Heterogeneity of germinal center B cells: New insights from single-cell studies

Single-cell RNA sequencing reveals the landscape of biomarker in allergic rhinitis patient undergoing intracervical lymphatic immunotherapy and related pan-cancer analysis

INTRODUCTION

Allergic rhinitis (AR) is one of the leading allergic diseases worldwide. Allergen immunotherapy (AIT) induces persistent specific allergen tolerance to achieve remission of the symptoms in AR patients. We creatively conducted the intra-cervical lymphatic immunotherapy (ICLIT) for AR patients. However, the underlying molecular mechanism of immune cell response of AIT in AR remains elusive.

METHOD

To investigate the transcriptome profile in AR patients who underwent ICLIT, we comprehensively investigated the transcriptional changes in B cells from peripheral blood mononuclear cells of AR patient by single-cell RNA sequencing. Immunoglobulins and relative key gene, which influences the B cell differentiation, was demonstrated. The biomarkers' association with different types of tumors was investigated.

RESULTS

Naive B cells, germinal center B cells, activated memory B cells, and memory B cells constituted the B cells subsets. The expression of IGHE, IGHGs, IGHA, IGHD, and IGHM from memory B cells was validated. Pseudotime analysis further indicated the dynamic change from the expression of the immunoglobulins in the memory B cells, suggesting that ITGB1 may contribute to the differentiation procedure of memory B cells. The cell-cell communication among these immune cells demonstrated the significantly enhanced CD23, BTLA signaling after ICLIT in AR patient. ITGB1 was upregulated in 13 tumors and downregulated in six others. High ITGB1 expression was linked to poor prognosis in eight types of tumors. ITGB1 expression showed correlations with tumor mutation burden, tissue purity, and microsatellite instability in different types of tumors.

DISCUSSION

ITGB1 was demonstrated as a potential biomarker for AR patients after ICLIT and is significant in identifying immune infiltration in tumor tissue and predicting tumor prognosis.

Spatially-resolved transcriptomics reveal macrophage heterogeneity and prognostic significance in diffuse large B-cell lymphoma

Macrophages are abundant immune cells in the microenvironment of diffuse large B-cell lymphoma (DLBCL). Macrophage estimation by immunohistochemistry shows varying prognostic significance across studies in DLBCL, and does not provide a comprehensive analysis of macrophage subtypes. Here, using digital spatial profiling with whole transcriptome analysis of CD68+ cells, we characterize macrophages in distinct spatial niches of reactive lymphoid tissues (RLTs) and DLBCL. We reveal transcriptomic differences between macrophages within RLTs (light zone /dark zone, germinal center/ interfollicular), and between disease states (RLTs/ DLBCL), which we then use to generate six spatially-derived macrophage signatures (MacroSigs). We proceed to interrogate these MacroSigs in macrophage and DLBCL single-cell RNA-sequencing datasets, and in gene-expression data from multiple DLBCL cohorts. We show that specific MacroSigs are associated with cell-of-origin subtypes and overall survival in DLBCL. This study provides a spatially-resolved whole-transcriptome atlas of macrophages in reactive and malignant lymphoid tissues, showing biological and clinical significance.

The fifth edition of the WHO classification of mature B-cell neoplasms: open questions for research

The fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) is the product of an evidence-based evolution of the revised fourth edition with wide multidisciplinary consultation. Nonetheless, while every classification incorporates scientific advances and aims to improve upon the prior version, medical knowledge remains incomplete and individual neoplasms may not be easily subclassified in a given scheme. Thus, optimal classification requires ongoing study, and there are certain aspects of some entities and subtypes that require further refinements. In this review, we highlight a selection of these challenging areas to prompt more research investigations. These include (1) a 'placeholder term' of splenic B-cell lymphoma/leukaemia with prominent nucleoli (SBLPN) to accommodate many of the splenic lymphomas previously classified as hairy cell leukaemia variant and B-prolymphocytic leukaemia, a clear new start to define their pathobiology; (2) how best to classify BCL2 rearrangement negative follicular lymphoma including those with BCL6 rearrangement, integrating the emerging new knowledge on various germinal centre B-cell subsets; (3) what is the spectrum of non-IG gene partners of MYC translocation in diffuse large B-cell lymphoma/high-grade B-cell lymphoma and how they impact MYC expression and clinical outcome; how best to investigate this in a routine clinical setting; and (4) how best to define high-grade B-cell lymphoma not otherwise specified and high-grade B-cell lymphoma with 11q aberrations to distinguish them from their mimics and characterise their molecular pathogenetic mechanism. Addressing these questions would provide more robust evidence to better define these entities/subtypes, improve their diagnosis and/or prognostic stratification, leading to better patient care. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis

Breast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance in the early diagnosis, treatment, and improved prognosis of BC patients. In this study, we downloaded breast cancer protein expression data from The Cancer Genome Atlas (TCGA) and combined proteomics with genomics to construct a proteomic-based prognostic model for BC. This model consists of nine proteins (HEREGULIN, IDO, PEA15, MERIT40_pS29, CIITA, AKT2, CD171 DVL3, and CABL9). The accuracy of the model in predicting the survival prognosis of BC patients was further validated through risk curve analysis, survival curve analysis, and independent prognostic analysis. We further confirmed the impact of differential expression of these nine key proteins on overall survival in BC patients, and the differential expression of the key proteins and their encoding genes was validated using immunohistochemical staining. Enrichment analysis revealed functional associations primarily related to PPAR signaling pathway, steroid hormone metabolism, chemokine signaling pathway, DNA conformation changes, immunoglobulin production, and immunoglobulin complex in the high- and low-risk groups. Immune infiltration analysis revealed differential expression of immune cells between the high- and low-risk groups, providing a theoretical basis for subsequent immunotherapy. The model constructed in this study can predict the survival of BC patients, and the identified key proteins may serve as biomarkers to aid in the early diagnosis of BC. Enrichment analysis and immune infiltration analysis provide a necessary theoretical basis for further exploration of the molecular mechanisms and subsequent immunotherapy.

Germinal center-dependent and -independent immune responses of tumor-infiltrating B cells in human cancers

B cells play essential roles in immunity, mainly through the production of high affinity plasma cells (PCs) and memory B (Bmem) cells. The affinity maturation and differentiation of B cells rely on the integration of B-cell receptor (BCR) intrinsic and extrinsic signals provided by antigen binding and the microenvironment, respectively. In recent years, tumor infiltrating B (TIL-B) cells and PCs (TIL-PCs) have been revealed as important players in antitumor responses in human cancers, but their interplay and dynamics remain largely unknown. In lymphoid organs, B-cell responses involve both germinal center (GC)-dependent and GC-independent pathways for Bmem cell and PC production. Affinity maturation of BCR repertoires occurs in GC reactions with specific spatiotemporal dynamics of signal integration by B cells. In general, the reactivation of high-affinity Bmem cells by antigens triggers GC-independent production of large numbers of PC without BCR rediversification. Understanding B-cell dynamics in immune responses requires the integration of multiple tools and readouts such as single-cell phenotyping and RNA-seq, in situ analyses, BCR repertoire analysis, BCR specificity and affinity assays, and functional tests. Here, we review how those tools have recently been applied to study TIL-B cells and TIL-PC in different types of solid tumors. We assessed the published evidence for different models of TIL-B-cell dynamics involving GC-dependent or GC-independent local responses and the resulting production of antigen-specific PCs. Altogether, we highlight the need for more integrative B-cell immunology studies to rationally investigate TIL-B cells as a leverage for antitumor therapies.

Focus on the 9th Annual Seminar organized by the Canceropôle Provence-Alpes-Côte-d'Azur: Highlights of the event

The 9th Annual Seminar of the Canceropôle Provence-Alpes-Côte-d'Azur took place on July, 5th-6th 2022 in Saint-Raphaël, south of France. Annual meeting of the regional scientific community working in the field of cancer research, this seminar brings together a large and diverse audience, with 285 people attending in 2022: PhD students, postdocs, PIs (Principal Investigators) and senior researchers, clinicians, patient associations, funding partners of the Canceropôle. This document reviews the major scientific results presented and key moments of the event.

Understanding repertoire sequencing data through a multiscale computational model of the germinal center

Sequencing of B-cell and T-cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes (lineages) and their frequencies and not about, for example, their affinity or antigen (Ag) specificity. To further characterize the identified clones, usually with special attention to the particularly abundant ones (dominant), additional time-consuming or expensive experiments are generally required. Here, we present an extension of a multiscale model of the germinal center (GC) that we previously developed to gain more insight in B-cell repertoires. We compare the extent that these simulated repertoires deviate from experimental repertoires established from single GCs, blood, or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor (same-clone) subclones. Our simulations suggest that low-abundance clones and subclones, might also be of interest since they may have high affinity for the Ag. We show that the fraction of plasma cells (PCs) with high B-cell receptor (BcR) mRNA content in the GC does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. Results from these simulations guide data interpretation and the design of follow-up experiments.

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

Motivation

Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression.

Results

We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies.

Availability and implementation

The R package and code used in this manuscript can be found at github.com/alexyermanos/echidna and also in the R package Platypus (Yermanos et al., 2021). Installation instructions and the vignette for Echidna is described in the Platypus Computational Ecosystem (https://alexyermanos.github.io/Platypus/index.html). Publicly available data and corresponding sample accession numbers can be found in Supplementary Tables S2 and S3.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Classifying Germinal Center Derived Lymphomas-Navigate a Complex Transcriptional Landscape

Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 "double hit lymphomas" (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.

Tracking Immunoglobulin Repertoire and Transcriptomic Changes in Germinal Center B Cells by Single-Cell Analysis

In response to T-cell-dependent antigens, mature B cells in the secondary lymphoid organs are stimulated to form germinal centers (GCs), which are histological structures deputed to antibody affinity maturation, a process associated with immunoglobulin gene editing by somatic hypermutation (SHM) and class switch recombination (CSR). GC B cells are heterogeneous and transition across multiple stages before being eliminated by apoptosis or committing to post-GC differentiation as memory B cells or plasma cells. In order to explore the dynamics of SHM and CSR during the GC reaction, we identified GC subpopulations by single-cell (sc) transcriptomics and analyzed the load of immunoglobulin variable (V) region mutations as well as the isotype class distribution in each subpopulation. The results showed that the large majority of GC B cells display a quantitatively similar mutational load in the V regions and analogous IGH isotype class distribution, except for the precursors of memory B cells (PreM) and plasma cells (PBL). PreM showed a bimodal pattern with about half of the cells displaying high V region germline identity and enrichment for unswitched IGH, while the rest of the cells carried a mutational load similar to the bulk of GC B cells and showed a switched isotype. PBL displayed a bias toward expression of IGHG and higher V region germline identity compared to the bulk of GC B cells. Genes implicated in SHM and CSR were significantly induced in specific GC subpopulations, consistent with the occurrence of SHM in dark zone cells and suggesting that CSR can occur within the GC.

Imaging the different timescales of germinal center selection

Germinal centers (GCs) are the site of antibody affinity maturation, a fundamental immunological process that increases the potency of antibodies and thereby their ability to protect against infection. GC biology is highly dynamic in both time and space, making it ideally suited for intravital imaging. Using multiphoton laser scanning microscopy (MPLSM), the field has gained insight into the molecular, cellular, and structural changes and movements that coordinate affinity maturation in real time in their native environment. On the other hand, several limitations of MPLSM have had to be overcome to allow full appreciation of GC events taking place across different timescales. Here, we review the technical advances afforded by intravital imaging and their contributions to our understanding of GC biology.