Investigation of Biological Factors Contributing to Individual Variation in Viral Titer after Oral Infection of Aedes aegypti Mosquitoes by Sindbis Virus

The mechanisms involved in determining arbovirus vector competence, or the ability of an arbovirus to infect and be transmitted by an arthropod vector, are still incompletely understood. It is well known that vector competence for a particular arbovirus can vary widely among different populations of a mosquito species, which is generally attributed to genetic differences between populations. What is less understood is the considerable variability (up to several logs) that is routinely observed in the virus titer between individual mosquitoes in a single experiment, even in mosquitoes from highly inbred lines. This extreme degree of variation in the virus titer between individual mosquitoes has been largely ignored in past studies. We investigated which biological factors can affect titer variation between individual mosquitoes of a laboratory strain of Aedes aegypti, the Orlando strain, after Sindbis virus infection. Greater titer variation was observed after oral versus intrathoracic infection, suggesting that the midgut barrier contributes to titer variability. Among the other factors tested, only the length of the incubation period affected the degree of titer variability, while virus strain, mosquito strain, mosquito age, mosquito weight, amount of blood ingested, and virus concentration in the blood meal had no discernible effect. We also observed differences in culture adaptability and in the ability to orally infect mosquitoes between virus populations obtained from low and high titer mosquitoes, suggesting that founder effects may affect the virus titer in individual mosquitoes, although other explanations also remain possible.

Image based annotation of Chemogenomic Libraries for Phenotypic Screening

Phenotypical screening is a widely used approach in drug discovery for the identification of small molecules with cellular activities. However, functional annotation of identified hits often poses a challenge. The development of small molecules with narrow or exclusive target selectivity such as chemical probes and chemogenomic (CG) libraries, greatly diminishes this challenge, but non-specific effects caused by compound toxicity or interference with basic cellular functions still poses a problem to associate phenotypic readouts with molecular targets. Hence, each compound should ideally be comprehensively characterized regarding its effects on general cell functions. Here, we report an optimized live-cell multiplexed assay that classifies cells based on nuclear morphology, presenting an excellent indicator for cellular responses such as early apoptosis and necrosis. This basic readout in combination with the detection of other general cell damaging activities of small molecules such as changes in cytoskeletal morphology, cell cycle and mitochondrial health provides a comprehensive time-dependent characterization of the effect of small molecules on cellular health in a single experiment. The developed high-content assay offers multi-dimensional comprehensive characterization that can be used to delineate generic effects regarding cell functions and cell viability, allowing an assessment of compound suitability for subsequent detailed phenotypic and mechanistic studies.

Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile

The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method’s main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure’s training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy.

Development of the “Applied Proteomics” Concept for Biotechnology Applications in Microalgae: Example of the Proteome Data in Nannochloropsis gaditana

Most of the marine ecosystems on our planet are still unknown. Among these ecosystems, microalgae act as a baseline due to their role as primary producers. The estimated millions of species of these microorganisms represent an almost infinite source of potentially active biocomponents offering unlimited biotechnology applications. This review considers current research in microalgae using the “omics” approach, which today is probably the most important biotechnology tool. These techniques enable us to obtain a large volume of data from a single experiment. The specific focus of this review is proteomics as a technique capable of generating a large volume of interesting information in a single proteomics assay, and particularly the concept of applied proteomics. As an example, this concept has been applied to the study of Nannochloropsis gaditana, in which proteomics data generated are transformed into information of high commercial value by identifying proteins with direct applications in the biomedical and agri-food fields, such as the protein designated UCA01 which presents antitumor activity, obtained from N. gaditana.

Validated Determination of NRG1 Ig-like Domain Structure by Mass Spectrometry Coupled with Computational Modeling

High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally-informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure leaving questions of unintentional bias and applicability to unknown structures unresolved. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 Å. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure.

Simultaneous determination of CKM angle γ and charm mixing parameters

A combination of measurements sensitive to the CP violation angle γ of the Cabibbo-Kobayashi-Maskawa unitarity triangle and to the charm mixing parameters that describe oscillations between D0 and $$ \overline{D} $$ D ¯ 0 mesons is performed. Results from the charm and beauty sectors, based on data collected with the LHCb detector at CERN’s Large Hadron Collider, are combined for the first time. This method provides an improvement on the precision of the charm mixing parameter y by a factor of two with respect to the current world average. The charm mixing parameters are determined to be $$ x=\left({0.400}_{-0.053}^{+0.052}\right)\% $$ x = 0.400 − 0.053 + 0.052 % and y = $$ \left({0.630}_{-0.030}^{+0.033}\right)\% $$ 0.630 − 0.030 + 0.033 % . The angle γ is found to be γ = $$ \left({65.4}_{-4.2}^{+3.8}\right){}^{\circ} $$ 65.4 − 4.2 + 3.8 ° and is the most precise determination from a single experiment.

DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data

Background Advances in droplet-based single-cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free “ambient” RNA predominantly caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell-containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells or empty droplets. Additionally, there are currently no methods available to detect droplets containing damaged cells, which comprise partially lysed cells, the original source of the ambient RNA. Results Here, we describe DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish them from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. We demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. Conclusions We implement DropletQC as an R package, which can be easily integrated into existing single-cell analysis workflows.

Multiparametric quantitative phase imaging for real-time, single cell, drug screening in breast cancer

Quantitative phase imaging (QPI) measures the growth rate of individual cells by quantifying changes in mass versus time. Here, we use the breast cancer cell lines MCF-7, BT-474, and MDA-MB-231 to validate QPI as a multiparametric approach for determining response to single-agent therapies. Our method allows for rapid determination of drug sensitivity, cytotoxicity, heterogeneity, and time of response for up to 100,000 individual cells or small clusters in a single experiment. We find that QPI EC50 values are concordant with CellTiter-Glo (CTG), a gold standard metabolic endpoint assay. In addition, we apply multiparametric QPI to characterize cytostatic/cytotoxic and rapid/slow responses and track the emergence of resistant subpopulations. Thus, QPI reveals dynamic changes in response heterogeneity in addition to average population responses, a key advantage over endpoint viability or metabolic assays. Overall, multiparametric QPI reveals a rich picture of cell growth by capturing the dynamics of single-cell responses to candidate therapies.