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Pham T, Liao R, Labaer J, Guo J. Multiplexed In Situ Protein Profiling with High-Performance Cleavable Fluorescent Tyramide. Molecules 2021; 26:molecules26082206. [PMID: 33921211 PMCID: PMC8070642 DOI: 10.3390/molecules26082206] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 12/29/2022] Open
Abstract
Understanding the composition, function and regulation of complex cellular systems requires tools that quantify the expression of multiple proteins at their native cellular context. Here, we report a highly sensitive and accurate protein in situ profiling approach using off-the-shelf antibodies and cleavable fluorescent tyramide (CFT). In each cycle of this method, protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and CFT. Subsequently, the fluorophores are efficiently cleaved by mild chemical reagents, which simultaneously deactivate HRP. Through reiterative cycles of protein staining, fluorescence imaging, fluorophore cleavage, and HRP deactivation, multiplexed protein quantification in single cells in situ can be achieved. We designed and synthesized the high-performance CFT, and demonstrated that over 95% of the staining signals can be erased by mild chemical reagents while preserving the integrity of the epitopes on protein targets. Applying this method, we explored the protein expression heterogeneity and correlation in a group of genetically identical cells. With the high signal removal efficiency, this approach also enables us to accurately profile proteins in formalin-fixed paraffin-embedded (FFPE) tissues in the order of low to high and also high to low expression levels.
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Affiliation(s)
| | | | | | - Jia Guo
- Correspondence: ; Tel.: +1-480-727-2096
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2
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Liao R, Pham T, Mastroeni D, Coleman PD, Labaer J, Guo J. Highly Sensitive and Multiplexed In-Situ Protein Profiling with Cleavable Fluorescent Streptavidin. Cells 2020; 9:E852. [PMID: 32244728 PMCID: PMC7226835 DOI: 10.3390/cells9040852] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/23/2020] [Accepted: 03/25/2020] [Indexed: 12/29/2022] Open
Abstract
The ability to perform highly sensitive and multiplexed in-situ protein analysis is crucial to advance our understanding of normal physiology and disease pathogenesis. To achieve this goal, we here develop an approach using cleavable biotin-conjugated antibodies and cleavable fluorescent streptavidin (CFS). In this approach, protein targets are first recognized by the cleavable biotin-labeled antibodies. Subsequently, CFS is applied to stain the protein targets. Though layer-by-layer signal amplification using cleavable biotin-conjugated orthogonal antibodies and CSF, the protein detection sensitivity can be enhanced at least 10-fold, compared with the current in-situ proteomics methods. After imaging, the fluorophore and the biotin unbound to streptavidin are removed by chemical cleavage. The leftover streptavidin is blocked by biotin. Upon reiterative analysis cycles, a large number of different proteins with a wide range of expression levels can be profiled in individual cells at the optical resolution. Applying this approach, we have demonstrated that multiple proteins are unambiguously detected in the same set of cells, regardless of the protein analysis order. We have also shown that this method can be successfully applied to quantify proteins in formalin-fixed paraffin-embedded (FFPE) tissues.
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Affiliation(s)
- Renjie Liao
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA; (R.L.); (T.P.); (J.L.)
| | - Thai Pham
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA; (R.L.); (T.P.); (J.L.)
| | - Diego Mastroeni
- ASU-Banner Neurodegenerative Disease Research Center, Biodesign Institute and School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA; (D.M.); (P.D.C.)
- L.J. Roberts Center for Alzheimer’s Research, Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Paul D. Coleman
- ASU-Banner Neurodegenerative Disease Research Center, Biodesign Institute and School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA; (D.M.); (P.D.C.)
- L.J. Roberts Center for Alzheimer’s Research, Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Joshua Labaer
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA; (R.L.); (T.P.); (J.L.)
| | - Jia Guo
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA; (R.L.); (T.P.); (J.L.)
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3
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Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies. Methods Mol Biol 2018. [PMID: 29476461 DOI: 10.1007/978-1-4939-7680-5_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.
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4
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Leggett SE, Sim JY, Rubins JE, Neronha ZJ, Williams EK, Wong IY. Morphological single cell profiling of the epithelial-mesenchymal transition. Integr Biol (Camb) 2017; 8:1133-1144. [PMID: 27722556 DOI: 10.1039/c6ib00139d] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Single cells respond heterogeneously to biochemical treatments, which can complicate the analysis of in vitro and in vivo experiments. In particular, stressful perturbations may induce the epithelial-mesenchymal transition (EMT), a transformation through which compact, sensitive cells adopt an elongated, resistant phenotype. However, classical biochemical measurements based on population averages over large numbers cannot resolve single cell heterogeneity and plasticity. Here, we use high content imaging of single cell morphology to classify distinct phenotypic subpopulations after EMT. We first characterize a well-defined EMT induction through the master regulator Snail in mammary epithelial cells over 72 h. We find that EMT is associated with increased vimentin area as well as elongation of the nucleus and cytoplasm. These morphological features were integrated into a Gaussian mixture model that classified epithelial and mesenchymal phenotypes with >92% accuracy. We then applied this analysis to heterogeneous populations generated from less controlled EMT-inducing stimuli, including growth factors (TGF-β1), cell density, and chemotherapeutics (Taxol). Our quantitative, single cell approach has the potential to screen large heterogeneous cell populations for many types of phenotypic variability, and may thus provide a predictive assay for the preclinical assessment of targeted therapeutics.
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Affiliation(s)
- Susan E Leggett
- Center for Biomedical Engineering, School of Engineering, Providence, RI 02912, USA and Pathobiology Graduate Program Brown University, Providence, RI 02912, USA.
| | - Jea Yun Sim
- Center for Biomedical Engineering, School of Engineering, Providence, RI 02912, USA
| | - Jonathan E Rubins
- Center for Biomedical Engineering, School of Engineering, Providence, RI 02912, USA
| | - Zachary J Neronha
- Center for Biomedical Engineering, School of Engineering, Providence, RI 02912, USA
| | | | - Ian Y Wong
- Center for Biomedical Engineering, School of Engineering, Providence, RI 02912, USA and Pathobiology Graduate Program Brown University, Providence, RI 02912, USA.
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5
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Gupta RK, Swain S, Kankanamge D, Priyanka PD, Singh R, Mitra K, Karunarathne A, Giri L. Comparison of Calcium Dynamics and Specific Features for G Protein-Coupled Receptor-Targeting Drugs Using Live Cell Imaging and Automated Analysis. SLAS DISCOVERY 2017; 22:848-858. [PMID: 28267930 DOI: 10.1177/2472555217693378] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
G protein-coupled receptors (GPCRs) are targets for designing a large fraction of the drugs in the pharmaceutical industry. For GPCR-targeting drug screening using cell-based assays, measurement of cytosolic calcium has been widely used to obtain dose-response profiles. However, it remains challenging to obtain drug-specific features due to cell-to-cell heterogeneity in drug-cell responses obtained from live cell imaging. Here, we present a framework combining live cell imaging of a cell population and a feature extraction method for classification of responses of drugs targeting GPCRs CXCR4 and α2AR. We measured the calcium dynamics using confocal microscopy and compared the responses for SDF-1α and norepinephrine. The results clearly show that the clustering patterns of responses for the two GPCRs are significantly different. Additionally, we show that different drugs targeting the same GPCR induce different calcium response signatures. We also implemented principal component analysis and k means for feature extraction and used nondominated (ND) sorting for ranking a group of drugs at various doses. The presented approach can be used to model a cell population as a mixture of subpopulations. It also offers specific advantages, such as higher spatial resolution, classification of responses, and ranking of drugs, potentially providing a platform for high-content drug screening.
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Affiliation(s)
- Rishikesh Kumar Gupta
- 1 Department of Chemical Engineering, Indian Institute of Technology-Hyderabad, Hyderabad, Telangana, India
| | - Sarpras Swain
- 1 Department of Chemical Engineering, Indian Institute of Technology-Hyderabad, Hyderabad, Telangana, India
| | - Dinesh Kankanamge
- 2 Department of Chemistry and Biochemistry, The University of Toledo, Toledo, OH, USA
| | - Pantula Devi Priyanka
- 1 Department of Chemical Engineering, Indian Institute of Technology-Hyderabad, Hyderabad, Telangana, India
| | - Ranjana Singh
- 1 Department of Chemical Engineering, Indian Institute of Technology-Hyderabad, Hyderabad, Telangana, India
| | - Kishalay Mitra
- 1 Department of Chemical Engineering, Indian Institute of Technology-Hyderabad, Hyderabad, Telangana, India
| | - Ajith Karunarathne
- 2 Department of Chemistry and Biochemistry, The University of Toledo, Toledo, OH, USA
| | - Lopamudra Giri
- 1 Department of Chemical Engineering, Indian Institute of Technology-Hyderabad, Hyderabad, Telangana, India
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6
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Gough A, Stern AM, Maier J, Lezon T, Shun TY, Chennubhotla C, Schurdak ME, Haney SA, Taylor DL. Biologically Relevant Heterogeneity: Metrics and Practical Insights. SLAS DISCOVERY 2017; 22:213-237. [PMID: 28231035 DOI: 10.1177/2472555216682725] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.
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Affiliation(s)
- Albert Gough
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Andrew M Stern
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - John Maier
- 3 Department of Family Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy Lezon
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Tong-Ying Shun
- 2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Chakra Chennubhotla
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Mark E Schurdak
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Steven A Haney
- 5 Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA
| | - D Lansing Taylor
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
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7
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Spagnolo DM, Gyanchandani R, Al-Kofahi Y, Stern AM, Lezon TR, Gough A, Meyer DE, Ginty F, Sarachan B, Fine J, Lee AV, Taylor DL, Chennubhotla SC. Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers. J Pathol Inform 2016; 7:47. [PMID: 27994939 PMCID: PMC5139455 DOI: 10.4103/2153-3539.194839] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 08/09/2016] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity. METHODS We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map. RESULTS We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score. CONCLUSIONS This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.
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Affiliation(s)
- Daniel M Spagnolo
- Program in Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Rekha Gyanchandani
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yousef Al-Kofahi
- GE Global Research Center, Diagnostics, Imaging and Biomedical Technologies, Niskayuna, NY, USA
| | - Andrew M Stern
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Timothy R Lezon
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Albert Gough
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Dan E Meyer
- GE Global Research Center, Diagnostics, Imaging and Biomedical Technologies, Niskayuna, NY, USA
| | - Fiona Ginty
- GE Global Research Center, Diagnostics, Imaging and Biomedical Technologies, Niskayuna, NY, USA
| | - Brion Sarachan
- GE Global Research Center, Software Science and Analytics Organization, Niskayuna, NY, USA
| | - Jeffrey Fine
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Adrian V Lee
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania
| | - D Lansing Taylor
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania
| | - S Chakra Chennubhotla
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
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8
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Handly LN, Yao J, Wollman R. Signal Transduction at the Single-Cell Level: Approaches to Study the Dynamic Nature of Signaling Networks. J Mol Biol 2016; 428:3669-82. [PMID: 27430597 PMCID: PMC5023475 DOI: 10.1016/j.jmb.2016.07.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 07/07/2016] [Accepted: 07/11/2016] [Indexed: 12/16/2022]
Abstract
Signal transduction, or how cells interpret and react to external events, is a fundamental aspect of cellular function. Traditional study of signal transduction pathways involves mapping cellular signaling pathways at the population level. However, population-averaged readouts do not adequately illuminate the complex dynamics and heterogeneous responses found at the single-cell level. Recent technological advances that observe cellular response, computationally model signaling pathways, and experimentally manipulate cells now enable studying signal transduction at the single-cell level. These studies will enable deeper insights into the dynamic nature of signaling networks.
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Affiliation(s)
- L Naomi Handly
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA 90095, USA
| | - Jason Yao
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA 90095, USA
| | - Roy Wollman
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA 90095, USA.
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9
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Janes KA. Single-cell states versus single-cell atlases - two classes of heterogeneity that differ in meaning and method. Curr Opin Biotechnol 2016; 39:120-125. [PMID: 27042975 DOI: 10.1016/j.copbio.2016.03.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2015] [Revised: 03/04/2016] [Accepted: 03/20/2016] [Indexed: 12/30/2022]
Abstract
Recent advances have created new opportunities to dissect cellular heterogeneity at the omics level. The enthusiasm for deep single-cell profiling has obscured a discussion of different types of heterogeneity and the most-appropriate techniques for studying each type. Here, I distinguish heterogeneity in regulation from heterogeneity in lineage. Snapshots of lineage heterogeneity provide a cell atlas that catalogs cellular diversity within complex tissues. Profiles of regulatory heterogeneity seek to interrogate one lineage deeply to capture an ensemble of single-cell states. Single-cell atlases require molecular signatures from many cells at a throughput afforded by mass cytometry-based, microfluidic-based, and microencapsulation-based methods. Single-cell states are more dependent on time, microenvironment, and low-abundance transcripts, emphasizing in situ methods that stress depth of profiling and quantitative accuracy.
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Affiliation(s)
- Kevin A Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908 USA.
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10
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Gough A, Shun TY, Lansing Taylor D, Schurdak M. A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens. Methods 2015; 96:12-26. [PMID: 26476369 DOI: 10.1016/j.ymeth.2015.10.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2015] [Revised: 10/12/2015] [Accepted: 10/13/2015] [Indexed: 12/14/2022] Open
Abstract
Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.
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Affiliation(s)
- Albert Gough
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA.
| | - Tong Ying Shun
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA
| | - D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA
| | - Mark Schurdak
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA
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11
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Lin JR, Fallahi-Sichani M, Sorger PK. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat Commun 2015; 6:8390. [PMID: 26399630 PMCID: PMC4587398 DOI: 10.1038/ncomms9390] [Citation(s) in RCA: 357] [Impact Index Per Article: 39.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 08/17/2015] [Indexed: 12/23/2022] Open
Abstract
Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications. Multiplexed single cell measurements provide insight into connections between cell state and phenotype. Here Lin et al. present CycIF, a high throughput, public domain immunofluorescence method for multiplexed single-cell analysis of adherent cells following live-cell imaging.
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Affiliation(s)
- Jia-Ren Lin
- HMS LINCS Center &Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts 02115 USA
| | - Mohammad Fallahi-Sichani
- Department of Systems Biology Harvard Medical School 200 Longwood Avenue, Boston, Massachusetts 02115 USA
| | - Peter K Sorger
- HMS LINCS Center &Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts 02115 USA.,Department of Systems Biology Harvard Medical School 200 Longwood Avenue, Boston, Massachusetts 02115 USA
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