Diagenode (Belgium)

In living cells, the genetic information stored in the DNA sequence is always associated with chromosomal and extra-chromosomal epigenetic information. Chromatin is formed by the DNA and associated proteins, in particular histones. Covalent histone modifications are important bearers of epigenetic information and as such have been increasingly studied since about the year 2000. One of the principal techniques to gather information about the association between DNA and modified histones is chromatin immunoprecipitation (ChIP), also combined with massive sequencing (ChIP-Seq). Automated ChIPmentation procedure is a convenient alternative to native chromatin immunoprecipitation (N-ChIP). It is now routinely used for ChIP-Seq in many model species, using in general roughly 106 cells per experiment. Such high cell numbers are sometimes difficult to produce. Using the human parasite Schistosoma mansoni, whose production requires sacrificing animals and should therefore be kept to a minimum, we show here that automated ChIPmentation is suitable for limited biological material. We define the operational limit as ≥20,000 Schistosoma cells with 30,000-300,000 cells as optimum. We also present a streamlined protocol for the preparation of ChIP input libraries.

We can’t know if Ray’s choice can be considered a happy ending, but I am sure his decision changed the rest of his life.

After learning about some basic theoretical notes on the MIDI protocol and the Arduino MIDI library in the previous chapter, it's time to see how it was possible to make a fully working drum machine using an alternative method to the expensive commercial pad sensors.

The most challenging part of the project was the design of the bottom box and the extra components needed to make the CNC (computer numerical control) mechanics work as needed. Until the bottom part of the Sand Machine was completed, it was impossible to make any test with a very low tolerance. See Figure 11-1.

The global Functional Annotation of Farm Animal Genomes initiative (FAANG) aims to improve animal breeding by improved genomic prediction via integration of functional genomics information. The GENESWitCH project has produced extensive functional genomics information for a variety of important tissues at early embryonic timepoints for both chickens and pigs. These datasets will be integrated to produce both tissue and time-point specific transcript, gene, and regulatory annotation for both species. In this paper, we describe the aims of the project, and the initial release of both raw and processed data.

Recurrence is frequent in pediatric ependymoma (EPN). Our longitudinal integrated analysis of 30 patient-matched repeated relapses (3.67 ± 1.76 times) over 13 years (5.8 ± 3.8) reveals stable molecular subtypes (RELA and PFA) and convergent DNA methylation reprogramming during serial relapses accompanied by increased orthotopic patient derived xenograft (PDX) (13/27) formation in the late recurrences. A set of differentially methylated CpGs (DMCs) and DNA methylation regions (DMRs) are found to persist in primary and relapse tumors (potential driver DMCs) and are acquired exclusively in the relapses (potential booster DMCs). Integrating with RNAseq reveals differentially expressed genes regulated by potential driver DMRs (CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and potential booster DMRs (PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). DMCs predicators of relapse are also identified in the primary tumors. This study provides a high-resolution epigenetic roadmap of serial EPN relapses and 13 orthotopic PDX models to facilitate biological and preclinical studies.

The age-related changes in hematopoiesis (ARCH) project is part of the Innovative Training Network (ITN) of the Marie-Sklodowska Curie Actions (MSCA) program, which provides doctorate training of excellence based on the exchange of ideas and competencies from the academic and private sectors.1 The ARCH project intends to outline hematopoietic stem cell (HSC) alterations with aging, how hematopoietic cell individuality is controlled at the transcriptional and epigenetic levels in normal hematopoiesis and in leukemias, and understand the crosstalk between intrinsic and extrinsic indications that support the proliferation of preleukemic and leukemic cells within the hematopoietic niche. We are 15 PhD students funded by this network, based around Europe, and our common aim is to understand functional changes in the hematopoietic system with age, how these changes link to the development of age-associated diseases and in parallel work towards the development of new treatments.2 Our projects kicked off just when the severe acute respiratory coronavirus-2 (SARS-CoV-2) emerged. Two and a half years later, SARS-CoV-2 continues to infect millions of people and has taken the lives of at least 6 million people worldwide.3 The COVID-19 outbreak brought along social isolation and feelings of uncertainty to everyone around the world, including doctorate students.4,5 Ironically, our projects have been more relevant than ever, as the pandemic has highlighted the important relationship between age-related changes in hematopoiesis and disease severity. Below, we aim to discuss the timeline of the ARCH project throughout the pandemic and how we managed to courageously pull through the hardships of doing research during a global pandemic within different settings (academia/institutes and industry). We provide recommendations to future PhD students on how to manage their PhD projects during global emergencies.

BackgroundTotal-RNA sequencing (total-RNA-seq) allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines have traditionally focused on particular biotypes, making assumptions that are not fullfilled by total-RNA-seq datasets. Transcripts from distinct RNA biotypes vary in length, biogenesis, and function, can overlap in a genomic region, and may be present in the genome with a high copy number. Consequently, reads from total-RNA-seq libraries may cause ambiguous genomic alignments, demanding for flexible quantification approaches.ResultsHere we present Multi-Graph count (MGcount), a total-RNA-seq quantification tool combining two strategies for handling ambiguous alignments. First, MGcount assigns reads hierarchically to small-RNA and long-RNA features to account for length disparity when transcripts overlap in the same genomic position. Next, MGcount aggregates RNA products with similar sequences where reads systematically multi-map using a graph-based approach. MGcount outputs a transcriptomic count matrix compatible with RNA-sequencing downstream analysis pipelines, with both bulk and single-cell resolution, and the graphs that model repeated transcript structures for different biotypes. The software can be used as a python module or as a single-file executable program.ConclusionsMGcount is a flexible total-RNA-seq quantification tool that successfully integrates reads that align to multiple genomic locations or that overlap with multiple gene features. Its approach is suitable for the simultaneous estimation of protein-coding, long non-coding and small non-coding transcript concentration, in both precursor and processed forms. Both source code and compiled software are available at https://github.com/hitaandrea/MGcount.

To elucidate cellular diversity and clonal evolution in tissues and tumors, one must resolve genomic heterogeneity in single cells. To this end, we have developed low-cost, mass-producible micro-/nanofluidic chips for DNA extraction from individual cells. These chips have modules that collect genomic DNA for sequencing or map genomic structure directly, on-chip, with denaturation-renaturation (D-R) optical mapping [Marie R, et al. (2013) Proc Natl Acad Sci USA 110:4893-4898]. Processing of single cells from the LS174T colorectal cancer cell line showed that D-R mapping of single molecules can reveal structural variation (SV) in the genome of single cells. In one experiment, we processed 17 fragments covering 19.8 Mb of the cell's genome. One megabase-large fragment aligned well to chromosome 19 with half its length, while the other half showed variable alignment. Paired-end single-cell sequencing supported this finding, revealing a region of complexity and a 50-kb deletion. Sequencing struggled, however, to detect a 20-kb gap that D-R mapping showed clearly in a megabase fragment that otherwise mapped well to the reference at the pericentromeric region of chromosome 4. Pericentromeric regions are complex and show substantial sequence homology between different chromosomes, making mapping of sequence reads ambiguous. Thus, D-R mapping directly, from a single molecule, revealed characteristics of the single-cell genome that were challenging for short-read sequencing.