Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics

Mature tertiary lymphoid structures evoke intra-tumoral T and B cell responses via progenitor exhausted CD4+ T cells in head and neck cancer

Tumor tertiary lymphoid structures (TLS), especially mature TLS (mTLS), have been associated with better prognosis and improved responses to immune checkpoint blockade (ICB), but the underlying mechanisms remain incompletely understood. Here, by performing single-cell RNA, antigen receptor sequencing and spatial transcriptomics on tumor tissue from head and neck squamous cell carcinoma (HNSCC) patients with different statuses of TLS, we observe that mTLS are enriched with stem-like T cells, and B cells at various maturation stages. Notably, progenitor exhausted CD4+ T cells, with features resembling follicular helper T cells, support these responses, by activating B cells to produce plasma cells in the germinal center, and interacting with DC-LAMP+ dendritic cells to support CD8+ T cell activation. Conversely, non-mTLS tumors do not promote local anti-tumor immunity which is abundant of immunosuppressive cells or a lack of stem-like B and T cells. Furthermore, patients with mTLS manifest improved overall survival and response to ICB compared to those with non-mTLS. Overall, our study provides insights into mechanisms underlying mTLS-mediated intra-tumoral immunity events against cancer.

Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data

Analysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi-omics technologies now reveal complex molecular interactions shaping cellular behaviour and tissue dynamics. This review highlights key technologies and computational methods that have advanced spatial domain identification and their pseudo-relations, as well as inference of intra- and inter-cellular molecular networks that drive disease progression. We also discuss strategies to address major challenges, including data sparsity, high-dimensionality, scalability, and heterogeneity. Furthermore, we outline how spatial multi-omics enables novel insights into disease mechanisms, advancing precision medicine and informing targeted therapies. KEY POINTS: Advancements in spatial multi-omics facilitate our understanding of tumour spatiotemporal heterogeneity. AI-driven multimodal models uncover complex molecular interactions that underlie cellular behaviours and tissue dynamics. Combining multi-omics technologies and AI-enabled bioinformatics tools helps predict critical disease stages, such as pre-cancer, advancing precision medicine, and informing targeted therapeutic strategies.

Deconvolution of cell types and states in spatial multiomics utilizing TACIT

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we develop TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000 cells; 51 cell types) from three niches (brain, intestine, gland), TACIT outperforms existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types reveals new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

De novo antibody identification in human blood from full-length single B cell transcriptomics and matching haplotype-resolved germline assemblies

Immunoglobulin (IGH, IGK, IGL) loci in the human genome are highly polymorphic regions that encode the building blocks of the light and heavy chain IG proteins that dimerize to form antibodies. The processes of V(D)J recombination and somatic hypermutation in B cells are responsible for creating an enormous reservoir of highly specific antibodies capable of binding a vast array of possible antigens. However, the antibody repertoire is fundamentally limited by the set of variable (V), diversity (D), and joining (J) alleles present in the germline IG loci. To better understand how the germline IG haplotypes contribute to the expressed antibody repertoire, we combined genome sequencing of the germline IG loci with single-cell transcriptome sequencing of B cells from the same donor. Sequencing and assembly of the germline IG loci captured the IGH locus in a single fully phased contig where the maternal and paternal contributions to the germline V, D, and J repertoire can be fully resolved. The B cells were collected following a measles, mumps, and rubella (MMR) vaccination, resulting in a population of cells that were activated in response to this specific immune challenge. Single-cell, full-length transcriptome sequencing of these B cells results in whole transcriptome characterization of each cell, as well as highly accurate consensus sequences for the somatically rearranged and hypermutated light and heavy chain IG transcripts. A subset of antibodies synthesized based on their consensus heavy and light chain transcript sequences demonstrate binding to measles antigens and neutralization of authentic measles virus.

DNA Origami Scaffold-Based Peptide-Major Histocompatibility Complex Multimers for Spatial Imaging of T Cells

Visualizing the spatial distribution of antigen-specific T cells is essential for understanding immune responses and improving therapeutic strategies. However, detecting low-affinity antigen-specific T cells and enhancing signals from low-abundance populations remain challenging due to limitations in sensitivity. Here, we report DNA origami scaffold-based peptide-major histocompatibility complex multimers (DOS-pMHCs) with precise spatial organization of pMHC and signaling molecules on the nanoscale for enhanced in situ visualization of antigen-specific T cells. The two-dimensional triangular DNA origami precisely organizes pMHCs and signaling molecules with high valency, significantly improving binding to antigen-specific T cells and signal amplification. These DOS-pMHCs facilitate enhanced visualization of antigen-specific T cells in lymphoid tissues compared to traditional tetramers. Moreover, we show that DOS-pMHCs enable the in situ detection of autoimmune T cells with lower affinity T cell receptors (TCRs), which are difficult to identify using traditional tetramers. This in situ detection strategy provides a powerful tool for mapping the spatial distribution of antigen-specific T cells, thus holding great potential for advancing our understanding of immune responses and guiding personalized immunotherapy.

DARLIN mouse for in vivo lineage tracing at high efficiency and clonal diversity

Lineage tracing is a powerful tool to study cell history and cell dynamics during tissue development and homeostasis. An increasingly popular approach for lineage tracing is to generate high-frequent mutations at given genomic loci, which can serve as genetic barcodes to label different cell lineages. However, current lineage tracing mouse models suffer from low barcode diversity and limited single-cell lineage coverage. We recently developed the DARLIN mouse model by incorporating three barcoding arrays within defined genomic loci and combining Cas9 and terminal deoxynucleotidyl transferase (TdT) to improve editing diversity in each barcode array. We estimated that DARLIN generates 1018 distinct lineage barcodes in theory, and enables the recovery of lineage barcodes in over 70% of cells in single-cell assays. In addition, DARLIN can be induced with doxycycline to generate stable lineage barcodes across different tissues at a defined stage. Here we provide a step-by-step protocol on applying the DARLIN system for in vivo lineage tracing, including barcode induction, estimation of induction efficiency, barcode analysis with bulk and single-cell sequencing, and computational analysis. The execution time of this protocol is ~1 week for experimental data collection and ~1 d for running the computational analysis pipeline. To execute this protocol, one should be familiar with sequencing library generation and Linux operation. DARLIN opens the door to study the lineage relationships and the underlying molecular regulations across various tissues at physiological context.

Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Profiling immune cell tissue niches in the spatial -omics era

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

A Temporal and Spatial Atlas of Adaptive Immune Responses in the Lymph Node Following Viral Infection

The spatial organization of adaptive immune cells within lymph nodes is critical for understanding immune responses during infection and disease. Here, we introduce AIR-SPACE, an integrative approach that combines high-resolution spatial transcriptomics with paired, high-fidelity long-read sequencing of T and B cell receptors. This method enables the simultaneous analysis of cellular transcriptomes and adaptive immune receptor (AIR) repertoires within their native spatial context. We applied AIR-SPACE to mouse popliteal lymph nodes at five distinct time points after Vaccinia virus footpad infection and constructed a comprehensive map of the developing adaptive immune response. Our analysis revealed heterogeneous activation niches, characterized by Interferon-gamma (IFN-γ) production, during the early stages of infection. At later stages, we delineated sub-anatomical structures within the germinal center (GC) and observed evidence that antibody-producing plasma cells differentiate and exit the GC through the dark zone. Furthermore, by combining clonotype data with spatial lineage tracing, we demonstrate that B cell clones are shared among multiple GCs within the same lymph node, reinforcing the concept of a dynamic, interconnected network of GCs. Overall, our study demonstrates how AIR-SPACE can be used to gain insight into the spatial dynamics of infection responses within lymphoid organs.

B cells in non-lymphoid tissues

B cells have long been understood to be drivers of both humoral and cellular immunity. Recent advances underscore this importance but also indicate that in infection, inflammatory disease and cancer, B cells function directly at sites of inflammation and form tissue-resident memory populations. The spatial organization and cellular niches of tissue B cells have profound effects on their function and on disease outcome, as well as on patient response to therapy. Here we review the role of B cells in peripheral tissues in homeostasis and disease, and discuss the newly identified cellular and molecular signals that are involved in regulating their activity. We integrate emerging data from multi-omic human studies with experimental models to propose a framework for B cell function in tissue inflammation and homeostasis.

Full-Length Immune Repertoire Reconstruction and Profiling at the Transcriptome Level Using Long-Read Sequencing

BACKGROUND

Due to the diversity of the immune repertoire (IR), reconstructing full-length IR using traditional short-read sequencing has proven challenging.

METHODS

A full-length IR sequencing (FLIRseq) work flow was developed with linear rolling circle amplification and nanopore sequencing. Its accuracy and quantification ability were verified by plasmid mixtures and commercial B-cell receptor/T-cell receptor sequencing (BCR/TCR-seq) based on short reads. IRs in tissues and the peripheral blood from 8 patients with acute lymphoblastic leukemia, 3 patients with allergic diseases, 4 patients with psoriasis, and 5 patients with prostate cancer were analyzed using FLIRseq.

RESULTS

FLIRseq reads had lower mismatch rates and gap rates, and higher identify rates than nanopore reads (all P < 2.2 × -16). The relative quantification of components by FLIRseq was consistent with the actual quantification (P > 0.05). FLIRseq had superiority over BCR/TCR-seq, providing the long complementarity-determining region 3, B-cell isotype, and the rarely used V gene sequence. FLIRseq observed an increase in clonotype diversity (P < 0.05) and a decrease in the percentage of abnormal BCRs/TCRs in patients with leukemia in remission. For patients with allergic diseases or psoriasis, FLIRseq provided direct insights into V(D)J recombination and specific immunoglobulin classes. Compared with that in prostate cancer tissues, the full-length V segment of the biased T-cell receptor β chain from lymphocytes in psoriatic tissues showed a more consistent AlphaFold2-predicted protein structure (P < 0.05).

CONCLUSIONS

FLIRseq enables unbiased and comprehensive analyses of direct V(D)J recombination and immunoglobulin classes, thereby contributing to characterizing pathogenic mechanisms, monitoring minimal residual disease, and customizing adoptive cell therapy.

Application of Single-Cell Genomics to Animal Models of Periodontitis and Peri-Implantitis

AIMS

This narrative review aims to synthesize current knowledge on integrating single-cell genomics technologies with animal models of periodontitis and peri-implantitis.

REVIEW

Single-cell RNA sequencing (scRNAseq) reveals cellular heterogeneity and specific cell roles in periodontitis and peri-implantitis, overcoming the limitations of bulk RNA sequencing. Under controlled conditions and genetic manipulation, animal models facilitate studying disease progression, gene functions and systemic disease links, aiding targeted therapy development. Knockout models have started to elucidate the impact of genetic mutations on periodontal disease and host responses. scRNAseq in animal models has been used to examine connections between periodontitis and systemic diseases, revealing altered immune environments and cellular interactions. Emerging studies are now applying these methods to animal models of peri-implantitis. Integrating these datasets into single-cell and spatially resolved atlases will enable future meta-analyses, providing deeper insights into disease mechanisms considering factors such as sex, strain, and age.

CONCLUSIONS

Integrating scRNAseq with animal models advances the understanding of periodontitis and peri-implantitis pathogenesis and precision therapies. The combined use of single-cell and spatial genomics and scRNAseq will further enhance data insights significantly for drug discovery and preclinical testing, making these technologies pivotal in validating animal models and translating findings into clinical practice.

Organismal mucosal immunology: A perspective through the eyes of game theory

In complex organisms, functional units must interact cohesively to maintain homeostasis, especially within mucosal barriers that house diverse, specialized cell exposed to constant environmental challenges. Understanding how homeostasis at mucosal barriers is maintained and how its disruption can lead to autoimmune diseases or cancer, requires a holistic view. Although omics approaches and systems immunology have become powerful tools, they are not without limitations; interpretations may reflect researchers' assumptions, even if other explanations exist. In this perspective, I propose that applying game theory concepts to mucosal immunology could help interpret complex data, offering fresh perspectives and supporting the exploration of alternative scenarios. By framing the mucosal immune system as a network of strategic interactions with multiple possible outcomes, game theory, which analyzes strategic interactions and decision-making processes, could illuminate novel cell types and functions, cell interactions, and responses to pathogens and commensals, leading to a more comprehensive understanding of immune homeostasis and diseases. In addition, game theory might encourage researchers to consider a broader range of possibilities, reduce the risk of myopic thinking, and ultimately enable a more refined and comprehensive understanding of the complexity of the immune system at mucosal barriers. This perspective aims to introduce game theory as a complementary framework for mucosal immunologists, encouraging them to incorporate these concepts into data interpretation and system modeling.

Recent advancement in the spatial immuno-oncology

Recent advancements in spatial transcriptomics and spatial proteomics enabled the high-throughput profiling of single or multi-cell types and cell states with spatial information. They transformed our understanding of the higher-order architectures and paired cell-cell interactions within a tumor microenvironment (TME). Within less than a decade, this rapidly emerging field has discovered much crucial fundamental knowledge and significantly improved clinical diagnosis in the field of immuno-oncology. This review summarizes the conceptual frameworks to understand spatial omics data and highlights the updated knowledge of spatial immuno-oncology.

Single Cell VDJ Sequencing of Normal and Malignant B and T Cells

Recent developments in single cell sequencing technologies enable researchers to examine heterogeneity of cell types and subclusters even deeper. First assays were only available for transcriptome analysis of up to 10,000 cells, but nowadays up to 60,000 cells or even more can be analyzed. Whereas initially only analysis of mRNA expression was possible, currently single cell methods multiplied, with extension of assays for examination of surface molecule expression, DNA accessibility (ATAC-seq), antigen specificity, and B or T cell receptor repertoires. Also, spatial transcriptomics or CRISPR screenings, augmenting classical CRISPR/Cas9 screens by combining them with transcriptomic data at single cell level, can be evaluated. The composition of B and T cell clones-of malignant cells in lymphomas and leukemia, as well as of infiltrating B or T cell clones in other types of cancer-is especially important in tumor research, as these clones may give valuable hints for tumor development and control. This chapter presents detailed methods for implementation and analysis of single cell B and/or T cell receptor repertoire sequencing on the Chromium system from 10× Genomics and the Rhapsody™ system from BD Bioscience.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Laser-Based Microdissection of Single Cells from Tissue Sections and PCR Analysis of Rearranged Immunoglobulin Genes from Isolated Normal and Malignant Human B Cells

Normal and malignant B cells carry rearranged immunoglobulin (Ig) variable region genes, which due to their practically limitless diversity represent ideal clonal markers for these cells. We describe here an approach to isolate single cells from frozen tissue sections by microdissection using a laser-based method. From the DNA of the isolated cells, rearranged IgH and Igκ genes are amplified in a semi-nested PCR approach, using a collection of IGV gene subgroup-specific primers recognizing nearly all IGV genes together with primers for the J genes. By sequence analysis of IGV region genes from distinct cells, the clonal relationship of the B-lineage cells can unequivocally be determined and related to the histological distribution of the cells. The approach is also useful to determine V, D and J gene usage. Moreover, the presence and pattern of somatic IGV gene mutations give valuable insight into the differentiation stage of the B cells.

Clinical Implications of Basic Research: Exploring the Transformative Potential of Spatial 'Omics in Uro-oncology

New spatial molecular technologies are poised to transform our understanding and treatment of urological cancers. By mapping the spatial molecular architecture of tumours, these platforms uncover the complex heterogeneity within and around individual malignancies, offering novel insights into disease development, progression, diagnosis, and treatment. They enable tracking of clonal phylogenetics in situ and immune-cell interactions in the tumour microenvironment. A whole transcriptome/genome/proteome-level spatial analysis is hypothesis generating, particularly in the areas of risk stratification and precision medicine. Current challenges include reagent costs, harmonisation of protocols, and computational demands. Nonetheless, the evolving landscape of the technology and evolving machine learning applications have the potential to overcome these barriers, pushing towards a future of personalised cancer therapy, leveraging detailed spatial cellular and molecular data. PATIENT SUMMARY: Tumours are complex and contain many different components. Although we have been able to observe some of these differences visually under the microscope, until recently, we have not been able to observe the genetic changes that underpin cancer development. Scientists are now able to explore molecular/genetic differences using approaches such as "spatial transcriptomics" and "spatial proteomics", which allow them to see genetic and cellular variation across a region of normal and cancerous tissue without destroying the tissue architecture. Currently, these technologies are limited by high associated costs, and a need for powerful and complex computational analysis workflows. Future advancements and results through these new technologies may assist patients and their doctors as they make decisions about treating their cancer.

Powerful microscopy technologies decode spatially organized cellular networks that drive response to immunotherapy in humans

In tumors, immune cells organize into networks of different sizes and composition, including complex tertiary lymphoid structures and recently identified networks centered around the chemokines CXCL9/10/11 and CCL19. New commercially available highly multiplexed microscopy using cyclical RNA in situ hybridization and antibody-based approaches have the potential to establish the organization of the immune response in human tissue and serve as a foundation for future immunology research.

Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues

The capability to spatially explore RNA biology in formalin-fixed paraffin-embedded (FFPE) tissues holds transformative potential for histopathology research. Here, we present pathology-compatible deterministic barcoding in tissue (Patho-DBiT) by combining in situ polyadenylation and computational innovation for spatial whole transcriptome sequencing, tailored to probe the diverse RNA species in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for 5 years. Furthermore, genome-wide single-nucleotide RNA variants can be captured to distinguish malignant subclones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis. Single-cell level Patho-DBiT dissects the spatiotemporal cellular dynamics driving tumor clonal architecture and progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to aid in clinical pathology evaluation.

Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope

Spatial and genomic heterogeneity of tumors are crucial factors influencing cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on somatic point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their localization in close to single-cell resolution by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

The future of immunology: a Lofoten perspective

This Future Challenges article summarizes views on future directions in immunological research presented at round-table discussions at the 4th Immunology workshop in the Lofoten Islands in Norway, held in August 2023, and subsequent responses to surveys sent to meeting participants. It also summarizes some of the conversations around the responsibility of scientists to communicate with the non-science community, and the approaches that we may use to meet this obligation.

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

The spatial multi-omics revolution in cancer therapy: Precision redefined

Spatially resolved multi-omics revolutionizes cancer therapy by decoding the cellular and molecular heterogeneity of the tumor microenvironment through spatial coordinates. This commentary discusses the roles of spatial multi-omics in identifying precise therapeutic targets and predicting treatment responses while also highlighting the challenges that impede its integration into precision medicine.

Decoding changes in tumor-infiltrating leukocytes through dynamic experimental models and single-cell technologies

The ability to characterize immune cells and explore the molecular interactions that govern their functions has never been greater, fueled in recent years by the revolutionary advance of single-cell analysis platforms. However, precisely how immune cells respond to different stimuli and where differentiation processes and effector functions operate remain incompletely understood. Inferring cellular fate within single-cell transcriptomic analyses is now omnipresent, despite the assumptions typically required in such analyses. Recently developed experimental models support dynamic analyses of the immune response, providing insights into the temporal changes that occur within cells and the tissues in which such transitions occur. Here we will review these approaches and discuss how these can be combined with single-cell technologies to develop a deeper understanding of the immune responses that should support the development of better therapeutic options for patients.

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

T cell trafficking in human chronic inflammatory diseases

Circulating T cells, which migrate from the periphery to sites of tissue inflammation, play a crucial role in the development of various chronic inflammatory conditions. Recent research has highlighted subsets of tissue-resident T cells that acquire migratory capabilities and re-enter circulation, referred to here as "recirculating T cells." In this review, we examine recent advancements in understanding the biology of T cell trafficking in diseases where T cell infiltration is pivotal, such as multiple sclerosis and inflammatory bowel diseases, as well as in metabolic disorders where the role of T cell migration is less understood. Additionally, we discuss current insights into therapeutic strategies aimed at modulating T cell circulation across tissues and the application of state-of-the-art technologies for studying recirculation in humans. This review underscores the significance of investigating T trafficking as a novel potential target for therapeutic interventions across a spectrum of human chronic inflammatory diseases.

Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

Advances and challenges in investigating B-cells via single-cell transcriptomics

Single-cell RNA sequencing (scRNAseq) and Variable, Diversity, Joining (VDJ) profiling have improved our understanding of B-cells. Recent scRNAseq-based approaches have led to the discovery of intermediate B-cell states, including preplasma cells and pregerminal centre B-cells, as well as unveiling protective roles for B-cells within tertiary lymphoid structures in respiratory infections and cancers. These studies have improved our understanding of transcriptional and epigenetic control of B-cell development and of atypical and memory B-cell differentiation. Advancements in temporal profiling in parallel with transcriptomic and VDJ sequencing have consolidated our understanding of the trajectory of B-cell clones over the course of infection and vaccination. Challenges remain in studying B-cell states across tissues in humans, in relating spatial location with B-cell phenotype and function, in examining antibody isotype switching events, and in unequivocal determination of clonal relationships. Nevertheless, ongoing multiomic assessments and studies of cellular interactions within tissues promise new avenues for improving humoral immunity and combatting autoimmune conditions.

A practical guide to spatial transcriptomics

Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.

Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

Single-cell immune repertoire analysis

Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.

Circulating microRNA-155-3p levels predicts response to first line immunotherapy in patients with metastatic renal cell carcinoma

Predictive biomarkers of response to immune checkpoint-based therapies (ICI) remain a critically unmet need in the management of advanced renal cell carcinoma (RCC). The complex interplay of the tumour microenvironment (TME) and the circulating immune response has proven to be challenging to decipher. MicroRNAs have gained increasing attention for their role in post-transcriptional gene expression regulation, particularly because they can have immunomodulatory properties. We evaluated the presence of immune-specific extracellular vesicle (EV) microRNAs in the plasma of patients with metastatic RCC (mRCC) prior to initiation of ICI. We found significantly lower levels of microRNA155-3p (miR155) in responders to ICI, when compared to non-responders. This microRNA has unique immunomodulatory properties, thus providing potential biological rationale for our findings. Our results support further work in exploring microRNAs as potential biomarkers of response to immunotherapy.

Seq-Scope Protocol: Repurposing Illumina Sequencing Flow Cells for High-Resolution Spatial Transcriptomics

Spatial transcriptomics (ST) technologies represent a significant advance in gene expression studies, aiming to profile the entire transcriptome from a single histological slide. These techniques are designed to overcome the constraints faced by traditional methods such as immunostaining and RNA in situ hybridization, which are capable of analyzing only a few target genes simultaneously. However, the application of ST in histopathological analysis is also limited by several factors, including low resolution, a limited range of genes, scalability issues, high cost, and the need for sophisticated equipment and complex methodologies. Seq-Scope-a recently developed novel technology-repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, thereby overcoming these limitations. Here we provide a detailed step-by-step protocol to implement Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell that allows for the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. In addition to detailing how to prepare a frozen tissue section for both histological imaging and sequencing library preparation, we provide comprehensive instructions and a streamlined computational pipeline to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Mapping and modelling human B cell maturation in the germinal centre

The maturation of B cells within the germinal centre (GC) is necessary for antigen-specific immune responses and memory. Dysfunction in the GC can lead to immunodeficiencies, autoimmune diseases, or lymphomas. Here we describe how recent advances in single-cell and spatial genomics have enabled new discoveries about the diversity of human GC B cell states. However, with the advent of these hypothesis-generating technologies, the field should now transition towards testing bioinformatic predictions using experimental models of the human GC. We review available experimental culture systems for modelling human B cell responses and discuss the potential limitations of different methods in capturing bona fide GC B cell states. Together, the combination of cell atlas-based mapping with experimental modelling of lymphoid tissues holds great promise to better understand the maturation of human B cells in the GC response and generate new insights into human immune health and disease.

De novo antibody discovery in human blood from full-length single B cell transcriptomics and matching haplotyped-resolved germline assemblies

Immunoglobulin (IGH, IGK, IGL) loci in the human genome are highly polymorphic regions that encode the building blocks of the light and heavy chain IG proteins that dimerize to form antibodies. The processes of V(D)J recombination and somatic hypermutation in B cells are responsible for creating an enormous reservoir of highly specific antibodies capable of binding a vast array of possible antigens. However, the antibody repertoire is fundamentally limited by the set of variable (V), diversity (D), and joining (J) alleles present in the germline IG loci. To better understand how the germline IG haplotypes contribute to the expressed antibody repertoire, we combined genome sequencing of the germline IG loci with single-cell transcriptome sequencing of B cells from the same donor. Sequencing and assembly of the germline IG loci captured the IGH locus in a single fully-phased contig where the maternal and paternal contributions to the germline V, D, and J repertoire can be fully resolved. The B cells were collected following a measles, mumps, and rubella (MMR) vaccination, resulting in a population of cells that were activated in response to this specific immune challenge. Single-cell, full-length transcriptome sequencing of these B cells resulted in whole transcriptome characterization of each cell, as well as highly-accurate consensus sequences for the somatically rearranged and hypermutated light and heavy chain IG transcripts. A subset of antibodies synthesized based on their consensus heavy and light chain transcript sequences demonstrated binding to measles antigens and neutralization of measles live virus.

Gut-associated lymphoid tissue: a microbiota-driven hub of B cell immunity

The diverse gut microbiota, which is associated with mucosal health and general wellbeing, maintains gut-associated lymphoid tissues (GALT) in a chronically activated state, including sustainment of germinal centers in a context of high antigenic load. This influences the rules for B cell engagement with antigen and the potential consequences. Recent data have highlighted differences between GALT and other lymphoid tissues. For example, GALT propagates IgA responses against glycans that show signs of having been generated in germinal centers. Other findings suggest that humans are among those species where GALT supports the diversification, propagation, and possibly selection of systemic B cells. Here, we review novel findings that identify GALT as distinctive, and able to support these processes.

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.