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Background Understanding the molecular interactions between cells, tissues or organs is key to understanding the functioning of a biological system as a whole. Results Here, we propose crossWGCNA : a co-expression-based method that identifies highly interacting genes unbiasedly and that we employ to study stroma-epithelium communication in breast cancer. CrossWGCNA can be applied to bulk, single cell and spatial transcriptomics data. We validate it both in silico and experimentally, and we provide a fully documented R package allowing users to employ it. Conclusions The wide applicability and agnostic nature of our tool make it complementary to existing methods overcoming the limitations arising from strong baseline assumptions. Graphical Abstract

Background CRISPR-Cas9 dropout screens are formidable tools for investigating biology with unprecedented precision and scale. However, biases in data lead to potential confounding effects on interpretation and compromise overall quality. The activity of Cas9 is influenced by structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend to generate similar gene-independent responses to CRISPR-Cas9 targeting (proximity bias), possibly due to Cas9-induced whole chromosome-arm truncations or other genomic structural features and different chromatin accessibility levels. Results We benchmarked eight computational methods, rigorously evaluating their ability to reduce both CN and proximity bias in the two largest publicly available cell-line-based CRISPR-Cas9 screens to date. We also evaluated the capability of each method to preserve data quality and heterogeneity by assessing the extent to which the processed data allows accurate detection of true positive essential genes, established oncogenetic addictions, and known/novel biomarkers of cancer dependency. Our analysis sheds light on the ability of each method to correct biases under different scenarios. AC-Chronos outperforms other methods in correcting both CN and proximity biases when jointly processing multiple screens of models with available CN information, whereas CRISPRcleanR is the top performing method for individual screens or when CN information is not available. In addition, Chronos and AC-Chronos yield a final dataset better able to recapitulate known sets of essential and non-essential genes. Conclusions Overall, our investigation provides guidance for the selection of the most appropriate bias-correction method, based on its strengths, weaknesses and experimental settings.

Glioblastoma is a major unmet clinical need characterized by striking inter- and intra-tumoral heterogeneity and a population of glioblastoma stem cells (GSCs), conferring aggressiveness and therapy resistance. GSCs communicate through a network of tumor–tumor connections (TTCs), including nanotubes and microtubes, promoting tumor progression. However, very little is known about the mechanisms underlying TTC formation and overall GSC morphology. As GSCs closely resemble neural progenitor cells during neurodevelopment, we hypothesized that GSCs’ morphological features affect tumor progression. We identified GSC morphology as a new layer of tumoral heterogeneity with important consequences on GSC proliferation. Strikingly, we showed that the neurodevelopmental morphoregulator ADD3 is sufficient and necessary for maintaining proper GSC morphology, TTC abundance, cell cycle progression, and chemoresistance, as well as required for cell survival. Remarkably, both the effects on cell morphology and proliferation depend on the stability of actin cytoskeleton. Hence, cell morphology and its regulators play a key role in tumor progression by mediating cell–cell communication. We thus propose that GSC morphological heterogeneity holds the potential to identify new therapeutic targets and diagnostic markers.

CRISPR-Cas9 screens stand as formidable tools for investigating biology with unprecedented precision and scale. One of their principal applications involves probing large panels of immortalised human cancer cell lines for viability reduction responses upon systematic genetic knock-out at a genome-wide level, to identify novel cancer dependencies and therapeutic targets. However, biases in CRISPR-Cas9 screens' data pose challenges, leading to potential confounding effects on their interpretation and compromising their overall quality. The mode of action of the Cas9 enzyme, exerted by the induction of DNA double-strand breaks at a locus targeted by a specifically designed single-guide RNA (sgRNA), is influenced by specific structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend to generate similar gene-independent responses to CRISPR-Cas9 targeting (proximity bias), possibly due to Cas9-induced whole chromosome-arm truncations or other unknown genomic structural features and different chromatin accessibility levels. Different computational methods have been proposed to correct these biases in silico, each based on different modelling assumptions. We have benchmarked seven of the latest methods, rigorously evaluating their effectiveness for the first time in reducing both CN and proximity bias in the two largest publicly available cell-line-based CRISPR-Cas9 screens to date. We have also evaluated the ability of each method in preserving data quality and heterogeneity by assessing the extent to which the processed data allows accurate detection of true positive essential genes, established oncogenetic addictions, and known/novel biomarkers of cancer dependency. Our analysis sheds light on the ability of each method to correct biases arising from structural properties and other possible unknown factors associated with CRISPR-Cas9 screen data under different scenarios. In particular, it shows that among all tested methods CRISPRcleanR outperforms other methods in correcting both CN and proximity biases, while Chronos yields a final dataset better able to recapitulate known sets of essential and non-essential genes. Overall, our investigation provides guidance for the selection of the most appropriate bias-correction method, based on its strengths, weaknesses and experimental settings.Competing Interest StatementFI receives funding from Open Targets, a public-private initiative involving academia and industry and performs consultancy for the joint CRUK-AstraZeneca Functional Genomics Centre. All other authors declare that they have no competing interests.

Glioblastoma is a major unmet clinical need characterized by striking inter- and intra-tumoral heterogeneity and a population of glioblastoma stem cells (GSCs), conferring aggressiveness and therapy resistance. GSCs communicate through a network of tumor-tumor connections (TTCs), including nanotubes and microtubes, promoting tumor progression. However, very little is known about the mechanisms underlying TTC formation and overall GSC morphology. As GSCs closely resemble neural progenitor cells during neurodevelopment, we hypothesised that GSCs’ morphological features affect tumour progression. We identified GSC morphology as a new layer of tumoral heterogeneity with important consequences on GSC proliferation. Strikingly, we showed that the neurodevelopmental morphoregulator ADD3, is sufficient and necessary for maintaining proper GSC morphology, TTC abundance and cell cycle progression as well as required for cell survival. Remarkably, both the effects on cell morphology and proliferation depend on the stability of actin cytoskeleton. Hence, cell morphology and its regulators play a key role in tumor progression by mediating cell-cell communication. We thus propose that GSC morphological heterogeneity holds the potential to identify new therapeutic targets and diagnostic markers.

This paper reports on a rare case of pregnancy after uterine artery embolization (UAE) for uterine arteriovenous malformation (AVM). Debate exists about persistence of fertility in women after UAE. Adverse effects of this technique can modify both uterine echostructure, inducing necrosis and infarction, endometrial atrophy and uterine artery rupture, and ovarian reserve, causing persistent amenorrhea. Ovarian reserve appears to be affected by UAE in pre-menopausal women. However, younger ovaries (according to biological ovarian age) exhibit a greater capacity for recovery after ovarian damage. Therefore, larger studies are needed for more conclusive results. A 28-year-old woman was admitted to our department due to life-threatening uterine bleeding, resulting in tachycardia, pallor, and sweating. The patient came with a history of two spontaneous miscarriages. After sonography and computed tomography, AVMs were identified at uterine fundus and anterior wall. The pathogenesis of infertility after UAE is not yet known. The peculiarity of this case was that, only few months later, the patient became pregnant and gave birth to a live fetus at 37 weeks with cesarean delivery.

Genome-wide recessive genetic screens using lentiviral CRISPR-guide RNA libraries are widely performed in mammalian cells to functionally characterise individual genes and for the discovery of new anti-cancer therapeutic targets. As the effectiveness of such powerful and precise tools for cancer pharmacogenomic is emerging, reference datasets for their quality assessment and the validation of the underlying experimental pipelines are becoming increasingly necessary. Here, we provide a dataset, an R package, and metrics for the assessment of novel experimental pipelines upon the execution of a single calibration viability screen of the HT-29 human colon cancer cell line, employing a commercially available genome-wide library of single guide RNAs: the Human Improved Genome-wide Knockout CRISPR (Sanger) Library. This dataset contains results from screening the HT-29 in multiple batches with the Sanger library, and outcomes from several levels of quality control tests on the resulting data. Data and accompanying R package can be used as a toolkit for benchmarking newly established experimental pipelines for CRISPR-Cas9 recessive screens, via the generation of a final quality-control report. Competing Interest Statement MJG has received research grants from AstraZeneca, GlaxoSmithKline, and Astex Pharmaceuticals, and is founder of Mosaic Therapeutic. FI has received funding from Open Targets, a public-private initiative involving academia and industry, and he performs consultancy for the joint CRUK-AstraZeneca Functional Genomics Centre and for Mosaic Therapeutics. Footnotes * https://figshare.com/articles/dataset/HT29_reference_dataset/20480544 * https://github.com/DepMap-Analytics/HT29benchmark