Statistical modeling of single target cell encapsulation.
Ontology highlight
ABSTRACT:
SUBMITTER: Moon S
PROVIDER: S-EPMC3140975 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.
| S-EPMC6765121 | biostudies-literature
Project description:BACKGROUND:Sensitive detection of low-frequency single nucleotide variants carries great significance in many applications. In cancer genetics research, tumor biopsies are a mixture of normal and tumor cells from various subpopulations due to tumor heterogeneity. Thus the frequencies of somatic variants from a subpopulation tend to be low. Liquid biopsies, which monitor circulating tumor DNA in blood to detect metastatic potential, also face the challenge of detecting low-frequency variants due to the small percentage of the circulating tumor DNA in blood. Moreover, in population genetics research, although pooled sequencing of a large number of individuals is cost-effective, pooling dilutes the signals of variants from any individual. Detection of low frequency variants is difficult and can be cofounded by sequencing artifacts. Existing methods are limited in sensitivity and mainly focus on frequencies around 2 % to 5 %; most fail to consider differential sequencing artifacts. RESULTS:We aimed to push down the frequency detection limit close to the position specific sequencing error rates by modeling the observed erroneous read counts with respect to genomic sequence contexts. 4 distributions suitable for count data modeling (using generalized linear models) were extensively characterized in terms of their goodness-of-fit as well as the performances on real sequencing data benchmarks, which were specifically designed for testing detection of low-frequency variants; two sequencing technologies with significantly different chemistry mechanisms were used to explore systematic errors. We found the zero-inflated negative binomial distribution generalized linear mode is superior to the other models tested, and the advantage is most evident at 0.5 % to 1 % range. This method is also generalizable to different sequencing technologies. Under standard sequencing protocols and depth given in the testing benchmarks, 95.3 % recall and 79.9 % precision for Ion Proton data, 95.6 % recall and 97.0 % precision for Illumina MiSeq data were achieved for SNVs with frequency?>?= 1 %, while the detection limit is around 0.5 %. CONCLUSIONS:Our method enables sensitive detection of low-frequency single nucleotide variants across different sequencing platforms and will facilitate research and clinical applications such as pooled sequencing, cancer early detection, prognostic assessment, metastatic monitoring, and relapses or acquired resistance identification.
| S-EPMC5001245 | biostudies-literature
Project description:Various signals in tissue microenvironments are often unevenly distributed around cells. Cellular responses to asymmetric cell-matrix adhesion in a 3D space remain generally unclear and are to be studied at the single-cell resolution. Here, the authors developed a droplet-based microfluidic approach to manufacture a pure population of single cells in a microscale layer of compartmentalized 3D hydrogel matrices with a tunable spatial presentation of ligands at the subcellular level. Cells elongate with an asymmetric presentation of the integrin adhesion ligand Arg-Gly-Asp (RGD), while cells expand isotropically with a symmetric presentation of RGD. Membrane tension is higher on the side of single cells interacting with RGD than on the side without RGD. Finite element analysis shows that a non-uniform isotropic cell volume expansion model is sufficient to recapitulate the experimental results. At a longer timescale, asymmetric ligand presentation commits mesenchymal stem cells to the osteogenic lineage. Cdc42 is an essential mediator of cell polarization and lineage specification in response to asymmetric cell-matrix adhesion. This study highlights the utility of precisely controlling 3D ligand presentation around single cells to direct cell polarity for regenerative engineering and medicine.
| S-EPMC9875620 | biostudies-literature
Project description:The number of fluorophores within a molecule complex can be revealed by single-molecule photobleaching imaging. A widely applied strategy to analyze intensity traces over time is the quantification of photobleaching step counts. However, several factors can limit and bias the detection of photobleaching steps, including noise, high numbers of fluorophores, and the possibility that several photobleaching events occur almost simultaneously. In this study, we propose a new approach, to our knowledge, to determine the fluorophore number that correlates the intensity decay of a population of molecule complexes with the decay of the number of visible complexes. We validated our approach using single and fourfold Atto-labeled DNA strands. As an example we estimated the subunit stoichiometry of soluble CD95L using GFP fusion proteins. To assess the precision of our method we performed in silico experiments showing that the estimates are not biased for experimentally observed intensity fluctuations and that the relative precision remains constant with increasing number of fluorophores. In case of fractional fluorescent labeling, our simulations predicted that the fluorophore number estimate corresponds to the product of the true fluorophore number with the labeling fraction. Our method, denoted by spot number and intensity correlation (SONIC), is fully automated, robust to noise, and does not require the counting of photobleaching events.
| S-EPMC4675885 | biostudies-literature
Project description:Statistical mixture modeling provides an opportunity for automated identification and resolution of cell subtypes in flow cytometric data. The configuration of cells as represented by multiple markers simultaneously can be modeled arbitrarily well as a mixture of Gaussian distributions in the dimension of the number of markers. Cellular subtypes may be related to one or multiple components of such mixtures, and fitted mixture models can be evaluated in the full set of markers as an alternative, or adjunct, to traditional subjective gating methods that rely on choosing one or two dimensions. Four color flow data from human blood cells labeled with FITC-conjugated anti-CD3, PE-conjugated anti-CD8, PE-Cy5-conjugated anti-CD4, and APC-conjugated anti-CD19 Abs was acquired on a FACSCalibur. Cells from four murine cell lines, JAWS II, RAW 264.7, CTLL-2, and A20, were also stained with FITC-conjugated anti-CD11c, PE-conjugated anti-CD11b, PE-Cy5-conjugated anti-CD8a, and PE-Cy7-conjugated-CD45R/B220 Abs, respectively, and single color flow data were collected on an LSRII. The data were fitted with a mixture of multivariate Gaussians using standard Bayesian statistical approaches and Markov chain Monte Carlo computations. Statistical mixture models were able to identify and purify major cell subsets in human peripheral blood, using an automated process that can be generalized to an arbitrary number of markers. Validation against both traditional expert gating and synthetic mixtures of murine cell lines with known mixing proportions was also performed. This article describes the studies of statistical mixture modeling of flow cytometric data, and demonstrates their utility in examples with four-color flow data from human peripheral blood samples and synthetic mixtures of murine cell lines.
| S-EPMC2840702 | biostudies-literature
Project description:We develop an R/Bioconductor package CancerInSilico to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for cell-based mathematical model, implemented for an off-lattice, cell-center Monte Carlo mathematical model. We also adapt this model to simulate the impact of growth suppression by targeted therapeutics in cancer and benchmark simulations against bulk in vitro experimental data. Sensitivity to parameters is evaluated and used to predict the relative impact of variation in cellular growth parameters and cell types on tumor heterogeneity in therapeutic response.
2020-07-09 | GSE114375 | GEO
Project description:Single-cell analysis is of critical importance in revealing cell-to-cell heterogeneity by characterizing individual cells and identifying minority sub-populations of interest. Droplet-based microfluidics has been widely used in the past decade to achieve high-throughput single-cell analysis. However, to maximize the proportion of single-cell emulsification is challenging due to cell sedimentation and aggregation. The purpose of this study was to investigate the influence of single-cell encapsulation and incubation through the use of neutral buoyancy. As a proof of concept, OptiPrep™ was used to create neutrally buoyant cell suspensions of THP-1, a human monocytic leukemia cell line, for single-cell encapsulation and incubation. We found that using a neutrally buoyant suspension greatly increased the efficiency of single-cell encapsulation in microdroplets and eliminated unnecessary cell loss. Moreover, the presence of OptiPrep™ was shown to not affect cellular viability. This method significantly improved the effectiveness of single-cell study in a non-toxic environment and is expected to broadly facilitate single-cell analysis.
| S-EPMC7019761 | biostudies-literature
Project description:Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (?4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.
| S-EPMC7146887 | biostudies-literature
Project description:The identification of T cell epitopes and their HLA (human leukocyte antigen) restrictions is important for applications such as the design of cellular vaccines for HIV. Traditional methods for such identification are costly and time-consuming. Recently, a more expeditious laboratory technique using ELISpot assays has been developed that allows for rapid screening of specific responses. However, this assay does not directly provide information concerning the HLA restriction of a response, a critical piece of information for vaccine design. Thus, we introduce, apply, and validate a statistical model for identifying HLA-restricted epitopes from ELISpot data. By looking at patterns across a broad range of donors, in conjunction with our statistical model, we can determine (probabilistically) which of the HLA alleles are likely to be responsible for the observed reactivities. Additionally, we can provide a good estimate of the number of false positives generated by our analysis (i.e., the false discovery rate). This model allows us to learn about new HLA-restricted epitopes from ELISpot data in an efficient, cost-effective, and high-throughput manner. We applied our approach to data from donors infected with HIV and identified many potential new HLA restrictions. Among 134 such predictions, six were confirmed in the lab and the remainder could not be ruled as invalid. These results shed light on the extent of HLA class I promiscuity, which has significant implications for the understanding of HLA class I antigen presentation and vaccine development.
| S-EPMC2014793 | biostudies-literature
Project description:Sickle cell disease is an inherited genetic disorder that causes anemia, pain crises, organ infarction, and infections in 13 million people worldwide. Previous studies have revealed changes in sialic acid levels associated with red blood cell sickling and showed that stressed red blood cells bare surface-exposed clustered terminal mannose structures mediating hemolysis, but detailed glycan structures and anti-glycan antibodies in sickle cell disease remain understudied. Here, we compiled results obtained through lectin arrays, glycan arrays, and mass spectrometry to interrogate red blood cell glycoproteins and glycan-binding proteins found in the plasma of healthy individuals and patients with sickle cell disease and sickle cell trait. Lectin arrays and mass spectrometry revealed an increase in α2,6 sialylation and a decrease in α2,3 sialylation and blood group antigens displayed on red blood cells. Increased binding of proteins to immunogenic asialo and sialyl core 1, Lewis A, and Lewis Y structures was observed in plasma from patients with sickle cell disease, suggesting a heightened anti-glycan immune response. Data modeling affirmed glycan expression and plasma protein binding changes in sickle cell disease but additionally revealed further changes in ABO blood group expression. Our data provide detailed insights into glycan changes associated with sickle cell disease and refer glycans as potential therapeutic targets.
| S-EPMC7948272 | biostudies-literature