The Si(100)(2 × 1) surface serves as a template for the formation of Mn wires at room temperature, which are the starting point for the annealing experiments discussed here. The evolution of the Mn surface structures as a function of annealing temperature was observed with scanning tunneling microscopy for the temperature range between 115 and 600 °C and establishes a surface phase diagram for the Mn−Si(100)(2 × 1) system. The stability of the Mn nanowires is limited; they break up below 115 °C (the lowest annealing temperature studied), and ultrasmall clusters are formed. These clusters are initially positioned on the terraces but migrate to the step edges at around 250 °C, which sets the limit for the Mn surface diffusion length. At around 300 °C the Mn adatoms move into subsurface sites, and the empty and filled states images strongly indicate that Mn acts as an acceptor in this near-surface region. A further increase in temperature leads to the formation of large crystallites (several tens of nanometers), which exhibit the characteristic shape associated with Mn silicides. The modification of the Si surface with temperature is characterized by the dramatic increase in the defect population (defect density and size and step edge roughness). The condensation of dimer vacancies begins around 200 °C and progresses to the formation of long dimer vacancy lines at elevated temperatures. The step edge roughness and the step edge formation energies were calculated for SA and SB steps, and their modulation with annealing temperature illustrates the impact of Mn on the defect and kink stabilities. These data will be used to perform a thermodynamic and kinetic modeling of defect population in the presence of Mn at moderate substrate temperatures. This study presents a surface phase diagram, which includes the evolution of the Mn nanostructures and the modification of the Si surface. The identification of near-surface layers of Mn acceptors is particularly relevant for the design of basic building blocks for future Si-based spintronics.
Single cell SNV analysis is an emerging and promising strategy to connect cell-level genetic variation to cell phenotypes. At the present, SNV detection from 10x Genomics scRNA-seq data is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gain of information of SNV assessments from individual cell scRNA-seq data, where the alignments are split by barcode prior to the variant call. For our analyses we use publicly available sequencing data on the human breast cancer cell line MCF7 cell line generated at consequent time-points during anti-cancer treatment. We analyzed SNV calls by three popular variant callers – GATK, Strelka2 and Mu-tect2, in combination with a method for cell-level tabulation of the sequencing read counts bearing SNV alleles – SCReadCounts. Our analysis shows that variant calls on individual cell alignments identify at least two-fold higher number of SNVs as compared to the pooled scRNA-seq. We demonstrate that scSNVs exclusively called in the single cell alignments (scSNVs) are substantially enriched in novel genetic variants and in coding functional annotations, in particular, stop-codon and missense substitutions. Furthermore, we find that the expression of some scSNVs correlates with the expression of their harbouring gene (cis-scReQTLs).Overall, our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes on the need of cell-level variant detection approaches and tools. Given the growing accumulation of scRNA-seq datasets, cell-level variant assessments are likely to significantly contribute to the understanding of the cellular heterogeneity and the relationship between genetics variants and functional phenotypes. In addition, cell-level variant assessments from scRNA-seq can be highly informative in cancer where they can help elucidate somatic mutations evolution and functionality.
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