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Park SA, Sipka T, Krivá Z, Lutfalla G, Nguyen-Chi M, Mikula K. Segmentation-based tracking of macrophages in 2D+time microscopy movies inside a living animal. Comput Biol Med 2023; 153:106499. [PMID: 36599208 DOI: 10.1016/j.compbiomed.2022.106499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/19/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.
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Affiliation(s)
- Seol Ah Park
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
| | - Tamara Sipka
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Zuzana Krivá
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
| | - Georges Lutfalla
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Mai Nguyen-Chi
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Karol Mikula
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
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Vahadane A, Sharma S, Mandal D, Dabbeeru M, Jakthong J, Garcia-Guzman M, Majumdar S, Lee CW. Development of an automated combined positive score prediction pipeline using artificial intelligence on multiplexed immunofluorescence images. Comput Biol Med 2023; 152:106337. [PMID: 36502695 DOI: 10.1016/j.compbiomed.2022.106337] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 11/05/2022] [Accepted: 11/16/2022] [Indexed: 11/25/2022]
Abstract
Immunotherapy targeting immune checkpoint proteins, such as programmed cell death ligand 1 (PD-L1), has shown impressive outcomes in many clinical trials but only 20%-40% of patients benefit from it. Utilizing Combined Positive Score (CPS) to evaluate PD-L1 expression in tumour biopsies to identify patients with the highest likelihood of responsiveness to anti-PD-1/PD-L1 therapy has been approved by the Food and Drug Administration for several solid tumour types. Current CPS workflow requires a pathologist to manually score the two-colour PD-L1 chromogenic immunohistochemistry image. Multiplex immunofluorescence (mIF) imaging reveals the expression of an increased number of immune markers in tumour biopsies and has been used extensively in immunotherapy research. Recent rapid progress of Artificial Intelligence (AI)-based imaging analysis, particularly Deep Learning, provides cost effective and high-quality solutions to healthcare. In this article, we propose an imaging pipeline that utilizes three-colour mIF images (DAPI, PD-L1, and Pan-cytokeratin) as input and predicts the CPS using AI techniques. Our novel pipeline is composed of three modules employing algorithms of image processing, machine learning, and deep learning techniques. The first module of quality check (QC) detects and removes the image regions contaminated with sectioning and staining artefacts. The QC module ensures that only image regions free of the three common artefacts are used for downstream analysis. The second module of nuclear segmentation uses deep learning to segment and count nuclei in the DAPI images wherein our specialized method can accurately separate touching nuclei. The third module of cell phenotyping calculates CPS by identifying and counting PD-L1 positive cells and tumour cells. These modules are data-efficient and require only few manual annotations for training purposes. Using tumour biopsies from a clinical trial, we found that the CPS from the AI-based models shows a high Spearman correlation (78%, p = 0.003) to the pathologist-scored CPS.
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Affiliation(s)
- Abhishek Vahadane
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | - Shreya Sharma
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | - Devraj Mandal
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | - Madan Dabbeeru
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | | | | | - Shantanu Majumdar
- Rakuten India Enterprise Private Ltd, Bagmane Pallavi Tower #20, 1st Cross, Raja Ram Mohan Roy Road, S. R. Nagar, Bengaluru, Karnataka, 560027, India
| | - Chung-Wein Lee
- Rakuten Medical Inc., 11080 Roselle Street, San Diego, CA, 92121, USA.
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Potts C, Schearer J, Sebrell TA, Bair D, Ayler B, Love J, Dankoff J, Harris PR, Zosso D, Bimczok D. MNPmApp: An image analysis tool to quantify mononuclear phagocyte distribution in mucosal tissues. Cytometry A 2022; 101:1012-1026. [PMID: 35569131 PMCID: PMC9663762 DOI: 10.1002/cyto.a.24657] [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: 06/22/2021] [Revised: 03/27/2022] [Accepted: 05/12/2022] [Indexed: 01/27/2023]
Abstract
Mononuclear phagocytes (MNPs) such as dendritic cells and macrophages perform key sentinel functions in mucosal tissues and are responsible for inducing and maintaining adaptive immune responses to mucosal pathogens. Positioning of MNPs at the epithelial interface facilitates their access to luminally-derived antigens and regulates MNP function through soluble mediators or surface receptor interactions. Therefore, accurately quantifying the distribution of MNPs within mucosal tissues as well as their spatial relationship with other cells is important to infer functional cellular interactions in health and disease. In this study, we developed and validated a MATLAB-based tissue cytometry platform, termed "MNP mapping application" (MNPmApp), that performs high throughput analyses of MNP density and distribution in the gastrointestinal mucosa based on digital multicolor fluorescence microscopy images and that integrates a Monte Carlo modeling feature to assess randomness of MNP distribution. MNPmApp identified MNPs in tissue sections of the human gastric mucosa with 98 ± 2% specificity and 76 ± 15% sensitivity for HLA-DR+ MNPs and 98 ± 1% specificity and 85 ± 12% sensitivity for CD11c+ MNPs. Monte Carlo modeling revealed that mean MNP-MNP distances for both HLA-DR+ and CD11c+ MNPs were significantly lower than anticipated based on random cell placement, whereas MNP-epithelial distances were similar to randomly placed cells. Surprisingly, H. pylori infection had no significant impact on the number of HLA-DR and CD11c MNPs or their distribution within the gastric lamina propria. However, our study demonstrated that MNPmApp is a reliable and user-friendly tool for unbiased quantitation of MNPs and their distribution at mucosal sites.
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Affiliation(s)
- Catherine Potts
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | - Julia Schearer
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT
| | - Thomas A Sebrell
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT
| | - Dominic Bair
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | | | - Jordan Love
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | - Jennifer Dankoff
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT
| | - Paul R. Harris
- Division of Pediatrics, Department of Pediatric Gastroenterology and Nutrition, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominique Zosso
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | - Diane Bimczok
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT
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Croci GA, Au-Yeung RKH, Reinke S, Staiger AM, Koch K, Oschlies I, Richter J, Poeschel V, Held G, Loeffler M, Trümper L, Rosenwald A, Ott G, Spang R, Altmann B, Ziepert M, Klapper W. SPARC-positive macrophages are the superior prognostic factor in the microenvironment of diffuse large B-cell lymphoma and independent of MYC rearrangement and double-/triple-hit status. Ann Oncol 2021; 32:1400-1409. [PMID: 34438040 DOI: 10.1016/j.annonc.2021.08.1991] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/04/2021] [Accepted: 08/16/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with respect to outcome. Features of the tumor microenvironment (TME) are associated with prognosis when assessed by gene expression profiling. However, it is uncertain whether assessment of the microenvironment can add prognostic information to the most relevant and clinically well-established molecular subgroups when analyzed by immunohistochemistry (IHC). PATIENTS AND METHODS We carried out a histopathologic analysis of biomarkers related to TME in a very large cohort (n = 455) of DLBCL treated in prospective trials and correlated with clinicopathologic and molecular data, including chromosomal rearrangements and gene expression profiles for cell-of-origin and TME. RESULTS The content of PD1+, FoxP3+ and CD8+, as well as vessel density, was not associated with outcome. However, we found a low content of CD68+ macrophages to be associated with inferior progression-free survival (PFS) and overall survival (OS; P = 0.023 and 0.040, respectively) at both univariable and multivariable analyses, adjusted for the factors of the International Prognostic Index (IPI), MYC break and BCL2/MYC and BCL6/MYC double-hit status. The subgroup of PDL1+ macrophages was not associated with survival. Instead, secreted protein acidic and cysteine rich (SPARC)-positive macrophages were identified as the subtype of macrophages most associated with survival. SPARC-positive macrophages and stromal cells directly correlated with favorable PFS and OS (both, P[log rank] <0.001, P[trend] < 0.001). The association of SPARC with prognosis was independent of the factors of the IPI, MYC double-/triple-hit status, Bcl2/c-myc double expression, cell-of-origin subtype and a recently published gene expression signature [lymphoma-associated macrophage interaction signature (LAMIS)]. CONCLUSIONS SPARC expression in the TME detected by a single IHC staining with fair-to-good interobserver reproducibility is a powerful prognostic parameter. Thus SPARC expression is a strong candidate for risk assessment in DLBCL in daily practice.
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Affiliation(s)
- G A Croci
- Institute of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Division of Pathology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - R K H Au-Yeung
- Institute of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Department of Pathology, Queen Mary Hospital, The University of Hong Kong, Hong Kong
| | - S Reinke
- Institute of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - A M Staiger
- Department of Clinical Pathology, Robert-Bosch-Krankenhaus, Stuttgart, Germany; Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart and University of Tuebingen, Tübingen, Germany
| | - K Koch
- Institute of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - I Oschlies
- Institute of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - J Richter
- Institute of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - V Poeschel
- Department of Internal Medicine 1 (Oncology, Hematology, Clinical Immunology, and Rheumatology), Saarland University Medical School, Homburg/Saar, Germany
| | - G Held
- DSHNHL Studiensekretariat, Universitätsklinikum des Saarlandes, Homburg, Germany
| | - M Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - L Trümper
- Department of Hematology and Oncology, Georg-August Universität, Göttingen, Germany
| | - A Rosenwald
- Institute of Pathology, Universität Würzburg and Comprehensive Cancer Center Mainfranken (CCCMF), Würzburg, Germany
| | - G Ott
- Department of Clinical Pathology, Robert-Bosch-Krankenhaus, Stuttgart, Germany; Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart and University of Tuebingen, Tübingen, Germany
| | - R Spang
- Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - B Altmann
- DSHNHL Studiensekretariat, Universitätsklinikum des Saarlandes, Homburg, Germany
| | - M Ziepert
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - W Klapper
- Institute of Pathology, Hematopathology Section and Lymph Node Registry, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
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Wagner M, Reinke S, Hänsel R, Klapper W, Braumann UD. An image dataset related to automated macrophage detection in immunostained lymphoma tissue samples. Gigascience 2021; 9:5803336. [PMID: 32161948 PMCID: PMC7066390 DOI: 10.1093/gigascience/giaa016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 12/20/2019] [Accepted: 02/17/2020] [Indexed: 02/07/2023] Open
Abstract
Background We present an image dataset related to automated segmentation and counting of macrophages in diffuse large B-cell lymphoma (DLBCL) tissue sections. For the classification of DLBCL subtypes, as well as for providing a prognosis of the clinical outcome, the analysis of the tumor microenvironment and, particularly, of the different types and functions of tumor-associated macrophages is indispensable. Until now, however, most information about macrophages has been obtained either in a completely indirect way by gene expression profiling or by manual counts in immunohistochemically (IHC) fluorescence-stained tissue samples while automated recognition of single IHC stained macrophages remains a difficult task. In an accompanying publication, a reliable approach to this problem has been established, and a large set of related images has been generated and analyzed. Results Provided image data comprise (i) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at 4 channels corresponding to CD14, CD163, Pax5, and DAPI; (ii) ”cartoon-like” total variation–filtered versions of these images, generated by Rudin-Osher-Fatemi denoising; (iii) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel; and (iv) automatically generated segmentation masks for macrophages (using information from CD14 and CD163 channels), B-cells (using information from Pax5 channel), and all cell nuclei (using information from DAPI channel). Conclusions A large set of IHC stained DLBCL specimens is provided together with segmentation masks for different cell populations generated by a reference method for automated image analysis, thus featuring considerable reuse potential.
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Affiliation(s)
- Marcus Wagner
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Härtelstr. 16-18, D-04107 Leipzig, Germany
| | - Sarah Reinke
- Department of Pathology, Hematopathology Section and Lymph Node Registry, University of Kiel/University Hospital Schleswig-Holstein, Arnold-Heller-Str. 3, Haus 14, D-24105 Kiel, Germany
| | - René Hänsel
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Härtelstr. 16-18, D-04107 Leipzig, Germany
| | - Wolfram Klapper
- Department of Pathology, Hematopathology Section and Lymph Node Registry, University of Kiel/University Hospital Schleswig-Holstein, Arnold-Heller-Str. 3, Haus 14, D-24105 Kiel, Germany
| | - Ulf-Dietrich Braumann
- Faculty of Engineering, Leipzig University of Applied Sciences (HTWK), P.O.B. 30 11 66, D-04251 Leipzig, Germany.,Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstr. 1, D-04103 Leipzig, Germany
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Hadjiiski L, Samala R, Chan HP. Image Processing Analytics: Enhancements and Segmentation. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00057-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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