Author Correction: Commensal-driven immune zonation of the liver promotes host defence.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03346-0.

Commensal-driven immune zonation of the liver promotes host defence.

The liver connects the intestinal portal vasculature with the general circulation, using a diverse array of immune cells to protect from pathogens that translocate from the gut1. In liver lobules, blood flows from portal triads that are situated in periportal lobular regions to the central vein via a polarized sinusoidal network. Despite this asymmetry, resident immune cells in the liver are considered to be broadly dispersed across the lobule. This differs from lymphoid organs, in which immune cells adopt spatially biased positions to promote effective host defence2,3. Here we used quantitative multiplex imaging, genetic perturbations, transcriptomics, infection-based assays and mathematical modelling to reassess the relationship between the localization of immune cells in the liver and host protection. We found that myeloid and lymphoid resident immune cells concentrate around periportal regions. This asymmetric localization was not developmentally controlled, but resulted from sustained MYD88-dependent signalling induced by commensal bacteria in liver sinusoidal endothelial cells, which in turn regulated the composition of the pericellular matrix involved in the formation of chemokine gradients. In vivo experiments and modelling showed that this immune spatial polarization was more efficient than a uniform distribution in protecting against systemic bacterial dissemination. Together, these data reveal that liver sinusoidal endothelial cells sense the microbiome, actively orchestrating the localization of immune cells, to optimize host defence.

Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in onco-hematology.

Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genomically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In onco-hematology, Hierarchical Dirichlet Mixture Models (HDMM) have become one of the preferred method to cluster the genomics data, that include the presence or absence of gene mutations and cytogenetics anomalies, into components. This work unfolds the standard workflow used in onco-hematology to improve patient stratification and proposes alternative approaches to characterize the components and to assign patient to them, as they are crucial tasks usually supported by a priori clinical knowledge. We propose (a) to compute the parameters of the multinomial components of the HDMM or (b) to estimate the parameters of the HDMM components as if they were Multivariate Fisher's Non-Central Hypergeometric (MFNCH) distributions. Then, our approach to perform patients assignments to the HDMM components is designed to essentially determine for each patient its most likely component. We show on simulated data that the patients assignment using the MFNCH-based approach can be superior, if not comparable, to using the multinomial-based approach. Lastly, we illustrate on real Acute Myeloid Leukemia data how the utilization of MFNCH-based approach emerges as a good trade-off between the rigorous multinomial-based characterization of the HDMM components and the common refinement of them based on a priori clinical knowledge.

MISpheroID: a knowledgebase and transparency tool for minimum information in spheroid identity.

Spheroids are three-dimensional cellular models with widespread basic and translational application across academia and industry. However, methodological transparency and guidelines for spheroid research have not yet been established. The MISpheroID Consortium developed a crowdsourcing knowledgebase that assembles the experimental parameters of 3,058 published spheroid-related experiments. Interrogation of this knowledgebase identified heterogeneity in the methodological setup of spheroids. Empirical evaluation and interlaboratory validation of selected variations in spheroid methodology revealed diverse impacts on spheroid metrics. To facilitate interpretation, stimulate transparency and increase awareness, the Consortium defines the MISpheroID string, a minimum set of experimental parameters required to report spheroid research. Thus, MISpheroID combines a valuable resource and a tool for three-dimensional cellular models to mine experimental parameters and to improve reproducibility.

Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy Images.

Most of the time, the deep analysis of a biological sample requires the acquisition of images at different time points, using different modalities and/or different stainings. This information gives morphological, functional, and physiological insights, but the acquired images must be aligned to be able to proceed with the co-localisation analysis. Practically speaking, according to Aristotle's principle, "The whole is greater than the sum of its parts", multi-modal image registration is a challenging task that involves fusing complementary signals. In the past few years, several methods for image registration have been described in the literature, but unfortunately, there is not one method that works for all applications. In addition, there is currently no user-friendly solution for aligning images that does not require any computer skills. In this work, DS4H Image Alignment (DS4H-IA), an open-source ImageJ/Fiji plugin for aligning multimodality, immunohistochemistry (IHC), and/or immunofluorescence (IF) 2D microscopy images, designed with the goal of being extremely easy to use, is described. All of the available solutions for aligning 2D microscopy images have also been revised. The DS4H-IA source code; standalone applications for MAC, Linux, and Windows; video tutorials; manual documentation; and sample datasets are publicly available.

Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.

Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.

Characterization and comparison of gene-centered human interactomes.

The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human interactome have been obtained through several experimental techniques and functional analyses. Moreover, these maps can be enriched through literature-mining approaches, and different combinations of various 'source' databases have been used in the literature. It is therefore unclear to which extent the various interactomes yield similar results when used in the context of interactome-based approaches in network biology. We compared a comprehensive list of human interactomes on the basis of topology, protein complexes, molecular pathways, pathway cross-talk and disease gene prediction. In a general context of relevant heterogeneity, our study provides a series of qualitative and quantitative parameters that describe the state of the art of human interactomes and guidelines for selecting interactomes in future applications.

Clinical relevance of clonal hematopoiesis in persons aged ≥80 years.

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with increased risk of cancers and inflammation-related diseases. This phenomenon becomes common in persons aged ≥80 years, in whom the implications of CHIP are not well defined. We performed a mutational screening in 1794 persons aged ≥80 years and investigated the relationships between CHIP and associated pathologies. Mutations were observed in one-third of persons aged ≥80 years and were associated with reduced survival. Mutations in JAK2 and splicing genes, multiple mutations (DNMT3A, TET2, and ASXL1 with additional genetic lesions), and variant allele frequency ≥0.096 had positive predictive value for myeloid neoplasms. Combining mutation profiles with abnormalities in red blood cell indices improved the ability of myeloid neoplasm prediction. On this basis, we defined a predictive model that identifies 3 risk groups with different probabilities of developing myeloid neoplasms. Mutations in DNMT3A, TET2, ASXL1, or JAK2 were associated with coronary heart disease and rheumatoid arthritis. Cytopenia was common in persons aged ≥80 years, with the underlying cause remaining unexplained in 30% of cases. Among individuals with unexplained cytopenia, the presence of highly specific mutation patterns was associated with myelodysplastic-like phenotype and a probability of survival comparable to that of myeloid neoplasms. Accordingly, 7.5% of subjects aged ≥80 years with cytopenia had presumptive evidence of myeloid neoplasm. In summary, specific mutational patterns define different risk of developing myeloid neoplasms vs inflammatory-associated diseases in persons aged ≥80 years. In individuals with unexplained cytopenia, mutational status may identify those subjects with presumptive evidence of myeloid neoplasms.

Follicular Lymphoma Microenvironment Traits Associated with Event-Free Survival.

The majority of patients with Follicular Lymphoma (FL) experience subsequent phases of remission and relapse, making the disease "virtually" incurable. To predict the outcome of FL patients at diagnosis, various clinical-based prognostic scores have been proposed; nonetheless, they continue to fail for a subset of patients. Gene expression profiling has highlighted the pivotal role of the tumor microenvironment (TME) in the FL prognosis; nevertheless, there is still a need to standardize the assessment of immune-infiltrating cells for the prognostic classification of patients with early or late progressing disease. We studied a retrospective cohort of 49 FL lymph node biopsies at the time of the initial diagnosis using pathologist-guided analysis on whole slide images, and we characterized the immune repertoire for both quantity and distribution (intrafollicular, IF and extrafollicular, EF) of cell subsets in relation to clinical outcome. We looked for the natural killer (CD56), T lymphocyte (CD8, CD4, PD1) and macrophage (CD68, CD163, MA4A4A)-associated markers. High CD163/CD8 EF ratios and high CD56/MS4A4A EF ratios, according to Kaplan-Meier estimates were linked with shorter EFS (event-free survival), with the former being the only one associated with POD24. In contrast to IF CD68+ cells, which represent a more homogeneous population, higher in non-progressing patients, EF CD68+ macrophages did not stratify according to survival. We also identify distinctive MS4A4A+CD163-macrophage populations with different prognostic weights. Enlarging the macrophage characterization and combining it with a lymphoid marker in the rituximab era, in our opinion, may enable prognostic stratification for low-/high-grade FL patients beyond POD24. These findings warrant validation across larger FL cohorts.

BRAQUE: Bayesian Reduction for Amplified Quantization in UMAP Embedding.

Single-cell biology has revolutionized the way we understand biological processes. In this paper, we provide a more tailored approach to clustering and analyzing spatial single-cell data coming from immunofluorescence imaging techniques. We propose Bayesian Reduction for Amplified Quantization in UMAP Embedding (BRAQUE) as an integrative novel approach, from data preprocessing to phenotype classification. BRAQUE starts with an innovative preprocessing, named Lognormal Shrinkage, which is able to enhance input fragmentation by fitting a lognormal mixture model and shrink each component towards its median, in order to help further the clustering step in finding more separated and clear clusters. Then, BRAQUE's pipeline consists of a dimensionality reduction step performed using UMAP, and a clustering performed using HDBSCAN on UMAP embedding. In the end, clusters are assigned to a cell type by experts, using effects size measures to rank markers and identify characterizing markers (Tier 1), and possibly characterize markers (Tier 2). The number of total cell types in one lymph node detectable with these technologies is unknown and difficult to predict or estimate. Therefore, with BRAQUE, we achieved a higher granularity than other similar algorithms such as PhenoGraph, following the idea that merging similar clusters is easier than splitting unclear ones into clear subclusters.

Random Walk Approximation for Stochastic Processes on Graphs.

We introduce the Random Walk Approximation (RWA), a new method to approximate the stationary solution of master equations describing stochastic processes taking place on graphs. Our approximation can be used for all processes governed by non-linear master equations without long-range interactions and with a conserved number of entities, which are typical in biological systems, such as gene regulatory or chemical reaction networks, where no exact solution exists. For linear systems, the RWA becomes the exact result obtained from the maximum entropy principle. The RWA allows having a simple analytical, even though approximated, form of the solution, which is global and easier to deal with than the standard System Size Expansion (SSE). Here, we give some theoretically sufficient conditions for the validity of the RWA and estimate the order of error calculated by the approximation with respect to the number of particles. We compare RWA with SSE for two examples, a toy model and the more realistic dual phosphorylation cycle, governed by the same underlying process. Both approximations are compared with the exact integration of the master equation, showing for the RWA good performances of the same order or better than the SSE, even in regions where sufficient conditions are not met.

Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach.

Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1 , and IR-bSSFP-B1 , the latter two designed to be more specific for B1+ parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary-based and the NN approaches. In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR-FISP MRF sequence exploiting both simulations and in vivo acquisitions. Results demonstrate that the NN approach outperforms the dictionary-based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T1 , T2 , and M0 maps that are in good agreement with those obtained with the original dictionary approach. k-SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.

Canine smooth muscle tumors: A clinicopathological study.

Canine smooth muscle tumors (SMTs) commonly develop in the alimentary and female genital tracts and less frequently in soft tissue. The definition of histological criteria of malignancy is less detailed for SMTs in dogs than in humans. This study evaluated the clinicopathologic features of canine SMTs and compared the veterinary and human medical criteria of malignancy. A total of 105 canine SMTs were evaluated histologically and classified according to both veterinary and human criteria. The Ki67 labeling index was assessed in all SMTs. Estrogen receptor (ER) and progesterone receptor (PR) expression was evaluated for soft tissue SMTs. Follow-up data were available in 25 cases. SMTs were diagnosed in the female genital tract (42%), alimentary tract (22%), and soft tissue (20%). Soft tissue SMTs frequently arose in the perigenital area, pelvic cavity, and retroperitoneum. A subset of soft tissue SMTs expressed ER and/or PR, resembling the gynecologic type of soft tissue SMT in humans. SMTs were less frequently malignant when assessed with human criteria than with veterinary criteria, better reflecting their benign behavior, especially in the genital tract where human criteria tolerate a higher mitotic count for leiomyoma. Decreased differentiation was correlated with increased proliferation, necrosis, and reduced desmin expression. Mitotic count, Ki67 labeling index, and necrosis were correlated with metastases and tumor-related death. Further prognostic studies are warranted to confirm the better performance of the human criteria when assessing SMT malignancy, especially genital cases, to confirm their usefulness in ER/PR-expressing soft tissue SMTs, and to better define the most useful prognostic parameters for canine SMTs.

Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation.

Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system's economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians.

Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization.

Purpose: In this work, we propose an implementation of the Bienenstock-Cooper-Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science. Methods: To improve the convergence efficiency of the BCM model, we combine the original plasticity rule with the optimization tools of modern deep learning. By numerical simulation on standard benchmark datasets, we prove the efficiency of the BCM model in learning, memorization capacity, and feature extraction. Results: In all the numerical simulations, the visualization of neuronal synaptic weights confirms the memorization of human-interpretable subsets of patterns. We numerically prove that the selectivity obtained by BCM neurons is indicative of an internal feature extraction procedure, useful for patterns clustering and classification. The introduction of competitiveness between neurons in the same BCM network allows the network to modulate the memorization capacity of the model and the consequent model selectivity. Conclusions: The proposed improvements make the BCM model a suitable alternative to standard machine learning techniques for both feature selection and classification tasks.

Impact of concurrency on the performance of a whole exome sequencing pipeline.

BACKGROUND: Current high-throughput technologies-i.e. whole genome sequencing, RNA-Seq, ChIP-Seq, etc.-generate huge amounts of data and their usage gets more widespread with each passing year. Complex analysis pipelines involving several computationally-intensive steps have to be applied on an increasing number of samples. Workflow management systems allow parallelization and a more efficient usage of computational power. Nevertheless, this mostly happens by assigning the available cores to a single or few samples' pipeline at a time. We refer to this approach as naive parallel strategy (NPS). Here, we discuss an alternative approach, which we refer to as concurrent execution strategy (CES), which equally distributes the available processors across every sample's pipeline. RESULTS: Theoretically, we show that the CES results, under loose conditions, in a substantial speedup, with an ideal gain range spanning from 1 to the number of samples. Also, we observe that the CES yields even faster executions since parallelly computable tasks scale sub-linearly. Practically, we tested both strategies on a whole exome sequencing pipeline applied to three publicly available matched tumour-normal sample pairs of gastrointestinal stromal tumour. The CES achieved speedups in latency up to 2-2.4 compared to the NPS. CONCLUSIONS: Our results hint that if resources distribution is further tailored to fit specific situations, an even greater gain in performance of multiple samples pipelines execution could be achieved. For this to be feasible, a benchmarking of the tools included in the pipeline would be necessary. It is our opinion these benchmarks should be consistently performed by the tools' developers. Finally, these results suggest that concurrent strategies might also lead to energy and cost savings by making feasible the usage of low power machine clusters.

Genomic history of the Italian population recapitulates key evolutionary dynamics of both Continental and Southern Europeans.

BACKGROUND: The cline of human genetic diversity observable across Europe is recapitulated at a micro-geographic scale by variation within the Italian population. Besides resulting from extensive gene flow, this might be ascribable also to local adaptations to diverse ecological contexts evolved by people who anciently spread along the Italian Peninsula. Dissecting the evolutionary history of the ancestors of present-day Italians may thus improve the understanding of demographic and biological processes that contributed to shape the gene pool of European populations. However, previous SNP array-based studies failed to investigate the full spectrum of Italian variation, generally neglecting low-frequency genetic variants and examining a limited set of small effect size alleles, which may represent important determinants of population structure and complex adaptive traits. To overcome these issues, we analyzed 38 high-coverage whole-genome sequences representative of population clusters at the opposite ends of the cline of Italian variation, along with a large panel of modern and ancient Euro-Mediterranean genomes. RESULTS: We provided evidence for the early divergence of Italian groups dating back to the Late Glacial and for Neolithic and distinct Bronze Age migrations having further differentiated their gene pools. We inferred adaptive evolution at insulin-related loci in people from Italian regions with a temperate climate, while possible adaptations to pathogens and ultraviolet radiation were observed in Mediterranean Italians. Some of these adaptive events may also have secondarily modulated population disease or longevity predisposition. CONCLUSIONS: We disentangled the contribution of multiple migratory and adaptive events in shaping the heterogeneous Italian genomic background, which exemplify population dynamics and gene-environment interactions that played significant roles also in the formation of the Continental and Southern European genomic landscapes.

Aneuploid acute myeloid leukemia exhibits a signature of genomic alterations in the cell cycle and protein degradation machinery.

BACKGROUND: Aneuploidy occurs in more than 20% of acute myeloid leukemia (AML) cases and correlates with an adverse prognosis. METHODS: To understand the molecular bases of aneuploid acute myeloid leukemia (A-AML), this study examined the genomic profile in 42 A-AML cases and 35 euploid acute myeloid leukemia (E-AML) cases. RESULTS: A-AML was characterized by increased genomic complexity based on exonic variants (an average of 26 somatic mutations per sample vs 15 for E-AML). The integration of exome, copy number, and gene expression data revealed alterations in genes involved in DNA repair (eg, SLX4IP, RINT1, HINT1, and ATR) and the cell cycle (eg, MCM2, MCM4, MCM5, MCM7, MCM8, MCM10, UBE2C, USP37, CK2, CK3, CK4, BUB1B, NUSAP1, and E2F) in A-AML, which was associated with a 3-gene signature defined by PLK1 and CDC20 upregulation and RAD50 downregulation and with structural or functional silencing of the p53 transcriptional program. Moreover, A-AML was enriched for alterations in the protein ubiquitination and degradation pathway (eg, increased levels of UHRF1 and UBE2C and decreased UBA3 expression), response to reactive oxygen species, energy metabolism, and biosynthetic processes, which may help in facing the unbalanced protein load. E-AML was associated with BCOR/BCORL1 mutations and HOX gene overexpression. CONCLUSIONS: These findings indicate that aneuploidy-related and leukemia-specific alterations cooperate to tolerate an abnormal chromosome number in AML, and they point to the mitotic and protein degradation machineries as potential therapeutic targets.

RUNX2 expression in thyroid and breast cancer requires the cooperation of three non-redundant enhancers under the control of BRD4 and c-JUN.

Aberrant reactivation of embryonic pathways is a common feature of cancer. RUNX2 is a transcription factor crucial during embryogenesis that is aberrantly reactivated in many tumors, including thyroid and breast cancer, where it promotes aggressiveness and metastatic spreading. Currently, the mechanisms driving RUNX2 expression in cancer are still largely unknown. Here we showed that RUNX2 transcription in thyroid and breast cancer requires the cooperation of three distantly located enhancers (ENHs) brought together by chromatin three-dimensional looping. We showed that BRD4 controls RUNX2 by binding to the newly identified ENHs and we demonstrated that the anti-proliferative effects of bromodomain inhibitors (BETi) is associated with RUNX2 transcriptional repression. We demonstrated that each RUNX2 ENH is potentially controlled by a distinct set of TFs and we identified c-JUN as the principal pivot of this regulatory platform. We also observed that accumulation of genetic mutations within these elements correlates with metastatic behavior in human thyroid tumors. Finally, we identified RAINs, a novel family of ENH-associated long non-coding RNAs, transcribed from the identified RUNX2 regulatory unit. Our data provide a new model to explain how RUNX2 expression is reactivated in thyroid and breast cancer and how cancer-driving signaling pathways converge on the regulation of this gene.