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Tissue development is a complex spatiotemporal process with multiple interdependent components. Anatomical, histological, sequencing, and evolutional strategies can be used to profile and explain tissue development from different perspectives. The introduction of scRNAseq methods and the computational tools allows to deconvolute developmental heterogeneity and draw a decomposed uniform map. In this manuscript, we decomposed the development of a human retina with a focus on the retinal ganglion cells (RGC). To increase the temporal resolution of retinal cell classes maturation state we assumed the working hypothesis that that maturation of retinal ganglion cells is a continuous, non-discrete process. We have assembled the scRNAseq atlas of human fetal retina from fetal week 8 to week 27 and applied the computational methods to unravel maturation heterogeneity into a uniform maturation track. We align RGC transcriptomes in pseudotime to map RGC developmental fate trajectories against the broader timeline of retinal development. Through this analysis, we identified the continuous maturation track of RGC and described the cell-intrinsic (DEGs, maturation gene profiles, regulons, transcriptional motifs) and -extrinsic profiles (neurotrophic receptors across maturation, cell-cell interactions) of different RGC maturation states. We described the genes involved in the retina and RGC maturation, including de novo RGC maturation drivers. We demonstrate the application of the human fetal retina atlas as a reference tool, allowing automated annotation and universal embedding of scRNAseq data. Altogether, our findings deepen the current knowledge of the retina and RGC maturation by bringing in the maturation dimension for the cell class vs. state analysis. We show how the pseudotime application contributes to developmental-oriented analyses, allowing to order the cells by their maturation state. This approach not only improves the downstream computational analysis but also provides a true maturation track transcriptomics profile., Competing Interests: Competing interests: The authors declare no conflict of interest.

The spectrum of somatic alterations in hematologic malignancies includes substitutions, insertions/deletions (indels), copy number alterations (CNAs), and a wide range of gene fusions; no current clinically available single assay captures the different types of alterations. We developed a novel next-generation sequencing-based assay to identify all classes of genomic alterations using archived formalin-fixed paraffin-embedded blood and bone marrow samples with high accuracy in a clinically relevant time frame, which is performed in our Clinical Laboratory Improvement Amendments-certified College of American Pathologists-accredited laboratory. Targeted capture of DNA/RNA and next-generation sequencing reliably identifies substitutions, indels, CNAs, and gene fusions, with similar accuracy to lower-throughput assays that focus on specific genes and types of genomic alterations. Profiling of 3696 samples identified recurrent somatic alterations that impact diagnosis, prognosis, and therapy selection. This comprehensive genomic profiling approach has proved effective in detecting all types of genomic alterations, including fusion transcripts, which increases the ability to identify clinically relevant genomic alterations with therapeutic relevance., (© 2016 by The American Society of Hematology.)

Purpose: Micropapillary urothelial carcinoma (MPUC) is a rare and aggressive form of bladder cancer. We conducted genomic analyses [next-generation sequencing (NGS)] of MPUC and non-micropapillary urothelial bladder carcinomas (non-MPUC) to characterize the genomic landscape and identify targeted treatment options., Experimental Design: DNA was extracted from 40 μm of formalin-fixed paraffin-embedded sections from 15 MPUC and 64 non-MPUC tumors. Sequencing (NGS) was performed on hybridization-captured, adaptor ligation-based libraries to high coverage for 3,230 exons of 182 cancer-related genes plus 37 introns from 14 genes frequently rearranged in cancer. The results were evaluated for all classes of genomic alteration., Results: Mutations in the extracellular domain of ERBB2 were identified in 6 of 15 (40%) of MPUC: S310F (four cases), S310Y (one case), and R157W (one case). All six cases of MPUC with ERBB2 mutation were negative for ERBB2 amplification and Erbb2 overexpression. In contrast, 6 of 64 (9.4%) non-MPUC harbored an ERBB2 alteration, including base substitution (three cases), amplification (two cases), and gene fusion (one case), which is higher than the 2 of 159 (1.3%) protein-changing ERBB2 mutations reported for urinary tract cancer in COSMIC. The enrichment of ERBB2 alterations in MPUC compared with non-MPUC is significant both between this series (P < 0.0084) and for all types of urinary tract cancer in COSMIC (P < 0.001)., Conclusions: NGS of MPUC revealed a high incidence of mutation in the extracellular domain of ERBB2, a gene for which there are five approved targeted therapies. NGS can identify genomic alteration, which inform treatment options for the majority of MPUC patients.

The "Marrainage" project involves the implementation and evaluation of an intervention to prevent negligence in at-risk families with at least one child between the age of 0 and 5. The goal of the intervention is to introduce into those families, working nonprofessionals who can help them create their own natural support network and improve the quality of the family environment. The sponsors offer the following support services: emotional support, counselling-information, physical assistance, social skills development, positive feedback, and material needs. The sponsors work most often with the mother. The services provided to children consist mainly of outings, games and personal care. Such interventions have positive effects on the family environment, particularly on the parents' abilities and the psychological well-being of the mother. On the other hand, there is no conclusive evidence of a positive impact on the use of the families' natural networks.