Name: non-contact, label-free monitoring of cells and extracellular matrix using raman spectroscopy
Uploaded: 2012-05-29T13:00:00
Duration: 13 min 48 s
Description: Watch this Scientific Journal Video about Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy at JoVE.com

We thank Steffen Koch for his technical support and Shannon Lee Layland (both Fraunhofer IGB Stuttgart) for his helpful suggestions on the manuscript. This work was financially supported by the Attract program of the Fraunhofer-Gesellschaft and the BMBF (both to K.S.-L.).

1. Preparation of the Biological Sample
<ol>
	<li>Preparation of living cells
	<ol>
		<li>Preparation of adherent cells
		<ol>
			<li>Seed in vitro-cultured or freshly isolated cells on a glass bottom dish (Greiner BioOne/Germany) and incubate them at 37 &deg;C and 5% CO2 until cell attachment is completed.</li>
			<li>Remove culture medium and wash gently three times with PBS prior measurement. Keep the cells covered with either PBS or cell culture medium throughout the whole measurement procedure.</li>
		</ol>
		</li>
		<li>Preparation of cells in suspension
		<ol>
			<li>Detach in vitro-cultured cells according to common protocols (e.g. trypsin-EDTA, cell scraping), centrifuge the cells and re-suspend the obtained cell pellet in PBS or cell culture medium.</li>
			<li>Transfer 100 &mu;l of the cell suspension (concentration: max. 100,000 cells/ml) to a glass bottom dish.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Preparation of native tissues
	<ol>
		<li>After harvesting the tissues, transfer them to sterile, ice-cold PBS and keep them no longer than 12 hours at 4 &deg;C or on ice prior measurement. Previous experiments have shown that a prolonged storage time (cold ischemia time &gt; 12 hours at 4 &deg;C) resulted in spectral changes due to natural degradation processes.</li>
		<li>Measurements must be performed with the tissue covered in PBS or medium to avoid damage of the ECM proteins and cells due to tissue drying.</li>
	</ol>
	</li>
</ol>
2. Raman Spectrometer
Our customized Raman spectrometer combines a standard fluorescence microscope (Olympus IX 71) with a Raman spectrometer that allows the direct comparison of bright-field and fluorescence images with the Raman spectra. The basic set up consists of a 784 nm diode laser (Toptica photonics AG/Germany), a notch filter for the separation of the Raman-scattered light from the excitation light, a microscope and a spectrograph (Kaiser Optical Systems Inc., Ann Arbor/USA) with a charge coupled device (CCD camera) optimized for the detection of spectral information (F-view from Soft Imaging Systems/Germany).
3. Control of Laser Function
<ol>
	<li>Start the software Andor Solis (Andor/United Kingdom) and set the temperature of the CCD camera to -60 &deg;C to minimize the noise caused by thermally induced currents in the camera.</li>
	<li>Place a silicon wafer on the microscope stage for the calibration procedure.</li>
	<li>Turn the laser on and set the power to 85 mW.</li>
	<li>Use the software Cell B (Olympus/Germany) to focus the laser onto the wafer until XY appears.</li>
	<li>Measure the silicon wafer with a single integration time of 1 sec using a 60x air objective.</li>
	<li>Change the unit of the x-axis from pixel number to Raman shift (cm-1) in the Andor Solis software.</li>
	<li>Vary the laser focus of the silicon peak at 520 cm-1 in the collected spectrum in order to find the maximum possible intensity for this Raman band. The minimum amount of counts must be higher than 11,000 to have a successful calibration.</li>
</ol>
4. Raman Spectroscopic Measurements
All measurements are performed at room temperature.
<ol>
	<li>Basic settings
	<ol>
		<li>Use a 60x water immersion objective (Olympus/Germany) with a numerical aperture of 1.2 to collect the spectrum of the samples.</li>
		<li>Change the acquisition settings to 10 integrations/ 10 seconds for a total of 100 seconds per measurements.</li>
	</ol>
	</li>
	<li>Measurement of adherent cells
	<ol>
		<li>Take the glass bottom dish with the cells and place it on the microscope stage.</li>
		<li>In order to obtain a better signal and ensure reproducibility, focus the laser on the cell nucleus, turn the microscope light off and start collecting the spectrum.</li>
		<li>Measure a reference spectrum of the background every 10 spectra by moving the laser focus beside the cell. It is important to consider that when changing the focus a new background must be collected for each focus depth.</li>
	</ol>
	</li>
	<li>Measurement of cells in suspension
	<ol>
		<li>Transfer 100 &mu;l of the cell suspension into the glass bottom dish and place it onto the microscope stage.</li>
		<li>Focus the laser on the center of the cell, turn the microscope light off and start collecting the spectrum.</li>
		<li>Measure a reference spectrum of the background every 10 spectra by moving the laser focus beside the cell. When changing the focus, a new background must be collected for the new focus depth.</li>
	</ol>
	</li>
	<li>Measurement of native tissues
	<ol>
		<li>Take the sample and place it into a glass bottom dish. The region of interest (ROI) should be oriented facing the bottom of the dish.</li>
		<li>Fill the dish with enough PBS to cover the sample.</li>
		<li>Place a cover glass over the sample to avoid any movement of the sample during measurements.</li>
		<li>Set the laser focus into the structure of interest (depth resolution is laser- and tissue-dependent) and start collecting the spectra.</li>
		<li>Collect a reference spectrum of the background every 10 spectra by moving the laser focus out of the whole tissue area. When changing the focus, a new background must be collected for the new focus depth.</li>
	</ol>
	</li>
	<li>Measurement of immunofluorescence (IF)-labeled cryosections
	<ol>
		<li>Section fresh, snap-frozen tissue samples using a standard cryotom and mount them on silica-coated cover glasses.</li>
		<li>Stain the cryosections following a routine protocol for IF, employing only a short fixation step (max. 10 minutes with 4% paraformaldehyde) and using appropriate antibodies for the detection of the protein of interest.</li>
		<li>Perform Raman measurements focusing in the area where fluorescence occurs.</li>
	</ol>
	</li>
	<li>Elastin degradation experiments
	<ol>
		<li>Place the ventricularis of the dissected porcine aortic valve leaflets (elastin-rich, blood inflow-side layer of the heart valve leaflet) facing to the bottom of the glass bottom dish.</li>
		<li>Measure the native tissue as a 'non-incubated control' at 30 random points across the whole tissue surface focusing in the fibrillar structures.</li>
		<li>Divide the sample into 3 sections and place them into separate 2.5 ml Eppendorf tubes filled with 2 ml of an elastase solution (5 U/ ml, Worthington/Germany).</li>
		<li>Incubate the tissue for either 15 or 30 minutes at 37 &deg;C.</li>
		<li>After incubation for either 15 or 30 minutes, remove the tissues from the Eppendorf tube and wash carefully with PBS in order to completely stop the enzymatic reaction.</li>
		<li>Measure each sample at 30 random points, focusing in the fibrillar structures.</li>
	</ol>
	</li>
</ol>
5. Data Processing and Analysis
<ol>
	<li>Raman spectra processing 
	The pre-treatment of the generated spectra was performed using OPUS software (Bruker Optik GmbH/Germany).
	<ol>
		<li>In order to reduce interfering signals from the glass and medium as well as to avoid variations caused by changes in the focus during measurements, subtract the corresponding background spectrum from the collected spectra.</li>
		<li>Reduce the spectra to the wavenumber region between 400-1800 cm-1, which offers the highest amount of information.</li>
		<li>If needed, normalize the spectra to the maximum peak. Normalization factors out the intensity fluctuations and systematic failures, simplifying the detection of structural changes in the sample spectra.</li>
		<li>Perform a baseline correction to increase the comparability between different experiments.</li>
	</ol>
	</li>
	<li>Raman spectra analysis 
	The Raman spectra were analyzed using PCA with The Unscrambler (CAMO/ Norway) software. This multivariate analysis detects differences and similarities within the spectral data sets. Every spectrum is plotted as a single point in a multidimensional space based on the collected counts for every Raman shift. Each principle component (PC) describes a certain quantity of the total information contained in the original data. The first PC is the one that contains the highest source of variation. Each following PC contains, in order, less information than the previous one. Every variable has a score and a loading on each PC. By plotting PCs (=scores), important sample correlations can be exposed. The loadings describe the contribution of each analyzed variable to the PCA.
	<ol>
		<li>Label each group of measurements by creating row ranges for every sample group.</li>
		<li>Use the following basic settings for the PCA: cross validation, the NIPALS algorithm, no rotation and start the analysis. These settings are spectra dependent.</li>
		<li>Perform the PCA.</li>
	</ol>
	</li>
</ol>
6. Representative Results
Raman spectra generated from adherent cells often reveal a low signal-to-noise ratio and a low overall signal intensity (Fig. 1).11 Due to the fact that the laser focus has to be set near the glass bottom, the influence of the interfering glass signal is rather high, causing masking of the actual sample signal. Consequently, the sample signal might be minimized or even eliminated during a subsequent background subtraction. Thus, we prefer to use cells in suspension for our Raman spectroscopic analysis, as they allow the detection of more detailed spectral information. However, the spectra of adherent and suspension cells exhibit the same main peaks differing only in their intensities.
For the characterization of different cell types within a suspension, no pre-treatment is required. The mean Raman spectra and standard deviations of human fibroblasts, mesenchymal stem cells (MSCs), chondrocytes and keratinocytes measured in suspension are depicted in Figure 2. All Raman spectra are similarly structured, with peaks originating from typical biomolecules such as proteins, nucleic acids and lipids (see Table 1).12 For these cell types, the spectral region between 600 and 1800 cm-1 contains most relevant spectral information, by which clear differences are detectable between the different cell types (Fig. 2A). Exemplary, we highlighted one spectral region (1280-1350 cm-1) displaying clear structural differences, which is assignable to molecular vibrations of collagen and lipids. In contrast, morphological analyses are not suitable for the identification and distinction of most cells (Fig. 2B-I). While the difference between chondrocytes and skin cells is observable (Fig. 2D,H versus B,F and E,I), fibroblasts and MSCs are difficult to separate using solely bright-field micrsocopy (Fig. 2B,F versus C,G).13
Raman spectroscopic analysis of native tissue, particularly of ECM proteins, requires that a ROI can be visualized by bright-field imaging in order to be able to focus on the respective structure. For the assignment of a protein to a specific fingerprint spectrum, we generated Raman spectra of commercially available pure proteins and immunohistologically stained cryosections. Here, we identified the fingerprint spectra of elastic fibers within native tissues comparing lyophilized elastin and immunofluorescence-stained cryosections employing an antibody against elastin. However, since elastin features a high autofluorescence, which is reflected in the Raman spectra, the data analysis is challenging (Fig. 3A). To reduce the systematic failure due to sample-specific properties such as autofluorescence, an appropriate processing of the data sets is crucial. In our data analyses, we used normalization to eliminate the significantly higher signal intensity of the pure elastin protein, and thus, we were able to generate comparable Raman spectra (Fig. 3B). Elastin is one of the most stable ECM proteins in the body and is therefore very difficult to degrade.14 In our experimental set up, we induced elastin degradation in healthy porcine aortic valve leaflets by performing an enzymatic digestion. Applying the multivariate analysis PCA, we identified significant differences between the Raman spectra of enzymatically-treated samples and native controls (Fig. 3C). These spectral differences were observed in the loading spectrum at 861, 1003 and 1664 cm-1. Expected structural changes in the elastin-containing fibers due to extended exposure times to elastase were shown by HART's staining (Fig. 4), which were also reflected in more distinct separable score clusters (Fig. 3C).
<img alt="Figure 1" src="/files/ftp_upload/3977/3977fig1.jpg" /> 
Figure 1. Mean Raman spectra of detached and adherent fibroblasts.
<img alt="Figure 2" src="/files/ftp_upload/3977/3977fig2.jpg" /> 
Figure 2. (A) Mean Raman spectra and standard deviations of four different primary isolated cell types (fibroblasts, MSCs, chondrocytes and keratinocytes). The frame highlights the spectral region of 1280 - 1350 cm-1 with structural differences. (B-E) Bright-field images of detached (B) fibroblasts, (C) MSCs, (D) chondrocytes and (E) keratinocytes. Scale bar equals 20 &mu;m. (F-I) Bright-field images of adherent (F) fibroblasts, (G) MSCs, (H) chondrocytes and (I) keratinocytes. Scale bar equals 200 &mu;m.
<img alt="Figure 3" src="/files/ftp_upload/3977/3977fig3.jpg" /> 
Figure 3. (A) Raman spectra without normalization of lyophilized elastin (blue line), immunofluorescence (IF)-labeled cryosections (orange line) and elastic fibers (red line) measured within native aortic valve leaflets. The high signal intensity of lyophilized elastin is caused by autofluorescence. (B) Raman spectra after normalization in order to eliminate systemic failures. (C) Scores and loadings of the comparison between non-treated control (red) and enzymatically-degraded (blue and green) elastic fibers within the native tissue.
<img alt="Figure 4" src="/files/ftp_upload/3977/3977fig4.jpg" /> 
Figure 4. HART's-stained porcine aortic valve leaflets. Elastic fibers are visualized in black. (A) and (B) show non-treated control and (C) and (D) depict tissue samples that were exposed to the elastin-degrading enzyme elastase for 30 minute.
<table border="1">
	<tbody>
		<tr>
			<td>Wavenumber in cm-1</td>
			<td colspan="2">Assignment12</td>
		</tr>
		<tr>
			<td>717-719</td>
			<td>C-N</td>
			<td>Phospholipids</td>
		</tr>
		<tr>
			<td>785-788</td>
			<td>DNA/RNA bases, 
			O-P-O backbone</td>
			<td>DNA/RNA</td>
		</tr>
		<tr>
			<td>1003- 1005</td>
			<td>Phenylalanine</td>
			<td>Protein</td>
		</tr>
		<tr>
			<td>1220-1280</td>
			<td>Amide III</td>
			<td>Protein</td>
		</tr>
		<tr>
			<td>1445-1447</td>
			<td>CH2</td>
			<td>Protein/Lipid</td>
		</tr>
		<tr>
			<td>1655-1680</td>
			<td>Amide I C=C</td>
			<td>Protein Lipid</td>
		</tr>
	</tbody>
</table>
Table 1. Raman bands that are detected within spectra of all cell types (fibroblasts, MSCs, chondrocytes and keratinocytes).

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Biotechnol. 2010, 101864-101864 (2010).</li><li>Gentleman, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+materials+differences+revealed+in+engineered+bone+as+a+function+of+cell-specific+differentiation.">Comparative materials differences revealed in engineered bone as a function of cell-specific differentiation.</a> Nat. Mater. 8, 763-770 (2009).</li><li>Notingher, I., Hench, L. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Raman+microspectroscopy:+a+noninvasive+tool+for+studies+of+individual+living+cells+in+vitro.">Raman microspectroscopy: a noninvasive tool for studies of individual living cells in vitro.</a> Expert. Rev. Med. 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No conflicts of interest declared.

Raman spectroscopy is a suitable tool to analyze biological samples, such as in vitro-cultured cells and ECM proteins as well as cells within native tissues.11,15,16 Here, we demonstrated that this non-contact, label-free technique allows the discrimination of different cell types and the detection of ECM protein degradation solely based on the intrinsic biomolecular composition of these biological samples.
The major advantage of Raman spectroscopy is the ability to non-invasively quantify the biochemical fingerprint of a sample by its resulting Raman spectra. In contrast to infrared spectroscopy, which yields similar information, Raman spectra can be collected from aqueous samples, as the Raman scatter from water is weak. In addition, Raman spectroscopy is solely based on the detection of backscattering of monochromatic light; therefore, no sample processing is required prior measurement. These attributes make Raman spectroscopy a promising alternative for potential in vivo imaging applications. In this regards, coherent anti-stokes Raman spectroscopy (CARS) is a very interesting technique since it enables faster and more sensitive acquisition of data based on the same vibrational signals utilized in our experiments.15,16 Other alternative methods including multiphoton-induced autofluorescence and second harmonic generation imaging have been previously proven to be suitable for monitoring biological samples non-or minimal invasively.17 However, these imaging modalities are associated with very high costs and are limited to autofluorescence-generating molecules. In addition, Raman spectrometer is easy to combine with conventional optical microscopes. These characteristics make Raman spectroscopy a valuable tool to study biological samples in physiological environments.
One of the current limitations of our Raman spectroscopy set up is the relatively small laser focus (250 nm full width half maximum (FWHM) lateral and 700 nm FWHM axial) that is created by a high numerical aperture objective (NA=1.2). Although a high numerical aperture allows to cover a good amount of the emitted Raman light yielding in a high signal-to-noise ratio, the high NA produces only a small collection focus within the sample that is typically much smaller than a cell. In order to compare Raman spectra of different cells, the collection of a representative spectrum is essential, which is difficult to obtain with a small focus area. To address this issue, we are working on a process to automate the signal collection at different points within the cell (= Raman spectroscopic mapping), resulting in a spectral averaging and yielding to a representative spectrum. Additionally, this technique will provide an overview of the distribution of specific Raman bands, for instance of the protein distribution within a cell.
Biological samples are highly complex and consist of a heterogeneous mixture of biomolecules that contribute to the collected Raman spectra. Therefore, the spectral pattern is highly complex and the monitoring of a single type of a molecule within a Raman spectrum is difficult to accomplish with the overlapping of different molecule signals. Additionally, intrinsic fluorescence of the sample may mask valuable information of weaker Raman signals. Interestingly, in some of our previous studies, we identified autofluorescence in the Raman spectra as the main differentiating factor between cell types (MSCs and fibroblasts) using an appropriate analysis tool.13 We also identified that changes in the overall Raman signal intensity can serve as an indicator for the state of collagen and collagen fibers within the ECM of aortic valve leaflets.9 However, when analyzing the state of elastin in these tissues, we were not able to detect similar results. As mentioned in the results section, we were only able to detect alterations of specific Raman bands in the elastase-treated samples when compared to the native controls. We did not see a decrease of the overall Raman signal in the enzymatically-treated samples as expected. These observations resulted in a score plot that did not reveal a clear cluster formation as seen in the previous study.9 In contrast, the influence of the enzymatic treatment was detectable within the PCA results. We assume that these discrepancies between the two ECM proteins, elastin and collagen, are based on morphological differences and different enzymatic degradation processes: within the aortic valve leaflet, the collagen-rich zone (fibrosa) is a continuous layer that becomes loosened due to the enzymatic treatment, whereby the elastin containing zone (ventricularis) has a network configuration that appears fragmented after exposure to elastase (Fig. 4). Single spot measurements were therefore not appropriate to detect such small ruptures within the elastin network. Here, a Raman mapping of the tissue would help to identify network breakdowns.
A further challenge in Raman spectroscopy of biological samples is to reduce the measurement times. One solution is to increase the laser power, which is suitable as long as the biological samples are not affected by photo-damage. All of our current experiments are proof-of-principle studies focusing on basic research; however, our overall goal is to implement Raman spectroscopy for clinical applications including regenerative medicine (e.g. quality control of tissue-engineered products), pre-transplantation graft monitoring and cancer diagnostics.

<table border="1">
	<tbody>
		<tr>
			<td>Name of the reagent</td>
			<td>Company</td>
			<td>Catalogue number</td>
			<td>Comments </td>
		</tr>
		<tr>
			<td>elastase</td>
			<td>Worthington</td>
			<td>LS006363</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>anti-elastin antibody</td>
			<td>Sigma</td>
			<td>HPA018111</td>
			<td>1:75; citrate buffer</td>
		</tr>
		<tr>
			<td>PBS</td>
			<td>Lonza</td>
			<td>17-512F</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>glass bottom dishes</td>
			<td>Greiner BioOne</td>
			<td>627860</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>The Unscrambler</td>
			<td>CAMO</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Opus</td>
			<td>Bruker</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
		</tr>
	</tbody>
</table>

non-contact, label-free monitoring of cells and extracellular matrix using raman spectroscopy

Non-destructive, non-contact and label-free technologies to monitor cell and tissue cultures are needed in the field of biomedical research.1-5 However, currently available routine methods require processing steps and alter sample integrity. Raman spectroscopy is a fast method that enables the measurement of biological samples without the need for further processing steps. This laser-based technology detects the inelastic scattering of monochromatic light.6 As every chemical vibration is assigned to a specific Raman band (wavenumber in cm-1), each biological sample features a typical spectral pattern due to their inherent biochemical composition.7-9 Within Raman spectra, the peak intensities correlate with the amount of the present molecular bonds.1 Similarities and differences of the spectral data sets can be detected by employing a multivariate analysis (e.g. principal component analysis (PCA)).10
Here, we perform Raman spectroscopy of living cells and native tissues. Cells are either seeded on glass bottom dishes or kept in suspension under normal cell culture conditions (37 &deg;C, 5% CO2) before measurement. Native tissues are dissected and stored in phosphate buffered saline (PBS) at 4 &deg;C prior measurements. Depending on our experimental set up, we then either focused on the cell nucleus or extracellular matrix (ECM) proteins such as elastin and collagen. For all studies, a minimum of 30 cells or 30 random points of interest within the ECM are measured. Data processing steps included background subtraction and normalization.

Watch this Scientific Journal Video about Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy at JoVE.com

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Raman spectroscopy is a suitable technique for the non-contact, label-free analysis of living cells, tissue-engineered constructs and native tissues. Source-specific spectral fingerprints can be generated and analyzed using multivariate analysis.

Non-destructive, non-contact and label-free technologies to monitor cell and tissue cultures are needed in the field of biomedical research.1-5 However, ...

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Research

JoVE Journal

Bioengineering

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

The mechanical microenvironment has been shown to act as a crucial regulator of tumor growth behavior and signaling, which is itself remodeled and modified as part of a set of complex, two-way mechanosensitive interactions. While the development of biologically-relevant 3D tumor models have facilitated mechanistic studies on the impact of matrix rheology on tumor growth, the inverse problem of mapping changes in the mechanical environment induced by tumors remains challenging. Here, we describe the implementation of particle-tracking microrheology (PTM) in conjunction with 3D models of pancreatic cancer as part of a robust and viable approach for longitudinally monitoring physical changes in the tumor microenvironment, in situ. The methodology described here integrates a system of preparing in vitro 3D models embedded in a model extracellular matrix (ECM) scaffold of Type I collagen with fluorescently labeled probes uniformly distributed for position- and time-dependent microrheology measurements throughout the specimen. In vitro tumors are plated and probed in parallel conditions using multiwell imaging plates. Drawing on established methods, videos of tracer probe movements are transformed via the Generalized Stokes Einstein Relation (GSER) to report the complex frequency-dependent viscoelastic shear modulus, G*(&#969;). Because this approach is imaging-based, mechanical characterization is also mapped onto large transmitted-light spatial fields to simultaneously report qualitative changes in 3D tumor size and phenotype. Representative results showing contrasting mechanical response in sub-regions associated with localized invasion-induced matrix degradation as well as system calibration, validation data are presented. Undesirable outcomes from common experimental errors and troubleshooting of these issues are also presented. The 96-well 3D culture plating format implemented in this protocol is conducive to correlation of microrheology measurements with therapeutic screening assays or molecular imaging to gain new insights into impact of treatments or biochemical stimuli on the mechanical microenvironment.

Particle-tracking microrheology can be used to non-destructively quantify and spatially map changes in extracellular matrix mechanical properties in 3D tumor models.

The mechanical microenvironment has been shown to act as a crucial regulator of tumor growth behavior and signaling, which is itself remodeled and ...

A Microfluidic-based Electrochemical Biochip for Label-free DNA Hybridization Analysis

Miniaturization of analytical benchtop procedures into the micro-scale provides significant advantages in regards to reaction time, cost, and integration of pre-processing steps. Utilizing these devices towards the analysis of DNA hybridization events is important because it offers a technology for real time assessment of biomarkers at the point-of-care for various diseases. However, when the device footprint decreases the dominance of various physical phenomena increases. These phenomena influence the fabrication precision and operation reliability of the device. Therefore, there is a great need to accurately fabricate and operate these devices in a reproducible manner in order to improve the overall performance. Here, we describe the protocols and the methods used for the fabrication and the operation of a microfluidic-based electrochemical biochip for accurate analysis of DNA hybridization events. The biochip is composed of two parts: a microfluidic chip with three parallel micro-channels made of polydimethylsiloxane (PDMS), and a 3 x 3 arrayed electrochemical micro-chip. The DNA hybridization events are detected using electrochemical impedance spectroscopy (EIS) analysis. The EIS analysis enables monitoring variations of the properties of the electrochemical system that are dominant at these length scales. With the ability to monitor changes of both charge transfer and diffusional resistance with the biosensor, we demonstrate the selectivity to complementary ssDNA targets, a calculated detection limit of 3.8 nM, and a 13% cross-reactivity with other non-complementary ssDNA following 20 min&#160;of incubation. This methodology can improve the performance of miniaturized devices by elucidating on the behavior of diffusion at the micro-scale regime and by enabling the study of DNA hybridization events.

We present a microfluidic-based electrochemical biochip for DNA hybridization detection. Following ssDNA probe functionalization, the specificity, sensitivity, and detection limit are studied with complementary and non-complementary ssDNA targets. Results illustrate the influence of the DNA hybridization events on the electrochemical system, with a detection limit of 3.8 nM.

Miniaturization of analytical benchtop procedures into the micro-scale provides significant advantages in regards to reaction time, cost, and integration ...

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Inter-cellular communication is an integral part of a complex system that helps in maintaining basic cellular activities. As a result, the malfunctioning of such signaling can lead to many disorders. To understand cell-to-cell signaling, it is essential to study the spatial and temporal nature of the secreted molecules from the cell without disturbing the local environment. Various assays have been developed to study protein secretion, however, these methods are typically based on fluorescent probes which disrupt the relevant signaling pathways. To overcome this limitation, a label-free technique is required.
In this paper, we describe the fabrication and application of a label-free localized surface plasmon resonance imaging (LSPRi) technology capable of detecting protein secretions from a single cell. The plasmonic nanostructures are lithographically patterned onto a standard glass coverslip and can be excited using visible light on commercially available light microscopes. Only a small fraction of the coverslip is covered by the nanostructures and hence this technique is well suited for combining common techniques such as fluorescence and bright-field imaging.
A multidisciplinary approach is used in this protocol which incorporates sensor nanofabrication and subsequent biofunctionalization, binding kinetics characterization of ligand and analyte, the integration of the chip and live cells, and the analysis of the measured signal. As a whole, this technology enables a general label-free approach towards mapping cellular secretions and correlating them with the responses of nearby cells.

Inter-cellular communication is critical for controlling various physiological activities within and outside the cell. This paper describes a protocol for measuring the spatio-temporal nature of single cell secretions. To achieve this, a multidisciplinary approach is used which integrates label-free nanoplasmonic sensing with live cell imaging.

Inter-cellular communication is an integral part of a complex system that helps in maintaining basic cellular activities. As a result, the malfunctioning ...

Label-free Single Molecule Detection Using Microtoroid Optical Resonators

Detecting small concentrations of molecules down to the single molecule limit has impact on areas such as early detection of disease, and fundamental studies on the behavior of molecules. Single molecule detection techniques commonly utilize labels such as fluorescent tags or quantum dots, however, labels are not always available, increase cost and complexity, and can perturb the events being studied. Optical resonators have emerged as a promising means to detect single molecules without the use of labels. Currently the smallest particle detected by a non-plasmonically-enhanced bare optical resonator system in solution is a 25 nm polystyrene sphere1. We have developed a technique known as Frequency Locking Optical Whispering Evanescent Resonator (FLOWER) that can surpass this limit and achieve label-free single molecule detection in aqueous solution2. As signal strength scales with particle volume, our work represents a &#62; 100x improvement in the signal to noise ratio (SNR) over the current state of the art. Here the procedures behind FLOWER are presented in an effort to increase its usage in the field.

We have developed a label-free biosensing system based on optical resonator technology known as Frequency Locking Optical Whispering Evanescent Resonator (FLOWER) that is capable of detecting single molecules in solution. Here the procedures behind this work are described and presented.

Detecting small concentrations of molecules down to the single molecule limit has impact on areas such as early detection of disease, and fundamental ...

Engineering Three-dimensional Epithelial Tissues Embedded within Extracellular Matrix

The architecture of branched organs such as the lungs, kidneys, and mammary glands arises through the developmental process of branching morphogenesis, which is regulated by a variety of soluble and physical signals in the microenvironment. Described here is a method created to study the process of branching morphogenesis by forming engineered three-dimensional (3D) epithelial tissues of defined shape and size that are completely embedded within an extracellular matrix (ECM). This method enables the formation of arrays of identical tissues and enables the control of a variety of environmental factors, including tissue geometry, spacing, and ECM composition. This method can also be combined with widely used techniques such as traction force microscopy (TFM) to gain more information about the interactions between cells and their surrounding ECM. The protocol can be used to investigate a variety of cell and tissue processes beyond branching morphogenesis, including cancer invasion.

This manuscript describes a soft lithography-based technique to engineer uniform arrays of three-dimensional (3D) epithelial tissues of defined geometry surrounded by extracellular matrix. This method is amenable to a wide variety of cell types and experimental contexts and allows for high-throughput screening of identical replicates.

The architecture of branched organs such as the lungs, kidneys, and mammary glands arises through the developmental process of branching morphogenesis, ...

Preparation of Extracellular Matrix Protein Fibers for Brillouin Spectroscopy

Brillouin spectroscopy is an emerging technique in the biomedical field. It probes the mechanical properties of a sample through the interaction of visible light with thermally induced acoustic waves or phonons propagating at a speed of a few km/sec. Information on the elasticity and structure of the material is obtained in a nondestructive contactless manner, hence opening the way to in vivo applications and potential diagnosis of pathology. This work describes the application of Brillouin spectroscopy to the study of biomechanics in elastin and trypsin-digested type I collagen fibers of the extracellular matrix. Fibrous proteins of the extracellular matrix are the building blocks of biological tissues and investigating their mechanical and physical behavior is key to establishing structure-function relationships in normal tissues and the changes which occur in disease. The procedures of sample preparation followed by measurement of Brillouin spectra using a reflective substrate are presented together with details of the optical system and methods of spectral data analysis.

We present a protocol for the application of Brillouin light scattering spectroscopy to elastin and trypsin-purified type I collagen fibers of the extracellular matrix to extract their full elastic properties.

Brillouin spectroscopy is an emerging technique in the biomedical field. It probes the mechanical properties of a sample through the interaction of ...

PIP-on-a-chip: A Label-free Study of Protein-phosphoinositide Interactions

Numerous cellular proteins interact with membrane surfaces to affect essential cellular processes. These interactions can be directed towards a specific lipid component within a membrane, as in the case of phosphoinositides (PIPs), to ensure specific subcellular localization and/or activation. PIPs and cellular PIP-binding domains have been studied extensively to better understand their role in cellular physiology. We applied a pH modulation assay on supported lipid bilayers (SLBs) as a tool to study protein-PIP interactions. In these studies, pH sensitive ortho-Sulforhodamine B conjugated phosphatidylethanolamine is used to detect protein-PIP interactions. Upon binding of a protein to a PIP-containing membrane surface, the interfacial potential is modulated (i.e. change in local pH), shifting the protonation state of the probe. A case study of the successful usage of the pH modulation assay is presented by using phospholipase C delta1 Pleckstrin Homology (PLC-&#948;1 PH) domain and phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) interaction as an example. The apparent dissociation constant (Kd,app) for this interaction was 0.39 &#177; 0.05 &#181;M, similar to Kd,app values obtained by others. As previously observed, the PLC-&#948;1 PH domain is PI(4,5)P2 specific, shows weaker binding towards phosphatidylinositol 4-phosphate, and no binding to pure phosphatidylcholine SLBs. The PIP-on-a-chip assay is advantageous over traditional PIP-binding assays, including but not limited to low sample volume and no ligand/receptor labeling requirements, the ability to test high- and low-affinity membrane interactions with both small and large molecules, and improved signal to noise ratio. Accordingly, the usage of the PIP-on-a-chip approach will facilitate the elucidation of mechanisms of a wide range of membrane interactions. Furthermore, this method could potentially be used in identifying therapeutics that modulate protein&#39;s capacity to interact with membranes.

Here we present a supported lipid bilayer in the context of a microfluidic platform to study protein-phosphoinositide interactions using a label-free method based on pH modulation.

Numerous cellular proteins interact with membrane surfaces to affect essential cellular processes. These interactions can be directed towards a specific ...

Fabricating a Kidney Cortex Extracellular Matrix-Derived Hydrogel

Extracellular matrix (ECM) provides important biophysical and biochemical cues to maintain tissue homeostasis. Current synthetic hydrogels offer robust mechanical support for in vitro cell culture but lack the necessary protein and ligand composition to elicit physiological behavior from cells. This manuscript describes a fabrication method for a kidney cortex ECM-derived hydrogel with proper mechanical robustness and supportive biochemical composition. The hydrogel is fabricated by mechanically homogenizing and solubilizing decellularized human kidney cortex ECM. The matrix preserves native kidney cortex ECM protein ratios while also enabling gelation to physiological mechanical stiffnesses. The hydrogel serves as a substrate upon which kidney cortex-derived cells can be maintained under physiological conditions. Furthermore, the hydrogel composition can be manipulated to model a diseased environment which enables the future study of kidney diseases.

Here we present a protocol to fabricate a kidney cortex extracellular matrix-derived hydrogel to retain the native kidney extracellular matrix (ECM) structural and biochemical composition. The fabrication process and its applications are described. Finally, a perspective on using this hydrogel to support kidney-specific cellular and tissue regeneration and bioengineering is discussed.

Extracellular matrix (ECM) provides important biophysical and biochemical cues to maintain tissue homeostasis. Current synthetic hydrogels offer robust ...

Implementation of Interference Reflection Microscopy for Label-free, High-speed Imaging of Microtubules

There are several methods for visualizing purified biomolecules near surfaces. Total-internal reflection fluorescence (TIRF) microscopy is a commonly used method, but has the drawback that it requires fluorescent labeling, which can interfere with the activity of the molecules. Also, photobleaching and photodamage are concerns. In the case of microtubules, we have found that images of similar quality to TIRF can be obtained using interference reflection microscopy (IRM). This suggests that IRM might be a general technique for visualizing the dynamics of large biomolecules and oligomers in vitro. In this paper, we show how a fluorescence microscope can be modified simply to obtain IRM images. IRM is easier and considerably cheaper to implement than other contrast techniques such as differential interference contrast microcopy or interferometric scattering microscopy. It is also less susceptible to surface defects and solution impurities than darkfield microscopy. Using IRM, together with the image analysis software described in this paper, the field of view and the frame rate is limited only by the camera; with a sCMOS camera and&#160;wide-field&#160;illumination&#160;microtubule length can be measured with precision up to 20 nm with a bandwidth of 10 Hz.&#160;

This protocol is a guide for implementing interference reflection microscopy on a standard fluorescence microscope for label-free, high-contrast, high-speed imaging of microtubules using in vitro surfaces assays.

There are several methods for visualizing purified biomolecules near surfaces. Total-internal reflection fluorescence (TIRF) microscopy is a commonly used ...

Optical Trapping of Plasmonic Nanoparticles for In Situ Surface-Enhanced Raman Spectroscopy Characterizations

Surface-enhanced Raman spectroscopy (SERS) enables the ultrasensitive detection of analyte molecules in various applications due to the enhanced electric field of metallic nanostructures. Salt-induced silver nanoparticle aggregation is the most popular method for generating SERS-active substrates; however, it is limited by poor reproducibility, stability, and biocompatibility. The present protocol integrates optical manipulation and SERS detection to develop an efficient analytical platform to address this. A 1064 nm trapping laser and a 532 nm Raman probe laser are combined in a microscope to assemble silver nanoparticles, which generate plasmonic hotspots for in situ SERS measurements in aqueous environments. Without aggregating agents, this dynamic plasmonic silver nanoparticle assembly enables an approximately 50-fold enhancement of the analyte molecule signal. Moreover, it provides spatial and temporal control to form the SERS-active assembly in as low as 0.05 nM analyte-coated silver nanoparticle solution, which minimizes the potential perturbation for in vivo analysis. Hence, this optical trapping-integrated SERS platform holds great potential for efficient, reproducible, and stable molecular analyses in liquids, especially in aqueous physiological environments.

Optical Trapping of Plasmonic Nanoparticles for In Situ Surface-Enhanced Raman Spectroscopy Characterizations

The present protocol describes a convenient approach to integrating optical trapping and surface-enhanced Raman spectroscopy (SERS) to manipulate plasmonic nanoparticles for sensitive molecular detection. Without aggregating agents, the trapping laser assembles plasmonic nanoparticles to enhance the SERS signals of target analytes for in situ spectroscopic measurements.

Surface-enhanced Raman spectroscopy (SERS) enables the ultrasensitive detection of analyte molecules in various applications due to the enhanced electric ...