Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Podsumowanie

Here, we present a protocol for the detection and quantification of Plasmodium falciparum in infected aqueous red blood cells using an attenuated total reflection infrared spectrometer and multivariate data analysis.

Streszczenie

We demonstrate a method of quantification and detection of parasites in aqueous red blood cells (RBCs) by using a simple benchtop Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectrometer in conjunction with Multivariate Data Analysis (MVDA). 3D7 P. falciparum were cultured to 10% parasitemia ring stage parasites and used to spike fresh donor isolated RBCs to create a dilution series between 0–1%. 10 µL of each sample were placed onto the center of the ATR diamond window to acquire the spectrum. The sample data was treated to improve the signal to noise ratio and to remove the contribution of water, and then the second derivative was applied to resolve spectral features. The data were then analyzed using two types of MVDA: first Principal Component Analysis (PCA) to determine any outliers and then Partial Least Squares Regression (PLS-R) to build the quantification model.

Wprowadzenie

Malaria is among the most devastating diseases of our time; over half the population lives at risk in endemic regions and it disproportionately burdens the poor^1,2,3,4. A large part of the issue is the asymptomatic carriers and early stage patients that act as reservoirs for mosquito vectors⁵, causing spikes of infection during wet seasons and allowing it to persist in communities. Malaria is caused by five Plasmodium parasites, the most deadly of which is P. falciparum which causes the most severe form of the disease².

Currently, techniques for the diagnosis of malaria are less than perfect. Optical microscopy, the current gold standard, can only detect 62-88 parasites/µL depending on the method used⁶. Furthermore, due to the intensiveness and high skill required, in many regions microscopy misdiagnoses >50% of cases, especially those with low parasitaemia levels², which can be directly attributed to the lack of resources in the area and results in the misuse of anti-malarial drugs. The other 2 main diagnostic methods are Rapid Diagnostic Tests (RDTs), which utilize antibodies for the detection, and Polymerase Chain Reaction (PCR) assays, which discriminate and quantify parasites from DNA. Currently, RDTs are only able to detect P. falciparum and P. vivax of at least 100 parasites/µL of blood resulting in rarer forms of the disease being untreated.^7,8 In contrast, PCR assays discriminate and quantify different species of Plasmodium at a sensitivity of 0.0004-5 parasites/µL of blood. However, it requires expensive reagents, equipment and technical skill, and thus is not suitable for the field application.

A highly sensitive, reliable and affordable technique is essential to improve diagnosis times, and thus improving patient outcomes, and making disease elimination possible. Attenuated Total Reflection Fourier transform (ATR-FTIR) spectroscopy offers a potential solution to this problem. Previous work has shown that it was possible to detect and quantify P. falciparum in methanol fixed blood films achieving the detection limits of <1 parasite/µL of blood (<0.0002 % parasitemia)⁹, which is comparable to PCR methods. Recent studies have shown that it is possible to detect and quantify parasites in aqueous samples and thus eliminate the fixation step. However, factors such as water vapor, spectral noise and data treatment need to be taken into consideration for optimal results¹⁰.

This protocol aims to show new users how to acquire ATR-FTIR spectra and prepare a regression model for the detection of P. falciparum from aqueous Red Blood Cells (RBC) samples¹⁰.

Protokół

Please consult appropriate Material Safety Data Sheets (MSDS) and seek appropriate Biosafety Level 2 (BSL-2) training. All culturing steps must be done in a BSL-2 cabinet using aseptic technique, meaning there is a risk of exposure to harmful UV radiation from decontamination steps, needle stick injuries, and potential biological exposure and infection if the parasite culture enters any injuries. Furthermore, stock blood from blood banks is only screened for certain diseases and the potential for spreading blood borne diseases is a potential risk. Seek immediate medical aid in the occurrence of injury.

1. Preparation and Measurement of 3D7 Plasmodium falciparum Parasite Dilution Series

NOTE: Plasmodium sp. culture is highly sensitive. Use fresh/unexpired reagents and feed the parasites regularly by changing the media. Feed cultures below 5% parasitemia every second day; feed cultures between 6-10% parasitemia once or twice a day; and feed cultures between 11–20% up to 4 times a day. Parasites that are beginning to starve will lose their shape and begin to contract. In such a case, feed and dilute immediately by changing the media and adding stock RBCs. Collect the donor blood in blood collection tubes containing heparin as the anticoagulant and measure within the first 6 h.

1. Culture a total of 30 mL of 3D7 strain Plasmodium falciparum parasites according to the standard protocol until the parasitemia reaches 10% rings.
2. Synchronization of parasites
   1. 11 h after shizogeny into the parasite lifecycle, resuspend the culture and transfer into a 50 mL conical tube using an automated pipette.
   2. Centrifuge the culture at 300–400 x g and standard laboratory conditions (25°C and 100 kPa) for 5 min.
   3. Remove the supernatant by drawing it up with a pipette or using a Pasteur pipette attached to a vacuum without disturbing the pellet. Discard waste media into a 10% bleach solution.
   4. Slowly add 12–15 mL of 4% sorbitol solution to the pellet using an automated pipette and mix the culture by capping and inverting the tube until the culture is homogenous.
   5. Incubate the culture at 37 °C for 15 min.
   6. Centrifuge the culture at 300–400 x g and standard laboratory conditions for 5 min.
   7. Remove the supernatant as described in step 1.2.3.
   8. Add 10–15 mL of 0.9% saline to the pellet and mix the solution by capping and inverting the tube until the culture is homogenous.
   9. Repeat the steps 1.2.6–1.2.8 twice more to wash all remnants of sorbitol.
3. Preparation of the parasitemia dilution series
   1. Wash stock RBCs as in steps 1.2.6–1.2.9.
   2. Centrifuge the culture at standard laboratory conditions, stock RBCs at 300–400 x g for 5 min and discard supernatant as in step 1.2.3.
   3. Label microcentrifuge tubes as 0%, 0.010%, 0.025%, 0.075%, 0.100%, 0.250%, 0.750% and 1.000%, and add 0 µL, 1 µL, 2.5 µL, 7.5 µL, 10 µL, 25 µL, 75 µL and 100 µL of culture respectively using a 0.2-20 µL pipette.
   4. Add 100 µL, 99 µL, 97.5 µL, 92.5 µL, 80 µL, 75 µL, 25 and 0 µL of stock RBCs to the tubes labelled 0%, 0.010%, 0.025%, 0.075%, 0.100%, 0.250%, 0.750%, and 1.000%, respectively.
   5. Centrifuge the fresh donor blood collected in anticoagulant tubes at 1,200 x g and standard laboratory conditions for 10 min.
   6. Remove the plasma by drawing it up with a pipette and discarding as described in step 1.2.3.
   7. Add 900 µL of isolated RBCs into each microcentrifuge tube and mix thoroughly by inverting 10x.
4. Spectral acquisition
   1. Using the accompanying spectral acquisition program, click "Instrument Set-Up" and set the data collection parameters to the following: 128 scans for background; 32 scans for sample; and resolution to 8/cm over the maximum sample range of the instrument.
   2. Set the temperature of the ATR-FTIR spectrometer per manufacturer's recommendation or overnight.
   3. Clean the crystal by gently scrubbing in a circular motion using lint free wipes dampened with ultrapure water. Then thoroughly dry using another lint free wipe.
   4. Take a background measurement of the air by clicking "Background Measurement". Every 20 min, clean the crystal as in step 1.4.3 and repeat this measurement.
   5. Open the live view by clicking "Preview". Observe a flat, horizontal baseline that indicates that the crystal is clean.
   6. Pipette 10 µL of deionized water directly on to the middle of the crystal and click "Measure Sample". Repeat this water measurement after every fifth sample.
   7. Dry the crystal using a lint free wipe and a gentle circular motion.
   8. Pipette 10 µL of sample and click "Measure Sample".
   9. Clean the crystal between samples as in step 1.4.3.
   10. Repeat until 3 replicates are acquired, making sure to randomize the order of both the samples and the replicates.

2. Multivariate Data Analysis (MVDA)

NOTE: Data treatment must be done over the whole dataset in order to avoid noise and the addition of peaks of non-biological origin. In contrast, analyze over the biologically relevant regions: 2980-2800/cm and 1750-850/cm.

1. Data treatment
   NOTE: Two software, e.g., Matlab (henceforth referred to as software 1) and The Unscrambler X (henceforth referred to as software 2) are used as examples for MVDA. Software 1 has the capability of performing MVDA without the addition of a graphical user interface (GUI). However, it is recommended to purchase a GUI (Table of Materials). The following instructions when referring to software 1 will assume that is in conjunction with the commercially available GUI toolbox, and the example data treatment script has been attached.
   1. Open the appropriate multivariate data analysis software, click "Import Data" and select the type of file for analysis.
      1. In software 1, input "Analysis" into the command window to open the GUI. Right click the X box to find "Import Data".
      2. In software 2, click the tab "File" to find "Import".
   2. Import sample, water and baseline spectra as data sets into the workspace by selecting all spectra in each set separately and clicking "Open" and give each set a short name, e.g., 'wat' for dataset of water spectra.
   3. Select new table/matrix by clicking "New Matrix/Vector" and generate a n×1 vector, where n is the number of samples. Input the parasitemia of each sample and give the vector the name 'Parasitemia'.
      1. In software 1, click "New Variable" in the command window.
      2. In software 2, click the icon "New Matrix".
   4. Plot data by clicking the "Plot Data Icon". Inspect the spectra for water vapor effects by clicking "Zoom" and zooming in on 1800-1400/cm; most clearly observed as short, sharp, narrow peaks along the slopes of the amide I and amide II bands.
   5. In cases of extreme water vapor, open edit/pre-process data tab, and select "Smoothing". Reduce the noise and/or strong water vapor contributions by smoothing the sample and water spectra using up to 25 points of smoothing or use a water vapor correction method.
   6. Correct non-horizontal baseline by using the baseline correction algorithm if appropriate, under the same edit/pre-process data tab in step 2.1.4.
   7. Average water spectra and copy the rows into an n×m matrix equivalent to the sample dataset and reduce it to 70% intensity by multiplying it by 0.7.
      1. In software 1, do so in the command window by inputting the following script "AverageWater=mean(WaterDataset)". Then copy-paste the rows to match the sample data set. To reduce the intensity, input "AverageWater70= AverageWater*0.70".
   8. Subtract average water spectra from each sample spectrum.
      1. In software 1, do so in the command window by inputting the following script "WaterCorrectedData=SampleDataset-AverageWater70".
   9. Open the edit/pre-process data tab to apply a second derivative function, and then normalize the data by selecting single normal variate (SNV) function and mean center data.
      1. In software 1, do so in one go. First select "Derivative", and input 25 points of smoothing, polynomial order of 3 and derivative order on the sample set using 25 points of smoothing and a Savitzky-Golay function. Then select "SNV" and "Mean Center". Click "Okay/Apply".
   10. Open the edit/pre-process data tab and in "Column Variables", select 2,980–2,800/cm and 1,750–850/cm by making sure only their boxes are ticked.
2. Data analysis
   1. Principal Component Analysis (PCA)
      1. Click "Analysis | Decomposition" and select PCA.
         1. In software 1, click "Build Model".
         2. In software 2, input the PCA methods. Input the optimal number of Principal Components (PCs) -in this work, 7- and a maximum of 100 iterations, and select cross-validation for the validation method. Click "Run".
      2. Observe the 95% confidence limit, the dashed ring, on the scores plot between PC1 and PC2.
      3. Mark the sample spectra with scores that occur outside the limit as potential outliers using the "Select Spectra Tool".
      4. If the marked spectra have high Hotellings T² values and high Q residuals exclude them from further analysis.
   2. Partial Least Squares Regression (PLS-R)
      1. Click "Analysis | Regression" and select "PLS-R".
         1. In software 1, right click Y block and select the vector "Parasitemia | Build Model".
         2. In software 2, input the PLS-R methods. Select the vector "Parasitemia" as the "Y reference" and the sample dataset as "X Data Set" and select cross-validation as the validation method. Click "Run".
      2. Analyze the regression model. R² values over 0.80 and root mean square error of cross-validation (RMSECV) values that are less than 0.1% parasitemia are acceptable models.
      3. Analyze the regression vector and identify the biological bands.

Wyniki

Partial Least Squares (PLS-R) plot and its associated regression vector, Figure 1a and 1b respectively, show that the signal from parasites are distinct enough from the RBCs that they can be used to form a linear regression model to be used for the prediction of parasites in future data sets.

A robust, linear PLS-R model was generated with an R-squared value of 0.87 and a root mean ...

Dyskusje

PLS-R model is a supervised multivariate method that finds a linear relationship, Y=bX+E, between the predictive variables X (here, the absorbance at each wavenumber) and a continuous variable Y (here, the parasitemia level). In short, the model combines the variables in X to create a new set of latent variables (LVs) that capture the variance on X correlated with Y, and computes a regression vector (b) that multiplied by a new spectrum results in the estimation of the y value. The strength of the model can be taken from...

Materiały

Name	Company	Catalog Number	Comments
Donor blood	-	-	Must be collected by a trained medical practitioner
Stock blood	Australian Red Cross`	-
3D7 Plasmodium falciparum	The Burnet Institute	-	Laboratory strain wild type equivalent
RPMI-1640 media with L-hepes without Sodium Bicarbonate	Sigma Aldrich	R6504
Albumax (Gibco)	Thermofisher	E003000PJ	Aliquot into 50 mL falcon tubes and store in freezer
Sodium Bicarbonate	Sigma Aldrich	S5761
Giemsa Stain	Sigma Aldrich	48900
Sorbitol	Sigma Aldrich	S1876	Must be filtered before use in culture
Blood collection tubes (Vacutainers)	Becton, Dickonson and Company	367671
Immersion Oil	Thermofisher	M3004
50 mL culture dishes	Falcon	353025
25mL pipette tips	Falcon	357515
10mL pipette tips	Falcon	357530
Microscope Slides	Sigma Aldrich	S8902
Centrifuge tubes 50 mL	Sigma Aldrich	T2318
Automated pipette controller	Integra-biosciences	155 015
Sorvall Legend X1R Centrifuge	Thermofisher	75004260
Forma™ 310 Direct Heat CO2 Incubators	Thermofisher	310TS
Nikon Eclipse E-100 Binocular Microscope	Nikon Instruments	E100_2CE-MRTK-1	When purchasing ensure that the 100x lens is an oil immersion lens
Bruker Alpha Ft-IR Spectrometer with ATR Quick Snap Attachment	Bruker	9308-3700	Be sure to request "Eco-ATR" attachment when purchasing
Matlab	Mathworks Inc		Multivariate data analysis software
The Unscrambler X	CAMO		Multivariate data analysis software
PLS-Toolbox	Mathworks, Inc.		GUI for Matlab