Unbiased estimation of chloroplast number in mesophyll cells: advantage of a genuine three-dimensional approach.
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ABSTRACT:
SUBMITTER: Kubinova Z
PROVIDER: S-EPMC3904715 | biostudies-literature | 2014 Feb
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Tumor mutational burden (TMB) measurement is limited by low tumor purity of samples, which can influence prediction of the immunotherapy response, particularly when using whole-exome sequencing-based TMB (wTMB). This issue could be overcome by targeted panel sequencing-based TMB (pTMB) with higher depth of coverage, which remains unexplored. METHODS:We comprehensively reanalyzed four public datasets of immune checkpoint inhibitor (ICI)-treated cohorts (adopting pTMB or wTMB) to test each biomarker's predictive ability for low purity samples (cut-off: 30%). For validation, paired genomic profiling with the same tumor specimens was performed to directly compare wTMB and pTMB in patients with breast cancer (paired-BRCA, n=165) and ICI-treated patients with advanced non-small-cell lung cancer (paired-NSCLC, n=156). RESULTS:Low tumor purity was common (range 30%-45%) in real-world samples from ICI-treated patients. In the survival analyzes of public cohorts, wTMB could not predict the clinical benefit of immunotherapy when tumor purity was low (log-rank p=0.874), whereas pTMB could effectively stratify the survival outcome (log-rank p=0.020). In the paired-BRCA and paired-NSCLC cohorts, pTMB was less affected by tumor purity, with significantly more somatic variants identified at low allele frequency (p<0.001). We found that wTMB was significantly underestimated in low purity samples with a large proportion of clonal variants undetected by whole-exome sequencing. Interestingly, pTMB more accurately predicted progression-free survival (PFS) after immunotherapy than wTMB owing to its superior performance in the low tumor purity subgroup (p=0.054 vs p=0.358). Multivariate analysis revealed pTMB (p=0.016), but not wTMB (p=0.32), as an independent predictor of PFS even in low-purity samples. The net reclassification index using pTMB was 21.7% in the low-purity subgroup (p=0.016). CONCLUSIONS:Our data suggest that TMB characterization with targeted deep sequencing might have potential strength in predicting ICI responsiveness due to its enhanced sensitivity for hard-to-detect variants at low-allele fraction. Therefore, pTMB could act as an invaluable biomarker in the setting of both clinical trials and practice outside of trials based on its reliable performance in mitigating the purity-related bias.
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Project description:Background and aimsExcess salinity inhibits the metabolism of various systems and induces structural changes, especially in chloroplasts. Although the chloroplast body seems to swell under salinity stress as observed by conventional transmission electron microscopy, previous studies are limited to 2-D data and lack quantitative comparisons because specimens need to be sliced into ultrathin sections. This study shows three-dimensionally the structural changes in a whole mesophyll cell responding to salinity stress by serial sectioning with a focused ion beam scanning electron microscope (FIB-SEM) and compares the differences in chloroplast structures based on reconstructed models possessing accurate numerical voxel values.MethodsLeaf blades of rice plants treated with 100 mm NaCl or without (control) for 4 d were fixed chemically and embedded in resin. The specimen blocks were sectioned and observed using the FIB-SEM, and then the sliced image stacks were reconstructed into 3-D models by image processing software.Key resultsOn the transverse sections of rice mesophyll cells, the chloroplasts in the control leaves appeared to be elongated meniscus lens shaped, while those in the salt-treated leaves appear to be expanded oval shaped. The 3-D models based on serial sectioning images showed that the chloroplasts in the control cells spread like sheets fitted to the shape of the cell wall and in close contact with the adjacent chloroplasts. In contrast, those in the salt-stressed cells curled up into a ball and fitted to cell protuberances without being in close contact with adjacent chloroplasts. Although the shapes of chloroplasts were clearly different between the two treatments, their volumes did not differ.ConclusionsThe 3-D reconstructed models of whole rice mesophyll cells indicated that chloroplasts under salt stress conditions were not swollen but became spherical without increasing their volume. This is in contrast to findings of previous studies based on 2-D images.
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Project description:Knowledge of the genetic relatedness between individuals is important in many research areas in quantitative genetics, conservation genetics, forensics, evolution, and ecology. In the absence of pedigree records, relatedness can be estimated from genetic marker data using a number of estimators. These estimators, however, make the critical assumption of a large random mating population without genetic structures. The assumption is frequently violated in the real world where geographic/social structures or nonrandom mating usually lead to genetic structures. In this study, I investigated two approaches to the estimation of relatedness between a pair of individuals from a subpopulation due to recent common ancestors (i.e., relatedness is defined and measured with the current focal subpopulation as reference). The indirect approach uses the allele frequencies of the entire population with and without accounting for the population structure, and the direct approach uses the allele frequencies of the current focal subpopulation. I found by simulations that currently widely applied relatedness estimators are upwardly biased under the indirect approach, but can be modified to become unbiased and more accurate by using Wright's F(st) to account for population structures. However, the modified unbiased estimators under the indirect approach are clearly inferior to the unmodified original estimators under the direct approach, even when small samples are used in estimating both allele frequencies and relatedness.
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Project description:Ultrathin sections of rice leaf blades observed two-dimensionally using a transmission electron microscope (TEM) show that the chlorenchyma is composed of lobed mesophyll cells, with intricate cell boundaries, and lined with chloroplasts. The lobed cell shape and chloroplast positioning are believed to enhance the area available for the gas exchange surface for photosynthesis in rice leaves. However, a cell image revealing the three-dimensional (3-D) ultrastructure of rice mesophyll cells has not been visualized. In this study, a whole rice mesophyll cell was observed using a focused ion beam scanning electron microscope (FIB-SEM), which provides many serial sections automatically, rapidly and correctly, thereby enabling 3-D cell structure reconstruction.Rice leaf blades were fixed chemically using the method for conventional TEM observation, embedded in resin and subsequently set in the FIB-SEM chamber. Specimen blocks were sectioned transversely using the FIB, and block-face images were captured using the SEM. The sectioning and imaging were repeated overnight for 200-500 slices (each 50?nm thick). The resultant large-volume image stacks ( x = 25??m, y = 25??m, z = 10-25??m) contained one or two whole mesophyll cells. The 3-D models of whole mesophyll cells were reconstructed using image processing software.The reconstructed cell models were discoid shaped with several lobes around the cell periphery. The cell shape increased the surface area, and the ratio of surface area to volume was twice that of a cylinder having the same volume. The chloroplasts occupied half the cell volume and spread as sheets along the cell lobes, covering most of the inner cell surface, with adjacent chloroplasts in close contact with each other.Cellular and sub-cellular ultrastructures of a whole mesophyll cell in a rice leaf blade are demonstrated three-dimensionally using a FIB-SEM. The 3-D models and numerical information support the hypothesis that rice mesophyll cells enhance their CO 2 absorption with increased cell surface and sheet-shaped chloroplasts.
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Project description:PurposeTo describe an unsupervised three-dimensional cardiac motion estimation network (CarMEN) for deformable motion estimation from two-dimensional cine MR images.Materials and methodsA function was implemented using CarMEN, a convolutional neural network that takes two three-dimensional input volumes and outputs a motion field. A smoothness constraint was imposed on the field by regularizing the Frobenius norm of its Jacobian matrix. CarMEN was trained and tested with data from 150 cardiac patients who underwent MRI examinations and was validated on synthetic (n = 100) and pediatric (n = 33) datasets. CarMEN was compared to five state-of-the-art nonrigid body registration methods by using several performance metrics, including Dice similarity coefficient (DSC) and end-point error.ResultsOn the synthetic dataset, CarMEN achieved a median DSC of 0.85, which was higher than all five methods (minimum-maximum median [or MMM], 0.67-0.84; P < .001), and a median end-point error of 1.7, which was lower than (MMM, 2.1-2.7; P < .001) or similar to (MMM, 1.6-1.7; P > .05) all other techniques. On the real datasets, CarMEN achieved a median DSC of 0.73 for Automated Cardiac Diagnosis Challenge data, which was higher than (MMM, 0.33; P < .0001) or similar to (MMM, 0.72-0.75; P > .05) all other methods, and a median DSC of 0.77 for pediatric data, which was higher than (MMM, 0.71-0.76; P < .0001) or similar to (MMM, 0.77-0.78; P > .05) all other methods. All P values were derived from pairwise testing. For all other metrics, CarMEN achieved better accuracy on all datasets than all other techniques except for one, which had the worst motion estimation accuracy.ConclusionThe proposed deep learning-based approach for three-dimensional cardiac motion estimation allowed the derivation of a motion model that balances motion characterization and image registration accuracy and achieved motion estimation accuracy comparable to or better than that of several state-of-the-art image registration algorithms.© RSNA, 2019Supplemental material is available for this article.
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Project description:AimsAssessment of preclinical models of vascular disease is paramount in the successful translation of novel treatments. The results of these models have traditionally relied on two-dimensional (2D) histological methodologies. Light sheet fluorescence microscopy (LSFM) is an imaging platform that allows for three-dimensional (3D) visualization of whole organs and tissues. In this study, we describe an improved methodological approach utilizing LSFM for imaging of preclinical vascular injury models while minimizing analysis bias.Methods and resultsThe rat carotid artery segmental pressure-controlled balloon injury and mouse carotid artery ligation injury were performed. Arteries were harvested and processed for LSFM imaging and 3D analysis, as well as for 2D area histological analysis. Artery processing for LSFM imaging did not induce vessel shrinkage or expansion and was reversible by rehydrating the artery, allowing for subsequent sectioning and histological staining a posteriori. By generating a volumetric visualization along the length of the arteries, LSFM imaging provided different analysis modalities including volumetric, area, and radial parameters. Thus, LSFM-imaged arteries provided more precise measurements compared to classic histological analysis. Furthermore, LSFM provided additional information as compared to 2D analysis in demonstrating remodelling of the arterial media in regions of hyperplasia and periadventitial neovascularization around the ligated mouse artery.ConclusionLSFM provides a novel and robust 3D imaging platform for visualizing and quantifying arterial injury in preclinical models. When compared with classic histology, LSFM outperformed traditional methods in precision and quantitative capabilities. LSFM allows for more comprehensive quantitation as compared to traditional histological methodologies, while minimizing user bias associated with area analysis of alternating, 2D histological artery cross-sections.
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Project description:Many different biological processes are represented by network graphs such as regulatory networks, metabolic pathways, and protein-protein interaction networks. Since genes that are linked on the networks usually have biologically similar functions, the linked genes form molecular modules to affect the clinical phenotypes/outcomes. Similarly, in large-scale genetic association studies, many SNPs are in high linkage disequilibrium (LD), which can also be summarized as a LD graph. In order to incorporate the graph information into regression analysis with high dimensional genomic data as predictors, we introduce a Bayesian approach for graph-constrained estimation (Bayesian GRACE) and regularization, which controls the amount of regularization for sparsity and smoothness of the regression coefficients. The Bayesian estimation with their posterior distributions can provide credible intervals for the estimates of the regression coefficients along with standard errors. The deviance information criterion (DIC) is applied for model assessment and tuning parameter selection. The performance of the proposed Bayesian approach is evaluated through simulation studies and is compared with Bayesian Lasso and Bayesian Elastic-net procedures. We demonstrate our method in an analysis of data from a case-control genome-wide association study of neuroblastoma using a weighted LD graph.
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