The authors would like to acknowledge financial support from the Ohio Third Frontier to the Ohio Imaging Research and Innovation Network (OIRAIN, 667750), and the National Natural Science Foundation of China (No. 81771876).

The protocol was approved by the Institutional Review Board at Wright State University, and informed consent was obtained from each participant prior to the experiment.
1. Subject preparation
<ol>
	<li>Power up the FD-fNIRS and DCS system to warm up for at least 10 min (see sections 2 and 3 for more details) before starting any measurements of the subject. An example of subject measurement with the compact DCS instrument is shown in Figure 1.</li>
	<li>First, use a tape measure to measure the distance between the nasion to inion on each subject&#8217;s head (Figure 2A).</li>
	<li>With the nasion as the starting point, mark the location that is 10% of the distance to the inion with an ink marker. This denotes the point between Fp1 and Fp2 of the EEG 10/20 montage (Figure 2A).</li>
	<li>Using an EEG 10/20 cap (see Table of Materials), adjust the cap so that the marked point is between Fp1 and Fp2.</li>
	<li>Mark the point between Fp1 and F7 (left cortex) and the point between Fp2 and F8 (right cortex). This represents the boundaries between the superior prefrontal cortex and dorsolateral prefrontal cortex (DLFC) and between the DLFC and inferior prefrontal cortex (IFC), respectively, for the left and right hemispheres (Figure 2A).</li>
	<li>Using a 3D-printed probe, place the multi-mode fibers (MMF) on the newly marked points (points &#8220;S&#8221; on Figure 2C) and connect each to the 785 nm laser light source (Figure 2B,C).</li>
	<li>Place the single-mode fibers (SMFs) 2.75 cm away from the MMF. Two fibers should be placed on the DLFC (locations &#8220;DLFC,1&#8221; and &#8220;DLFC,2&#8221;) and one on the IFC (location &#8220;IFC&#8221;). The placement of the SMF is replicated on each side of the cortex for a total of six SMFs (Figure 2c).</li>
	<li>Place another SMF 1 cm below the MMF at location &#8220;Ds&#8221; in both sides of the cortex (for the detection of blood flow in the scalp) and connect each of the SMFs to individual single-photon counting machines (Figure 2C).</li>
</ol>
p class=&#34;jove_title&#34;&#62;2. FD-fNIRS settings and calibration

<ol>
	<li>Turn off any lights and turn on the FD-fNIRS system to prepare for calibration. 
	CAUTION: As a general precaution, do not look directly at the light sources and fiber outputs, as this may cause eye damage. Use an IR sensor card (Table of Materials).</li>
	<li>Avoid unnecessary exposure of the detectors to room light levels to maintain noise-free operation and avoid damage to the detectors.</li>
	<li>Warm the light sources and detectors by powering up the system and letting it run for at least 10 min (preferably, 20 min minimum and 1 h maximum for optimal accuracy and stability) with the light on, modulation on, and detector voltage on.</li>
	<li>Run GUI-based data acquisition software. Adjust the detector gain to achieve an optimal signal with the sensor attached and secured to a calibration phantom (polydimethylsiloxane-based phantom of known optical properties, see Table of Materials) by pressing the &#8220;auto-bias&#8221; button. If the overvoltage warning flashes, lower the gain.</li>
	<li>After adjusting the detector gain to get the maximum signal, disconnect one of the source fibers from the detector and verify that the direct current (DC) is less than 20 counts per measurement period for the corresponding source fiber. If it is greater than this value, there may be excessive room light leaking into the detector45. If this is the case, the system should be powered down, then any excess light in the room should be blocked/removed and steps 2.4&#8211;2.5 repeated.</li>
	<li>Verify the proper signal level from every source and detector. The system defines this as above 100 and below 1,500 counts per measurement cycle.</li>
	<li>Perform calibration by pressing the &#8220;Calibrate&#8221; button in the GUI. The system will take measurements and apply calibration factors to correctly measure the optical properties of the known phantom. These calibration factors are saved and applied automatically to the in vivo measurements.</li>
	<li>Log the calibration data, which will provide a record of system performance on a standard phantom.</li>
</ol>
3. DCS settings
CAUTION: As a general precaution, do not look at the light sources and fiber outputs directly to avoid potential eye damage. Use the IR sensor card (see Table of Materials).
<ol>
	<li>Avoid unnecessary exposure of the detectors (i.e., room light) to obtain accurate raw data for the autocorrelation curves and prevent damage to the detectors.</li>
	<li>Warm up the DCS laser light sources and SPCM (see Table of Materials) by switching them to the &#8220;on&#8221; position and allowing them to run for at least 10 min (preferably, 20 min minimum and 1 h maximum for optimal accuracy and stability).</li>
	<li>Run GUI-based DCS data acquisition software, which displays the photon counts for each detector and semi-quantitative real-time blood flow values. Adjust the fiber position, angle (fiber face should be perpendicular to the skin surface), and data acquisition timing to obtain a signal of at least 5,000 counts/s (for an adequate signal to noise ratio) and below 1,000,000 counts/s (to avoid damaging detectors) (Figure 3A).</li>
	<li>Verify sufficient photon count levels (from step 3.3) from each detector by checking the photon count level and near real-time autocorrelation curves shown on the monitor.</li>
	<li>Verify sufficient fiber contact without any ambient light leakage by checking the y-intercept of the autocorrelation curve displayed on the monitor. The optimal value is ~1.5 without the use of polarizers (Figure 3B).</li>
	<li>Verify that the probe and measurements are not prone to motion artifacts by tightening the elastic band so that it is tight enough to resist motion but loose enough to prevent any discomfort to the subject. The user should also check the autocorrelation curves on the monitor concurrently such that the autocorrelation curve decays to 1 for longer correlation time (&#964; &#62; 10 ms) (Figure 3C).</li>
</ol>
4. Data collection
<ol>
	<li>Instruct the subject to minimize any movements during the 8 min measurement.</li>
	<li>Turn off the lights and make sure that the subject is seated in a comfortable position with his/her eyes closed.</li>
	<li>Perform a baseline FD-fNIRS measurements using by placing the FD-fNIRS system optical probe on the forehead adjacent to the DCS probe. Then, press the &#8220;Acquire&#8221; button in the FD-fNIRS acquisition GUI. This data will provide static optical properties, absorption parameters, and scattering parameters (&#956;a,&#160;&#956;&#39;s) that will be used for quantification of the dynamic optical parameter, CBFi17,20.</li>
	<li>After completion of the FD-fNIRS measurements, begin data acquisition on the optical DCS measurements by pressing the &#8220;Run&#8221; button in the DCS data acquisition GUI. Collect data for a total of 8 min with a maximum 2 s integration time (less is preferred, depending on the signal-to-noise ratio for each subject).</li>
	<li>If necessary, repeat the experiment within 1 h of the initial experiment or repeat the experiment during a similar time of day to reduce external variations such as fatigue, stimulants, or temperature.</li>
</ol>
5. Data analysis
<ol>
	<li>For FD-fNIRS data, extract the optical absorption and scattering properties (&#956;a,&#160;&#956;&#39;s) that are processed by the slope method46,47,48,49,50,51,52,53.</li>
	<li>For DCS, since post-processing is needed, import the auto-correlation raw data from each of the eight channels into the data analysis software.</li>
	<li>CBF-related parameter quantification is detailed in recent reviews6,27,54. Briefly, from the normalized intensity autocorrelation function (g2 [r,&#964;]), extract the normalized diffuse electric field temporal autocorrelation function (g1 [r,&#964;]) using the Siegert relation: g2 (r,&#964;) = 1 + &#946; | g 1 (r,&#964;)|2. 
	NOTE: &#946; is a constant, proportional to the number of spatial modes detected6,17,27,55,56, ranges from 0 to 1, and obtained by fitting the (normalized) electric field autocorrelation function g1.</li>
	<li>To obtain a blood flow-related parameter (&#945;DB) from the fit, use the analytical solution for g16,27,54 and fit the data to the model or decay rate: 
	EQUATION ONE 
	NOTE: In the equation above, ko&#160;is the wavenumber of light in the medium, &#945; is a factor proportionate to tissue blood volume fraction, and DB is the effective Brownian coefficient. &#945;DB&#160;can be defined as the blood flow index (BFI)6,54 or CBFi17. Here, CBFi is used.</li>
	<li>Fit the model using the optical parameters obtained from FD-fNIRS. The main parameters to fit for are CBFi and &#946;. 
	NOTE: Figure 3A shows representative data that is sufficient for analysis. DCS data is discarded if (1) the autocorrelation function is significantly lower that 1.5 (&#946; &#60; 0.5) (i.e., in the case of Figure 3B, where the function is ~1.2, &#946; &#60; 0.2, due to room light leakage) or if (2) the autocorrelation curve does not decay to 1 for longer correlation time (&#964; &#62; 10 ms) (i.e., in the case of Figure 3C, where the motion artifact, such head or probe movement, leads to unusable data).</li>
	<li>Detrend the quantified results using a second-order polynomial fit to remove slow drift (Figure 4A).</li>
	<li>Use a zero-phase second-order Butterworth filter with a passband of 0.009&#8211;0.080 Hz to remove any unwanted brain frequencies such as Mayer waves (Figure 4A).</li>
	<li>Use linear regression to obtain the residuals from each channel against the short distance measurement to remove the superficial scalp signals on each side of the cortex (Figure 4B).</li>
	<li>Calculate the Pearson&#8217;s correlation coefficient between each pair of channels to identify the resting state functional connectivity between brain regions (Figure 5).</li>
	<li>Transform the correlation value into a z-value using a Fisher Z transformation and perform a t-test to obtain the p-value (Figure 5). Use false discovery rate (FDR) for multiple comparisons correction.</li>
</ol>

The authors declare no competing financial interests.

To determine whether CBF as measured by DCS accurately detected RSFC, two areas of the brain with known RSFC properties were examined. Functional connectivity between DLFC regions and between DLFC and IFC is assumed to exist57,58,59. Connectivity between two sites within the left and right DLFC was chosen, because the intra-regional connectivity is usually higher. Also, connectivity between the IFC and the DLFC was chosen, as th...

When the brain is in a resting state, it demonstrates a high synchronization of spontaneous activity in functionally related regions, which can be located close in proximity or from a distance. These in-sync regions are known as functional networks1,2,3,4,5,6,7,8,9. This phenomenon was first uncovered by a functional magnetic resonance imaging (fMRI) study using blood oxygen level-dependent (BOLD) signals that indicate oxygenation levels of the cerebral blood5,10, also known as resting state functional connectivity (RSFC). Abnormalities in RSFC have been associated with brain disorders such as autism11, Alzheimer&#8217;s12, and depression13. Thus, RSFC is a valuable tool for studying patients with disorders who have trouble performaning task-based assessments. However, many patients, such as young autistic children, are poor candidates for assessment by fMRI, as it requires remaining still inside of a confined space for extended periods of time14,15. Optical imaging is fast and wearable; thus, it is suitable for a majority of patients, particularly the pediatric population16,17,18,19,20,21,22,23,24. Utilizing these advantages, functional near-infrared spectroscopy (fNIRS), which can quantify hemoglobin concentration and oxygen saturation parameters in the brain, is used to measure RSFC in humans (including the pediatric population4,8,25 and patients with autism11).
Optical diffuse correlation spectroscopy (DCS), a relatively new optical technique, can quantify cerebral blood flow, which is an important parameter that associates oxygen supply with metabolism6,17,26,27,28,29. Optical flow contrast quantified by DCS has been shown to have higher sensitivity in the brain compared to oxygenation contrast30. Thus, utilizing DCS-derived CBF parameters for assessing RSFC is advantageous.
DCS is sensitive to moving blood cells. When diffusing photons scatter from moving blood cells, this causes the intensity of detected light to fluctuate over time. DCS measures a time-based intensity autocorrelation function and its decay rate are dependent on the optical parameters and blood flow. These values are ultimately used to obtain the cerebral blood flow index (CBFi). With faster moving blood cells, the intensity autocorrelation function decays faster. Therefore, information about motion deep beneath the tissue surface can be derived (e.g., in the brain) from measurements of diffusing light fluctuations over time27,31,32,33,34,35. DCS is a technique complementary to the widely known fNIRS that measures blood oxygenation17,36. Since both fNIRS and DCS are optical brain imaging techniques with high temporal resolution in the range of milliseconds, the optical imaging set-ups are far less sensitive to motion artifacts than fMRI. They have also been successfully used for functional brain imaging in pediatric populations, including very young infants16. Previously, superficial blood flow measurements have been used to assess RSFC in preclinical studies in mice37. Here, blood flow parameters are used to quantify RSFC in nine healthy adults as a proof-of-concept study38,39.
In this study, a commercial FD-fNIRS system and custom DCS system is used (see Table of Materials). The DCS that was built in-house is comprised of two 785 nm, 100 mW, long coherence length continuous-wave lasers that are coupled to an FC connector and eight single-photon counting machines (SPCM) connected to an auto-correlator. A custom software graphical user interface (GUI) was also made specifically for this system to display and save the photon counts, autocorrelation curves, and semi-quantitative blood flow of each SPCM channel in real-time. The parts in this system are commonly used for DCS16,17,31,32,40,42,43,44, and the results obtained have also been verified in-house and used in a recent study39.

<table border="0" cellpadding="0" cellspacing="0" style="width:1012px;" width="1011">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">3D Printed Probe</td>
			<td style="width:121px;">In-house</td>
			<td style="width:272px;">N/A</td>
			<td style="width:339px;">3D printed PLA probe (Craftbot, Craft unique)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">785nm, 100mW, CW, FC coupled Laser</td>
			<td style="width:121px;">CrystaLaser</td>
			<td style="width:272px;">DL785-100-S</td>
			<td>DCS component (light source)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Auto-correlator</td>
			<td style="width:121px;">Correlator.com</td>
			<td style="width:272px;">Flex05-8ch</td>
			<td>DCS component (output g2 curve to PC)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Data Acquisition GUI</td>
			<td>In-house</td>
			<td>N/A</td>
			<td>GUI coded in LabVIEW to run the DCS system</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Data analysis software</td>
			<td>In-house</td>
			<td>N/A</td>
			<td>Matlab code used for obtaining RSFC results</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">EEG Electrode Cap</td>
			<td>OpenBCI</td>
			<td>N/A</td>
			<td>EEG mesh cap with standard 10/20 positions</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Multi-mode fiber</td>
			<td style="width:121px;">OZ Optics</td>
			<td style="width:272px;">QMMJ-3,2.5-IRVIS-600/630-3PCBK-3</td>
			<td>DCS component (source fiber)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Oxiplex calibration phantom</td>
			<td>ISS</td>
			<td>75019, 75020</td>
			<td>Set of 2 PDMS Calibration Phantom</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Oxiplex muscle probe</td>
			<td style="width:121px;">ISS</td>
			<td>86010</td>
			<td>4 channel muscle probe</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Oxiplex Oximeter</td>
			<td style="width:121px;">ISS</td>
			<td>95205</td>
			<td>FD-fNIRS (690nm, 830nm)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Power meter</td>
			<td style="width:121px;">Thorlabs</td>
			<td style="width:272px;">PM100D</td>
			<td>Laser light power adjuster</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Sensor card</td>
			<td style="width:121px;">Thorlabs</td>
			<td style="width:272px;">F-IRC1-S</td>
			<td>laser IR beam viewer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Single-mode fiber</td>
			<td style="width:121px;">OZ Optics</td>
			<td style="width:272px;">SMJ-3S2.5-780-5/125-3PCBK-3</td>
			<td>DCS component (detector fiber)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Single-Photon Counting Machine</td>
			<td>Excelitas</td>
			<td>SPMC-NIR-1x2-FC</td>
			<td>DCS component (detector)</td>
		</tr>
	</tbody>
</table>

cerebral blood flow-based resting state functional connectivity of the human brain using optical diffuse correlation spectroscopy

To obtain a comprehensive understanding of the human brain, utilization of cerebral blood flow (CBF) as a source of contrast is desired because it is a key hemodynamic parameter related to cerebral oxygen supply. Resting state low frequency fluctuations based on oxygenation contrast have been shown to provide correlations between functionally connected regions. The presented protocol uses optical diffuse correlation spectroscopy (DCS) to assess blood flow-based resting state functional connectivity (RSFC) in the human brain. Results of CBF-based RSFC in human frontal cortex indicate that intra-regional RSFC is significantly higher in the left and right cortices compared to inter-regional RSFC in both cortices. This protocol should be of interest to researchers who employ multi-modal imaging techniques to study human brain function, especially in the pediatric population.

The feasibility of using DCS to measure functional connectivity was successfully demostrated39. The resting state functional connectivity in the prefrontal cortices of nine subjects was measured. The results (mean &#177; SD) indicated a higher correlation in the intra-regional region of the left (0.64 &#177; 0.25) and right (0.62 &#177; 0.23) cortices, compared to the inter-regional region of the left (0.32 &#177; 0.32), (0.34 &#177; 0.27) and right (0.34 &#177; 0.29), (0.34 &#177; 0.26) cortices....

Watch this Scientific Journal Video about Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy at JoVE.com

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

This protocol demonstrates how to measure resting state functional connectivity in the human prefrontal cortex using a custom-made diffuse correlation spectroscopy instrument. The report also discuss practical aspects of the experiment as well as detailed steps for analyzing the data.

To obtain a comprehensive understanding of the human brain, utilization of cerebral blood flow (CBF) as a source of contrast is desired because it is a ...

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6.5K Views.  Wright State University. This protocol demonstrates how to measure resting state functional connectivity in the human prefrontal cortex using a custom-made diffuse correlation spectroscopy instrument. The report also discuss practical aspects of the experiment as well as detailed steps for analyzing the data.

Video: Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Improved Method for the Preparation of a Human Cell-based, Contact Model of the Blood-Brain Barrier

The blood-brain barrier (BBB) comprises impermeable but adaptable brain capillaries which tightly control the brain environment. Failure of the BBB has been implied in the etiology of many brain pathologies, creating a need for development of human in vitro BBB models to assist in clinically-relevant research. Among the numerous BBB models thus far described, a static (without flow), contact BBB model, where astrocytes and brain endothelial cells (BECs) are cocultured on the opposite sides of a porous membrane, emerged as a simplified yet authentic system to simulate the BBB with high throughput screening capacity. Nevertheless the generation of such model presents few technical challenges. Here, we describe a protocol for preparation of a contact human BBB model utilizing a novel combination of primary human BECs and immortalized human astrocytes. Specifically, we detail an innovative method for cell-seeding on inverted inserts as well as specify insert staining techniques and exemplify how we use our model for BBB-related research.

Establishment of human models of the blood-brain barrier (BBB) can benefit research into brain conditions associated with BBB failure. We describe here an improved technique for preparation of a contact BBB model, which permits coculturing of human astrocytes and brain endothelial cells on the opposite sides of a porous membrane.

The blood-brain barrier (BBB) comprises impermeable but adaptable brain capillaries which tightly control the brain environment. Failure of the BBB has ...

Nanomanipulation of Single RNA Molecules by Optical Tweezers

A large portion of the human genome is transcribed but not translated. In this post genomic era, regulatory functions of RNA have been shown to be increasingly important. As RNA function often depends on its ability to adopt alternative structures, it is difficult to predict RNA three-dimensional structures directly from sequence. Single-molecule approaches show potentials to solve the problem of RNA structural polymorphism by monitoring molecular structures one molecule at a time. This work presents a method to precisely manipulate the folding and structure of single RNA molecules using optical tweezers. First, methods to synthesize molecules suitable for single-molecule mechanical work are described. Next, various calibration procedures to ensure the proper operations of the optical tweezers are discussed. Next, various experiments are explained. To demonstrate the utility of the technique, results of mechanically unfolding RNA hairpins and a single RNA kissing complex are used as evidence. In these examples, the nanomanipulation technique was used to study folding of each structural domain, including secondary and tertiary, independently. Lastly, the limitations and future applications of the method are discussed.

Optical tweezers have been used to study RNA folding by stretching individual molecules from their 5&#8217; and 3&#8217; ends. Here common procedures are described to synthesize RNA molecules for tweezing, calibration of the instrument, and methods to manipulate single molecules.

A large portion of the human genome is transcribed but not translated. In this post genomic era, regulatory functions of RNA have been shown to be ...

Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum

For a biosensing platform to have clinical relevance in point-of-care (POC) settings, assay sensitivity, reproducibility, and ability to reliably monitor analytes against the background of human serum are crucial.
Nanoimprinting lithography (NIL) was used to fabricate, at a low cost, sensing areas as large as 1.5 mm x 1.5 mm. The sensing surface was made of high-fidelity arrays of nanoholes, each with an area of about 140 nm2. The great reproducibility of NIL made it possible to employ a one-chip, one-measurement strategy on 12 individually manufactured surfaces, with minimal chip-to-chip variation. These nanoimprinted localized surface plasmon resonance (LSPR) chips were extensively tested on their ability to reliably measure a bioanalyte at concentrations varying from 2.5 to 75 ng/mL amidst the background of a complex biofluid-in this case, human serum. The high fidelity of NIL enables the generation of large sensing areas, which in turn eliminates the need for a microscope, as this biosensor can be easily interfaced with a commonly available laboratory light source. These biosensors can detect cardiac troponin in serum with a high sensitivity, at a limit of detection (LOD) of 0.55 ng/mL, which is clinically relevant. They also show low chip-to-chip variance (due to the high quality of the fabrication process). The results are commensurable with widely used enzyme-linked immunosorbent assay (ELISA)-based assays, but the technique retains the advantages of an LSPR-based sensing platform (i.e., amenability to miniaturization and multiplexing, making it more feasible for POC applications).

This work describes a nanoimprinting lithography method to fabricate high-quality sensing arrays that work on the principle of extraordinary optical transmission. The biosensor is low-cost, robust, easy to use, and can detect cardiac troponin I in serum at clinically relevant concentrations (99th percentile cutoff &#8764;10-400 pg/mL, depending on the assay).

For a biosensing platform to have clinical relevance in point-of-care (POC) settings, assay sensitivity, reproducibility, and ability to reliably monitor ...

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy

This protocol describes how to make agarose-based tissue-mimicking phantoms and demonstrates how to determine their optical properties using a conventional optical system with an integrating sphere. Measuring systems for the acquisition of the diffuse reflectance and total transmittance spectra are constructed with a broadband white light source, a light guide, an achromatic lens, an integrating sphere, a sample holder, an optical fiber probe, and a multi-channel spectrometer. An acrylic mold consisting of two rectangular acrylic pieces and a U-shaped acrylic piece is constructed to create an epidermal phantom and a dermal phantom with whole blood. The application of a sodium dithionite (Na2S2O4) solution to the dermal phantom enables the researcher to deoxygenate hemoglobin in red blood cells distributed in the dermal phantom. The inverse Monte Carlo simulation with the diffuse reflectance and total transmittance spectra measured by a spectrometer with an integrating sphere is performed to determine the absorption coefficient spectrum &#181;a(&#955;) and the reduced scattering coefficient spectrum &#181;s&#39;(&#955;) of each layer phantom. A two-layered phantom mimicking the diffuse reflectance of human skin tissue is also demonstrated by piling up the epidermal phantom on the dermal phantom.

Here, we demonstrate how agarose-based tissue-mimicking optical phantoms are made and how their optical properties are determined using a conventional optical system with an integrating sphere.

This protocol describes how to make agarose-based tissue-mimicking phantoms and demonstrates how to determine their optical properties using a ...

Nondestructive Monitoring of Degradable Scaffold-Based Tissue-Engineered Blood Vessel Development Using Optical Coherence Tomography

Engineered vascular grafts with structural and mechanical properties similar to natural blood vessels are expected to meet the growing demand for arterial bypass. Characterization of the growth dynamics and remodeling process of degradable polymer scaffold-based tissue-engineered blood vessels (TEBVs) with pulsatile stimulation is crucial for vascular tissue engineering. Optical imaging techniques stand out as powerful tools for monitoring vascularization of engineered tissue enabling high-resolution imaging in real-time culture. This paper demonstrates a nondestructive and fast real-time imaging strategy to monitor the growth and remodeling of TEBVs in long-term culture by using optical coherence tomography (OCT). Geometric morphology is evaluated, including vascular remodeling process, wall thickness, and comparison of TEBV thickness in different culture time points and presence of pulsatile stimulation. Finally, OCT provides practical possibilities for real-time observation of the degradation of polymer in the reconstructing tissues under pulsatile stimulation or not and in each vessel segment, by compared with the assessment of polymer degradation using scanning electron microscopic(SEM) and polarized microscope.

A step by step protocol for nondestructive and long-period monitoring the process of vascular remodeling and scaffold degradation in real-time culture of biodegradable polymeric scaffold-based tissue-engineered blood vessels with pulsatile stimulation using optical coherence tomography is described here.

Engineered vascular grafts with structural and mechanical properties similar to natural blood vessels are expected to meet the growing demand for arterial ...

Lucifer Yellow - A Robust Paracellular Permeability Marker in a Cell Model of the Human Blood-brain Barrier

The blood-brain barrier BBB consists of endothelial cells that form a barrier between the systemic circulation and the brain to prevent the exchange of non-essential ions and toxic substances. Tight junctions (TJ) effectively seal the paracellular space in the monolayers resulting in an intact barrier. This study describes a LY-based fluorescence assay that can be used to determine its apparent permeability coefficient (Papp) and in turn can be used to determine the kinetics of the formation of confluent monolayers and the resulting tight junction barrier integrity in hCMEC/D3 monolayers. We further demonstrate an additional utility of this assay to determine TJ functional integrity in transfected cells. Our data from the LY Papp assay shows that the hCMEC/D3 cells seeded in a transwell setup effectively limit LY paracellular transport 7 days-post culture. As an additional utility of the presented assay, we also demonstrate that the DNA nanoparticle transfection does not alter LY paracellular transport in hCMEC/D3 monolayers.

We present a fluorescence assay to demonstrate that Lucifer Yellow (LY) is a robust marker to determine the apparent paracellular permeability of hCMEC/D3 cell monolayers, an in vitro model of the human blood-brain barrier. We used this assay to determine the kinetics of a confluent monolayer formation in cultured hCMEC/D3 cells.

The blood-brain barrier BBB consists of endothelial cells that form a barrier between the systemic circulation and the brain to prevent the exchange of ...

Intra-cardiac Side-Firing Light Catheter for Monitoring Cellular Metabolism using Transmural Absorbance Spectroscopy of Perfused Mammalian Hearts

Absorbance spectroscopy of cardiac muscle provides non-destructive assessment of cytosolic and mitochondrial oxygenation via myoglobin and cytochrome absorbance respectively. In addition, numerous aspects of the mitochondrial metabolic status such as membrane potential and substrate entry can also be estimated. To perform cardiac wall transmission optical spectroscopy, a commercially available side-firing optical fiber catheter is placed in the left ventricle of the isolated perfused heart as a light source. Light passing through the heart wall is collected with an external optical fiber to perform optical spectroscopy of the heart in near real- time. The transmission approach avoids numerous surface scattering interference occurring in widely used reflection approaches. Changes in transmural absorbance spectra were deconvolved using a library of chromophore reference spectra, providing quantitative measures of all the known cardiac chromophores simultaneously. This spectral deconvolution approach eliminated intrinsic errors that may result from using common dual wavelength methods applied to overlapping absorbance spectra, as well as provided a quantitative evaluation of the goodness of fit. A custom program was designed for data acquisition and analysis, which permitted the investigator to monitor the metabolic state of the preparation during the experiment. These relatively simple additions to the standard heart perfusion system provide a unique insight into the metabolic state of the heart wall in addition to conventional measures of contraction, perfusion, and substrate/oxygen extraction.

Here we introduce a method for using an intra-ventricle optical catheter in perfused hearts to perform absorbance spectroscopy across the heart wall. The data obtained provides robust information on tissue oxygen tension as well as substrate utilization and membrane potential simultaneously with cardiac performance measures in this ubiquitous preparation.

Absorbance spectroscopy of cardiac muscle provides non-destructive assessment of cytosolic and mitochondrial oxygenation via myoglobin and cytochrome ...

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Neutrophil swarming is a cooperative process by which neutrophils seal off a site of infection and promote tissue reorganization. Swarming has classically been studied in vivo in animal models showing characteristic patterns of cell migration. However, in vivo models have several limitations, including intercellular mediators that are difficult to access and analyze, as well as the inability to directly analyze human neutrophils. Because of these limitations, there is a need for an in vitro platform that studies swarming with human neutrophils and provides easy access to the molecular signals generated during swarming. Here, a multistep microstamping process is used to generate a bioparticle microarray that stimulates swarming by mimicking an in vivo infection. The bioparticle microarray induces neutrophils to swarm in a controlled and stable manner. On the microarray, neutrophils increase in speed and form stable swarms around bioparticle clusters. Additionally, supernatant generated by the neutrophils was analyzed and 16 proteins were discovered to have been differentially expressed over the course of swarming. This in vitro swarming platform facilitates direct analysis of neutrophil migration and protein release in a reproducible, spatially controlled manner.

This protocol generates bioparticle microarrays that provide spatially controlled neutrophil swarming. It provides easy access to the mediators that neutrophils release during migration and allows for quantitative imaging analysis.

Neutrophil swarming is a cooperative process by which neutrophils seal off a site of infection and promote tissue reorganization. Swarming has classically ...

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Mimicking in vivo environmental conditions is crucial for in vitro studies on complex life machinery. However, current techniques targeting live cells and organs are either highly expensive, like robotics, or lack nanoliter volume and millisecond time accuracy in liquid manipulation. We herein present the design and fabrication of a microfluidic system, which consists of 1,500 culture units, an array of enhanced peristaltic pumps and an on-site mixing modulus. To demonstrate the capacities of the microfluidic device, neural stem cell (NSC) spheres are maintained in the proposed system. We observed that when the NSC sphere is exposed to CXCL in day 1 and EGF in day 2, the round-shaped conformation is well maintained. Variation in the input order of 6 drugs causes morphological changes to the NSC sphere and the expression level representative marker for NSC stemness (i.e., Hes5 and Dcx). These results indicate that dynamic and complex environmental conditions have great effects on NSC differentiation and self-renewal, and the proposed microfluidic device is a suitable platform for high throughput studies on the complex life machinery.

We present a microfluidic system for high throughput studies on complex life machinery, which consists of 1500 culture units, an array of enhanced peristaltic pumps and an on-site mixing modulus. The microfluidic chip allows for the analysis of the highly complex and dynamic micro-environmental conditions in vivo.

Mimicking in vivo environmental conditions is crucial for in vitro studies on complex life machinery. However, current techniques targeting live cells and ...

Functional Transcranial Doppler Ultrasound for Monitoring Cerebral Blood Flow

Functional transcranial Doppler ultrasound (fTCD) is the use of transcranial Doppler ultrasound (TCD) to study neural activation occurring during stimuli such as physical movement, activation of tactile sensors in the skin, and viewing images. Neural activation is inferred from an increase in the cerebral blood flow velocity (CBFV) supplying the region of the brain involved in processing sensory input. For example, viewing bright light causes increased neural activity in the occipital lobe of the cerebral cortex, leading to increased blood flow in the posterior cerebral artery, which supplies the occipital lobe. In fTCD, changes in CBFV are used to estimate changes in cerebral blood flow (CBF).
With its high temporal resolution measurement of blood flow velocities in the major cerebral arteries, fTCD complements other established functional imaging techniques. The goal of this Methods paper is to give step-by-step instructions for using fTCD to perform a functional imaging experiment. First, the basic steps for identifying the middle cerebral artery (MCA) and optimizing the signal will be described. Next, placement of a fixation device for holding the TCD probe in place during the experiment will be described. Finally, the breath-holding experiment, which is a specific example of a functional imaging experiment using fTCD, will be demonstrated.

Functional transcranial Doppler ultrasound complements other functional imaging modalities, with its high temporal resolution measurement of stimulus-induced changes in cerebral blood flow within the basal cerebral arteries. This Methods paper gives step-by-step instructions for using functional transcranial Doppler ultrasound to perform a functional imaging experiment.&#160;

Functional transcranial Doppler ultrasound (fTCD) is the use of transcranial Doppler ultrasound (TCD) to study neural activation occurring during stimuli ...