The authors gratefully acknowledge the support of the Breast Cancer Research Foundation and the Advanced Medical Research Foundation.

All the methods described here have been approved by the Boston Children&#39;s Hospital Institutional Biosafety Committee.
1. Cell Preparation
<ol>
	<li>Thawing KHOS-A and -N cells
	<ol>
		<li>Warm up culture medium, i.e., Dulbecco&#39;s modified Eagle medium supplemented with 10% (vol/vol) fetal calf serum (FBS) and 1% (vol/vol) penicillin/streptomycin.</li>
		<li>Take the cryogenic vials of cells from the liquid nitrogen tank, immerse the bottom of the vials into warm water in a 37 &#176;C water bath, and gently shake the cryogenic vials to accelerate the thawing process. Keep the lid of cryogenic vials from contacting water in order to avoid contamination. As soon as cells are thawed, sterilize the cryogenic vial by spraying 70% ethanol solution and wiping the vial.</li>
		<li>In a laminar flow hood, immediately aspirate the cell suspension from the cryogenic vial, and gently suspend in 10 mL warmed culture medium (described in 1.1.1). Centrifuge cell suspensions at approximately 200 x g for 5 - 7 min.</li>
		<li>Resuspend the cell pellet with 10 mL warmed culture medium, and transfer into a T75 flask. Gently pipette several times to suspend cells evenly using a 10-mL pipet.</li>
		<li>Place the flask into a 37 &#176;C incubator with 5% (vol/vol) CO2 and humidified atmosphere. Allow cells to attach for at least 12 h.</li>
		<li>Incubate cells for approximately 3 - 7 days until 80 - 100% confluent. Change the culture medium every 2 - 3 days.</li>
	</ol>
	</li>
	<li>Splitting KHOS-A and -N cells
	<ol>
		<li>Remove the culture media from the flask.</li>
		<li>Wash the cells with 5 mL sterile PBS (1x) without calcium and magnesium, to remove non adherent cells or fraction.</li>
		<li>Add 1 mL 0.05% Trypsin-EDTA solution into the flask, and briefly swirl the flask to ensure that the trypsin-EDTA is dispersed evenly.</li>
		<li>Incubate the flask for 2 - 3 min in a 37 &#176;C incubator or 3 - 5 min at room temperature. Check the cells under a microscope at approximately 400x&#160;magnification to make sure that the cells are rounded up and detached.</li>
		<li>Once the cells are detached, add 10 mL culture medium and gently pipette the medium up and down several times to break up cell aggregates into a single cell suspension using a 10 mL pipet.</li>
		<li>Count cells using a cell counter. Seed the cell suspension into new T75 flasks at a density of 2 &#215; 106 cells or a ratio of 1:4.</li>
		<li>Allow cells to grow to 80 - 100% confluence before seeding for the QPI experiment.</li>
	</ol>
	</li>
	<li>Seeding KHOS-A and -N cells for QPI
	<ol>
		<li>Seed KHOS-A and -N cells in 6-well plates at the density of 50,000 - 300,000 cells/well with 5 mL culture medium after counting with a cell counter. 
		NOTE: The seeding density is dependent on the cell proliferation rate, and the imaging time period in order to have &#60;100% confluence during imaging acquisition.</li>
		<li>Incubate cells for at least 12 h to allow attachment.</li>
		<li>Change the medium with fresh media before conducting QPI.</li>
	</ol>
	</li>
</ol>
2. QPI Acquisition
<ol>
	<li>Cover slip set up
	<ol>
		<li>Clean the cover slip by rinsing several times with running deionized water and sterilize with 75% ethanol solution by immersion for 15 min. Put the cover slip into a sterile laminar flow hood to dry.</li>
		<li>Carefully place the cover slip onto a 6-well plate to avoid obvious bubbles. Ensure that the bottom of the cover slip is immersed in the culture media (minimum 5 mL/well).</li>
		<li>Place the plate into the incubator (37 &#176;C, 5% CO2, and humidified atmosphere) to equilibrate for at least 15 min. If fog forms on the cover slip, use a sterile cotton swab to wipe it clean.</li>
	</ol>
	</li>
	<li>Imaging cells with a microscope
	<ol>
		<li>Open the software to initialize the system, including self calibration of exposure time, pattern contrast, and hologram noise. Make sure the values are acceptable (shown in green or yellow range).</li>
		<li>Place the 6-well plate onto the stage of the QPI microscope.</li>
		<li>Click &#34;Live Capture,&#34;&#160;and select &#34;Manual&#34; in &#34;Software focus.&#34;&#160;Coarsely focus the phase images of cells by adjusting the work distance in &#34;Microscope setting&#34; to obtain outlines for cells in phase images. Change to &#34;Automatic&#34; in &#34;Software focus.&#34;</li>
		<li>Click &#34;New experiment&#34; to create a new experiment. Start with the selection of image acquisition positions using the mouse or the arrow keys on the numerical pad. Click &#34;Remember&#34; after each selection. Normally 3 - 5 locations for each sample are selected.</li>
		<li>Set up the imaging intervals and time period in the &#34;Timelapse&#34; section. 
		NOTE: To track cancer cells clearly, the intervals should be shorter than 5 min. 48 hours is set as the time period for the combination QPI analysis.</li>
		<li>Start the experiment by clicking &#34;Capture,&#34;&#160;which will automatically focus and acquire the images at the setting time points.</li>
	</ol>
	</li>
</ol>
3. Data Analysis
<ol>
	<li>Cell morphology analysis
	<ol>
		<li>Click &#34;Identify cells.&#34;&#160;Adjust the numeric setting for &#34;Background threshold&#34; so that cell areas are separated well from background noise. Adjust the setting number for &#34;Object size&#34; to make sure that each cell has one nucleus. Maintain consistent parameters for samples that need to be compared, as these may affect the final area and volume measurements. Manually modify cell segments by using &#34;Manual changes,&#34;&#160;if needed.</li>
		<li>Use the &#34;Analyze data&#34; function in the software to analyze cells. Start a new data analysis by clicking &#34;New analysis.&#34;&#160;Drag selected images into the &#34;Source frames&#34; tab and cell morphology parameters including cell area (&#181;m2), optical thickness (&#181;m), and volume (&#181;m3) for every individual cell. Choose scatter plot or histogram mode and export data as tables or figures.</li>
	</ol>
	</li>
	<li>Cell proliferation analysis
	<ol>
		<li>Select at least 5 images for the different time points of interest (e.g., initial, 12 h, 24 h, 36 h, and 48 h). Record cell numbers that are provided by the software.</li>
		<li>To estimate the doubling time, plot cell numbers after normalizing with the initial numbers and fit with exponential growth curves.</li>
	</ol>
	</li>
	<li>Cell motion analysis
	<ol>
		<li>Use the &#34;Track cells&#34; function in the software. Click &#34;New analysis&#34; to start a new data analysis. Drag series of images for a selected time period (e.g., 4 h after 24 h incubation) into the &#34;Source frames&#34; tab. Randomly choose 10 - 30 cells by clicking cells under the &#34;Add cells&#34; selection mode. Exclude the cells at the edges and those moving out of the field of view.</li>
		<li>Carefully check the tracking in the series of images. In the event that the system loses track of a cell or tracks the wrong cell, manually adjust the tracking cell by clicking &#34;Identify&#34; to identify cells or clicking &#34;Modify location&#34; under the &#34;Select&#34; mode to modify the cell location.</li>
		<li>Choose rose plot of cell trajectories in &#34;Plot movement&#34; or plot for the other motion-related parameters in &#34;Plot features,&#34;&#160;such as motility speed (&#181;m/h), motility (&#181;m), migration (&#181;m), and migration directness. Export Data as tables or figures.</li>
	</ol>
	</li>
</ol>

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No conflicts of interest declared.

In this study, we describe an in vitro, non-invasive, and label-free method using QPI to quantitatively characterize the angiogenic and non-angiogenic phenotypes of human osteosarcoma cells. Multiple cellular parameters have been analyzed simultaneously by this integrated, high-throughput method, including cell area, cell thickness, cell volume, proliferation rate, doubling time, migration directness, motility speed, migration, and motility.
Compared with the conventional assays that ...

One of the earliest checkpoints in the development and progression of a solid tumor is the acquisition of the angiogenic phenotype, a hallmark of cancer. This progression involves a variety of biochemical and molecular processes1,2,3. A technical challenge in the study of this key step in tumor progression is the lack of tools to continuously and quantitatively characterize and differentiate between angiogenic and non-angiogenic phenotypes of live cancer cells in an unbiased manner. The traditional assays being used to investigate the cellular behaviors of angiogenic and non-angiogenic cells usually require expensive reagents and instruments, for example, cell proliferation/migration assays4,5,6,7,8,9,10,11,12,13,14 or complementary in vivo evaluations4,5,6,8,15,16, as well as requiring significant expertise and intensive time and labor consumption.
Recently, quantitative phase imaging (QPI) has emerged as a novel technique that enables time-lapse and labeling-free assessment of a variety of cell morphology and behavior parameters17,18,19,20,21,22. Unlike conventional optical microscopy, QPI quantifies variations of phase shift pixel by pixel after light passes through an optical object, and reconstructs a holograph with converted optical thickness and volume, thus enabling the direct analysis of live cells and the following features: (1) quantitative imaging, (2) non-invasive and time-lapse imaging, (3) label-free imaging, and (4) simultaneous multi-parameter imaging. These features make QPI a powerful tool to assess and understand pathological processes at cellular level.
In a recent study, we utilized QPI to quantitatively characterize and differentiate between angiogenic KHOS-A and non-angiogenic KHOS-N phenotypes of human osteosarcoma cells in a systematic and quantitative manner, combining analyses of cell morphology, proliferation, and motility23. Using image analysis software, a panel of cell morphological and behavior parameters were quantitatively compared between angiogenic and non-angiogenic human osteosarcoma cells and five characteristic differences were identified between these two phenotypes. This novel approach provides an integrated and quantitative platform for assessing a variety of biologically relevant cellular characteristics.

<table border="0" cellpadding="0" cellspacing="0" width="908">
	<tbody>
		<tr height="33">
			<td height="33" style="height:34px;width:227px;">T75 flask</td>
			<td style="width:227px;">Corning, NY, USA</td>
			<td style="width:227px;">353136</td>
			<td style="width:227px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:34px;width:227px;">6-well plates&#160;</td>
			<td style="width:227px;">Corning, NY, USA</td>
			<td style="width:227px;">3506</td>
			<td></td>
		</tr>
		<tr height="48">
			<td height="48" style="height:48px;width:227px;">Dulbecco&#8217;s modified Eagle medium (DMEM)</td>
			<td style="width:227px;">Thermo Fisher Scientific, MA, USA</td>
			<td style="width:227px;">11965092</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:34px;width:227px;">Fetal bovine serum (FBS)&#160;</td>
			<td style="width:227px;">Atlanta Biologicals, GA, USA</td>
			<td style="width:227px;">S11550</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:34px;width:227px;">Penicillin Streptomycin</td>
			<td style="width:227px;">Thermo Fisher Scientific, MA, USA</td>
			<td style="width:227px;">15140122</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:34px;width:227px;">Phosphate buffered saline (PBS)</td>
			<td style="width:227px;">Thermo Fisher Scientific, MA, USA</td>
			<td style="width:227px;">10010023</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:34px;width:227px;">Beckman Z1 Coulter counter</td>
			<td style="width:227px;">Beckman Coulter, IN, USA</td>
			<td style="width:227px;">Z1&#160;</td>
			<td></td>
		</tr>
		<tr height="51">
			<td height="51" style="height:51px;width:227px;">HoloMonitor M4</td>
			<td style="width:227px;">Phase Holographic Imaging Phi AB, Lund, Sweden</td>
			<td style="width:227px;">M4</td>
			<td>Microscope</td>
		</tr>
		<tr height="51">
			<td height="51" style="height:51px;width:227px;">Hololid</td>
			<td style="width:227px;">Phase Holographic Imaging Phi AB, Lund, Sweden</td>
			<td style="width:227px;">PHI 8020</td>
			<td></td>
		</tr>
		<tr height="51">
			<td height="51" style="height:51px;width:227px;">HStudioM4</td>
			<td style="width:227px;">Phase Holographic Imaging Phi AB, Lund, Sweden</td>
			<td style="width:227px;">HStudioM4</td>
			<td>Software</td>
		</tr>
	</tbody>
</table>

a time-lapse, label-free, quantitative phase imaging study of dormant and active human cancer cells

The acquisition of the angiogenic phenotype is an essential component of the escape from tumor dormancy. Although several classic in vitro assays (e.g., proliferation, migration, and others) and in vivo models have been developed to investigate and characterize angiogenic and non-angiogenic cell phenotypes, these methods are time and labor intensive, and often require expensive reagents and instruments, as well as significant expertise. In a recent study, we used a novel quantitative phase imaging (QPI) technique to conduct time-lapse and labeling-free characterizations of angiogenic and non-angiogenic human osteosarcoma KHOS cells. A panel of cellular parameters, including cell morphology, proliferation, and motility, were quantitatively measured and analyzed using QPI. This novel and quantitative approach provides the opportunity to continuously and non-invasively study relevant cellular processes, behaviors, and characteristics of cancer cells and other cell types in a simple and integrated manner. This report describes our experimental protocol, including cell preparation, QPI acquisition, and data analysis.

Figure 1 depicts a typical cell morphology characterization. Images are presented as holographs (Figure 1A-B) and 2D images (Figure 1C-D). Optical cell thicknesses (calculated from the refractive index and optical path length) are quantified via line profile or a whole cell measurement. Scatter plots of the area and thickness of KHOS-A and KHOS-N cells measured for a...

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A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

Dormant and active cancer cell phenotypes were characterized using quantitative phase imaging. Cell proliferation, migration, and morphology assays were integrated and analyzed in one simple method.

The acquisition of the angiogenic phenotype is an essential component of the escape from tumor dormancy. Although several classic in vitro assays (e.g., ...

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JoVE Journal

Cancer Research

7.4K Views.  Boston Children's Hospital. Dormant and active cancer cell phenotypes were characterized using quantitative phase imaging. Cell proliferation, migration, and morphology assays were integrated and analyzed in one simple method.

Video: A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies

Renal cell carcinoma (RCC) affects &#62; 60,000 people in the United States annually, and ~ 30% of RCC patients have multiple metastases at the time of diagnosis. Metastatic RCC (mRCC) is incurable, with a median survival time of only 18 months. Immune-based interventions (e.g., interferon (IFN) and interleukin (IL)-2) induce durable responses in a fraction of mRCC patients, and multikinase inhibitors (e.g., sunitinib or sorafenib) or anti-VEGF receptor monoclonal antibodies (mAb) are largely palliative, as complete remissions are rare. Such shortcomings in current therapies for mRCC patients provide the rationale for the development of novel treatment protocols. A key component in the preclinical testing of new therapies for mRCC is a suitable animal model. Beneficial features that recapitulate the human condition include a primary renal tumor, renal tumor metastases, and an intact immune system to investigate any therapy-driven immune effector responses and the formation of tumor-induced immunosuppressive factors. This report describes an orthotopic mRCC mouse model that has all of these features. We describe an intrarenal implantation technique using the mouse renal adenocarcinoma cell line Renca, followed by the assessment of tumor growth in the kidney (primary site) and lungs (metastatic site).

Implementation of an orthotopic model of renal cell carcinoma in immunocompetent mice affords the investigator a clinically-relevant system defined by the presence of a primary renal tumor and lung metastases in the same animal. This system can be used to preclinically test a variety of treatments in vivo.

Renal cell carcinoma (RCC) affects &#62; 60,000 people in the United States annually, and ~ 30% of RCC patients have multiple metastases at the time of ...

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer

Phosphoproteomics involves the large-scale study of phosphorylated proteins. Protein phosphorylation is a critical step in many signal transduction pathways and is tightly regulated by kinases and phosphatases. Therefore, characterizing the phosphoproteome may provide insights into identifying novel targets and biomarkers for oncologic therapy. Mass spectrometry provides a way to globally detect and quantify thousands of unique phosphorylation events. However, phosphopeptides are much less abundant than non-phosphopeptides, making biochemical analysis more challenging. To overcome this limitation, methods to enrich phosphopeptides prior to the mass spectrometry analysis are required. We describe a procedure to extract and digest proteins from tissue to yield peptides, followed by an enrichment for phosphotyrosine (pY) and phosphoserine/threonine (pST) peptides using an antibody-based and/or titanium dioxide (TiO2)-based enrichment method. After the sample preparation and mass spectrometry, we subsequently identify and quantify phosphopeptides using liquid chromatography-mass spectrometry and analysis software.

This protocol describes a procedure to extract and enrich phosphopeptides from prostate cancer cell lines or tissues for an analysis of the phosphoproteome via mass spectrometry-based proteomics.

Phosphoproteomics involves the large-scale study of phosphorylated proteins. Protein phosphorylation is a critical step in many signal transduction ...

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Understanding the impact of the microenvironment on the phenotype of cells is a difficult problem due to the complex mixture of both soluble growth factors and matrix-associated proteins in the microenvironment in vivo. Furthermore, readily available reagents for the modeling of microenvironments in vitro typically utilize complex mixtures of proteins that are incompletely defined and suffer from batch to batch variability. The microenvironment microarray (MEMA) platform allows for the assessment of thousands of simple combinations of microenvironment proteins for their impact on cellular phenotypes in a single assay. The MEMAs are prepared in well plates, which allows the addition of individual ligands to separate wells containing arrayed extracellular matrix (ECM) proteins. The combination of the soluble ligand with each printed ECM forms a unique combination. A typical MEMA assay contains greater than 2,500 unique combinatorial microenvironments that cells are exposed to in a single assay. As a test case, the breast cancer cell line MCF7 was plated on the MEMA platform. Analysis of this assay identified factors that both enhance and inhibit the growth and proliferation of these cells. The MEMA platform is highly flexible and can be extended for use with other biological questions beyond cancer research.

The purpose of the method presented here is to show how microenvironment microarrays (MEMA) can be fabricated and used to interrogate the impact of thousands of simple combinatorial microenvironments on the phenotype of cultured cells.

Understanding the impact of the microenvironment on the phenotype of cells is a difficult problem due to the complex mixture of both soluble growth ...

Yeast As a Chassis for Developing Functional Assays to Study Human P53

The finding that the well-known mammalian P53 protein can act as a transcription factor (TF) in the yeast S. cerevisiae has allowed for the development of different functional assays to study the impacts of 1) binding site [i.e., response element (RE)] sequence variants on P53 transactivation specificity or 2) TP53 mutations, co-expressed cofactors, or small molecules on P53 transactivation activity. Different basic and translational research applications have been developed. Experimentally, these approaches exploit two major advantages of the yeast model. On one hand, the ease of genome editing enables quick construction of qualitative or quantitative reporter systems by exploiting isogenic strains that differ only at the level of a specific P53-RE to investigate sequence-specificity of P53-dependent transactivation. On the other hand, the availability of regulated systems for ectopic P53 expression allows the evaluation of transactivation in a wide range of protein expression. Reviewed in this report are extensively used systems that are based on color reporter genes, luciferase, and the growth of yeast to illustrate their main methodological steps and to critically assess their predictive power. Moreover, the extreme versatility of these approaches can be easily exploited to study different TFs including P63 and P73, which are other members of TP53 gene family.

Presented here are four protocols to construct and exploit yeast Saccharomyces cerevisiae reporter strains to study human P53 transactivation potential, impacts of its various cancer-associated mutations, co-expressed interacting proteins, and the effects of specific small molecules.

The finding that the well-known mammalian P53 protein can act as a transcription factor (TF) in the yeast S. cerevisiae has allowed for the development of ...

Time-Lapse 2D Imaging of Phagocytic Activity in M1 Macrophage-4T1 Mouse Mammary Carcinoma Cells in Co-cultures

Tumor-associated macrophages (TAMs) have been identified as an important component for tumor growth, invasion, metastasis, and resistance to cancer therapies. However, tumor-associated macrophages can be harmful to the tumor depending on the tumor microenvironment and can reversibly alter their phenotypic characteristics by either antagonizing the cytotoxic activity of immune cells or enhancing anti-tumor response. The molecular actions of macrophages and their interactions with tumor cells (e.g., phagocytosis) have not been extensively studied. Therefore, the interaction between immune cells (M1/M2-subtype TAM) and cancer cells in the tumor microenvironment is now a focus of cancer immunotherapy research. In the present study, a live cell coculture model of induced M1 macrophages and mouse mammary 4T1 carcinoma cells was developed to assess the phagocytic activity of macrophages using a time-lapse video feature using phase-contrast, fluorescent, and differential interference contrast (DIC) microscopy. The present method can observe and document multipoint live-cell imaging of phagocytosis. Phagocytosis of 4T1 cells by M1 macrophages can be observed using fluorescent microscopy before staining 4T1 cells with carboxyfluorescein succinimidyl ester (CFSE). The current publication describes how to coculture macrophages and tumor cells in a single imaging dish, polarize M1 macrophages, and record multipoint events of macrophages engulfing 4T1 cells during 13 h of coculture.

Macrophage phagocytic activity against cancer cells, specifically 4T1 mouse mammary carcinoma cells, was imaged in this study. The live cell coculture model was established and observed using a combination of fluorescent and differential interference contrast microscopy. This assessment was imaged using imaging software to develop multipoint time-lapse video.

Tumor-associated macrophages (TAMs) have been identified as an important component for tumor growth, invasion, metastasis, and resistance to cancer ...

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less aggressive breast tumors, the 5-year survival rate of TNBC patients is 77% due to their characteristic drug-resistant phenotype and metastatic burden. Toward this end, murine models have been established aimed at identifying novel therapeutic strategies limiting TNBC tumor growth and metastatic spread. This work describes a practical guide for the TNBC orthotopic model where MDA-MB-231 breast cancer cells suspended in a basement membrane matrix are implanted in the fourth mammary fat pad, which closely mimics the cancer cell behavior in humans. Measurement of tumors by caliper, lung metastasis assessment via in vivo and ex vivo imaging, and molecular detection are discussed. This model provides an excellent platform to study therapeutic efficacy and is especially suitable for the study of the interaction between the primary tumor and distal metastatic sites.

This work presets an advanced protocol to accurately assess tumor loading by detection of green fluorescent protein and bioluminescence signals as well as the integration of quantitative molecular detection technique.

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less ...

Combined Conditional Knockdown and Adapted Sphere Formation Assay to Study a Stemness-Associated Gene of Patient-derived Gastric Cancer Stem Cells

Cancer stem cells (CSCs) are implicated in tumor initiation, development and recurrence after treatment, and have become the center of attention of many studies in the last decades. Therefore, it is important to develop methods to investigate the role of key genes involved in cancer cell stemness. Gastric cancer (GC) is one of the most common and mortal types of cancers. Gastric cancer stem cells (GCSCs) are thought to be the root of gastric cancer relapse, metastasis and drug resistance. Understanding GCSCs&#160;biology is needed to advance the development of targeted therapies and eventually to reduce mortality among patients. In this protocol, we present an experimental design using a conditional knockdown system and an adapted sphere formation assay to study the effect of clusterin on the stemness of patient-derived GCSCs. The protocol can be easily adapted to study both in vitro and in vivo function of stemness-associated genes in different types of CSCs.

In this protocol, we present an experimental design using a conditional knockdown system and an adapted sphere formation assay to study the effect of clusterin on the stemness of patient-derived GCSCs. The protocol can be easily adapted to study both in vitro and in vivo function of stemness-associated genes in different types of CSCs.

Cancer stem cells (CSCs) are implicated in tumor initiation, development and recurrence after treatment, and have become the center of attention of many ...

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous metastasis models are available. Thus, the experimental metastasis model involving tail-vein injection of suitable cell lines is a mainstay of metastasis research. When cancer cells are injected into the lateral tail-vein, the lung is their preferred site of colonization. A potential limitation of this technique is the accurate quantification of the metastatic lung tumor burden. While some investigators count macrometastases of a pre-defined size and/or include micrometastases following sectioning of tissue, others determine the area of metastatic lesions relative to normal tissue area. Both of these quantification methods can be exceedingly difficult when the metastatic burden is high. Herein, we demonstrate an intravenous injection model of lung metastasis followed by an advanced method for quantifying metastatic tumor burden using image analysis software. This process allows for investigation of multiple end-point parameters, including average metastasis size, total number of metastases, and total metastasis area, to provide a comprehensive analysis. Furthermore, this method has been reviewed by a veterinary pathologist board-certified by the American College of Veterinary Pathologists (SEK) to ensure accuracy.

Intravenous injection of cancer cells is often used in metastasis research, but the metastatic tumor burden can be difficult to analyze. Herein, we demonstrate a tail-vein injection model of metastasis and include a novel approach to analyze the resulting metastatic lung tumor burden.

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous ...

Immunofluorescence Imaging of DNA Damage and Repair Foci in Human Colon Cancer Cells

The purpose of the manuscript is to provide a step-by-step protocol for performing immunofluorescence microscopy to study the radiation-induced DNA damage response induced by neutron-gamma mixed-beam used in boron neutron capture therapy (BNCT). Specifically, the proposed methodology is applied for the detection of repair proteins activation which can be visualized as foci using antibodies specific to DNA double-strand breaks (DNA-DSBs). DNA repair foci were assessed by immunofluorescence in colon cancer cells (HCT-116) after irradiation with the neutron-mixed beam. DNA-DSBs are the most genotoxic lesions and are repaired in mammalian cells by two major pathways: non-homologous end-joining pathway (NHEJ) and homologous recombination repair (HRR). The frequencies of foci, immunochemically stained, for commonly used markers in radiobiology like &#947;-H2AX, 53BP1 are associated with DNA-DSB number and are considered as efficient and sensitive markers for monitoring the induction and repair of DNA-DSBs. It was established that &#947;-H2AX foci attract repair proteins, leading to a higher concentration of repair factors near a DSB. To monitor DNA damage at the cellular level, immunofluorescence analysis for the presence of DNA-PKcs representative repair protein foci from the NHEJ pathway and Rad52 from the HRR pathway was planned. We have developed and introduced a reliable immunofluorescence staining protocol for the detection of radiation-induced DNA damage response with antibodies specific for repair factors from NHEJ and HRR pathways and observed radiation-induced foci (RIF). The proposed methodology can be used for investigating repair protein that is highly activated in the case of neutron-mixed beam radiation, thereby indicating the dominance of the repair pathway.

Radiation-induced DNA damage response activated by neutron mixed-beam used in boron neutron capture therapy (BNCT) has not been fully established. This protocol provides a step-by-step procedure to detect radiation-induced foci (RIF) of repair proteins by immunofluorescence staining in human colon cancer cell lines after irradiation with the neutron-mixed beam.

The purpose of the manuscript is to provide a step-by-step protocol for performing immunofluorescence microscopy to study the radiation-induced DNA damage ...

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor microenvironment is an important step toward understanding mechanisms that regulate tumor progression. While this can be accomplished through current intravital imaging techniques, it remains challenging due to the heterogeneous nature of tissues and the need for spatial context within the experimental observation. To this end, we have developed an intravital imaging workflow that pairs collagen second harmonic generation imaging, endogenous fluorescence from the metabolic co-factor NAD(P)H, and fluorescence lifetime imaging microscopy (FLIM) as a means to non-invasively compartmentalize the tumor microenvironment into basic domains of the tumor nest, the surrounding stroma or ECM, and the vasculature. This non-invasive protocol details the step-by-step process ranging from the acquisition of time-lapse images of mammary tumor models to post-processing analysis and image segmentation. The primary advantage of this workflow is that it exploits metabolic signatures to contextualize the dynamically changing live tumor microenvironment without the use of exogenous fluorescent labels, making it advantageous for human patient-derived xenograft (PDX) models and future clinical use where extrinsic fluorophores are not readily applicable.

The intravital imaging method described here utilizes collagen second harmonic generation and endogenous fluorescence from the metabolic co-factor NAD(P)H to non-invasively segment an unlabeled tumor microenvironment into tumor, stromal, and vascular compartments for in-depth analysis of 4D intravital images.

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor ...