Multiplexed Ion Beam Imaging Research Articles

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

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

39800 Immune Checkpoint Blockade during Periprosthetic Joint Infection

ABSTRACT IMPACT: If immune checkpoint blockade increases bacterial clearance with or without antibiotics in vitro, clinical application would be almost immediate and dramatic creating a seismic shift in the current therapeutic paradigm of periprosthetic joint infection. OBJECTIVES/GOALS: Periprosthetic joint infection (PJI) is a major cause of failure after joint replacement. Currently, the treatment of PJI relies on removing biofilm contaminated implants. Some of the bacteria within biofilm undergo a phenotypic shift becoming small colony variants (SCVs). SCVs induce local immunosuppression through PD-1/L1 signaling. METHODS/STUDY POPULATION: We will infect cultured human macrophages and bone marrow aspirate with stable Staphylococcus aureus SVCs and treat with anti-PD-1 or anti-PD-L1 monoclonal antibodies with and without antibiotics (e.g., gentamycin, cefazolin, vancomycin, rifampicin) and assess the residual bacterial viability. We will utilize multiplexed ion beam imaging to quantify PD-1/L1 expression in human tissue from patients with a chronic PJI and compare those to patients undergoing an aseptic revision. Patients with a chronic PJI are likely to have increased expression of PD-1/L1 as their tissue samples are prospectively screened. RESULTS/ANTICIPATED RESULTS: SCVs reduce the phagocytic activity of macrophages and can survive intracellularly. SCVs also induce anti-inflammatory M2-macrophage polarization and recruit a heterogeneous group of immature monocytes and granulocytes called myeloid-derived suppressor cells (MDSC) to the periprosthetic microenvironment. M2-macrophages and MDSCs then produce an immunosuppressive cytokine milieu characterized by increased IL-10 and decreased TNF-α. Clinically isolated SCVs up-regulate the expression of PD-L1 and PD-L2 on the surface of macrophages, representing a mechanism by which SCVs induce host immunosuppression and survive immune clearance. Our preliminary data show PD-L1 expression during septic PJI, but not in aseptic revisions. DISCUSSION/SIGNIFICANCE OF FINDINGS: If immune checkpoint blockade is shown to increase bacterial clearance with or without antibiotics, host immunomodulation would represent a novel class of therapeutic adjuvants to assist surgical debridement and antibiotic administration that could be superimposed on existing treatment algorithms to improve PJI related outcomes.

Abstract PD5-08: The human tumor atlas network (HTAN) breast pre cancer atlas: A multi-omic integrative analysis of ductal carcinoma in situ (DCIS) and correlation with clinical outcomes

Abstract Introduction. As nonobligate precursors of invasive disease, pre-cancers provide a unique vantage point from which to study the molecular pathways and evolutionary dynamics that lead to the development of life-threatening cancers. Ductal carcinoma in situ (DCIS) is the most commonly diagnosed precursor of breast cancer, with variable propensity for invasive progression. In order to address the problems of over- and under-treatment, we performed a multimodal, integrated profile of DCIS with clinical outcomes with which to develop and validate predictors of invasive progression. Methods. We present observations on DNA, RNA, and protein expression on two independent patient cohorts of DCIS, diagnosed from 1981 to 2014, from the Translational Breast Cancer Research Consortium (TBCRC 038) and the Washington University Repository of Archival Human Breast Tissue (RAHBT). Patients initially diagnosed with DCIS, with either DCIS or invasive recurrence (cases; mean follow up 5.8 years) were matched to those without recurrence (controls; mean follow up 10.3 years), based upon age at diagnosis and year of diagnosis. Results. We present genomic and cellular changes that correlate with both disease states and patient outcomes in DCIS. DCIS can be clustered by classification systems developed for IBC. Specific immune cell types and pathways correlate with longitudinal outcome. Luminal cell adhesion and metabolism pathways are upregulated in controls and cases, respectively. Highly multiplexed ion beam imaging (MIBI) was used to validate RNA seq findings, and to provide single cell-level spatial context for molecular alterations.Conclusion. We have performed an integrated multi-omic analysis of DCIS and associated tumor micorenvironment. Our multi-scale approach employs in situ methods to generate a spatially resolved atlas of breast precancers where different modalities can be directly compared to each other, and correlated with conventional pathology findings and clinical outcome. The PreCancer Atlas represents a complex multi-modal database for DCIS study, whose design allows for future discovery and hypothesis generation. Table 1. Breast Pre-cancer Atlas Multi-scale Characterization AssaysAssayScaleType of DataIntegration and validation with other assaysRNA-seq (Single duct, single cell, TME)Cell, duct, organ, normal tissue1. Whole transcriptome gene expression profiling per single duct (also enabling CNV and cell type prediction)2. Whole transcriptome gene expression profiling per single duct1. Prediction of CNV confirmed by DNA-seq (single duct) and FISH (single cell)2. Prediction of cell type composition (Cibersort) confirmed by multiplex IHC and multicolor flow cytometryLow-pass whole genome DNA-seqDuct and adjacent normalCNV profiling per single ductAnalysis of CNV supported by RNA-seq (single duct) and MIBI (single cell)Whole genome sequencingDuct and adjacent normalMutation status per single ductMutational analysis confirmed by RNA-seqMultiplex IHC (MIBI & Cyclic multicolor)Cell1. Cell type2. Proteomic analysisAnalysis of cell type supported by RNA-seq of ducts (Cibersort) and single cellsH&E MorphometricsCell, duct, organSpatial location of cell types, organization of ductsAnalysis of H&E images correlated with FISH data Citation Format: Shelley Hwang, Siri H Strand, Belen Rivero, Lorraine King, Tyler Risom, Bryan Harmon, Fergus Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla McAuliffe, Julie Nangia, Ana Maria Storniolo, Alastair Thompson, Gaorav Gupta, Joanna Lee, Jennifer Tseng, Robyn Burns, ChunFang Zhu, Magda Matusiak, Shirley X Zhu, Jason Wang, Jose Seoane, Jen Tappenden, Daisy Ding, Dadong Zhang, Jingqin Luo, Sujay Vennam, Sushama Varma, Lunden Simpson, Luis Cisneros, Timmothy Hardman, Lauren Anderson, Cody Straub, Sucheta Srivastava, Deb J Veis, Christina Curtis, Rob Tibshirani, Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia Polyak, Carlo Maley, Jeff Marks, Graham Colditz, Robert B West. The human tumor atlas network (HTAN) breast pre cancer atlas: A multi-omic integrative analysis of ductal carcinoma in situ (DCIS) and correlation with clinical outcomes [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD5-08.

Abstract PD5-07: Mapping the tumor and microenvironmental evolution underlying DCIS progression throughmultiplexed ion beam imaging

Abstract Ductal Carcinoma in Situ (DCIS) is a pre-invasive lesion that accounts for nearly 20% of new breast cancer diagnoses. Of these cases, about half will progress to invasive breast cancer (IBC) within ten years. However, because diagnostic criteria for delineating low risk lesions from those likely to progress to IBC have not been identified, many patients are receiving unnecessary chemotherapy and surgery that can result in therapy-related morbidity and death. With this in mind, we used Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and RNA-seq laser-capture microdissection (SMART-3SEQ) to construct a comprehensive spatial atlas describing the structure, function, and cellular composition of DCIS. MIBI-TOF and SMART-3SEQ were used to compare lesions from patients that later developed IBC with those from age- and history matched DCIS controls without recurrence. Using a 37-marker staining panel to interrogate 137 lesions, we identified 17 distinct cell populations of epithelial, stromal, and immune cells that were arranged in recurrent cellular microenvironments specific for DCIS or invasive disease. We observe a coordinated shift in the immune and stromal compartments as invasive disease arises, including a robust influx of lymphocytes and expansion reactive stromal phenotypes in synchronous DCIS + IBC, which was distinct from the macrophage-dominant, highly vascularized microenvironment of recurrent IBC. Further, single cell segmentation using a deep learning model was combined with pixel-level coexpression analysis to extensively evaluate how the thickness, continuity, and phenotype of ductal myoepithelium changes as tumors progress from a pre-invasive state. These data were incorporated into a comprehensive model which was subsequently used to identify a subset of features that correlate with disease-free survival following DCIS tumor resection. Taken together, these features represent important prognostic metrics that can be used to separate pre-invasive from indolent DCIS tumors, and allow for tailored therapy that improves patient outcomes and quality of life in this disease. Citation Format: Michael Angelo. Mapping the tumor and microenvironmental evolution underlying DCIS progression throughmultiplexed ion beam imaging [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD5-07.

Nanoscopic subcellular imaging enabled by ion beam tomography

Multiplexed ion beam imaging (MIBI) has been previously used to profile multiple parameters in two dimensions in single cells within tissue slices. Here, a mathematical and technical framework for three-dimensional (3D) subcellular MIBI is presented. Ion-beam tomography (IBT) compiles ion beam images that are acquired iteratively across successive, multiple scans, and later assembled into a 3D format without loss of depth resolution. Algorithmic deconvolution, tailored for ion beams, is then applied to the transformed ion image series, yielding 4-fold enhanced ion beam data cubes. To further generate 3D sub-ion-beam-width precision visuals, isolated ion molecules are localized in the raw ion beam images, creating an approach coined as SILM, secondary ion beam localization microscopy, providing sub-25 nm accuracy in original ion images. Using deep learning, a parameter-free reconstruction method for ion beam tomograms with high accuracy is developed for low-density targets. In cultured cancer cells and tissues, IBT enables accessible visualization of 3D volumetric distributions of genomic regions, RNA transcripts, and protein factors with 5 nm axial resolution using isotope-enrichments and label-free elemental analyses. Multiparameter imaging of subcellular features at near macromolecular resolution is implemented by the IBT tools as a general biocomputation pipeline for imaging mass spectrometry.

Virus-Dependent Immune Conditioning of Tissue Microenvironments

A thorough understanding of complex spatial host-disease interactions in situ is necessary in order to develop effective preventative measures and therapeutic strategies. Here, we developed Protein And Nucleic acid IN situ Imaging (PANINI) and coupled it with Multiplexed Ion Beam Imaging (MIBI) to sensitively and simultaneously quantify DNA, RNA, and protein levels within the microenvironments of tissue compartments. The PANINI-MIBI approach was used to measure over 30 parameters simultaneously across large sections of archival lymphoid tissues from non-human primates that were healthy or infected with simian immunodeficiency virus (SIV), a model that accurately recapitulates human immunodeficiency virus infection (HIV). This enabled multiplexed dissection of cellular phenotypes, functional markers, viral DNA integration events, and viral RNA transcripts as resulting from viral infection. The results demonstrated immune coordination from an unexpected upregulation of IL10 in B cells in response to SIV infection that correlated with macrophage M2 polarization, thus conditioning a potential immunosuppressive environment that allows for viral production. This multiplexed imaging strategy also allowed characterization of the coordinated microenvironment around latently or actively infected cells to provide mechanistic insights into the process of viral latency. The spatial multi-modal framework presented here is applicable to deciphering tissue responses in other infectious diseases and tumor biology.

Abstract PO-092: Immune profiling of the tumor microenvironment using multiplexed ion beam imaging (MIBI)

Abstract The growing application of immunotherapy and its ability, or inability, to modulate the tumor microenvironment (TME) has exposed a need for techniques capable of simultaneously tracking large numbers of biomarkers in complex tissue at sub-cellular resolution. To address this, MIBI uses tissue processed similar to standard immunohistochemistry (IHC) methods but with the advantage of being able to stain the tissue simultaneously with a panel of up to 40 metal labeled antibodies. The expression of proteins is imaged by MIBI in a single scan at subcellular spatial resolution by combining the high resolution of an ion beam and the sensitivity of time-of-flight secondary ion mass spectrometry (ToF-SIMS). To characterize the immune infiltrates present in a set of 10 non-small-cell lung carcinoma (NSCLC) samples, a panel of 22 metal labeled antibodies was used to stain the tissue. The stained tissue was then analyzed by MIBI and the masses of detected species were assigned to target biomolecules given the unique label of each antibody. Multi-step processing and segmentation utilizing highly expressed nuclear, membrane, and cytoplasmic markers were performed to accurately determine cell boundaries and enable quantitative analyses of marker expression, cell classification, and the spatial relationships between cell types. Results comparing the abundance of tumor cells, vascular cells, and immune cell subsets between samples identified “cold” tumors with few immune cells and inflamed samples that varied in the type of immune populations present. Immune checkpoint markers (LAG3, PD-1, PD-L1, TIM-3) were also quantified at single cell resolution. In conclusion, this study shows that MIBI offers high-parameter capability necessary to (1) identify the wide range of cell types present within the complex tumor immune landscape, (2) the spatial relationships between these different cell populations, and (3) the expression status of key immunoregulatory proteins. Citation Format: Jason Ptacek, Srimoyee Ghosh, Lourdes Pablo, Bin Feng, Hailei Zhang, Yari Sigal, Jay G. Tarolli, Murat Aksoy, Yi Zhang, Rachel Finck, Jessica Finn. Immune profiling of the tumor microenvironment using multiplexed ion beam imaging (MIBI) [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-092.

Abstract PR05: Mapping the tumor and microenvironmental evolution underlying DCIS progression through multiplexed ion beam imaging

Abstract Ductal Carcinoma in Situ (DCIS) is a pre-invasive lesion that accounts for nearly 20% of new breast cancer diagnoses. Of these cases, about half will progress to invasive breast cancer (IBC) within ten years. However, because diagnostic criteria for delineating low risk lesions from those likely to progress to IBC have not been identified, many patients are receiving unnecessary chemotherapy and surgery that can result in therapy-related morbidity and death. With this in mind, we used Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and RNA-seq laser-capture microdissection (SMART-3SEQ) to construct a comprehensive spatial atlas describing the structure, function, and cellular composition of DCIS. MIBI-TOF and SMART-3SEQ were used to compare lesions from patients that later developed IBC with those from age- and history matched DCIS controls without recurrence. Using a 37-marker staining panel to interrogate 137 lesions, we identified 30 distinct cell populations of the epithelial, stromal, and immune lineages that were arranged in recurrent cellular microenvironments specific for DCIS or invasive disease. We observe a coordinated shift in the immune and stromal compartments as invasive disease arises, including an expansion of immune cell diversity and transition to reactive stromal phenotypes in synchronous DCIS + IBC, which was distinct from the macrophage-dominant microenvironment of recurrent IBC. Single-cell segmentation using a deep learning model was combined with pixel-level coexpression analysis to determine how thickness, continuity, and phenotype of ductal myoepithelium changes as tumors progress from a pre-invasive state. These data were incorporated into a comprehensive model which was subsequently used to identify a subset of features that correlate with disease-free survival following DCIS tumor resection. Taken together, these features represent important prognostic metrics that can be used to separate pre-invasive from indolent DCIS tumors, and allow for tailored therapy that improves patient outcomes and quality of life in this disease. Citation Format: Tyler Risom, Belen Rivero, Candace Liu, Alex Baranski, Siri Strand, Noah Greenwald, Erin McCaffrey, Sushama Varma, Leeat Keren, Sucheta Srivastava, Chunfang Zhu, Sujay Vennam, Shelley Hwang, Graham Colditz, Sean Bendall, Robert West, Michael Angelo. Mapping the tumor and microenvironmental evolution underlying DCIS progression through multiplexed ion beam imaging [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PR05.

Abstract PO-041: Multiplexed ion beam imaging to describe tumor-immune microenvironment and tumor heterogeneity in neuroblastoma

Abstract Neuroblastoma is a tumor of the peripheral sympathetic nervous system, accounting for 10% of pediatric cancer associated deaths and the most common cancer diagnosed during infancy(1,2). This neural crest derived malignancy is characterized by clinical heterogeneity ranging from spontaneous regression in some patients to treatment resistance, metastasis and death in others(3,4). Here, we report the application of Multiplexed Ion Beam Imaging (MIBI), a single-cell, high-dimensional imaging method to determine the expression and co-expression of antigens on neuroblastoma cells and describe the local immune environment. We have optimized a 40-antibody panel to capture adrenergic or mesenchymal identity(5,6), infiltrating immune cells, tissue architecture and known neuroblastoma proteins. We optimized our antibody staining and determined tissue specificity using a unique tissue microarray which included 32 NBL cores but also additional childhood solid tumors including osteosarcoma, glioblastoma, Ewing’s sarcoma, rhabdomyosarcoma, atypical teratoid rhabdoid tumor, and Wilms tumor. Healthy pediatric tissues from various organs including kidney, liver, and tonsil were also included. We have uncovered divergent neuroblastoma cell populations differentiated by co-expression of CD57, Ki67 and GPC2 as well as mesenchymal (FN1 and SNAI2/SLUG) and adrenergic (TH, GATA3 and PHOX2B) antibodies, demonstrating promising early results of our application of MIBI. We will describe infiltrating immune populations and spatial relationships to these unique tumor cell populations. Our work demonstrates the feasibility of using MIBI to make translatable discoveries in neuroblastoma and other childhood cancers. With this we will lay the foundation for future discovery and validation of novel targets for immunotherapy in neuroblastoma. Citation Format: Marte B. Kammersgaard, Marc Bosse, Daniel Martinez, Kristopher R. Bosse, John M. Maris, Crystal L. Mackall, Robert M. Angelo, Kara L. Davis. Multiplexed ion beam imaging to describe tumor-immune microenvironment and tumor heterogeneity in neuroblastoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-041.

Abstract IA22: Single cell proteomics to capture human dysplasia and dysfunction

Abstract Single cell analysis, starting with the earliest low parameter fluorescent cytometry and microscopy experiments, helped define the major cell subsets of human cellular systems as we understand them today. Now, a novel combination of elemental mass spectrometry with single cell analysis (mass cytometry – CyTOF, Science 2011) and imaging (multiplexed ionbeam imaging – MIBI, Nature Med. 2014, Cell 2018) offers routine examination of >50 parameters without fluorescent agents or interference from spectral overlap using heavy metal isotopes as reporters. With these platforms, we have reached new levels of organizational understanding in human pathobiology. Here, we will review the evolution of these technologies and present our latest efforts to create assays with these platforms in order to quantify multiple levels of regulation including metabolism, proteome dynamics, and chromatin content regulating the epigenome. Further to this, we will highlight recent efforts to leverage structural characteristics of leukemia dysplasias to facilitate automated identification and classification from multiplexed single cell datasets (Tsai & Glass et al. Nature Med. 2020). Overall, these new approaches can reveal new layers of human cellular organization and structure that can be exploited to understand dysfunction in general and provide avenues for more automated and robust clinical classification. Citation Format: Sean Bendall. Single cell proteomics to capture human dysplasia and dysfunction [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr IA22.

Abstract PO-078: Single-cell transcriptomic analysis of highly-multiplexed IHC images via antigen mapping

Abstract Recently developed technologies for digital imaging and highly multiplexed immunohistochemistry (mIHC) are enabling complex quantitative descriptions of tissue architecture. Imaging mass cytometry, multiplexed ion beam imaging, and co-detection by indexing can be used to profile the expression of dozens of proteins in a tissue section with single-cell resolution. Despite this progress, the amount of cell types and states that can be simultaneously identified by mIHC in a tissue section is still limited. Standard methods for identifying cell populations cluster cells according to expression similarities of the profiled antigens. These clusters are then manually annotated using previous knowledge of cell population markers. However, this is generally a partial, subjective, and biased process. Clusters often differ little in their antigenic profile and the interpretation of those differences is unclear. Moreover, the design of comprehensive antibody panels that include specific markers for every cell type and state present in the tissue is usually unfeasible. To overcome these limitations and increase the level of annotation of mIHC data, we propose an approach for enriching mIHC slides with single-cell RNA-seq data. Currently available single-cell RNA-seq technologies profile the expression of thousands of genes in each cell, allowing researchers to finely classify cells based on their gene expression profile. Some of the most recent approaches, like CITE-seq, REAP-seq, and Ab-seq, allow augmenting single-cell transcriptomes with concurrent protein measurements by staining dissociated cells with oligo-tagged antibodies. These approaches can be therefore used to determine the quantitative relation between gene and antigen expression levels. Here, we build upon CITE-seq and computational methods for the integration of single-cell omics data to identify and annotate cell populations in mIHC images in an automated way based on single-cell gene expression data from the same tissue. Our method, Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA), performs transcriptome-guided annotation of mIHC datasets. It increases the phenotypic resolution of histological analyses by enabling annotation of subtle cell populations, spatial patterns of transcription, and interactions between cell types. We use the murine spleen as a test system to benchmark the stability and performance of STvEA and demonstrate its utility by using it to annotate published CODEX and CyTOF datasets of this organ. Citation Format: Kiya W. Govek, Emma C. Troisi, Zhen Miao, Steven Woodhouse, Pablo G. Camara. Single-cell transcriptomic analysis of highly-multiplexed IHC images via antigen mapping [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-078.

Abstract IA04: Relating single cell function to tissue structure in human tumors using MIBI-TOF

Abstract Understanding the role of distinct cellular phenotypes in tissue function, development, and pathogenesis requires tools that can rapidly and consistently quantify the expression of multiple proteins while preserving spatial information. To meet this need, we have developed Multiplexed Ion Beam Imaging by Time-Of-Flight (MIBI-TOF). MIBI-TOF uses secondary ion mass spectrometry to visualize up to 50 metal labeled antibodies at subcellular resolution simultaneously with histochemical stains and native biological elements. We have used this capability to study how single cell function and tissue structure interact at the tumor immune microenvironment, the human maternal-fetal interface, and in infectious disease. These studies have led us to develop scalable, automated, and extensible tools for image thresholding, single cell segmentation, cell clustering, and cell neighborhood analyses that can be deployed on any tissue. Through NIH HuBMAP and CIMAC initiatives, we are currently working to deploy this computational pipeline as well as preformulated antibody staining master mixes as a streamlined, end-to-end workflow for routine high dimensional tissue analysis in basic and translational research. Lastly, we are currently working to develop brighter primary ion sources and new metal reporter constructs that will permit full tissue section scanning while extending multiplexed detection to include RNA and DNA. Citation Format: R. Michael Angelo. Relating single cell function to tissue structure in human tumors using MIBI-TOF [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr IA04.

Single-cell metabolic profiling of human cytotoxic T cells.

Cellular metabolism regulates immune cell activation, differentiation and effector functions but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. Here, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using a high-dimensional antibody-based approach. We employed mass cytometry (CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naïve and memory CD8+ T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with imaging mass spectrometry (MIBI-TOF), we uncovered the spatial organization of metabolic programs, which indicated exclusion of metabolically repressed immune cells from the tumor-immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.

Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types

AbstractAn ability to characterize the cellular composition and spatial organization of the tumor microenvironment (TME) using multiplexed IHC has been limited by the techniques available. Here we show the applicability of multiplexed ion beam imaging (MIBI) for cell phenotype identification and analysis of spatial relationships across numerous tumor types. Formalin-fixed paraffin-embedded (FFPE) samples from tumor biopsies were simultaneously stained with a panel of 15 antibodies, each labeled with a specific metal isotope. Multi-step processing produced images of the TME that were further segmented into single cells. Frequencies of different cell subsets and the distributions of nearest neighbor distances between them were calculated using this data. A total of 50 tumor specimens from 15 tumor types were characterized for their immune profile and spatial organization. Most samples showed infiltrating cytotoxic T cells and macrophages present amongst tumor cells. Spatial analysis of the TME in two ovarian serous carcinoma images highlighted differences in the degree of mixing between tumor and immune cells across samples. Identification of admixed PD-L1+ macrophages and PD-1+ T cells in an urothelial carcinoma sample allowed for the detailed observations of immune cell subset spatial arrangement. These results illustrate the high-parameter capability of MIBI at a sensitivity and resolution uniquely suited to understanding the complex tumor immune landscape including the spatial relationships of immune and tumor cells and expression of immunoregulatory proteins.The ability to characterize the cellular composition and spatial organization of the tumor microenvironment has been limited by the techniques available to image the necessary number of biomarkers for broad phenotyping at a subcellular resolution. This study demonstrates the capabilities of Multiplexed Ion Beam Imaging (MIBI) for cell phenotype identification and their spatial relationships across multiple tumor types.

Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma

SummaryTo define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.

Single-nucleus RNA-seq of frozen archival primary pancreatic ductal adenocarcinoma uncovers multi-compartment intratumoral heterogeneity associated with neoadjuvant treatment.

4633 Background: Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease and existing molecular subtypes do not inform clinical decisions. Previously identified bulk transcriptomic subtypes of PDAC were often unintentionally driven by “contaminating” stroma. RNA extraction from pancreatic tissue is difficult and prior single-cell RNA-seq efforts have been limited by suboptimal dissociation/RNA quality and poor performance in the setting of neoadjuvant treatment. We developed a robust single-nucleus RNA-seq (sNuc-seq) technique compatible with frozen archival PDAC specimens. Methods: Single nuclei suspensions were extracted from frozen primary PDAC specimens (n = 27) derived from patients with (borderline)-resectable PDAC who underwent surgical resection with or without neoadjuvant chemoradiotherapy (CRT). Approximately 170,000 nuclei were processed with the 10x Genomics Single Cell 3’ v3 pipeline and gene expression libraries were sequenced (Illumina HiSeq X). Results: Distinct nuclei clusters with gene expression profiles/inferred copy number variation analysis consistent with neoplastic, acinar, ductal, fibroblast, endothelial, endocrine, lymphocyte, and myeloid populations were identified with proportions similar to corresponding multiplexed ion beam imaging. Non-negative matrix factorization revealed intra-tumoral heterogeneity shared across patients. Neoplastic cells featured eight distinct transcriptional topics characterized by developmental (epithelial, mesenchymal, endoderm progenitor, neural progenitor) and environmental (anabolic, catabolic, cycling, hypoxic) programs. CAFs exhibited four different transcriptional topics (activated/desmoplastic, myofibroblast, neurogenic, osteochondral). Differential gene expression and gene set enrichment analyses demonstrated that CRT was associated with an enrichment in myogenic programs in CAFs, activation pathways in immune cells, and type I/II interferons in malignant cells. CRT was also associated with a depletion in developmental programs within malignant cells. Conclusions: We uncovered significant intratumoral heterogeneity and treatment-associated differences in the malignant, fibroblast, and immune compartments of PDAC using sNuc-seq. Deconvolution of clinically-annotated bulk RNA-seq cohorts and characterization of intercellular interactions with receptor-ligand analysis and spatial transcriptomics are ongoing.

Isotopically Encoded Nanotags for Multiplexed Ion Beam Imaging.

High-dimensional profiling of markers and analytes using approaches, such as barcoded fluorescent imaging with repeated labeling and mass cytometry has allowed visualization of biological processes at the single-cell level. To address limitations of sensitivity and mass-channel capacity, a nanobarcoding platform is developed for multiplexed ion beam imaging (MIBI) using secondary ion beam spectrometry that utilizes fabricated isotopically encoded nanotags. Use of combinatorial isotope distributions in 100 nm sized nanotags expands the labeling palette to overcome the spectral bounds of mass channels. As a proof-of-principle, a four-digit (i.e., 0001-1111) barcoding scheme is demonstrated to detect 16 variants of 2H, 19F, 79/81Br, and 127I elemental barcode sets that are encoded in silica nanoparticle matrices. A computational debarcoding method and an automated machine learning analysis approach are developed to extract barcodes for accurate quantification of spatial nanotag distributions in large ion beam imaging areas up to 0.6 mm2. Isotopically encoded nanotags should boost the performance of mass imaging platforms, such as MIBI and other elemental-based bioimaging approaches.

225: In-depth characterization of immune cells in preeclampsia using Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF)

225: In-depth characterization of immune cells in preeclampsia using Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF)

Immune monitoring usingmass cytometry and related high-dimensional imaging approaches.

The cellular complexity and functional diversity of the human immune system necessitate the use of high-dimensional single-cell tools to uncover its role in multifaceted diseases such as rheumatic diseases, as well as other autoimmune and inflammatory disorders. Proteomic technologies that use elemental (heavy metal) reporter ions, such as mass cytometry (also known as CyTOF) and analogous high-dimensional imaging approaches (including multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC)), have been developed from their low-dimensional counterparts, flow cytometry and immunohistochemistry, to meet this need. A growing number of studies have been published that use these technologies to identify functional biomarkers and therapeutic targets in rheumatic diseases, but the full potential of their application to rheumatic disease research has yet to be fulfilled. This Review introduces the underlying technologies for high-dimensional immune monitoring and discusses aspects necessary for their successful implementation, including study design principles, analytical tools and future developments for the field of rheumatology.

Mass Cytometry Imaging for the Study of Human Diseases-Applications and Data Analysis Strategies.

High parameter imaging is an important tool in the life sciences for both discovery and healthcare applications. Imaging Mass Cytometry (IMC) and Multiplexed Ion Beam Imaging (MIBI) are two relatively recent technologies which enable clinical samples to be simultaneously analyzed for up to 40 parameters at subcellular resolution. Importantly, these “Mass Cytometry Imaging” (MCI) modalities are being rapidly adopted for studies of the immune system in both health and disease. In this review we discuss, first, the various applications of MCI to date. Second, due to the inherent challenge of analyzing high parameter spatial data, we discuss the various approaches that have been employed for the processing and analysis of data from MCI experiments.