1
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Shannon MJ, Eisman SE, Lowe AR, Sloan TFW, Mace EM. cellPLATO - an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data. J Cell Sci 2024; 137:jcs261887. [PMID: 38738282 DOI: 10.1242/jcs.261887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/01/2024] [Indexed: 05/14/2024] Open
Abstract
Advances in imaging, segmentation and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this 'phenomics' type data. Here, we present 'Cell PLasticity Analysis Tool' (cellPLATO), a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a 'behavioural ID' at each timepoint, and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human natural killer (NK) cell migration on ICAM-1 or VCAM-1. We find eight behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and four trajectories based on sequences of these behaviours over time. Therefore, by using cellPLATO, we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.
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Affiliation(s)
- Michael J Shannon
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Medical Center, NYC, NY 10032, USA
| | - Shira E Eisman
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Medical Center, NYC, NY 10032, USA
| | - Alan R Lowe
- Institute for the Physics of Living Systems, Institute for Structural and Molecular Biology and London Centre for Nanotechnology, University College London, London WC1H 0AH, UK
| | | | - Emily M Mace
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Medical Center, NYC, NY 10032, USA
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2
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Xiong J, Kaur H, Heiser CN, McKinley ET, Roland JT, Coffey RJ, Shrubsole MJ, Wrobel J, Ma S, Lau KS, Vandekar S. GammaGateR: semi-automated marker gating for single-cell multiplexed imaging. Bioinformatics 2024; 40:btae356. [PMID: 38833684 PMCID: PMC11193056 DOI: 10.1093/bioinformatics/btae356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 04/20/2024] [Accepted: 06/03/2024] [Indexed: 06/06/2024] Open
Abstract
MOTIVATION Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. RESULTS To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. AVAILABILITY AND IMPLEMENTATION The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.
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Affiliation(s)
- Jiangmei Xiong
- Department of Biostatistics, Vanderbilt University, 2525 West End Avenue, Suite 1100, Nashville, TN 37203-1741, United States
| | - Harsimran Kaur
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, 340 Light Hall, 2215 Garland Ave, Nashville, TN 37232, United States
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
| | - Cody N Heiser
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, 340 Light Hall, 2215 Garland Ave, Nashville, TN 37232, United States
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- Regeneron Pharmaceuticals, 777 Old Saw Mill River Road, Tarrytown, NY 10591, United States
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- GlaxoSmithKline, 410 Blackwell St, Durham, NC 27701, United States
| | - Joseph T Roland
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- Department of Surgery, Vanderbilt University Medical Center, 2215 Garland Ave Medical Research Building IV, Nashville, TN 37232, United States
| | - Robert J Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- Department of Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN 37232, United States
| | - Martha J Shrubsole
- Department of Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN 37232, United States
| | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Rd, Atlanta, GA 30322, United States
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt University, 2525 West End Avenue, Suite 1100, Nashville, TN 37203-1741, United States
| | - Ken S Lau
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, 340 Light Hall, 2215 Garland Ave, Nashville, TN 37232, United States
- Epithelial Biology Center, Vanderbilt University Medical Center, MRBIV 10415-E, 2213 Garland Avenue, Nashville, TN 37232, United States
- Regeneron Pharmaceuticals, 777 Old Saw Mill River Road, Tarrytown, NY 10591, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, 10475 Medical Research Building IV, 2215 Garland Avenue, Nashville, TN 37232, United States
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University, 2525 West End Avenue, Suite 1100, Nashville, TN 37203-1741, United States
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3
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Rumberger JL, Greenwald NF, Ranek JS, Boonrat P, Walker C, Franzen J, Varra SR, Kong A, Sowers C, Liu CC, Averbukh I, Piyadasa H, Vanguri R, Nederlof I, Wang XJ, Van Valen D, Kok M, Hollmann TJ, Kainmueller D, Angelo M. Automated classification of cellular expression in multiplexed imaging data with Nimbus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.02.597062. [PMID: 38895405 PMCID: PMC11185540 DOI: 10.1101/2024.06.02.597062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.
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Affiliation(s)
- J. Lorenz Rumberger
- Max-Delbruck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany
- Helmholtz Imaging
| | - Noah F. Greenwald
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Jolene S. Ranek
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Potchara Boonrat
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Cameron Walker
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Jannik Franzen
- Max-Delbruck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Helmholtz Imaging
- Charité University Medicine, Berlin, Germany
| | | | - Alex Kong
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Cameron Sowers
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Candace C. Liu
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Inna Averbukh
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Hadeesha Piyadasa
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Rami Vanguri
- Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Iris Nederlof
- Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Xuefei Julie Wang
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Marleen Kok
- Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Travis J. Hollmann
- Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Dagmar Kainmueller
- Max-Delbruck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Helmholtz Imaging
- Potsdam University, Digital Engineering Faculty, Germany
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, California, USA
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4
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Zhu B, Gao S, Chen S, Yeung J, Bai Y, Huang AY, Yeo YY, Liao G, Mao S, Jiang ZG, Rodig SJ, Shalek AK, Nolan GP, Jiang S, Ma Z. Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.12.593710. [PMID: 38798592 PMCID: PMC11118457 DOI: 10.1101/2024.05.12.593710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sheng Gao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Shuxiao Chen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Y Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Guanrui Liao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Zhenghui G Jiang
- Division of Gastroenterology/Liver Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zongming Ma
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
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5
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Couckuyt A, Rombaut B, Saeys Y, Van Gassen S. Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools. Bioinformatics 2024; 40:btae179. [PMID: 38632080 PMCID: PMC11052654 DOI: 10.1093/bioinformatics/btae179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/12/2024] [Accepted: 04/16/2024] [Indexed: 04/19/2024] Open
Abstract
MOTIVATION We describe a new Python implementation of FlowSOM, a clustering method for cytometry data. RESULTS This implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot. AVAILABILITY AND IMPLEMENTATION The FlowSOM Python implementation is freely available on GitHub: https://github.com/saeyslab/FlowSOM_Python.
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Affiliation(s)
- Artuur Couckuyt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
| | - Benjamin Rombaut
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
| | - Yvan Saeys
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
| | - Sofie Van Gassen
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
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6
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Zhu B, Bai Y, Yeo YY, Lu X, Rovira-Clavé X, Chen H, Yeung J, Gerber GK, Angelo M, Shalek AK, Nolan GP, Jiang S. A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583400. [PMID: 38496402 PMCID: PMC10942342 DOI: 10.1101/2024.03.04.583400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiaowei Lu
- Mass Spectrometry Core Facility, Stanford University, Stanford, CA, United States
| | - Xavier Rovira-Clavé
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Han Chen
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Biological and Medical Informatics program, UCSF, San Francisco, CA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Georg K Gerber
- Division of Computational Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Health Sciences and Technology, Harvard University and MIT, Cambridge, MA, USA
| | - Mike Angelo
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
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7
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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8
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Durkee MS, Ai J, Casella G, Cao T, Chang A, Halper-Stromberg A, Jabri B, Clark MR, Giger ML. Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574920. [PMID: 38260318 PMCID: PMC10802447 DOI: 10.1101/2024.01.09.574920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The rapid development of highly multiplexed microscopy systems has enabled the study of cells embedded within their native tissue, which is providing exciting insights into the spatial features of human disease [1]. However, computational methods for analyzing these high-content images are still emerging, and there is a need for more robust and generalizable tools for evaluating the cellular constituents and underlying stroma captured by high-plex imaging [2]. To address this need, we have adapted spectral angle mapping - an algorithm used widely in hyperspectral image analysis - to compress the channel dimension of high-plex immunofluorescence images. As many high-plex immunofluorescence imaging experiments probe unique sets of protein markers, existing cell and pixel classification models do not typically generalize well. Pseudospectral angle mapping (pSAM) uses reference pseudospectra - or pixel vectors - to assign each pixel in an image a similarity score to several cell class reference vectors, which are defined by each unique staining panel. Here, we demonstrate that the class maps provided by pSAM can directly provide insight into the prevalence of each class defined by reference pseudospectra. In a dataset of high-plex images of colon biopsies from patients with gut autoimmune conditions, sixteen pSAM class representation maps were combined with instance segmentation of cells to provide cell class predictions. Finally, pSAM detected a diverse set of structure and immune cells when applied to a novel dataset of kidney biopsies imaged with a 43-marker panel. In summary, pSAM provides a powerful and readily generalizable method for evaluating high-plex immunofluorescence image data.
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Affiliation(s)
| | - Junting Ai
- Department of Medicine, Section on Rheumatology, The University of Chicago, Chicago, IL, USA, 60637
| | - Gabriel Casella
- Department of Radiology, The University of Chicago, Chicago, IL, USA, 60637
- Department of Medicine, Section on Rheumatology, The University of Chicago, Chicago, IL, USA, 60637
| | - Thao Cao
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, USA, 60637
| | - Anthony Chang
- Department of Pathology, The University of Chicago, Chicago, IL, USA, 60637
| | - Ariel Halper-Stromberg
- Department of Medicine, Section on Gastroenterology, Hepatology & Nutrition, The University of Chicago, Chicago, IL, USA, 60637
| | - Bana Jabri
- Department of Medicine, Section on Gastroenterology, Hepatology & Nutrition, The University of Chicago, Chicago, IL, USA, 60637
| | - Marcus R. Clark
- Department of Medicine, Section on Rheumatology, The University of Chicago, Chicago, IL, USA, 60637
| | - Maryellen L. Giger
- Department of Radiology, The University of Chicago, Chicago, IL, USA, 60637
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9
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Kumar G, Pandurengan RK, Parra ER, Kannan K, Haymaker C. Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications. Front Immunol 2023; 14:1288802. [PMID: 38179056 PMCID: PMC10765501 DOI: 10.3389/fimmu.2023.1288802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.
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Affiliation(s)
| | | | | | - Kasthuri Kannan
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States
| | - Cara Haymaker
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States
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10
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Si Y, Lee C, Hwang Y, Yun JH, Cheng W, Cho CS, Quiros M, Nusrat A, Zhang W, Jun G, Zöllner S, Lee JH, Kang HM. FICTURE: Scalable segmentation-free analysis of submicron resolution spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.04.565621. [PMID: 37961699 PMCID: PMC10635162 DOI: 10.1101/2023.11.04.565621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.
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Affiliation(s)
- Yichen Si
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA
| | - ChangHee Lee
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Yongha Hwang
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, 48109-2200, USA
- Space Planning and Analysis, University of Michigan Medical School, Ann Arbor, Michigan 48109-2800, USA
| | - Jeong H. Yun
- Channing Division of Network Medicine, and Division of Pulmonary and Critical Care, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Weiqiu Cheng
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA
| | - Chun-Seok Cho
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, 48109-2200, USA
| | - Miguel Quiros
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, 48109, USA
| | - Asma Nusrat
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, 48109, USA
| | - Weizhou Zhang
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, College of Medicine, Gainesville, FL 32610-0275, USA
| | - Goo Jun
- Human Genetics Center, School of Public Health, University of Texas Health Science Center Houston, Houston, TX 77030, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA
| | - Jun Hee Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, 48109-2200, USA
| | - Hyun Min Kang
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA
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11
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Shannon MJ, Eisman SE, Lowe AR, Sloan T, Mace EM. cellPLATO: an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.28.564355. [PMID: 37961659 PMCID: PMC10634992 DOI: 10.1101/2023.10.28.564355] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Advances in imaging, cell segmentation, and cell tracking now routinely produce microscopy datasets of a size and complexity comparable to transcriptomics or proteomics. New tools are required to process this 'phenomics' type data. Cell PLasticity Analysis TOol (cellPLATO) is a Python-based analysis software designed for measurement and classification of diverse cell behaviours based on clustering of parameters of cell morphology and motility. cellPLATO is used after segmentation and tracking of cells from live cell microscopy data. The tool extracts morphological and motility metrics from each cell per timepoint, before being using them to segregate cells into behavioural subtypes with dimensionality reduction. Resultant cell tracks have a 'behavioural ID' for each cell per timepoint corresponding to their changing behaviour over time in a sequence. Similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Trajectories and underlying behaviours generate a phenotypic fingerprint for each experimental condition, and representative cells are mathematically identified and graphically displayed for human understanding of each subtype. Here, we use cellPLATO to investigate the role of IL-15 in modulating NK cell migration on ICAM-1 or VCAM-1. We find 8 behavioural subsets of NK cells based on their shape and migration dynamics, and 4 trajectories of behaviour. Therefore, using cellPLATO we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.
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Affiliation(s)
- Michael J Shannon
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York NY 10032
| | - Shira E Eisman
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York NY 10032
| | - Alan R Lowe
- Institute for the Physics of Living Systems, Institute for Structural and Molecular Biology and London Centre for Nanotechnology, University College London, London WC1H 0AH, United Kingdom
| | | | - Emily M Mace
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York NY 10032
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12
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Willie E, Yang P, Patrick E. The impact of similarity metrics on cell-type clustering in highly multiplexed in situ imaging cytometry data. BIOINFORMATICS ADVANCES 2023; 3:vbad141. [PMID: 37928340 PMCID: PMC10625459 DOI: 10.1093/bioadv/vbad141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/23/2023] [Accepted: 10/07/2023] [Indexed: 11/07/2023]
Abstract
Motivation The advent of highly multiplexed in situ imaging cytometry assays has revolutionized the study of cellular systems, offering unparalleled detail in observing cellular activities and characteristics. These assays provide comprehensive insights by concurrently profiling the spatial distribution and molecular features of numerous cells. In navigating this complex data landscape, unsupervised machine learning techniques, particularly clustering algorithms, have become essential tools. They enable the identification and categorization of cell types and subsets based on their molecular characteristics. Despite their widespread adoption, most clustering algorithms in use were initially developed for cell suspension technologies, leading to a potential mismatch in application. There is a critical gap in the systematic evaluation of these methods, particularly in determining the properties that make them optimal for in situ imaging assays. Addressing this gap is vital for ensuring accurate, reliable analyses and fostering advancements in cellular biology research. Results In our extensive investigation, we evaluated a range of similarity metrics, which are crucial in determining the relationships between cells during the clustering process. Our findings reveal substantial variations in clustering performance, contingent on the similarity metric employed. These variations underscore the importance of selecting appropriate metrics to ensure accurate cell type and subset identification. In response to these challenges, we introduce FuseSOM, a novel ensemble clustering algorithm that integrates hierarchical multiview learning of similarity metrics with self-organizing maps. Through a rigorous stratified subsampling analysis framework, we demonstrate that FuseSOM outperforms existing best-practice clustering methods specifically tailored for in situ imaging cytometry data. Our work not only provides critical insights into the performance of clustering algorithms in this novel context but also offers a robust solution, paving the way for more accurate and reliable in situ imaging cytometry data analysis. Availability and implementation The FuseSOM R package is available on Bioconductor and is available under the GPL-3 license. All the codes for the analysis performed can be found at Github.
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Affiliation(s)
- Elijah Willie
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Pengyi Yang
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong, China
- Computational Systems Biology Group, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW 2145, Australia
| | - Ellis Patrick
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong, China
- Centre for Cancer Research, The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia
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Xiong J, Kaur H, Heiser CN, McKinley ET, Roland JT, Coffey RJ, Shrubsole MJ, Wrobel J, Ma S, Lau KS, Vandekar S. GammaGateR: semi-automated marker gating for single-cell multiplexed imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.558645. [PMID: 37781604 PMCID: PMC10541135 DOI: 10.1101/2023.09.20.558645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Motivation Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. Results To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. Availability and Implementation The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.
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Affiliation(s)
| | - Harsimran Kaur
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
| | - Cody N Heiser
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Regeneron Pharmaceuticals, USA
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- GlaxoSmithKline, USA
| | - Joseph T Roland
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Surgery, Vanderbilt University Medical Center, USA
| | - Robert J Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Medicine, Vanderbilt University Medical Center, USA
| | | | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Emory University, USA
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt University, USA
| | - Ken S Lau
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Surgery, Vanderbilt University Medical Center, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, USA
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