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Fan X, Sun AR, Young RSE, Afara IO, Hamilton BR, Ong LJY, Crawford R, Prasadam I. Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications. Bone Res 2024; 12:7. [PMID: 38311627 PMCID: PMC10838951 DOI: 10.1038/s41413-023-00304-6] [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: 09/05/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 02/06/2024] Open
Abstract
Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.
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Affiliation(s)
- Xiwei Fan
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Antonia Rujia Sun
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Reuben S E Young
- Central Analytical Research Facility, Queensland University of Technology, Brisbane, QLD, Australia
- Molecular Horizons, University of Wollongong, Wollongong, NSW, Australia
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland, Brisbane, QLD, Australia
| | - Brett R Hamilton
- Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, QLD, Australia
| | - Louis Jun Ye Ong
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ross Crawford
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Indira Prasadam
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia.
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Ferkowicz MJ, Winfree S, Sabo AR, Kamocka MM, Khochare S, Barwinska D, Eadon MT, Cheng YH, Phillips CL, Sutton TA, Kelly KJ, Dagher PC, El-Achkar TM, Dunn KW. Large-scale, three-dimensional tissue cytometry of the human kidney: a complete and accessible pipeline. J Transl Med 2021; 101:661-676. [PMID: 33408350 PMCID: PMC8363780 DOI: 10.1038/s41374-020-00518-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/05/2020] [Accepted: 11/07/2020] [Indexed: 02/08/2023] Open
Abstract
The advent of personalized medicine has driven the development of novel approaches for obtaining detailed cellular and molecular information from clinical tissue samples. Tissue cytometry is a promising new technique that can be used to enumerate and characterize each cell in a tissue and, unlike flow cytometry and other single-cell techniques, does so in the context of the intact tissue, preserving spatial information that is frequently crucial to understanding a cell's physiology, function, and behavior. However, the wide-scale adoption of tissue cytometry as a research tool has been limited by the fact that published examples utilize specialized techniques that are beyond the capabilities of most laboratories. Here we describe a complete and accessible pipeline, including methods of sample preparation, microscopy, image analysis, and data analysis for large-scale three-dimensional tissue cytometry of human kidney tissues. In this workflow, multiphoton microscopy of unlabeled tissue is first conducted to collect autofluorescence and second-harmonic images. The tissue is then labeled with eight fluorescent probes, and imaged using spectral confocal microscopy. The raw 16-channel images are spectrally deconvolved into 8-channel images, and analyzed using the Volumetric Tissue Exploration and Analysis (VTEA) software developed by our group. We applied this workflow to analyze millimeter-scale tissue samples obtained from human nephrectomies and from renal biopsies from individuals diagnosed with diabetic nephropathy, generating a quantitative census of tens of thousands of cells in each. Such analyses can provide useful insights that can be linked to the biology or pathology of kidney disease. The approach utilizes common laboratory techniques, is compatible with most commercially-available confocal microscope systems and all image and data analysis is conducted using the VTEA image analysis software, which is available as a plug-in for ImageJ.
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Affiliation(s)
- Michael J Ferkowicz
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Seth Winfree
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Angela R Sabo
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Malgorzata M Kamocka
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Suraj Khochare
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Daria Barwinska
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Michael T Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Ying-Hua Cheng
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Carrie L Phillips
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Division of Pathology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Timothy A Sutton
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Katherine J Kelly
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Pierre C Dagher
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Tarek M El-Achkar
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
| | - Kenneth W Dunn
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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Rashid R, Gaglia G, Chen YA, Lin JR, Du Z, Maliga Z, Schapiro D, Yapp C, Muhlich J, Sokolov A, Sorger P, Santagata S. Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer. Sci Data 2019; 6:323. [PMID: 31848351 PMCID: PMC6917801 DOI: 10.1038/s41597-019-0332-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 11/21/2019] [Indexed: 12/31/2022] Open
Abstract
In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.
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Affiliation(s)
- Rumana Rashid
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Giorgio Gaglia
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States
| | - Yu-An Chen
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States
| | - Jia-Ren Lin
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States
| | - Ziming Du
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States
| | - Zoltan Maliga
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States
| | - Denis Schapiro
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Clarence Yapp
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Jeremy Muhlich
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Artem Sokolov
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Peter Sorger
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States.
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States.
| | - Sandro Santagata
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States.
- Department of Oncologic Pathology, Dana Farber Cancer Institute, Boston, MA, United States.
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Gieseler A, Hillert R, Krusche A, Zacher KH. Theme 5 Human cell biology and pathology. Amyotroph Lateral Scler Frontotemporal Degener 2019; 20:188-205. [PMID: 31702463 DOI: 10.1080/21678421.2019.1646993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background: The delay from onset of the first symptoms to a definite ALS diagnosis depends also on the elusiveness of the initial clinical manifestations. The lack of disease-specific biomarkers to detect early pathology when ALS is supposed complicates the situation. This latency reduces the therapeutic time frame, in which neuron-rescuing strategies exert their greatest chance to work. Various biomarkers are currently promised, but none of them are specific enough to allow monitoring of disease progression. This, as well as the heterogeneity of the disease concerning clinical onset pattern and survival rates, makes difficult the correct stratification of patients into clinical trials, masking the potential positive outcome in some patients.Objective: Our main objective is to establish and test an early diagnostic tool based on microscopic immune cell monitoring of ALS patients' blood samples by using the Toponome Imaging System (TIS).Methods: TIS is based on automatically controlled microscopic device involving conjugated dye-tag incubation, protein-tag-dye-imaging, and tag-dye bleaching (1). This leads to the collection of at least 21 cycle images of fixated peripheral blood mononuclear cells (PBMCs) isolated from freshly drawn blood of ALS patients and healthy "control" donors. Resulting data sets contain combinatorial molecular information about the spatial protein network, called toponome. The PBMC toponome architectures are quantitatively analyzed as a threshold-binary code with 1 = protein is present and 0 = protein is absent.Results: Preliminary screening data of PBMCs from 4 ALS patients reveal a subpopulation of lymphocytes expressing a specific surface protein pattern, called "ALS toponome". These aberrant T cells could not be found in blood samples of controls. We observe that the number of these cells correlate with the ALS progression rate of patients, supporting the conclusion that these cells may be causal for the disease.Discussion and conclusion: Although these findings open up a potential strategy to detect early ALS disease and to monitor disease progression, a statistical analysis with many more patients, as well as data based differentiation to other neurodegenerative diseases, is mandatory. A clinical trial initiated by our faceALS foundation with at least 60 patients classified in three subsets (1. control, 2. ALS, and 3. Multiple Sclerosis (MS)) and in close cooperation with leading ALS centres in Germany is still in progress. The detection of specific and/or aberrant immune cells in blood samples of ALS patients may provide a key to understand disease onset and progression, could be used for the "staging" of disease, and contribute to effective therapy options.
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Affiliation(s)
- Anne Gieseler
- FaceALS foundation, Centre for Neuroscientific Innovation and Technology (ZENIT), Magdeburg, Germany
| | - Reyk Hillert
- FaceALS foundation, Centre for Neuroscientific Innovation and Technology (ZENIT), Magdeburg, Germany
| | - Andreas Krusche
- FaceALS foundation, Centre for Neuroscientific Innovation and Technology (ZENIT), Magdeburg, Germany
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Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging. Nat Protoc 2019; 14:2900-2930. [PMID: 31534232 DOI: 10.1038/s41596-019-0206-y] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 06/03/2019] [Indexed: 12/27/2022]
Abstract
Multiplexed tissue imaging enables precise, spatially resolved enumeration and characterization of cell types and states in human resection specimens. A growing number of methods applicable to formalin-fixed, paraffin-embedded (FFPE) tissue sections have been described, the majority of which rely on antibodies for antigen detection and mapping. This protocol provides step-by-step procedures for confirming the selectivity and specificity of antibodies used in fluorescence-based tissue imaging and for the construction and validation of antibody panels. Although the protocol is implemented using tissue-based cyclic immunofluorescence (t-CyCIF) as an imaging platform, these antibody-testing methods are broadly applicable. We demonstrate assembly of a 16-antibody panel for enumerating and localizing T cells and B cells, macrophages, and cells expressing immune checkpoint regulators. The protocol is accessible to individuals with experience in microscopy and immunofluorescence; some experience in computation is required for data analysis. A typical 30-antibody dataset for 20 FFPE slides can be generated within 2 weeks.
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6
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Verma M, Asakura Y, Murakonda BSR, Pengo T, Latroche C, Chazaud B, McLoon LK, Asakura A. Muscle Satellite Cell Cross-Talk with a Vascular Niche Maintains Quiescence via VEGF and Notch Signaling. Cell Stem Cell 2018; 23:530-543.e9. [PMID: 30290177 PMCID: PMC6178221 DOI: 10.1016/j.stem.2018.09.007] [Citation(s) in RCA: 196] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 06/19/2018] [Accepted: 09/07/2018] [Indexed: 12/20/2022]
Abstract
Skeletal muscle is a complex tissue containing tissue resident muscle stem cells (satellite cells) (MuSCs) important for postnatal muscle growth and regeneration. Quantitative analysis of the biological function of MuSCs and the molecular pathways responsible for a potential juxtavascular niche for MuSCs is currently lacking. We utilized fluorescent reporter mice and muscle tissue clearing to investigate the proximity of MuSCs to capillaries in 3 dimensions. We show that MuSCs express abundant VEGFA, which recruits endothelial cells (ECs) in vitro, whereas blocking VEGFA using both a vascular endothelial growth factor (VEGF) inhibitor and MuSC-specific VEGFA gene deletion reduces the proximity of MuSCs to capillaries. Importantly, this proximity to the blood vessels was associated with MuSC self-renewal in which the EC-derived Notch ligand Dll4 induces quiescence in MuSCs. We hypothesize that MuSCs recruit capillary ECs via VEGFA, and in return, ECs maintain MuSC quiescence though Dll4.
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Affiliation(s)
- Mayank Verma
- Medical Scientist Training Program, University of Minnesota Medical School, Minneapolis, MN, USA; Stem Cell Institute, University of Minnesota Medical School, Minneapolis, MN, USA; Paul & Sheila Wellstone Muscular Dystrophy Center, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Yoko Asakura
- Stem Cell Institute, University of Minnesota Medical School, Minneapolis, MN, USA; Paul & Sheila Wellstone Muscular Dystrophy Center, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Bhavani Sai Rohit Murakonda
- Stem Cell Institute, University of Minnesota Medical School, Minneapolis, MN, USA; Paul & Sheila Wellstone Muscular Dystrophy Center, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Thomas Pengo
- University of Minnesota Informatics Institute, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Claire Latroche
- San Raffaele Telethon Institute for Gene Therapy, Milan, Italy
| | | | - Linda K McLoon
- Stem Cell Institute, University of Minnesota Medical School, Minneapolis, MN, USA; Paul & Sheila Wellstone Muscular Dystrophy Center, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Ophthalmology and Visual Neurosciences, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Atsushi Asakura
- Stem Cell Institute, University of Minnesota Medical School, Minneapolis, MN, USA; Paul & Sheila Wellstone Muscular Dystrophy Center, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, USA.
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7
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Gettinger SN, Choi J, Mani N, Sanmamed MF, Datar I, Sowell R, Du VY, Kaftan E, Goldberg S, Dong W, Zelterman D, Politi K, Kavathas P, Kaech S, Yu X, Zhao H, Schlessinger J, Lifton R, Rimm DL, Chen L, Herbst RS, Schalper KA. A dormant TIL phenotype defines non-small cell lung carcinomas sensitive to immune checkpoint blockers. Nat Commun 2018; 9:3196. [PMID: 30097571 PMCID: PMC6086912 DOI: 10.1038/s41467-018-05032-8] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Accepted: 06/07/2018] [Indexed: 02/07/2023] Open
Abstract
The biological determinants of sensitivity and resistance to immune checkpoint blockers are not completely understood. To elucidate the role of intratumoral T-cells and their association with the tumor genomic landscape, we perform paired whole exome DNA sequencing and multiplexed quantitative immunofluorescence (QIF) in pre-treatment samples from non-small cell lung carcinoma (NSCLC) patients treated with PD-1 axis blockers. QIF is used to simultaneously measure the level of CD3+ tumor infiltrating lymphocytes (TILs), in situ T-cell proliferation (Ki-67 in CD3) and effector capacity (Granzyme-B in CD3). Elevated mutational load, candidate class-I neoantigens or intratumoral CD3 signal are significantly associated with favorable response to therapy. Additionally, a "dormant" TIL signature is associated with survival benefit in patients treated with immune checkpoint blockers characterized by elevated TILs with low activation and proliferation. We further demonstrate that dormant TILs can be reinvigorated upon PD-1 blockade in a patient-derived xenograft model.
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Affiliation(s)
- S N Gettinger
- Medical Oncology and Yale Cancer Center, New Haven, CT, 06511, USA
| | - J Choi
- Department of Genetics, Yale School of Medicine, New Haven, CT, 06511, USA
| | - N Mani
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06511, USA
- Translational Immuno-oncology Laboratory, Yale Cancer Center, New Haven, CT, 06511, USA
| | - M F Sanmamed
- Immunobiology, Yale School of Medicine, New Haven, CT, 06511, USA
| | - I Datar
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06511, USA
- Translational Immuno-oncology Laboratory, Yale Cancer Center, New Haven, CT, 06511, USA
| | - Ryan Sowell
- Immunobiology, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Victor Y Du
- Immunobiology, Yale School of Medicine, New Haven, CT, 06511, USA
| | - E Kaftan
- Medical Oncology and Yale Cancer Center, New Haven, CT, 06511, USA
- Translational Immuno-oncology Laboratory, Yale Cancer Center, New Haven, CT, 06511, USA
| | - S Goldberg
- Medical Oncology and Yale Cancer Center, New Haven, CT, 06511, USA
| | - W Dong
- Department of Genetics, Yale School of Medicine, New Haven, CT, 06511, USA
| | - D Zelterman
- Yale School of Public Health, New Haven, CT, 06511, USA
| | - K Politi
- Medical Oncology and Yale Cancer Center, New Haven, CT, 06511, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06511, USA
| | - P Kavathas
- Immunobiology, Yale School of Medicine, New Haven, CT, 06511, USA
- Laboratory Medicine, Yale School of Medicine, New Haven, CT, 06511, USA
| | - S Kaech
- Immunobiology, Yale School of Medicine, New Haven, CT, 06511, USA
| | - X Yu
- Yale School of Public Health, New Haven, CT, 06511, USA
| | - H Zhao
- Department of Genetics, Yale School of Medicine, New Haven, CT, 06511, USA
- Yale School of Public Health, New Haven, CT, 06511, USA
| | - J Schlessinger
- Department of Pharmacology, Yale School of Medicine, New Haven, CT, 06511, USA
| | - R Lifton
- Department of Genetics, Yale School of Medicine, New Haven, CT, 06511, USA
| | - D L Rimm
- Medical Oncology and Yale Cancer Center, New Haven, CT, 06511, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06511, USA
| | - L Chen
- Immunobiology, Yale School of Medicine, New Haven, CT, 06511, USA
| | - R S Herbst
- Medical Oncology and Yale Cancer Center, New Haven, CT, 06511, USA
| | - K A Schalper
- Medical Oncology and Yale Cancer Center, New Haven, CT, 06511, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06511, USA.
- Translational Immuno-oncology Laboratory, Yale Cancer Center, New Haven, CT, 06511, USA.
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Zaidan AA, Zaidan BB, Albahri OS, Alsalem MA, Albahri AS, Yas QM, Hashim M. A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. HEALTH AND TECHNOLOGY 2018. [DOI: 10.1007/s12553-018-0223-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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