A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer.

Breast cancer is a heterogeneous disease. Tumor cells and associated healthy cells form ecosystems that determine disease progression and response to therapy. To characterize features of breast cancer ecosystems and their associations with clinical data, we analyzed 144 human breast tumor and 50 non-tumor tissue samples using mass cytometry. The expression of 73 proteins in 26 million cells was evaluated using tumor and immune cell-centric antibody panels. Tumors displayed individuality in tumor cell composition, including phenotypic abnormalities and phenotype dominance. Relationship analyses between tumor and immune cells revealed characteristics of ecosystems related to immunosuppression and poor prognosis. High frequencies of PD-L1+ tumor-associated macrophages and exhausted T cells were found in high-grade ER+ and ER- tumors. This large-scale, single-cell atlas deepens our understanding of breast tumor ecosystems and suggests that ecosystem-based patient classification will facilitate identification of individuals for precision medicine approaches targeting the tumor and its immunoenvironment.

Elucidating Compound Mechanism of Action by Network Perturbation Analysis.

Genome-wide identification of the mechanism of action (MoA) of small-molecule compounds characterizing their targets, effectors, and activity modulators represents a highly relevant yet elusive goal, with critical implications for assessment of compound efficacy and toxicity. Current approaches are labor intensive and mostly limited to elucidating high-affinity binding target proteins. We introduce a regulatory network-based approach that elucidates genome-wide MoA proteins based on the assessment of the global dysregulation of their molecular interactions following compound perturbation. Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small-molecule MoA and compound similarity.

Occupational therapy in the management of haemophilia patients: A scoping review.

BACKGROUND: Haemophilia is a haematological disease, although most haemorrhages occur in the locomotor system. Patients are physically disabled from an early age and have a poorer perception of quality of life. In the day-to-day lives of patients and their families, psychosocial well-being, the disease's physical, personal, and social impact, as well as work-related problems are the most complicated aspects of the disease that need to be addressed. OBJECTIVE: To identify the role of occupational therapy in managing patients with haemophilia and to analyse the therapeutic potential of occupational therapy in treating these patients. METHODS: A scoping review was conducted to identify the role of occupational therapy in managing patients with haemophilia and to analyse the therapeutic potential of occupational therapy in treating these patients. The review was registered in the international registry PROSPERO (Id: CRD42022319637). The databases consulted were SCOPUS, PubMed, PsycINFO, Web of Science and Science Direct, including all studies published until 14 August 2023. RESULTS: No single study was found that specifically developed an occupational therapy intervention for patients with haemophilia. Measurement instruments have been identified, specific for patients with haemophilia and generic, that can be useful for the functional evaluation of these patients in the occupational therapy approach. Different studies showed the importance of multidisciplinary treatment, including occupational therapy. CONCLUSIONS: The use of occupational therapy could be effective in improving autonomy and quality of life in haemophilia patients. Therefore, it is of paramount importance to conduct research studies within the field of occupational therapy.

Exploring the interplay of frailty, physical function, physical activity, nutritional status, and their association with quality of life and depressive symptoms in older adults with the frailty phenotype.

OBJECTIVE: This study aimed to explore the interplay between frailty, physical function, physical activity, nutritional status, and their impact on the quality of life and depressive status in older adults with frailty. METHODS: A cross-sectional study involving 235 pre-frail/frail older adults residing in Spanish communities was conducted. Frailty was assessed using Fried's criteria, physical function was evaluated using the Short Physical Performance Battery, and physical activity levels were measured via wrist-worn accelerometers. Nutritional status was determined using the Mini-Nutritional Assessment alongside anthropometric measurements. Quality of life was gauged using the EuroQoL 5-Dimension 5-Level, while depressive status was assessed using the Yesavage 15-item Geriatric Depression Scale. Multivariate linear regression and logistic regression analyses were employed to elucidate the associations of these factors with quality of life and depression. RESULTS: Our findings revealed significant correlations between various factors and quality of life. Notably, reported fatigue (ß = -0.276, p = 0.002), performance in the 4-m gait test (ß = -0.242, p = 0.001), the score on the short version of the Mini-Nutritional Assessment (ß = 0.312, p = 0.002), and engagement in light physical activity (ß = 0.180, p = 0.023) were all found to be associated with quality of life. In terms of depressive symptoms, the Mini-Nutritional Assessment score emerged as a protective factor (Odds ratio, OR: 0.812, p < 0.001), as did participation in moderate physical activity (OR: 0.988, p = 0.028). Conversely, fatigue (OR: 3.277, p = 0.003) and a slow gait speed (OR: 1.136, p = 0.045) were identified as risk factors for depressive symptoms. CONCLUSIONS: This study underscores the detrimental association of fatigue and slow gait speed on both quality of life and depressive status among older adults with frailty. In contrast, engaging in physical activity and addressing malnutrition risk emerge as critical protective factors for enhancing quality of life and ameliorating depressive symptoms in this population. CLINICAL TRIAL REGISTRATION: This is a study that uses cross-sectional data from a trial registered at ClinicalTrials.gov (Identifier: NCT05610605).

Does Tension Headache Have a Central or Peripheral Origin? Current State of Affairs.

PURPOSE OF REVIEW: The aim of this narrative review is to analyze the evidence about a peripheral or central origin of a tension headache attack in order to provide a further clarification for an appropriate approach. RECENT FINDINGS: Tension headache is a complex and multifactorial pathology, in which both peripheral and central factors could play an important role in the initiation of an attack. Although the exact origin of a tension headache attack has not been conclusively established, correlations have been identified between certain structural parameters of the craniomandibular region and craniocervical muscle activity. Future research should focus on improving our understanding of the pathology with the ultimate goal of improving diagnosis. The pathogenesis of tension-type headache involves both central and peripheral mechanisms, being the perpetuation over time of the headache attacks what would favor the evolution of an episodic tension-type headache to a chronic tension-type headache. The unresolved question is what factors would be involved in the initial activation in a tension headache attack. The evidence that favors a peripheral origin of the tension headache attacks, that is, the initial events occur outside the brain barrier, which suggests the action of vascular and musculoskeletal factors at the beginning of a tension headache attack, factors that would favor the sensitization of the peripheral nervous system as a result of sustained sensory input.

TITAN: T-cell receptor specificity prediction with bimodal attention networks.

MOTIVATION: The activity of the adaptive immune system is governed by T-cells and their specific T-cell receptors (TCR), which selectively recognize foreign antigens. Recent advances in experimental techniques have enabled sequencing of TCRs and their antigenic targets (epitopes), allowing to research the missing link between TCR sequence and epitope binding specificity. Scarcity of data and a large sequence space make this task challenging, and to date only models limited to a small set of epitopes have achieved good performance. Here, we establish a k-nearest-neighbor (K-NN) classifier as a strong baseline and then propose Tcr epITope bimodal Attention Networks (TITAN), a bimodal neural network that explicitly encodes both TCR sequences and epitopes to enable the independent study of generalization capabilities to unseen TCRs and/or epitopes. RESULTS: By encoding epitopes at the atomic level with SMILES sequences, we leverage transfer learning and data augmentation to enrich the input data space and boost performance. TITAN achieves high performance in the prediction of specificity of unseen TCRs (ROC-AUC 0.87 in 10-fold CV) and surpasses the results of the current state-of-the-art (ImRex) by a large margin. Notably, our Levenshtein-based K-NN classifier also exhibits competitive performance on unseen TCRs. While the generalization to unseen epitopes remains challenging, we report two major breakthroughs. First, by dissecting the attention heatmaps, we demonstrate that the sparsity of available epitope data favors an implicit treatment of epitopes as classes. This may be a general problem that limits unseen epitope performance for sufficiently complex models. Second, we show that TITAN nevertheless exhibits significantly improved performance on unseen epitopes and is capable of focusing attention on chemically meaningful molecular structures. AVAILABILITY AND IMPLEMENTATION: The code as well as the dataset used in this study is publicly available at https://github.com/PaccMann/TITAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

On the feasibility of deep learning applications using raw mass spectrometry data.

SUMMARY: In recent years, SWATH-MS has become the proteomic method of choice for data-independent-acquisition, as it enables high proteome coverage, accuracy and reproducibility. However, data analysis is convoluted and requires prior information and expert curation. Furthermore, as quantification is limited to a small set of peptides, potentially important biological information may be discarded. Here we demonstrate that deep learning can be used to learn discriminative features directly from raw MS data, eliminating hence the need of elaborate data processing pipelines. Using transfer learning to overcome sample sparsity, we exploit a collection of publicly available deep learning models already trained for the task of natural image classification. These models are used to produce feature vectors from each mass spectrometry (MS) raw image, which are later used as input for a classifier trained to distinguish tumor from normal prostate biopsies. Although the deep learning models were originally trained for a completely different classification task and no additional fine-tuning is performed on them, we achieve a highly remarkable classification performance of 0.876 AUC. We investigate different types of image preprocessing and encoding. We also investigate whether the inclusion of the secondary MS2 spectra improves the classification performance. Throughout all tested models, we use standard protein expression vectors as gold standards. Even with our naïve implementation, our results suggest that the application of deep learning and transfer learning techniques might pave the way to the broader usage of raw mass spectrometry data in real-time diagnosis. AVAILABILITY AND IMPLEMENTATION: The open source code used to generate the results from MS images is available on GitHub: https://ibm.biz/mstransc. The raw MS data underlying this article cannot be shared publicly for the privacy of individuals that participated in the study. Processed data including the MS images, their encodings, classification labels and results can be accessed at the following link: https://ibm.box.com/v/mstc-supplementary. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems.

Computational models of biological systems can exploit a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod's main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community, as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings and a YouTube channel with talks and lectures of interest for the modelling community. For more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has inspired and supported the SysMod community. Its members get progressively more involved and actively contribute to the annual COSI meeting and several related community workshops and meetings, focusing on specific topics, including particular techniques for computational modelling or standardisation efforts.

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

PaccMann: a web service for interpretable anticancer compound sensitivity prediction.

The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of complementary sources of information might strengthen the accuracy of a drug efficacy prediction model. Here, we present a web-based platform for the Prediction of AntiCancer Compound sensitivity with Multimodal Attention-based Neural Networks (PaccMann). PaccMann is trained on public transcriptomic cell line profiles, compound structure information and drug sensitivity screenings, and outperforms state-of-the-art methods on anticancer drug sensitivity prediction. On the open-access web service (https://ibm.biz/paccmann-aas), users can select a known drug compound or design their own compound structure in an interactive editor, perform in-silico drug testing and investigate compound efficacy on publicly available or user-provided transcriptomic profiles. PaccMann leverages methods for model interpretability and outputs confidence scores as well as attention heatmaps that highlight the genes and chemical sub-structures that were more important to make a prediction, hence facilitating the understanding of the model's decision making and the involved biochemical processes. We hope to serve the community with a toolbox for fast and efficient validation in drug repositioning or lead compound identification regimes.

Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders.

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors, and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based encoder significantly outperforms a baseline model trained on Morgan fingerprints and a selection of encoders based on SMILES, as well as the previously reported state-of-the-art for multimodal drug sensitivity prediction (R2 = 0.86 and RMSE = 0.89). Moreover, the explainability of our approach is demonstrated by a thorough analysis of the attention weights. We show that the attended genes significantly enrich apoptotic processes and that the drug attention is strongly correlated with a standard chemical structure similarity index. Finally, we report a case study of two receptor tyrosine kinase (RTK) inhibitors acting on a leukemia cell line, showcasing the ability of the model to focus on informative genes and submolecular regions of the two compounds. The demonstrated generalizability and the interpretability of our model testify to its potential for in silico prediction of anticancer compound efficacy on unseen cancer cells, positioning it as a valid solution for the development of personalized therapies as well as for the evaluation of candidate compounds in de novo drug design.

MAZ induces MYB expression during the exit from quiescence via the E2F site in the MYB promoter.

Most E2F-binding sites repress transcription through the recruitment of Retinoblastoma (RB) family members until the end of the G1 cell-cycle phase. Although the MYB promoter contains an E2F-binding site, its transcription is activated shortly after the exit from quiescence, before RB family members inactivation, by unknown mechanisms. We had previously uncovered a nuclear factor distinct from E2F, Myb-sp, whose DNA-binding site overlapped the E2F element and had hypothesized that this factor might overcome the transcriptional repression of MYB by E2F-RB family members. We have purified Myb-sp and discovered that Myc-associated zinc finger proteins (MAZ) are major components. We show that various MAZ isoforms are present in Myb-sp and activate transcription via the MYB-E2F element. Moreover, while forced RB or p130 expression repressed the activity of a luciferase reporter driven by the MYB-E2F element, co-expression of MAZ proteins not only reverted repression, but also activated transcription. Finally, we show that MAZ binds the MYB promoter in vivo, that its binding site is critical for MYB transactivation, and that MAZ knockdown inhibits MYB expression during the exit from quiescence. Together, these data indicate that MAZ is essential to bypass MYB promoter repression by RB family members and to induce MYB expression.

Physical frailty and gait speed in community elderly: a systematic review. / Fragilidade física e velocidade da marcha em idosos da comunidade: uma revisão sistemática.

OBJECTIVE: To identify the outcomes of studies on gait speed and its use as a marker of physical frailty in community elderly. METHOD: Systematic review of the literature performed in the following databases: LILACS, SciELO, MEDLINE/PubMed, ScienceDirect, Scopus and ProQuest. The studies were evaluated by STROBE statement, and the PRISMA recommendations were adopted. RESULTS: There were 6,303 studies, and 49 of them met the inclusion criteria. Of the total number of studies, 91.8% described the way of measuring gait speed. Of these, 28.6% used the distance of 4.6 meters, and 34.7% adopted values below 20% as cutoff points for reduced gait speed, procedures in accordance with the frailty phenotype. Regarding the outcomes, in 30.6% of studies, there was an association between gait speed and variables of disability, frailty, sedentary lifestyle, falls, muscular weakness, diseases, body fat, cognitive impairment, mortality, stress, lower life satisfaction, lower quality of life, napping duration, and poor performance in quantitative parameters of gait in community elderly. CONCLUSION: The results reinforce the association between gait speed, physical frailty and health indicator variables in community elderly.

Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues.

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Proteomic-based stratification of intermediate-risk prostate cancer patients.

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

Potential Role of Occupational Therapist Intervention in Elementary School for Children with Additional Support Needs: A Systematic Review.

(1) Background: The main activity children engage with is learning through play or formal education. The aim of this systematic review is to analyze the role of occupational therapy in the school setting for children with additional support needs or disabilities. (2) Method: We conducted a systematic review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. This systematic review was registered in PROSPERO (CDR42022314271). The search was performed in the following databases: ERIC, Dialnet Plus, PubMed, OTseeker, Cochrane, Scopus, CINAHL, and PsycINFO. (3) Results: In total, 1954 studies were identified, from which 18 articles were selected. These studies were heterogeneous and showed different types of intervention of the occupational therapist in school environments. (4) Conclusions: The main conclusions highlighted the effectiveness of the occupational therapist within the school environment, the importance of an interdisciplinary team to cover the special needs students within the school and the need for intrinsic motivation for an active and inclusive participation of the students with special needs. However, there is a need for more homogeneous studies with a larger sample size that specifically focus on the school context and include the involvement of occupational therapists in order to replicate the findings obtained.

MonoNet: enhancing interpretability in neural networks via monotonic features.

Motivation: Being able to interpret and explain the predictions made by a machine learning model is of fundamental importance. Unfortunately, a trade-off between accuracy and interpretability is often observed. As a result, the interest in developing more transparent yet powerful models has grown considerably over the past few years. Interpretable models are especially needed in high-stake scenarios, such as computational biology and medical informatics, where erroneous or biased models' predictions can have deleterious consequences for a patient. Furthermore, understanding the inner workings of a model can help increase the trust in the model. Results: We introduce a novel structurally constrained neural network, MonoNet, which is more transparent, while still retaining the same learning capabilities of traditional neural models. MonoNet contains monotonically connected layers that ensure monotonic relationships between (high-level) features and outputs. We show how, by leveraging the monotonic constraint in conjunction with other post hoc strategies, we can interpret our model. To demonstrate our model's capabilities, we train MonoNet to classify cellular populations in a single-cell proteomic dataset. We also demonstrate MonoNet's performance in other benchmark datasets in different domains, including non-biological applications (in the Supplementary Material). Our experiments show how our model can achieve good performance, while providing at the same time useful biological insights about the most important biomarkers. We finally carry out an information-theoretical analysis to show how the monotonic constraint actively contributes to the learning process of the model. Availability and implementation: Code and sample data are available at https://github.com/phineasng/mononet. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Biomechanical Assessments in Woodwind Musicians: A Systematic Review.

Biomechanical methods are frequently used to provide information about the kinematics and kinetics of posture and movement during musical performance. The aim of this review was to identify and analyze the biomechanical methods performed on woodwind musicians to understand their musculoskeletal demands. A systemic review was carried out following the guidelines of the document Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It was registered in PROSPERO (code 430304).The databases PubMed, Cochrane, CINAHL, Scopus, and Web of Science were consulted between January 2000 and March 2022. The search in the databases identified 1625 articles, and 16 different studies were finally included in the review, with a sample size of 390 participants. Pressure sensors, surface electromyography, infrared thermography, goniometry in two dimensions, and ultrasound topometry in three dimensions were biomechanical methods useful to broaden the knowledge of musculoskeletal demands during musical practice. Piezoresistive pressure sensors were the most widely used method. The great heterogeneity of the studies limited the comparability of the results. The findings raised the need to increase both the quantity and the quality of studies in future research.

Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires.

The adaptive immune system has the extraordinary ability to produce a broad range of immunoglobulins that can bind a wide variety of antigens. During adaptive immune responses, activated B cells duplicate and undergo somatic hypermutation in their B-cell receptor (BCR) genes, resulting in clonal families of diversified B cells that can be related back to a common ancestor. Advances in high-throughput sequencing technologies have enabled the high-throughput characterization of B-cell repertoires, however, the accurate identification of clonally related BCR sequences remains a major challenge. In this study, we compare three different clone identification methods on both simulated and experimental data, and investigate their impact on the characterization of B-cell diversity. We observe that different methods lead to different clonal definitions, which affects the quantification of clonal diversity in repertoire data. Our analyses show that direct comparisons between clonal clusterings and clonal diversity of different repertoires should be avoided if different clone identification methods were used to define the clones. Despite this variability, the diversity indices inferred from the repertoires' clonal characterization across samples show similar patterns of variation regardless of the clonal identification method used. We find the Shannon entropy to be the most robust in terms of the variability of diversity rank across samples. Our analysis also suggests that the traditional germline gene alignment-based method for clonal identification remains the most accurate when the complete information about the sequence is known, but that alignment-free methods may be preferred for shorter sequencing read lengths. We make our implementation freely available as a Python library cdiversity.

Convergent evolution and B-cell recirculation in germinal centers in a human lymph node.

Germinal centers (GCs) play a central role in generating an effective immune response against infectious pathogens, and failures in their regulating mechanisms can lead to the development of autoimmune diseases and cancer. Although previous works study experimental systems of the immune response with mouse models that are immunized with specific antigens, our study focused on a real-life situation, with an ongoing GC response in a human lymph node (LN) involving multiple asynchronized GCs reacting simultaneously to unknown antigens. We combined laser capture microdissection of individual GCs from human LN with next-generation repertoire sequencing to characterize individual GCs as distinct evolutionary spaces. In line with well-characterized GC responses in mice, elicited by immunization with model antigens, we observe a heterogeneous clonal diversity across individual GCs from the same human LN. Still, we identify shared clones in several individual GCs, and phylogenetic tree analysis combined with paratope modeling suggest the re-engagement and rediversification of B-cell clones across GCs and expanded clones exhibiting shared antigen responses across distinct GCs, indicating convergent evolution of the GCs.