scholarly journals 4D chromosome reconstruction elucidates the spatial reorganization of the mammalian X-chromosome

2021 ◽  
Author(s):  
Anna Lappala ◽  
Chen-Yu Wang ◽  
Andrea Kriz ◽  
Hunter Michalk ◽  
Kevin Tan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Principal Component ◽  
Reaction Diffusion ◽  
Chromosome Architecture ◽  
3D Space ◽  
Xist Rna ◽  
Spatial Reorganization ◽  
Chromosome Reconstruction ◽  
Scale Structures

AbstractChromosomes are segmented into domains and compartments; yet, how these structures are spatially related in 3D is unclear. Here, by directly integrating Hi-C capture experiments and 3D modeling, we use X-inactivation as a model to examine the time evolution of 3D chromosome architecture during substantial changes in gene expression. First, we show that gene expression A/B compartments are consistent with phase separation in 3D space. Second, we show that residuals of smaller scale structures persist through transitions, despite further large-scale reorganization into the final inactive configuration, comprising two “megadomains”. Interestingly, these previously hidden residual structures were not detectable in 2D Hi-C maps or principal component analyses. Third, time-dependent reaction-diffusion simulations reveal how Xist RNA particles diffuse across the 3D X-superstructure as it reorganizes. Our 4DHiC pipeline helps satisfy the growing demand for methodologies that produce 3D chromosome reconstructions directly from 2D datasets, which are consistent with the empirical data.

Large-scale analysis of longitudinal skin gene expression in systemic sclerosis reveals relationships of immune cell and fibroblast activity with skin thickness and a trend towards normalisation over time

2021 ◽  
pp. annrheumdis-2021-221352
Author(s):  
Brian Skaug ◽  
Marka A Lyons ◽  
William R Swindell ◽  
Gloria A Salazar ◽  
Minghua Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systemic Sclerosis ◽  
Immunohistochemical Staining ◽  
Large Scale ◽  
Immune Cell ◽  
Treatment Strategies ◽  
Principal Component ◽  
Skin Thickness ◽  
High Baseline ◽  
Over Time

ObjectivesDetermine relationships between skin gene expression and systemic sclerosis (SSc) clinical disease features, and changes in skin gene expression over time.MethodsA total of 339 forearm skin biopsies were obtained from 113 SSc patients and 44 matched healthy controls. 105 SSc patients had a second biopsy, and 76 had a third biopsy. Global gene expression profiling was performed, and differentially expressed genes and cell type-specific signatures in SSc were evaluated for relationships to modified Rodnan Skin Score (mRSS) and other clinical variables. Changes in skin gene expression over time were analysed by mixed effects models and principal component analysis. Immunohistochemical staining was performed to validate conclusions.ResultsGene expression dysregulation was greater in SSc patients with affected skin than in those with unaffected skin. Immune cell and fibroblast signatures positively correlated with mRSS. High baseline immune cell and fibroblast signatures predicted higher mRSS over time, but were not independently predictive of longitudinal mRSS after adjustment for baseline mRSS. In early diffuse cutaneous SSc, immune cell and fibroblast signatures declined over time, and overall skin gene expression trended towards normalisation. On immunohistochemical staining, most early diffuse cutaneous SSc patients with high baseline T cell and macrophage numbers had declines in these numbers at follow-up.ConclusionsSkin thickness in SSc is related to dysregulated immune cell and fibroblast gene expression. Skin gene expression changes over time in early diffuse SSc, with a tendency towards normalisation. These observations are relevant for understanding SSc pathogenesis and could inform treatment strategies and clinical trial design.

Four-dimensional chromosome reconstruction elucidates the spatiotemporal reorganization of the mammalian X chromosome

2021 ◽  
Vol 118 (42) ◽  
pp. e2107092118
Author(s):  
Anna Lappala ◽  
Chen-Yu Wang ◽  
Andrea Kriz ◽  
Hunter Michalk ◽  
Kevin Tan ◽  
...  
Keyword(s):  
X Chromosome ◽  
Large Scale ◽  
Three Dimensions ◽  
Rna Molecules ◽  
Chromosome Architecture ◽  
Inactive X Chromosome ◽  
Mixing Dynamics ◽  
Chromosome Reconstruction ◽  
Slow Mixing ◽  
3D Information

Chromosomes are segmented into domains and compartments, but how these structures are spatially related in three dimensions (3D) is unclear. Here, we developed tools that directly extract 3D information from Hi-C experiments and integrate the data across time. With our “4DHiC” method, we use X chromosome inactivation (XCI) as a model to examine the time evolution of 3D chromosome architecture during large-scale changes in gene expression. Our modeling resulted in several insights. Both A/B and S1/S2 compartments divide the X chromosome into hemisphere-like structures suggestive of a spatial phase-separation. During the XCI, the X chromosome transits through A/B, S1/S2, and megadomain structures by undergoing only partial mixing to assume new structures. Interestingly, when an active X chromosome (Xa) is reorganized into an inactive X chromosome (Xi), original underlying compartment structures are not fully eliminated within the Xi superstructure. Our study affirms slow mixing dynamics in the inner chromosome core and faster dynamics near the surface where escapees reside. Once established, the Xa and Xi resemble glassy polymers where mixing no longer occurs. Finally, Xist RNA molecules initially reside within the A compartment but transition to the interface between the A and B hemispheres and then spread between hemispheres via both surface and core to establish the Xi.

scholarly journals A Review of Feature Extraction Software for Microarray Gene Expression Data

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Ching Siang Tan ◽  
Wai Soon Ting ◽  
Mohd Saberi Mohamad ◽  
Weng Howe Chan ◽  
Safaai Deris ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Extraction ◽  
Gene Expression Data ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Reduced Representation ◽  
Local Linear Embedding

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Exposure Assessment ◽  
Large Scale ◽  
First Generation ◽  
Cancer Epidemiology ◽  
Policy Changes ◽  
The Past ◽  
New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

2021 ◽  
Vol 503 (1) ◽  
pp. 270-291
Author(s):  
F Navarete ◽  
A Damineli ◽  
J E Steiner ◽  
R D Blum
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Rotation Axis ◽  
Principal Component ◽  
Young Stellar Objects ◽  
High Mass ◽  
Continuum Emission ◽  
Rotating Disc ◽  
Stellar Objects ◽  
K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

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