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Phenotypic Characterization by Mass Cytometry of the Microenvironment in Ovarian Cancer and Impact of Tumor Dissociation Methods. Cancers (Basel) 2021; 13:cancers13040755. [PMID: 33670410 PMCID: PMC7918057 DOI: 10.3390/cancers13040755] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/02/2021] [Accepted: 02/09/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary High-grade serous ovarian cancer (HGSOC) is the deadliest gynecological malignancy. Despite increasing research on HGSOC, biomarkers for individualized selection of therapy are scarce. In this study, we develop a multiparametric mass cytometry antibody panel to identify differences in the cellular composition of the microenvironment of tumor tissues dissociated to single-cell suspensions. We also investigate how dissociation methods impact results. Application of our antibody panel to HGSOC tissues showed its ability to identify established main cell subsets and subpopulations of these cells. Comparisons between dissociation methods revealed differences in cell fractions for one immune, two stromal, and three tumor cell subpopulations, while functional marker expression was not affected by the dissociation method. The interpatient disparities identified in the tumor microenvironment were more significant than those identified between differently dissociated tissues from one patient, indicating that the panel facilitates the mapping of individual tumor microenvironments in HGSOC patients. Abstract Improved molecular dissection of the tumor microenvironment (TME) holds promise for treating high-grade serous ovarian cancer (HGSOC), a gynecological malignancy with high mortality. Reliable disease-related biomarkers are scarce, but single-cell mapping of the TME could identify patient-specific prognostic differences. To avoid technical variation effects, however, tissue dissociation effects on single cells must be considered. We present a novel Cytometry by Time-of-Flight antibody panel for single-cell suspensions to identify individual TME profiles of HGSOC patients and evaluate the effects of dissociation methods on results. The panel was developed utilizing cell lines, healthy donor blood, and stem cells and was applied to HGSOC tissues dissociated by six methods. Data were analyzed using Cytobank and X-shift and illustrated by t-distributed stochastic neighbor embedding plots, heatmaps, and stacked bar and error plots. The panel distinguishes the main cellular subsets and subpopulations, enabling characterization of individual TME profiles. The dissociation method affected some immune (n = 1), stromal (n = 2), and tumor (n = 3) subsets, while functional marker expressions remained comparable. In conclusion, the panel can identify subsets of the HGSOC TME and can be used for in-depth profiling. This panel represents a promising profiling tool for HGSOC when tissue handling is considered.
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Lanz TV, Pröbstel AK, Mildenberger I, Platten M, Schirmer L. Single-Cell High-Throughput Technologies in Cerebrospinal Fluid Research and Diagnostics. Front Immunol 2019; 10:1302. [PMID: 31244848 PMCID: PMC6579921 DOI: 10.3389/fimmu.2019.01302] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 05/22/2019] [Indexed: 01/08/2023] Open
Abstract
High-throughput single-cell technologies have recently emerged as essential tools in biomedical research with great potential for clinical pathology when studying liquid and solid biopsies. We provide an update on current single-cell methods in cerebrospinal fluid research and diagnostics, focusing on high-throughput cell-type specific proteomic and genomic technologies. Proteomic methods comprising flow cytometry and mass cytometry as well as genomic approaches including immune cell repertoire and single-cell transcriptomic studies are critically reviewed and future directions discussed.
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Affiliation(s)
- Tobias V. Lanz
- Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Anne-Katrin Pröbstel
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Departments of Medicine and Biomedicine, Neurologic Clinic and Policlinic, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Iris Mildenberger
- Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Michael Platten
- Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- DKTK Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lucas Schirmer
- Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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Herring CA, Banerjee A, McKinley ET, Simmons AJ, Ping J, Roland JT, Franklin JL, Liu Q, Gerdes MJ, Coffey RJ, Lau KS. Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut. Cell Syst 2017; 6:37-51.e9. [PMID: 29153838 DOI: 10.1016/j.cels.2017.10.012] [Citation(s) in RCA: 121] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 08/17/2017] [Accepted: 10/13/2017] [Indexed: 12/19/2022]
Abstract
Modern single-cell technologies allow multiplexed sampling of cellular states within a tissue. However, computational tools that can infer developmental cell-state transitions reproducibly from such single-cell data are lacking. Here, we introduce p-Creode, an unsupervised algorithm that produces multi-branching graphs from single-cell data, compares graphs with differing topologies, and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. We have applied p-Creode to mass cytometry, multiplex immunofluorescence, and single-cell RNA-seq data. As a test case, we validate cell-state-transition trajectories predicted by p-Creode for intestinal tuft cells, a rare, chemosensory cell type. We clarify that tuft cells are specified outside of the Atoh1-dependent secretory lineage in the small intestine. However, p-Creode also predicts, and we confirm, that tuft cells arise from an alternative, Atoh1-driven developmental program in the colon. These studies introduce p-Creode as a reliable method for analyzing large datasets that depict branching transition trajectories.
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Affiliation(s)
- Charles A Herring
- Epithelial Biology Center, Vanderbilt University Medical Center, 2213 Garland Avenue, 10475 MRB IV, Nashville, TN 37232, USA; Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Amrita Banerjee
- Epithelial Biology Center, Vanderbilt University Medical Center, 2213 Garland Avenue, 10475 MRB IV, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, 2213 Garland Avenue, 10475 MRB IV, Nashville, TN 37232, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Alan J Simmons
- Epithelial Biology Center, Vanderbilt University Medical Center, 2213 Garland Avenue, 10475 MRB IV, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jie Ping
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Joseph T Roland
- Epithelial Biology Center, Vanderbilt University Medical Center, 2213 Garland Avenue, 10475 MRB IV, Nashville, TN 37232, USA
| | - Jeffrey L Franklin
- Epithelial Biology Center, Vanderbilt University Medical Center, 2213 Garland Avenue, 10475 MRB IV, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Qi Liu
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Michael J Gerdes
- Life Sciences Division, GE Global Research, Niskayuna, NY 12309, USA
| | - Robert J Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, 2213 Garland Avenue, 10475 MRB IV, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, TN 37232, USA
| | - Ken S Lau
- Epithelial Biology Center, Vanderbilt University Medical Center, 2213 Garland Avenue, 10475 MRB IV, Nashville, TN 37232, USA; Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
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Diggins KE, Greenplate AR, Leelatian N, Wogsland CE, Irish JM. Characterizing cell subsets using marker enrichment modeling. Nat Methods 2017; 14:275-278. [PMID: 28135256 PMCID: PMC5330853 DOI: 10.1038/nmeth.4149] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 12/22/2016] [Indexed: 12/21/2022]
Abstract
Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.
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Affiliation(s)
- K. E. Diggins
- Department of Cancer Biology and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - A. R. Greenplate
- Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - N. Leelatian
- Department of Cancer Biology and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - C. E. Wogsland
- Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - J. M. Irish
- Department of Cancer Biology and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
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