Immunoinformatics (Amsterdam, Netherlands)最新文献

{"title":"Association of pyroptosis and severeness of COVID-19 as revealed by integrated single-cell transcriptome data analysis","authors":"Qian Xu ,&nbsp;Yongjian Yang ,&nbsp;Xiuren Zhang ,&nbsp;James J. Cai","doi":"10.1016/j.immuno.2022.100013","DOIUrl":"10.1016/j.immuno.2022.100013","url":null,"abstract":"<div><p>Cytokine storm and inflammatory cytokine release syndrome are often found to be associated with severe instances of the 2019 coronavirus disease (COVID-19). However, factors that contribute to the development of the COVID-19-associated cytokine storm and intensify the hyperinflammatory response are not well known. Here, we integratively analyzed scRNAseq data of 37,607 immune cells of eight different cell types from four studies involving COVID-19 patients in either moderate or severe conditions. Our analysis showed that pyroptosis—a lytic, inflammatory type of programmed cell death—may play a critical role in the SARS-CoV-2-induced cytokine storm. The expression of the key markers of pyroptosis, such as pro-inflammatory cytokine genes <em>IL1B</em> and <em>IL18</em>, is significantly higher in moderate and severe COVID-19 patients than in healthy controls. The pattern is more pronounced in macrophages and neutrophils than in adaptive immune cells such as T cells and B cells. Furthermore, the lack of interferon-gamma (IFN-γ) and overexpression of ninjurin 1 (<em>NINJ1</em>) in macrophages may exacerbate the systemic inflammation, as shown in severe COVID-19 patients. Finally, we developed a scoring metric to quantitatively assess single cell's pyroptotic state and demonstrated the use of this pyroptosis signature score to scRNAseq data. Taken together, our study underscores the importance of the pyroptosis pathway and highlights its clinical relevance, suggesting that pyroptosis is a cellular process that can be a potential target for the treatment of COVID-19 associated diseases.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"6 ","pages":"Article 100013"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9155487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling rheumatoid arthritis: A hybrid modelling framework to describe pannus formation in a small joint","authors":"Fiona R. Macfarlane ,&nbsp;Mark A.J. Chaplain ,&nbsp;Raluca Eftimie","doi":"10.1016/j.immuno.2022.100014","DOIUrl":"https://doi.org/10.1016/j.immuno.2022.100014","url":null,"abstract":"<div><p>Rheumatoid arthritis (RA) is a chronic inflammatory disorder that causes pain, swelling and stiffness in the joints, and negatively impacts the life of affected patients. The disease does not have a cure yet, as there are still many aspects of this complex disorder that are not fully understood. While mathematical models can shed light on some of these aspects, to date there are few such models that can be used to better understand the disease. As a first step in the mechanistic understanding of RA, in this study we introduce a new hybrid mathematical modelling framework that describes pannus formation in a small proximal interphalangeal (PIP) joint. We perform numerical simulations with this new model, to investigate the impact of different levels of immune cells (macrophages and fibroblasts) on the degradation of bone and cartilage. Since many model parameters are unknown and cannot be estimated due to a lack of experiments, we also perform a sensitivity analysis of model outputs to various model parameters (single parameters or combinations of parameters). Finally, we discuss how our model could be applied to investigate current treatments for RA, for example, methotrexate, TNF-inhibitors or tocilizumab, which can impact different model parameters.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"6 ","pages":"Article 100014"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119022000064/pdfft?md5=a2ba71d57b0678c431dbc4f7f7da7143&pid=1-s2.0-S2667119022000064-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136426394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data","authors":"Teresa Rubio ,&nbsp;Maria Chernigovskaya ,&nbsp;Susanna Marquez ,&nbsp;Cristina Marti ,&nbsp;Paula Izquierdo-Altarejos ,&nbsp;Amparo Urios ,&nbsp;Carmina Montoliu ,&nbsp;Vicente Felipo ,&nbsp;Ana Conesa ,&nbsp;Victor Greiff ,&nbsp;Sonia Tarazona","doi":"10.1016/j.immuno.2022.100012","DOIUrl":"10.1016/j.immuno.2022.100012","url":null,"abstract":"<div><p>T<strong>-</strong>cell receptor (TCR) analysis is relevant for the study of immune system diseases. The expression of TCRs is usually measured with targeted sequencing approaches where TCR genes are selectively amplified. However, many non-targeted RNA-seq experiments also contain reads of TCR genes, which could be leveraged for TCR expression analysis while reducing sample requirements and costs. Moreover, a step-by-step pipeline for the processing of transcriptome RNA-seq reads to deliver immune repertoire data is missing, and these types of analyses are usually not included in RNA-seq studies of immunological conditions. This represents a missed opportunity for complementing them with the analysis of the immune repertoire.</p><p>We present a Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data. We used a case study where TCR repertoire profiles were recovered from bulk RNA-seq of isolated CD4 T cells from control patients, cirrhotic patients without and with Minimal Hepatic Encephalopathy (MHE). MHE is a neuropsychiatric syndrome, mediated by peripheral inflammation, that may affect cirrhotic patients. After the recovery of 498-1,114 distinct TCR beta chains per patient, repertoire analysis of patients resulted in few public clones, high diversity and elevated within-repertoire sequence similarity, independently of immune status. Additionally, TCRs associated with celiac disease and inflammatory bowel disease were significantly overrepresented in MHE patient repertoires. The provided computational pipeline functions as a resource to facilitate TCR profiling from RNA-seq data boosting immunophenotype analyses of immunological diseases.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"6 ","pages":"Article 100012"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119022000040/pdfft?md5=91b0d931e25aed0127e660a05b33b628&pid=1-s2.0-S2667119022000040-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47122221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunoinformatics: Pushing the boundaries of immunology research and medicine","authors":"Miyo K. Chatanaka ,&nbsp;Antigona Ulndreaj ,&nbsp;Dorsa Sohaei ,&nbsp;Ioannis Prassas","doi":"10.1016/j.immuno.2021.100007","DOIUrl":"10.1016/j.immuno.2021.100007","url":null,"abstract":"<div><p>Immunology has come a long way, from its early religious beginnings thousands of years ago, to the explosion of immunological data in the 21st century. Thanks to discoveries in immunology, our world has seen tremendous progress in how we understand and treat disease. However, a lot of unmet clinical needs remain, which require focused, real-time collaboration at the clinical and scientific research forefronts. Moreover, the current exponential growth in the generation of research data makes it impossible to handle, analyze, visualize, and interpret such data without the use of advanced computational tools. We think immunoinformatics- a discipline at the intersection of immunology and computer science- will greatly increase efficiency in research productivity and disease treatment.</p><p>This perspective paper aims to emphasize the role of immunoinformatics toward pushing the boundaries of immunology research. It will also illustrate its clinical applications, including disease prevention, diagnosis, prognosis, treatment, monitoring, as well as in drug discovery.</p><p>We believe informatics approaches will be implemented increasingly more frequently in research. Thus, here we also discuss a set of fundamental prerequisites to facilitate the efficient and ethical integration of informatics in research and ensure immunological advancements provide maximum benefits to society.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"5 ","pages":"Article 100007"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119021000070/pdfft?md5=0b5f69c50a14d9b7b39314988f9c3a5a&pid=1-s2.0-S2667119021000070-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48314913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in T-cell receptor repertoire analysis: Bridging the gap with multimodal single-cell RNA sequencing","authors":"Sebastiaan Valkiers ,&nbsp;Nicky de Vrij ,&nbsp;Sofie Gielis ,&nbsp;Sara Verbandt ,&nbsp;Benson Ogunjimi ,&nbsp;Kris Laukens ,&nbsp;Pieter Meysman","doi":"10.1016/j.immuno.2022.100009","DOIUrl":"10.1016/j.immuno.2022.100009","url":null,"abstract":"<div><p>T cells exercise a multitude of functions such as cytotoxicity, secretion of immunomodulating cytokines or regulation of tolerance, collectively resulting in an effective control of immune-related disease. Through the unique mechanism of V(D)J recombination, T cells express a highly specific receptor complex known as the T-cell receptor (TCR). Single-cell sequencing technologies have paved the road for interrogating the transcriptome and the paired αβ TCR repertoire of a single T cell in tandem. In contrast, conventional bulk methods are restricted to only one layer of information. This combination of transcriptomic- and repertoire information can provide novel insight into the functional character of T cell immunity. Recently, single-cell technologies have gained in popularity due to improvements in throughput, decrease in cost and the ability for multimodal experiments that integrate different information layers. Consequently, this prompts the need for the development of novel computational tools that integrate transcriptomic profiles and corresponding features of the TCR repertoire. Here we discuss the current progress in the field of single-cell T cell sequencing, with a focus on the multimodality of new approaches that allow the paired profiling of cellular phenotype and clonotype information. In addition, this review provides detailed descriptions of recent computational developments for analyzing single-cell TCR sequencing data in an integrative manner using novel computational approaches. Finally, we present an overview of the available software tools that can be used to perform integrative analysis of gene expression and TCR profiles.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"5 ","pages":"Article 100009"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119022000015/pdfft?md5=e87c5318f3f2c272c5efee686fd3aa55&pid=1-s2.0-S2667119022000015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42482558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"To what extent does MHC binding translate to immunogenicity in humans?","authors":"Lee Chloe H. ,&nbsp;Agne Antanaviciute ,&nbsp;Paul R. Buckley ,&nbsp;Alison Simmons ,&nbsp;Hashem Koohy","doi":"10.1016/j.immuno.2021.100006","DOIUrl":"https://doi.org/10.1016/j.immuno.2021.100006","url":null,"abstract":"","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"3 ","pages":"Article 100006"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119021000069/pdfft?md5=e730de59356c9e0615546b7fb00aeb30&pid=1-s2.0-S2667119021000069-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137440062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review","authors":"Amelie Echle ,&nbsp;Narmin Ghaffari Laleh ,&nbsp;Peter L. Schrammen ,&nbsp;Nicholas P. West ,&nbsp;Christian Trautwein ,&nbsp;Titus J. Brinker ,&nbsp;Stephen B. Gruber ,&nbsp;Roman D. Buelow ,&nbsp;Peter Boor ,&nbsp;Heike I. Grabsch ,&nbsp;Philip Quirke ,&nbsp;Jakob N. Kather","doi":"10.1016/j.immuno.2021.100008","DOIUrl":"10.1016/j.immuno.2021.100008","url":null,"abstract":"<div><p>Microsatellite instability (MSI) or deficient mismatch repair (dMMR) is a clinically important genetic feature affecting 10–15% of colorectal cancer (CRC) patients. Patients with metastatic MSI/dMMR CRC are eligible for therapy with immune checkpoint inhibitors, making MSI/dMMR the most important immuno-oncological biomarker in CRC. Gold standard tests for detection of MSI/dMMR in CRC are based on wet laboratory tests such as immunohistochemistry (IHC) or DNA extraction with subsequent polymerase chain reaction (PCR). However, since 2019, advances in Deep Learning (DL), an Artificial Intelligence (AI) technology, have enabled the prediction of MSI/dMMR directly from digitized routine haematoxylin and eosin (H&amp;E) histopathology slides with high accuracy. In addition to the initial proof-of-concept publication in 2019, twelve subsequent studies have refined, improved, and further validated this approach. At this moment, MSI/dMMR prediction using Deep Learning has become a widely used benchmark task for academic studies in the field of computational pathology. Beyond academic use, this assay has attracted commercial interest from companies with the possibility of approval as a diagnostic device in the near future. In this review, we summarize and quantitatively compare the existing evidence on Deep-Learning-based detection of MSI/dMMR in CRC and discuss the need for further improvement and potential for integration into routine pathological workflows. Ultimately, this DL-based method could facilitate the identification of patients eligible for treatment with immune checkpoint inhibitors by pre-screening or replacement of current methods.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"3 ","pages":"Article 100008"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119021000082/pdfft?md5=b00fd87b897720f255bdae9f6ca98fd3&pid=1-s2.0-S2667119021000082-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47994461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immune checkpoint therapy modeling of PD-1/PD-L1 blockades reveals subtle difference in their response dynamics and potential synergy in combination","authors":"Kamran Kaveh ,&nbsp;Feng Fu","doi":"10.1016/j.immuno.2021.100004","DOIUrl":"https://doi.org/10.1016/j.immuno.2021.100004","url":null,"abstract":"<div><p>Immune checkpoint therapy is one of the most promising immunotherapeutic methods that are likely able to give rise to durable treatment response for various cancer types. Despite much progress in the past decade, there are still critical open questions with particular regards to quantifying and predicting the efficacy of treatment and potential optimal regimens for combining different immune checkpoint blockades. To shed light on this issue, here we develop clinically-relevant, dynamical systems models of cancer immunotherapy with a focus on the immune checkpoint PD-1/PD-L1 blockades. Our model allows the acquisition of adaptive immune resistance in the absence of treatment, whereas immune checkpoint blockades can reverse such resistance and boost anti-tumor activities of effector cells. Our numerical analysis predicts that anti-PD-1 agents are commonly less effective than anti-PD-L1 agents for a wide range of model parameters. We also observe that combination treatment of anti-PD-1 and anti-PD-L1 blockades leads to a desirable synergistic effect. Our modeling framework lays the ground for future data-driven analysis on combination therapeutics of immune checkpoint treatment regimes and thorough investigation of optimized treatment on a patient-by-patient basis.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 ","pages":"Article 100004"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.immuno.2021.100004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92022448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ImmunoInformatics: at the crossroads between immunology and informatics, and beyond","authors":"Niels Halama,&nbsp;Doron Levy","doi":"10.1016/j.immuno.2021.100001","DOIUrl":"https://doi.org/10.1016/j.immuno.2021.100001","url":null,"abstract":"","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 ","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.immuno.2021.100001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92022447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CelltrackR: An R package for fast and flexible analysis of immune cell migration data","authors":"Inge M.N. Wortel ,&nbsp;Annie Y. Liu ,&nbsp;Katharina Dannenberg ,&nbsp;Jeffrey C. Berry ,&nbsp;Mark J. Miller ,&nbsp;Johannes Textor","doi":"10.1016/j.immuno.2021.100003","DOIUrl":"10.1016/j.immuno.2021.100003","url":null,"abstract":"<div><p>Visualization of cell migration via time-lapse microscopy has greatly advanced our understanding of the immune system. However, subtle differences in migration dynamics are easily obscured by biases and imaging artifacts. While several analysis methods have been suggested to address these issues, an integrated tool implementing them is currently lacking. Here, we present celltrackR, an R package containing a diverse set of state-of-the-art analysis methods for (immune) cell tracks. CelltrackR supports the complete pipeline for track analysis by providing methods for data management, quality control, extracting and visualizing migration statistics, clustering tracks, and simulating cell migration. CelltrackR supports the analysis of both 2D and 3D cell tracks. CelltrackR is an open-source package released under the GPL-2 license, and is freely available on both GitHub and CRAN. Although the package was designed specifically for immune cell migration data, many of its methods will also be of use in other research areas dealing with moving objects.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"1 ","pages":"Article 100003"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.immuno.2021.100003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9272309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}