scholarly journals Bayesian Estimation of 3D Chromosomal Structure from Single Cell Hi-C Data

2018 ◽  
Author(s):  
Michael Rosenthal ◽  
Darshan Bryner ◽  
Fred Huffer ◽  
Shane Evans ◽  
Anuj Srivastava ◽  
...  
Keyword(s):  
Single Cell ◽  
Bayesian Estimation ◽  
Chromosome Structure ◽  
Structural Features ◽  
Multiscale Approach ◽  
Chromosomal Structure ◽  
Structural Inference ◽  
Contact Information ◽  
Using Data ◽  
Local Variability

AbstractThe problem of 3D chromosome structure inference from Hi-C datasets is important and challenging. While bulk Hi-C datasets contain contact information derived from millions of cells, and can capture major structural features shared by the majority of cells in the sample, they do not provide information about local variability between cells. Single cell Hi-C can overcome this problem, but contact matrices are generally very sparse, making structural inference more problematic. We have developed a Bayesian multiscale approach, named SIMBA3D, to infer 3D structures of chromosomes from single cell Hi-C while including the bulk Hi-C data and some regularization terms as a prior. We study the landscape of solutions for each single-cell Hi-C dataset as a function of prior strength and demonstrate clustering of solutions using data from the same cell.

scholarly journals Bayesian Estimation of Three-Dimensional Chromosomal Structure from Single-Cell Hi-C Data

2019 ◽  
Vol 26 (11) ◽  
pp. 1191-1202 ◽  
Author(s):  
Michael Rosenthal ◽  
Darshan Bryner ◽  
Fred Huffer ◽  
Shane Evans ◽  
Anuj Srivastava ◽  
...  
Keyword(s):  
Single Cell ◽  
Bayesian Estimation ◽  
Three Dimensional ◽  
Chromosomal Structure

scholarly journals Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Patrick S. Stumpf ◽  
Xin Du ◽  
Haruka Imanishi ◽  
Yuya Kunisaki ◽  
Yuichiro Semba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bone Marrow ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transfer Learning ◽  
Biomedical Research ◽  
Human Cell ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Using Data

AbstractBiomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

scholarly journals Analysis of the structural variability of topologically associated domains as revealed by Hi-C

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Natalie Sauerwald ◽  
Akshat Singhal ◽  
Carl Kingsford
Keyword(s):  
Structural Changes ◽  
Chromosome Structure ◽  
Three Dimensional ◽  
Replication Timing ◽  
Building Blocks ◽  
Structural Features ◽  
Genetic Sequence ◽  
Experimental Variation ◽  
Topologically Associated Domains ◽  
Integral Role

Abstract Three-dimensional chromosome structure plays an integral role in gene expression and regulation, replication timing, and other cellular processes. Topologically associated domains (TADs), building blocks of chromosome structure, are genomic regions with higher contact frequencies within the region than outside the region. A central question is the degree to which TADs are conserved or vary between conditions. We analyze 137 Hi-C samples from 9 studies under 3 measures to quantify the effects of various sources of biological and experimental variation. We observe significant variation in TAD sets between both non-replicate and replicate samples, and provide initial evidence that this variability does not come from genetic sequence differences. The effects of experimental protocol differences are also measured, demonstrating that samples can have protocol-specific structural changes, but that TADs are generally robust to lab-specific differences. This study represents a systematic quantification of key factors influencing comparisons of chromosome structure, suggesting significant variability and the potential for cell-type-specific structural features, which has previously not been systematically explored. The lack of observed influence of heredity and genetic differences on chromosome structure suggests that factors other than the genetic sequence are driving this structure, which plays an important role in human disease and cellular functioning.

scholarly journals Whole slide imaging is a high-throughput method to assess Candida biofilm formation

2020 ◽  
Author(s):  
Maximilian W. D. Raas ◽  
Thiago P. Silva ◽  
Jhamine C. O. Freitas ◽  
Lara M. Campos ◽  
Rodrigo L. Fabri ◽  
...  
Keyword(s):  
Single Cell ◽  
Biofilm Formation ◽  
Large Scale ◽  
Structural Features ◽  
Whole Slide Imaging ◽  
Bright Field ◽  
Biofilm Growth ◽  
Fluorescent Markers ◽  
Golden Standard ◽  
Important Addition

AbstractNew strategies that enable fast and accurate visualization of Candida biofilms are necessary to better study their structure and response to antifungals agents. Here, we applied whole slide imaging (WSI) to study biofilm formation of Candida species. Three relevant biofilm-forming Candida species (C. albicans ATCC 10231, C. glabrata ATCC 2001, and C. tropicalis ATCC 750) were cultivated on glass coverslips both in presence and absence of widely used antifungals. Accumulated biofilms were stained with fluorescent markers and scanned in both bright-field and fluorescence modes using a WSI digital scanner. WSI enabled clear assessment of both size and structural features of Candida biofilms. Quantitative analyses readily detected reductions in biofilm-covered surface area upon antifungal exposure. Furthermore, we show that the overall biofilm growth can be adequately assessed across both bright-field and fluorescence modes. At the single-cell level, WSI proved adequate, as morphometric parameters evaluated with WSI did not differ significantly from those obtained with scanning electron microscopy, considered as golden standard at single-cell resolution. Thus, WSI allows for reliable visualization of Candida biofilms enabling both large-scale growth assessment and morphometric characterization of single-cell features, making it an important addition to the available microscopic toolset to image and analyze fungal biofilm growth.

scholarly journals Dynamics of the compartmentalized Streptomyces chromosome during metabolic differentiation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Virginia S. Lioy ◽  
Jean-Noël Lorenzi ◽  
Soumaya Najah ◽  
Thibault Poinsignon ◽  
Hervé Leh ◽  
...  
Keyword(s):  
Chromosome Structure ◽  
Gene Clusters ◽  
Structural Features ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Linear Chromosome ◽  
Chromosome Architecture ◽  
Specialized Metabolites ◽  
Streptomyces Ambofaciens ◽  
Core Genes

AbstractBacteria of the genus Streptomyces are prolific producers of specialized metabolites, including antibiotics. The linear chromosome includes a central region harboring core genes, as well as extremities enriched in specialized metabolite biosynthetic gene clusters. Here, we show that chromosome structure in Streptomyces ambofaciens correlates with genetic compartmentalization during exponential phase. Conserved, large and highly transcribed genes form boundaries that segment the central part of the chromosome into domains, whereas the terminal ends tend to be transcriptionally quiescent compartments with different structural features. The onset of metabolic differentiation is accompanied by a rearrangement of chromosome architecture, from a rather ‘open’ to a ‘closed’ conformation, in which highly expressed specialized metabolite biosynthetic genes form new boundaries. Thus, our results indicate that the linear chromosome of S. ambofaciens is partitioned into structurally distinct entities, suggesting a link between chromosome folding, gene expression and genome evolution.

General Screening of Surface Alloys for Catalysis

2020 ◽  
Author(s):  
Matthew Montemore ◽  
Chukwudi F. Nwaokorie ◽  
Gbolade O. Kayode
Keyword(s):  
Electronic Structure ◽  
Catalytic Performance ◽  
Structural Features ◽  
Design Parameters ◽  
Surface Alloys ◽  
Catalytic Surfaces ◽  
Challenges And Opportunities ◽  
Adsorption Energies ◽  
Using Data ◽  
Structure Properties

Intensive research in catalysis has resulted in design parameters for many important catalytic reactions; however, designing new catalysts remains difficult, partly due to the time and expense needed to screen a large number of potential catalytic surfaces. Here, we create a general, efficient model that can be used to screen surface alloys for many reactions without any quantum-based calculations. This model allows the prediction of the adsorption energies of a variety of species (explicitly shown for C, N, O, OH, H, S, K, F) on metal alloy surfaces that include combinations of nearly all of the d-block metals. We find that a few simple structural features, chosen using data-driven techniques and physical understanding, can be used to predict electronic structure properties. These electronic structure properties are then used to predict adsorption energies, which are in turn used to predict catalytic performance. This framework is interpretable and gives insight into how underlying structural features affect adsorption and catalytic performance. We apply the model to screen more than 10<sup>7</sup> unique surface sites on approximately 10<sup>6</sup> unique surfaces for 7 important reactions. We identify novel surfaces with high predicted catalytic performance, and demonstrate challenges and opportunities in catalyst development using surface alloys. This work shows the utility of a general, reusable model that can be applied in new contexts without requiring new data to be generated.<br>

Single Crystal Automated Refinement (SCAR): A Data-Driven Method for Solving Inorganic Structures

2019 ◽  
Author(s):  
Gayatri Viswanathan ◽  
Anton Oliynyk ◽  
Erin Antono ◽  
Julia Ling ◽  
Bryce Meredig ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Single Crystal ◽  
Structural Features ◽  
Single Crystal Diffraction ◽  
Data Set ◽  
Inorganic Solids ◽  
Automatic Structure ◽  
Structure Solution ◽  
Extended Solids ◽  
Using Data

<p>Single crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure solution and refinement is not always straightforward. Methods to simplify and rationalize the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure solution. In this work, we propose a new method that uses single crystal data to solve the crystal structures of inorganic, extended solids called “Single Crystal Automated Refinement (<i>SCAR</i>).” The approach was developed using data mining and machine-learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for <i>SCAR</i> is presented followed by the implementation of <i>SCAR</i> to solve two newly synthesized and previously unreported phases, ZrAu<sub>0.5</sub>Os<sub>0.5</sub> and Nd<sub>4</sub>Mn<sub>2</sub>AuGe<sub>4</sub>. The structure solutions are found to be comparable with manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed <i>SCAR</i> program is thusly verified to be a fast and reliable assistant in solving even complex single crystal diffraction data for extended inorganic solids.</p>

Single Crystal Automated Refinement (SCAR): A Data-Driven Method for Solving Inorganic Structures

2019 ◽  
Author(s):  
Gayatri Viswanathan ◽  
Anton Oliynyk ◽  
Erin Antono ◽  
Julia Ling ◽  
Bryce Meredig ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Single Crystal ◽  
Structural Features ◽  
Single Crystal Diffraction ◽  
Data Set ◽  
Inorganic Solids ◽  
Automatic Structure ◽  
Structure Solution ◽  
Extended Solids ◽  
Using Data

<p>Single crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure solution and refinement is not always straightforward. Methods to simplify and rationalize the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure solution. In this work, we propose a new method that uses single crystal data to solve the crystal structures of inorganic, extended solids called “Single Crystal Automated Refinement (<i>SCAR</i>).” The approach was developed using data mining and machine-learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for <i>SCAR</i> is presented followed by the implementation of <i>SCAR</i> to solve two newly synthesized and previously unreported phases, ZrAu<sub>0.5</sub>Os<sub>0.5</sub> and Nd<sub>4</sub>Mn<sub>2</sub>AuGe<sub>4</sub>. The structure solutions are found to be comparable with manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed <i>SCAR</i> program is thusly verified to be a fast and reliable assistant in solving even complex single crystal diffraction data for extended inorganic solids.</p>

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