energy purchases (1,943.6 kcal/household member/day, 95% CI 1,901.7-1,985.6), a smaller proportion of energy from fruits and vegetables (6.0%, 95% CI 5.8%-6.3%), and a greater proportion of energy from less healthy food and beverages (54.6%, 95% CI 54.0%-55.1%) than other classes. A greater proportion of energy from sweet snacks was observed for households in the classes SSB (18.5%, 95% CI 18.1%-19.0%) and Diet (18.8%, 95% CI 18.3%-19.3%). The main limitation of our analyses, in common with other studies, is that our data do not include information on food and beverage purchases that are consumed outside the home. Amongst households that regularly purchase beverages, those that mainly purchased high volumes of SSBs or diet beverages were at greater risk of obesity and tended to purchase less healthy foods, including a high proportion of energy from sweet snacks. These households might additionally benefit from policies targeting unhealthy foods, such as sweet snacks, as a way of reducing excess energy intake.