Cellular imaging and texture analysis distinguish differences in cellular dynamics in mouse brain tumors

Gazdzinski, Lisa M.; Nieman, Brian J.

doi:10.1002/mrm.24790

Cited by 6 publications

(6 citation statements)

References 31 publications

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“…GL261 cells, a murine GBM cell line 44 (National Cancer Institute, Charles River Labs), were transfected with a plasmid encoding for DsRed (pDsRed2-N1, Clontech, Mountain View, USA). Cells that stably express DsRed were selected using Geneticin (0.5 mg/ml, Gibco) and cultured as monolayers in RPMI1640 + GlutaMAX-1 (Gibco 61870) supplemented with 10% Fetal Calf Serum (Thermo Scientific) and Geneticin (0.5 mg/ml, Gibco).…”

Section: Methodsmentioning

confidence: 99%

Phenotypic dynamics of microglial and monocyte-derived cells in glioblastoma-bearing mice

Ricard

Tchoghandjian

Luche

et al. 2016

Sci Rep

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Inflammatory cells, an integral component of tumor evolution, are present in Glioblastomas multiforme (GBM). To address the cellular basis and dynamics of the inflammatory microenvironment in GBM, we established an orthotopic syngenic model by grafting GL261-DsRed cells in immunocompetent transgenic LysM-EGFP//CD11c-EYFP reporter mice. We combined dynamic spectral two-photon imaging with multiparametric cytometry and multicolor immunostaining to characterize spatio-temporal distribution, morphology and activity of microglia and blood-derived infiltrating myeloid cells in live mice. Early stages of tumor development were dominated by microglial EYFP+ cells invading the tumor, followed by massive recruitment of circulating LysM-EGFP+ cells. Fluorescent invading cells were conventional XCR1+ and monocyte-derived dendritic cells distributed in subpopulations of different maturation stages, located in different areas relative to the tumor core. The lethal stage of the disease was characterized by the progressive accumulation of EGFP+/EYFP+ monocyte-derived dendritic cells. This local phenotypic regulation of monocyte subtypes marked a transition in the immune response.

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Section: Methodsmentioning

confidence: 99%

Phenotypic dynamics of microglial and monocyte-derived cells in glioblastoma-bearing mice

Ricard

Tchoghandjian

Luche

et al. 2016

Sci Rep

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“…An overview of these mouse models and relevant information can be found in Figure 1 and Table 1. Weiner 1999 [161][162][163][164][165] GBM: glioblastoma, Treg: regulatory T cell, DC: dendritic cell, MHC-I/II: major histocompatibility complex I or II, TAM: tumor associated macrophage, APC: antigen presenting cell, PD-1: programmed cell death protein 1, TIGIT: T cell immunoglobulin and ITIM domain, TGF-β: transforming growth factor β, TIM-3: T cell immunoglobulin and mucin-domain containing 3, LAG-3: lymphocyte activation gene 3, RT: radiotherapy, TMZ: Temozolomide, ICB: immune checkpoint blockade. Bold: highlight.…”

Section: Oldest Available Immunocompetent Mouse Models For Gbmmentioning

confidence: 99%

“…The 4C8 model was established in 1999 from clonal cell lines of a glial tumor (MOCH-1) in B6D2F1 mice [161]. Gazdzinski et al [162] compared the characteristics of this model with the GL261 model. The 4C8 model is less aggressive, the tumor has higher cell density, less necrosis and invasiveness with a more normal vasculature and less mitotic cells as compared to the GL261 model.…”

Section: C8 251 Origins and Tumor Characteristicsmentioning

confidence: 99%

Immunocompetent Mouse Models in the Search for Effective Immunotherapy in Glioblastoma

Wouters

Bevers

Riva

et al. 2020

Cancers

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Glioblastoma (GBM) is the most aggressive intrinsic brain tumor in adults. Despite maximal therapy consisting of surgery and radio/chemotherapy, GBM remains largely incurable with a median survival of less than 15 months. GBM has a strong immunosuppressive nature with a multitude of tumor and microenvironment (TME) derived factors that prohibit an effective immune response. To date, all clinical trials failed to provide lasting clinical efficacy, despite the relatively high success rates of preclinical studies to show effectivity of immunotherapy. Various factors may explain this discrepancy, including the inability of a single mouse model to fully recapitulate the complexity and heterogeneity of GBM. It is therefore critical to understand the features and limitations of each model, which should probably be combined to grab the full spectrum of the disease. In this review, we summarize the available knowledge concerning immune composition, stem cell characteristics and response to standard-of-care and immunotherapeutics for the most commonly available immunocompetent mouse models of GBM.

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“…This can be particularly true for longitudinal studies, where the same subject is scanned at multiple time points. In the case of early brain growth (Studholme, 2011; Szulc et al, 2013) or the growth of a tumor (Gazdzinski and Nieman, 2014), the anatomy of the brain changes to such an extent that insufficient homology exists to accurately register early time points to late ones. In spite of these difficulties, it is often possible to accurately register adjacent time points together if the time-series was densely sampled (Lerch et al, Manuscript in preparation).…”

Section: Introductionmentioning

confidence: 99%

“…In the case of pathology, such as brain tumor growth, homology can only be found with a sufficiently sampled time-series, but is lost due to idiosyncratic tumor growth across subjects. (Tumor data courtesy Lisa Gazdzinski and Brian Nieman, The Hospital for Sick Children; see Gazdzinski and Nieman, 2014). …”

Section: Introductionmentioning

confidence: 99%

Pydpiper: a flexible toolkit for constructing novel registration pipelines

Friedel

Eede

Pipitone

et al. 2014

Front. Neuroinform.

100

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Using neuroimaging technologies to elucidate the relationship between genotype and phenotype and brain and behavior will be a key contribution to biomedical research in the twenty-first century. Among the many methods for analyzing neuroimaging data, image registration deserves particular attention due to its wide range of applications. Finding strategies to register together many images and analyze the differences between them can be a challenge, particularly given that different experimental designs require different registration strategies. Moreover, writing software that can handle different types of image registration pipelines in a flexible, reusable and extensible way can be challenging. In response to this challenge, we have created Pydpiper, a neuroimaging registration toolkit written in Python. Pydpiper is an open-source, freely available software package that provides multiple modules for various image registration applications. Pydpiper offers five key innovations. Specifically: (1) a robust file handling class that allows access to outputs from all stages of registration at any point in the pipeline; (2) the ability of the framework to eliminate duplicate stages; (3) reusable, easy to subclass modules; (4) a development toolkit written for non-developers; (5) four complete applications that run complex image registration pipelines “out-of-the-box.” In this paper, we will discuss both the general Pydpiper framework and the various ways in which component modules can be pieced together to easily create new registration pipelines. This will include a discussion of the core principles motivating code development and a comparison of Pydpiper with other available toolkits. We also provide a comprehensive, line-by-line example to orient users with limited programming knowledge and highlight some of the most useful features of Pydpiper. In addition, we will present the four current applications of the code.

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Cellular imaging and texture analysis distinguish differences in cellular dynamics in mouse brain tumors

Abstract: The redistribution of a cellular label during tumor growth is characteristic of a tumor model. The label distribution map reflects more than simple differences in cell proliferation and is likely influenced by differences in the tumor microenvironment.

Cited by 6 publications

References 31 publications

Phenotypic dynamics of microglial and monocyte-derived cells in glioblastoma-bearing mice

Phenotypic dynamics of microglial and monocyte-derived cells in glioblastoma-bearing mice

Immunocompetent Mouse Models in the Search for Effective Immunotherapy in Glioblastoma

Pydpiper: a flexible toolkit for constructing novel registration pipelines

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