Decoding the regulatory network of early blood development from single-cell gene expression measurements.

Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.We thank J. Downing (St. Jude Children's Research Hospital, Memphis, TN, USA) for the Runx1-ires-GFP mouse. Research in the authors' laboratory is supported by the Medical Research Council, Biotechnology and Biological Sciences Research Council, Leukaemia and Lymphoma Research, the Leukemia and Lymphoma Society, Microsoft Research and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute. V.M. is supported by a Medical Research Council Studentship and Centenary Award and S.W. by a Microsoft Research PhD Scholarship.This is the accepted manuscript for a paper published in Nature Biotechnology 33, 269–276 (2015) doi:10.1038/nbt.315

Preparation, characterization, DFT calculations and ethylene oligomerization studies of iron(II) complexes bearing 2-(1H-benzimidazol-2-yl)-phenol derivatives

<p>Five 2-(1<i>H</i>-benzimidazol-2-yl)-phenol derivatives including 1H (HL₁), 5-chloro-(HL₂), 5-methyl-(HL₃), 5,6-dichloro-(HL₄), and 5,6-dimethyl-(HL₅) were synthesized by the reaction of their corresponding benzene-1,2-diamine precursors and 2-hydroxybenzaldehyde which subsequently was employed in complexation with Fe(II) to prepare complexes <b>C1</b>–<b>C5</b>, respectively. Indeed, in all complexes, the ligands were coordinated as bidentate, via the C=N nitrogen and hydroxy oxygen atom of benzimidazole moiety and phenol ring, respectively. The compounds were characterized by FTIR, UV–vis, ¹H- and ¹³C-NMR spectropscopy, ICP, and elemental analysis (C, H, and N). The purity of these compounds was determined by melting point (m.p )and TLC. The synthesized ligands and complexes were geometrically optimized by Gaussian09 software at B3LYP/TZVP level of theory and satisfactory theoretical–experimental agreement was achieved for analysis of IR data of the compounds. Catalytic behavior of the iron(II) complexes was investigated for ethylene reactivity. On activation with diethylaluminum chloride (Et₂AlCl), iron(II) complex (<b>C4</b>) showed the highest activity (1686 kg oligomers.mol⁻¹(Fe).h⁻¹) for ethylene oligomerization when it contains chlorine substituents and exhibits good selectivity for linear 1-butene. The steric and electronic effects of ligands were investigated in detail on the influence of their catalytic activities.</p

Smoothed <i>κ</i>-velo projection of velocities in the HSPC dataset.

Velocities were smoothed by averaging over the 30 nearest neighbours. Neighbourhoods are calculated in S space. Non-smoothed projection in main Fig 5B. (TIFF)</p

The u-s phase portrait of <i>Acly</i>, <i>Dpysl2</i> and <i>Gnaz</i> (raw counts, after normalisation and after recovering of dynamics).

The u-s phase portrait of Acly, Dpysl2 and Gnaz (from the pancreas endocrinogenesis dataset), which are all genes with insufficient unspliced counts. Here, we show how scVelo would recover the dynamics if these genes were not filtered out. (TIFF)</p