Immunoinformatics (Amsterdam, Netherlands)最新文献

{"title":"Deciphering the role of molecular mimicry in the etiopathogenesis of Autoimmune Hemolytic Anemia using an immunoinformatics approach.","authors":"Pratyusha Patidar ,&nbsp;Arihant Jain ,&nbsp;Tulika Prakash","doi":"10.1016/j.immuno.2025.100047","DOIUrl":"10.1016/j.immuno.2025.100047","url":null,"abstract":"<div><div>Autoimmune hemolytic anemia (AIHA) is a chronic autoimmune disease characterized by the self-destruction of red blood cells (RBCs). For investigating the role molecular mimicry in the onset of AIHA manifestations, we identified the microbial epitopes as precipitating factors in the disease etiopathology using an integrated immunoinformatics pipeline which includes sequence homology search between microbial and RBC proteins, followed by B-cell and T-cell epitope prediction. These epitopes were further subjected to a homology search with the human gut microbial proteins. Eight out of the ten analysed infectious agents, including Hepatitis C Virus (HCV), Cytomegalovirus (CMV), Epstein-Barr Virus (EBV), Herpes Simplex Virus (HSV), Human Papillomavirus (HPV), Human Immunodeficiency Virus (HIV), <em>Mycoplasma pneumoniae</em> (MP), and <em>Treponema pallidum</em> (TP), possessed B-cell and T-cell epitopes. Interestingly, EBV, HSV, MP, and TP displayed conformational B-cell epitopes, which overlapped with their linear B-cell epitopes. HLA DRB1_0305 was found to exhibit binding with several bacterial epitopes indicating its predisposing potential to AIHA. Further, we report cross-reactive microbial epitopes against RBC proteins that have been experimentally proven to be associated with AIHA indicating a high possibility of those epitopes causing AIHA. Additionally, many B-cell and T-cell epitopes exhibited exact homologies with various human gut microbial proteins. The functional annotation highlighted the involvement of specialized RBC functions, such as cytoskeleton organization, ammonium homeostasis, signalling transduction, in the underlying disease mechanism. These findings suggest that infection-causing pathogens and gut microbes might have a plausible association with AIHA in the context of molecular mimicry.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"17 ","pages":"Article 100047"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395696","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":"Comparative analysis of SLA-1 and SLA-2 genetic diversity in exotic, hybrid, and local pig breeds of Cameroon in relation to adaptive immunity against African swine virus","authors":"Ebanja Joseph Ebwanga ,&nbsp;Jess Bouhuijzen Wenger ,&nbsp;Robert Adamu Shey ,&nbsp;Nadine Buys ,&nbsp;Rob Lavigne ,&nbsp;Stephen Mbigha Ghogomu ,&nbsp;Jan Paeshuyse","doi":"10.1016/j.immuno.2025.100048","DOIUrl":"10.1016/j.immuno.2025.100048","url":null,"abstract":"<div><div>African swine fever is a severe hemorrhagic swine disease that greatly affects smallholder pig farm productivity in low-income countries as well as some developed countries. Research has shown that the indigenous pigs and wild suids in Africa are either tolerant or resistant to the disease. Also, resistance to disease and favourable production traits are attributed to polymorphism within the major histocompatibility complex (MHC), which is crucial for the vertebrate's adaptive immune response. The polymorphism within the swine leukocyte antigen (SLA) is attributable to host-pathogen co-evolution which results in improved resistance to disease as well as adaptation to diverse environments. While this makes the SLA essential for comparative diversity studies, comparative SLA studies are absent in this context. We undertook SLA-1 and SLA-2 exon-2 comparative genetic diversity study within the locally adapted (local) breed, hybrid (a cross between local and exotic), and the exotic breed of pigs in Cameroon using the polymerase chain reaction sequence-based typing method on 41 animals. Our data analyses provide evidence of positive balancing selection as well as conserved private alleles within the local breeds, the highest expected heterozygosity within the tolerant population while the exotic population had the highest number of haplotypes for both SLA-1 and SLA-2 . The results from this study contribute to our expanding knowledge of SLA genetic diversity while providing the first SLA data for the indigenous and exotic breeds of pigs in Cameroon.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"17 ","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453878","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":"Scifer: An R/Bioconductor package for large-scale integration of Sanger sequencing and flow cytometry data of index-sorted single cells","authors":"Rodrigo Arcoverde Cerveira ,&nbsp;Klara Lenart ,&nbsp;Marcel Martin ,&nbsp;Matthew James Hinchcliff ,&nbsp;Fredrika Hellgren ,&nbsp;Kewei Ye ,&nbsp;Juliana Assis Geraldo ,&nbsp;Taras Kreslavsky ,&nbsp;Sebastian Ols ,&nbsp;Karin Loré","doi":"10.1016/j.immuno.2024.100046","DOIUrl":"10.1016/j.immuno.2024.100046","url":null,"abstract":"<div><div>Sanger sequencing remains widely used in various experimental contexts, often in combination with flow cytometry for indexing specific cell populations. However, existing software lacks the capability to automate quality control (QC) of raw Sanger sequencing data and integrate it with flow cytometry information on a large scale. Here, we introduce scifer, an R package now available in the latest release of Bioconductor (3.20) showcasing its effectiveness in seamlessly integrating these types of data as demonstrated by analyses of B cell and T cell receptor sequences. Scifer preprocesses raw data from index sorts and immune receptor Sanger sequencing. It identifies high-quality sequences based on selected parameters, such as length, Phred scores, and heavy-chain complementarity-determining region 3 (HCDR3) quality. As a result, the quality of germline assignments is significantly increased and spurious variable gene mutations are reduced. Scifer is automated and can process thousands of sequences in less than an hour. Its output provides quality control reports, FASTA files, summarized tables, and electropherograms for manual inspection. In summary, scifer is a user-friendly software that speeds up the analysis of immune receptor repertoire sequences, offering wide applicability.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"16 ","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663038","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":"Lessons learned from the IMMREP23 TCR-epitope prediction challenge","authors":"Morten Nielsen ,&nbsp;Anne Eugster ,&nbsp;Mathias Fynbo Jensen ,&nbsp;Manisha Goel ,&nbsp;Andreas Tiffeau-Mayer ,&nbsp;Aurelien Pelissier ,&nbsp;Sebastiaan Valkiers ,&nbsp;María Rodríguez Martínez ,&nbsp;Barthélémy Meynard-Piganeeau ,&nbsp;Victor Greiff ,&nbsp;Thierry Mora ,&nbsp;Aleksandra M. Walczak ,&nbsp;Giancarlo Croce ,&nbsp;Dana L Moreno ,&nbsp;David Gfeller ,&nbsp;Pieter Meysman ,&nbsp;Justin Barton","doi":"10.1016/j.immuno.2024.100045","DOIUrl":"10.1016/j.immuno.2024.100045","url":null,"abstract":"<div><div>Here, we present the findings from IMMREP23, the second benchmark competition focused on predicting the specificity of TCR-pMHC interactions.</div><div>The interaction of T cell receptors (TCR) towards their pMHC target is a cornerstone of the cellular immune system. Over the last decade, substantial progress has been made within the field of TCR specificity prediction, providing proof of concept for predicting TCR-pMHC interactions in a narrow space of “seen” pMHC targets where substantial training data is available. However, a significant challenge persists in extending the predictive capability to novel “unseen” pMHC targets. Furthermore, the performance of proposed methods is often challenged when evaluated outside the initial publication and data sets.</div><div>To address these issues, IMMREP23 challenge invited participants to predict, for a given test set of TCR-pMHC pairs, the likelihood that a pair would bind. A total of 53 teams participated, providing a total of 398 submissions.</div><div>The benchmark confirms that current methods achieve reasonable performance in the \"seen\" pMHC setting. However, most participating methods had close to random performance on the subset of “unseen” peptides, underlining that this prediction challenge remains essentially unsolved.</div><div>Finally, another key lesson from the benchmark is the critical issue of data leakage. Specifically, the data set construction procedure employed in IMMREP23 led to biases in the negative test data set. These biases were identified by several participating teams, and complicated the interpretation of the benchmark results. Based on these results, we put forward suggestions on how future competitions could avoid such data leakages and biases.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"16 ","pages":"Article 100045"},"PeriodicalIF":0.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426792","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":"Multicohort analysis identifies conserved transcriptional interactions between humans and Plasmodium falciparum","authors":"Bárbara Fernandes Silva ,&nbsp;Nágila Isleide Silva ,&nbsp;Pedro Felipe Loyola Souza ,&nbsp;Tiago Paiva Guimarães ,&nbsp;Luiz Gustavo Gardinassi","doi":"10.1016/j.immuno.2024.100044","DOIUrl":"10.1016/j.immuno.2024.100044","url":null,"abstract":"<div><p>Malaria is caused by <em>Plasmodium</em>, a parasite that replicates inside and ruptures erythrocytes, causing an intense inflammatory response. Advances in high-throughput sequencing technologies have enabled the simultaneous study of the gene expression in humans and <em>P. falciparum</em>. However, the high-dimensional correlational networks generated in previous studies challenge the interpretation of the underlying biology, whereas associations found in one cohort might not replicate in independent samples due confounding factors affecting gene expression. We combined multicohort analysis of correlations with a hierarchical grouping approach to improve the discovery and interpretation of transcriptional associations between humans and <em>P. falciparum</em>. We analyzed nine public dual-transcriptomes acquired from whole blood of individuals infected with <em>P. falciparum</em>. Blood Transcription Modules (BTM) were used to reduce the dimension of host transcriptomes and Spearman's correlation analysis was used to identify host-parasite associations. Following, we performed meta-analysis of correlations with Stouffer's method and Bonferroni correction that resulted in a major transcriptional meta-network between humans and <em>P. falciparum</em>. We identified, for example, positive correlations between <em>PAK1, NFKBIA, BIRC2, NLRC4, TLR4, RIPK2</em> expression and <em>PF3D7_1205800</em>, a putative <em>P. falciparum</em> high mobility group protein B3 (HMGB3). We also applied a leave-one-out strategy to prevent influence of confounding factors, resulting in highly conserved associations between host genes related to inflammation, immune cells, and glycerophospholipid metabolism with <em>PF3D7_1223400</em>, which encodes a putative phospholipid-transporting ATPase. Paired metabolomics and transcriptomics data revealed negative correlation between <em>PF3D7_1223400</em> expression and the relative abundance of 1-linoleoyl-GPG. Collectively, our study provides data-driven hypotheses about molecular mechanisms of host-parasite interaction.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"16 ","pages":"Article 100044"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000144/pdfft?md5=0bb5fec5def6b5a092876014fea42924&pid=1-s2.0-S2667119024000144-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274294","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":"In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics","authors":"Thi Nhu Thao Nguyen ,&nbsp;Madge Martin ,&nbsp;Christophe Arpin ,&nbsp;Samuel Bernard ,&nbsp;Olivier Gandrillon ,&nbsp;Fabien Crauste","doi":"10.1016/j.immuno.2024.100043","DOIUrl":"10.1016/j.immuno.2024.100043","url":null,"abstract":"<div><p>The CD8 T cell immune response operates at multiple temporal and spatial scales, including all the early complex biochemical and biomechanical processes, up to long term cell population behavior.</p><p>In order to model this response, we devised a multiscale agent-based approach using <span>Simuscale</span> software. Within each agent (cell) of our model, we introduced a gene regulatory network (GRN) based upon a piecewise deterministic Markov process formalism. Cell fate – differentiation, proliferation, death – was coupled to the state of the GRN through rule-based mechanisms. Cells interact in a 3D computational domain and signal to each other via cell–cell contacts, influencing the GRN behavior.</p><p>Results show the ability of the model to correctly capture both population behavior and molecular time-dependent evolution. We examined the impact of several parameters on molecular and population dynamics, and demonstrated the add-on value of using a multiscale approach by showing the influence of molecular parameters, particularly protein degradation rates, on the outcome of the response, such as effector and memory cell counts.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"15 ","pages":"Article 100043"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000132/pdfft?md5=92c4f652893809c6f3e06131e312c290&pid=1-s2.0-S2667119024000132-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173664","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":"Data mining antibody sequences for database searching in bottom-up proteomics","authors":"Xuan-Tung Trinh ,&nbsp;Rebecca Freitag ,&nbsp;Konrad Krawczyk ,&nbsp;Veit Schwämmle","doi":"10.1016/j.immuno.2024.100042","DOIUrl":"10.1016/j.immuno.2024.100042","url":null,"abstract":"<div><p>Mass spectrometry-based proteomics facilitates the identification and quantification of thousands of proteins but encounters challenges in measuring human antibodies due to their vast diversity. Bottom-up proteomics methods primarily rely on database searches, comparing experimental peptide values to theoretical database sequences. While the human body can produce millions of distinct antibodies, current databases, such as UniProtKB/Swiss-Prot, contain only 1095 sequences (as of January 2024), potentially hindering antibody identification via mass spectrometry. Therefore, expanding the database is crucial for discovering new antibodies. Recent genomic studies have amassed millions of human antibody sequences in the Observed Antibody Space (OAS) database, yet this data remains underutilized. Leveraging this vast collection, we conduct efficient database searches in publicly available proteomics data, focusing on SARS-CoV-2. In our study, thirty million heavy antibody sequences from 146 SARS-CoV-2 patients in the OAS database were digested <em>in silico</em> to obtain 18 million unique peptides. These peptides form the basis for new bottom-up proteomics databases. We used those databases for searching new antibody peptides in publicly available SARS-CoV-2 human plasma samples in the Proteomics Identification Database (PRIDE). This approach avoids false positives in antibody peptide identification as confirmed by searching against negative controls (brain samples) and employing different database sizes. We show that new antibody peptides were found in previous plasma samples and expect that the newly discovered antibody peptides can be further employed to develop therapeutic antibodies. The method will be broadly applicable to find characteristic antibodies for other diseases.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"15 ","pages":"Article 100042"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000120/pdfft?md5=6bc5ac01ada92397791db50d32ef768f&pid=1-s2.0-S2667119024000120-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076922","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":"Navigating the immunosuppressive brain tumor microenvironment using spatial biology","authors":"Samuel S. Widodo ,&nbsp;Marija Dinevska ,&nbsp;Stanley S. Stylli ,&nbsp;Adriano L. Martinelli ,&nbsp;Marianna Rapsomaniki ,&nbsp;Theo Mantamadiotis","doi":"10.1016/j.immuno.2024.100041","DOIUrl":"10.1016/j.immuno.2024.100041","url":null,"abstract":"<div><p>With the application of spatial biology, the detection and identification of the diverse cell types present in the tumor microenvironment, including specific immune subsets, is possible at single cell resolution. Since spatial biology analysis of tumor tissue allows multiple biological parameters to be measured, including cell type, cell number, cell state, as well as the precise location and the spatial relationship of every cell to other cells and histopathological hallmarks, a vast amount of data is generated. The power of this is realized when correlating the spatial biology data with clinical data for each patient, from which the tissue was collected during biopsy or surgery, conducted as part of the patient's diagnosis and treatment. Aside from the enormous leap in chemistry and molecular biology technology required to develop the analytical tools for spatial biology, collection, analysis of cells in the tumor microenvironment has been possible only with the development of computational tools capable of deciphering tumor tissue complexity to predict tumor evolution and response to treatment and the role of immune cells in regulating tumor biology. Here we describe how spatial biology analysis, combined with computational analysis have been used to deconstruct the complexity of the brain tumor microenvironment and shed light on why brain tumors exhibit extreme immunosuppression. We also discuss how the understanding gained using spatial biology has shed light on how tumor immunosuppression can be overcome.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"15 ","pages":"Article 100041"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000119/pdfft?md5=04d68aa94c0735faff67ea6c15c37656&pid=1-s2.0-S2667119024000119-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997366","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":"T-cell receptor binding prediction: A machine learning revolution","authors":"Anna Weber ,&nbsp;Aurélien Pélissier ,&nbsp;María Rodríguez Martínez","doi":"10.1016/j.immuno.2024.100040","DOIUrl":"10.1016/j.immuno.2024.100040","url":null,"abstract":"<div><p>Recent advancements in immune sequencing and experimental techniques are generating extensive T cell receptor (TCR) repertoire data, enabling the development of models to predict TCR binding specificity. Despite the computational challenges posed by the vast diversity of TCRs and epitopes, significant progress has been made. This review explores the evolution of computational models designed for this task, emphasizing machine learning efforts, including early unsupervised clustering approaches, supervised models, and recent applications of Protein Language Models (PLMs), deep learning models pretrained on extensive collections of unlabeled protein sequences that capture crucial biological properties.</p><p>We survey the most prominent models in each category and offer a critical discussion on recurrent challenges, including the lack of generalization to new epitopes, dataset biases, and shortcomings in model validation designs. Focusing on PLMs, we discuss the transformative impact of Transformer-based protein models in bioinformatics, particularly in TCR specificity analysis. We discuss recent studies that exploit PLMs to deliver notably competitive performances in TCR-related tasks, while also examining current limitations and future directions. Lastly, we address the pressing need for improved interpretability in these often opaque models, and examine current efforts to extract biological insights from large black box models.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"15 ","pages":"Article 100040"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000107/pdfft?md5=d53078634a01ebcc5850282ff7db1fa1&pid=1-s2.0-S2667119024000107-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141961980","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":"In silico single-cell metabolism analysis unravels a new transition stage of CD8 T cells 4 days post-infection","authors":"Christophe Arpin ,&nbsp;Franck Picard ,&nbsp;Olivier Gandrillon","doi":"10.1016/j.immuno.2024.100038","DOIUrl":"https://doi.org/10.1016/j.immuno.2024.100038","url":null,"abstract":"<div><p>CD8 T cell proper differentiation during antiviral responses relies on metabolic adaptations. Herein, we investigated global metabolic activity in single CD8 T cells along an <em>in vivo</em> response by estimating metabolic fluxes from single-cell RNA-sequencing data. The approach was validated by the observation of metabolic variations known from experimental studies on global cell populations, while adding temporally detailed information and unravelling yet undescribed sections of CD8 T cell metabolism that are affected by cellular differentiation. Furthermore, inter-cellular variability in gene expression level, highlighted by single cell data, and heterogeneity of metabolic activity 4 days post-infection, revealed a new transition stage accompanied by a metabolic switch in activated cells differentiating into full-blown effectors.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"14 ","pages":"Article 100038"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000089/pdfft?md5=97df354a875ea3c5bae8550349b1ee7d&pid=1-s2.0-S2667119024000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141243599","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}