MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure

Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution

The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME—by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments—while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.

Mapping the Proteoform Landscape of Five Human Tissues

A functional understanding of the human body requires structure-function studies of proteins at scale. The chemical structure of proteins is controlled at the transcriptional, translational, and post-translational levels, creating a variety of products with modulated functions within the cell. The term "proteoform" encapsulates this complexity at the level of chemical composition. Comprehensive mapping of the proteoform landscape in human tissues necessitates analytical techniques with increased sensitivity and depth of coverage. Here, we took a top-down proteomics approach, combining data generated using capillary zone electrophoresis (CZE) and nanoflow reversed-phase liquid chromatography (RPLC) hyphenated to mass spectrometry to identify and characterize proteoforms from the human lungs, heart, spleen, small intestine, and kidneys. CZE and RPLC provided complementary post-translational modification and proteoform selectivity, thereby enhancing the overall proteome coverage when used in combination. Of the 11,466 proteoforms identified in this study, 7373 (64%) were not reported previously. Large differences in the protein and proteoform level were readily quantified, with initial inferences about proteoform biology operative in the analyzed organs. Differential proteoform regulation of defensins, glutathione transferases, and sarcomeric proteins across tissues generate hypotheses about how they function and are regulated in human health and disease.

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets

Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.

Advances in mass cytometry and its applicability to digital pathology in clinical-translational cancer research

The development and subsequent adaptation of mass cytometry for the histological analysis of tissue sections has allowed the simultaneous spatial characterization of multiple components. This is useful to find the correlation between the genotypic and phenotypic profile of tumor cells and their environment in clinical-translational studies. In this revision, we provide an overview of the most relevant hallmarks in the development, implementation and application of multiplexed imaging in the study of cancer and other conditions. A special focus is placed on studies based on imaging mass cytometry (IMC) and multiplexed ion beam imaging (MIBI). The purpose of this review is to help our readers become familiar with the verification techniques employed on this tool and outline the multiple applications reported in the literature. This review will also provide guidance on the use of IMC or MIBI in any field of biomedical research.

Metalloproteomics for Biomedical Research: Methodology and Applications

Metals are essential components in life processes and participate in many important biological processes. Dysregulation of metal homeostasis is correlated with many diseases. Metals are also frequently incorporated into diagnosis and therapeutics. Understanding of metal homeostasis under (patho)physiological conditions and the molecular mechanisms of action of metallodrugs in biological systems has positive impacts on human health. As an emerging interdisciplinary area of research, metalloproteomics involves investigating metal-protein interactions in biological systems at a proteome-wide scale, has received growing attention, and has been implemented into metal-related research. In this review, we summarize the recent advances in metalloproteomics methodologies and applications. We also highlight emerging single-cell metalloproteomics, including time-resolved inductively coupled plasma mass spectrometry, mass cytometry, and secondary ion mass spectrometry. Finally, we discuss future perspectives in metalloproteomics, aiming to attract more original research to develop more advanced methodologies, which could be utilized rapidly by biochemists or biologists to expand our knowledge of how metal functions in biology and medicine. Expected final online publication date for the Annual Review of Biochemistry, Volume 91 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts

Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.

The immunoregulatory landscape of human tuberculosis granulomas

Tuberculosis (TB) in humans is characterized by formation of immune-rich granulomas in infected tissues, the architecture and composition of which are thought to affect disease outcome. However, our understanding of the spatial relationships that control human granulomas is limited. Here, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) to image 37 proteins in tissues from patients with active TB. We constructed a comprehensive atlas that maps 19 cell subsets across 8 spatial microenvironments. This atlas shows an IFN-γ-depleted microenvironment enriched for TGF-β, regulatory T cells and IDO1⁺ PD-L1⁺ myeloid cells. In a further transcriptomic meta-analysis of peripheral blood from patients with TB, immunoregulatory trends mirror those identified by granuloma imaging. Notably, PD-L1 expression is associated with progression to active TB and treatment response. These data indicate that in TB granulomas, there are local spatially coordinated immunoregulatory programs with systemic manifestations that define active TB.

Squidpy: a scalable framework for spatial omics analysis

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.

Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

Advances in Surface Enhanced Raman Spectroscopy for in Vivo Imaging in Oncology

In the last two decades, the application of surface enhanced Raman scattering (SERS) nanoparticles for preclinical cancer imaging has attracted increasing attention. Raman imaging with SERS nanoparticles offers unparalleled sensitivity, providing a platform for molecular targeting, and granting multiplexed and multimodal imaging capabilities. Recent progress has been facilitated not only by the optimization of the SERS contrast agents themselves, but also by the developments in Raman imaging approaches and instrumentation. In this article, we review the principles of Raman scattering and SERS, present advances in Raman instrumentation specific to cancer imaging, and discuss the biological means of ensuring selective in vivo uptake of SERS contrast agents for targeted, multiplexed, and multimodal imaging applications. We offer our perspective on areas that must be addressed in order to facilitate the clinical translation of SERS contrast agents for in vivo imaging in oncology.

From imaging a single cell to implementing precision medicine: an exciting new era

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE

Synaptic molecular characterization is limited for Alzheimer’s disease (AD). Our newly invented mass cytometry–based method, synaptometry by time of flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), nonhuman primates (NHPs), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed previously unidentified findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF.

Oligonucleotide conjugated antibody strategies for cyclic immunostaining

A number of highly multiplexed immunostaining and imaging methods have advanced spatial proteomics of cancer for improved treatment strategies. While a variety of methods have been developed, the most widely used methods are limited by harmful signal removal techniques, difficulties with reagent production and antigen sensitivity. Multiplexed immunostaining employing oligonucleotide (oligos)-barcoded antibodies is an alternative approach that is growing in popularity. However, challenges remain in consistent conjugation of oligos to antibodies with maintained antigenicity as well as non-destructive, robust and cost-effective signal removal methods. Herein, a variety of oligo conjugation and signal removal methods were evaluated in the development of a robust oligo conjugated antibody cyclic immunofluorescence (Ab-oligo cyCIF) methodology. Both non- and site-specific conjugation strategies were assessed to label antibodies, where site-specific conjugation resulted in higher retained binding affinity and antigen-specific staining. A variety of fluorescence signal removal methods were also evaluated, where incorporation of a photocleavable link (PCL) resulted in full fluorescence signal removal with minimal tissue disruption. In summary, this work resulted in an optimized Ab-oligo cyCIF platform capable of generating high dimensional images to characterize the spatial proteomics of the hallmarks of cancer.

Cancer as a tool for preclinical psychoneuroimmunology

Cancer represents a novel homeostatic challenge to the host system. How the brain senses and responds to changes in peripheral physiology elicited by tumor growth is a largely untapped area of research. This is especially relevant given the widespread prevalence of systemic problems that people with various types of cancer experience. These include disruptions in sleep/wake cycles, cognitive function, depression, and changes in appetite/food intake, among others. Critically, many of these problems are evident prior to diagnosis, indicating that their etiology is potentially distinct from the effects of cancer treatment or the stress of a cancer diagnosis. Psychoneuroimmunology (PNI) is well equipped to tackle these types of problems, as it uses approaches from multiple disciplines to understand how specific stimuli (endogenous and environmental) are transduced into neural, endocrine, and immune signals that ultimately regulate health and behavior. In this article, I first provide a brief historical perspective of cancer and PNI, introduce the idea of cancer as a systemic homeostatic challenge, and provide examples from preclinical literature supporting this hypothesis. Given the rise of advanced tools in neuroscience (e.g., calcium imaging), we can now monitor and manipulate genetically defined neural circuits over the extended time scales necessary to disentangle distal communication between peripheral tumors and the brain.

High-Dimensional Tissue Profiling by Multiplexed Ion Beam Imaging

Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF) enables high-dimensional imaging in situ of clinical specimens at single-cell resolution. In MIBI-TOF, tissue sections are stained with dozens of metal-labeled antibodies, whose abundance and location are read by secondary ionization mass spectrometry. The result is a multi-dimensional image, depicting sub-cellular expression and localization for dozens of distinct proteins in situ. Here, we describe the staining and imaging procedures of a MIBI-TOF experiment.

Targeted Mass Spectrometry Enables Multiplexed Quantification of Immunomodulatory Proteins in Clinical Biospecimens

Immunotherapies are revolutionizing cancer care, producing durable responses and potentially cures in a subset of patients. However, response rates are low for most tumors, grade 3/4 toxicities are not uncommon, and our current understanding of tumor immunobiology is incomplete. While hundreds of immunomodulatory proteins in the tumor microenvironment shape the anti-tumor response, few of them can be reliably quantified. To address this need, we developed a multiplex panel of targeted proteomic assays targeting 52 peptides representing 46 proteins using peptide immunoaffinity enrichment coupled to multiple reaction monitoring-mass spectrometry. We validated the assays in tissue and plasma matrices, where performance figures of merit showed over 3 orders of dynamic range and median inter-day CVs of 5.2% (tissue) and 21% (plasma). A feasibility study in clinical biospecimens showed detection of 48/52 peptides in frozen tissue and 38/52 peptides in plasma. The assays are publicly available as a resource for the research community.

Multiplexed Ion Beam Imaging: Insights into Pathobiology

Next-generation tools for multiplexed imaging have driven a new wave of innovation in understanding how single-cell function and tissue structure are interrelated. In previous work, we developed multiplexed ion beam imaging by time of flight, a highly multiplexed platform that uses secondary ion mass spectrometry to image dozens of antibodies tagged with metal reporters. As instrument throughput has increased, the breadth and depth of imaging data have increased as well. To extract meaningful information from these data, we have developed tools for cell identification, cell classification, and spatial analysis. In this review, we discuss these tools and provide examples of their application in various contexts, including ductal carcinoma in situ, tuberculosis, and Alzheimer's disease. We hope the synergy between multiplexed imaging and automated image analysis will drive a new era in anatomic pathology and personalized medicine wherein quantitative spatial signatures are used routinely for more accurate diagnosis, prognosis, and therapeutic selection. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Single-cell analysis of regions of interest (SCARI) using a photosensitive tag

The functional activity and differentiation potential of cells are determined by their interactions with surrounding cells. Approaches that allow unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a tradeoff between spatial resolution and cell profiling depth. Here, we develop a photocage-based technology that allows isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including primary human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran) cage coupled to a set of nanobodies allows high-resolution photo-uncaging of different cell types in areas of interest. Single-cell RNA-sequencing of spatially defined CD8+ T cells is used to exemplify the feasibility of identifying location-dependent cell states. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples.

The Application of Single-Cell Technologies in Cardiovascular Research

Cardiovascular diseases (CVDs) are the leading cause of deaths in the world. The intricacies of the cellular composition and tissue microenvironment in heart and vasculature complicate the dissection of molecular mechanisms of CVDs. Over the past decade, the rapid development of single-cell omics technologies generated vast quantities of information at various biological levels, which have shed light on the cellular and molecular dynamics in cardiovascular development, homeostasis and diseases. Here, we summarize the latest single-cell omics techniques, and show how they have facilitated our understanding of cardiovascular biology. We also briefly discuss the clinical value and future outlook of single-cell applications in the field.

Atlas of clinically distinct cell states and ecosystems across human solid tumors

Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.

Single cell biology-a Keystone Symposia report

Single cell biology has the potential to elucidate many critical biological processes and diseases, from development and regeneration to cancer. Single cell analyses are uncovering the molecular diversity of cells, revealing a clearer picture of the variation among and between different cell types. New techniques are beginning to unravel how differences in cell state-transcriptional, epigenetic, and other characteristics-can lead to different cell fates among genetically identical cells, which underlies complex processes such as embryonic development, drug resistance, response to injury, and cellular reprogramming. Single cell technologies also pose significant challenges relating to processing and analyzing vast amounts of data collected. To realize the potential of single cell technologies, new computational approaches are needed. On March 17-19, 2021, experts in single cell biology met virtually for the Keystone eSymposium "Single Cell Biology" to discuss advances both in single cell applications and technologies.

SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment

Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. Here we report on the spatial single nuclear metabolomics (SEAM) method, a flexible platform combining high-spatial-resolution imaging mass spectrometry and a set of computational algorithms that can display multiscale and multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We first applied SEAM to a range of wild-type mouse tissues, then delineated a consistent pattern of metabolic zonation in mouse liver. We further studied the spatial metabolic profile in the human fibrotic liver. We discovered subpopulations of hepatocytes with special metabolic features associated with their proximity to the fibrotic niche, and validated this finding by spatial transcriptomics with Geo-seq. These demonstrations highlighted SEAM's ability to explore the spatial metabolic profile and tissue histology at the single-cell level, leading to a deeper understanding of tissue metabolic organization.

Personalized models of heterogeneous 3D epithelial tumor microenvironments: Ovarian cancer as a model

Intractable human diseases such as cancers, are context dependent, unique to both the individual patient and to the specific tumor microenvironment. However, conventional cancer treatments are often nonspecific, targeting global similarities rather than unique drivers. This limits treatment efficacy across heterogeneous patient populations and even at different tumor locations within the same patient. Ultimately, this poor efficacy can lead to adverse clinical outcomes and the development of treatment-resistant relapse. To prevent this and improve outcomes, it is necessary to be selective when choosing a patient's optimal adjuvant treatment. In this review, we posit the use of personalized, tumor-specific models (TSM) as tools to achieve this remarkable feat. First, using ovarian cancer as a model disease, we outline the heterogeneity and complexity of both the cellular and extracellular components in the tumor microenvironment. Then we examine the advantages and disadvantages of contemporary cancer models and the rationale for personalized TSM. We discuss how to generate precision 3D models through careful and detailed analysis of patient biopsies. Finally, we provide clinically relevant applications of these versatile personalized cancer models to highlight their potential impact. These models are ideal for a myriad of fundamental cancer biology and translational studies. Importantly, these approaches can be extended to other carcinomas, facilitating the discovery of new therapeutics that more effectively target the unique aspects of each individual patient's TME. STATEMENT OF SIGNIFICANCE: In this article, we have presented the case for the application of biomaterials in developing personalized models of complex diseases such as cancers. TSM could bring about breakthroughs in the promise of precision medicine. The critical components of the diverse tumor microenvironments, that lead to treatment failures, include cellular- and extracellular matrix- heterogeneity, and biophysical signals to the cells. Therefore, we have described these dynamic components of the tumor microenvironments, and have highlighted how contemporary biomaterials can be utilized to create personalized in vitro models of cancers. We have also described the application of the TSM to predict the dynamic patterns of disease progression, and predict effective therapies that can produce durable responses, limit relapses, and treat any minimal residual disease.

Fabrication approaches for high-throughput and biomimetic disease modeling

There is often a tradeoff between in vitro disease modeling platforms that capture pathophysiologic complexity and those that are amenable to high-throughput fabrication and analysis. However, this divide is closing through the application of a handful of fabrication approaches-parallel fabrication, automation, and flow-driven assembly-to design sophisticated cellular and biomaterial systems. The purpose of this review is to highlight methods for the fabrication of high-throughput biomaterial-based platforms and showcase examples that demonstrate their utility over a range of throughput and complexity. We conclude with a discussion of future considerations for the continued development of higher-throughput in vitro platforms that capture the appropriate level of biological complexity for the desired application. STATEMENT OF SIGNIFICANCE: There is a pressing need for new biomedical tools to study and understand disease. These platforms should mimic the complex properties of the body while also permitting investigation of many combinations of cells, extracellular cues, and/or therapeutics in high-throughput. This review summarizes emerging strategies to fabricate biomimetic disease models that bridge the gap between complex tissue-mimicking microenvironments and high-throughput screens for personalized medicine.

Effect of intranasal corticosteroid treatment on allergen-induced changes in group 2 innate lymphoid cells in allergic rhinitis with mild asthma

BACKGROUND

Allergic rhinitis is characterized by rhinorrhea, nasal congestion, sneezing and nasal pruritus. Group 2 innate lymphoid cells (ILC2s), CD4⁺ T cells and eosinophils in nasal mucosa are increased significantly after nasal allergen challenge (NAC). Effects of intranasal corticosteroids (INCS) on ILC2s remain to be investigated.

METHODS

Subjects (n = 10) with allergic rhinitis and mild asthma were enrolled in a single-blind, placebo-controlled, sequential treatment study and treated twice daily with intranasal triamcinolone acetonide (220 µg) or placebo for 14 days, separated by a 7-day washout period. Following treatment, subjects underwent NAC and upper airway function was assessed. Cells from the nasal mucosa and blood, sampled 24 h post-NAC, underwent flow cytometric enumeration for ILC2s, CD4⁺ T and eosinophil progenitor (EoPs) levels. Cell differentials and cytokine levels were assessed in nasal lavage.

RESULTS

Treatment with INCS significantly attenuated ILC2s, IL-5⁺ /IL-13⁺ ILC2s, HLA-DR⁺ ILC2s and CD4⁺ T cells in the nasal mucosa, 24 h post-NAC. EoP in nasal mucosa was significantly increased, while mature eosinophils were significantly decreased, 24 h post-NAC in INCS versus placebo treatment arm. Following INCS treatment, IL-2, IL-4, IL-5 and IL-13 were significantly attenuated 24 h post-NAC accompanied by significant improvement in upper airway function.

CONCLUSION

Pre-treatment with INCS attenuates allergen-induced increases in ILC2s, CD4⁺ T cells and terminal differentiation of EoPs in the nasal mucosa of allergic rhinitis patients with mild asthma, with little systemic effect. Attenuation of HLA-DR expression by ILC2s may be an additional mechanism by which steroids modulate adaptive immune responses in the upper airways.

An overview of multiplexed analyses of CAR T-cell therapies: insights and potential

INTRODUCTION

Cancer immunotherapy is a rapidly growing field with exponential advancement in engineered immune cell-based therapies. For instance, an engineered chimeric antigen receptor (CAR) can be introduced in T-cells or other immune cells and adoptively transferred to target and kill cancer cells in hematologic malignancies or solid tumors. The first CAR-T-cell (CAR-T) therapy has been developed against CD19, a B-cell marker expressed on lymphoma and lymphoblastic leukemia. To allow for personalized treatment, proteomics approaches could provide insights into biomarkers for CAR-T therapy efficacy and toxicity.

AREAS COVERED

We researched the most recent technology methods of biomarker evaluation used in the laboratory and clinical setting. Publications of CAR-T biomarkers were then systematically reviewed to provide a narrative of the most validated biomarkers of CAR-T efficacy and toxicity. Examples of biomarkers include CAR-T functionality and phenotype as well as interleukin-6 and other cytokines.

EXPERT COMMENTARY

Biomarkers of CAR-T efficacy and toxicity have been identified, but still need to be validated and standardized across institutions. Moreover, few are used in the clinical setting due to limitations in real-time technology. Expansion of biomarker research could provide better understanding of patient response and risk of life-threatening side effects with potential for improved precision medicine.

Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques

Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.

Uncovering Spatiotemporal Heterogeneity of High-Grade Gliomas: From Disease Biology to Therapeutic Implications

Glioblastomas (GBM) are the most common and aggressive tumors of the central nervous system. Rapid tumor growth and diffuse infiltration into healthy brain tissue, along with high intratumoral heterogeneity, challenge therapeutic efficacy and prognosis. A better understanding of spatiotemporal tumor heterogeneity at the histological, cellular, molecular, and dynamic levels would accelerate the development of novel treatments for this devastating brain cancer. Histologically, GBM is characterized by nuclear atypia, cellular pleomorphism, necrosis, microvascular proliferation, and pseudopalisades. At the cellular level, the glioma microenvironment comprises a heterogeneous landscape of cell populations, including tumor cells, non-transformed/reactive glial and neural cells, immune cells, mesenchymal cells, and stem cells, which support tumor growth and invasion through complex network crosstalk. Genomic and transcriptomic analyses of gliomas have revealed significant inter and intratumoral heterogeneity and insights into their molecular pathogenesis. Moreover, recent evidence suggests that diverse dynamics of collective motion patterns exist in glioma tumors, which correlate with histological features. We hypothesize that glioma heterogeneity is not stochastic, but rather arises from organized and dynamic attributes, which favor glioma malignancy and influences treatment regimens. This review highlights the importance of an integrative approach of glioma histopathological features, single-cell and spatially resolved transcriptomic and cellular dynamics to understand tumor heterogeneity and maximize therapeutic effects.

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence

Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.

Spatial omics and multiplexed imaging to explore cancer biology

Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

Engineering stromal heterogeneity in cancer

Based on our exponentially increasing knowledge of stromal heterogeneity from advances in single-cell technologies, the notion that stromal cell types exist as a spectrum of unique subpopulations that have specific functions and spatial distributions in the tumor microenvironment has significant impact on tumor modeling for drug development and personalized drug testing. In this Review, we discuss the importance of incorporating stromal heterogeneity and tumor architecture, and propose an overall approach to guide the reconstruction of stromal heterogeneity in vitro for tumor modeling. These next-generation tumor models may support the development of more precise drugs targeting specific stromal cell subpopulations, as well as enable improved recapitulation of patient tumors in vitro for personalized drug testing.

Subcellular localization of biomolecules and drug distribution by high-definition ion beam imaging

Simultaneous visualization of the relationship between multiple biomolecules and their ligands or small molecules at the nanometer scale in cells will enable greater understanding of how biological processes operate. We present here high-definition multiplex ion beam imaging (HD-MIBI), a secondary ion mass spectrometry approach capable of high-parameter imaging in 3D of targeted biological entities and exogenously added structurally-unmodified small molecules. With this technology, the atomic constituents of the biomolecules themselves can be used in our system as the “tag” and we demonstrate measurements down to ~30 nm lateral resolution. We correlated the subcellular localization of the chemotherapy drug cisplatin simultaneously with five subnuclear structures. Cisplatin was preferentially enriched in nuclear speckles and excluded from closed-chromatin regions, indicative of a role for cisplatin in active regions of chromatin. Unexpectedly, cells surviving multi-drug treatment with cisplatin and the BET inhibitor JQ1 demonstrated near total cisplatin exclusion from the nucleus, suggesting that selective subcellular drug relocalization may modulate resistance to this important chemotherapeutic treatment. Multiplexed high-resolution imaging techniques, such as HD-MIBI, will enable studies of biomolecules and drug distributions in biologically relevant subcellular microenvironments by visualizing the processes themselves in concert, rather than inferring mechanism through surrogate analyses.

Multiplexed ion beam imaging can provide subcellular localisation information but with limited resolution. Here the authors report an ion beam imaging method with nanoscale resolution which they use to assess the subcellular distribution of cisplatin.

Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement for Multiplexed Images

Multiplex imaging technologies are now routinely capable of measuring more than 40 antibody-labeled parameters in single cells. However, lateral spillage of signals in densely packed tissues presents an obstacle to the assignment of high-dimensional spatial features to individual cells for accurate cell-type annotation. We devised a method to correct for lateral spillage of cell surface markers between adjacent cells termed REinforcement Dynamic Spillover EliminAtion (REDSEA). The use of REDSEA decreased contaminating signals from neighboring cells. It improved the recovery of marker signals across both isotopic (i.e., Multiplexed Ion Beam Imaging) and immunofluorescent (i.e., Cyclic Immunofluorescence) multiplexed images resulting in a marked improvement in cell-type classification.

High-Dimensional Image Analysis using Histocytometry

Histocytometry is a technique for processing multiparameter microscopy images using computational approaches to identify and quantify cellular phenotypes. It allows for spatial analyses of cellular phenotypes in relation to each other and within defined spatial regions. The benefit of this technique over manual annotation and characterization of cells is a high degree of automation/throughput, significantly decreased user bias, and increased reproducibility. Recently, an increase in freely available software amenable to or deliberately designed for histocytometry has resulted in these complex analyses being available to a broader base of users who have amassed multi-component microscopic imaging data. This article provides an overview of a histocytometry pipeline, focusing on the strategic planning and software requirements to allow readers to perform cell segmentation, phenotyping, and spatial analyses to advance their research outputs. © 2021 Wiley Periodicals LLC.

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

Tumors are not only aggregates of malignant cells but also well-organized complex ecosystems. The immunological components within tumors, termed the tumor immune microenvironment (TIME), have long been shown to be strongly related to tumor development, recurrence and metastasis. However, conventional studies that underestimate the potential value of the spatial architecture of the TIME are unable to completely elucidate its complexity. As innovative high-flux and high-dimensional technologies emerge, researchers can more feasibly and accurately detect and depict the spatial architecture of the TIME. These findings have improved our understanding of the complexity and role of the TIME in tumor biology. In this review, we first epitomized some representative emerging technologies in the study of the spatial architecture of the TIME and categorized the description methods used to characterize these structures. Then, we determined the functions of the spatial architecture of the TIME in tumor biology and the effects of the gradient of extracellular nonspecific chemicals (ENSCs) on the TIME. We also discussed the potential clinical value of our understanding of the spatial architectures of the TIME, as well as current limitations and future prospects in this novel field. This review will bring spatial architectures of the TIME, an emerging dimension of tumor ecosystem research, to the attention of more researchers and promote its application in tumor research and clinical practice.

Multiplexed Ion Beam Imaging Readout of Single-Cell Immunoblotting

Improvements in single-cell protein analysis are required to study the cell-to-cell variation inherent to diseases, including cancer. Single-cell immunoblotting (scIB) offers proteoform detection specificity, but often relies on fluorescence-based readout and is therefore limited in multiplexing capability. Among rising multiplexed imaging methods is multiplexed ion beam imaging by time-of-flight (MIBI-TOF), a mass spectrometry imaging technology. MIBI-TOF employs metal-tagged antibodies that do not suffer from spectral overlap to the same degree as fluorophore-tagged antibodies. We report for the first-time MIBI-TOF of single-cell immunoblotting (scIB-MIBI-TOF). The scIB assay subjects single-cell lysate to protein immunoblotting on a microscale device consisting of a 50- to 75-μm thick hydrated polyacrylamide (PA) gel matrix for protein immobilization prior to in-gel immunoprobing. We confirm antibody-protein binding in the PA gel with indirect fluorescence readout of metal-tagged antibodies. Since MIBI-TOF is a layer-by-layer imaging technique, and our protein target is immobilized within a 3D PA gel layer, we characterize the protein distribution throughout the PA gel depth by fluorescence confocal microscopy and confirm that the highest signal-to-noise ratio is achieved by imaging the entirety of the PA gel depth. Accordingly, we report the required MIBI-TOF ion dose strength needed to image varying PA gel depths. Lastly, by imaging ∼42% of PA gel depth with MIBI-TOF, we detect two isoelectrically separated TurboGFP (tGFP) proteoforms from individual glioblastoma cells, demonstrating that highly multiplexed mass spectrometry-based readout is compatible with scIB.

National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: IV. The 2020 Highly morbid forms report

Chronic graft-versus-host disease (GVHD) can be associated with significant morbidity, in part because of nonreversible fibrosis, which impacts physical functioning (eye, skin, lung manifestations) and mortality (lung, gastrointestinal manifestations). Progress in preventing severe morbidity and mortality associated with chronic GVHD is limited by a complex and incompletely understood disease biology and a lack of prognostic biomarkers. Likewise, treatment advances for highly morbid manifestations remain hindered by the absence of effective organ-specific approaches targeting "irreversible" fibrotic sequelae and difficulties in conducting clinical trials in a heterogeneous disease with small patient numbers. The purpose of this document is to identify current gaps, to outline a roadmap of research goals for highly morbid forms of chronic GVHD including advanced skin sclerosis, fasciitis, lung, ocular and gastrointestinal involvement, and to propose strategies for effective trial design. The working group made the following recommendations: (1) Phenotype chronic GVHD clinically and biologically in future cohorts, to describe the incidence, prognostic factors, mechanisms of organ damage, and clinical evolution of highly morbid conditions including long-term effects in children; (2) Conduct longitudinal multicenter studies with common definitions and research sample collections; (3) Develop new approaches for early identification and treatment of highly morbid forms of chronic GVHD, especially biologically targeted treatments, with a special focus on fibrotic changes; and (4) Establish primary endpoints for clinical trials addressing each highly morbid manifestation in relationship to the time point of intervention (early versus late). Alternative endpoints, such as lack of progression and improvement in physical functioning or quality of life, may be suitable for clinical trials in patients with highly morbid manifestations. Finally, new approaches for objective response assessment and exploration of novel trial designs for small populations are required.

Glycan Imaging Mass Spectrometry: Progress in Developing Clinical Diagnostic Assays for Tissues, Biofluids, and Cells

N-glycan imaging mass spectrometry (IMS) can rapidly and reproducibly identify changes in disease-associated N-linked glycosylation that are linked with histopathology features in standard formalin-fixed paraffin-embedded tissue samples. It can detect multiple N-glycans simultaneously and has been used to identify specific N-glycans and carbohydrate structural motifs as possible cancer biomarkers. Recent advancements in instrumentation and sample preparation are also discussed. The tissue N-glycan IMS workflow has been adapted to new glass slide-based assays for effective and rapid analysis of clinical biofluids, cultured cells, and immunoarray-captured glycoproteins for detection of changes in glycosylation associated with disease.

Highly Multiplexed Phenotyping of Immunoregulatory Proteins in the Tumor Microenvironment by CODEX Tissue Imaging

Immunotherapies are revolutionizing cancer treatment by boosting the natural ability of the immune system. In addition to antibodies against traditional checkpoint molecules or their ligands (i.e., CTLA-4, PD-1, and PD-L1), therapies targeting molecules such as ICOS, IDO-1, LAG-3, OX40, TIM-3, and VISTA are currently in clinical trials. To better inform clinical care and the design of therapeutic combination strategies, the co-expression of immunoregulatory proteins on individual immune cells within the tumor microenvironment must be robustly characterized. Highly multiplexed tissue imaging platforms, such as CO-Detection by indEXing (CODEX), are primed to meet this need by enabling >50 markers to be simultaneously analyzed in single-cells on formalin-fixed paraffin-embedded (FFPE) tissue sections. Assembly and validation of antibody panels is particularly challenging, with respect to the specificity of antigen detection and robustness of signal over background. Herein, we report the design, development, optimization, and application of a 56-marker CODEX antibody panel to eight cutaneous T cell lymphoma (CTCL) patient samples. This panel is comprised of structural, tumor, and immune cell markers, including eight immunoregulatory proteins that are approved or currently undergoing clinical trials as immunotherapy targets. Here we provide a resource to enable extensive high-dimensional, spatially resolved characterization of the tissue microenvironment across tumor types and imaging modalities. This framework provides researchers with a readily applicable blueprint to study tumor immunology, tissue architecture, and enable mechanistic insights into immunotherapeutic targets.

Nanoprojectile Secondary Ion Mass Spectrometry for Analysis of Extracellular Vesicles

We describe a technique based on secondary ion mass spectrometry with nanoprojectiles (NP-SIMS) for determining the protein content of extracellular vesicles, EVs, via tagged antibodies. The technique uses individual gold nanoprojectiles (e.g., Au₄₀₀⁴⁺ and Au₂₈₀₀⁸⁺), separated in time and space, to bombard a surface. For each projectile impact (10-20 nm in diameter), the co-emitted molecules are mass analyzed and recorded as an individual mass spectrum. Examining these individual mass spectra for co-localized species allows for nanoscale mass spectrometry to be performed. The high lateral resolution of this technique is well suited for analyzing nano-objects. SIMS is generally limited to analyzing small molecules (below ∼1500 Da); therefore, we evaluated three molecules (eosin, erythrosine, and BHHTEGST) as prospective mass spectrometry tags. We tested these on a model surface comprising a mixture of all three tags conjugated to antibodies and found that NP-SIMS could detect all three tags from a single projectile impact. Applying the method, we tagged two surface proteins common in urinary EVs, CD63 and CD81, with anti-CD63-erythrosine and anti-CD81-BHHTEGST. We found that NP-SIMS could determine the relative abundance of the two proteins and required only a few hundred or thousand EVs in the analysis region to detect the presence of the tagged antibodies.

Immunometabolism of Tissue-Resident Macrophages - An Appraisal of the Current Knowledge and Cutting-Edge Methods and Technologies

Tissue-resident macrophages exist in unique environments, or niches, that inform their identity and function. There is an emerging body of literature suggesting that the qualities of this environment, such as the types of cells and debris they eat, the intercellular interactions they form, and the length of time spent in residence, collectively what we call habitare, directly inform their metabolic state. In turn, a tissue-resident macrophage’s metabolic state can inform their function, including whether they resolve inflammation and protect the host from excessive perturbations of homeostasis. In this review, we summarize recent work that seeks to understand the metabolic requirements for tissue-resident macrophage identity and maintenance, for how they respond to inflammatory challenges, and for how they perform homeostatic functions or resolve inflammatory insults. We end with a discussion of the emerging technologies that are enabling, or will enable, in situ study of tissue-resident macrophage metabolism.

Clinical and research applications of multiplexed immunohistochemistry and in situ hybridization

Over the past decade, invention and adoption of novel multiplexing technologies for tissues have made increasing impacts in basic and translational research and, to a lesser degree, clinical medicine. Platforms capable of highly multiplexed immunohistochemistry or in situ RNA measurements promise evaluation of protein or RNA targets at levels of plex and sensitivity logs above traditional methods - all with preservation of spatial context. These methods promise objective biomarker quantification, markedly increased sensitivity, and single-cell resolution. Increasingly, development of novel technologies is enabling multi-omic interrogations with spatial correlation of RNA and protein expression profiles in the same sample. Such sophisticated methods will provide unprecedented insights into tissue biology, biomarker science, and, ultimately, patient health. However, this sophistication comes at significant cost, requiring extensive time, practical knowledge, and resources to implement. This review will describe the technical features, advantages, and limitations of currently available multiplexed immunohistochemistry and spatial transcriptomic platforms. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Bridging scales: From cell biology to physiology using in situ single-cell technologies

Biological organization crosses multiple spatial scales: from molecular, cellular, to tissues and organs. The proliferation of molecular profiling technologies enables increasingly detailed cataloging of the components at each scale. However, the scarcity of spatial profiling has made it challenging to bridge across these scales. Emerging technologies based on highly multiplexed in situ profiling are paving the way to study the spatial organization of cells and tissues in greater detail. These new technologies provide the data needed to cross the scale from cell biology to physiology and identify the fundamental principles that govern tissue organization. Here, we provide an overview of these key technologies and discuss the current and future insights these powerful techniques enable.

Mass Cytometry, Imaging Mass Cytometry, and Multiplexed Ion Beam Imaging Use in a Clinical Setting

Mass cytometry (MC), imaging mass cytometry (IMC), and multiplexed ion beam imaging (MIBI) represent a new generation of tools to understand increasingly complex systems. Although these technologies differ in their intended applications, with MC being most similar to flow cytometry, and IMC/MIBI being similar to immunohistochemistry, they all share a time of flight mass spectrometry (TOF MS) platform. These TOF MS platforms use metal conjugated antibodies as opposed to fluorophores, increasing the measurable parameters up to approximately 50 with a theoretic limit approximately 100 parameters. These tools are being adapted to understand highly complex systems in basic and clinical research.

MAUI (MBI Analysis User Interface)-An image processing pipeline for Multiplexed Mass Based Imaging

Mass Based Imaging (MBI) technologies such as Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and Imaging Mass Cytometry (IMC) allow for the simultaneous measurement of the expression levels of 40 or more proteins in biological tissue, providing insight into cellular phenotypes and organization in situ. Imaging artifacts, resulting from the sample, assay or instrumentation complicate downstream analyses and require correction by domain experts. Here, we present MBI Analysis User Interface (MAUI), a series of graphical user interfaces that facilitate this data pre-processing, including the removal of channel crosstalk, noise and antibody aggregates. Our software streamlines these steps and accelerates processing by enabling real-time and interactive parameter tuning across multiple images.

High-dimensional Imaging technologies allow to simultaneously measure the expression levels of dozens of proteins in biological tissue, providing insight into single-cell phenotypes and organization in situ. Imaging artifacts, resulting from the sample, assay or instrumentation complicate downstream analyses and require correction by domain experts. Here, we present MAUI, a series of graphical user interfaces that facilitate this data pre-processing, including the removal of channel crosstalk, noise and antibody aggregates. MAUI accelerates and automates these steps, such that reproducible, high-quality data will be the input for subsequent stages of analysis.

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

Regulatory and IgE+ B Cells in Allergic Asthma

Allergic asthma is triggered by inhalation of environmental allergens resulting in bronchial constriction and inflammation, which leads to clinical symptoms such as wheezing, coughing, and difficulty breathing. Asthmatic airway inflammation is initiated by inflammatory mediators released by granulocytic cells. However, the immunoglobulin E (IgE) antibody is necessary for the initiation of the allergic cascade, and IgE is produced and released exclusively by memory B cells and plasma cells. Acute allergen exposure has also been shown to increase IgE levels in the airways of patients diagnosed with allergic asthma; however, more studies are needed to understand local airway inflammation. Additionally, regulatory B cells (B_regs) have been shown to modulate IgE-mediated inflammatory processes in allergic asthma pathogenesis, particularly in mouse models of allergic airway disease. However, the levels and function of these IgE⁺ B cells and B_regs remain to be elucidated in human models of asthma. The overall objective for this chapter is to provide detailed methodological, and insightful technological advances to study the biology of B cells in allergic asthma pathogenesis. Specifically, we will describe how to investigate the frequency and function of IgE⁺ B cells and B_regs in allergic asthma, and the kinetics of these cells after allergen exposure in a human asthma model.

Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy

Advances in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or even millions of individual cells from malignant tumours at the single-cell level of resolution. This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and the details of the evolutionary trajectory of each tumour. Single-cell transcriptomics, the ability to track individual T cell clones through paired sequencing of the T cell receptor genes and high-dimensional single-cell spatial analysis are all areas of particular relevance to immuno-oncology. Multidimensional biomarker signatures will increasingly be crucial to guiding clinical decision-making in each patient with cancer. High-dimensional single-cell technologies are likely to provide the resolution and richness of data required to generate such clinically relevant signatures in immuno-oncology. In this Perspective, we describe advances made using transformative single-cell analysis technologies, especially in relation to clinical response and resistance to immunotherapy, and discuss the growing utility of single-cell approaches for answering important research questions.

Deep learning-based point-scanning super-resolution imaging

Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefiting from arbitrarily defined pixel sizes. The resolution, speed, sample preservation and signal-to-noise ratio (SNR) of point-scanning systems are difficult to optimize simultaneously. We show these limitations can be mitigated via the use of deep learning-based supersampling of undersampled images acquired on a point-scanning system, which we term point-scanning super-resolution (PSSR) imaging. We designed a 'crappifier' that computationally degrades high SNR, high-pixel resolution ground truth images to simulate low SNR, low-resolution counterparts for training PSSR models that can restore real-world undersampled images. For high spatiotemporal resolution fluorescence time-lapse data, we developed a 'multi-frame' PSSR approach that uses information in adjacent frames to improve model predictions. PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed and sensitivity. All the training data, models and code for PSSR are publicly available at 3DEM.org.