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><tbody><tr><td>T75 flask</td><td>Corning, NY, USA</td><td>353136</td><td></td></tr><tr><td>6-well plates&amp;nbsp;</td><td>Corning, NY, USA</td><td>3506</td><td></td></tr><tr><td>Dulbecco&amp;rsquo;s modified Eagle medium (DMEM)</td><td>Thermo Fisher Scientific, MA, USA</td><td>11965092</td><td></td></tr><tr><td>Fetal bovine serum (FBS)&amp;nbsp;</td><td>Atlanta Biologicals, GA, USA</td><td>S11550</td><td></td></tr><tr><td>Penicillin Streptomycin</td><td>Thermo Fisher Scientific, MA, USA</td><td>15140122</td><td></td></tr><tr><td>Phosphate buffered saline (PBS)</td><td>Thermo Fisher Scientific, MA, USA</td><td>10010023</td><td></td></tr><tr><td>Beckman Z1 Coulter counter</td><td>Beckman Coulter, IN, USA</td><td>Z1&amp;nbsp;</td><td></td></tr><tr><td>HoloMonitor M4</td><td>Phase Holographic Imaging Phi AB, Lund, Sweden</td><td>M4</td><td>Microscope</td></tr><tr><td>Hololid</td><td>Phase Holographic Imaging Phi AB, Lund, Sweden</td><td>PHI 8020</td><td></td></tr><tr><td>HStudioM4</td><td>Phase Holographic Imaging Phi AB, Lund, Sweden</td><td>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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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

Long-term Live-cell Imaging to Assess Cell Fate in Response to Paclitaxel

Live-cell imaging is a powerful technique that can be used to directly visualize biological phenomena in single cells over extended periods of time. Over the past decade, new and innovative technologies have greatly enhanced the practicality of live-cell imaging. Cells can now be kept in focus and continuously imaged over several days while maintained under 37 &#176;C and 5% CO2 cell culture conditions. Moreover, multiple fields of view representing different experimental conditions can be acquired simultaneously, thus providing high-throughput experimental data. Live-cell imaging provides a significant advantage over fixed-cell imaging by allowing for the direct visualization and temporal quantitation of dynamic cellular events. Live-cell imaging can also identify variation in the behavior of single cells that would otherwise have been missed using population-based assays. Here, we describe live-cell imaging protocols to assess cell fate decisions following treatment with the anti-mitotic drug paclitaxel. We demonstrate methods to visualize whether mitotically arrested cells die directly from mitosis or slip back into interphase. We also describe how the fluorescent ubiquitination-based cell cycle indicator (FUCCI) system can be used to assess the fraction of interphase cells born from mitotic slippage that are capable of re-entering the cell cycle. Finally, we describe a live-cell imaging method to identify nuclear envelope rupture events.

Live-cell imaging provides a wealth of information on single cells or whole populations that is unattainable by fixed cell imaging alone. Here, live-cell imaging protocols to assess cell fate decisions following treatment with the anti-mitotic drug paclitaxel are described.

Live-cell imaging is a powerful technique that can be used to directly visualize biological phenomena in single cells over extended periods of time. Over ...

Longitudinal Intravital Imaging of Brain Tumor Cell Behavior in Response to an Invasive Surgical Biopsy

Biopsies are standard of care for cancer treatment and are clinically beneficial as they allow solid tumor diagnosis, prognosis, and personalized treatment determination. However, perturbation of the tumor architecture by biopsy and other invasive procedures has been associated with undesired effects on tumor progression, which need to be studied in depth to further improve the clinical benefit of these procedures. Conventional static approaches, which only provide a snapshot of the tumor, are limited in their ability to reveal the impact of biopsy on tumor cell behavior such as migration, a process closely related to tumor malignancy. In particular, tumor cell migration is the key in highly aggressive brain tumors, where local tumor dissemination makes total tumor resection virtually impossible. The development of multiphoton imaging and chronic imaging windows allows scientists to study this dynamic process in living animals over time. Here, we describe a method for the high-resolution longitudinal imaging of brain tumor cells before and after a biopsy in the same living animal. This approach makes it possible to study the impact of this procedure on tumor cell behavior (migration, invasion, and proliferation). Furthermore, we discuss the advantages and limitations of this technique, as well as the ability of this methodology to study changes in the cancer cell behavior for other surgical interventions, including tumor resection or the implantation of chemotherapy wafers.

Here we describe a method for high-resolution time-lapse multiphoton imaging of brain tumor cells before and after invasive surgical intervention (e.g., biopsy) within the same living animal. This method allows studying the impact of these invasive surgical procedures on tumor cells&#39; migratory, invasive, and proliferative behavior at a single cell level.

Biopsies are standard of care for cancer treatment and are clinically beneficial as they allow solid tumor diagnosis, prognosis, and personalized ...

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 ...

Time-lapse Imaging of Primary Preneoplastic Mammary Epithelial Cells Derived from Genetically Engineered Mouse Models of Breast Cancer

Time-lapse imaging can be used to compare behavior of cultured primary preneoplastic mammary epithelial cells derived from different genetically engineered mouse models of breast cancer. For example, time between cell divisions (cell lifetimes), apoptotic cell numbers, evolution of morphological changes, and mechanism of colony formation can be quantified and compared in cells carrying specific genetic lesions. Primary mammary epithelial cell cultures are generated from mammary glands without palpable tumor. Glands are carefully resected with clear separation from adjacent muscle, lymph nodes are removed, and single-cell suspensions of enriched mammary epithelial cells are generated by mincing mammary tissue followed by enzymatic dissociation and filtration. Single-cell suspensions are plated and placed directly under a microscope within an incubator chamber for live-cell imaging. Sixteen 650 &mu;m x 700 &mu;m fields in a 4x4 configuration from each well of a 6-well plate are imaged every 15 min for 5 days. Time-lapse images are examined directly to measure cellular behaviors that can include mechanism and frequency of cell colony formation within the first 24 hr of plating the cells (aggregation versus cell proliferation), incidence of apoptosis, and phasing of morphological changes. Single-cell tracking is used to generate cell fate maps for measurement of individual cell lifetimes and investigation of cell division patterns. Quantitative data are statistically analyzed to assess for significant differences in behavior correlated with specific genetic lesions.

Time-lapse imaging is used to assess behavior of primary preneoplastic mammary epithelial cells derived from genetically engineered mouse models of breast cancer risk to determine if there are correlations between specific behavioral parameters and distinct genetic lesions.

Time-lapse  imaging can be used to compare behavior of cultured primary preneoplastic  mammary epithelial cells derived from different genetically ...

Medicine

Through the Looking Glass: Time-lapse Microscopy and Longitudinal Tracking of Single Cells to Study Anti-cancer Therapeutics

The response of single cells to anti-cancer drugs contributes significantly in determining the population response, and therefore is a major contributing factor in the overall outcome. Immunoblotting, flow cytometry and fixed cell experiments are often used to study how cells respond to anti-cancer drugs. These methods are important, but they have several shortcomings. Variability in drug responses between cancer and normal cells, and between cells of different cancer origin, and transient and rare responses are difficult to understand using population averaging assays and without being able to directly track and analyze them longitudinally. The microscope is particularly well suited to image live cells. Advancements in technology enable us to routinely image cells at a resolution that enables not only cell tracking, but also the observation of a variety of cellular responses. We describe an approach in detail that allows for the continuous time-lapse imaging of cells during the drug response for essentially as long as desired, typically up to 96 hr. Using variations of the approach, cells can be monitored for weeks. With the employment of genetically encoded fluorescent biosensors numerous processes, pathways and responses can be followed. We show examples that include tracking and quantification of cell growth and cell cycle progression, chromosome dynamics, DNA damage, and cell death. We also discuss variations of the technique and its flexibility, and highlight some common pitfalls.

Here, we describe a method of long-term time-lapse microscopy to longitudinally track single cells in response to anti-cancer therapeutics.

The response of single cells to anti-cancer drugs contributes significantly in determining the population response, and therefore is a major contributing ...

Extended Time-lapse Intravital Imaging of Real-time Multicellular Dynamics in the Tumor Microenvironment

In the tumor microenvironment, host stromal cells interact with tumor cells to promote tumor progression, angiogenesis, tumor cell dissemination and metastasis. Multicellular interactions in the tumor microenvironment can lead to transient events including directional tumor cell motility and vascular permeability. Quantification of tumor vascular permeability has frequently used end-point experiments to measure extravasation of vascular dyes. However, due to the transient nature of multicellular interactions and vascular permeability, the kinetics of these dynamic events cannot be discerned. By labeling cells and vasculature with injectable dyes or fluorescent proteins, high-resolution time-lapse intravital microscopy has allowed the direct, real-time visualization of transient events in the tumor microenvironment. Here we describe a method for using multiphoton microscopy to perform extended intravital imaging in live mice to directly visualize multicellular dynamics in the tumor microenvironment. This method details cellular labeling strategies, the surgical preparation of a mammary skin flap, the administration of injectable dyes or proteins by tail vein catheter and the acquisition of time-lapse images. The time-lapse sequences obtained from this method facilitate the visualization and quantitation of the kinetics of cellular events of motility and vascular permeability in the tumor microenvironment.

This protocol describes the use of multiphoton microscopy to perform extended time-lapse imaging of multicellular interactions in real time, in vivo at single cell resolution.

In the tumor microenvironment, host stromal cells interact with tumor cells to promote tumor progression, angiogenesis, tumor cell dissemination and ...

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell types.

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification ...

Immunology and Infection

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

Precise control of the initiation and subsequent progression through the various phases of the cell cycle are of paramount importance in proliferating cells. Cell cycle division is an integral part of growth and reproduction and deregulation of key cell cycle components have been implicated in the precipitating events of carcinogenesis 1,2. Molecular agents in anti-cancer therapies frequently target biological pathways responsible for the regulation and coordination of cell cycle division 3. Although cell cycle kinetics tend to vary according to cell type, the distribution of cells amongst the four stages of the cell cycle is rather consistent within a particular cell line due to the consistent pattern of mitogen and growth factor expression. Genotoxic events and other cellular stressors can result in a temporary block of cell cycle progression, resulting in arrest or a temporary pause in a particular cell cycle phase to allow for instigation of the appropriate response mechanism. 
The ability to experimentally observe the behavior of a cell population with reference to their cell cycle progression stage is an important advance in cell biology. Common procedures such as mitotic shake off, differential centrifugation or flow cytometry-based sorting are used to isolate cells at specific stages of the cell cycle 4-6. These fractionated, cell cycle phase-enriched populations are then subjected to experimental treatments. Yield, purity and viability of the separated fractions can often be compromised using these physical separation methods. As well, the time lapse between separation of the cell populations and the start of experimental treatment, whereby the fractionated cells can progress from the selected cell cycle stage, can pose significant challenges in the successful implementation and interpretation of these experiments.
Other approaches to study cell cycle stages include the use of chemicals to synchronize cells. Treatment of cells with chemical inhibitors of key metabolic processes for each cell cycle stage are useful in blocking the progression of the cell cycle to the next stage. For example, the ribonucleotide reductase inhibitor hydroxyurea halts cells at the G1/S juncture by limiting the supply of deoxynucleotides, the building blocks of DNA. Other notable chemicals include treatment with aphidicolin, a polymerase alpha inhibitor for G1 arrest, treatment with colchicine and nocodazole, both of which interfere with mitotic spindle formation to halt cells in M phase and finally, treatment with the DNA chain terminator 5-fluorodeoxyridine to initiate S phase arrest 7-9. Treatment with these chemicals is an effective means of synchronizing an entire population of cells at a particular phase. With removal of the chemical, cells rejoin the cell cycle in unison. Treatment of the test agent following release from the cell cycle blocking chemical ensures that the drug response elicited is from a uniform, cell cycle stage-specific population. However, since many of the chemical synchronizers are known genotoxic compounds, teasing apart the participation of various response pathways (to the synchronizers vs. the test agents) is challenging. 
Here we describe a metabolic labeling method for following a subpopulation of actively cycling cells through their progression from the DNA replication phase, through to the division and separation of their daughter cells. Coupled with flow cytometry quantification, this protocol enables for measurement of kinetic progression of the cell cycle in the absence of either mechanically- or chemically- induced cellular stresses commonly associated with other cell cycle synchronization methodologies 10. In the following sections we will discuss the methodology, as well as some of its applications in biomedical research.

Tracking subtle changes in the progression and kinetics of cell cycle stages can be accomplished by use of a combination of metabolic labeling of nucleic acids with BrdU and total genomic DNA staining via Propidium Iodide. This method avoids the need of chemical synchronization of cycling cells, thereby preventing the introduction of non-specific DNA damage, which in turn affects cell cycle progression.

Precise control of the initiation and subsequent progression through the various phases of the cell cycle are of paramount importance in proliferating ...

Biology

Time-Resolved Fluorescence Imaging and Analysis of Cancer Cell Invasion in the 3D Spheroid Model

The invasion of cancer cells from the primary tumor into the adjacent healthy tissues is an early step in metastasis. Invasive cancer cells pose a major clinical challenge because no efficient method exist for their elimination once their dissemination is underway. A better understanding of the mechanisms regulating cancer cell invasion may lead to the development of novel potent therapies. Due to their physiological resemblance to tumors, spheroids embedded in collagen I have been extensively utilized by researchers to study the mechanisms governing cancer cell invasion into the extracellular matrix (ECM). However, this assay is limited by (1) a lack of control over the embedding of spheroids into the ECM; (2) high cost of collagen I and glass bottom dishes, (3) unreliable immunofluorescent labeling, due to the inefficient penetration of antibodies and fluorescent dyes and (4) time-consuming image processing and quantification of the data. To address these challenges, we optimized the three-dimensional (3D) spheroid protocol to image fluorescently labeled cancer cells embedded in collagen I, either using time-lapse videos or longitudinal imaging, and analyze cancer cell invasion. First, we describe the fabrication of a spheroid imaging device (SID) to embed spheroids reliably and in a minimal collagen I volume, reducing the assay cost. Next, we delineate the steps for robust fluorescence labeling of live and fixed spheroids. Finally, we offer an easy-to-use Fiji macro for image processing and data quantification. Altogether, this simple methodology provides a reliable and affordable platform to monitor cancer cell invasion in collagen I. Furthermore, this protocol can be easily modified to fit the users&#8217; needs.

Presented here is a protocol for the fabrication of a spheroid imaging device. This device enables dynamic or longitudinal fluorescence imaging of cancer cell spheroids. The protocol also offers a simple image processing procedure for the analysis of cancer cell invasion.

The invasion of cancer cells from the primary tumor into the adjacent healthy tissues is an early step in metastasis. Invasive cancer cells pose a major ...

Quantifying Cell Death Initiation Through Continuous Imaging

Studying Cell Death Initiation Using a Digital Microscope

Hypersensitive response (HR)-conferred resistance is an effective defense response that can be determined by the N resistance genes. HR is manifested as the formation of cell death zones on inoculated leaves. Here, a protocol for studying the rate of cell death initiation by imaging inoculated leaves in the time between the cell death initiation and the cell death appearance using a digital microscope is presented. The digital microscope enables a continuous imaging process in desired intervals, which allows an accurate determination of cell death initiation rate up to minutes exactly, as opposed to hours in traditional methods. Imaging with the digital microscope is also independent of light and can therefore be used during day and night without disturbing the circadian rhythm of the plant. Different pathosystems resulting in programmed cell death development could be studied using this protocol with minor modifications. Overall, the protocol thus allows simple, accurate, and inexpensive identification of cell death initiation rate.

Author Spotlight: A Streamlined Approach to Studying Cell Death Initiation in Hypersensitive Response 

Here we present a protocol for studying the rate of programmed cell death initiation by continuously imaging inoculated leaves following cell death induction.

Hypersensitive response (HR)-conferred resistance is an effective defense response that can be determined by the N resistance genes. HR is manifested as ...