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Supporting Information Supporting Information Rapid Discrimination of Malaria and Dengue Infected Patient Sera using Raman Spectroscopy Sandip K. Patel1, Nishant Rajora1, Saurabh Kumar1, Aditi Sahu2, Sanjay K. Kochar3, C. Murali Krishna2* and Sanjeeva Srivastava1* 1 Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India 2 Chilakapati lab, ACTREC, Tata Memorial Center, Kharghar, Navi Mumbai-410210, India 3 Department of Medicine, Malaria Research Center, S.P. Medical College, Bikaner 334003, India *Correspondence: 1. Professor Sanjeeva Srivastava, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India E-mail: [email protected]; Phone: +91-22-2576-7779, Fax: +91-22-2572-3480 2. Professor Chilakapati Murali Krishna, Chilakapati lab, ACTREC, Tata Memorial Center, Kharghar, Navi Mumbai-410210, India E-mail: [email protected], [email protected] 1. SI Materials and Methods (pn:2) 2. TableS1: Investigation of hematological and biochemical parameters of the dengue and malaria patient sera and healthy subjected included in the study (pn:4) 3. Table S2: Complete details of all statistically significant (p < 0.05 and fold change <1.5) altered metabolites in malaria and dengue obtained from mass spectrometry (Q-TOF) analysis. (pn:5) 1 SI Materials and Methods Subject Recruitment and Sample Collection. Blood samples were collected from 37 malaria (27 for model generation, 12 for validation; 2 samples were overlapping) and 39 dengue (29 for model generation and 12 for validation; 2 samples were overlapping) patients and 54 HC patients. Malaria cases were confirmed by the thick blood smear microscopy method and RDT, while dengue infections were diagnosed by an IgM antibody capture enzyme-linked immunosorbent assay. Raman spectroscopy Spectra acquisition: After passive thawing, samples were subjected to Raman spectroscopy by placing 30 μl volume on calcium fluoride (CaF2) window and spectra were recorded using fiber optic Raman microprobe (Horiba-Jobin-Yvon, France). This system consists of laser (785 nm, Process Instruments) as an excitation source and a HE 785 spectrograph (Horiba-Jobin-Yvon, France) coupled with CCD (Synapse, Horiba-Jobin-Yvon) as dispersion and detection elements, respectively. Optical filtering of unwanted noise, including Rayleigh signals, is accomplished through ‘Superhead’, the other component of the system. Optical fibers were employed to carry the incident light from the excitation source to the sample and to collect the Raman scattered light from the sample to the detection system. The Raman microprobe was assembled by coupling a 40× microscope objective (Nikon, Japan) to the superhead. Spectra were acquired in the range of 500−3500 cm−1 for 20 s and averaged over three accumulations, acquisition details, laser power = 30 mW, and excitation wavelength (λex) = 785 nm. Eight spectra were recorded from each sample 1. Data analysis: Raman data analysis was performed following previously described protocol 2. Briefly, Raman spectra were corrected for CCD-response and background spectral contaminations from substrate and fiber signals. For multivariate data analysis, first derivative of the spectra was computed using the Savitzky-Golay method (window size 3). Derivatized spectra were interpolated in the region of 800-1800 cm-1 and subjected to supervised principal components linear discriminant (PC-LDA) analysis. For supervised PC-LDA approach, standard models of pathologically certified groups: malaria, dengue, and HC were developed, and analysis was carried out in 2- and 3-model systems. PC- LDA was followed by leave-one-out cross validation (LOOCV). Test predictions were carried out on the 3-model system. PC-LDA, LOOCV, and test prediction analyses were performed using a MATLAB (Mathworks Inc.) based in-house software 3. The resulting PC-LDA analysis was presented as scatter plots and confusion matrix, where the diagonal elements represent the true positive predictions while non-diagonal elements represent misclassifications across groups. The efficiency of the RS model for discriminating malaria and dengue with HC was analysed using ROC [plot of true positives (sensitivity) vs. false positives (1- specificity)] plotted using GraphPad Prism software package (version 6.02). For spectral comparisons, the background-subtracted spectra were baseline-corrected by fitting a fifth order polynomial function, smoothed (Savitzky–Golay, 3) and vector-normalized to compute the average representative spectra of each pathological group. 2 Mass spectrometry Metabolite extraction: Serum samples were extracted in methanol in a ratio of 1: 4 (v/v) following previously described protocol 4. Mass spectrometry runs parameters and data acquisitions: Metabolites were analyzed using an Agilent 1260 Infinity HPLC system coupled to an Agilent 6550 HRLC system. Metabolite (1µl) was separated at a gradient of 5% mobile phase B increasing linearly to 100% B in 50 min and was held at until 60 min, mobile phase A: 0.1 % formic acid in water, mobile phase B: 0.1 % formic acid in 90% acetonitrile, flow-rate 0.3 ml/min, temperature 40°C. Agilent MassHunterTM software was used for untargeted data acquisition in centroid mode over the mass range m/z 50–1000 with a scan rate of 1.5 spectra/sec. Data processing and analysis: Data was deconvoluted into individual chemical peaks in Agilent MassHunter™ Qualitative Analysis B.05.01 using molecular feature extractor (MFE) with an absolute height filter set to 5,000 counts. A separate, complementary “targeted” data mining approach was used that was based on a list of annotated compounds with empirical formulas. An algorithm, “Find by Mass” was used to find compounds in LC/MS data files. The list of compounds and their associated formulas was derived from METLIN. Results of the analyses was a series of extracted ion chromatograms (EICs) that were saved as CEF files and were used for subsequent statistical analysis and data visualization in mass profiler professional (MPP) version 12.6 software (Agilent Technologies, Santa Clara, CA). A complete list of metabolites expressed differentially (fold change > 2; p < 0.05) in malaria and dengue as compared to HC is tabulated in Table S2. 3 TableS1: Investigation of hematological and biochemical parameters of the dengue and malaria patient sera and healthy subjected included in the study Healthy (n=54) Malaria (n=37) p-value Dengue (n=39) p-value Mean ± SD (Max - Min) Mean ± SD (Max - Min) Mean ± SD (Max - Min) Sex F=12; M=42 F=6; M=31 F=7; M=32 Age 21.5±4.6(35-18) 29.1±10.8(56-15) 28.7±9.7(55-16) Haematological parameters Hemoglobin (g/dl) 12.3±1.6(14.3-8.1) 9.4±3.0(14.2-3.2) 1.00E-05 12.8±2.4(18.8-5.0) 0.242 56191.9±29025.1(122000- Platelets/μl 68805.6±40651.7(200000-39300) 0.1052 30843.6±28085.3(104000-2700) 1.00E-05 2600) Biochemical parameters Creatinine (mg/dl) 0.8 0.1(1.07-0.51) 2.0±1.4(4.6-0.0) 1.00E-05 5.5±15.4(85. -0.7) 1.00E-04 Total bilirubin (mg%) 1.5±2.4(9.0-0.25) 1.5±1.0(4.6-0.6) 1.00E-05 1.3±1.1(6.6-0.6) 6.94E-4 Urea (mg/dl) 33.6±9.0(55.4-21.2) 70.6±46.1(156.38-21.0) 4.2E-04 47.6±25.4(126.0-22.0) 0.141 AST (1U/l) 25.6±7.5(40.0-12.38) 32.9±7.7(51.0-17.6) 6.00E-05 39.5±21.6(89.0-13.0) 4.00E-04 ALT (1U/l) 28.8±10.3(50-12.38) 49.3±47.0(201-12.4) 3.6E-05 46.5±31.5(194.2-20.0) 1.00E-05 ALP (µkat/l) 67.5±28.6(110.0-17.21) 168.8±137.8(501-12.0) 1.00E-05 121.4±69.9(400.0-59.0) 1.00E-05 4 Table S2: Complete details of all statistically significant (p < 0.05 and fold change > 2) altered metabolites in malaria and dengue obtained from mass spectrometry (Q-TOF) analysis. Regulation Regulation Compound FC (D vs H) FC (M vs H) Mass CAS Number KEGG ID (D vs H) (M vs H) (+/-)14,15-EpETrE 7.22706 up 1.09E+07 up 383.0678 72509-76-3 C06995 Phosphoric acid -1 down 962662.7 up 176.0437 923-37-5 D-Glucose 15.772747 up 860830.8 up 200.0315 57728-59-3 Leucine -1 down 817288.44 up 614.1229 153-18-4 C05625 D-Galactose 30.251202 up 770244.25 up 449.3047 126-27-2 C12552 Histidine 139.3417 up 746023.5 up 244.0571 54955-36-1 myo-Inositol -1 down 695352.44 up 310.15 60-99-1 C07192 Asparagine 24.431307 up 694322.7 up 365.0577 103237-52-1 Acetoacetic acid 158917.64 up 641903.1 up 208.0848 487-90-1 C00931 Benzoic acid -1 down 641887 up 776.5494 473-32-5 Dihydroxyacetone (glycerone) 98.77884 up 431303.44 up 445.0632 19046-78-7 C03794 Ethanolamine -1 down 404604.28 up 384.2424 116285-37-1 Ceramide (d18:1/12:0) -1 down 377302.47 up 262.0682 2627-69-2 Maltose 16.973713 up 366541.22 up 190.0025 34875-87-1 Glyceric acid 91836.63 up 360219.6 up 258.0363 54955-32-7 Hypoxanthine -1 down 337194.22 up 187.0237 6061-96-7 Glycolaldehyde 1069.2494 up 313634.8 up 427.0613 80210-62-4 C08114 Inosine -1 down 292967.16 up 356.1999 83-43-2 C07197 Creatine 1195.8191 up 272420.75 up 199.0262 4432-31-9 Canavanine -1 down 271589.72 up 477.2118 C11131 Pyridoxine (Vitamin B6) -1 down 260782.05 up 908.6259 31867-59-1 C03543 Kynurenine -1 down 230422.62 up 292.1772 51781-06-7 C06874 Glucosamine 3.7021706 up 225207.48 up 462.2861 483-18-1 C09421 5 L-DOPA 4.486463 up 203769.89 up 506.3114 C11999 QH2 -1 down 193540.36 up 135.0542 21768-45-6 C06056 Dipalmitoylphosphatidic acid 61.05689 up 193439.83 up 775.5522 98677-33-9 Ethanol 3.7754045 up 189612.4 up 487.3262 C03640 Bilirubin 61.97075 up 184579.55 up 299.1878 549-91-7 C11816 Biliverdin IX -1 down 184297.17 up 298.1259 159126-30-4 Homogentisic acid -1 down 177600.52 up 327.2341 C11898 Trimethylamine -1 down 163461.58 up 375.3042 29560-24-5 C05440 N-Acetyl-L-glutamic
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