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Glioblastoma is the most common type of malignant brain tumor among adults and is currently a non-curable disease due primarily to its highly invasive phenotype, and the lack of successful current therapies. Despite surgical resection and post-surgical treatment patients ultimately develop recurrence of the tumour. Several signalling molecules have been implicated in the development, progression and aggressiveness of glioblastoma. The present study reviewed the role of interleukin (IL)-6, a cytokine known to be important in activating several pro-oncogenic signaling pathways in glioblastoma. The current study particularly focused on the contribution of IL-6 in recurrent glioblastoma, with particular focus on glioblastoma stem cells and resistance to therapy.

Genetic maps have been fundamental to building our understanding of disease genetics and evolutionary processes. The gametes of an individual contain all of the information required to perform a de novo chromosome-scale assembly of an individual’s genome, which historically has been performed with populations and pedigrees. Here, we discuss how single-cell gamete sequencing offers the potential to merge the advantages of short-read sequencing with the ability to build personalized genetic maps and open up an entirely new space in personalized genetics.

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Profiling gametes of an individual enables the construction of personalised haplotypes and meiotic crossover landscapes, now achievable at larger scale than ever through the availability of high-throughput single-cell sequencing technologies. However, haplotyping single gametes from high-throughput single-cell DNA sequencing datasets using existing methods requires intensive processing. Here we introduce an efficient software toolset using modern programming languages for the common tasks of haplotyping haploid gamete genomes and calling crossovers (sgcocaller), and constructing and visualising individualised crossover landscapes (comapr) from single gametes. With additional data pre-possessing, the tools can also be applied to bulk sequenced samples.

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Profiling gametes of an individual enables the construction of personalised haplotypes and meiotic crossover landscapes, now achievable at larger scale than ever through the availability of high-throughput single-cell sequencing technologies. However, high-throughput single-gamete data commonly have low depth of coverage per gamete, which challenges existing gamete-based haplotype phasing methods. In addition, haplotyping a large number of single gametes from high-throughput single-cell DNA sequencing data and constructing meiotic crossover profiles using existing methods requires intensive processing. Here, we introduce efficient software tools for the essential tasks of generating personalised haplotypes and calling crossovers in gametes from single-gamete DNA sequencing data (sgcocaller), and constructing, visualising, and comparing individualised crossover landscapes from single gametes (comapr). With additional data pre-possessing, the tools can also be applied to bulk-sequenced samples. We demonstrate that sgcocaller is able to generate impeccable phasing results for high-coverage datasets, on which it is more accurate and stable than existing methods, and also performs well on low-coverage single-gamete sequencing datasets for which current methods fail. Our tools achieve highly accurate results with user-friendly installation, comprehensive documentation, efficient computation times and minimal memory usage.

Immunofluorescent staining is commonly used to generate images to characterise cytological phenotypes. The manual quantification of DNA double-strand breaks and their repair intermediates during meiosis using image data requires a series of subjective steps, from image selection to the counting of particular events per nucleus. Here we describe synapsis, a Bioconductor package, which includes a set of functions to automate the process of identifying meiotic nuclei and quantifying key double-strand break formation and repair events in a rapid, scalable and reproducible workflow, and compare it to manual user quantification. The software can be extended for other applications in meiosis research, such as incorporating machine learning approaches to categorise meiotic substages.