Analyzing proteome topology and function by automated multidimensional fluorescence microscopy

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.

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.

Viewing 3D spatial biology with highly-multiplexed Raman imaging: from spectroscopy to biotechnology

Understansding complex biological systems requires the simultaneous characterization of a large number of interacting components in their native 3D environment with high spatial resolution. Highly-multiplexed Raman imaging is an emerging general strategy for detecting biomarkers with scalable multiplexity and ultra-sensitivity based on a series of stimulated Raman scattering (SRS) techniques. Here we review recent advances in highly-multiplexed Raman imaging and how they contribute to the technological revolution in 3D spatial biology, focusing on the developmental pathway from spectroscopy study to biotechnology invention. We envision highly-multiplexed Raman imaging is taking off, which will greatly facilitate our understanding in biological and medical research fields.

Early-life vitamin A treatment rescues neonatal infection-induced durably impaired tolerogenic properties of celiac lymph nodes

Gut-draining mesenteric and celiac lymph nodes (mLNs and celLNs) critically contribute to peripheral tolerance toward food and microbial antigens by supporting the de novo induction of regulatory T cells (Tregs). These tolerogenic properties of mLNs and celLNs are stably imprinted within stromal cells (SCs) by microbial signals and vitamin A (VA), respectively. Here, we report that a single, transient gastrointestinal infection in the neonatal, but not adult, period durably abrogates the efficient Treg-inducing capacity of celLNs by altering the subset composition and gene expression profile of celLNSCs. These cells carry information about the early-life pathogen encounter until adulthood and durably instruct migratory dendritic cells entering the celLN with reduced tolerogenic properties. Mechanistically, transiently reduced VA levels cause long-lasting celLN functional impairment, which can be rescued by early-life treatment with VA. Together, our data highlight the therapeutic potential of VA to prevent sequelae post gastrointestinal infections in infants.

Ultrasensitive and Multiplexed Protein Imaging with Clickable and Cleavable Fluorophores

Single-cell spatial proteomic analysis holds great promise to advance our understanding of the composition, organization, interaction, and function of the various cell types in complex biological systems. However, the current multiplexed protein imaging technologies suffer from low detection sensitivity, limited multiplexing capacity, or are technically demanding. To tackle these issues, here, we report the development of a highly sensitive and multiplexed in situ protein profiling method using off-the-shelf antibodies. In this approach, the protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and cleavable fluorophores via click chemistry. Through repeated cycles of target staining, fluorescence imaging, and fluorophore cleavage, many proteins can be profiled in single cells in situ. Applying this approach, we successfully quantified 28 different proteins in human formalin-fixed paraffin-embedded (FFPE) tonsil tissue, which represents the highest multiplexing capacity among the tyramide signal amplification (TSA) methods. Based on their unique protein expression patterns and their microenvironment, ∼820,000 cells in the tissue are classified into distinct cell clusters. We also explored the cell-cell interactions between these varied cell clusters and observed that different subregions of the tissue are composed of cells from specific clusters.

Mast cell-derived interleukin-4 mediates activation of dendritic cell during toll-like receptor 2-mediated inflammation

Introduction

During an innate inflammation, immune cells form distinct pro- and anti-inflammatory regions around pathogen-containing core-regions. Mast cells are localized in an anti-inflammatory microenvironment during the resolution of an innate inflammation, suggesting antiinflammatory roles of these cells.

Methods

High-content imaging was used to investigated mast cell-dependent changes in the regional distribution of immune cells during an inflammation, induced by the toll-like receptor (TLR)-2 agonist zymosan.

Results

The distance between the zymosan-containing core-region and the anti-inflammatory region, described by M2-like macrophages, increased in mast cell-deficient mice. Absence of mast cells abolished dendritic cell (DC) activation, as determined by CD86-expression and localized the DCs in greater distance to zymosan particles. The CD86- DCs had a higher expression of the pro-inflammatory interleukins (IL)-1β and IL-12/23p40 as compared to activated CD86+ DCs. IL-4 administration restored CD86 expression, cytokine expression profile and localization of the DCs in mast cell-deficient mice. The IL-4 effects were mast cell-specific, since IL-4 reduction by eosinophil depletion did not affect activation of DCs.

Discussion

We found that mast cells induce DC activation selectively at the site of inflammation and thereby determine their localization within the inflammation. Overall, mast cells have antiinflammatory functions in this inflammation model and limit the size of the pro-inflammatory region surrounding the zymosan-containing core region.

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

Multiplexed imaging technologies have made it possible to interrogate complex tumor 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 label-free unsupervised context. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane markers to construct an a posteriori probability estimate of each pixel belonging to the nucleus, cell membrane, or background. It uses this estimate to segment each cell into its nuclear and cell-membrane compartments. We show that UNSEG is more internally consistent and better at generalizing to the complexity of tissue morphology than current deep learning methods. This allows UNSEG to unambiguously identify the cytoplasmic compartment of a cell, which we employ to demonstrate its use in an exemplar biological scenario. Within the UNSEG framework, we also introduce a new perturbed watershed algorithm capable of stably and automatically segmenting a cluster of cell nuclei into individual cell nuclei that increases the accuracy of classical watershed. Perturbed watershed can also be used as a standalone algorithm that researchers can incorporate within their supervised or unsupervised learning approaches to extend classical watershed, particularly in the multiplexed imaging context. Finally, as part of developing UNSEG, we have generated a high-quality annotated gastrointestinal tissue (GIT) dataset, which we anticipate will be useful for the broader research community. We demonstrate the efficacy of UNSEG on the GIT dataset, publicly available datasets, and on a range of practical scenarios. In these contexts, we also discuss the possibility of bias inherent in quantification of segmentation accuracy based on F 1 score. Segmentation, despite its long antecedents, remains a challenging problem, particularly in the context of tissue samples. UNSEG, an easy-to-use algorithm, provides an unsupervised approach to overcome this bottleneck, and as we discuss, can help improve deep learning based segmentation methods by providing a bridge between unsupervised and supervised learning paradigms.

Quantitative performance assessment of Ultivue multiplex panels in formalin-fixed, paraffin-embedded human and murine tumor specimens

We present a rigorous validation strategy to evaluate the performance of Ultivue multiplex immunofluorescence panels. We have quantified the accuracy and precision of four different multiplex panels (three human and one mouse) in tumor specimens with varying levels of T cell density. Our results show that Ultivue panels are typically accurate wherein the relative difference in cell proportion between a multiplex image and a 1-plex image is less than 20% for a given biomarker. Ultivue panels exhibited relatively high intra-run precision (CV ≤ 25%) and relatively low inter-run precision (CV >> 25%) which can be remedied by using local intensity thresholding to gate biomarker positivity. We also evaluated the reproducibility of cell-cell distance estimates measured from multiplex images which show high intra- and inter-run precision. We introduce a new metric, multiplex labeling efficiency, which can be used to benchmark the overall fidelity of the multiplex data across multiple batch runs. Taken together our results provide a comprehensive characterization of Ultivue panels and offer practical guidelines for analyzing multiplex images.

Meloxicam treatment disrupts the regional structure of innate inflammation sites by targeting the pro-inflammatory effects of prostanoids

BACKGROUND AND PURPOSE

Non-steroidal anti-inflammatory drugs (NSAIDs) are the most widely prescribed drugs in the world due to their analgesic, antipyretic and anti-inflammatory effects. However, NSAIDs inhibit prostanoid synthesis, interfering with their pro-inflammatory and anti-inflammatory functions and potentially prolonging acute inflammation.

EXPERIMENTAL APPROACH

We used high-content immunohistochemistry to define the impact of meloxicam treatment on spatially separated pro-inflammatory and anti-inflammatory processes during innate inflammation in mice induced by zymosan. This allowed us to determine the effect of meloxicam treatment on the organization of pro-inflammatory and anti-inflammatory microenvironments, thereby identifying relevant changes in immune cell localization, recruitment and activation.

KEY RESULTS

Meloxicam treatment reduced zymosan-induced thermal hypersensitivity at early time points but delayed its resolution. High-content immunohistochemistry revealed that the pro-inflammatory area was smaller after treatment, diminishing neutrophil recruitment, M1-like macrophage polarization, and especially phagocytosis by neutrophils and macrophages. The polarization of macrophages towards the M2-like anti-inflammatory phenotype was unaffected, and the number of anti-inflammatory eosinophils actually increased.

CONCLUSION AND IMPLICATIONS

High-content immunohistochemistry was able to identify relevant meloxicam-mediated effects on inflammatory processes based on alterations in the regional structure of inflammation sites. Meloxicam delayed the clearance of pathogens by inhibiting pro-inflammatory processes, causing prolonged inflammation. Our data suggest that the prescription of NSAIDs as a treatment during an acute pathogen-driven inflammation should be reconsidered in patients with compromised immune systems.

High-throughput proteomic analysis of chronic inflammatory skin diseases: Psoriasis and atopic dermatitis

Common characteristics in the pathogenesis of psoriasis (PS) and atopic dermatitis (AD) have been presumed, but only a few studies have clearly supported this. The current aim was to find possible similarities and differences in protein expression patterns between these two major chronic inflammatory skin diseases. High-throughput tandem mass spectrometry proteomic analysis was performed using full thickness skin samples from adult PS patients, AD patients and healthy subjects. We detected a combined total of 3045 proteins in the three study groups. According to principal component analysis, there was significant overlap between the proteomic profiles of PS and AD, and both clearly differed from that of healthy skin. The following validation of selected proteins with western blot analysis showed similar tendencies in expression levels and produced statistically significant results. The expression of periostin (POSTN) was consistently high in AD and very low or undetectable in PS (5% FDR corrected p < 0.001), suggesting POSTN as a potential biomarker to distinguish these diseases. Immunohistochemistry further confirmed higher POSTN expression in AD compared to PS skin. Overall, our findings support the concept that these two chronic skin diseases might share considerably more common mechanisms in pathogenesis than has been suspected thus far.

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.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

MarShie: a clearing protocol for 3D analysis of single cells throughout the bone marrow at subcellular resolution

Analyzing immune cell interactions in the bone marrow is vital for understanding hematopoiesis and bone homeostasis. Three-dimensional analysis of the complete, intact bone marrow within the cortex of whole long bones remains a challenge, especially at subcellular resolution. We present a method that stabilizes the marrow and provides subcellular resolution of fluorescent signals throughout the murine femur, enabling identification and spatial characterization of hematopoietic and stromal cell subsets. By combining a pre-processing algorithm for stripe artifact removal with a machine-learning approach, we demonstrate reliable cell segmentation down to the deepest bone marrow regions. This reveals age-related changes in the marrow. It highlights the interaction between CX3CR1+ cells and the vascular system in homeostasis, in contrast to other myeloid cell types, and reveals their spatial characteristics after injury. The broad applicability of this method will contribute to a better understanding of bone marrow biology.

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.

Unveiling Macrophage Heterogeneity and Their Spatial Distribution Using Multiplexed Tissue Imaging

Macrophages display a high degree of phenotypic diversity and plasticity, which is influenced by their location within the tissue microenvironment. Co-Detection by Indexing (CODEX), a multiplexed imaging technique, allows the simultaneous detection of multiple membrane and cellular markers that enable the accurate identification of tissue-resident hematopoietic and non-hematopoietic cells, while conferring spatial information at a single-cell level. Here we describe the use of CODEX to visualize the phenotypic and spatial heterogeneity of murine tissue-resident macrophages in several organs, and a pipeline to characterize their cellular microenvironments and interactions.

Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel.

Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images

Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.

Ultrasensitive and multiplexed protein imaging with clickable and cleavable fluorophores

Single-cell spatial proteomic analysis holds great promise to advance our understanding of the composition, organization, interaction and function of the various cell types in complex biological systems. However, the current multiplexed protein imaging technologies suffer from low detection sensitivity, limited multiplexing capacity or technically demanding. To tackle these issues, here we report the development of a highly sensitive and multiplexed in situ protein profiling method using off-the-shelf antibodies. In this approach, the protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and cleavable fluorophores via click chemistry. Through reiterative cycles of target staining, fluorescence imaging, and fluoropohore cleavage, many proteins can be profiled in single cells in situ. Applying this approach, we successfully quantified 28 different proteins in a human formalin-fixed paraffin-embedded (FFPE) tonsil tissue, which represents the highest multiplexing capacity among the tyramide signal amplification (TSA) methods. Based on their unique protein expression patterns and their microenvironment, ~820,000 cells in the tissue are classified into distinct cell clusters. We also explored the cell-cell interactions between these varied cell clusters and observed different subregions of the tissue are composed of cells from specific clusters.

Quantitative multiplexed imaging technologies for single-cell analysis to assess predictive markers for immunotherapy in thoracic immuno-oncology: promises and challenges

The past decade has witnessed a revolution in cancer treatment by the shift from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular the immune-checkpoint inhibitors (ICIs). These immunotherapies selectively release the host immune system against the tumour and have shown unprecedented durable remission for patients with cancers that were thought incurable such as advanced non-small cell lung cancer (aNSCLC). The prediction of therapy response is based since the first anti-PD-1/PD-L1 molecules FDA and EMA approvals on the level of PD-L1 tumour cells expression evaluated by immunohistochemistry, and recently more or less on tumour mutation burden in the USA. However, not all aNSCLC patients benefit from immunotherapy equally, since only around 30% of them received ICIs and among them 30% have an initial response to these treatments. Conversely, a few aNSCLC patients could have an efficacy ICIs response despite low PD-L1 tumour cells expression. In this context, there is an urgent need to look for additional robust predictive markers for ICIs efficacy in thoracic oncology. Understanding of the mechanisms that enable cancer cells to adapt to and eventually overcome therapy and identifying such mechanisms can help circumvent resistance and improve treatment. However, more than a unique universal marker, the evaluation of several molecules in the tumour at the same time, particularly by using multiplex immunostaining is a promising open room to optimise the selection of patients who benefit from ICIs. Therefore, urgent further efforts are needed to optimise to individualise immunotherapy based on both patient-specific and tumour-specific characteristics. This review aims to rethink the role of multiplex immunostaining in immuno-thoracic oncology, with the current advantages and limitations in the near-daily practice use.

DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging

Imaging mass cytometry (IMC) is a highly multiplexed, antibody-based imaging method that captures heterogeneous spatial protein expression patterns at subcellular resolution. Here we report the extension of IMC to low-abundance markers through incorporation of the DNA-based signal amplification by exchange reaction, immuno-SABER. We applied SABER-IMC to image the tumor immune microenvironment in human melanoma by simultaneous imaging of 18 markers with immuno-SABER and 20 markers without amplification. SABER-IMC enabled the identification of immune cell phenotypic markers, such as T cell co-receptors and their ligands, that are not detectable with IMC.

Excessive sodium chloride ingestion promotes inflammation and kidney fibrosis in aging mice

In aging kidneys, a decline of function resulting from extracellular matrix (ECM) deposition and organ fibrosis is regarded as "physiological." Whether a direct link between high salt intake and fibrosis in aging kidney exists autonomously from arterial hypertension is unclear. This study explores kidney intrinsic changes (inflammation, ECM derangement) induced by a high-salt diet (HSD) in a murine model lacking arterial hypertension. The contribution of cold shock Y-box binding protein (YB-1) as a key orchestrator of organ fibrosis to the observed differences is determined by comparison with a knockout strain (Ybx1ΔRosaERT+TX). Comparisons of tissue from mice fed with normal-salt diet (NSD, standard chow) or high-salt diet (HSD, 4% NaCl in chow; 1% NaCl in water) for up to 16 mo revealed that with HSD tubular cell numbers decrease and tubulointerstitial scarring [periodic acid-Schiff (PAS), Masson's trichrome, Sirius red staining] prevails. In Ybx1ΔRosaERT+TX animals tubular cell damage, a loss of cell contacts with profound tubulointerstitial alterations, and tubular cell senescence was seen. A distinct tubulointerstitial distribution of fibrinogen, collagen type VI, and tenascin-C was detected under HSD, transcriptome analyses determined patterns of matrisome regulation. Temporal increase of immune cell infiltration was seen under HSD of wild type, but not Ybx1ΔRosaERT+TX animals. In vitro Ybx1ΔRosaERT+TX bone marrow-derived macrophages exhibited a defect in polarization (IL-4/IL-13) and abrogated response to sodium chloride. Taken together, HSD promotes progressive kidney fibrosis with premature cell aging, ECM deposition, and immune cell recruitment that is exacerbated in Ybx1ΔRosaERT+TX animals.NEW & NOTEWORTHY Short-term experimental studies link excessive sodium ingestion with extracellular matrix accumulation and inflammatory cell recruitment, yet long-term data are scarce. Our findings with a high-salt diet over 16 mo in aging mice pinpoints to a decisive tipping point after 12 mo with tubular stress response, skewed matrisome transcriptome, and immune cell infiltration. Cell senescence was aggravated in knockout animals for cold shock Y-box binding protein (YB-1), suggesting a novel protective protein function.

A review on deep learning applications in highly multiplexed tissue imaging data analysis

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

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 Protein Imaging through PACIFIC: Photoactive Immunofluorescence with Iterative Cleavage

Multiplex protein imaging technologies enable deep phenotyping and provide rich spatial information about biological samples. Existing methods have shown great success but also harbored trade-offs between various pros and cons, underscoring the persisting necessity to expand the imaging toolkits. Here we present PACIFIC: photoactive immunofluorescence with iterative cleavage, a new modality of multiplex protein imaging methods. PACIFIC achieves iterative multiplexing by implementing photocleavable fluorophores for antibody labeling with one-step spin-column purification. PACIFIC requires no specialized instrument, no DNA encoding, or chemical treatments. We demonstrate that PACIFIC can resolve cellular heterogeneity in both formalin-fixed paraffin-embedded (FFPE) samples and fixed cells. To further highlight how PACIFIC assists discovery, we integrate PACIFIC with live-cell tracking and identify phosphor-p70S6K as a critical driver that governs U87 cell mobility. Considering the cost, flexibility, and compatibility, we foresee that PACIFIC can confer deep phenotyping capabilities to anyone with access to traditional immunofluorescence platforms.

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.

Distinct tissue niches direct lung immunopathology via CCL18 and CCL21 in severe COVID-19

Prolonged lung pathology has been associated with COVID-19, yet the cellular and molecular mechanisms behind this chronic inflammatory disease are poorly understood. In this study, we combine advanced imaging and spatial transcriptomics to shed light on the local immune response in severe COVID-19. We show that activated adventitial niches are crucial microenvironments contributing to the orchestration of prolonged lung immunopathology. Up-regulation of the chemokines CCL21 and CCL18 associates to endothelial-to-mesenchymal transition and tissue fibrosis within these niches. CCL21 over-expression additionally links to the local accumulation of T cells expressing the cognate receptor CCR7. These T cells are imprinted with an exhausted phenotype and form lymphoid aggregates that can organize in ectopic lymphoid structures. Our work proposes immune-stromal interaction mechanisms promoting a self-sustained and non-resolving local immune response that extends beyond active viral infection and perpetuates tissue remodeling.

High-multiplex tissue imaging in routine pathology-are we there yet?

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.

Eosinophil-derived IL-4 is necessary to establish the inflammatory structure in innate inflammation

Pathogen-induced inflammation comprises pro- and anti-inflammatory processes, which ensure pathogen removal and containment of the proinflammatory activities. Here, we aimed to identify the development of inflammatory microenvironments and their maintenance throughout the course of a toll-like receptor 2-mediated paw inflammation. Within 24 h after pathogen-injection, the immune cells were organized in three zones, which comprised a pathogen-containing "core-region", a bordering proinflammatory (PI)-region and an outer anti-inflammatory (AI)-region. Eosinophils were present in all three inflammatory regions and adapted their cytokine profile according to their localization. Eosinophil depletion reduced IL-4 levels and increased edema formation as well as mechanical and thermal hypersensitivities during resolution of inflammation. Also, in the absence of eosinophils PI- and AI-regions could not be determined anymore, neutrophil numbers increased, and efferocytosis as well as M2-macrophage polarization were reduced. IL-4 administration restored in eosinophil-depleted mice PI- and AI-regions, normalized neutrophil numbers, efferocytosis, M2-macrophage polarization as well as resolution of zymosan-induced hypersensitivity. In conclusion, IL-4-expressing eosinophils support the resolution of inflammation by enabling the development of an anti-inflammatory framework, which encloses proinflammatory regions.

Multiplex Immunofluorescence: A Powerful Tool in Cancer Immunotherapy

Traditional immunohistochemistry (IHC) has already become an essential method of diagnosis and therapy in cancer management. However, this antibody-based technique is limited to detecting a single marker per tissue section. Since immunotherapy has revolutionized the antineoplastic therapy, developing new immunohistochemistry strategies to detect multiple markers simultaneously to better understand tumor environment and predict or assess response to immunotherapy is necessary and urgent. Multiplex immunohistochemistry (mIHC)/multiplex immunofluorescence (mIF), such as multiplex chromogenic IHC and multiplex fluorescent immunohistochemistry (mfIHC), is a new and emerging technology to label multiple biomarkers in a single pathological section. The mfIHC shows a higher performance in cancer immunotherapy. This review summarizes the technologies, which are applied for mfIHC, and discusses how they are employed for immunotherapy research.

Interferon-Driven Immune Dysregulation in Common Variable Immunodeficiency-Associated Villous Atrophy and Norovirus Infection

PURPOSE

About 15% of patients with common variable immunodeficiency (CVID) develop a small intestinal enteropathy, which resembles celiac disease with regard to histopathology but evolves from a distinct, poorly defined pathogenesis that has been linked in some cases to chronic norovirus (NV) infection. Interferon-driven inflammation is a prominent feature of CVID enteropathy, but it remains unknown how NV infection may contribute.

METHODS

Duodenal biopsies of CVID patients, stratified according to the presence of villous atrophy (VA), IgA plasma cells (PCs), and chronic NV infection, were investigated by flow cytometry, multi-epitope-ligand cartography, bulk RNA-sequencing, and RT-qPCR of genes of interest.

RESULTS

VA development was connected to the lack of intestinal (IgA+) PC, a T helper 1/T helper 17 cell imbalance, and increased recruitment of granzyme+CD8+ T cells and pro-inflammatory macrophages to the affected site. A mixed interferon type I/III and II signature occurred already in the absence of histopathological changes and increased with the severity of the disease and in the absence of (IgA+) PCs. Chronic NV infection exacerbated this signature when compared to stage-matched NV-negative samples.

CONCLUSIONS

Our study suggests that increased IFN signaling and T-cell cytotoxicity are present already in mild and are aggravated in severe stages (VA) of CVID enteropathy. NV infection preempts local high IFN-driven inflammation, usually only seen in VA, at milder disease stages. Thus, revealing the impact of different drivers of the pathological mixed IFN type I/III and II signature may allow for more targeted treatment strategies in CVID enteropathy and supports the goal of viral elimination.

Correlative Multi-Modal Microscopy: A Novel Pipeline for Optimizing Fluorescence Microscopy Resolutions in Biological Applications

The modern fluorescence microscope is the convergence point of technologies with different performances in terms of statistical sampling, number of simultaneously analyzed signals, and spatial resolution. However, the best results are usually obtained by maximizing only one of these parameters and finding a compromise for the others, a limitation that can become particularly significant when applied to cell biology and that can reduce the spreading of novel optical microscopy tools among research laboratories. Super resolution microscopy and, in particular, molecular localization-based approaches provide a spatial resolution and a molecular localization precision able to explore the scale of macromolecular complexes in situ. However, its use is limited to restricted regions, and consequently few cells, and frequently no more than one or two parameters. Correlative microscopy, obtained by the fusion of different optical technologies, can consequently surpass this barrier by merging results from different spatial scales. We discuss here the use of an acquisition and analysis correlative microscopy pipeline to obtain high statistical sampling, high content, and maximum spatial resolution by combining widefield, confocal, and molecular localization microscopy.

MALDI HiPLEX-IHC: multiomic and multimodal imaging of targeted intact proteins in tissues

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is one of the most widely used methods for imaging the spatial distribution of unlabeled small molecules such as metabolites, lipids and drugs in tissues. Recent progress has enabled many improvements including the ability to achieve single cell spatial resolution, 3D-tissue image reconstruction, and the precise identification of different isomeric and isobaric molecules. However, MALDI-MSI of high molecular weight intact proteins in biospecimens has thus far been difficult to achieve. Conventional methods normally require in situ proteolysis and peptide mass fingerprinting, have low spatial resolution, and typically detect only the most highly abundant proteins in an untargeted manner. In addition, MSI-based multiomic and multimodal workflows are needed which can image both small molecules and intact proteins from the same tissue. Such a capability can provide a more comprehensive understanding of the vast complexity of biological systems at the organ, tissue, and cellular levels of both normal and pathological function. A recently introduced top-down spatial imaging approach known as MALDI HiPLEX-IHC (MALDI-IHC for short) provides a basis for achieving this high-information content imaging of tissues and even individual cells. Based on novel photocleavable mass-tags conjugated to antibody probes, high-plex, multimodal and multiomic MALDI-based workflows have been developed to image both small molecules and intact proteins on the same tissue sample. Dual-labeled antibody probes enable multimodal mass spectrometry and fluorescent imaging of targeted intact proteins. A similar approach using the same photocleavable mass-tags can be applied to lectin and other probes. We detail here several examples of MALDI-IHC workflows designed to enable high-plex, multiomic and multimodal imaging of tissues at a spatial resolution as low as 5 µm. This approach is compared to other existing high-plex methods such as imaging mass cytometry, MIBI-TOF, GeoMx and CODEX. Finally, future applications of MALDI-IHC are discussed.

Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network

Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://github.com/GoodGuy398/GraphLoc . The proposed GraphLoc model consists of three parts. The first part is a graph convolutional network (GCN) module, which utilizes the predicted contact maps to construct protein graph, taking benefit of protein information accordingly. The second part is the multi-head attention module, which learns the weighted contribution of different amino acids in different feature representation subspace, and weighted average the feature map across all amino acid nodes. The last part is a fully connected layer that maps the flatten graph representation vector to another vector with a category number dimension, followed by a softmax layer to predict the protein subcellular localization.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes

Cells in complex organisms undergo frequent functional changes, but few methods allow comprehensive longitudinal profiling of living cells. Here we introduce scission-accelerated fluorophore exchange (SAFE), a method for multiplexed temporospatial imaging of living cells with immunofluorescence. SAFE uses a rapid bioorthogonal click chemistry to remove immunofluorescent signals from the surface of labeled cells, cycling the nanomolar-concentration reagents in seconds and enabling multiple rounds of staining of the same samples. It is non-toxic and functional in both dispersed cells and intact living tissues. We demonstrate multiparameter (n ≥ 14), non-disruptive imaging of murine peripheral blood mononuclear and bone marrow cells to profile cellular differentiation. We also show longitudinal multiplexed imaging of bone marrow progenitor cells as they develop into neutrophils over 6 days and real-time multiplexed cycling of living mouse hepatic tissues. We anticipate that SAFE will find broad utility for investigating physiologic dynamics in living systems.

An integrated microfluidic device for multiplexed imaging of spatial gene expression patterns of Drosophila embryos

To reveal the underlying mechanism of the biological function of multicellular systems, it is important to obtain comprehensive spatial gene expression profiles. Among the emerging single-cell spatial-omics techniques, immunofluorescence (IF)-based iterative multiplexed imaging is a promising approach. However, the conventional method is usually costly, time-consuming, labor-intensive, and has low throughput. Moreover, it has yet to be demonstrated in intact multicellular organisms. Here, we developed an integrated microfluidic system to overcome these challenges for quantitatively measuring multiple protein profiles sequentially in situ in the same Drosophila embryo. We designed an array of hydrodynamic trapping sites to automatically capture over ten Drosophila embryos with orientation selectivity at more than 90% trapping rates. We also optimized the geometry of confinement and the on-chip IF protocol to achieve the same high signal-to-noise ratio as the off-chip traditional IF experiments. Moreover, we developed an efficient de-staining protocol by combining on-chip antibody stripping and fluorophore bleaching. Using the same secondary antibody to sequentially stain different genes, we confirmed that the de-stained genes have no detectable interference with the subsequently stained genes, and the gene expression profiles are preserved after multiple cycles of staining and de-staining processes. This preliminary test shows that our newly developed integrated microfluidic system can be a powerful tool for multiplexed imaging of Drosophila embryos. Our work opens a new avenue to design microfluidic chips for multicellular organisms and single-cell spatial-omics techniques.

Improving and evaluating deep learning models of cellular organization

MOTIVATION

Cells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both traditional and deep learning methods to build accurate models of cell organization. Most cellular organelles are distinct objects with defined boundaries that do not overlap, while the pixel resolution of most imaging methods is n sufficient to resolve these boundaries. Thus while cell organization is conceptually object-based, most current methods are pixel-based. Using extensive image collections in which particular organelles were fluorescently labeled, deep learning methods can be used to build conditional autoencoder models for particular organelles. A major advance occurred with the use of a U-net approach to make multiple models all conditional upon a common reference, unlabeled image, allowing the relationships between different organelles to be at least partially inferred.

RESULTS

We have developed improved Generative Adversarial Networks-based approaches for learning these models and have also developed novel criteria for evaluating how well synthetic cell images reflect the properties of real images. The first set of criteria measure how well models preserve the expected property that organelles do not overlap. We also developed a modified loss function that allows retraining of the models to minimize that overlap. The second set of criteria uses object-based modeling to compare object shape and spatial distribution between synthetic and real images. Our work provides the first demonstration that, at least for some organelles, deep learning models can capture object-level properties of cell images.

AVAILABILITY AND IMPLEMENTATION

http://murphylab.cbd.cmu.edu/Software/2022_insilico.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Heparan Sulfate Glycosaminoglycan Is Predicted to Stabilize Inflammatory Infiltrate Formation and RANKL/OPG Ratio in Severe Periodontitis in Humans

Since chronically inflamed periodontal tissue exhibits extracellular matrix (ECM) degradation, the possible alternative to standard periodontitis treatment is to restore ECM by supplementing its components, including heparan sulfate glycosaminoglycan (HS GAG). Supplementation of the degraded ECM with synthetic derivatives of HS GAGs has been shown to be effective for periodontal tissue regeneration in experimental animal models of periodontitis. However, the potential of HS GAG supplementation for the treatment of periodontal disease in humans is still unknown. Here, we used a statistical model to investigate the role of HS GAG on inflammatory infiltrate formation and alveolar bone resorption in humans with severe periodontitis. The model was based on data from immunofluorescence staining (IF) of human gingiva samples, and reconstruction of a subset of HS GAG -related proteins from STRING reactome database. According to predictions, increased expression of native HS GAG might stabilize the accumulation of gingival inflammatory infiltrate (represented by the general inflammatory cell marker CD45) and alveolar bone resorption (represented by Receptor Activator of Nuclear ΚΒ ligand (RANKL) and osteoprotegerin (OPG) ratio) but could not restore them to healthy tissue levels. Therefore, supplementation of native HS GAG may be of limited benefits for the treatment of sever periodontitis in humans.

Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis

The definitive diagnosis and early treatment of many immune-mediated inflammatory diseases (IMIDs) is hindered by variable and overlapping clinical manifestations. Psoriatic arthritis (PsA), which develops in ~30% of people with psoriasis, is a key example. This mixed-pattern IMID is apparent in entheseal and synovial musculoskeletal structures, but a definitive diagnosis often can only be made by clinical experts or when an extensive progressive disease state is apparent. As with other IMIDs, the detection of multimodal molecular biomarkers offers some hope for the early diagnosis of PsA and the initiation of effective management and treatment strategies. However, specific biomarkers are not yet available for PsA. The assessment of new markers by genomic and epigenomic profiling, or the analysis of blood and synovial fluid/tissue samples using proteomics, metabolomics and lipidomics, provides hope that complex molecular biomarker profiles could be developed to diagnose PsA. Importantly, the integration of these markers with high-throughput histology, imaging and standardized clinical assessment data provides an important opportunity to develop molecular profiles that could improve the diagnosis of PsA, predict its occurrence in cohorts of individuals with psoriasis, differentiate PsA from other IMIDs, and improve therapeutic responses. In this review, we consider the technologies that are currently deployed in the EU IMI2 project HIPPOCRATES to define biomarker profiles specific for PsA and discuss the advantages of combining multi-omics data to improve the outcome of PsA patients.

Multiplexed Single-Cell in Situ Protein Profiling

The ability to profile a large number of different proteins in individual cells in their native cellular locations is critical to accelerate our understanding of normal cell physiology and disease pathogenesis. Bulk cell protein quantification masks the cell heterogeneity in complex biological systems, while conventional immunofluorescence or immunohistochemistry are limited by their low multiplexing capacity. Recent technological advances in multiplexed protein imaging approaches allow many distinct proteins to be analyzed in single cells in situ. These methods will bring new insights into various biological and biomedical fields, such as cell type and subtype classification, signaling network regulation, tissue architecture, and disease diagnosis and prognosis, along with treatment monitoring. In this Review, we will describe the recent advances of multiplexed single-cell in situ protein profiling technologies, discuss their unique advantages and limitations, highlight their applications in biology and medicine, present the current challenges, and propose potential solutions.

Multiplexed single-cell analysis of FNA allows accurate diagnosis of salivary gland tumors

Diagnosing salivary gland tumors (SGTs) through fine-needle aspiration (FNA) biopsies is challenging due to the overlapping cytomorphologic features between benign and malignant tumors. The authors developed an innovative, multiplexed cycling technology for the rapid analyses of single cells obtained from FNA that can facilitate the molecular analyses and diagnosis of SGTs. Antibodies against 29 protein markers associated with 7 SGT subtypes were validated and chemically modified via custom linker-bio-orthogonal probes (FAST). Single-cell homogenates and FNA samples were profiled by FAST cyclic imaging and computational analysis. A prediction model was generated using a training set of 151,926 cells from primary SGTs (N = 26) and validated on a separate cohort (N = 30). Companion biomarker testing, such as neurotrophic tyrosine receptor kinase (NTRK), was also assessed with the FAST technology. The FAST molecular diagnostic assay was able to distinguish between benign and malignant SGTs with an accuracy of 0.86 for single-cell homogenate samples and 0.88 for FNA samples. Profiling of multiple markers as compared to a single marker increased the diagnostic accuracy (0.82 as compared to 0.65-0.74, respectively), independent of the cell number sampled. NTRK expression was also assessed by the FAST assay, highlighting the potential therapeutic application of this technology. Application of the novel multiplexed single-cell technology facilitates rapid biomarker testing from FNA samples at low cost. The customizable and modular FAST-FNA approach has relevance to multiple pathologies and organ systems where cytologic samples are often scarce and/or indeterminate resulting in improved diagnostic workflows and timely therapeutic clinical decision-making.

Next-Generation Pathology Using Multiplexed Immunohistochemistry: Mapping Tissue Architecture at Single-Cell Level

Single-cell omics aim at charting the different types and properties of all cells in the human body in health and disease. Over the past years, myriads of cellular phenotypes have been defined by methods that mostly required cells to be dissociated and removed from their original microenvironment, thus destroying valuable information about their location and interactions. Growing insights, however, are showing that such information is crucial to understand complex disease states. For decades, pathologists have interpreted cells in the context of their tissue using low-plex antibody- and morphology-based methods. Novel technologies for multiplexed immunohistochemistry are now rendering it possible to perform extended single-cell expression profiling using dozens of protein markers in the spatial context of a single tissue section. The combination of these novel technologies with extended data analysis tools allows us now to study cell-cell interactions, define cellular sociology, and describe detailed aberrations in tissue architecture, as such gaining much deeper insights in disease states. In this review, we provide a comprehensive overview of the available technologies for multiplexed immunohistochemistry, their advantages and challenges. We also provide the principles on how to interpret high-dimensional data in a spatial context. Similar to the fact that no one can just “read” a genome, pathological assessments are in dire need of extended digital data repositories to bring diagnostics and tissue interpretation to the next level.

The emerging landscape of spatial profiling technologies

Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.

Extending applications of AFM to fluidic AFM in single living cell studies

In this article, a review of a series of applications of atomic force microscopy (AFM) and fluidic Atomic Force Microscopy (fluidic AFM, hereafter fluidFM) in single-cell studies is presented. AFM applications involving single-cell and extracellular vesicle (EV) studies, colloidal force spectroscopy, and single-cell adhesion measurements are discussed. FluidFM is an offshoot of AFM that combines a microfluidic cantilever with AFM and has enabled the research community to conduct biological, pathological, and pharmacological studies on cells at the single-cell level in a liquid environment. In this review, capacities of fluidFM are discussed to illustrate (1) the speed with which sequential measurements of adhesion using coated colloid beads can be done, (2) the ability to assess lateral binding forces of endothelial or epithelial cells in a confluent cell monolayer in an appropriate physiological environment, and (3) the ease of measurement of vertical binding forces of intercellular adhesion between heterogeneous cells. Furthermore, key applications of fluidFM are reviewed regarding to EV absorption, manipulation of a single living cell by intracellular injection, sampling of cellular fluid from a single living cell, patch clamping, and mass measurements of a single living cell.

Biology, geometry and information

The main thesis developed in this article is that the key feature of biological life is the a biological process can control and regulate other processes, and it maintains that ability over time. This control can happen hierarchically and/or reciprocally, and it takes place in three-dimensional space. This implies that the information that a biological process has to utilize is only about the control, but not about the content of those processes. Those other processes can be vastly more complex that the controlling process itself, and in fact necessarily so. In particular, each biological process draws upon the complexity of its environment.

Comparison of the Toponomes of Alveolar Macrophages From Wild Type and Surfactant Protein A Knockout Mice and Their Response to Infection

Background

Surfactant protein-A (SP-A) plays a critical role in lung innate immunity by regulating alveolar macrophages (AM), expression of inflammatory mediators, and other host defense proteins. The toponome imaging system (TIS), a serial immunostainer, was used to study the AM toponome because it characterizes the localization of multiple markers and identifies marker combinations in each pixel as combinatorial molecular phenotypes (CMPs). We used TIS to study the AM toponome from wild type (WT) and SP-A knockout (KO) mice and changes following Klebsiella pneumoniae exposure.

Methods

WT or KO mice received intratracheal K. pneumoniae or vehicle and AM were obtained by bronchoalveolar lavage after one hour. AM were attached to slides and underwent TIS analysis. Images were analyzed to characterize all pixels. AM CMPs from WT vehicle (n=3) and infected (n=3) mice were compared to each other and to AM from KO (n=3 vehicle; n=3 infected). Histograms provided us with a tool to summarize the representation of each marker in a set of CMPs.

Results

Using the histograms and other tools we identified markers of interest and observed that: 1) Both comparisons had conserved (present in all group members) CMPs, only in vehicle AM and only in infected AM, or common to both vehicle and infected AM, (i.e., unaffected by the condition). 2) the CMP number decreased with infection in WT and KO versus vehicle controls. 3) More infection-specific CMPs in WT vs KO AM. 4) When AM from WT and KO vehicle or infected were compared, there were more unique CMPs exclusive to the KO AM. 5) All comparisons showed CMPs shared by both groups.

Conclusions

The decrease of CMPs exclusive to infected AM in KO mice may underlie the observed susceptibility of KO mice to infection. However, both KO groups had more exclusive CMPs than the corresponding WT groups, perhaps indicating a vigorous effort by KO to overcome deficits in certain proteins and CMPs that are dysregulated by the absence of SP-A. Moreover, the presence of shared CMPs in the compared groups indicates that regulation of these CMPs is not dependent on either infection or the presence or absence of SP-A.

Epidermotropie von Immunzellen unterscheidet Pyoderma gangraenosum vom Ulcus cruris venosum

HINTERGRUND UND ZIELE

Pyoderma gangraenosum ist eine ulzerierende, autoinflammatorische Erkrankung. Es gibt keine eindeutigen histopathologischen Merkmale zur Differenzierung von anderen Ursachen chronischer Wunden wie dem Ulcus cruris venosum. Ziel dieser Studie war es, histopathologische Merkmale von Pyoderma gangraenosum und Unterschiede zu venösen Ulzerationen zu detektieren.

PATIENTEN UND METHODIK

Acht Gewebeproben von Pyoderma gangraenosum, zwölf Proben von Ulcus cruris venosum und sechs Proben von gesunder Haut wurden einer immunhistologischen Multi-Antigen-Analyse unterzogen. Das Immuninfiltrat und seine räumliche Verteilung wurden anhand von Fluoreszenzbildern mit einer Gewebezytometriesoftware analysiert.

ERGEBNISSE

Die dichte epidermale Präsenz von CD45RO⁺ -T-Gedächtnis-Zellen und die Rarefizierung von CD1a⁺ -Langerhans-Zellen in der Epidermis waren Marker für Pyoderma gangraenosum, welche auch auf eine epidermale Immunreaktion schließen lassen. Darüber hinaus konnte dermal eine hohe Anzahl CD11c⁺ CD68⁺ pro-inflammatorischer M1-Makrophagen nachgewiesen werden. Diese überstieg die Anzahl der in venösen Ulzerationen beobachteten Makrophagen deutlich.

SCHLUSSFOLGERUNGEN

Die histopathologischen Unterschiede zwischen Pyoderma gangraenosum und Ulcus cruris venosum können zur Unterscheidung der beiden Erkrankungen herangezogen werden und somit eine wichtige Hilfe zur schnellen Einleitung einer adäquaten Therapie sein. Darüber hinaus deuten unsere Daten auf einen antigengesteuerten Prozess in der Epidermis hin, möglicherweise unter Beteiligung von CD1a⁺ Langerhans-Zellen.

A framework for multiplex imaging optimization and reproducible analysis

Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, mplexable, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.

An approach for tissue image analysis applicable to highly multiplexed immunofluorescence imaging of the spatial distribution of multiple protein biomarkers is proposed, here applied to the analysis of multiplex IF using the multiplex imaging platform, CyCIF.

PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements

Ultra-multiplexed fluorescence imaging requires the use of spectrally overlapping fluorophores to label proteins and then to unmix the images of the fluorophores. However, doing this remains a challenge, especially in highly heterogeneous specimens, such as the brain, owing to the high degree of variation in the emission spectra of fluorophores in such specimens. Here, we propose PICASSO, which enables more than 15-color imaging of spatially overlapping proteins in a single imaging round without using any reference emission spectra. PICASSO requires an equal number of images and fluorophores, which enables such advanced multiplexed imaging, even with bandpass filter-based microscopy. We show that PICASSO can be used to achieve strong multiplexing capability in diverse applications. By combining PICASSO with cyclic immunofluorescence staining, we achieve 45-color imaging of the mouse brain in three cycles. PICASSO provides a tool for multiplexed imaging with high accessibility and accuracy for a broad range of researchers.