This work was supported by grants from the National Institutes of Health (U54CA143906 to K.G.P., O.J.T.M and R01HL101972 to O.J.T.M.) and an Oregon Medical Research Foundation Early Clinical Investigator Award (K.G.P.). O.J.T.M. is an American Heart Association Established Investigator (13EIA12630000). We thank Dr. Eric Anderson of the Knight Cancer Institute for preparing cell samples used in this work.

1. Microscope Specifications
To carry out imaging in the correct fashion the microscope should have the following specifications:
<ol>
	<li>Possess both differential interference contrast (DIC) and brightfield (BF) contrast.</li>
	<li>Have computer-controlled z-axis movement.</li>
	<li>Have an adjustable aperture stop to vary the condenser lens numerical aperture. An aperture with a graded rule or electronic readout is required to know the value of the numerical aperture. The numerical aperture should range from 0.1 for NIQPM up to 0.9 (or higher) for HTDIC.</li>
	<li>The microscope should have a narrow band color filter to be utilized for bright field imaging. This filter is required to fix the refractive increment, a wavelength-dependent facture used to convert phase to mass density for NIQP.</li>
</ol>
2. Differential Interference Contrast (DIC) Z-stack Acquisition
<ol>
	<li>Open the SlideBook software and create a new slide for image collection.</li>
	<li>Next, open the Focus Window. Under the Filter Set section, select DIC. On the Scope tab, under the Condenser section, adjust the Aperture slide bar to the furthest right position (open all the way). This provides high numerical aperture illumination and enhances optical sectioning of the specimen.</li>
	<li>Focus on the sample using a DIC objective. Adjust the halogen lamp intensity until the sample is easily visible. To ensure the camera is not being saturated, select the Camera tab in the Focus Window and check that the histogram of pixel intensities is within the dynamic range of the camera.</li>
	<li>Open the Image Capture window. In the Capture Type section, check the 3D box. In the 3D Capture section, select the Range around center and Return to current location boxes and specify the Z-direction range, number of planes, and step size. The Z-direction range should incorporate the entire sample.</li>
	<li>In the Filter Set section of the Image Capture window, check the DIC box and specify exposure time. In the Image Information section, name the image (optional), and select Start to initiate the imaging.</li>
</ol>
3. Bright Field (BF) Z-stack Acquisition
<ol>
	<li>Once the microscope has finished collecting the DIC Z-stack of the sample, open the Focus Window and select Open under the Filter Set section. Adjust the Aperture slide bar to the furthest left position (closed all the way) to provide low NA illumination.</li>
	<li>Change the microscope light-path filter to green. Adjust the halogen lamp intensity until the sample is visible. Ensure there is no saturation of the camera by selecting the Camera tab in the Focus Window and checking that the histogram of pixel intensities is within the specified range.</li>
	<li>Open the Image Capture window. The 3D Capture settings from the DIC Z-stack acquisition will be displayed.</li>
	<li>In the Filter Set section of the Image Capture window, check the OPEN box and specify exposure time. In the Image Information section, name the image (optional), and select Start to initiate Z-stack image acquisition.</li>
</ol>
4. Exporting Z-stack Images
<ol>
	<li>Open a Z-stack capture. Change the view to 100%. Adjust the histogram of the pixel values to be displayed between 0 and the maximum pixel value of the camera (4,095 for 12 bit cameras, 65,535 for 16 bit.).</li>
	<li>Select View &#62; Export &#62; TIFF Series. This will export the Z-stack as a series of TIFF images (one for each Z plane). Save the TIFF series in a separate folder with a name that has an underscore at the end (i.e.&#160;"DIC Stack 1_"). Repeat this with each DIC or BF Z-stack.</li>
</ol>
5. Volume Measurements
<ol>
	<li>Open the HTDIC MATLAB program entitled "JoVE_HTDIC_v1.m".</li>
	<li>Under Section 0 of the HTDIC program, update the dependencies directory variable. Copy and paste the directory containing the hilbert_transform_dic.m and sobel_edge_detect.m files from explorer (PC) in between the single quotes following "dependencies_directory =". ExecuteSection 0 of the JoVE_HTDIC_v1.m program.</li>
	<li>In Section 1, update the "images_directory". Again, copy and paste the directory containing the through-focus images in .tif form in between the single quotes. ONLY RUN SECTION 1 ONCE.</li>
	<li>Alignment and rotation of images for Hilbert transform Run Section 2 of the code. A dialog box titled "Define HTDIC Parameters" will appear. Five numbers are required from the user: the focal plane number where the DIC image of the sample is in focus, lateral resolution (&#956;m/pixel in the image), the axial resolution (this is 0.1 for image acquisition in 0.1 &#956;m axial steps), the rotation angle of the DIC image needed to perform the Hilbert transform, typical values are 45 and -135. Lastly, enter the region of interest size, this parameter defines the length of a side of a box that will subsequently appear - typical value is 400. Click Ok.</li>
	<li>An image of the DIC focal plane specified by the focal plane number will appear with a blue box. Position the box over the feature of interest, e.g. a cell. The box need not be square. Once the box has been positioned over the desired region, double click inside the box.</li>
	<li>Another figure will now appear: this figure contains a cropped and rotated image of the region of interest selected in the previous step. The contrast of the image should be such that dark features appear on the left while bright features appear on the right. Drag the blue box over the region of interest, reshape as necessary.</li>
	<li>Section 3 generates a mask for the z-stack cube. There are two types of masks available: a rectangular mask to create a rectangle around the cell and a free-hand tool to outline the cell of interest by hand.</li>
	<li>To generate a rectangular mask, uncomment line 167 and comment line 170 (comment a line out by placing a "%" at the beginning of that line). Execute Section 3 of the program. Click in the image and drag the mouse to begin defining the rectangular mask. Double click on the box to accept it.</li>
	<li>To generate a hand-drawn mask, comment line 167 and uncomment line 170 and execute Section 3. Click and draw the desired mask with the mouse. Double click on the mask to accept it.</li>
	<li>Run Section 4to construct the Hilbert transformed DIC image stack and the corresponding DIC image stack of the area of interest. The mask constructed in Section 3 will be applied to the Hilbert transform DIC stack and the regular DIC stack when "maskON" is set to 1 at line 179. Setting "maskON" to 0 will construct image stacks without applying the mask.</li>
	<li>Run Section 5 to optimize the image segmentation of the xz cross sectional images of the region of interest. Figure 500, produced by the program, appears displaying three different types of contrast. The success of the algorithm to find the borders of the cell is reliant on a combination of the mask used and the value of "threshold" at line 229 of the program. Begin with a value of 0.5. Adjust the value of threshold and rerun this section of the program until the proper outlining is achieved in one of the columns.</li>
	<li>If the outlining was best in column 1, use Section 6 to determine the volume using DIC image segmentation. If column 2 gave optimal results, run Section 7 to determine cell volume from Hilbert-transformed DIC imagery. If column 3 gave the optimal results, run Section 8 to determine volume using Fourier-filtered Hilbert-transformed DIC imagery.</li>
	<li>The measured volume of the sample, reported in cubic &#956;m (fl) is presented in the title of Figure 600, 700, or 800 produced by the program depending on which volume measurement is chosen.</li>
</ol>
6. Mass Measurements
<ol>
	<li>Open the NIQPM MATLAB program entitled "JoVE_NIQPM_v1.m".</li>
	<li>Under Section 0 of the NIQPM program, update the location of three directories needed to run the program. These are the "dependencies_directory," the "brightfield_directory," and the "dic_directory."</li>
	<li>Next, run Section 1. This section of code generates a bright field image cube of the full field of the entire set of images. ONLY RUN SECTION 1 ONCE.</li>
	<li>Next, run Section 2. A dialog box titled "Define NIQPM Parameters" will appear. Four numbers are required from the user: the focal plane number where the bright field image of the sample is in focus, lateral resolution (&#956;m/pixel in the image), the axial resolution (this is 0.1 for image acquisition in 0.1 &#956;m axial steps), and the region of interest size, this parameter defines the length of a side of a box that will subsequently appear - a typical value is 200. Click Ok.</li>
	<li>An image of the bright field focal plane specified by the focal plane number will appear with a blue box.
	<ol>
		<li>If the image is not in focus, double click in the blue box and rerun Section 2, be sure to adjust the focal plane number in the dialog box.</li>
		<li>If the image is in focus, drag the blue box around the image and resize it by selecting the nodes of the box and dragging them as necessary. Position the box around the feature of interest, e.g. a cell. The box need not be square. Double click inside the box to accept it.</li>
	</ol>
	</li>
	<li>Next, run Section 3 to construct a stack of bright field images cropped to the region of interest.</li>
	<li>To generate the phase map, pseudo DIC image, and comparisons to bright field imagery and the true DIC image, run Section 4.</li>
	<li>With the pseudo DIC and true DIC images as similar as possible the mass density map, total mass, and histogram of the cell density can be determined by running Section 5a or 5b. Section 5a carries out automated boarder detection of the field - optimize "threshold" at line 300 - typical values vary between 0.1-1. Rerun this section as needed to optimize the value of threshold. Section 5b carries out mass determination, etc. after the user outlines the cell of interest.</li>
</ol>

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The authors have no financial interests in the work presented.

In general, NIQPM is a diffraction-limited technique validated on optical path-lengths ranging from 0.25-44.7. Validation was performed on n = 1.596 polystyrene spheres ranging in diameter from 0.11-9.8 &#956;m suspended in Fluoromount&#160;G (data not shown). Cells possess optical path-lengths that range from nearly 0-7.
When measuring the density distribution of a specimen one may find that the pseudo DIC image looks fine while the density map possesses unwanted background contributions. Thi...

The use of optical microscopy is now ubiquitous in the investigation of cellular organisms. Due to their low endogenous absorbance and weak scattering properties over the visible optical spectrum, cells do not strongly affect the amplitude of optical waves traversing them and thus appear semitransparent when imaged with standard bright field microscopes. Cellular specimens do, however, slow down optical waves traveling through them in a manner that is linearly related to the amount of local mass density in a particular region of space through which the light travels. Utilization of this heterogeneous time lag or &#8220;phase&#8221; profile of optical waves transmitted through microscopic specimens was first described in 1935 by Frits Zernike1 and experimentally realized by Zernikein 19422. Zernike was awarded the Nobel prize in 1953 for this achievement. Zeiss commercialized this modality in 19453.&#160;In 1955, Smith and Nomarski would present their initial work on the use4 and theory5 of differential interference contrast (DIC) microscopy, a modality that uses the spatial gradient of phase as a contrast mechanism. DIC was commercialized by Zeiss in 1965in close collaboration with Nomarski6. In 1981, two laboratories demonstrated the first recorded live cell DIC imagery with the incorporation of video cameras into the optics train of the DIC microscope7,8. The era of live cell imaging was born.
Since this time, the execution of both phase contrast and DIC on commercial microscopes has largely been unchanged. These methods are principally utilized by biologists to produce images of cells for qualitative purposes: monitoring of morphology, tracking of subcellular structures, and investigations of membrane dynamics9. These techniques are qualitative in their &#8220;off-the-shelf&#8221; configuration as both phase and DIC images are arbitrary functions of the light source intensity, the illumination optics settings, and CCD camera gain, gamma, and exposure settings.
A small legion of physicists and optical engineers has endeavored to make commercial imaging modalities quantitative. Among the first efforts were two letters to Nature in 1952 and 1953 in which the physician-turned-biophysicist Robert Barer demonstrated the use of phase microscopy to determine cellular dry mass of cells by estimating phase shifts through these cell types using a commercially available phase microscope10,11.&#160;The field has developed a multitude of techniques over the ensuing years based around three basic label-free contrast mechanisms: phase microscopy10,11, DIC microscopy12-17, and bright field18-22 to determine optical path-length, phase, mass density, refractive index, and cellular volume.
In parallel, a large collection of custom optical instruments have also been developed since the 1950s, and have made far-reaching optical measurements ranging from applications in parasite growth23, to documenting the cell cycle24 to investigating membrane dynamics of red blood cells25.&#160;In particular, the past ten years has seen a wealth of label-free quantitative microscopy in the form of diffraction phase microscopy26, tomographic phase microscopy27, digital holographic microscopy28, phase sensitive optical coherence microscopy29, spatial light interference microscopy30, Hilbert phase microscopy31, and quantitative phase microscopy32. Despite their collective successes, these instruments have not been disseminated to the larger field of biological researchers owing, mostly, to their complex instrumentation and computational requirements.
Herein we describe the use of a standard optical microscope to perform quantitative measurements of mass, volume, and density on cellular specimens through a combination of bright field and DIC imagery. Two primary approaches are presented: noninterferometric quantitative phase microscopy (NIQPM), to perform measurements of total cell mass and subcellular density distribution, and Hilbert transform differential interference contrast microscopy (HTDIC), to determine volume. The primary advantages of NIQPM and HTDIC lay in their technological accessibility. The imaging conditions required for their successful execution are within the scope of normal operation of most commercially available microscopes. Additionally, the post-processing algorithms are stable, quick, and robust &#8211; having been implemented in MATLAB using fast Fourier transform (FFT) based algorithms whenever possible.
NIQPM is a method to reconstruct phase and the axially integrated mass density of cellular specimens from bright field imagery. Summation of this axially integrated mass density over the area of the specimen gives the total dry mass content of the specimen. The NIQPM protocol is based on the experimental foundations laid by Paganin and Nugent18,19 &#8211; in which it was demonstrated that the phase profile of a cell could be reconstructed from through-focus bright field imagery of the sample &#8211; and the theoretical work of Frank, Altmeyer, and Wernicke20 &#8211; on solving the paraxial wave models in an efficient FFT-based manner. The connection of phase to the dry mass density is based upon the work by Barer10,11 and Popescu33.
Volumetric information can be obtained from through-focus DIC imagery under high NA illumination conditions that enable optical sectioning of the specimen along the optical axis. Hilbert transform processing on the DIC image stacks greatly enhances edge detection algorithms for localization of the specimen borders in three dimensions by separating the gray values of the specimen intensity from those of the background. This work originates with Arinson&#160;et al.34 although we have introduced both Fourier filtering methods to enhance contrast and a Sobel-based edge detection method for automated volumetric analysis of the sample. We have also validated HTDIC previously on polystyrene spheres ranging in size from the diffraction limit up to 20 &#956;m in diameter36.
While both NIQPM and HTDIC are technologically accessible owing to their development on commercial microscopes, the methods are fundamentally limited by the hardware configuration of the microscopes themselves. The primary limitations of these techniques are two-fold: slow z-stack acquisition time on commercial scopes, due to translation of the entire sample stage as opposed to just the objective lens, currently limits the investigation of phenomena faster than roughly 1 frame/minute, and secondly, diffraction effects restrict the utility of NIQPM and HTDIC to objects ranging in size from 0.2 up to 10 and 20 &#956;m in diameter, respectively. Hence, the specimen and its associated time dynamics of interest must meet certain size and temporal constraints to enable the use of these methods on typical &#8220;off-the-shelf&#8221; instruments. Excitingly, most fixed cellular specimens are readily investigated with these methods.
An overview of the NIQPM and HTDIC protocols are given in Figure 1. In Figure 2 we illustrate optimal and suboptimal through-focus imaging under both low and high NA illumination conditions for both bright field and DIC imagery. Figures 3 and 4 demonstrate the parameter dependence of the NIQPM algorithm highlighting successful and unsuccessful implementations. Figure 5 illustrates the steps involved in the HTDIC image-processing algorithm and demonstrates the optimal implementation of the algorithm to determine cellular volume.

<table border="0" cellpadding="0" cellspacing="0" width="675">
	<tbody>
		<tr>
			<td>Zeiss Axio Imager 2 microscope</td>
			<td>Carl Zeiss MicroImaging GmbH, Germany</td>
			<td>Axio Imager D2</td>
			<td>Microscope</td>
		</tr>
		<tr>
			<td>Green filter (&#955; = 540 &#177; 25 nm)</td>
			<td>Chroma Technology Corp., Bellows Falls, Vermont</td>
			<td>D540/25x</td>
			<td>Green filter</td>
		</tr>
		<tr>
			<td>SlideBook 5.5 software</td>
			<td>Intelligent Imaging Innovations, Denver, Colorado</td>
			<td></td>
			<td>Image acquistion software</td>
		</tr>
		<tr>
			<td>Polystyrene microspheres</td>
			<td>Bangs Laboratory, Inc., Fishers, IN</td>
			<td>PS06N</td>
			<td>Polystyrene spheres</td>
		</tr>
		<tr>
			<td>Fluoromount-G</td>
			<td>SouthernBiotech, Birmingham, Alabama</td>
			<td>0100-01</td>
			<td>Mounting media</td>
		</tr>
	</tbody>
</table>

quantitative optical microscopy: measurement of cellular biophysical features with a standard optical microscope

We describe the use of a standard optical microscope to perform quantitative measurements of mass, volume, and density on cellular specimens through a combination of bright field and differential interference contrast imagery. Two primary approaches are presented: noninterferometric quantitative phase microscopy (NIQPM), to perform measurements of total cell mass and subcellular density distribution, and Hilbert transform differential interference contrast microscopy (HTDIC)&#160;to determine volume. NIQPM is based on a simplified model of wave propagation, termed the paraxial approximation, with three underlying assumptions: low numerical aperture (NA) illumination, weak scattering, and weak absorption of light by the specimen. Fortunately, unstained cellular specimens satisfy these assumptions and low NA illumination is easily achieved on commercial microscopes. HTDIC is used to obtain volumetric information from through-focus DIC imagery under high NA illumination conditions. High NA illumination enables enhanced sectioning of the specimen along the optical axis. Hilbert transform processing on the DIC image stacks greatly enhances edge detection algorithms for localization of the specimen borders in three dimensions by separating the gray values of the specimen intensity from those of the background. The primary advantages of NIQPM and HTDIC lay in their technological accessibility using &#8220;off-the-shelf&#8221; microscopes. There are two basic limitations of these methods: slow z-stack acquisition time on commercial scopes currently abrogates the investigation of phenomena faster than 1 frame/minute, and secondly, diffraction effects restrict the utility of NIQPM and HTDIC to objects from 0.2 up to 10 (NIQPM) and 20 (HTDIC) &#956;m in diameter, respectively. Hence, the specimen and its associated time dynamics of interest must meet certain size and temporal constraints to enable the use of these methods. Excitingly, most fixed cellular specimens are readily investigated with these methods.

Correct sample illumination during through-focus image acquisition is critical to the successful implementation of the NIQPM and HTDIC algorithms. In Figure 2 we illustrate low and high NA illumination under both DIC and bright field contrast for a polystyrene sphere and the human colorectal adenocarcinoma cell line SW620. Figures 2A, 2C, 2I, and 2K demonstrate optimal imaging for NIQPM. Figures 2F, 2H, 2N, and 2P demons...

Watch this Scientific Journal Video about Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope at JoVE.com

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

We describe the use of a standard optical microscope to perform quantitative measurements of cellular mass, volume, and density through a combination of bright field and differential interference contrast imagery.

We describe the use of a standard optical microscope to perform quantitative measurements of mass, volume, and density on cellular specimens through a ...

quantitative-optical-microscopy-measurement-cellular-biophysical

Research

JoVE Journal

Bioengineering

15.4K Views.  Oregon Health & Science University, School of Medicine. We describe the use of a standard optical microscope to perform quantitative measurements of cellular mass, volume, and density through a combination of bright field and differential interference contrast imagery.

Video: Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Three-dimensional Optical-resolution Photoacoustic Microscopy

Optical microscopy, providing valuable insights at the cellular and organelle levels, has been widely recognized as an enabling biomedical technology. As the mainstays of in vivo three-dimensional (3-D) optical microscopy, single-/multi-photon fluorescence microscopy and optical coherence tomography (OCT) have demonstrated their extraordinary sensitivities to fluorescence and optical scattering contrasts, respectively. However, the optical absorption contrast of biological tissues, which encodes essential physiological/pathological information, has not yet been assessable. 
The emergence of biomedical photoacoustics has led to a new branch of optical microscopy optical-resolution photoacoustic microscopy (OR-PAM)1, where the optical irradiation is focused to the diffraction limit to achieve cellular1 or even subcellular2 level lateral resolution. As a valuable complement to existing optical microscopy technologies, OR-PAM brings in at least two novelties. First and most importantly, OR-PAM detects optical absorption contrasts with extraordinary sensitivity (i.e., 100%). Combining OR-PAM with fluorescence microscopy3 or with optical-scattering-based OCT4 (or with both) provides comprehensive optical properties of biological tissues. Second, OR-PAM encodes optical absorption into acoustic waves, in contrast to the pure optical processes in fluorescence microscopy and OCT, and provides background-free detection. The acoustic detection in OR-PAM mitigates the impacts of optical scattering on signal degradation and naturally eliminates possible interferences (i.e., crosstalks) between excitation and detection, which is a common problem in fluorescence microscopy due to the overlap between the excitation and fluorescence spectra. 
Unique for optical absorption imaging, OR-PAM has demonstrated broad biomedical applications since its invention, including, but not limited to, neurology5, 6, ophthalmology7, 8, vascular biology9, and dermatology10. In this video, we teach the system configuration and alignment of OR-PAM as well as the experimental procedures for in vivo functional microvascular imaging.

Optical-resolution photoacoustic microscopy (OR-PAM) is an emerging technology capable of imaging optical absorption contrasts in vivo with cellular resolution and sensitivity. Here, we provide a visualized instruction on the experimental protocols of OR-PAM, including system configuration, system alignment, typical in vivo experimental procedures, and functional imaging schemes.

Optical microscopy, providing valuable insights at the cellular and organelle levels, has been widely recognized as an enabling biomedical technology. As ...

A Cre-Lox P Recombination Approach for the Detection of Cell Fusion In Vivo

The ability of two or more cells of the same type to fuse has been utilized in metazoans throughout evolution to form many complex organs, including skeletal muscle, bone and placenta. Contemporary studies demonstrate fusion of cells of the same type confers enhanced function. For example, when the trophoblast cells of the placenta fuse to form the syncytiotrophoblast, the syncytiotrophoblast is better able to transport nutrients and hormones across the maternal-fetal barrier than unfused trophoblasts1-4. More recent studies demonstrate fusion of cells of different types can direct cell fate. The "reversion" or modification of cell fate by fusion was once thought to be limited to cell culture systems. But the advent of stem cell transplantation led to the discovery by us and others that stem cells can fuse with somatic cells in vivo and that fusion facilitates stem cell differentiation5-7. Thus, cell fusion is a regulated process capable of promoting cell survival and differentiation and thus could be of central importance for development, repair of tissues and even the pathogenesis of disease.
Limiting the study of cell fusion, is lack of appropriate technology to 1) accurately identify fusion products and to 2) track fusion products over time. Here we present a novel approach to address both limitations via induction of bioluminescence upon fusion (Figure 1); bioluminescence can be detected with high sensitivity in vivo8-15. We utilize a construct encoding the firefly luciferase (Photinus pyralis) gene placed adjacent to a stop codon flanked by LoxP sequences. When cells expressing this gene fuse with cells expressing the Cre recombinase protein, the LoxP sites are cleaved and the stop signal is excised allowing transcription of luciferase. Because the signal is inducible, the incidence of false-positive signals is very low. Unlike existing methods which utilize the Cre/LoxP system16, 17, we have incorporated a "living" detection signal and thereby afford for the first time the opportunity to track the kinetics of cell fusion in vivo.
To demonstrate the approach, mice ubiquitously expressing Cre recombinase served as recipients of stem cells transfected with a construct to express luciferase downstream of a floxed stop codon. Stem cells were transplanted via intramyocardial injection and after transplantation intravital image analysis was conducted to track the presence of fusion products in the heart and surrounding tissues over time. This approach could be adapted to analyze cell fusion in any tissue type at any stage of development, disease or adult tissue repair.

A Cre-Lox P Recombination Approach for the Detection of Cell Fusion In Vivo

A method to track cell fusion in living organisms over time is described. The approach utilizes Cre-LoxP recombination to induce luciferase expression upon cell fusion. The luminescent signal generated can be detected in living organisms using biophotonic imaging systems with a sensitivity of detection of &tilde;1,000 cells in peripheral tissues.

The ability of two or more cells of the same type to fuse has been utilized in metazoans throughout evolution to form many complex organs, including ...

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

Collagen fibrils are present in the extracellular matrix of animal tissue to provide structural scaffolding and mechanical strength. These native collagen fibrils have a characteristic banding periodicity of ~67 nm and are formed in vivo through the hierarchical assembly of Type I collagen monomers, which are 300 nm in length and 1.4 nm in diameter. In vitro, by varying the conditions to which the monomer building blocks are exposed, unique structures ranging in length scales up to 50 microns can be constructed, including not only native type fibrils, but also fibrous long spacing and segmental long spacing collagen. Herein, we present procedures for forming the three different collagen structures from a common commercially available collagen monomer. Using the protocols that we and others have published in the past to make these three types typically lead to mixtures of structures. In particular, unbanded fibrils were commonly found when making native collagen, and native fibrils were often present when making fibrous long spacing collagen. These new procedures have the advantage of producing the desired collagen fibril type almost exclusively. The formation of the desired structures is verified by imaging using an atomic force microscope.

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

Simple and reproducible procedures are described for making three structurally distinct collagen assemblies from a common commercially available Type I collagen monomer. Native type, fibrous long spacing or segmental long spacing collagen can be constructed by varying the conditions to which the 300 nm long and 1.4 nm diameter monomer building block is exposed.

Collagen fibrils are present in the  extracellular matrix of animal tissue to provide structural scaffolding and  mechanical strength. These native ...

Preparation of Mica Supported Lipid Bilayers for High Resolution Optical Microscopy Imaging

Supported lipid bilayers (SLBs) are widely used as a model for studying membrane properties (phase separation, clustering, dynamics) and its interaction with other compounds, such as drugs or peptides. However SLB characteristics differ depending on the support used. 
Commonly used techniques for SLB imaging and measurements are single molecule fluorescence microscopy, FCS and atomic force microscopy (AFM). Because most optical imaging studies are carried out on a glass support, while AFM requires an extremely flat surface (generally mica), results from these techniques cannot be compared directly, since the charge and smoothness properties of these materials strongly influence diffusion. Unfortunately, the high level of manual dexterity required for the cutting and gluing thin slices of mica to the glass slide presents a hurdle to routine use of mica for SLB preparation. Although this would be the method of choice, such prepared mica surfaces often end up being uneven (wavy) and difficult to image, especially with small working distance, high numerical aperture lenses. Here we present a simple and reproducible method for preparing thin, flat mica surfaces for lipid vesicle deposition and SLB preparation. Additionally, our custom made chamber requires only very small volumes of vesicles for SLB formation. The overall procedure results in the efficient, simple and inexpensive production of high quality lipid bilayer surfaces that are directly comparable to those used in AFM studies.

We present a method of preparing mica supported lipid bilayers for high resolution microscopy. Mica is transparent and flat on an atomic scale, but rarely used in imaging because of handling difficulties; our preparation results in even deposition of the mica sheet, and reduces the material used in bilayer preparation.

Supported lipid bilayers (SLBs) are widely used as a model for studying membrane properties (phase separation, clustering, dynamics) and its interaction ...

Fluorescence-quenching of a Liposomal-encapsulated Near-infrared Fluorophore as a Tool for In Vivo Optical Imaging

Optical imaging offers a wide range of diagnostic modalities and has attracted a lot of interest as a tool for biomedical imaging. Despite the enormous number of imaging techniques currently available and the progress in instrumentation, there is still a need for highly sensitive probes that are suitable for in vivo imaging. One typical problem of available preclinical fluorescent probes is their rapid clearance in vivo, which reduces their imaging sensitivity. To circumvent rapid clearance, increase number of dye molecules at the target site, and thereby reduce background autofluorescence, encapsulation of the near-infrared fluorescent dye, DY-676-COOH in liposomes and verification of its potential for in vivo imaging of inflammation was done. DY-676 is known for its ability to self-quench at high concentrations. We first determined the concentration suitable for self-quenching, and then encapsulated this quenching concentration into the aqueous interior of PEGylated liposomes. To substantiate the quenching and activation potential of the liposomes we use a harsh freezing method which leads to damage of liposomal membranes without affecting the encapsulated dye. The liposomes characterized by a high level of fluorescence quenching were termed Lip-Q. We show by experiments with different cell lines that uptake of Lip-Q is predominantly by phagocytosis which in turn enabled the characterization of its potential as a tool for in vivo imaging of inflammation in mice models. Furthermore, we use a zymosan-induced edema model in mice to substantiate the potential of Lip-Q in optical imaging of inflammation in vivo. Considering possible uptake due to inflammation-induced enhanced permeability and retention (EPR) effect, an always-on liposome formulation with low, non-quenched concentration of DY-676-COOH (termed Lip-dQ) and the free DY-676-COOH were compared with Lip-Q in animal trials.

Fluorescence-quenching of a Liposomal-encapsulated Near-infrared Fluorophore as a Tool for In Vivo Optical Imaging

The use of fluorophores for in vivo imaging can be greatly limited by opsonization, rapid clearance, low detection sensitivity and cytotoxic effects on the host. Encapsulation of fluorophores in liposomes by film hydration and extrusion leads to fluorescence quenching and protection which enables in vivo imaging with high detection sensitivity.

Optical imaging offers a wide range of diagnostic modalities and has attracted a lot of interest as a tool for biomedical imaging. Despite the enormous ...

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Fluorescence assays are the most common readouts used in droplet microfluidics due to their bright signals and fast time response. Applications such as multiplex assays, enzyme evolution, and molecular biology enhanced cell sorting require the detection of two or more colors of fluorescence. Standard multicolor detection systems that couple free space lasers to epifluorescence microscopes are bulky, expensive, and difficult to maintain. In this paper, we describe a scheme to perform multicolor detection by exciting discrete regions of a microfluidic channel with lasers coupled to optical fibers. Emitted light is collected by an optical fiber coupled to a single photodetector. Because the excitation occurs at different spatial locations, the identity of emitted light can be encoded as a temporal shift, eliminating the need for more complicated light filtering schemes. The system has been used to detect droplet populations containing four unique combinations of dyes and to detect sub-nanomolar concentrations of fluorescein.

Multicolor fluorescence detection in droplet microfluidics typically involves bulky and complex epifluorescence microscope-based detection systems. Here we describe a compact and modular multicolor detection scheme that utilizes an array of optical fibers to temporally encode multicolor data collected by a single photodetector.

Fluorescence assays are the most common readouts used in droplet microfluidics due to their bright signals and fast time response. Applications such as ...

Targeted Plasma Membrane Delivery of a Hydrophobic Cargo Encapsulated in a Liquid Crystal Nanoparticle Carrier

The controlled delivery of drug/imaging agents to cells is critical for the development of therapeutics and for the study of cellular signaling processes. Recently, nanoparticles (NPs) have shown significant promise in the development of such delivery systems. Here, a liquid crystal NP (LCNP)-based delivery system has been employed for the controlled delivery of a water-insoluble dye, 3,3&#8242;-dioctadecyloxacarbocyanine perchlorate (DiO), from within the NP core to the hydrophobic region of a plasma membrane bilayer. During the synthesis of the NPs, the dye was efficiently incorporated into the hydrophobic LCNP core, as confirmed by multiple spectroscopic analyses. Conjugation of a PEGylated cholesterol derivative to the NP surface (DiO-LCNP-PEG-Chol) enabled the binding of the dye-loaded NPs to the plasma membrane in HEK 293T/17 cells. Time-resolved laser scanning confocal microscopy and F&#246;rster resonance energy transfer (FRET) imaging confirmed the passive efflux of DiO from the LCNP core and its insertion into the plasma membrane bilayer. Finally, the delivery of DiO as a LCNP-PEG-Chol attenuated the cytotoxicity of DiO; the NP form of DiO exhibited ~30-40% less toxicity compared to DiOfree delivered from bulk solution. This approach demonstrates the utility of the LCNP platform as an efficient modality for the membrane-specific delivery and modulation of hydrophobic molecular cargos.

A liquid crystal nanoparticle (LCNP) nanocarrier is exploited as a vehicle for the controlled delivery of a hydrophobic cargo to the plasma membrane of living cells.

The controlled delivery of drug/imaging agents to cells is critical for the development of therapeutics and for the study of cellular signaling processes. ...

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

The rapid development of new optical imaging techniques is dependent on the availability of low-cost, customizable, and easily reproducible standards. By replicating the imaging environment, costly animal experiments to validate a technique may be circumvented. Predicting and optimizing the performance of in vivo and ex vivo imaging techniques requires testing on samples that are optically similar to tissues of interest. Tissue-mimicking optical phantoms provide a standard for evaluation, characterization, or calibration of an optical system. Homogenous polymer optical tissue phantoms are widely used to mimic the optical properties of a specific tissue type within a narrow spectral range. Layered tissues, such as the epidermis and dermis, can be mimicked by simply stacking these homogenous slab phantoms. However, many in vivo imaging techniques are applied to more spatially complex tissue where three dimensional structures, such as blood vessels, airways, or tissue defects, can affect the performance of the imaging system. 
This protocol describes the fabrication of a tissue-mimicking phantom that incorporates three-dimensional structural complexity using material with optical properties of tissue. Look-up tables provide India ink and titanium dioxide recipes for optical absorption and scattering targets. Methods to characterize and tune the material optical properties are described. The phantom fabrication detailed in this article has an internal branching mock airway void; however, the technique can be broadly applied to other tissue or organ structures.

Optical tissue phantoms are essential tools for calibration and characterization of optical imaging systems and validation of theoretical models. This article details a method for phantom fabrication that includes replication of tissue optical properties and three-dimensional tissue structure.

The rapid development of new optical imaging techniques is dependent on the availability of low-cost, customizable, and easily reproducible standards. By ...

Light-sheet Fluorescence Microscopy for the Study of the Murine Heart

Light-sheet fluorescence microscopy has been widely used for rapid image acquisition with a high axial resolution from micrometer to millimeter scale. Traditional light-sheet techniques involve the use of a single illumination beam directed orthogonally at sample tissue. Images of large samples that are produced using a single illumination beam contain stripes or artifacts and suffer from a reduced resolution due to the scattering and absorption of light by the tissue. This study uses a dual-sided illumination beam and a simplified CLARITY optical clearing technique for the murine heart. These techniques allow for deeper imaging by removing lipids from the heart and produce a large field of imaging, greater than 10 x 10 x 10 mm3. As a result, this strategy enables us to quantify the ventricular dimensions, track the cardiac lineage, and localize the spatial distribution of cardiac-specific proteins and ion-channels from the post-natal to adult mouse hearts with sufficient contrast and resolution.

This study uses a dual-sided illumination light-sheet fluorescence microscopy (LSFM) technique combined with optical clearing to study the murine heart.

Light-sheet fluorescence microscopy has been widely used for rapid image acquisition with a high axial resolution from micrometer to millimeter scale. ...

Conducting Multiple Imaging Modes with One Fluorescence Microscope

Fluorescence microscopy is a powerful tool to detect biological molecules in situ and monitor their dynamics and interactions in real-time. In addition to conventional epi-fluorescence microscopy, various imaging techniques have been developed to achieve specific experimental goals. Some of the widely used techniques include single-molecule fluorescence resonance energy transfer (smFRET), which can report conformational changes and molecular interactions with angstrom resolution, and single-molecule detection-based super-resolution (SR) imaging, which can enhance the spatial resolution approximately ten&#160;to twentyfold compared to diffraction-limited microscopy. Here we present a customer-designed integrated system, which merges multiple imaging methods in one microscope, including conventional epi-fluorescent imaging, single-molecule detection-based SR imaging, and multi-color single-molecule detection, including smFRET imaging. Different imaging methods can be achieved easily and reproducibly by switching optical elements. This set-up is easy to adopt by any research laboratory in biological sciences with a need for routine and diverse imaging experiments at a reduced cost and space relative to building separate microscopes for individual purposes.

Here we present a practical guide of building an integrated microscopy system, which merges conventional epi-fluorescent imaging, single-molecule detection-based super-resolution imaging, and multi-color single-molecule detection, including single-molecule fluorescence resonance energy transfer imaging, into one set-up in a cost-efficient way.

Fluorescence microscopy is a powerful tool to detect biological molecules in situ and monitor their dynamics and interactions in real-time. In addition to ...