Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer

Despite advances in treatment, lung cancer survival rates remain low. A better understanding of the cellular heterogeneity and interplay of cancer-associated fibroblasts (CAFs) within the tumor microenvironment will support the development of personalized therapies. We report a spatially resolved single-cell imaging mass cytometry (IMC) analysis of CAFs in a non-small cell lung cancer cohort of 1,070 patients. We identify four prognostic patient groups based on 11 CAF phenotypes with distinct spatial distributions and show that CAFs are independent prognostic factors for patient survival. The presence of tumor-like CAFs is strongly correlated with poor prognosis. In contrast, inflammatory CAFs and interferon-response CAFs are associated with inflamed tumor microenvironments and higher patient survival. High density of matrix CAFs is correlated with low immune infiltration and is negatively correlated with patient survival. In summary, our data identify phenotypic and spatial features of CAFs that are associated with patient outcome in NSCLC.

Spatial multimodal analysis revealed tertiary lymphoid structures as a risk stratification indicator in combined hepatocellular-cholangiocarcinoma

The microenvironment created by tertiary lymphoid structures (TLSs) can support and regulate immune responses, affecting the prognosis and immune treatment of patients. Nevertheless, the actual importance of TLSs for predicting the prognosis of combined hepatocellular-cholangiocarcinoma (cHCC-CCA) patients remains unclear. Herein, using spatial transcriptomic analysis, we revealed that a gene signature of TLSs specific to cHCC-CCA was associated with high-intensity immune infiltration. Then, a novel scoring system was developed to evaluate the distribution and frequency of TLSs in intra-tumoral and extra-tumoral regions (iTLS and eTLS scores) in 146 cHCC-CCA patients. iTLS score was positively associated with promising prognosis, likely due to the decreased frequency of suppressive immune cell like Tregs, and the ratio of CD163+ macrophages to macrophages in intra-tumoral TLSs via imaging mass cytometry, while improved prognosis is not necessarily indicated by a higher eTLS score. Overall, this study highlights the potential of TLSs as a prognostic factor and an indicator of immune therapy in cHCC-CCA.

Imaging Mass Cytometry for In Situ Immune Profiling

The complexities and cellular heterogeneity associated with tissues necessitate the concurrent detection of markers beyond the limitations of conventional imaging approaches in order to spatially resolve the relationships between immune cell populations and their environments. This is a necessary complement to single-cell suspension-based methods to inform a better understanding of the events that may underlie pathological conditions. Imaging mass cytometry is a high-dimensional imaging modality that allows for the concurrent detection of up to 40 protein markers of interest across tissues at subcellular resolution. Here, we present an optimized staining protocol for imaging mass cytometry with modifications that integrate RNAscope. This unique addition enables combined protein and single-molecule RNA detection, thereby expanding the utility of imaging mass cytometry to researchers investigating low abundance or noncoding targets. In general, the procedure described is broadly applicable for comprehensive immune profiling of host-pathogen interactions, tumor microenvironments and inflammatory conditions, all within the tissue contexture.

Distinct lung cell signatures define the temporal evolution of diffuse alveolar damage in fatal COVID-19

BACKGROUND

Lung damage in severe COVID-19 is highly heterogeneous however studies with dedicated spatial distinction of discrete temporal phases of diffuse alveolar damage (DAD) and alternate lung injury patterns are lacking. Existing studies have also not accounted for progressive airspace obliteration in cellularity estimates. We used an imaging mass cytometry (IMC) analysis with an airspace correction step to more accurately identify the cellular immune response that underpins the heterogeneity of severe COVID-19 lung disease.

METHODS

Lung tissue was obtained at post-mortem from severe COVID-19 deaths. Pathologist-selected regions of interest (ROIs) were chosen by light microscopy representing the patho-evolutionary spectrum of DAD and alternate disease phenotypes were selected for comparison. Architecturally normal SARS-CoV-2-positive lung tissue and tissue from SARS-CoV-2-negative donors served as controls. ROIs were stained for 40 cellular protein markers and ablated using IMC before segmented cells were classified. Cell populations corrected by ROI airspace and their spatial relationships were compared across lung injury patterns.

FINDINGS

Forty patients (32M:8F, age: 22-98), 345 ROIs and >900k single cells were analysed. DAD progression was marked by airspace obliteration and significant increases in mononuclear phagocytes (MnPs), T and B lymphocytes and significant decreases in alveolar epithelial and endothelial cells. Neutrophil populations proved stable overall although several interferon-responding subsets demonstrated expansion. Spatial analysis revealed immune cell interactions occur prior to microscopically appreciable tissue injury.

INTERPRETATION

The immunopathogenesis of severe DAD in COVID-19 lung disease is characterised by sustained increases in MnPs and lymphocytes with key interactions occurring even prior to lung injury is established.

FUNDING

UK Research and Innovation/Medical Research Council through the UK Coronavirus Immunology Consortium, Barbour Foundation, General Sir John Monash Foundation, Newcastle University, JGW Patterson Foundation, Wellcome Trust.

High-content multimodal analysis supports the IL-7/IL-7 receptor axis as a relevant therapeutic target in primary Sjögren's syndrome

OBJECTIVE

While the involvement of IL-7/IL-7R axis in pSS has been described in relation to T cells, little is known about the contribution of this pathway in relationship with other immune cells, and its implication in autoimmunity. Using high-content multiomics data, we aimed at characterizing IL-7R expressing cells and the involvement of IL-7/IL-7R pathway in pSS pathophysiology.

METHODS

An IL-7 signature established using RNA-sequencing of human PBMCs incubated with IL-7 was applied to 304 pSS patients, and on RNA-Seq datasets from tissue biopsies. High-content immunophenotyping using flow and imaging mass cytometry was developed to characterize peripheral and in situ IL-7R expression.

RESULTS

We identified a blood 4-gene IL-7 module (IKZF4, KIAA0040, PGAP1 and SOS1) associated with anti-SSA/Ro positiveness in patients as well as disease activity, and a tissue 5-gene IL-7 module (IL7R, PCED1B, TNFSF8, ADAM19, MYBL1) associated with infiltration severity. We confirmed expression of IL-7R on T cells subsets, and further observed upregulation of IL-7R on double-negative (DN) B cells, and especially DN2 B cells. IL-7R expression was increased in pSS compared to sicca patients with variations seen according to the degree of infiltration. When expressed, IL-7R was mainly found on epithelial cells, CD4+ and CD8+ T cells, switched memory B cells, DN B cells and M1 macrophages.

CONCLUSION

This exhaustive characterization of the IL-7/IL-7R pathway in pSS pathophysiology established that two IL-7 gene modules discriminate pSS patients with a high IL-7 axis involvement. Their use could guide the implementation of an anti-IL-7R targeted therapy in a precision medicine approach.

Plasmacytoid Dendritic Cells Are Not Major Producers of Type 1 IFN in Cutaneous Lupus: An In-Depth Immunoprofile of Subacute and Discoid Lupus

The immunologic drivers of cutaneous lupus erythematosus (CLE) and its clinical subtypes remain poorly understood. We sought to characterize the immune landscape of discoid lupus erythematosus and subacute CLE using multiplexed immunophenotyping. We found no significant differences in immune cell percentages between discoid lupus erythematosus and subacute CLE (P > .05) with the exception of an increase in TBK1 in discoid lupus erythematosus (P < .05). Unbiased clustering grouped subjects into 2 major clusters without respect to clinical subtype. Subjects with a history of smoking had increased percentages of neutrophils, disease activity, and endothelial granzyme B compared with nonsmokers. Despite previous assumptions, plasmacytoid dendritic cells (pDCs) did not stain for IFN-1. Skin-eluted and circulating pDCs from subjects with CLE expressed significantly less IFNα than healthy control pDCs upon toll-like receptor 7 stimulation ex vivo (P < .0001). These data suggest that discoid lupus erythematosus and subacute CLE have similar immune microenvironments in a multiplexed investigation. Our aggregated analysis of CLE revealed that smoking may modulate disease activity in CLE through neutrophils and endothelial granzyme B. Notably, our data suggest that pDCs are not the major producers of IFN-1 in CLE. Future in vitro studies to investigate the role of pDCs in CLE are needed.

Imaging mass cytometry reveals tissue-specific cellular immune phenotypes in the mouse knee following ACL injury

Objective

To develop an imaging mass cytometry method for identifying complex cell phenotypes, inter-cellular interactions, and population changes in the synovium and infrapatellar fat pad (IFP) of the mouse knee following a non-invasive compression injury.

Design

Fifteen male C57BL/6 mice were fed a high-fat diet for 8 weeks prior to random assignment to sham, 0.88 ​mm, or 1.7 ​mm knee compression displacement at 24 weeks of age. 2-weeks after loading, limbs were prepared for histologic and imaging mass cytometry analysis, focusing on myeloid immune cell populations in the synovium and IFP.

Results

1.7 ​mm compression caused anterior cruciate ligament (ACL) rupture, development of post-traumatic osteoarthritis, and a 2- to 3-fold increase in cellularity of synovium and IFP tissues compared to sham or 0.88 ​mm compression. Imaging mass cytometry identified 11 myeloid cell subpopulations in synovium and 7 in IFP, of which approximately half were elevated 2 weeks after ACL injury in association with the vasculature. Notably, two monocyte/macrophage subpopulations and an MHC IIhi population were elevated 2-weeks post-injury in the synovium but not IFP. Vascular and immune cell interactions were particularly diverse in the synovium, incorporating 8 unique combinations of 5 myeloid cell populations, including a monocyte/macrophage population, an MHC IIhi population, and 3 different undefined F4/80+ myeloid populations.

Conclusions

Developing an imaging mass cytometry method for the mouse enabled us to identify a diverse array of synovial and IFP vascular-associated myeloid cell subpopulations. These subpopulations were differentially elevated in synovial and IFP tissues 2-weeks post injury, providing new details on tissue-specific immune regulation.

Self-supervised deep learning for highly efficient spatial immunophenotyping

BACKGROUND

Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets.

METHODS

This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model.

FINDINGS

With 1% annotations (18-114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to -0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression.

INTERPRETATION

By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data.

FUNDING

This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

Molecular architecture of proliferative lupus nephritis as elucidated using 50-plex imaging mass cytometry proteomics

Due to unique advantages that allow high-dimensional tissue profiling, we postulated imaging mass cytometry (IMC) may shed novel insights on the molecular makeup of proliferative lupus nephritis (LN). This study interrogates the spatial expression profiles of 50 target proteins in LN and control kidneys. Proliferative LN glomeruli are marked by podocyte loss with immune infiltration dominated by CD45RO+, HLA-DR+ memory CD4 and CD8 T-cells, and CD163+ macrophages, with similar changes in tubulointerstitial regions. Macrophages are the predominant HLA-DR expressing antigen presenting cells with little expression elsewhere, while macrophages and T-cells predominate cellular crescents. End-stage sclerotic glomeruli are encircled by an acellular fibro-epithelial Bowman's space surrounded by immune infiltrates, all enmeshed in fibronectin. Proliferative LN also shows signs indicative of epithelial to mesenchymal plasticity of tubular cells and parietal epithelial cells. IMC enabled proteomics is a powerful tool to delineate the spatial architecture of LN at the protein level.

Single-cell profiling of low-stage endometrial cancers identifies low epithelial vimentin expression as a marker of recurrent disease

BACKGROUND

Identification of aggressive low-stage endometrial cancers is challenging. So far, studies have failed to pinpoint robust features or biomarkers associated with risk of recurrence for these patients.

METHODS

Imaging mass cytometry was used to examine single-cell expression of 23 proteins in 36 primary FIGO IB endometrial cancers, of which 17 recurred. Single-cell information was extracted for each tumor and unsupervised clustering was used to identify cellular phenotypes. Distinct phenotypes and cellular neighborhoods were compared in relation to recurrence. Cellular differences were validated in a separate gene expression dataset and the TCGA EC dataset. Vimentin protein expression was evaluated by IHC in pre-operative samples from 518 patients to validate its robustness as a prognostic marker.

FINDINGS

The abundance of epithelial, immune or stromal cell types did not associate with recurrence. Clustering of patients based on tumor single cell marker expression revealed distinct patient clusters associated with outcome. A cell population neighboring CD8+ T cells, defined by vimentin, ER, and PR expressing epithelial cells, was more prevalent in non-recurrent tumors. Importantly, lower epithelial vimentin expression and lower gene expression of VIM associated with worse recurrence-free survival. Loss and low expression of vimentin was validated by IHC as a robust marker for recurrence in FIGO I stage disease and predicted poor prognosis also when including all patients and in endometrioid patients only.

INTERPRETATION

This study reveals distinct characteristics in low-stage tumors and points to vimentin as a clinically relevant marker that may aid in identifying a here to unidentified subgroup of high-risk patients.

FUNDING

A full list of funding that contributed to this study can be found in the Acknowledgements section.

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

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

Imaging Mass Cytometry in Immuno-Oncology

In situ profiling of the tumor-immune microenvironment (TiME) requires the ability to co-localize and detect multiple proteins simultaneously. Imaging mass cytometry (IMC), using the Hyperion™ imaging system is a novel multiplex imaging modality that currently enables detection of up to 50 markers on fixed tissues at subcellular resolution and thus has the potential to inform both pre-clinical and clinical research by providing investigators with spatially resolved information about the TiME. Here we provide an overview of the IMC workflow from sample fixation to analysis, with a focus on multiplex panel design and tissue staining.

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

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

Immuno-PET Monitoring of CD8+ T Cell Infiltration Post ICOS Agonist Antibody Treatment Alone and in Combination with PD-1 Blocking Antibody Using a 89Zr Anti-CD8+ Mouse Minibody in EMT6 Syngeneic Tumor Mouse

PURPOSE

The presence and functional competence of intratumoral CD8⁺ T cells is often a barometer for successful immunotherapeutic responses in cancer. Despite this understanding and the extensive number of clinical-stage immunotherapies focused on potentiation (co-stimulation) or rescue (checkpoint blockade) of CD8⁺ T cell antitumor activity, dynamic biomarker strategies are often lacking. To help fill this gap, immuno-PET nuclear imaging has emerged as a powerful tool for in vivo molecular imaging of antibody targeting. Here, we took advantage of immuno-PET imaging using ⁸⁹Zr-IAB42M1-14, anti-mouse CD8 minibody, to characterize CD8⁺ T-cell tumor infiltration dynamics following ICOS (inducible T-cell co-stimulator) agonist antibody treatment alone and in combination with PD-1 blocking antibody in a model of mammary carcinoma.

PROCEDURES

Female BALB/c mice with established EMT6 tumors received 10 µg, IP of either IgG control antibodies, ICOS agonist monotherapy, or ICOS/PD-1 combination therapy on days 0, 3, 5, 7, 9, 10, or 14. Imaging was performed at 24 and 48 h post IV dose of ⁸⁹Zr IAB42M1-14. In addition to ⁸⁹Zr-IAB42M1-14 uptake in tumor and tumor-draining lymph node (TDLN), 3D radiomic features were extracted from PET/CT images to identify treatment effects. Imaging mass cytometry (IMC) and immunohistochemistry (IHC) was performed at end of study.

RESULTS

⁸⁹Zr-IAB42M1-14 uptake in the tumor was observed by day 11 and was preceded by an increase in the TDLN as early as day 4. The spatial distribution of ⁸⁹Zr-IAB42M1-14 was more uniform in the drug treated vs. control tumors, which had spatially distinct tracer uptake in the periphery relative to the core of the tumor. IMC analysis showed an increased percentage of cytotoxic T cells in the ICOS monotherapy and ICOS/PD-1 combination group compared to IgG controls. Additionally, temporal radiomics analysis demonstrated early predictiveness of imaging features.

CONCLUSION

To our knowledge, this is the first detailed description of the use of a novel immune-PET imaging technique to assess the kinetics of CD8⁺ T-cell infiltration into tumor and lymphoid tissues following ICOS agonist and PD-1 blocking antibody therapy. By demonstrating the capacity for increased spatial and temporal resolution of CD8⁺ T-cell infiltration across tumors and lymphoid tissues, these observations underscore the widespread potential clinical utility of non-invasive PET imaging for T-cell-based immunotherapy in cancer.

Immuno Tomography (IT) and Imaging Mass Cytometry (IMC) for constructing spatially resolved, multiplexed 3D IMC data sets

PURPOSE

We have previously used Immuno Tomography (IT) to identify label-retaining stem cell populations in the cornea and meibomian gland. While this method provides the unique ability to quantify stem cell populations comprised of 1-4 cells, the number of antigens that can be sequentially used to characterize these unique cells is limited by antigen stability after antibody stripping and re-probing. To address this deficiency, we have evaluated the capability of Imaging Mass Cytometry™ (IMC™) to generate multiplexed images using metal-conjugated antibodies to label IT plastic sections and generate 3-dimensional IMC data sets (3D IMC).

METHODS

K5-H2B-GFP mice, 56 days after doxycycline chase, were sacrificed and eyelid tissue processed for IT. A total of 400 serial, plastic sections, 2 μm thick, were then probed using metal-tagged antibodies specific for sox 9, collagen type I, E-cadherin, Ki67, GFP, αSMA, vimentin, and DNA intercalator. Multiplexed images were then generated using an Imaging Mass Cytometry system (Fluidigm®), and 3D reconstructions were assembled.

RESULTS

All 8 metal-labeled tags were detected and their images were successfully assembled into 3D IMC data sets. GFP-labeled nuclei were identified within the meibomian glands in comparable numbers to those previously reported for slow-cycling meibomian gland stem cells.

CONCLUSIONS

These findings demonstrate that IMC can be used on plastic sections to generate multiplexed, 3D data sets that can be reconstructed to show the spatial localization of meibomian gland stem cells. We propose that 3D IMC might prove valuable in more fully characterizing stem cell populations in different tissues.

Single-cell profiling of human dura and meningioma reveals cellular meningeal landscape and insights into meningioma immune response

BACKGROUND

Recent investigations of the meninges have highlighted the importance of the dura layer in central nervous system immune surveillance beyond a purely structural role. However, our understanding of the meninges largely stems from the use of pre-clinical models rather than human samples.

METHODS

Single-cell RNA sequencing of seven non-tumor-associated human dura samples and six primary meningioma tumor samples (4 matched and 2 non-matched) was performed. Cell type identities, gene expression profiles, and T cell receptor expression were analyzed. Copy number variant (CNV) analysis was performed to identify putative tumor cells and analyze intratumoral CNV heterogeneity. Immunohistochemistry and imaging mass cytometry was performed on selected samples to validate protein expression and reveal spatial localization of select protein markers.

RESULTS

In this study, we use single-cell RNA sequencing to perform the first characterization of both non-tumor-associated human dura and primary meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, we characterize a functionally diverse and heterogenous landscape of non-immune cells including endothelial cells and fibroblasts. Through imaging mass cytometry, we highlight the spatial relationship among immune cell types and vasculature in non-tumor-associated dura. Utilizing T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. Finally, we report copy number variant heterogeneity within our meningioma samples.

CONCLUSIONS

Our comprehensive investigation of both the immune and non-immune cellular landscapes of human dura and meningioma at single-cell resolution builds upon previously published data in murine models and provides new insight into previously uncharacterized roles of human dura.

Detecting respiratory chain defects in osteoblasts from osteoarthritic patients using imaging mass cytometry

Osteoporosis is a skeletal disease which is characterised by reduced bone mass and microarchitecture, with a subsequent loss of strength that predisposes to fragility and risk of fractures. The pathogenesis of falling bone mineral density, ultimately leading to a diagnosis of osteoporosis is incompletely understood but the disease is currently thought to be multifactorial. Humans are known to accumulate mitochondrial mutations and respiratory chain deficiency with age and mounting evidence suggests that this may indeed be the overarching cause intrinsic to the changing phenotype in advancing age and age-related disease. Mitochondrial mutations are detectable from the age of about 30 years onwards. Mitochondria contain their own genome which encodes 13 essential mitochondrial proteins and accumulates somatic variants at up to 10 times the rate of the nuclear genome. Once the concentration of any pathogenic mitochondrial genome variant exceeds a threshold, respiratory chain deficiency and cellular dysfunction occur. The Polg^D257A/D257A mouse model is a knock-in mutant that expresses a proof-reading-deficient version of PolgA, a nuclear encoded subunit of mtDNA polymerase. These mice are a useful model of age-related accumulation of mtDNA mutations in humans since their defective proof-reading mechanism leads to a mitochondrial DNA mutation rate 3-5 times higher than in wild-type mice. These mice showed enhanced levels of age-related osteoporosis along with respiratory chain deficiency in osteoblasts. To explore whether respiratory chain deficiency is also seen in human osteoblasts, we developed a protocol and analysis framework for imaging mass cytometry in bone tissue sections to analyse osteoblasts in situ. By comparing bone tissue sampled at one timepoint from femoral neck of 10 older healthy volunteers aged 40-85 with samples from young patients aged 1-19, we have identified complex I defect in osteoblasts from 6 out of 10 older volunteers, complex II defect in 2 out of 10 older volunteers, complex IV defect in 1 out of 10 older volunteers and complex V defect in 4 out of 10 older volunteers. These observations are consistent with findings from the Polg^D257A/D257A mouse model and suggest that respiratory chain deficiency, as a consequence of the accumulation of age-related pathogenic mitochondrial DNA mutations, may play a significant role in the pathogenesis of human age-related osteoporosis.

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

In situ imaging for tumor microbiome interactions via imaging mass cytometry on single-cell level

Co-detection of multiplex cancer subtypes and bacteria subtypes in situ is crucial for understanding tumor microbiome interactions in tumor microenvironment. Current standard techniques such as immunohistochemical staining and immunofluorescence staining are limited for their multiplicity. Simultaneously visualizing detailed cell subtypes and bacteria distribution across the same pathological section remains a major technical challenge. Herein, we developed a rapid semi-quantitative method for in situ imaging of bacteria and multiplex cell phenotypes on the same solid tumor tissue sections. We designed a panel of antibody probes labeled with mass tags, namely prokaryotic and eukaryotic cell hybrid probes for in situ imaging (PEHPSI). For application demonstration, PEHPSI stained two bacteria subtypes (lipopolysaccharides (LPS) for Gram-negative bacteria and lipoteichoic acid (LTA) for Gram-positive bacteria) simultaneously with four types of immune cells (leukocytes, CD8+T-cells, B-cells and macrophages) and four breast cancer subtypes (classified by a panel of 12 human proteins) on the same tissue section. We unveiled that breast cancer cells are commonly enriched with Gram-negative bacteria and almost absent of Gram-positive bacteria, regardless of the cancer subtypes (triple-negative breast cancer (TNBC), HER2+, Luminal A and Luminal B). Further analysis revealed that on the single-cell level, Gram-negative bacteria have a significant correlation with CD8+T-cells only in HER2+ breast cancer, while PKCD, ER, PR and Ki67 are correlated with Gram-negative bacteria in the other three subtypes of breast cancers. On the cell population level, in TNBC, CD19 expression intensity is up-regulated by approximately 25% in bacteria-enriched cells, while for HER2+, Luminal A and Luminal B breast cancers, the intensity of biomarkers associated with the malignancy, metastasis and proliferation of cancer cells (PKCD, ISG15 and IFI6) is down-regulated by 29-38%. The flexible and expandable PEHPSI system permits intuitive multiplex co-visualization of bacteria and mammalian cells, which facilitates future research on tumor microbiome and tumor pathogenesis. This article is protected by copyright. All rights reserved.

Imaging mass cytometry for high-dimensional tissue profiling in the eye

Background

Imaging mass cytometry (IMC) combines the principles of flow cytometry and mass spectrometry (MS) with laser scanning spatial resolution and offers unique advantages for the analysis of tissue samples in unprecedented detail. In contrast to conventional immunohistochemistry, which is limited in its application by the number of possible fluorochrome combinations, IMC uses isoptope-coupled antibodies that allow multiplex analysis of up to 40 markers in the same tissue section simultaneously.

Methods

In this report we use IMC to analyze formalin-fixed, paraffin-embedded conjunctival tissue. We performed a 18-biomarkers IMC analysis of conjunctival tissue to determine and summarize the possibilities, relevance and limitations of IMC for deciphering the biology and pathology of ocular diseases.

Results

Without modifying the manufacturer’s protocol, we observed positive and plausible staining for 12 of 18 biomarkers. Subsequent bioinformatical single-cell analysis and phenograph clustering identified 24 different cellular clusters with distinct expression profiles with respect to the markers used.

Conclusions

IMC enables highly multiplexed imaging of ocular samples at subcellular resolution. IMC is an innovative and feasible method, providing new insights into ocular disease pathogenesis that will be valuable for basic research, drug discovery and clinical diagnostics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-021-02099-8.

MATISSE: a method for improved single cell segmentation in imaging mass cytometry

Background

Visualizing and quantifying cellular heterogeneity is of central importance to study tissue complexity, development, and physiology and has a vital role in understanding pathologies. Mass spectrometry-based methods including imaging mass cytometry (IMC) have in recent years emerged as powerful approaches for assessing cellular heterogeneity in tissues. IMC is an innovative multiplex imaging method that combines imaging using up to 40 metal conjugated antibodies and provides distributions of protein markers in tissues with a resolution of 1 μm² area. However, resolving the output signals of individual cells within the tissue sample, i.e., single cell segmentation, remains challenging. To address this problem, we developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation), a method that combines high-resolution fluorescence microscopy with the multiplex capability of IMC into a single workflow to achieve improved segmentation over the current state-of-the-art.

Results

MATISSE results in improved quality and quantity of segmented cells when compared to IMC-only segmentation in sections of heterogeneous tissues. Additionally, MATISSE enables more complete and accurate identification of epithelial cells, fibroblasts, and infiltrating immune cells in densely packed cellular areas in tissue sections. MATISSE has been designed based on commonly used open-access tools and regular fluorescence microscopy, allowing easy implementation by labs using multiplex IMC into their analysis methods.

Conclusion

MATISSE allows segmentation of densely packed cellular areas and provides a qualitative and quantitative improvement when compared to IMC-based segmentation. We expect that implementing MATISSE into tissue section analysis pipelines will yield improved cell segmentation and enable more accurate analysis of the tissue microenvironment in epithelial tissue pathologies, such as autoimmunity and cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01043-y.

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.

The landscape of myeloid and astrocyte phenotypes in acute multiple sclerosis lesions

Activated myeloid cells and astrocytes are the predominant cell types in active multiple sclerosis (MS) lesions. Both cell types can adopt diverse functional states that play critical roles in lesion formation and resolution. In order to identify phenotypic subsets of myeloid cells and astrocytes, we profiled two active MS lesions with thirteen glial activation markers using imaging mass cytometry (IMC), a method for multiplexed labeling of histological sections. In the acutely demyelinating lesion, we found multiple distinct myeloid and astrocyte phenotypes that populated separate lesion zones. In the post-demyelinating lesion, phenotypes were less distinct and more uniformly distributed. In both lesions cell-to-cell interactions were not random, but occurred between specific glial subpopulations and lymphocytes. Finally, we demonstrated that myeloid, but not astrocyte phenotypes were activated along a lesion rim-to-center gradient, and that marker expression in glial cells at the lesion rim was driven more by cell-extrinsic factors than in cells at the center. This proof-of-concept study demonstrates that highly multiplexed tissue imaging, combined with the appropriate computational tools, is a powerful approach to study heterogeneity, spatial distribution and cellular interactions in the context of MS lesions. Identifying glial phenotypes and their interactions at different lesion stages may provide novel therapeutic targets for inhibiting acute demyelination and low-grade, chronic inflammation.