Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis.

The success of marker-based approaches for dissecting haematopoiesis in mouse and human is reliant on the presence of well-defined cell surface markers specific for diverse progenitor populations. An inherent problem with this approach is that the presence of specific cell surface markers does not directly reflect the transcriptional state of a cell. Here, we used a marker-free approach to computationally reconstruct the blood lineage tree in zebrafish and order cells along their differentiation trajectory, based on their global transcriptional differences. Within the population of transcriptionally similar stem and progenitor cells, our analysis reveals considerable cell-to-cell differences in their probability to transition to another committed state. Once fate decision is executed, the suppression of transcription of ribosomal genes and upregulation of lineage-specific factors coordinately controls lineage differentiation. Evolutionary analysis further demonstrates that this haematopoietic programme is highly conserved between zebrafish and higher vertebrates.The study was supported by Cancer Research UK grant number C45041/A14953 (to A.C. and E.A.), European Research Council project 677501 – ZF_Blood (to A.C.) and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute. The authors would like to thank WTSI Cytometry Core Facility for their help with index cell sorting and the Core Sanger Web Team for hosting the cloud web application. The authors would also like to thank the CRUK Cambridge Institute Genomics Core Facility for their contribution in sequencing the data

CD4-Transgenic Zebrafish Reveal Tissue-Resident Th2- and Regulatory T Cell-like Populations and Diverse Mononuclear Phagocytes.

CD4+ T cells are at the nexus of the innate and adaptive arms of the immune system. However, little is known about the evolutionary history of CD4+ T cells, and it is unclear whether their differentiation into specialized subsets is conserved in early vertebrates. In this study, we have created transgenic zebrafish with vibrantly labeled CD4+ cells allowing us to scrutinize the development and specialization of teleost CD4+ leukocytes in vivo. We provide further evidence that CD4+ macrophages have an ancient origin and had already emerged in bony fish. We demonstrate the utility of this zebrafish resource for interrogating the complex behavior of immune cells at cellular resolution by the imaging of intimate contacts between teleost CD4+ T cells and mononuclear phagocytes. Most importantly, we reveal the conserved subspecialization of teleost CD4+ T cells in vivo. We demonstrate that the ancient and specialized tissues of the gills contain a resident population of il-4/13b-expressing Th2-like cells, which do not coexpress il-4/13a Additionally, we identify a contrasting population of regulatory T cell-like cells resident in the zebrafish gut mucosa, in marked similarity to that found in the intestine of mammals. Finally, we show that, as in mammals, zebrafish CD4+ T cells will infiltrate melanoma tumors and obtain a phenotype consistent with a type 2 immune microenvironment. We anticipate that this unique resource will prove invaluable for future investigation of T cell function in biomedical research, the development of vaccination and health management in aquaculture, and for further research into the evolution of adaptive immunity.European Research Council (Grant IDs: ERC-2011-StG-282059 (PROMINENT), 677501 (ZF_Blood)), Biotechnology and Biological Sciences Research Council (Grant ID: BB/L007401/1), Dowager Countess Eleanor Peel Trust (Grant ID: TH-PRCL.FID2228), Medical Research Council, Department for International Development (Career Development Award Fellowship MR/J009156/1), Medical Research Foundation (Grant ID: R/140419), Cancer Research UK (Grant ID: C45041/A14953), Wellcome Trust and Medical Research Council to the Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute (core support grant)This is the final version of the article. It first appeared from The American Association of Immunologists via https://doi.org/10.4049/​jimmunol.160095

Updated Field Synopsis and Systematic Meta-Analyses of Genetic Association Studies in Cutaneous Melanoma: The MelGene Database

We updated a field synopsis of genetic associations of cutaneous melanoma (CM) by systematically retrieving and combining data from all studies in the field published as of August 31, 2013. Data were available from 197 studies, which included 83,343 CM cases and 187,809 controls and reported on 1,126 polymorphisms in 289 different genes. Random-effects meta-analyses of 81 eligible polymorphisms evaluated in >4 data sets confirmed 20 single-nucleotide polymorphisms across 10 loci (TYR, AFG3L1P, CDK10, MYH7B, SLC45A2, MTAP, ATM, CLPTM1L, FTO, and CASP8) that have previously been published with genome-wide significant evidence for association (P<5 × 10−8) with CM risk, with certain variants possibly functioning as proxies of already tagged genes. Four other loci (MITF, CCND1, MX2, and PLA2G6) were also significantly associated with 5 × 10−8<P<1 × 10−3. In supplementary meta-analyses derived from genome-wide association studies, one additional locus located 11 kb upstream of ARNT (chromosome 1q21) showed genome-wide statistical significance with CM. Our approach serves as a useful model in analyzing and integrating the reported germline alterations involved in CM

Analysis of endothelial-to-haematopoietic transition at the single cell level identifies cell cycle regulation as a driver of differentiation

Funder: INTENS EU fp8 consortiumFunder: ERC advanced grant New-CholAbstract: Background: Haematopoietic stem cells (HSCs) first arise during development in the aorta-gonad-mesonephros (AGM) region of the embryo from a population of haemogenic endothelial cells which undergo endothelial-to-haematopoietic transition (EHT). Despite the progress achieved in recent years, the molecular mechanisms driving EHT are still poorly understood, especially in human where the AGM region is not easily accessible. Results: In this study, we take advantage of a human pluripotent stem cell (hPSC) differentiation system and single-cell transcriptomics to recapitulate EHT in vitro and uncover mechanisms by which the haemogenic endothelium generates early haematopoietic cells. We show that most of the endothelial cells reside in a quiescent state and progress to the haematopoietic fate within a defined time window, within which they need to re-enter into the cell cycle. If cell cycle is blocked, haemogenic endothelial cells lose their EHT potential and adopt a non-haemogenic identity. Furthermore, we demonstrate that CDK4/6 and CDK1 play a key role not only in the transition but also in allowing haematopoietic progenitors to establish their full differentiation potential. Conclusion: We propose a direct link between the molecular machineries that control cell cycle progression and EHT

Single-cell transcriptional analysis reveals ILC-like cells in zebrafish.

Innate lymphoid cells (ILCs) are important mediators of the immune response and homeostasis in barrier tissues of mammals. However, the existence and function of ILCs in other vertebrates are poorly understood. Here, we use single-cell RNA sequencing to generate a comprehensive atlas of zebrafish lymphocytes during tissue homeostasis and after immune challenge. We profiled 14,080 individual cells from the gut of wild-type zebrafish, as well as of rag1-deficient zebrafish that lack T and B cells, and discovered populations of ILC-like cells. We uncovered a rorc-positive subset of ILCs that could express cytokines associated with type 1, 2, and 3 responses upon immune challenge. Specifically, these ILC-like cells expressed il22 and tnfa after exposure to inactivated bacteria or il13 after exposure to helminth extract. Cytokine-producing ILC-like cells express a specific repertoire of novel immune-type receptors, likely involved in recognition of environmental cues. We identified additional novel markers of zebrafish ILCs and generated a cloud repository for their in-depth exploration.The study was supported by Cancer Research UK grant number C45041/A14953 (to A.C. and E.I.A.), European Research Council project 677501 – ZF_Blood (to A.C. and P.M.S.), EMBO Long-Term Fellowship ALTF-807-2015 (to P.P.H), ANR grant 17-CE15-0017-01 – ZF-ILC (to P.P.H) and ANR-16-CE20-0002-03 (to J.-P.L), H2020-MSCA-IF-2015 grant 708128 – ZF-ILC (to P.P.H), ANR-10-LABX-73 (‘revive’ to P. Herbomel) and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute

DNA microarray image processing based on advance pattern recognition techniques

In the present thesis, a novel gridding technique, as well as, two new segmentation methods applied to complementary DNA (cDNA) microarray images is proposed. More precise, a new gridding method based on continuous wavelet transform (CWT) was performed. Line profiles of x and y axis were calculated, resulting to 2 different signals. These signals were independently processed by means of CWT at 15 different levels, using daubechies 4 mother wavelet. A summation, point by point, was performed on the processed signals, in order to suppress noise and enhance spot’s differences. Additionally, a wavelet based hard thresholding filter was applied to each signal for the task of alleviating the noise of the signals. 10 real microarray images were used in order to visually assess the performance of our gridding method. Each microarray image contained 4 sub-arrays, each sub-array 40x40 spots, thus, 6400 spots totally. According to our results, the accuracy of our algorithm was 98% in all 10 images and in all spots. Additionally, processing time was less than 3 sec on a 1024×1024×16 microarray image, rendering the method a promising technique for an efficient and fully automatic gridding processing. Following the gridding process, the Gaussian Mixture Model (GMM) and the Fuzzy GMM algorithms were applied to each cell, with the purpose of discriminating foreground from background. In addition, markov random field (MRF), as well as, a proposed wavelet based MRF model (SMRF) were implemented. The segmentation abilities of all the algorithms were evaluated by means of the segmentation matching factor (SMF), the Coefficient of Determination (r²), and the concordance correlation (pc). Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r², and pc (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.Τα τελευταία χρόνια παρατηρείται ραγδαία ανάπτυξη της τεχνολογίας των μικροσυστοιχιών (microarrays) με αποτέλεσμα την ποιοτική και ποσοτική μέτρηση της έκφρασης χιλιάδων γονιδίων ταυτοχρόνως σ’ ένα και μόνο πείραμα. Εικόνες μικροσυστοιχιών, στις οποίες έχει λάβει χώρα υβριδοποίηση δείγματος DNA, χρησιμοποιούνται ευρέως για την εξαγωγή αξιόπιστων αποτελεσμάτων γονιδιακής έκφρασης και προσδιορισμό των μηχανισμών που ελέγχουν την ενεργοποίηση των γονιδίων σ’ έναν οργανισμό. Συνεπώς, η δημιουργία κατάλληλων υπολογιστικών τεχνικών για την επεξεργασία των εικόνων αυτών συντελεί καθοριστικά στην εξαγωγή ορθών και έγκυρων αποτελεσμάτων. Στη παρούσα Διδακτορική Διατριβή αναπτύχθηκε στο πρώτο στάδια μια νέα πλήρως αυτοματοποιημένη τεχνική διευθυνσιοδότησης και στο δεύτερο στάδιο δύο νέες τεχνικές τμηματοποίησης. Πιο συγκεκριμένα, αναπτύχθηκε μια νέα μέθοδος διευθυνσιοδότησης η οποία βασίζεται στο συνεχή μετασχηματισμό κυματιδίου (Continuous Wavelet Transform CWT) για την αυτόματη εύρεση των κέντρων των κηλίδων, καθώς και των ορίων μεταξύ δύο διαδοχικών κηλίδων. Στη συνέχεια αναπτύχθηκαν δύο νέες μέθοδοι κατάτμησης της εικόνας για τον διαχωρισμό των κηλίδων από το φόντο, οι οποίες βασίζονται στη τεχνική μίξης ασαφών μοντέλων Γκάους (Fuzzy Gaussian Mixture Models FGMM) καθώς και στη τεχνική συνδυασμού τυχαίων πεδίων Μαρκόφ (Markov Random Field MRF) και μετασχηματισμού κυματιδίου (Wavelet Transform WT) (SMRF). Με σκοπό την αξιολόγηση (validation) των προτεινόμενων μεθόδων της παρούσας Διδακτορικής Διατριβής, δημιουργήθηκαν και χρησιμοποιήθηκαν τόσο πραγματικές εικόνες μικροσυστοιχιών, καθώς και απομιμούμενες (simulated) σύμφωνα με μεθοδολογία η οποία προτείνεται από τη διεθνή βιβλιογραφία. Όσον αφορά την διευθυνσιοδότηση, χρησιμοποιώντας οπτική ανασκόπηση για κάθε κηλίδα χωριστά σε όλες τις πραγματικές εικόνες, δημιουργήθηκαν δύο κατηγορίες, ανάλογα με το αν οι γραμμές του πλέγματος εφάπτονταν πάνω σε κάποια κηλίδα ή όχι. Η προτεινόμενη μεθοδολογία ήταν ακριβής σε ποσοστό 98% στον ακριβή εντοπισμό των κηλίδων σε όλες τις εικόνες. Σύγκριση ανάμεσα στην απόδοση των GMM, FGMM, MRF και SMRF στις απομιμούμενες εικόνες σε διαφορετικά επίπεδα θορύβου πραγματοποιήθηκε και τα αποτελέσματα σε όλα τα μετρικά, segmentation matching factor (SMF), coefficient of variation (r²), και coefficient of determination (p), μας έδειξαν ότι η μέθοδος SMRF είναι πιο αξιόπιστη στο να μπορέσει να αναδείξει την πραγματική περιφέρεια της κηλίδας, τόσο σε εικόνες με μεγάλο λόγο σήματος προς θόρυβο, όσο και σε μικρό λόγο. Ενδεικτικά αποτελέσματα σε 1 db SNR για την περίπτωση του SMRF είναι SMF=92.66, r²=0.923, και p=0.88, ακολουθούμενο από το MRF ( SMF=92.15, r²=0.91, και p=0.85), FGMM (SMF=91.07, r²=0.92, και p=0.86)και GMM (SMF=90.73, r²=0.89, και p=0.83). Στη συνέχεια πάρθηκαν αποτελέσματα τα οποία προέκυψαν από τη χρήση πραγματικών εικόνων μικροσυστοιχιών. Και σε αυτή τη περίπτωση, αναδείχθηκε η υπεροχή του WMRF, έναντι των άλλων αλγορίθμων ταξινόμησης μέση τιμή MAE=497 και CV=0.88. Τέλος, θα πρέπει να τονιστεί ότι τα παραπάνω μετρικά υπολογίστηκαν και σε αποτελέσματα από δύο ευρέως χρησιμοποιούμενα πακέτα επεξεργασίας εικόνων μικροσυστοιχιών, τα οποία χρησιμοποιούνται και είναι διαθέσιμα. Πιο συγκεκριμένα, χρησιμοποιήθηκαν το SCANALYSE και το SPOT, τα οποία χρησιμοποιούν τις τεχνικές τμηματοποίησης Fixed Circle και Seeded Region Growing, αντίστοιχα. Στη περίπτωση αυτή η τεχνική SMRF κατάφερε να υπολογίσει καλύτερα αποτελέσματα από τα δύο αυτά πακέτα. Πιο συγκεκριμένα η τεχνική GMM πέτυχε MAE=1470 και CV=1.29, η τεχνική FGMM πέτυχε MAE=1430 και CV=1.21, η τεχνική MRF πέτυχε MAE=1215 και CV=1.15, η τεχνική WMRF πέτυχε MAE=497 και CV=0.88, η τεχνική FC του λογισμικού πακέτου SCANALYZE πέτυχε MAE=503 και CV=0.90, και τέλος η τεχνική SRG του λογισμικού πακέτου SPOT πέτυχε MAE=1180 και CV=0.93

C-PAmP: large scale analysis and database construction containing high scoring computationally predicted antimicrobial peptides for all the available plant species.

BACKGROUND: Antimicrobial peptides are a promising alternative to conventional antibiotics. Plants are an important source of such peptides; their pharmacological properties are known since antiquity. Access to relevant information, however, is not straightforward, as there are practically no major repositories of experimentally validated and/or predicted plant antimicrobial peptides. PhytAMP is the only database dedicated to plant peptides with confirmed antimicrobial action, holding 273 entries. Data on such peptides can be otherwise retrieved from generic repositories. DESCRIPTION: We present C-PAmP, a database of computationally predicted plant antimicrobial peptides. C-PAmP contains 15,174,905 peptides, 5-100 amino acids long, derived from 33,877 proteins of 2,112 plant species in UniProtKB/Swiss-Prot. Its web interface allows queries based on peptide/protein sequence, protein accession number and species. Users can view the corresponding predicted peptides along with their probability score, their classification according to the Collection of Anti-Microbial Peptides (CAMP), and their PhytAMP id where applicable. Moreover, users can visualise protein regions with a high concentration of predicted antimicrobial peptides. In order to identify potential antimicrobial peptides we used a classification algorithm, based on a modified version of the pseudo amino acid concept. The classifier tested all subsequences ranging from 5 to 100 amino acids of the plant proteins in UniProtKB/Swiss-Prot and stored those classified as antimicrobial with a high probability score (>90%). Its performance measures across a 10-fold cross-validation are more than satisfactory (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90) and it succeeded in classifying 99.5% of the PhytAMP peptides correctly. CONCLUSIONS: We have compiled a major repository of predicted plant antimicrobial peptides using a highly performing classification algorithm. Our repository is accessible from the web and supports multiple querying options to optimise data retrieval. We hope it will greatly benefit drug design research by significantly limiting the range of plant peptides to be experimentally tested for antimicrobial activity

BiDaS: a web-based Monte Carlo BioData Simulator based on sequence/feature characteristics

BiDaS is a web-application that can generate massive Monte Carlo simulated sequence or numerical feature data sets (e. g. dinucleotide content, composition, transition, distribution properties) based on small user-provided data sets. BiDaS server enables users to analyze their data and generate large amounts of: (i) Simulated DNA/RNA and aminoacid (AA) sequences following practically identical sequence and/or extracted feature distributions with the original data. (ii) Simulated numerical features, presenting identical distributions, while preserving the exact 2D or 3D between-feature correlations observed in the original data sets. The server can project the provided sequences to multidimensional feature spaces based on: (i) 38 DNA/RNA features describing conformational and physicochemical nucleotide sequence features from the B-DNA-VIDEO database, (ii) 122 DNA/RNA features based on conformational and thermodynamic dinucleotide properties from the DiProDB database and (iii) Pseudo-aminoacid composition of the initial sequences. To the best of our knowledge, this is the first available web-server that allows users to generate vast numbers of biological data sets with realistic characteristics, while keeping between-feature associations. These data sets can be used for a wide variety of current biological problems, such as the in-depth study of gene, transcript, peptide and protein groups/families; the creation of large data sets from just a few available members and the strengthening of machine learning classifiers. All simulations use advanced Monte Carlo sampling techniques. The BiDaS web-application is available at http://bioserver-3.bioacademy.gr/Bioserver/BiDaS/

ChemBioServer 2.0: An Advanced Web Server for Filtering, Clustering and Networking of Chemical Compounds Facilitating Both Drug Discovery and Repurposing

ChemBioServer 2.0 is the advanced sequel of a web-server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of chemical compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. The updated web server is available in the URL: http://chembioserver.vi-seem.eu/ </p

C-PAmP predictions for 6 antimicrobial regions in protein O24006 of Impatiens balsamina (Balsam) in comparison with the corresponding annotations in UniProtKB/Swiss-Prot.

<p>C-PAmP predictions for 6 antimicrobial regions in protein O24006 of Impatiens balsamina (Balsam) in comparison with the corresponding annotations in UniProtKB/Swiss-Prot.</p