The overall goal of this procedure is to build a regression model for the prediction of malaria parasitemia using ATR-FTIR spectroscopy with multivariate data analysis. This method can help expedite point-of-care diagnostics which can help improve patient outcomes in the future. The main advantage of this technique is that while the model may take some time to build, it is highly portable, user-friendly, highly sensitive, and low-cost.
Though this method enables the quantification of malaria parasites, it can also be used to observe phenotypic response to environmental changes such as changes in parasite composition due to antimicrobial agents. The idea developed from work we did at the Australian Synchrotron on malaria-infected cells. This was at the single-cell level.
From there, we decided to use ATR-FTIR spectroscopy to actually develop the diagnostic test. Prior to starting the synchronization procedure, culture 30 milliliters of 3D7 strain Plasmodium falciparum parasites as described in the text protocol. Using an automated pipette, resuspend the culture and transfer it to a 50-milliliter conical tube.
Centrifuge the culture at 300 to 400 times g under standard laboratory conditions for five minutes. Remove the supernatant by drawing it up with an automated pipette without disturbing the pellet. Discard the waste media into a 10%bleach solution.
Slowly add 12 to 15 milliliters of a 4%sorbitol solution to the pellet and mix the culture by capping and inverting the tube until the culture is homogeneous. Incubate the culture at 37 degrees Celsius for 15 minutes. After 15 minutes, centrifuge the culture for five minutes.
Use an automated pipette to remove the supernatant without disturbing the pellet. Add 10 to 15 milliliters of 0.9%saline to the pellet and mix the solution by capping and inverting the tube until the culture is homogeneous. Repeat the centrifugation and saline solution wash two more times to remove all remnants of sorbitol.
To begin this procedure, centrifuge the stock red blood cells, or RBCs. Remove the supernatant and wash with 0.9%saline a total of three times. Centrifuge the stock RBCs and the culture at 300 to 400 times g for five minutes and discard the supernatant from each tube.
Label eight microcentrifuge tubes as indicated. Then use a 0.2 to 20 microliter pipette to add the appropriate amount of culture to each tube. Next, add the appropriate amount of stock RBCs to each tube to obtain the desired parasitemia dilution.
Centrifuge the fresh donor blood collected in anticoagulant tubes at 1, 200 times g and standard laboratory conditions for 10 minutes. Remove the plasma by drawing it up with a pipette and discarding it into a 10%bleach solution. Add 900 microliters of isolated donor RBCs into each microcentrifuge tube and mix thoroughly by inverting 10 times.
A simple benchtop Attenuated Total Reflection Fourier Transform Infrared, or ATR-FTIR spectrometer will be used to acquire the spectrum. Prepare and clean the crystal by using lint-free wipes dampened with ultra-pure water to gently scrub the crystal in a circular motion. Then, use another lint-free wipe to thoroughly dry the crystal.
Take a background measurement of the air by clicking Background Measurement. Every 20 minutes, clean the crystal and repeat this measurement. Open the live view by clicking Preview.
Observe a flat, horizontal baseline that indicates the crystal is clean. Pipette 10 microliters of deionized water directly onto the middle of the crystal and click Measure Sample. Dry the crystal using a lint-free wipe and a gentle circular motion.
Pipette 10 microliters of sample onto the middle of the crystal and click Measure Sample. Clean the crystal between samples. In this demonstration, multivariate data analysis will be performed using MATLAB.
To begin data treatment, input analysis into the command window to open the graphic user interface. Right-click the X box to find Import Data. Select the type of file for analysis.
Import sample, water, and baseline spectra as datasets into the workplace by selecting all spectra in each set separately, clicking Open, and giving each set a short name. Click New Variable in the command window and give the vector the name Parasitemia. Input the Parasitemia of each sample.
Right-click the X box to find Plot Data. Click the Plot Data icon to plot the data. Inspect the spectra for water vapor effects by clicking Zoom and zooming in on 1, 800 to 1, 400 per centimeter, most clearly observed as short, sharp, narrow peaks along the slopes of the Amide I and Amide II bands.
In cases of extreme water vapor, open the Edit Preprocess data tab and select Smoothing. Reduce the noise and/or strong water vapor contributions by smoothing the sample and water spectra. After further data treatment as described in the text protocol, open the Edit Data tab, and in column variables, select 2980 to 2800 per centimeter and 1750 to 850 per centimeter by making sure only their boxes are ticked.
The data is now ready for analysis. To perform principal component analysis, or PCA, click Analysis Decomposition and select PCA. Click Build Model.
Observe the 95%confidence limit, the dashed ring, on the scores plot between PC 1 and PC 2. Use the Select Spectra tool to mark the sample spectra with scores that occur outside the limit as potential outliers. To perform partial least squares regression, or PLSR, click Analysis Regression and select PLSR.
Right-click the Y block and select the vector Parasitemia build model. Analyze the regression model and the regression vector and identify the biological bands. A partial least squares regression plot of RBCs spiked between zero to one percent Parasitemia generated an R-squared value of 0.87 and a root mean squared cross validation error of 0.13%Parasitemia.
This demonstrates the ability of the model to predict the Parasitemia in each sample with the known Parasitemia on the x-axis and the predicted Parasitemia on the y-axis. The associated regression coefficient describes the contribution of each band to the predictive capability of the model. The more intense the band, the more it contributes to the model, and thus, how the parasite alters the chemical composition of the blood.
The results show that the signal from parasites are distinct enough from the RBCs that they can be used to form a linear regression model for the prediction of parasites in future datasets. Once mastered, this technique can take less than three minutes per sample if performed properly. While attempting this procedure, it is important to remember to clean the ATR crystals of all residue.
Following this procedure, other malaria species such P vivax and P malariae can be modeled in order to answer such questions like, Is ATR-FTIR spectroscopy sensitive enough to distinguish between species? After its development, this technique paved the way for researchers in the field of biospectroscopy to explore the detection and quantifications of other blood-borne pathogens and even analyze in the blood such as glucose and urea. After watching this video, you should have a good understanding of ATR-FTIR spectroscopy and NVDA analysis to build a regression model.
Don't forget that working with patient blood and malaria samples can be dangerous, and appropriate biosafety level training and containment should always be done when attempting this procedure.
