Quantitative phase imaging has gained popularity in bioimaging because it can avoid the need for cell staining, which, in some cases, is difficult or impossible. However, as a result, quantitative phase imaging does not provide the labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for quantitative phase imaging techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed using tomographic phase microscopy in the flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy data and microfluidic cyto-fluorimeter outputs. This is a remarkable step towards directly extracting specific three-dimensional intracellular structures from the phase contrast data in a typical flow cytometry configuration.

Background HIF1A (Hypoxia-Inducible-Factor 1A) expression in solid tumors is relevant to establish resistance to therapeutic approaches. The use of compounds direct against hypoxia signaling and HIF1A does not show clinical efficiency because of changeable oxygen concentrations in solid tumor areas. The identification of HIF1A targets expressed in both normoxia and hypoxia and of HIF1A/hypoxia signatures might meliorate the prognostic stratification and therapeutic successes in patients with high-risk solid tumors. Methods In this study, we conducted a combined analysis of RNA expression and DNA methylation of neuroblastoma cells silenced or unsilenced for HIF1A expression, grown in normoxia and hypoxia conditions. Results The analysis of pathways highlights HIF-1 (heterodimeric transcription factor 1) activity in normoxia in metabolic process and HIF-1 activity in hypoxia in neuronal differentiation process. HIF1A driven transcriptional response in hypoxia depends on epigenetic control at DNA methylation status of gene regulatory regions. Furthermore, low oxygen levels generate HIF1A-dependent or HIF1A-independent signatures, able to stratify patients according to risk categories. Conclusions These findings may help to understand the molecular mechanisms by which low oxygen levels reshape gene signatures and provide new direction for hypoxia targeting in solid tumor. Electronic supplementary material The online version of this article (10.1186/s12881-019-0767-1) contains supplementary material, which is available to authorized users.

Progresses over the past years have extensively improved our capacity to use genome-scale analyses-including high-density genotyping and exome and genome sequencing-to identify the genetic basis of pediatric tumors. In particular, exome sequencing has contributed to the evidence that about 10% of children and adolescents with tumors have germline genetic variants associated with cancer predisposition. In this review, we provide an overview of genetic variations predisposing to solid pediatric tumors (medulloblastoma, ependymoma, astrocytoma, neuroblastoma, retinoblastoma, Wilms tumor, osteosarcoma, rhabdomyosarcoma, and Ewing sarcoma) and outline the biological processes affected by the involved mutated genes. A careful description of the genetic basis underlying a large number of syndromes associated with an increased risk of pediatric cancer is also reported. We place particular emphasis on the emerging view that interactions between germline and somatic alterations are a key determinant of cancer development. We propose future research directions, which focus on the biological function of pediatric risk alleles and on the potential links between the germline genome and somatic changes. Finally, the importance of developing new molecular diagnostic tests including all the identified risk germline mutations and of considering the genetic predisposition in screening tests and novel therapies is emphasized.

The contribution of coding mutations to oncogenesis has been largely clarified, whereas little is known about somatic mutations in noncoding DNA and their role in driving tumors remains controversial. Here, we used an alternative approach to interpret the functional significance of noncoding somatic mutations in promoting tumorigenesis. Noncoding somatic mutations of 151 neuroblastomas were integrated with ENCODE data to locate somatic mutations in regulatory elements specifically active in neuroblastoma cells, nonspecifically active in neuroblastoma cells, and nonactive. Within these types of elements, transcription factors (TF) were identified whose binding sites were enriched or depleted in mutations. For these TFs, a gene expression signature was built to assess their implication in neuroblastoma. DNA-and RNA-sequencing data were integrated to assess the effects of those mutations on mRNA levels. The pathogenicity of mutations was significantly higher in transcription factor binding site (TFBS) of regulatory elements specifically active in neuroblastoma cells, as compared with the others. Within these elements, there were 18 over-represented TFs involved mainly in cell-cycle phase transitions and 15 under-represented TFs primarily regulating cell differentiation. A gene expression signature based on over-represented TFs correlated with poor survival and unfavorable prognostic markers. Moreover, recurrent mutations in TFBS of over-represented TFs such as EZH2 affected MCF2L and ADP-ribosylhydrolase like 1 expression, among the others. We propose a novel approach to study the involvement of regulatory variants in neuroblastoma that could be extended to other cancers and provide further evidence that alterations of gene expression may have relevant effects in neuroblastoma development. Significance: These findings propose a novel approach to study regulatory variants in neuroblastoma and suggest that noncoding somatic mutations have relevant implications in neuroblastoma development.