The overall goal of this procedure is to monitor cells and tissue through a non-contact and marker free method. This is achieved through Ramen spectroscopy, a laser-based technology that detects the inelastic scattering of monochromatic light, producing a typical spectral pattern for each biological sample based on its inherent biochemical composition using a standard fluorescence microscope coupled to a ramen spectrometer. Direct comparison of brightfield and fluorescence images with ramen Spectra is used to monitor numerous biological samples.
Each chemical vibration is assigned to a specific Raman band, and the peak intensity is correlate with the amount of the present molecular bonds. Similarities and differences of the spectral data sets are detected. By employing a multivariate analysis, the resulting spectra can be used to check the integrity of tissues after harvesting a biopsy or to prove the sterility of isolated cells.
In addition, this technique enables the characterization and identification of different cell types in vitro. Ultimately, ramen spectroscopy is a promising laser-based technique for tissue engineering and clinical applications Through non-contact label-free analysis, Demonstrating the procedure will be Daniel Cabal Barrio, a technician, and Miriam Tala, a PhD student for my laboratory. The main advantage of this technique over existing routine methods, such as histology and immunofluorescence imaging, is that no sample processing is required.
Though this method can provide insight into single cell state and matrix components, it can also be applied to multicellular tissues and organs. To prepare customized ramen spectra, a standard fluorescence microscope is coupled to a ramen spectrometer that allows the direct comparison of fluorescence images with ramen spectra. The basic setup consists of a 784 nanometer diode laser, a notch filter for the separation of the ramen, scattered light from the excitation light, a microscope, and a spectrograph with a charge coupled device optimized for the detection of spectral information.
To control laser function, start the software and or solace and set the temperature of the CCD camera to negative 60 degrees Celsius to minimize the noise caused by thermally induced currents in the camera. Place a silicon wafer on the microscope stage for the calibration procedure. Turn the laser on and set the power to 85 milliwatts.
Use the software cell B to focus the laser onto the wafer until XY appears. Measure the silicon wafer with a single integration time of one second. Using a 60 X air objective, change the unit of the X axis from pixel number to ramen.
Shift in the and or Soli software vary the laser focus of the Silicon Peak at wave number 520 in the collected spectrum. In order to find the maximum possible intensity for this ramen band, the minimum amount of counts must be higher than 11, 000 to have a successful calibration prior to spectral measurement, prepare the biological samples as directed in the written protocol. Accompanying this video, all measurements are performed at room temperature.
Using a 60 x water immersion objective with a numerical aperture of 1.2, spectra should be collected using 10 integrations per 10 seconds for a total of 100 seconds per measurement. To perform measurement of a adherent cells, place a glass bottom dish holding the cells onto the microscope stage. 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.
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. Alternatively, in order to measure cells and suspension, transfer 100 microliters of the cell suspension into a glass bottom dish.
After placing onto the microscope stage, focus the laser on the center of the cell, turn the microscope light off, and start collecting the spectrum again. Measure a reference spectrum of the background every 10 spectrum by moving the laser focus beside the cell. To measure the spectra of native tissues, place the sample into a glass bottom dish.
The region of interest should be oriented facing the bottom of the dish. Fill the dish with enough PBS to cover the sample. Place a cover glass over the sample to avoid any movement of the sample.
During measurements, set the laser focus into the structure of interest and start collecting the spectra. Collect a reference spectrum of the background every 10 spectra by moving the laser focus out of the whole tissue area, always collecting a new background for each focus depth for spectral measurement of immunofluorescence labeled cryo sections. Section fresh snap frozen tissue samples.
Using a standard cryo ome, mount them on silica coated cover glasses. Stain the cryo sections following a routine protocol for immunofluorescence, employing only a short fixation step and using appropriate antibodies for the detection of the protein of interest performed. Ramen measurements focusing in the area where fluorescence occurs.
To demonstrate elastin degradation experiments, the ventriculars of dissected porcine aortic valve leaflets are positioned facing the bottom of the glass. Bottom dish. Measure the native tissue as a non incubated control at 30 random points across the whole tissue surface.
Focusing in the fibrillar structures divided the sample into two sections and placed them into separate 2.5 milliliter eend tubes filled with two milliliters of an elastase solution. Incubate the tissue for either 15 or 30 minutes at 37 degrees Celsius. After incubation, remove the tissues from the einor tube and wash carefully with PBS in order to completely stop the enzymatic reaction.
Measure each sample at 30 random points focusing in the fibrillar structures. Raman Spectra processing begins with the pre-treatment of the generated spectra using Opus software. First, subtract the corresponding background spectrum from the collected spectra to reduce interfering signals from the glass and medium, as well as to avoid variations caused by changes in the focus.
During measurements, reduce the spectra to the wave number region between 401, 800, which offers the highest amount of information if needed. Normalize the spectra to the maximum peak normalization, factors out the intensity fluctuations in systematic failures, simplifying the detection of structural changes in the sample spectra. Perform a baseline correction to increase the comparability between different experiments.
Analyze the Raman Spectra using principle component analysis or PCA with the unscrambler 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 ramen shift.
Each principle component, abbreviated 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, important sample correlations can be exposed. The loadings describe the contribution of each analyzed variable to the PCA. Label, each group of measurements by creating row ranges for every sample group, use the following basic settings for the PCA cross validation, the Nip Al algorithm, no rotation, and start the analysis.
These settings are spectra dependent. Finally, perform the PCA raw ramen spectra generated from adherent cells often reveal a low signal to noise ratio and a low overall signal intensity. The laser focus has to be set near the glass bottom, which masks the actual sample signal.
Consequently, the sample signal might be minimized or even eliminated during a subsequent background subtraction. Conversely, cells and suspension allow for 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 entities for the characterization of different cell types within a suspension, no pretreatment is required.
The mean Raman spectra and standard deviations of human fibroblasts, mesenchymal stem cells, chondrocytes, and keratinocytes measured in suspension are depicted here. All ramen spectra are similarly structured with peaks originating from typical biomolecules such as proteins, nucleic acids, and lipids. For these cell types, the spectral region between wave number 601, 800 contains the most relevant spectral information by which clear differences are detectable between the different cell types.
Exemplary one spectral region displays clear structural differences, which are assignable to molecular vibrations of collagen and lipids. In contrast, morphological analyses are not suitable for the identification and distinction of most cells. While the difference between chondrocytes and skin cells is observable, fibroblasts and mesenchymal stem cells are difficult to separate.
Using solely bright field microscopy for the assignment of native tissue to a specific fingerprint spectrum, Raman Spectra were generated of commercially available pure proteins and immuno histologically stained cryo sections. Here, fingerprint spectra of elastic fibers were identified within native tissues by comparing to lyophilized, elastin and immuno fluorescently stained cryo sections labeled using an antibody against elastin. However, since Elastin features a high autofluorescence, which is reflected in the Raman Spectra, the data analysis is challenging to reduce the systematic failure due to sample specific properties such as autofluorescence and appropriate processing of the data sets is crucial.
In the data analysis. Normalization was used to eliminate the significantly higher signal intensity of the pure elastin protein resulting in comparable Raman spectra. Elastin is one of the most stable extracellular matrix proteins in the body and is therefore very difficult to degrade.Here.
Elastin degradation was induced in healthy porcine aortic valve leaflets by performing an enzymatic digestion applying the multivariate analysis, PCA significant differences were identified between the Raman spectra of enzymatically treated samples and native controls. These spectral differences were observed in the loading spectrum at Wave Numbers 861 1003 and 1, 664 expected structural changes in the elastin containing fibers due to extended exposure times to elastase were reflected in more distinct separable score Clusters shown here are scores of the comparison between non-treated control and enzymatically degraded elastic fibers within the native tissue. These expected structural changes are also evident in the heart's stained porcine aortic valve leaflets as visualized in this inset by comparing the non-treated control with the tissue samples that were exposed to the elastin integrating enzyme elastase for 30 minutes.
This technique paves the way for researchers in the fields of cell and matrix biology as well as regenerative medicine and tissue engineering to perform not only real time cell phenotype analysis, but also to identify crucial cell cell and cell matrix interactions in Z two within a native environment.
