scholarly journals McSNAC: A software to approximate first-order signaling networks from mass cytometry data

2021 ◽  
Author(s):  
Darren Wethington ◽  
Sayak Mukherjee ◽  
Jayajit Das
Keyword(s):  
Single Cell ◽  
In Silico ◽  
Single Cells ◽  
Signaling Network ◽  
Signaling Networks ◽  
Enzymatic Reactions ◽  
Signaling Proteins ◽  
Protein Species ◽  
Mass Cytometry ◽  
First Order

AbstractMass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful to dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. With a proper computational analysis, timestamped CyTOF data of signaling proteins could help develop predictive and mechanistic models for signaling kinetics. These models would be useful for predicting the effects of perturbations in cells, or comparing signaling networks across cell groups. We propose our Mass cytometry Signaling Network Analysis Code, or McSNAC, a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption breaks down often as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of the protein species that are involved in signaling. We carry out a series of in silico experiments here to show that 1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; 2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured. These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from timestamped CyTOF data.

All Roads Lead to Rome: Phenotypic Channeling to Uniform Differentiation Structures from Diverse Leukemia Genotypes by Mass Cytometry

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. SCI-22-SCI-22
Author(s):  
Garry Nolan
Keyword(s):  
Stem Cells ◽  
B Cell ◽  
Single Cell ◽  
Conflicts Of Interest ◽  
Single Cells ◽  
Lymphoblastic Leukemia ◽  
Myelogenous Leukemia ◽  
Signaling Networks ◽  
Mass Cytometry ◽  
Human Cord Blood

Abstract Emerging single-cell technologies have been pivotal in uncovering an extensive degree of heterogeneity between and within tissues (1). Analysis of single-cell data has shed light on many different cellular processes (2-7) and recent technological advances have enabled the study of a large number of parameters in single cells at unparalleled resolution. One such technology, mass cytometry (8), can measure up to 45 parameters simultaneously in tens of thousands of individual cells. Using mass cytometry and genomic sequencing of conventionally sorted subpopulations show that acute myelogenous leukemia (AML) in a given patient can simultaneously occupy multiple stages of differentiation. Occupation of these stages was correlated with the presence, or absence, of unique exonic mutation fingerprints. In another cancer, B-cell acute lymphoblastic leukemia (ALL), outgrowth of tumor at pro and pre-B cell stages was nearly always uniquely at a single stage - contrary to the results in AML. This suggests that evolutionary “niche” searching is not only for physical space in cancers, but also involves utilization of differentiation machinery as an additional elaboration mechanism. Each differentiation stage in both AML and B-cell ALL was characterized by utilization of cognate signaling networks which showed differential susceptibility to drug action. Using such deep profiling and signaling delineation approaches at the single-cell level will allow for fine structured indexing of patient disease and further tailoring of disease management. In addition, it will allow “heterogeneous” tumors to be organized by a maturation index associated with a granular catalog of mutations that drive cells to occupy these pseudo-differentiation niches. 1. Bendall, S.C., et al., A deep profiler's guide to cytometry.Trends Immunol, 2012. 33(7): p. 323-32. 2. Petilla Interneuron Nomenclature Group, et al., Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.Nat Rev Neurosci, 2008. 9(7): p. 557-68. 3. Irish, J.M., et al., Single cell profiling of potentiated phospho-protein networks in cancer cells.Cell, 2004. 118(2): p. 217-28. 4. Sachs, K., et al., Causal protein-signaling networks derived from multiparameter single-cell data.Science, 2005. 308(5721): p. 523-9. 5. Majeti, R., C.Y. Park, and I.L. Weissman, Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood. Cell Stem Cell, 2007. 1(6): p. 635-45. 6. Tarnok, A., H. Ulrich, and J. Bocsi, Phenotypes of stem cells from diverse origin.Cytometry A, 2010. 77(1): p. 6-10. 7. O'Brien, C.A., A. Kreso, and J.E. Dick, Cancer stem cells in solid tumors: an overview.Semin Radiat Oncol, 2009. 19(2): p. 71-7. 8. Bandura, D.R., et al., Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem, 2009. 81(16): p. 6813-22. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Single Cell Detection of the p53 Protein by Mass Cytometry

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3699
Author(s):  
Oda Helen Eck Fagerholt ◽  
Monica Hellesøy ◽  
Stein-Erik Gullaksen ◽  
Bjørn Tore Gjertsen
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
P53 Protein ◽  
Single Cells ◽  
Cell Detection ◽  
Signaling Proteins ◽  
Malignant Cells ◽  
Mass Cytometry ◽  
Post Translational Modifications ◽  
Single Cell Profiling

Purpose: The p53 protein and its post-translational modifications are distinctly expressed in various normal cell types and malignant cells and are usually detected by immunohistochemistry and flow cytometry in contemporary diagnostics. Here, we describe an approach for simultaneous multiparameter detection of p53, its post-translational modifications and p53 pathway-related signaling proteins in single cells using mass cytometry. Method: We conjugated p53-specific antibodies to metal tags for detection by mass cytometry, allowing the detection of proteins and their post-translational modifications in single cells. We provide an overview of the antibody validation process using relevant biological controls, including cell lines treated in vitro with a stimulus (irradiation) known to induce changes in the expression level of p53. Finally, we present the potential of the method through investigation of primary samples from leukemia patients with distinct TP53 mutational status. Results: The p53 protein can be detected in cell lines and in primary samples by mass cytometry. By combining antibodies for p53-related signaling proteins with a surface marker panel, we show that mass cytometry can be used to decipher the single cell p53 signaling pathway in heterogeneous patient samples. Conclusion: Single cell profiling by mass cytometry allows the investigation of the p53 functionality through examination of relevant downstream signaling proteins in normal and malignant cells. Our work illustrates a novel approach for single cell profiling of p53.

scholarly journals Multiplexed single‐cell mass cytometry reveals distinct inhibitory effects on intracellular phosphoproteins by midostaurin in combination with chemotherapy in AML cells

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Emma Rörby ◽  
Jörgen Adolfsson ◽  
Erik Hultin ◽  
Thomas Gustafsson ◽  
Kourosh Lotfi ◽  
...  
Keyword(s):  
Single Cell ◽  
Intracellular Signaling ◽  
Kinase Inhibitor ◽  
Cell Mass ◽  
Receptor Expression ◽  
Leukemic Cells ◽  
Human Leukemia ◽  
Signaling Proteins ◽  
Mass Cytometry ◽  
Standard Chemotherapy

Abstract Background Fms-related tyrosine kinase 3 (FLT3) receptor serves as a prognostic marker and therapeutic target in acute myeloid leukemia (AML). Approximately one-third of AML patients carry mutation in FLT3, associated with unfavourable prognosis and high relapse rate. The multitargeted kinase inhibitor midostaurin (PKC412) in combination with standard chemotherapy (daunorubicin and cytarabine) was recently shown to increase overall survival of AML patients. For that reason, PKC412 has been approved for treatment of AML patients with FLT3-mutation. PKC412 synergizes with standard chemotherapy, but the mechanism involved is not fully understood and the risk of relapse is still highly problematic. Methods By utilizing the unique nature of mass cytometry for single cell multiparameter analysis, we have explored the proteomic effect and intracellular signaling response in individual leukemic cells with internal tandem duplication of FLT3 (FLT3-ITD) after midostaurin treatment in combination with daunorubicin or cytarabine. Results We have identified a synergistic inhibition of intracellular signaling proteins after PKC412 treatment in combination with daunorubicin. In contrast, cytarabine antagonized phosphorylation inhibition of PKC412. Moreover, we found elevated levels of FLT3 surface expression after cytarabine treatment. Interestingly, the surface localization of FLT3 receptor increased in vivo on the blast cell population of two AML patients during day 3 of induction therapy (daunorubicin; once/day from day 1–3 and cytarabine; twice/day from day 1–7). We found FLT3 receptor expression to correlate with intracellular cytarabine (AraC) response. AML cell line cultured with AraC with or without PKC412 had an antagonizing phosphorylation inhibition of pAKT (p = 0.042 and 0.0261, respectively) and pERK1/2 (0.0134 and 0.0096, respectively) in FLT3high compared to FLT3low expressing cell populations. Conclusions Our study provides insights into how conventional chemotherapy affects protein phosphorylation of vital signaling proteins in human leukemia cells. The results presented here support further investigation of novel strategies to treat FLT3-mutated AML patients with PKC412 in combination with chemotherapy agents and the potential development of novel treatment strategies.

scholarly journals Robustness and applicability of functional genomics tools on scRNA-seq data

2019 ◽  
Author(s):  
Christian H. Holland ◽  
Jovan Tanevski ◽  
Jan Gleixner ◽  
Manu P. Kumar ◽  
Elisabetta Mereu ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Single Cell ◽  
In Silico ◽  
Single Cells ◽  
Real Data ◽  
Drop Out ◽  
Rna Seq ◽  
Knock Out ◽  
Genomics Tools

AbstractMany tools have been developed to extract functional and mechanistic insight from bulk transcriptome profiling data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events, low library sizes and a comparatively large number of samples/cells. It is thus not clear if functional genomics tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. To address this question, we performed benchmark studies on in silico and in vitro single-cell RNA-seq data. We included the bulk-RNA tools PROGENy, GO enrichment and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compared them against the tools AUCell and metaVIPER, designed for scRNA-seq. For the in silico study we simulated single cells from TF/pathway perturbation bulk RNA-seq experiments. Our simulation strategy guarantees that the information of the original perturbation is preserved while resembling the characteristics of scRNA-seq data. We complemented the in silico data with in vitro scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on both the simulated and real data revealed comparable performance to the original bulk data. Additionally, we showed that the TF and pathway activities preserve cell-type specific variability by analysing a mixture sample sequenced with 13 scRNA-seq different protocols. Our analyses suggest that bulk functional genomics tools can be applied to scRNA-seq data, outperforming dedicated single cell tools. Furthermore we provide a benchmark for further methods development by the community.

scholarly journals Inferring the structures of signaling motifs from paired dynamic traces of single cells

2019 ◽  
Author(s):  
R. A. Haggerty ◽  
Jeremy E. Purvis
Keyword(s):  
Time Series ◽  
Single Cell ◽  
Single Cells ◽  
Underlying Mechanism ◽  
Repeated Measurements ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Signaling Mechanism ◽  
Signaling Dynamics ◽  
Cell To Cell Variability

AbstractIndividual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained by a single signaling motif that deterministically maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in single cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell’s upstream signal was randomly paired with another cell’s downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be embedded within the dynamical patterns of single cells.Author SummaryCells use molecular signaling networks to translate dynamically changing stimuli into appropriate downstream responses. Specialized network structures, or motifs, allow cells to properly decode a variety of temporal input signals. In this paper, we present an algorithm that infers signaling motifs from multiple examples of an upstream signal paired with its downstream response in the same cell. We compare the predictive power of single-cell versus averaged time-series traces and the incremental benefit of adding more single-cell traces to the algorithm. We use this approach to understand how yeast respond to environmental stresses.

scholarly journals Multi-Dimensional Immuno-Profiling of Drosophila Hemocytes by Single Cell Mass Cytometry

2020 ◽  
Author(s):  
József Á. Balog ◽  
Viktor Honti ◽  
Éva Kurucz ◽  
Beáta Kari ◽  
László G. Puskás ◽  
...  
Keyword(s):  
Single Cell ◽  
Complex Analysis ◽  
Single Cells ◽  
Cell Mass ◽  
Antigen Expression ◽  
Bioinformatic Analysis ◽  
Multidimensional Analysis ◽  
Cell Mediated Immunity ◽  
Mass Cytometry ◽  
Cell Surface Antigens

AbstractSingle cell mass cytometry (SCMC) combines features of traditional flow cytometry (FACS) with mass spectrometry and allows the measurement of several parameters at the single cell level, thus permitting a complex analysis of biological regulatory mechanisms. We optimized this platform to analyze the cellular elements, the hemocytes, of the Drosophila innate immune system. We have metal-conjugated six antibodies against cell surface antigens (H2, H3, H18, L1, L4, P1), against two intracellular antigens (3A5, L2) and one anti-IgM for the detection of L6 surface antigen, as well as one anti-GFP for the detection of crystal cells in the immune induced samples. We investigated the antigen expression profile of single cells and hemocyte populations in naive, in immune induced states, in tumorous mutants (hopTum bearing a driver mutation and l(3)mbn1 carrying deficiency of a tumor suppressor) as well as in stem cell maintenance defective hdcΔ84 mutant larvae. Multidimensional analysis enabled the discrimination of the functionally different major hemocyte subsets, lamellocytes, plasmatocytes, crystal cell, and delineated the unique immunophenotype of the mutants. We have identified sub-populations of L2+/P1+ (l(3)mbn1), L2+/L4+/P1+ (hopTum) transitional phenotype cells in the tumorous strains and a sub-population of L4+/P1+ cells upon immune induction. Our results demonstrated for the first time, that mass cytometry, a recent single cell technology combined with multidimensional bioinformatic analysis represents a versatile and powerful tool to deeply analyze at protein level the regulation of cell mediated immunity of Drosophila.

scholarly journals Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE

2018 ◽  
Author(s):  
Axel Theorell ◽  
Yenan Troi Bryceson ◽  
Jakob Theorell
Keyword(s):  
Single Cell ◽  
Open Source ◽  
Rna Sequencing ◽  
Biological Markers ◽  
State Of The Art ◽  
Single Cells ◽  
Data Mining Algorithm ◽  
Mass Cytometry ◽  
Sequencing Data ◽  
Biologically Relevant

AbstractTechnological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make this highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi-and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better as the best performing state-of-the-art clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. An open source R implementation of DEPECHE is available at https://github.com/theorell/DepecheR.Author summaryDEPECHE-a data-mining algorithm for mega-variate dataModern experimental technologies facilitate an array of single cells measurements, e.g. at the RNA-level, generating enormous datasets with thousands of annotated biological markers for each of thousands of cells. To analyze such datasets, researchers routinely apply automated or semi-automated techniques to order the cells into biologically relevant groups. However, even after such groups have been generated, it is often difficult to interpret the biological meaning of these groups since the definition of each group often dependends on thousands of biological markers. Therefore, in this article, we introduce DEPECHE, an algorithm designed to simultaneously group cells and enhance interpretability of the formed groups. DEPECHE defines groups only with respect to biological markers that contribute significantly to differentiate the cells in the group from the rest of the cells, yielding more succinct group definitions. Using the open source R software DepecheR on RNA sequencing data and mass cytometry data, the number of defining markers were reduced up to 1000-fold, thereby increasing interpretability vastly, while maintaining or improving the biological relevance of the groups formed compared to state-of-the-art algorithms.

Network-Based Discovery of Prognostic Markers in Pediatric AML by Multi-Dimensional Single Cell Mass Cytometry

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1411-1411
Author(s):  
Karen Sachs ◽  
Garry P. Nolan ◽  
Wendy J. Fantl ◽  
Erin F. Simonds ◽  
Sean C. Bendall ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Bone Marrow ◽  
Clinical Outcome ◽  
Single Cell ◽  
Intracellular Signaling ◽  
Cell Mass ◽  
Signaling Networks ◽  
Surface Markers ◽  
Mass Cytometry ◽  
Pediatric Aml

Abstract Abstract 1411 Background: Currently available indicators of risk stratification for acute myeloid leukemia (AML) approximate a patient's true prognosis (Rubnitz, J.E., et al., Lancet Oncol, 2010). While signaling networks are central to biological systems they do not lend themselves to easy characterization. As such, network characteristics are not incorporated into risk stratification. There exists a need for computational techniques that enable elucidation of a patient's global signaling state. Flow cytometry is an essential tool for classifying AML surface markers by surface marker expression. Previous work has shown that intracellular signaling responses can be predictive of clinical outcome (Irish et al., Cell 2004; Kornblau et al., Clin. Cancer Res. 2010, Kornblau et al. Blood Cancer J 2011). We hypothesized that there may exist additional layers of information in both cell phenotypes and signaling responses which were not recognized in the low-parameter systems used in these studies, and which, upon characterization, may yield prognostic benefit. Methods: 31-parameter single cell mass cytometry was used to measure the simultaneous co-expression of 16 surface markers and 15 intracellular signaling epitopes in pediatric AML diagnosis bone marrow samples (n=15) and healthy adult bone marrow controls (n=3). Signaling dynamics were measured under 18 stimulation conditions including cytokines and small molecule kinase inhibitors. Single cell resolution enables statistical extraction of correlative relationships which exist among the measured variables. Network inference elucidated the regulatory signaling structure in each patient (Sachs et al. Science 2005, Itani et al. JMLR 2008), and a statistical tool called a decision tree was used to predict the patient's relapse status (Figure 1). Results: The analysis selects network features predictive of clinical outcome, here, relapse status. Of the network edges with a strong statistical signal, the most informative were the relationships between pS6 and pSTAT1, pAKT and cleaved Caspase3, pAMPK and pRb, pRb and pP38, pAMPK and pErk and pCREB and pCbl. Despite the small dataset size, the decision tree correctly classified >85% of the patients, an improvement over a random model which returns 50% accuracy. (The predicted value was excluded from training to avoid “memorization” by the classification model). For a small subset of the patients (3) who relapsed, paired relapse samples were also available. Strikingly, of the network features which consistently differed between the diagnostic sample and its paired relapse counterpart, two overlapped with those found to have predictive information in the above: pAMPK – pRb and pRb – pP38. These results implicate a coordination of metabolism, cell cycle and survival Conclusions: Statistical classification typically relies on large numbers of profiled samples; thus the ability to predict relapse status from diagnostic samples in this small pilot study is a strong indication of the utility of this approach. Predictive accuracy relied on network features and was not achieved using univariate measurements alone. Furthermore, the features extracted by the predictive model determine which correlations help reveal the prognostic fate of a tumor at diagnosis, potentially providing mechanistic insight. These correlative relationships corroborate the roles of metabolism, survival and cell cycle in AML. The application of 31-parameter mass cytometry at the single cell level provided a level of detail that enabled characterization of signaling relationships at high resolution. This proof of principle study will inform future studies of AML signaling networks and prognostic biomarkers. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Profiling Cell Signaling Networks at Single-cell Resolution

2020 ◽  
Vol 19 (5) ◽  
pp. 744-756 ◽  
Author(s):  
Xiao-Kang Lun ◽  
Bernd Bodenmiller
Keyword(s):  
Cell Signaling ◽  
Single Cell ◽  
Signaling Network ◽  
Signaling Networks ◽  
Network Heterogeneity ◽  
Network Analyses ◽  
Cell Measurement ◽  
A Cell ◽  
Cell Signaling Networks ◽  
Many Sources

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

665 Spatial single-cell analysis of colorectal cancer tumour using multiplexed imaging mass cytometry

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A702-A702
Author(s):  
Minh Tran ◽  
Andrew Su ◽  
HoJoon Lee ◽  
Richard Cruz ◽  
Lance Pflieger ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Cancer Cells ◽  
Tissue Section ◽  
Single Cells ◽  
Cell Types ◽  
Mass Cytometry ◽  
Tissue Sections ◽  
Tissue Region ◽  
Hyperion Data

BackgroundCancer research experiments often require the dissociation of cells from their native tissue before molecular profiling, leading to the loss of spatial tissue context. The cancer genomics research has shifted from mostly profiling tumour DNA mutations towards the current frontier of investigating individual genes and gene products in single cells and their immediate microenvironments. Information at this level with the spatial context enables us to link cancer–causing mutations and environmental factors to outcomes in cell signalling, responses and survival that will lead to solutions for diagnosing, predicting progression and treating cancers in different individuals. In this project we aim to capture tissue morphology, cancer cell types, multi-parameter protein contents of single cells in within morphologically intact tissue sections of colorectal tumours from 52 patients.MethodsUsing Hyperion Imaging Mass Cytometry (IMC), we simultaneously profiled 16 protein markers for each tissue section, capturing molecular signatures of tissue architecture, cancer cells, and immune cells. IMC uses laser beam to accurately ablate every 1µm2 of tissue region, generating data at subcellular resolution for FFPE tissue sections on a glass microscopy slide. We selected 2–8 regions of interest (ROI), each containing approximately 2098 cells. The ROI sizes range from 141µm x 500µm to 1121µm x 1309µm. We developed an analysis pipeline to process raw Hyperion imaging data (IMCtools), define cellular masks with information about nuclei, membrane, cytoplasm (using CellProfiler and Ilastick), and analyses cellular communities (HistoCAT). We also generated whole exome sequencing data and histopathological mages from sections of the same tissue blocks.ResultsBy measuring 16 multiplexed proteins, for each tissue region we were able to identify up to seven cell types and preserved their spatial location within the tissue (figure 1A). Through the spatial map of the cell types to the tissue, we showed the heterogeneity of the tumour microenvironment, such as the infiltration of macrophages and B-cells to the cancer regions (figure 1A). We found cancer cells consistently marked as positive for p53 and Ki67 proteins. Moreover, we could measure the level of p53 in every individual cell within each tissue section (figure 1B). The quantitative measurement of p53 by imaging mass cytometry was correlated with the result from traditional genomic sequencing of p53 mutations and with the histopathological annotation.Abstract 665 Figure 1Characterizing the complexity of colorectal cancer. A) Cell types within a region of interest, defined by 16 markers. Cancer cells are consistent to the. B) Quantifying p53 expression from Hyperion dataConclusionsApplying the Hyperion technology, we could acquire rich information from each of the precious cancer samples. The spatial data at single-cell resolution enabled us to assess the heterogeneity of tumour tissue by defining cell types, immune infiltration, and cancer-immune cell interaction within an undissociated tissue section. Future analysis and application of Hyperion data would allow us to find better predictors for colorectal cancer tissue with more accurate diagnosis and prognosis.Ethics ApprovalThis study was approved by the Institutional Review Board (#1050191) at Intermountain Healthcare (Salt Lake City, UT USA)

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