MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors

Application of spatial omics in gastric cancer

Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.

Protocol for assessing GD2 on formalin-fixed paraffin-embedded tissue sections using immunofluorescence staining

The detection of disialoganglioside GD2 on tumor biopsies, especially in paraffin-embedded tissues, has been challenging due to the glycolipid structure of GD2 and its membrane anchorage. Here, we present an immunofluorescence protocol for the reliable assessment of GD2 on formalin-fixed paraffin-embedded (FFPE) tissues. We describe steps for antigen retrieval with Tris-EDTA buffer and staining with unconjugated anti-GD2 antibody (clone 14.G2a) and horse radish peroxidase (HRP)-conjugated secondary antibody. We then detail procedures for signal amplification using the tyramide signal amplification technique. For complete details on the use and execution of this protocol, please refer to Fischer-Riepe et al.1.

The evolution of immune profiling: will there be a role for nanoparticles?

Immune profiling provides insights into the functioning of the immune system, including the distribution, abundance, and activity of immune cells. This understanding is essential for deciphering how the immune system responds to pathogens, vaccines, tumors, and other stimuli. Analyzing diverse immune cell types facilitates the development of personalized medicine approaches by characterizing individual variations in immune responses. With detailed immune profiles, clinicians can tailor treatment strategies to the specific immune status and needs of each patient, maximizing therapeutic efficacy while minimizing adverse effects. In this review, we discuss the evolution of immune profiling, from interrogating bulk cell samples in solution to evaluating the spatially-rich molecular profiles across intact preserved tissue sections. We also review various multiplexed imaging platforms recently developed, based on immunofluorescence and imaging mass spectrometry, and their impact on the field of immune profiling. Identifying and localizing various immune cell types across a patient's sample has already provided important insights into understanding disease progression, the development of novel targeted therapies, and predicting treatment response. We also offer a new perspective by highlighting the unprecedented potential of nanoparticles (NPs) that can open new horizons in immune profiling. NPs are known to provide enhanced detection sensitivity, targeting specificity, biocompatibility, stability, multimodal imaging features, and multiplexing capabilities. Therefore, we summarize the recent developments and advantages of NPs, which can contribute to advancing our understanding of immune function to facilitate precision medicine. Overall, NPs have the potential to offer a versatile and robust approach to profile the immune system with improved efficiency and multiplexed imaging power.

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.

Host-pathogen interactions from a metabolic perspective: methods of investigation

Metabolism shapes immune homeostasis in health and disease. This review presents the range of methods that are currently available to investigate the dialog between metabolism and immunity at the systemic, tissue and cellular levels, particularly during infection.

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Multiplexed imaging technologies have made it possible to interrogate complex tissue microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning-based segmentation methods demonstrating human level performance have emerged. However, limited work has been done in developing such generalist methods within the unsupervised context. Here we present an easy-to-use unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data via leveraging a Bayesian-like framework, and nucleus and cell membrane markers. We show that UNSEG is internally consistent and better at generalizing to the complexity of tissue morphology than current deep learning methods, allowing it to unambiguously identify the cytoplasmic compartment of a cell, and localize molecules to their correct sub-cellular compartment. We also introduce a perturbed watershed algorithm for stably and automatically segmenting a cluster of cell nuclei into individual nuclei that increases the accuracy of classical watershed. Finally, we demonstrate the efficacy of UNSEG on a high-quality annotated gastrointestinal tissue dataset we have generated, on publicly available datasets, and in a range of practical scenarios.

Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled Enabling Global Image Data Sharing in the Life Sciences, which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data. In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

Applications of single-cell technologies in drug discovery for tumor treatment

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

Preclinical Development of CAR T Cells with Antigen-Inducible IL18 Enforcement to Treat GD2-Positive Solid Cancers

PURPOSE

Cytokine-engineering of chimeric antigen receptor-redirected T cells (CAR T cells) is a promising principle to overcome the limited activity of canonical CAR T cells against solid cancers.

EXPERIMENTAL DESIGN

We developed an investigational medicinal product, GD2IL18CART, consisting of CAR T cells directed against ganglioside GD2 with CAR-inducible IL18 to enhance their activation response and cytolytic effector functions in the tumor microenvironment. To allow stratification of patients according to tumor GD2 expression, we established and validated immunofluorescence detection of GD2 on paraffin-embedded tumor tissues.

RESULTS

Lentiviral all-in-one vector engineering of human T cells with the GD2-specific CAR with and without inducible IL18 resulted in cell products with comparable proportions of CAR-expressing central memory T cells. Production of IL18 strictly depends on GD2 antigen engagement. GD2IL18CART respond to interaction with GD2-positive tumor cells with higher IFNγ and TNFα cytokine release and more effective target cytolysis compared with CAR T cells without inducible IL18. GD2IL18CART further have superior in vivo antitumor activity, with eradication of GD2-positive tumor xenografts. Finally, we established GMP-compliant manufacturing of GD2IL18CART and found it to be feasible and efficient at clinical scale.

CONCLUSIONS

These results pave the way for clinical investigation of GD2IL18CART in pediatric and adult patients with neuroblastoma and other GD2-positive cancers (EU CT 2022- 501725-21-00). See related commentary by Locatelli and Quintarelli, p. 3361.

Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

Making Multiplexed Imaging Flexible: Combining Essential Markers With Established Antibody Panels

Multiplexed immunofluorescence (IF) can be achieved using different commercially available platforms, often making use of conjugated antibodies detected in iterative cycles. A growing portfolio of pre-conjugated antibodies is offered by the providers, as well as the possibility for in-house conjugation. For many conjugation methods and kits, there are limitations in which antibodies can be used, and conjugation results are sometimes irreproducible. The conjugation process can limit or slow down the progress of studies requiring conjugation of essential markers needed for a given project. Here, we demonstrate a protocol combining manual indirect immunofluorescence (IF) of primary antibodies, followed by antibody elution and staining with multiplexed panels of commercially pre-conjugated antibodies on the PhenoCycler platform. We present detailed protocols for applying the workflow on fresh frozen and formalin fixed paraffin embedded tissue sections. We also provide a ready to use workflow for coregistration of the images and demonstrate this for two examples.

Colorectal cancer-associated fibroblasts inhibit effector T cells via NECTIN2 signaling

Cancer-associated fibroblasts play a crucial role within the tumor microenvironment. However, a comprehensive characterization of CAF in colorectal cancer (CRC) is still missing. We combined scRNA-seq and spatial proteomics to decipher fibroblast heterogeneity in healthy human colon and CRC at high resolution. Analyzing nearly 23,000 fibroblasts, we identified 11 distinct clusters and verified them by spatial proteomics. Four clusters, consisting of myofibroblastic CAF (myCAF)-like, inflammatory CAF (iCAF)-like and proliferating fibroblasts as well as a novel cluster, which we named "T cell-inhibiting CAF" (TinCAF), were primarily found in CRC. This new cluster was characterized by the expression of immune-interacting receptors and ligands, including CD40 and NECTIN2. Co-culture of CAF and T cells resulted in a reduction of the effector T cell compartment, impaired proliferation, and increased exhaustion. By blocking its receptor interaction, we demonstrated that NECTIN2 was the key driver of T cell inhibition. Analysis of clinical datasets showed that NECTIN2 expression is a poor prognostic factor in CRC and other tumors. In conclusion, we identified a new class of immuno-suppressive CAF with features rendering them a potential target for future immunotherapies.

Oligo cyc-DEP: On-chip cyclic immunofluorescence profiling of cell-derived nanoparticles

We present a follow-on technique for the cyclic-immunofluorescence profiling of suspension particles isolated using dielectrophoresis. The original lab-on-chip technique ("cyc-DEP" [cyclic immunofluorescent imaging on dielectrophoretic chip]) was designed for the multiplex surveillance of circulating biomarkers. Nanoparticles were collected from low-volume liquid biopsies using microfluidic dielectrophoretic chip technology. Subsequent rounds of cyclic immunofluorescent labeling and quenching were imaged and quantified with a custom algorithm to detect multiple proteins. While cyc-DEP improved assay multiplicity, long runtimes threatened its clinical adoption. Here, we modify the original cyc-DEP platform to reduce assay runtimes. Nanoparticles were formulated from human prostate adenocarcinoma cells and collected using dielectrophoresis. Three proteins were labeled on-chip with a mixture of short oligonucleotide-conjugated antibodies. The sample was then incubated with complementary fluorophore-conjugated oligonucleotides, which were dehybridized using an ethylene carbonate buffer after each round of imaging. Oligonucleotide removal exhibited an average quenching efficiency of 98 ± 3% (n = 12 quenching events), matching the original cyc-DEP platform. The presented "oligo cyc-DEP" platform achieved clinically relevant sample-to-answer times, reducing the duration for three rounds of cyclic immunolabeling from approximately 20 to 6.5 h-a 67% decrease attributed to rapid fluorophore removal and the consolidated co-incubation of antibodies.

A novel dual-release scaffold for fluorescent labels improves cyclic immunofluorescence

Cyclic immunofluorescence is a powerful method to generate high-content imaging datasets for investigating cell biology and developing therapies. This method relies on fluorescent labels that determine the quality of immunofluorescence and the maximum number of staining cycles that can be performed. Here we present a novel fluorescent labelling strategy, based on antibodies conjugated to a scaffold containing two distinct sites for enzymatic cleavage of fluorophores. The scaffold is composed of a dextran decorated with short ssDNA that upon hybridization with complementary dye-modified oligos result in fluorescent molecules. The developed fluorescent labels exhibit specific staining and remarkable brightness in flow cytometry and fluorescence microscopy. We showed that the combination of DNase-mediated degradation of DNA and dextranse-mediated degradation of the dextran as two complementary enzymatic release mechanisms in one molecule, improves signal erasure from labelled epitopes. We envision that such dual-release labels with high brightness and efficient and specific erasure will advance multiplexed cyclic immunofluorescence approaches and thereby will contribute to gaining new insights in cell biology.

Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine

Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.

Sopa: a technology-invariant pipeline for analyses of image-based spatial omics

Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa ( https://github.com/gustaveroussy/sopa ), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.

Expand available targets for CAR-T therapy to overcome tumor drug resistance based on the "Evolutionary Traps"

Based on the concept of "Evolutionary Traps", targeting survival essential genes obtained during tumor drug resistance can effectively eliminate resistant cells. While, it still faces limitations. In this study, lapatinib-resistant cells were used to test the concept of "Evolutionary Traps" and no suitable target stand out because of the identified genes without accessible drug. However, a membrane protein PDPN, which is low or non-expressed in normal tissues, is identified as highly expressed in lapatinib-resistant tumor cells. PDPN CAR-T cells were developed and showed high cytotoxicity against lapatinib-resistant tumor cells in vitro and in vivo, suggesting that CAR-T may be a feasible route for overcoming drug resistance of tumor based on "Evolutionary Trap". To test whether this concept is cell line or drug dependent, we analyzed 21 drug-resistant tumor cell expression profiles reveal that JAG1, GPC3, and L1CAM, which are suitable targets for CAR-T treatment, are significantly upregulated in various drug-resistant tumor cells. Our findings shed light on the feasibility of utilizing CAR-T therapy to treat drug-resistant tumors and broaden the concept of the "Evolutionary Trap".

Spatial omics techniques and data analysis for cancer immunotherapy applications

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

Fluorophore multimerization on a PEG backbone as a concept for signal amplification and lifetime modulation

Fluorescent labels have strongly contributed to many advancements in bioanalysis, molecular biology, molecular imaging, and medical diagnostics. Despite a large toolbox of molecular and nanoscale fluorophores to choose from, there is still a need for brighter labels, e.g., for flow cytometry and fluorescence microscopy, that are preferably of molecular nature. This requires versatile concepts for fluorophore multimerization, which involves the shielding of dyes from other chromophores and possible quenchers in their neighborhood. In addition, to increase the number of readout parameters for fluorescence microscopy and eventually also flow cytometry, control and tuning of the labels' fluorescence lifetimes is desired. Searching for bright multi-chromophoric or multimeric labels, we developed PEGylated dyes bearing functional groups for their bioconjugation and explored their spectroscopic properties and photostability in comparison to those of the respective monomeric dyes for two exemplarily chosen fluorophores excitable at 488 nm. Subsequently, these dyes were conjugated with anti-CD4 and anti-CD8 immunoglobulins to obtain fluorescent conjugates suitable for the labeling of cells and beads. Finally, the suitability of these novel labels for fluorescence lifetime imaging and target discrimination based upon lifetime measurements was assessed. Based upon the results of our spectroscopic studies including measurements of fluorescence quantum yields (QY) and fluorescence decay kinetics we could demonstrate the absence of significant dye-dye interactions and self-quenching in these multimeric labels. Moreover, in a first fluorescence lifetime imaging (FLIM) study, we could show the future potential of this multimerization concept for lifetime discrimination and multiplexing.

Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche

Clonal hematopoiesis (CH) defines a premalignant state predominantly found in older persons that increases the risk of developing hematologic malignancies and age-related inflammatory diseases. However, the risk for malignant transformation or non-malignant disorders is variable and difficult to predict, and defining the clinical relevance of specific candidate driver mutations in individual carriers has proved to be challenging. In addition to the cell-intrinsic mechanisms, mutant cells rely on and alter cell-extrinsic factors from the bone marrow (BM) niche, which complicates the prediction of a mutant cell's fate in a shifting pre-malignant microenvironment. Therefore, identifying the insidious and potentially broad impact of driver mutations on supportive niches and immune function in CH aims to understand the subtle differences that enable driver mutations to yield different clinical outcomes. Here, we review the changes in the aging BM niche and the emerging evidence supporting the concept that CH can progressively alter components of the local BM microenvironment. These alterations may have profound implications for the functionality of the osteo-hematopoietic niche and overall bone health, consequently fostering a conducive environment for the continued development and progression of CH. We also provide an overview of the latest technology developments to study the spatiotemporal dependencies in the CH BM niche, ideally in the context of longitudinal studies following CH over time. Finally, we discuss aspects of CH carrier management in clinical practice, based on work from our group and others.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Microenvironmental reorganization in brain tumors following radiotherapy and recurrence revealed by hyperplexed immunofluorescence imaging

The tumor microenvironment plays a crucial role in determining response to treatment. This involves a series of interconnected changes in the cellular landscape, spatial organization, and extracellular matrix composition. However, assessing these alterations simultaneously is challenging from a spatial perspective, due to the limitations of current high-dimensional imaging techniques and the extent of intratumoral heterogeneity over large lesion areas. In this study, we introduce a spatial proteomic workflow termed Hyperplexed Immunofluorescence Imaging (HIFI) that overcomes these limitations. HIFI allows for the simultaneous analysis of > 45 markers in fragile tissue sections at high magnification, using a cost-effective high-throughput workflow. We integrate HIFI with machine learning feature detection, graph-based network analysis, and cluster-based neighborhood analysis to analyze the microenvironment response to radiation therapy in a preclinical model of glioblastoma, and compare this response to a mouse model of breast-to-brain metastasis. Here we show that glioblastomas undergo extensive spatial reorganization of immune cell populations and structural architecture in response to treatment, while brain metastases show no comparable reorganization. Our integrated spatial analyses reveal highly divergent responses to radiation therapy between brain tumor models, despite equivalent radiotherapy benefit.

High-dimensional in situ proteomics imaging to assess γδ T cells in spatial biology

This study presents a high-dimensional immunohistochemistry approach to assess human γδ T cell subsets in their native tissue microenvironments at spatial resolution, a hitherto unmet scientific goal due to the lack of established antibodies and required technology. We report an integrated approach based on multiplexed imaging and bioinformatic analysis to identify γδ T cells, characterize their phenotypes, and analyze the composition of their microenvironment. Twenty-eight γδ T cell microenvironments were identified in tissue samples from fresh frozen human colon and colorectal cancer where interaction partners of the immune system, but also cancer cells were discovered in close proximity to γδ T cells, visualizing their potential contributions to cancer immunosurveillance. While this proof-of-principle study demonstrates the potential of this cutting-edge technology to assess γδ T cell heterogeneity and to investigate their microenvironment, future comprehensive studies are warranted to associate phenotypes and microenvironment profiles with features such as relevant clinical characteristics.

Advancements in technology for characterizing the tumor immune microenvironment

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.

Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging

Deciphering cellular components and the spatial interaction network of the tumor immune microenvironment (TIME) of solid tumors is pivotal for understanding biologically relevant cross-talks and, ultimately, advancing therapies. Multiplexed tissue imaging provides a powerful tool to elucidate spatial complexity in a holistic manner. We established and cross-validated a comprehensive immunophenotyping panel comprising over 121 markers for multiplexed tissue imaging using MACSima™ imaging cyclic staining (MICS) alongside an end-to-end analysis workflow. Applying this panel and workflow to primary cancer tissues, we characterized tumor heterogeneity, investigated potential therapeutical targets, conducted in-depth profiling of cell types and states, sub-phenotyped T cells within the TIME, and scrutinized cellular neighborhoods of diverse T cell subsets. Our findings highlight the advantage of spatial profiling, revealing immunosuppressive molecular signatures of tumor-associated myeloid cells interacting with neighboring exhausted, PD1high T cells in the TIME of hepatocellular carcinoma (HCC). This study establishes a robust framework for spatial exploration of TIMEs in solid tumors and underscores the potency of multiplexed tissue imaging and ultra-deep cell phenotyping in unraveling clinically relevant tumor components.

High-Plex Assessment of Biomarkers in Tumors

The assessment of biomarkers plays a critical role in the diagnosis and treatment of many cancers. Biomarkers not only provide diagnostic, prognostic, or predictive information but also can act as effective targets for new pharmaceutical therapies. As the utility of biomarkers increases, it becomes more important to utilize accurate and efficient methods for biomarker discovery and, ultimately, clinical assessment. High-plex imaging studies, defined here as assessment of 8 or more biomarkers on a single slide, have become the method of choice for biomarker discovery and assessment of biomarker spatial context. In this review, we discuss methods of measuring biomarkers in slide-mounted tissue samples, detail the various high-plex methods that allow for the simultaneous assessment of multiple biomarkers in situ, and describe the impact of high-plex biomarker assessment on the future of anatomic pathology.

Glioblastoma-Associated Mesenchymal Stem/Stromal Cells and Cancer-Associated Fibroblasts: Partners in Crime?

Tumor-associated mesenchymal stem/stromal cells (TA-MSCs) have been recognized as attractive therapeutic targets in several cancer types, due to their ability to enhance tumor growth and angiogenesis and their contribution to an immunosuppressive tumor microenvironment (TME). In glioblastoma (GB), mesenchymal stem cells (MSCs) seem to be recruited to the tumor site, where they differentiate into glioblastoma-associated mesenchymal stem/stromal cells (GA-MSCs) under the influence of tumor cells and the TME. GA-MSCs are reported to exert important protumoral functions, such as promoting tumor growth and invasion, increasing angiogenesis, stimulating glioblastoma stem cell (GSC) proliferation and stemness, mediating resistance to therapy and contributing to an immunosuppressive TME. Moreover, they could act as precursor cells for cancer-associated fibroblasts (CAFs), which have recently been identified in GB. In this review, we provide an overview of the different functions exerted by GA-MSCs and CAFs and the current knowledge on the relationship between these cell types. Increasing our understanding of the interactions and signaling pathways in relevant models might contribute to future regimens targeting GA-MSCs and GB-associated CAFs to inhibit tumor growth and render the TME less immunosuppressive.

Tissue niches and immunopathology through the lens of spatial tissue profiling techniques

Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.

Preclinical Evaluation of Novel Folate Receptor 1-Directed CAR T Cells for Ovarian Cancer

Treatment options for ovarian cancer patients are limited, and a high unmet clinical need remains for targeted and long-lasting, efficient drugs. Genetically modified T cells expressing chimeric antigen receptors (CAR), are promising new drugs that can be directed towards a defined target and have shown efficient, as well as persisting, anti-tumor responses in many patients. We sought to develop novel CAR T cells targeting ovarian cancer and to assess these candidates preclinically. First, we identified potential CAR targets on ovarian cancer samples. We confirmed high and consistent expressions of the tumor-associated antigen FOLR1 on primary ovarian cancer samples. Subsequently, we designed a series of CAR T cell candidates against the identified target and demonstrated their functionality against ovarian cancer cell lines in vitro and in an in vivo xenograft model. Finally, we performed additional in vitro assays recapitulating immune suppressive mechanisms present in solid tumors and developed a process for the automated manufacturing of our CAR T cell candidate. These findings demonstrate the feasibility of anti-FOLR1 CAR T cells for ovarian cancer and potentially other FOLR1-expressing tumors.

Deterministic reprogramming of neutrophils within tumors

Neutrophils are increasingly recognized as key players in the tumor immune response and are associated with poor clinical outcomes. Despite recent advances characterizing the diversity of neutrophil states in cancer, common trajectories and mechanisms governing the ontogeny and relationship between these neutrophil states remain undefined. Here, we demonstrate that immature and mature neutrophils that enter tumors undergo irreversible epigenetic, transcriptional, and proteomic modifications to converge into a distinct, terminally differentiated dcTRAIL-R1+ state. Reprogrammed dcTRAIL-R1+ neutrophils predominantly localize to a glycolytic and hypoxic niche at the tumor core and exert pro-angiogenic function that favors tumor growth. We found similar trajectories in neutrophils across multiple tumor types and in humans, suggesting that targeting this program may provide a means of enhancing certain cancer immunotherapies.

Endogenous Signaling Molecule Activating (ESMA) CARs: A Novel CAR Design Showing a Favorable Risk to Potency Ratio for the Treatment of Triple Negative Breast Cancer

As chimeric antigen receptor (CAR) T cell therapy continues to gain attention as a valuable treatment option against different cancers, strategies to improve its potency and decrease the side effects associated with this therapy have become increasingly relevant. Herein, we report an alternative CAR design that incorporates transmembrane domains with the ability to recruit endogenous signaling molecules, eliminating the need for stimulatory signals within the CAR structure. These endogenous signaling molecule activating (ESMA) CARs triggered robust cytotoxic activity and proliferation of the T cells when directed against the triple-negative breast cancer (TNBC) cell line MDA-MB-231 while exhibiting reduced cytokine secretion and exhaustion marker expression compared to their cognate standard second generation CARs. In a NOD SCID Gamma (NSG) MDA-MB-231 xenograft mouse model, the lead candidate maintained longitudinal therapeutic efficacy and an enhanced T cell memory phenotype. Profound tumor infiltration by activated T cells repressed tumor growth, further manifesting the proliferative capacity of the ESMA CAR T cell therapy. Consequently, ESMA CAR T cells entail promising features for improved clinical outcome as a solid tumor treatment option.

Charting the tumor microenvironment with spatial profiling technologies

In recent years technologies that can achieve readouts at cellular resolution such as single-cell RNA sequencing (scRNA-seq) have provided a comprehensive characterization of the cellular proportions and phenotypes that populate the tumor microenvironment (TME). However, because of the sample dissociation steps required by these protocols, they fail to capture information related to the intricate spatial context in which cells operate as well as their dense networks of interactions. Spatial profiling technologies have recently emerged as a valuable way to investigate the physical organization of cells crowding the TME in intact tissues. In this review we first discuss how spatial profiling technologies have propelled TME characterization, and then explore their potential to improve both diagnosis and prognosis for cancer patients in the clinic.

The dawn of spatial omics

Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful-but still hard to navigate-landscape.

CellSighter: a neural network to classify cells in highly multiplexed images

Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighter's design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets.

Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists

Our understanding of the biology and management of human disease has undergone a remarkable evolution in recent decades. Improved understanding of the roles of complex immune populations in the tumor microenvironment has advanced our knowledge of antitumor immunity, and immunotherapy has radically improved outcomes for many advanced cancers. Digital pathology has unlocked new possibilities for the assessment and discovery of the tumor microenvironment, such as quantitative and spatial image analysis. Despite these advances, tissue-based evaluations for diagnosis and prognosis continue to rely on traditional practices, such as hematoxylin and eosin staining, supplemented by the assessment of single biomarkers largely using chromogenic immunohistochemistry (IHC). Such approaches are poorly suited to complex quantitative analyses and the simultaneous evaluation of multiple biomarkers. Thus, multiplex staining techniques have significant potential to improve diagnostic practice and immuno-oncology research. The different approaches to achieve multiplexed IHC and immunofluorescence are described in this study. Alternatives to multiplex immunofluorescence/IHC include epitope-based tissue mass spectrometry and digital spatial profiling (DSP), which require specialized platforms not available to most clinical laboratories. Virtual multiplexing, which involves digitally coregistering singleplex IHC stains performed on serial sections, is another alternative to multiplex staining. Regardless of the approach, analysis of multiplexed stains sequentially or simultaneously will benefit from standardized protocols and digital pathology workflows. Although this is a complex and rapidly advancing field, multiplex staining is now technically feasible for most clinical laboratories and may soon be leveraged for routine diagnostic use. This review provides an update on the current state of the art for tissue multiplexing, including the capabilities and limitations of different techniques, with an emphasis on potential relevance to clinical diagnostic practice.

Spatial mapping of cellular senescence: emerging challenges and opportunities

Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.

Recent advancements in tumour microenvironment landscaping for target selection and response prediction in immune checkpoint therapies achieved through spatial protein multiplexing analysis

Immune checkpoint therapies have significantly advanced cancer treatment. Nevertheless, the high costs and potential adverse effects associated with these therapies highlight the need for better predictive biomarkers to identify patients who are most likely to benefit from treatment. Unfortunately, the existing biomarkers are insufficient to identify such patients. New high-dimensional spatial technologies have emerged as a valuable tool for discovering novel biomarkers by analysing multiple protein markers at a single-cell resolution in tissue samples. These technologies provide a more comprehensive map of tissue composition, cell functionality, and interactions between different cell types in the tumour microenvironment. In this review, we provide an overview of how spatial protein-based multiplexing technologies have fuelled biomarker discovery and advanced the field of immunotherapy. In particular, we will focus on how these technologies contributed to (i) characterise the tumour microenvironment, (ii) understand the role of tumour heterogeneity, (iii) study the interplay of the immune microenvironment and tumour progression, (iv) discover biomarkers for immune checkpoint therapies (v) suggest novel therapeutic strategies.

Circulating tumour cells for early detection of clinically relevant cancer

Given that cancer mortality is usually a result of late diagnosis, efforts in the field of early detection are paramount to reducing cancer-related deaths and improving patient outcomes. Increasing evidence indicates that metastasis is an early event in patients with aggressive cancers, often occurring even before primary lesions are clinically detectable. Metastases are usually formed from cancer cells that spread to distant non-malignant tissues via the blood circulation, termed circulating tumour cells (CTCs). CTCs have been detected in patients with early stage cancers and, owing to their association with metastasis, might indicate the presence of aggressive disease, thus providing a possible means to expedite diagnosis and treatment initiation for such patients while avoiding overdiagnosis and overtreatment of those with slow-growing, indolent tumours. The utility of CTCs as an early diagnostic tool has been investigated, although further improvements in the efficiency of CTC detection are required. In this Perspective, we discuss the clinical significance of early haematogenous dissemination of cancer cells, the potential of CTCs to facilitate early detection of clinically relevant cancers, and the technological advances that might improve CTC capture and, thus, diagnostic performance in this setting.

Deciphering the tumour immune microenvironment cell by cell

Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

Inflammation in multiple sclerosis: consequences for remyelination and disease progression

Despite the large number of immunomodulatory or immunosuppressive treatments available to treat relapsing-remitting multiple sclerosis (MS), treatment of the progressive phase of the disease has not yet been achieved. This lack of successful treatment approaches is caused by our poor understanding of the mechanisms driving disease progression. Emerging concepts suggest that a combination of persisting focal and diffuse inflammation within the CNS and a gradual failure of compensatory mechanisms, including remyelination, result in disease progression. Therefore, promotion of remyelination presents a promising intervention approach. However, despite our increasing knowledge regarding the cellular and molecular mechanisms regulating remyelination in animal models, therapeutic increases in remyelination remain an unmet need in MS, which suggests that mechanisms of remyelination and remyelination failure differ fundamentally between humans and demyelinating animal models. New and emerging technologies now allow us to investigate the cellular and molecular mechanisms underlying remyelination failure in human tissue samples in an unprecedented way. The aim of this Review is to summarize our current knowledge regarding mechanisms of remyelination and remyelination failure in MS and in animal models of the disease, identify open questions, challenge existing concepts, and discuss strategies to overcome the translational roadblock in the field of remyelination-promoting therapies.

Precancerous Lesions of the Head and Neck Region and Their Stromal Aberrations: Piecemeal Data

Head and neck squamous cell carcinomas (HNSCCs) develop through a series of precancerous stages from a pool of potentially malignant disorders (PMDs). Although we understand the genetic changes that lead to HNSCC, our understanding of the role of the stroma in the progression from precancer to cancer is limited. The stroma is the primary battleground between the forces that prevent and promote cancer growth. Targeting the stroma has yielded promising cancer therapies. However, the stroma at the precancerous stage of HNSCCs is poorly defined, and we may miss opportunities for chemopreventive interventions. PMDs already exhibit many features of the HNSCC stroma, such as inflammation, neovascularization, and immune suppression. Still, they do not induce cancer-associated fibroblasts or destroy the basal lamina, the stroma's initial structure. Our review aims to summarize the current understanding of the transition from precancer to cancer stroma and how this knowledge can reveal opportunities and limitations for diagnostic, prognostic, and therapeutic decisions to benefit patients. We will discuss what may be needed to fulfill the promise of the precancerous stroma as a target to prevent progression to cancer.

Spatial profiling technologies illuminate the tumor microenvironment

The tumor microenvironment (TME) is composed of many different cellular and acellular components that together drive tumor growth, invasion, metastasis, and response to therapies. Increasing realization of the significance of the TME in cancer biology has shifted cancer research from a cancer-centric model to one that considers the TME as a whole. Recent technological advancements in spatial profiling methodologies provide a systematic view and illuminate the physical localization of the components of the TME. In this review, we provide an overview of major spatial profiling technologies. We present the types of information that can be extracted from these data and describe their applications, findings and challenges in cancer research. Finally, we provide a future perspective of how spatial profiling could be integrated into cancer research to improve patient diagnosis, prognosis, stratification to treatment and development of novel therapeutics.

Breast Cancer Survival Outcomes and Tumor-Associated Macrophage Markers: A Systematic Review and Meta-Analysis

INTRODUCTION

Tumor-associated macrophages (TAMs) in breast cancer are associated with a poor prognosis. Early studies of TAMs were largely limited to the pan-macrophage marker CD68, however, more recently, an increasing number of studies have used CD163, a marker expressed by alternatively activated M2 macrophages and TAM subsets. We hypothesized that CD163-positive (CD163+) TAMs would be a better predictor of survival outcomes in breast cancer compared to CD68+  TAMs.

METHODS

We performed a systematic literature search of trials (from 1900 to August 2020) reporting overall survival (OS) or progression-free survival (PFS), breast cancer-specific survival (BCSS), TAM phenotype, and density. Thirty-two studies with 8446 patients were included. Meta-analyses were carried out on hazard ratios (HRs) for survival outcomes of breast cancer patients with a high density of TAMs (CD68+ and/or CD163+) compared to a low density of TAMs.

RESULTS

A high density of TAMs (CD68+ and/or CD163+) was associated with decreased OS (HR 1.69, 95% CI 1.37-2.07) and reduced PFS (HR 1.64; 95% CI 1.35-1.99). Subgrouping by CD marker type showed a lower OS for high density of CD163+ TAMs (HR 2.24; 95% CI 1.71-2.92) compared to a high density of CD68+  TAMs (HR 1.5; 95% CI 1.12-2). A high density of TAMs (CD68+  and/or CD163+) in triple-negative breast cancer (TNBC) cases was associated with lower OS (HR 2.81, 95% CI 1.35-5.84).

CONCLUSION

Compared to CD68+  TAMs, a high density of CD163+ TAMs that express a similar phenotype to M2 macrophages are a better predictor of poor survival outcomes in breast cancer.

A 31-plex panel for high-dimensional single-cell analysis of murine preclinical models of solid tumors by imaging mass cytometry

Currently, the study of resistance mechanisms and disease progression in cancer relies on the capacity to analyze tumors as a complex ecosystem of healthy and malignant cells. Therefore, one of the current challenges is to decipher the intra-tumor heterogeneity and especially the spatial distribution and interactions of the different cellular actors within the tumor. Preclinical mouse models are widely used to extend our understanding of the tumor microenvironment (TME). Such models are becoming more sophisticated and allow investigating questions that cannot be addressed in clinical studies. Indeed, besides studying the tumor cell interactions within their environment, mouse models allow evaluating the efficacy of new drugs and delivery approaches, treatment posology, and toxicity. Spatially resolved analyses of the intra-tumor heterogeneity require global approaches to identify and localize a large number of different cell types. For this purpose, imaging mass cytometry (IMC) is a major asset in the field of human immuno-oncology. However, the paucity of validated IMC panels to study TME in pre-clinical mouse models remains a critical obstacle to translational or basic research in oncology. Here, we validated a panel of 31 markers for studying at the single-cell level the TME and the immune landscape for discovering/characterizing cells with complex phenotypes and the interactions shaping the tumor ecosystem in mouse models.

Single-cell high-dimensional imaging mass cytometry: one step beyond in oncology

Solid tumors have a dynamic ecosystem in which malignant and non-malignant (endothelial, stromal, and immune) cell types constantly interact. Importantly, the abundance, localization, and functional orientation of each cell component within the tumor microenvironment vary significantly over time and in response to treatment. Such intratumoral heterogeneity influences the tumor course and its sensitivity to treatments. Recently, high-dimensional imaging mass cytometry (IMC) has been developed to explore the tumor ecosystem at the single-cell level. In the last years, several studies demonstrated that IMC is a powerful tool to decipher the tumor complexity. In this review, we summarize the potential of this technology and how it may be useful for cancer research (from preclinical to clinical studies).

Notch-dependent cooperativity between myeloid lineages promotes Langerhans cell histiocytosis pathology

Langerhans cell histiocytosis (LCH) is a potentially fatal neoplasm characterized by the aberrant differentiation of mononuclear phagocytes, driven by mitogen-activated protein kinase (MAPK) pathway activation. LCH cells may trigger destructive pathology yet remain in a precarious state finely balanced between apoptosis and survival, supported by a unique inflammatory milieu. The interactions that maintain this state are not well known and may offer targets for intervention. Here, we used single-cell RNA-seq and protein analysis to dissect LCH lesions, assessing LCH cell heterogeneity and comparing LCH cells with normal mononuclear phagocytes within lesions. We found LCH discriminatory signatures pointing to senescence and escape from tumor immune surveillance. We also uncovered two major lineages of LCH with DC2- and DC3/monocyte-like phenotypes and validated them in multiple pathological tissue sites by high-content imaging. Receptor-ligand analyses and lineage tracing in vitro revealed Notch-dependent cooperativity between DC2 and DC3/monocyte lineages during expression of the pathognomonic LCH program. Our results present a convergent dual origin model of LCH with MAPK pathway activation occurring before fate commitment to DC2 and DC3/monocyte lineages and Notch-dependent cooperativity between lineages driving the development of LCH cells.

Spatial omics technologies at multimodal and single cell/subcellular level

Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications.

Spatial multi-omics analyses of the tumor immune microenvironment

In the past decade, single-cell technologies have revealed the heterogeneity of the tumor-immune microenvironment at the genomic, transcriptomic, and proteomic levels and have furthered our understanding of the mechanisms of tumor development. Single-cell technologies have also been used to identify potential biomarkers. However, spatial information about the tumor-immune microenvironment such as cell locations and cell-cell interactomes is lost in these approaches. Recently, spatial multi-omics technologies have been used to study transcriptomes, proteomes, and metabolomes of tumor-immune microenvironments in several types of cancer, and the data obtained from these methods has been combined with immunohistochemistry and multiparameter analysis to yield markers of cancer progression. Here, we review numerous cutting-edge spatial 'omics techniques, their application to study of the tumor-immune microenvironment, and remaining technical challenges.