Added: Randal Brian - Date: 19.07.2021 08:12 - Views: 25562 - Clicks: 5289
Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. Tissue clearing is one of the most powerful strategies for a comprehensive analysis of disease progression.
Here, we established an integrated pipeline that combines tissue clearing, 3D imaging, and machine learning and applied to a mouse tumour model of experimental lung metastasis using human lung adenocarcinoma A cells. This pipeline provided the spatial information of the tumour microenvironment. Hence, our integrated pipeline for 3D profiling will help the understanding of the tumour microenvironment. During cancer progression, cancer cells interact with various kinds of non-transformed stromal cells, including fibroblasts, endothelial cells, and immune cells 1.
This group of cellular components is known to constitute the tumour microenvironment. The tumour microenvironment often enhances proliferative and invasive tgf nudes of cancer cells. The tumour microenvironment in the metastatic site also determines the establishment tgf nudes metastasis, which partially s for organ tropism of cancer metastasis. Recently, the diversity of the tumour microenvironment was suggested to be involved in cancer progression and resistance to conventional cancer therapy 23.
Detailed observation of the tumour microenvironment is thus essential for the development of novel cancer treatment. Although researchers have tried to mimic the interactions between cancer cells and the tumour microenvironments in vivo, methods to monitor the multidimensional structure of cancer are still lacking. Tissue clearing technique has emerged as one of the most powerful strategies for a comprehensive and unbiased analysis of disease progression.
Over the past decades, various kinds of tissue clearing methods have been used especially for whole-brain clearing in neuroscience 11 These technologies are widely used in clearing of whole-body or other organs 13tgf nudes15 as well as in visualization of cancer cells in vivo 16 Here, the CUBIC protocol was improved and developed as an integrated pipeline for automated 3D profiling of the tumour microenvironment. Our findings showed several kinds of interactions between cancer cells and the tumour microenvironments that are critical for the metastatic colonization of cancer cells. The application of this enhanced tissue clearing protocol combined with integrated 3D imaging and automated computer analysis will further allow the efficient and accurate probing of the tumour microenvironment.
In the present study, we optimized the current tissue clearing protocols Fig. The combination of these clearing cocktails allowed whole-lung clearing in one day Fig. Although brain and liver were not delipidated to achieve sufficient transparency for whole-organ profiling, pancreas and spleen became almost completely clear in one day Fig. LSFM light-sheet fluorescence microscope. AmCherry cells were intravenously injected in mice day 0. AmCherry cells were intravenously injected in mice hour 0.
Mice were administered with DyLight conjugated anti-CD42c antibody immediately. Then, the lung was subjected to whole-organ clearing protocol, followed by 3D imaging hour 1. Then, the lung was subjected to whole-organ clearing protocol, followed by 3D imaging day 7. Representative images are shown. Figure schematic created with biorender. Next, we applied this clearing protocol to the examination of lung metastasis.
In this model, mCherry-expressing human lung adenocarcinoma cells A were intravenously injected into nude mice. Lungs were extracted from mice and cleared based on this protocol.
Microscopic examinations revealed the formation of metastatic colonization of mCherry-positive cells within the entire lungs, as expected Fig. At the same time, lungs were immunostained or labelled in vivo using antibodies against protein markers of each cellular component in the tumour microenvironments Fig. As a result, we confirmed that these cellular components of the tumour microenvironment could be visualized with a single-cell resolution in a 3D manner.
A marker for cellular proliferation, Ki, was also stained in these samples Fig. These motivated us to use our clearing protocol to visualize potential spatial relationships between cancer cells and the tumour microenvironment. To explore tgf nudes between cancer cells and tumour microenvironment, we established an automated tgf nudes analysis method with pixel classification based on machine learning Fig. In this method, which calculates gaussian smoothing, difference of gaussians, and hessian of gaussian eigenvalues, we do not need to set a threshold value of al intesnsity and thus are allowed for an unbiased analysis compared to the al intensity-dependent computational method.
Using this method, raw images were classified with four annotations and each al was classified as a component of tumour microenvironment in the lung of mice bearing cancer cells Fig. To analyse their spatial relationships, minimal distance between each cellular component was measured after the classification Fig. These suggested that our clearing protocol may provide spatial information on cancer cells and tumour microenvironment instantly when used with automated image analysis methods.
Whole-lung imaging of cancer cells and the tumour microenvironment. AmCherry cells were intravenously injected into mice day 0. After pixel classification by ilastik, the original bit images were converted into binary images. We set four annotations: yellow annotations are true al, blue annotations are al leakage along the Z -axis, red annotations are tissue autofluorescence, and green annotations are background al. Representative result of two independent experiments.
To distinguish the two cancer populations, prestimulated and unstimulated cancer cells were labelled with different fluorescent proteins, mCherry and green fluorescent protein GFPrespectively, prior to the determination of their distributions using the clearing protocol and 3D imaging Fig.
These trends were confirmed when the total tumour volumes were determined Fig. Then, the lung was subjected to whole-organ clearing protocol day 14followed by 3D imaging. Unstimulated AGFP cells were intravenously injected in mice day 0. Taken together, these data suggested that several cellular tgf nudes in the tumour microenvironment, e.
Then, gene expression level was determined by RNA-seq analysis. Fragments per kilobase of exon per million re mapped FPKM values of each gene are shown. Representative enrichment plots of the hallmarks are shown in c. Heat map showing the expression of genes listed in the hallmarks in d. Genes which were extracted in either or both hallmarks are listed. To validate the involvement of platelets in metastatic colonization of cancer cells, platelets in mice-bearing cancer cells were depleted by neutralizing antibody against mouse CD42b or rat isotype control antibody Fig.
Considering that platelet depletion is thought to be sustained for 3 days, platelets might play an important role in the colonization tgf nudes cancer cells in the early stages of cancer colonization. This time duration was in accordance with the time-course experiments in Fig. Next, the spatial relationship between cancer cells and platelets was examined using 3D imaging Fig.
The distribution patterns of both cells are displayed in Fig. Mice were pre-treated with DyLight conjugated anti-CD42c antibody. Then, the lung was subjected to whole-organ clearing protocol hour 1followed by 2D imaging.
The arrows indicate the co-localization of cancer cells and platelets. Mice were pre-treated with anti-CD42b neutralizing antibody or control IgG. Representative images are shown in b. Quantification of the metastatic colony and the metastatic tumour volume of cancer cells are shown in c. Mice were pretreated with DyLight conjugated anti-CD42c antibody. Then, the lung was subjected to whole-organ clearing protocol hour 1followed by 3D imaging.
The arrows indicate co-localization of cancer cells and platelets. The of mCherry-positive and DyLight positive colonies is indicated. We thus focused our investigation on macrophages in the tumour microenvironments. The spatial relationship between cancer cells and macrophages was assessed using 3D imaging Fig. Since lymphatic vessels act as tracts for macrophage migration 20the proximity of both may suggest that accumulation of macrophages was derived from migration, not from an increase in tissue resident macrophages. Cells were obtained from the lung of the nude mice 1 day after injection of cancer cells and cultured for one day.
Then, the lung was subjected to whole-organ clearing protocol day 1 and immunostained with anti-Iba1 antibody, followed by 2D imaging. The arrows indicate co-localization of cancer cells and macrophages. Mice were pretreated with clodronate or control liposome 3 and 1 day before cancer cell injections. Representative images are shown in c. Quantification of the metastatic tgf nudes and the metastatic tumour volume are shown in d.Tgf nudes
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Whole-organ analysis of TGF-β-mediated remodelling of the tumour microenvironment by tissue clearing