Oxidation Induced Doping of Nanoparticles Revealed by in Situ X-ray Absorption Studies

Doping is a well-known approach to modulate the electronic and optical properties of nanoparticles (NPs). However, doping at nanoscale is still very challenging, and the reasons for that are not well understood. We studied the formation and doping process of iron and iron oxide NPs in real time by in situ synchrotron X-ray absorption spectroscopy. Our study revealed that the mass flow of the iron triggered by oxidation is responsible for the internalization of the dopant (molybdenum) adsorbed at the surface of the host iron NPs. The oxidation induced doping allows controlling the doping levels by varying the amount of dopant precursor. Our in situ studies also revealed that the dopant precursor substantially changes the reaction kinetics of formation of iron and iron oxide NPs. Thus, in the presence of dopant precursor we observed significantly faster decomposition rate of iron precursors and substantially higher stability of iron NPs against oxidation. The same doping mechanism and higher stability of host metal NPs against oxidation was observed for cobalt-based systems. Since the internalization of the adsorbed dopant at the surface of the host NPs is driven by the mass transport of the host, this mechanism can be potentially applied to introduce dopants into different oxidized forms of metal and metal alloy NPs providing the extra degree of compositional control in material design.Fil: Kwon, Soon Gu. Argonne National Laboratory; Estados UnidosFil: Chattopadhyay, Soma. Argonne National Laboratory; Estados Unidos. Illinois Institute of Technology; Estados UnidosFil: Koo, Bonil. Argonne National Laboratory; Estados UnidosFil: Dos Santos Claro, Paula Cecilia. Argonne National Laboratory; Estados UnidosFil: Shibata, Tomohiro. Argonne National Laboratory; Estados UnidosFil: Requejo, Felix Gregorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Giovanetti, Lisandro Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Liu, Yuzi. Argonne National Laboratory; Estados UnidosFil: Johnson, Christopher. Argonne National Laboratory; Estados UnidosFil: Prakapenka, Vitali. University of Chicago; Estados UnidosFil: Lee, Byeongdu. Argonne National Laboratory; Estados UnidosFil: Shevchenko, Elena V.. Argonne National Laboratory; Estados Unido

CdTe/CdSe/CdTe heterostructure nanorods and I-III-VI₂ nanocrystals: synthesis and characterization

textSemiconductor nanocrystals are interesting candidates as new light-absorbing materials for photovoltaic (PV) devices. They can be dispersed in solvents and cheaply deposited at low-temperature on various substrates. Also, the nanocrystals have unique optical properties depending on their size due to the quantum size effect and moreover it is easy to uniformly control their stoichiometry. CdTe/CdSe/CdTe heterostructure nanorods and I-III-VI₂ nanocrystals were selected to synthesize and investigate in order to utilize the benefits of colloidal nanocrystals described above. Colloidal nanorods with linear CdTe/CdSe/CdTe heterojunctions were synthesized by sequential reactant injection. After CdTe deposition at the ends of initially formed CdSe nanorods, continued heating in solution leads to Se-Te interdiffusion across the heterojunctions and coalescence to decreased aspect ratio. The Se-Te interdiffusion rates were measured by mapping the composition profile using nanobeam energy dispersive X-ray spectroscopy (EDS). The rate of nanorod coalescence was also measured and compared to model predictions using a continuum viscous flow model. The synthetic method of monodisperse chalcopyrite (tetragonal) CuInSe₂ nanocrystals was also developed. The nanocrystals have trigonal pyramidal shape with one polar and three non-polar surface facets. When drop-cast onto carbon substrates, the nanocrystals self-assemble into close-packed monolayers with triangular (honeycomb) lattice structure. Moreover, the effect of excess Cu precursor (CuCl) was studied for the formation of monodisperse trigonal pyramidal CuInSe₂ nanocrystals. The formation mechanism of monodisperse trigonal pyramidal CuInSe₂ nanocrystals was suggested with regard to excess amount of CuCl precursor, based on the nucleationgrowth model of colloidal nanocrystal formation. A new wurtzite phase of CuInS₂, CuInSe₂, and Cu(InxGa1-x)Se₂ (CIGS) was observed in nanocrystals synthesized by heating metal precursors and Se-(or S-)urea in alkylamine. X-ray diffraction (XRD) showed the predominant phase to be wurtzite (hexagonal) instead of chalcopyrite (tetragonal). High resolution transmission electron microscopy (TEM), however, revealed polytypism in the nanocrystals, with the wurtzite phase interfaced with significant chalcopyrite domains.Chemical Engineerin

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license

DNA methylation heterogeneity profiles of 928 CCLE cell lines

Motivation Bisulfite sequencing data carry invaluable information about epigenetic states of a cell population beyond DNA methylation levels. Phased DNA methylation states (DNA methylation pattern; i.e., an array of DNA methylation states of CpGs simultaneously covered by a single read) can serve as a local barcode representing the epigenetic state of a single cell. Therefore we can compute approximate epigenetic diversity through measuring the diversity of DNA methylation patterns (inter-molecule / inter-cellular heterogeneity). On the other hand, DNA methylation patterns also inform us of the local disorder of DNA methylation states, which already have been shown to have prognostic potential (Landau et al., 2014). To facilitate studies on such concept of DNA methylation heterogeneity, we developed an efficient software named Metheor and here provide a comprehensive DNA methylation profiles of 928 cancer cell lines from cancer cell line encyclopedia (CCLE) computed by Metheor. Data processing Raw reduced representation bisulfite sequencing  (RRBS) reads for 928 CCLE cell lines were downloaded under SRA study accession SRP186687, and preprocessed using Trim Galore! v0.6.7 with --rrbs option. Reads were then aligned to hg38 reference genome using Bismark v0.23.1. The resulting alignments are used to compute DNA methylation heterogeneity levels (see below) through Metheor v0.1.0. Seven measures for DNA methylation heterogeneity Profiles of seven DNA methylation heterogeneity measures are provided in this dataset. Proportion of discordant reads (PDR) Local pairwise methylation disorder (LPMD) Methylation haplotype load (MHL) Epipolymorphism (PM) Methylation entropy (ME) Fraction of discordant read pairs (FDRP) Quantitative fraction of discordant pairs (qFDRP) For a more detailed description of those measures, please refer to this GitHub repository. Data tables We provide 7 tables for DNA methylation heterogeneity profiles and an additional table that contains the average methylation level information. ccle.pdr.csv: Table for average proportion of discordant reads (PDR) for various genomic contexts ccle.lpmd.csv:Table for average local pairwise methylation disorder (LPMD) for various genomic contexts ccle.mhl.csv: Table for average methylation haplotype load (MHL) for various genomic contexts ccle.pm.csv: Table for average epipolymorphism (PM) for various genomic contexts ccle.me.csv: Table for average methylation entropy (ME) for various genomic contexts. ccle.fdrp.csv: Table for average FDRP levels for various genomic contexts. ccle.qfdrp.csv: Table for average qFDRP levels for various genomic contexts. ccle.beta.csv: Table for average DNA methylation levels for various genomic contexts. Schema for data tables All data tables are in comma-separated values (csv) format sharing the following columns: cell_line_name: Identifier for the cell line. run_accession: SRA run accession of the corresponding RRBS data. tissue: Tissue collection site. disease: Full disease type (e.g., carcinoma (ductal carcinoma), carcinoma (squamous_cell_carcinoma), or lymphoid_noeplasm (Hodgkin_lymphoma)) disease_primary: General disease type (e.g., carcinoma or lymphoid_neoplasm). disease_secondary: Specific disease type (e.g., ductal carcinoma, squamous_cell_carcinoma or Hodgkin_lymphoma). disease_stage: Indicates whether tissue sample is from primary or metastatic site. age_at_sampling: Age of tissue donor at sampling if known. Otherwise, values are left empty. sex: Sex of tissue donor if known. Otherwise, values are left empty. ethnicity: Ethnicity of tissue donor if known. Otherwise, values are left empty. genomewide: Genomewide average DNA methylation heterogeneity levels. promoter: Average DNA methylation heterogeneity levels at promoters of protein-coding genes. cgi: Average DNA methylation heterogeneity levels at CpG islands. Annotations were downloaded from UCSC table browser. cpg_shore: Average DNA methylation heterogeneity levels at CpG shores. CpG shores are defined as 2kb regions flanking upstream or downstream of CpG islands. Regions overlapping CpG islands were excluded. cpg_shelf: Average DNA methylation heterogeneity levels at CpG shelves. CpG shelves are defined as 2kb regions flanking upstream or downstream of (CpG island + CpG shore) regions. Regions overlapping CpG islands or shores were excluded. methylation_canyon: Average DNA methylation heterogeneity levels at methylation canyons. DNA methylation canyons are defined as broad (> 3.5kb) under-methylated regions (Jeong et al., 2014), and their hg38 annotations were downloaded from (Su et al., 2018). exon: Average DNA methylation heterogeneity levels at exons of protein coding genes. intron: Average DNA methylation heterogeneity levels at introns of protein coding genes. gene_body: Average DNA methylation heterogeneity levels at gene bodies of protein coding genes. LINE: Average DNA methylation heterogeneity levels at LINEs. Annotations were downloaded from UCSC table browser (hg38, Repeats-RepeatMasker). SINE: Average DNA methylation heterogeneity levels at SINEs LTR: Average DNA methylation heterogeneity levels at LTR retrotransposons Availability of Metheor The source code for Metheor can be found at https://github.com/dohlee/metheor You can install Metheor using conda at commandline: $ conda install -c dohlee metheor</p

Schematic illustration of local pairwise methylation discordance (LPMD).

Schematic illustration of local pairwise methylation discordance (LPMD).</p

Promoter PDRs of tumor suppressors and oncogenes.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.</div

Overview of Metheor.

(A) The input for Metheor is bisulfite read alignment tagged with Bismark methylation call strings. Using each of the seven subcommands shown, Metheor computes the corresponding DNA methylation heterogeneity measure. If reads were aligned with a tool other than Bismark, Metheor can still add tag for methylation call string with metheor tag subcommand to make alignment file compatible for Metheor run. (B) Schematic diagram for DNA methylation heterogeneity measures and benchmark settings in this study. [5] denote the Perl script provided by the authors along with the article proposing the utility of MHL. (C, D) Schematic diagram illustrating (C) read-centric algorithm and (D) CpG-centric algorithm for the computation of DNA methylation heterogeneity. The advantages (plus symbol) and disadvantages (minus symbol) are shown below the diagrams. (E) Distribution of the average number of CpGs per sequencing read for the RRBS data from 928 CCLE cell lines. (F) Genomewide average levels of proportion of discordant reads (PDR) and local pairwise methylation discordance (LPMD) against varying read lengths. (G) Schematic illustration for the definition of local pairwise methylation discordance (LPMD) and examples. The proportion of reads having different DNA methylation states for a pair of CpGs (red arrows) are computed.</p

Characteristics of LPMD across 928 cancer cell lines.

(A) Genomewide average methylation levels and LPMD levels grouped by tissue types. Black vertical lines denote groupwise average levels of methylation and LPMD levels. Black horizontal bars on the right side denote the standard deviation of corresponding values. (B) Genomewide average methylation levels and LPMD levels grouped by disease types. Disease types from haematopoietic and lymphoid tissues are highlighted in red. (C, D) Correlation between mRNA expression and (C) genomewide average LPMD or (D) genomewide average methylation level. Genes are ranked according to the p-values of the corresponding correlation coefficients. P-values were adjusted using Benjamini-Hochberg procedure. (E, F) Correlation between DNMT3A expression and (E) genomewide average LPMD or (F) genomewide average methylation level. (G, H) Trends of fixed-distance average LPMD values. Shades denote 95% confidence interval. In (H), Cell lines were divided into two groups based on the median DNMT3A expression. (I) Difference of fixed-distance average LPMD values between DNMT3AHigh and DNMT3ALow groups.</p

Benchmarking the running time of Metheor against WSHPackage when only a subset of CpGs are considered.

Benchmarking the running time of Metheor against WSHPackage when only a subset of CpGs are considered.</p

Schematic illustration of epipolymorphism and methylation entropy.

Schematic illustration of epipolymorphism and methylation entropy.</p