Breast cancer is a heterogeneous disease. Tumor cells and associated healthy cells form ecosystems that determine disease progression and response to therapy. To characterize features of breast ...cancer ecosystems and their associations with clinical data, we analyzed 144 human breast tumor and 50 non-tumor tissue samples using mass cytometry. The expression of 73 proteins in 26 million cells was evaluated using tumor and immune cell-centric antibody panels. Tumors displayed individuality in tumor cell composition, including phenotypic abnormalities and phenotype dominance. Relationship analyses between tumor and immune cells revealed characteristics of ecosystems related to immunosuppression and poor prognosis. High frequencies of PD-L1+ tumor-associated macrophages and exhausted T cells were found in high-grade ER+ and ER− tumors. This large-scale, single-cell atlas deepens our understanding of breast tumor ecosystems and suggests that ecosystem-based patient classification will facilitate identification of individuals for precision medicine approaches targeting the tumor and its immunoenvironment.
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•Single-cell proteomics reveals tumor and immune cell diversity in tumor ecosystems•Breast cancer exhibits tumor cell phenotypic abnormalities and tumor individuality•PD-L1+ TAMs and exhausted T cells are abundant in high-grade ER− and ER+ tumors•Tumor-immune relationships in the tumor ecosystem are patient-stratifying
A single-cell atlas of cancer and immune cells reveals distinct tumor ecosystems across breast cancer patients, informing prognosis and, potentially, therapy selection.
Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding ...how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.
Understanding tumor complexity and applying that knowledge to advance patient care is a cornerstone of cancer research. However, tumor heterogeneity obfuscates this vision.Contemporary research contains a diverse set of technologies and computational tools for detecting, characterizing, and quantifying tumor heterogeneity at the molecular, architectural, organ, patient, or population level.Evaluation of the clinical relevance of heterogeneity metrics and creating actionable intelligence for the clinics, requires a common lexicon across disciplines, and a unified look toward multimodal data acquisition and computational analysis, the constraints they present, and how they partake in whole workflows.
A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor heterogeneity is believed to be key for therapy failure and differences in prognosis in cancer ...patients. Comprehending tumor heterogeneity, especially at the protein level, is critical for tracking tumor evolution, and showing the presence of different phenotypical variants and their location with respect to tissue architecture. Although a variety of techniques is available for quantifying protein expression, the heterogeneity observed in the tissue is rarely addressed. The proposed method is validated in breast cancer fresh-frozen tissues derived from five patients. Protein expression is quantified on the tissue regions of interest (ROI) with a resolution of up to 100 μm in diameter. High heterogeneity values across the analyzed patients in proteins such as cytokeratin 7, β-actin and epidermal growth factor receptor (EGFR) using a Shannon entropy analysis are observed. Additionally, ROIs are clustered according to their expression levels, showing their location in the tissue section, and highlighting that similar phenotypical variants are not always located in neighboring regions. Interestingly, a patient with a phenotype related to increased aggressiveness of the tumor presents a unique protein expression pattern. In summary, a workflow for the localized extraction and protein analysis of regions of interest from frozen tissues, enabling the evaluation of tumor heterogeneity at the protein level is presented.
We present easyFRAP, a versatile tool that assists quantitative and qualitative analysis of fluorescence recovery after photobleaching (FRAP) data. The user can handle simultaneously large data sets ...of raw data, visualize fluorescence recovery curves, exclude low quality data, perform data normalization, extract quantitative parameters, perform batch analysis and save the resulting data and figures for further use. Our tool is implemented as a single-screen Graphical User Interface (GUI) and is highly interactive, as it permits parameterization and visual data quality assessment at various points during the analysis.
easyFRAP is free software, available under the General Public License (GPL). Executable and source files, supplementary material and sample data sets can be downloaded at: ccl.med.upatras.gr/easyfrap.html.
Fluorescence recovery after photobleaching (FRAP) is a cutting-edge live-cell functional imaging technique that enables the exploration of protein dynamics in individual cells and thus permits the ...elucidation of protein mobility, function, and interactions at a single-cell level. During a typical FRAP experiment, fluorescent molecules in a defined region of interest within the cell are bleached by a short and powerful laser pulse, while the recovery of the fluorescence in the region is monitored over time by time-lapse microscopy. FRAP experimental setup and image acquisition involve a number of steps that need to be carefully executed to avoid technical artifacts. Equally important is the subsequent computational analysis of FRAP raw data, to derive quantitative information on protein diffusion and binding parameters. Here we present an integrated in vivo and in silico protocol for the analysis of protein kinetics using FRAP. We focus on the most commonly encountered challenges and technical or computational pitfalls and their troubleshooting so that valid and robust insight into protein dynamics within living cells is gained.
Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, ...CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course. CellCycleTRACER reveals signaling relationships and cell heterogeneity that were otherwise masked.
Once-per-cell cycle replication is regulated through the assembly onto chromatin of multisubunit protein complexes that license DNA for a further round of replication. Licensing consists of the ...loading of the hexameric MCM2–7 complex onto chromatin during G1 phase and is dependent on the licensing factor Cdt1. In vitro experiments have suggested a two-step binding mode for minichromosome maintenance (MCM) proteins, with transient initial interactions converted to stable chromatin loading. Here, we assess MCM loading in live human cells using an in vivo licensing assay on the basis of fluorescence recovery after photobleaching of GFP-tagged MCM protein subunits through the cell cycle. We show that, in telophase, MCM2 and MCM4 maintain transient interactions with chromatin, exhibiting kinetics similar to Cdt1. These are converted to stable interactions from early G1 phase. The immobile fraction of MCM2 and MCM4 increases during G1 phase, suggestive of reiterative licensing. In late G1 phase, a large fraction of MCM proteins are loaded onto chromatin, with maximal licensing observed just prior to S phase onset. Fluorescence loss in photobleaching experiments show subnuclear concentrations of MCM-chromatin interactions that differ as G1 phase progresses and do not colocalize with sites of DNA synthesis in S phase.
Background: MCM2–7 loading onto chromatin licenses origins for replication.
Results: MCMs exhibit transient interactions with chromatin in late mitosis, stable binding in G1 phase and increased loading in late G1 phase.
Conclusion: Multilevel regulation of MCM2–7 loading to chromatin occurs during mitosis and preceding the G1/S phase transition.
Significance: The dynamics of the DNA licensing system within live human cells reveal multiple control points.
Abstract
Summary
Tumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial ...tumor profiling, however computational resources that exploit the measurements to quantify tumor heterogeneity in a spatially aware manner are largely missing. We present ATHENA (Analysis of Tumor HEterogeNeity from spAtial omics measurements), a computational framework that facilitates the visualization, processing and analysis of tumor heterogeneity from spatial omics measurements. ATHENA uses graph representations of tumors and bundles together a large collection of established and novel heterogeneity scores that quantify different aspects of the complexity of tumor ecosystems.
Availability and implementation
ATHENA is available as a Python package under an open-source license at: https://github.com/AI4SCR/ATHENA. Detailed documentation and step-by-step tutorials with example datasets are also available at: https://ai4scr.github.io/ATHENA/. The data presented in this article are publicly available on Figshare at https://figshare.com/articles/dataset/zurich_pkl/19617642/2.
Supplementary information
Supplementary data are available at Bioinformatics online.
Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell ...protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches.
With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an ...open-source Python library for cell type identification and quantification of tumor ecosystem heterogeneity in patient cohorts. We provide a step-by-step protocol on the application of scQUEST on our previously generated human breast cancer single-cell atlas using mass cytometry and discuss how it can be adapted and extended for other datasets and analyses.
For complete details on the use and execution of this protocol, please refer to Wagner et al. (2019).
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•Tumor ecosystems exhibit a strong degree of phenotypic heterogeneity•scQUEST facilitates the analysis of large cytometry datasets with millions of cells•scQUEST implements scores to quantify tumor heterogeneity based on phenotypic profiles•All scores are easily customizable allowing users to adapt them to their needs
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an open-source Python library for cell type identification and quantification of tumor ecosystem heterogeneity in patient cohorts. We provide a step-by-step protocol on the application of scQUEST on our previously generated human breast cancer single-cell atlas using mass cytometry and discuss how it can be adapted and extended for other datasets and analyses.
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