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Motivation: Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable. Results: We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated.

Immune escape represents a major driver of acute myeloid leukemia (AML) reemergence after allogeneic hematopoietic cell transplantation (allo-HCT), with up to 40% of relapses prompted by nongenomic loss of HLA class II expression in leukemia cells. By integrative analysis of gene expression, DNA methylation, and chromatin accessibility in paired diagnosis/relapse primary samples and in the respective patient-derived xenografts (PDX), we identify the polycomb repressive complex 2 (PRC2) as a key epigenetic driver of this immune escape modality. We report that loss of expression of HLA class II molecules is accompanied by a PRC2-dependent reduction in chromatin accessibility. Pharmacologic inhibition of PRC2 subunits rescues HLA class II expression in AML relapses in vitro and in vivo, with consequent recovery of leukemia recognition by CD4+ T cells. Our results uncover a novel link between epigenetics and leukemia immune escape, which may rapidly translate into innovative strategies to cure or prevent AML posttransplantation relapse.

Single cell profiling has become a common practice to investigate the complexity of tissues, organs and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or from the very same cells. Despite development of computational methods for data integration is an active research field, most of the available strategies have been devised for the joint analysis of two modalities and cannot accommodate a high number of them. To solve this problem, we here propose a multiomic data integration framework based on Wasserstein Generative Adversarial Networks (MOWGAN) suitable for the analysis of paired or unpaired data with high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

The oncogenic role of common fragile sites (CFS), focal and pervasive gaps in the cancer genome arising from replicative stress, remains controversial. Exploiting the TCGA dataset, we found that in most CFS the genes residing within the associated focal deletions are down-regulated, including proteins involved in tumour immune recognition. In a subset of CFS, however, the residing genes are surprisingly overexpressed. Within the most frequent CFS in this group, FRA4F, which is deleted in up to 18% of cancer cases and harbours the CCSER1 gene, we identified a region which includes an intronic, antisense pseudogene, TMSB4XP8. TMSB4XP8 focal ablation or transcriptional silencing elicits the overexpression of CCSER1, through a cis-acting mechanism. CCSER1 overexpression increases proliferation and triggers centrosome amplifications, multinuclearity, and aberrant mitoses. Accordingly, FRA4F is associated in patient samples to mitotic genes deregulation and genomic instability. As a result, cells overexpressing CCSER1 become sensitive to the treatment with aurora kinase inhibitors. Our findings point to a novel tumourigenic mechanism where focal deletions increase the expression of a new class of “dormant” oncogenes.

Gene editing by engineered nucleases has revolutionized the field of gene therapy by enabling targeted and precise modification of the genome. However, the limited availability of methods for clonal tracking of edited cells has resulted in a paucity of information on the diversity, abundance and behavior of engineered clones. Here we detail the wet laboratory and bioinformatic BAR-Seq pipeline, a strategy for clonal tracking of cells harboring homology-directed targeted integration of a barcoding cassette. We present the BAR-Seq web application, an online, freely available and easy-to-use software that allows performing clonal tracking analyses on raw sequencing data without any computational resources or advanced bioinformatic skills. BAR-Seq can be applied to most editing strategies, and we describe its use to investigate the clonal dynamics of human edited hematopoietic stem/progenitor cells in xenotransplanted hosts. Notably, BAR-Seq may be applied in both basic and translational research contexts to investigate the biology of edited cells and stringently compare editing protocols at a clonal level. Our BAR-Seq pipeline allows library preparation and validation in a few days and clonal analyses of edited cell populations in 1 week.

Targeted gene editing in hematopoietic stem cells (HSCs) is a promising treatment for several diseases. However, the limited efficiency of homology-directed repair (HDR) in HSCs and the unknown impact of the procedure on clonal composition and dynamics of transplantation have hampered clinical translation. Here, we apply a barcoding strategy to clonal tracking of edited cells (BAR-Seq) and show that editing activates p53, which substantially shrinks the HSC clonal repertoire in hematochimeric mice, although engrafted edited clones preserve multilineage and self-renewing capacity. Transient p53 inhibition restored polyclonal graft composition. We increased HDR efficiency by forcing cell-cycle progression and upregulating components of the HDR machinery through transient expression of the adenovirus 5 E4orf6/7 protein, which recruits the cell-cycle controller E2F on its target genes. Combined E4orf6/7 expression and p53 inhibition resulted in HDR editing efficiencies of up to 50% in the long-term human graft, without perturbing repopulation and self-renewal of edited HSCs. This enhanced protocol should broaden applicability of HSC gene editing and pave its way to clinical translation.

Single cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, schist, that is compatible with the popular scanpy framework.

Background: Analysis of scATAC-seq data has been recently scaled to thousands of cells. While processing of other types of single cell data was boosted by the implementation of alignment-free techniques, pipelines available to process scATAC-seq data still require large computational resources. We propose here an approach based on pseudoalignment, which reduces the execution times and hardware needs at little cost for precision. Methods: Public data for 10k PBMC were downloaded from 10x Genomics web site. Reads were aligned to various references derived from DNase I Hypersensitive Sites (DHS) using kallisto and quantified with bustools. We compared our results with the ones publicly available derived by cellranger-atac. We subsequently tested our approach on scATAC-seq data for K562 cell line. Results: We found that kallisto does not introduce biases in quantification of known peaks; cells groups identified are consistent with the ones identified from standard method. We also found that cell identification is robust when analysis is performed using DHS-derived reference in place of de novo identification of ATAC peaks. Lastly, we found that our approach is suitable for reliable quantification of gene activity based on scATAC-seq signal, thus allows for efficient labelling of cell groups based on marker genes. Conclusions: Analysis of scATAC-seq data by means of kallisto produces results in line with standard pipelines while being considerably faster; using a set of known DHS sites as reference does not affect the ability to characterize the cell populations.

A Corrigendum on Targeted NGS Platforms for Genetic Screening and Gene Discovery in Primary Immunodeficiencies by Cifaldi, C., Brigida, I., Barzaghi, F., Zoccolillo, M., Ferradini, V., Petricone, D., et al. (2019). Front. Immunol. 10:316. doi: 10.3389/fimmu.2019.00316 In the published article, there was an error in affiliation “18.” Instead of “Pediatric and Rheumatology Unit, Giannina Gaslini Institute, Genoa, Italy,” it should be “Center for Autoinflammatory Diseases and Immunodeficiencies, IRCCS Giannina Gaslini, Genoa, Italy.” The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Background: Primary Immunodeficiencies (PIDs) are a heterogeneous group of genetic immune disorders. While some PIDs can manifest with more than one phenotype, signs, and symptoms of various PIDs overlap considerably. Recently, novel defects in immune-related genes and additional variants in previously reported genes responsible for PIDs have been successfully identified by Next Generation Sequencing (NGS), allowing the recognition of a broad spectrum of disorders. Objective: To evaluate the strength and weakness of targeted NGS sequencing using custom-made Ion Torrent and Haloplex (Agilent) panels for diagnostics and research purposes. Methods: Five different panels including known and candidate genes were used to screen 105 patients with distinct PID features divided in three main PID categories: T cell defects, Humoral defects and Other PIDs. The Ion Torrent sequencing platform was used in 73 patients. Among these, 18 selected patients without a molecular diagnosis and 32 additional patients were analyzed by Haloplex enrichment technology. Results: The complementary use of the two custom-made targeted sequencing approaches allowed the identification of causative variants in 28.6% (n = 30) of patients. Twenty-two out of 73 (34.6%) patients were diagnosed by Ion Torrent. In this group 20 were included in the SCID/CID category. Eight out of 50 (16%) patients were diagnosed by Haloplex workflow. Ion Torrent method was highly successful for those cases with well-defined phenotypes for immunological and clinical presentation. The Haloplex approach was able to diagnose 4 SCID/CID patients and 4 additional patients with complex and extended phenotypes, embracing all three PID categories in which this approach was more efficient. Both technologies showed good gene coverage. Conclusions: NGS technology represents a powerful approach in the complex field of rare disorders but its different application should be weighted. A relatively small NGS target panel can be successful

The DNA-damage response (DDR) arrests cell-cycle progression until damage is removed. DNA-damage-induced cellular senescence is associated with persistent DDR. The molecular bases that distinguish transient from persistent DDR are unknown. Here we show that a large fraction of exogenously induced persistent DDR markers is associated with telomeric DNA in cultured cells and mammalian tissues. In yeast, a chromosomal DNA double-strand break next to a telomeric sequence resists repair and impairs DNA ligase 4 recruitment. In mammalian cells, ectopic localization of telomeric factor TRF2 next to a double-strand break induces persistent DNA damage and DDR. Linear, but not circular, telomeric DNA or scrambled DNA induces a prolonged checkpoint in normal cells. In terminally differentiated tissues of old primates, DDR markers accumulate at telomeres that are not critically short. We propose that linear genomes are not uniformly reparable and that telomeric DNA tracts, if damaged, are irreparable and trigger persistent DDR and cellular senescence.

Kinetochores are nucleoprotein assemblies responsible for the attachment of chromosomes to spindle microtubules during mitosis. The KMN network, a crucial constituent of the outer kinetochore, creates an interface that connects microtubules to centromeric chromatin. The NDC80, MIS12, and KNL1 complexes form the core of the KMN network. We recently reported the structural organization of the human NDC80 complex. In this study, we extend our analysis to the human MIS12 complex and show that it has an elongated structure with a long axis of ∼22 nm. Through biochemical analysis, crosslinking-based methods, and negative-stain electron microscopy, we investigated the reciprocal organization of the subunits of the MIS12 complex and their contacts with the rest of the KMN network. A highlight of our findings is the identification of the NSL1 subunit as a scaffold supporting interactions of the MIS12 complex with the NDC80 and KNL1 complexes. Our analysis has important implications for understanding kinetochore organization in different organisms.