Biology subjects

Peters, B.

Publications and source records attributed to Peters, B..

68 records · Page 4Linked to original sources

bioRxiv2019

Improved prediction of MHC II antigen presentation throughintegration and motif deconvolution of mass spectrometryMHC eluted ligand data

Major Histocompatibility Complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyse and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Further, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0, demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes and development of personalized immunotherapies.

bioinformatics↗

bioRxiv2019

Benchmarking predictions of MHC class I restricted T cell epitopes

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected mice, considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top 0.04% (N = 277) of peptides in the VACV proteome (N = 767,788). These performance metrics provide guidance for immunologists as to which prediction methods to use. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.\n\nAuthor summaryComputational prediction tools are used to screen peptides to identify potential T cell epitope candidates. These tools, developed using machine learning methods, save time and resources in many immunological studies including vaccine discovery and cancer neoantigen identification. In addition to the already existing methods several epitope prediction tools are being developed these days but they lack a comprehensive and uniform evaluation to see which method performs best. In this study we did a comprehensive evaluation of publicly accessible MHC I restricted T cell epitope prediction tools using a recently published dataset of Vaccinia virus epitopes. We found that methods based on artificial neural network architecture and trained on both MHC binding and ligand elution data showed very high performance (NetMHCPan-4.0 and MHCFlurry). This benchmark analysis will help immunologists to choose the right prediction method for their desired work and will also serve as a framework for tool developers to evaluate new prediction methods.

immunology↗

bioRxiv2019

Uncovering the cell type specificity of blood sample derived gene signatures using RNA expression data

Analysis of transcriptomic data derived from blood samples is complicated by the complex mixture of cell types such samples contain. Transcriptomic signatures derived from such samples are often driven by a particular cell lineage within the mixture. Identifying this most contributing lineage can help to provide a biological interpretation of the signature. We created a web application CellTypeScore which quantifies and visually represents the expression level of signature genes in common blood cell types. This is done by constructing an interactive stacked bar plot with the bars representing expression of genes across blood cell types. Summed scores serve as a measure of how highly the combined signature is expressed in different cell types. An online version of the application can be found at https://tools.dice-database.org/celltypescore/.

bioinformatics↗

bioRxiv2019

Context information supports serial dependence of multiple visual objects across memory episodes

Visual perception operates in an object-based manner, by integrating associated features via attention. Working memory allows a flexible access to a limited number of currently relevant objects, even when they are occluded or physically no longer present. Recently, it has been shown that we compensate for small changes of an objects feature over memory episodes, which can support its perceptual stability. This phenomenon was termed serial dependence and has mostly been studied in situations that comprised only a single relevant object. However, since we are typically confronted with situations where several objects have to be perceived and held in working memory, the central question of how we selectively create temporal stability of several objects has remained unsolved. As different objects can be distinguished by their accompanying context features, like their color or temporal position, we tested whether serial dependence is supported by the congruence of context features across memory episodes. Specifically, we asked participants to remember the motion directions of two sequentially presented colored dot fields per trial. At the end of a trial one motion direction was cued for continuous report either by its color (Experiment 1) or serial position (Experiment 2). We observed serial dependence, i.e., an attractive bias of currently toward previously memorized objects, between current and past motion directions that was clearly enhanced when items had the same color or serial position across trials. This bias was particularly pronounced for the context feature that was used for cueing and for the target of the previous trial. Together, these findings demonstrate that coding of current object representations depends on previous representations, especially when they share similar content and context features. Apparently the binding of content and context features is not completely erased after a memory episode, but it is carried over to subsequent episodes. As this reflects temporal dependencies in natural settings, the present findings reveal a mechanism that integrates corresponding bundles of content and context features to support stable representations of individualized objects over time.

neuroscience↗

bioRxiv2019

Design principles of ROS dynamic networks relevant to precision therapies for age-related diseases

The eminently complex regulatory network protecting the cell against oxidative stress, surfaces in several disease maps, including that of Parkinsons disease (PD). How this molecular networking achieves its various functionalities and how processes operating at the seconds-minutes time scale cause a disease at a time scale of multiple decennia is enigmatic.\n\nBy computational analysis, we here disentangle the reactive oxygen species (ROS) regulatory network into a hierarchy of subnetworks that each correspond to a different functionality. The detailed dynamic model of ROS management obtained integrates these functionalities and fits in vitro data sets from two different laboratories.\n\nThe model shows effective ROS-management for a century, followed by a sudden systems collapse due to the loss of p62 protein. PD related conditions such as lack of DJ-1 protein or increased -synuclein accelerated the systems collapse. Various in-silico interventions (e.g. addition of antioxidants or caffeine) slowed down the collapse of the system in silico, suggesting the model may help discover new medicinal and nutritional therapies.

systems biology↗

bioRxiv2019

A Modified Injector and Sample Acquisition Protocol Can Improve Data Quality and Reduce Inter-Instrument Variability of the Helios Mass Cytometer

Mass cytometry is a powerful tool for high dimensional single cell characterization. Since the introduction of the first commercial CyTOF mass cytometer by DVS Sciences in 2009, mass cytometry technology has matured and become more widely utilized, with sequential platform upgrades designed to address specific limitations and to expand the capabilities of the platform. Fluidigms 3rd-generation Helios mass cytometer introduced a number of upgrades over the previous CyTOF2. One of these new features is a modified narrow bore sample injector that generates smaller ion clouds, which is expected to improve sensitivity and throughput. However, following rigorous testing we find that the narrow-bore sample injector may have unintended negative consequences on data quality and result in lower median signal intensities and higher coefficients of variation in antibody expression. We describe an alternative Helios acquisition protocol using a wider bore injector, which largely mitigates these data quality issues. We directly compare these two protocols in a multi-site study of 10 Helios instruments across 7 institutions and show that the modified protocol improves data quality and reduces inter-instrument variability. These findings highlight and address an important source of technical variability in mass cytometry experiments that is of particular relevance in the setting of multi-center studies.

immunology↗

bioRxiv2018

NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks

Predicting epitopes recognized by cytotoxic T cells has been a long standing challenge within the field of immuno- and bioinformatics. While reliable predictions of peptide binding are available for most Major Histocompatibility Complex class I (MHCI) alleles, prediction models of T cell receptor (TCR) interactions with MHC class I-peptide complexes remain poor due to the limited amount of available training data. Recent next generation sequencing projects have however generated a considerable amount of data relating TCR sequences with their cognate HLA-peptide complex target. Here, we utilize such data to train a sequence-based predictor of the interaction between TCRs and peptides presented by the most common human MHCI allele, HLA-A*02:01. Our model is based on convolutional neural networks, which are especially designed to meet the challenges posed by the large length variations of TCRs. We show that such a sequence-based model allows for the identification of TCRs binding a given cognate peptide-MHC target out of a large pool of non-binding TCRs.

bioinformatics↗

bioRxiv2018

Gene Regulatory Cross Networks: Inferring Gene Level Cell-to-Cell Communications of Immune Cells

BackgroundGene level cell-to-cell communications are crucial part of biology as they may be potential targets of drugs and vaccines against a disease condition of interest. Yet, there are only few studies that propose algorithms on this particularly important research field.\n\nResultsIn this study, we first overview the current literature and define two general terms for the types of approaches in general for gene level cell-to-cell communications: Gene Regulatory Cross Networks (GRCN) and Gene Co-Expression Cross Networks (GCCN). We then propose two algorithms for each type, named as GRCNone and GCCNone. We applied them to reveal communications among 8 different immune cell types and evaluate their performances mainly via membrane protein database. Also, we show the biological relevance of the predicted cross-networks with pathway enrichment analysis. We then provide an approach that prioritize the targets by ranking them before experimental validations.\n\nConclusionsWe establish two main approaches and propose algorithms for genome-wide scale gene level cell-to-cell communications between any two different cell-types. This study aims accelerating this relatively new avenue of research in cross-networks and points out the gap of it with the well-established single cell type gene networks. The proposed algorithms have the potential to reveal gene level interactions between normal and disease cell types. For instance, they might reveal the interaction of genes between tumor and normal cells, which are the potential drug-targets and thus can help finding new cures that might prevent the prevailing of tumor cells.

bioinformatics↗

bioRxiv2018

No cell is an island: circulating T cell:monocyte complexes are markers of immune perturbations

Our results highlight for the first time that a significant proportion of cell doublets in flow cytometry, previously believed to be the result of technical artefacts and thus ignored in data acquisition and analysis, are the result of true biological interaction between immune cells. In particular, we show that cell:cell doublets pairing a T cell and a monocyte can be directly isolated from human blood, and high resolution microscopy shows polarized distribution of LFA1/ICAM1 in many doublets, suggesting in vivo formation. Intriguingly, T cell:monocyte complex frequency and phenotype fluctuate with the onset of immune perturbations such as infection or immunization, reflecting expected polarization of immune responses. Overall these data suggest that cell doublets reflecting T cell-monocyte in vivo immune interactions can be detected in human blood and that the common approach in flow cytometry to avoid studying cell:cell complexes should be revisited.

immunology↗

bioRxiv2018

Attention samples objects held in working memory at a theta rhythm

Attention selects relevant information regardless of whether it is physically present or internally stored in working memory. Perceptual research has shown that attentional selection of external information is better conceived as rhythmic prioritization than as stable allocation. Here we tested this principle using information processing of internal representations held in working memory. Participants memorized four spatial positions that formed the endpoints of two objects. One of the positions was cued for a delayed match-non-match test. When uncued positions were probed, participants responded faster to uncued positions located on the same object as the cued position than to those located on the other object, revealing object-based attention in working memory. Manipulating the interval between cue and probe at a high temporal resolution revealed that reaction times oscillated at a theta rhythm of 6 Hz. Moreover, oscillations showed an anti-phase relationship between memorized but uncued positions on the same versus other object as the cued position, suggesting that attentional prioritization fluctuated rhythmically in an object-based manner. Our results demonstrate the highly rhythmic nature of attentional selection in working memory. Moreover, the striking similarity between rhythmic attentional selection of mental representations and perceptual information suggests that attentional oscillations are a general mechanism of information processing in human cognition. These findings have important implications for current, attention-based models of working memory.

neuroscience↗

bioRxiv2018

3’ Branch Ligation: A Novel Method to Ligate Non-Complementary DNA to Recessed or Internal 3’OH Ends in DNA or RNA

Nucleic acid ligases are crucial enzymes that repair breaks in DNA or RNA during synthesis, repair and recombination. Various molecular tools have been developed using the diverse activities of DNA/RNA ligases. Herein, we demonstrate a non-conventional ability of T4 DNA ligase to join 5 phosphorylated blunt-end double-stranded DNA to DNA breaks at 3 recessive ends, gaps, or nicks to form a 3 branch structure. Therefore, this base pairing-independent ligation is termed 3 branch ligation (3BL). In an extensive study of optimal ligation conditions, similar to blunt-end ligation, the presence of 10% PEG-8000 in the ligation buffer significantly increased ligation efficiency. A low level of nucleotide preference was observed at the junction sites using different synthetic DNAs. Furthermore, we discovered that T4 DNA ligase efficiently ligated DNA to the 3 recessed end of RNA, not to that of DNA, in a DNA/RNA hybrid, whereas RNA ligases are less efficient in this reaction. These novel properties of T4 DNA ligase can be utilized as a broad molecular technique in many important applications. We performed a proof-of-concept study of a new directional tagmentation protocol for next generation sequencing (NGS) library construction that eliminates inverted adapters and allows sample barcode insertion adjacent to genomic DNA. 3BL after single transposon tagmentation can theoretically achieve 100% usable template, and our empirical data demonstrate that the new approach produced higher yield compared with traditional double transposon or Y transposon tagmentation. We further explore the potential use of 3BL for preparing targeted RNA NGS libraries with mitigated structure-based bias and adapter dimer problems.

molecular biology↗

bioRxiv2018

Single tube bead-based DNA co-barcoding for cost effective and accurate sequencing, haplotyping, and assembly

Obtaining accurate sequences from long DNA molecules is very important for genome assembly and other applications. Here we describe single tube long fragment read (stLFR), a technology that enables this a low cost. It is based on adding the same barcode sequence to sub-fragments of the original long DNA molecule (DNA co-barcoding). To achieve this efficiently, stLFR uses the surface of microbeads to create millions of miniaturized barcoding reactions in a single tube. Using a combinatorial process up to 3.6 billion unique barcode sequences were generated on beads, enabling practically non-redundant co-barcoding with 50 million barcodes per sample. Using stLFR, we demonstrate efficient unique co-barcoding of over 8 million 20-300 kb genomic DNA fragments. Analysis of the genome of the human genome NA12878 with stLFR demonstrated high quality variant calling and phasing into contigs up to N50 34 Mb. We also demonstrate detection of complex structural variants and complete diploid de novo assembly of NA12878. These analyses were all performed using single stLFR libraries and their construction did not significantly add to the time or cost of whole genome sequencing (WGS) library preparation. stLFR represents an easily automatable solution that enables high quality sequencing, phasing, SV detection, scaffolding, cost-effective diploid de novo genome assembly, and other long DNA sequencing applications.

genomics↗

bioRxiv2017

DAFi: A Directed Recursive Filtering and Clustering Approach to Data-Driven Identification of Cell Populations from Polychromatic Flow Cytometry Data

Computational methods for identification of cell populations from high-dimensional flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. We found that combining recursive filtering and clustering with constraints converted from the user manual gating strategy can effectively identify overlapping and rare cell populations from smeared data that would have been difficult to resolve by either a single run of data clustering or manual segregation. We named this new method DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell-based biomarkers, but also makes the results interpretable to experimental scientists as in supervised classification through mapping and merging the high-dimensional data clusters into the user-defined 2D gating hierarchy. By recursive data filtering before clustering, DAFi can uncover small local clusters which are otherwise difficult to identify due to the statistical interference of the irrelevant major clusters. Quantitative assessment of cell type specific characteristics demonstrates that the population proportions calculated by DAFi, while being highly consistent with those by expert centralized manual gating, have smaller technical variance than those from individual manual gating analysis. Visual examination of the dot plots showed that the boundaries of the DAFi-identified cell populations followed the natural shapes of the data distributions. To further exemplify the utility of DAFi, we show that DAFi can incorporate the FLOCK clustering method to identify novel cell-based biomarkers. Implementation of DAFi supports options including clustering, bisecting, slope-based gating, and reversed filtering to meet various auto-gating needs from different scientific use cases.

bioinformatics↗

bioRxiv2017

NetMHCpan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data

Cytotoxic T cells are of central importance in the immune systems response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC (major histocompatibility complex) class I molecules. Peptide binding to MHC molecules is the single most selective step in the antigen presentation pathway. On the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has therefore attracted large attention.\n\nIn the past, predictors of peptide-MHC interaction have in most cases been trained on binding affinity data. Recently an increasing amount of MHC presented peptides identified by mass spectrometry has been published containing information about peptide processing steps in the presentation pathway and the length distribution of naturally presented peptides. Here, we present NetMHCpan-4.0, a method trained on both binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increased predictive performance compared to state-of-the-art when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

bioinformatics↗