key: cord-0293858-yp33nhfc
authors: Ember, K. J. I.; Daoust, F.; Mahfoud, M.; Dallaire, F.; Zamani, E.; Tran, T.; Plante, A.; Diop, M.-K.; Nguyen, T.; St-Georges-Robillard, A.; Ksantini, N.; Lanthier, J.; Filiatrault, A.; Sheehy, G.; Quach, C.; Trudel, D.; Leblond, F.
title: Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning
date: 2021-09-23
journal: nan
DOI: 10.1101/2021.09.21.21262619
sha: 96ea88fdd1d254b013289da7f4a6a176d8d56bc9
doc_id: 293858
cord_uid: yp33nhfc

Significance: The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise. Aim: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. Approach: We present a workflow for collecting, preparing and imaging dried saliva supernatant droplets using a non-invasive, label-free technique known as Raman spectroscopy to detect changes in the molecular profile of saliva associated with COVID-19 infection. Results: Using machine learning and droplet segmentation, amongst all confounding factors, we discriminated between COVID-positive and negative individuals yielding receiver operating coefficient (ROC) curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity, 75% specificity) and females (84% sensitivity, 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%. Conclusion:These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.

inversely proportional to wavelength. Since the ground state is more populous at thermal equilibrium, the Stokes shift is more prevalent and commonly used in Raman spectroscopy. 16, 17 In a Raman spectrometer, a detector is used to measure light that has been inelastically scattered from a sample after it has passed through a series of filters, and these measurements are converted into a Raman spectrum. This is a plot of the intensity of the scattered light against the Raman shift in wavenumbers. 18 Intensity of a Raman peak at a particular Raman shift increases as the concentration of molecules responsible for the peak increases. A Raman spectrum can therefore be thought of as a molecular fingerprint giving information about the molecules present in the sample through the analysis of the position, height, and width of peaks present in the spectrum. Raman spectroscopy can identify biomolecular features (such as lipids, proteins, nucleic acids, and amino acids) within biological samples. In recent years, clinical applications of this light-based technique have gained traction in oncology, [19] [20] [21] inflammatory diseases, 22, 23 transplantation 24 and virology. 25, 26 For example, Camacho et al. 25 developed surface enhanced Raman sensors for the detection of the Zika virus by functionalizing coreshell nanoparticles with Zika ZIKV NS1 antibodies and reported a sensibility of 10 ng/mL.

While not all metabolic impacts of SARS-CoV-2 are known, some have been identified or can be anticipated from similar diseases. The severe acute respiratory syndrome (SARS) virus, SARS-CoV-1, has been shown to induce long-term metabolic changes in formerly infected patients. Elevated serum levels of cysteine, alanine, aspartic acid, succinic acid, and lactic acid were observed. 27 The transmission vectors of the SARS-CoV-2 virus have been identified to be through the propagation of aerosols of biofluids such as saliva carrying a viral load. 28, 29 Reports have shown the high expression of angiotensin-converting enzyme II (ACE2) in epithelial cells of the oral cavity, 30 ACE2 being a receptor which is involved in the mechanism of entry of the virus into cells.

Furthermore, it has been hypothesized that COVID-19 may cause bacterial infection and other diseases of the salivary gland. 31 This could theoretically be detected by Raman spectroscopy, as the concentration of analytes is correlated with the intensity, width, and specificity of Raman peaks.

In a pandemic context, fast near real-time screening is required to track and contain the propagation of a virus. Airports, schools, workplaces, and remote communities would benefit from a rapid, reagent-free screening technique. The sensitivity and the label-free approach of Raman spectroscopy makes it a candidate for fast, robust, low-cost, and transportable means of viral screening to complement the diagnosis capacity of the gold standards. Any COVID screening tool should be applicable to all users, regardless of symptoms, age, sex, time-of-day of sample collection or diet. This poses a challenge when developing a label-free Raman spectroscopic approach as saliva composition is affected by time of day, sex, age and potentially other underlying health conditions. [32] [33] [34] [35] [36] [37] [38] A 2021 study (n = 30 for COVID positive volunteers) found that Raman microspectroscopy could detect COVID-19 infection in saliva with 84% sensitivity and 92% specificity. 39 However, the clinical cohort was restricted to elderly volunteers who had presented to hospitals and the study design required saliva collection to occur prior to breakfast. Thirty-six percent of positive cases were severely or critically ill, and a further 30% had symptoms and evidence of pneumonia upon imaging. Another recent study (n = 29 for COVID positive volunteers) determined infrared spectroscopy could be used to discriminate between COVID-infected and non-infected saliva with 93% sensitivity and 82% specificity. 40 As for the previous study, both the COVID-positive and negative cohorts consisted of hospitalized, symptomatic patients requiring treatment. Most labelfree tests would be aimed at screening non-hospitalized people who may be symptomatic or asymptomatic with different levels of severity of COVID-19 infection.

were not reported for this dataset, including age, sex and comorbidities. To be applicable in a setting outside of hospitals, a label-free screening technique must be applicable to people of all ages, with or without symptoms, regardless of diets, smoking status, with samples collected at any time of day.

Here, we present the results of a study demonstrating that Raman spectroscopy combined with machine learning is a candidate for real-world COVID-19 screening in the general population. The study design was developed to ensure that the number of samples collected allowed us to match COVID-positive and COVID-negative samples in terms of potential confounding factors including sex at birth, age, COVID symptoms, body mass index and prescription drugs taken. We studied saliva samples taken from volunteers at a COVID-19 testing clinic, including asymptomatic volunteers and those with respiratory and non-respiratory symptoms. Samples were taken from people aged >10 years old to <61 years old.

The experimental workflow is shown in Figure 1 Volunteers presenting between 10 am and 2 pm were asked to complete a questionnaire Figure 1 : Workflow from saliva collection to determination of COVID infection status from Raman spectra. Volunteers donated between 1 and 5 ml of saliva into a 50 ml tube. The liquid was pipetted into a 1.5 ml microcentrifuge tube which was centrifuged. The saliva supernatant was then stored at -80 o C. Supernatant was thawed, vortexed and mounted on an aluminum slide. After 45 minutes of drying, spectra were acquired using a Renishaw InVia Raman spectrometer. This figure was created with BioRender.com. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in reporting their symptoms and associated biological and environmental factors that could affect saliva composition e.g., age, sex at birth and comorbidities. When applicable, volunteers were asked to remove any lipstick or lip balm using makeup removal wipes (About Face Cleansing Wipes, Micronova Manufacturing Inc., Torrance, California).

Each volunteer was instructed to first rinse their mouth three times with bottled water to remove food debris. Volunteers then waited 5-10 minutes for saliva to accumulate before spitting in a 50 ml Falcon tube to collect a minimum of 1.5 ml of the biofluid.

Tubes were then immediately stored in a refrigerator at 4°C. Samples were transported to the Centre de Recherche du CHUM (CRCHUM) on ice. Samples were classified as being from asymptomatic, non-respiratory symptomatic or symptomatic volunteers based on symptoms reported in the questionnaire. "Respiratory" symptoms included those that might result in significant amounts of mucus in saliva such as runny nose, difficulty breathing, sore throat, coughing or wheezing. "Non-respiratory" symptoms included all other symptoms including headache, muscle aches and tiredness. The classification of each sample as being obtained from a COVID-19 positive or negative volunteer was based on PCR tests either based on nasopharyngeal or saliva swabs.

Saliva samples were processed and imaged at biosafety containment level 2 (BSL2) in biosafety cabinets. 1 ml of whole saliva samples were transferred to 1 ml microcentrifuge tubes and centrifuged at 4000 rpm for 30 minutes at 4°C. The supernatant was pipetted into one cryotube, mixed and then 40-500 µl were aliquoted into five separate 1.8 ml cryotubes. The part of the supernatant near the pellet was discarded and the pellet was retained in the microcentrifuge tube. Pellet and supernatant samples were stored at -80°C. Prior to Raman spectroscopy interrogation, saliva supernatant samples were thawed at room temperature for 30 minutes, vortexed for 40 seconds and 10 µl were pipetted onto an aluminum slide. Droplets were allowed to dry at room temperature for at least 45 minutes.

Model saliva was made using concentrations listed in Supplementary Table S1 . As for human saliva supernatant, it was frozen at -80°C and thawed at room temperature for 30 min, vortexed for 40 s and 10 µl of sample were pipetted onto an aluminum slide and dried. Bovine submaxillary mucin was used to generate the model saliva due to limitations in availability of human mucin. To address this limitation, Raman spectra from a limited available quantity of human mucin type I were also acquired for more accurate human mucin peak assignment in human saliva supernatant samples.

Dried saliva supernatant samples and model saliva were imaged using an inVia™ confocal Raman microscope (Renishaw, Gloucester, UK) in reflection mode.

Brightfield montage images of each droplet were obtained with a 5x lens. Brightfield montage images of the central crystalline region and edge region were obtained using a 50x objective. For Raman spectral acquisitions, the excitation consisted of a 785 nm 40 mW laser in line-focus mode (3 µm x 8 µm spot size) with a 1200 l/mm grating. Spectra were acquired in the fingerprint region (between 602 and 1726 cm -1 ) from three separate morphological regions within each droplet: "edge" (the perimeter of the droplet), "on crystal" (inside visible crystals in the center of the droplet) and "off crystal" (outside visible crystals in the center of the droplet). Ten spectra were acquired in each region and 8-10 repeat measurements were taken from each point. Spectra were taken from random "on crystal" and "off crystal" regions in the densest region of the droplet, and All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint in a zig-zag pattern within the edge to capture spectra from the very edge and slightly closer to the center (Figure 2 ). Each measurement was made ensuring 60-70% of the Raman microscope sensor dynamical range was used to minimize the impact of shot noise, resulting in an acquisition time of 2-10 s for "edge" and 15-40 s for center spectra ("on crystal" and "off crystal"), depending on the level of sample autofluorescence.

Solid compounds placed on aluminum slides were imaged using the Renishaw InVia microscope. 25 spectra were taken from each compound using the 50x lens between 105-1727 and 2601-3359 cm -1 .

The following data preprocessing steps were applied to every individual measurement: 1) removal of cosmic rays was imperative due to the long acquisition time, 2) smoothing using a Savitzky-Golay filter of order 3 with a window size of 11, 3) background subtraction of signals produced by the aluminum slides and autofluorescence using a custom adaptation of the rolling ball algorithm 41 , 4) cropping the region below 1100 cm -1 due to the large variances at lower wavenumber shifts, 5) averaging of the repeat measurements taken at a given spatial point, and 6) standard normal variate (SNV) normalization. 21

For data quality reasons, a limited number of spectra (from 3 patients for "on crystal", from 5 patients for "edge") were removed before training the machine learning models.

Exclusion was based on abnormally high levels of residual stochastic noise (after background removal) or the presence of unusual spectral shapes unrelated to All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted  biomolecular content. For each spectrum, a Gaussian fitting procedure was applied to all peaks of biological origin fitted to extract its position, its height, and its width; a total of 24 peaks were extracted using this procedure. These -along with the relative intensity of 700 individual bands in a spectrum-represent the data from which machine learning models could be trained.

A feature selection technique was applied to reduce the size of the feature set prior to model machine learning model training. Feature reduction is necessary because some spectral regions either provide no useful information for the classification or are perhaps too correlated. Algorithms typically run with at least one parameter, known as hyperparameter, that needs to be initialized. A variance-based algorithm was used to coarsely reduce the number of features. Features presenting a large variance have a higher weight and by sorting them according to their weight, only the k-best features are retained, where k was varied between 5 and 80 for the spectral features and 5 and 72 for the peak features. A Random Forest classifier (RF) with 200 estimators is then used to further reduce the feature set, this time with a multivariate technique rather than a univariate one. Again, this was done independently for spectral and peak features. In the end, the final number features will range between 20 and 200, with an average of 90.

A multiple-instance learning (MIL) approach was favored to be at the core of the classification algorithm. In such a scenario, all the spectra (instances) belonging to a given patient are represented by a bag to which a label is associated (COVID positive or negative) rather than labelling the instances individually. Labels associated to each patient (bag) correspond to the result from their PCR test. More specifically, the selected method is a multiple-instance learning via embedded instance selection (MILES) All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint algorithm that compute a similarity measure between instances to map them into a different feature space 42 . Instead of being represented by multiple instances from the original feature space, each bag is now represented by a single instance, based on the similarity measure from the new feature space. Under MILES, the mapping is done using an intermediate instance pool (IIP) which contains all the instances. It is also possible to create a discriminative instance pool (DIP) which is a subset of the IIP represented only by the best instances. This method is called a MIL with discriminative mapping (MILDM) algorithm 43 .

The similarity function has a hyperparameter, σ 2 , that varies between 1 and 70. For high σ 2 values, the similarity function tends to 1, and 0 for small σ 2 values. The size of the DIP is defined by a hyperparameter, mdip, that varies between 5 and 60% of the total number of spectra. Following the mapping, for both MILES and MILDM algorithms, the same feature selection algorithm used for peak and peak features is applied to the new mapped feature space.

The number of features in the MILES and MILDM spaces are defined by hyperparameters kMILES and kMILDM that vary between 5 and 60% of the total number of spectra, and between 5 and mdip, respectively. A RF classifier with 200 estimators is applied subsequently. Once the bags have been mapped to the new feature space, either with MILES or MILDM, a standard Support Vector Machine (SVM) algorithm is used for the training. Because we used a linear SVM, the only hyperparameter is the regularization parameter C which corresponds to the penalty term and was varied between 1 and 50.

As the values for the hyperparameters of the feature selection and classification algorithms are not known a priori, many combinations need to be considered. A combination is formed by randomly selecting a value for each hyperparameter within their respective range. A 5-fold cross validation (CV) procedure is used to assess the classification performance for each hyperparameter combination. Once applied on the validation set, the trained model will output All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; a classification probability, continuous between 0 and 1, for each sample in the validation set.

This procedure is repeated until all folds have been used once as a validation set. The model performance is assessed with a receiver operating characteristic (ROC) curve from which we can compute the accuracy, sensitivity, and specificity associated to the point with the minimal distance to the upper left corner and the area-under-curve (AUC). The ROC curve is computed by comparing the classification probabilities from the validation sets to their pathological labels, either 0 (COVID negative) or 1 (COVID positive), by varying the threshold value between 0 and 1.

In order to test the repeatability of the results, all these steps were repeated five times, allowing the data to be split differently between the folds. The final performance assessment is an average ROC curve with its own AUC associated. This procedure is repeated until all the desired hyperparameter combinations have been considered and the set of hyperparameters with the highest performance corresponds to the final model.

We developed a technique for obtaining Raman spectra from saliva supernatant in a way that minimized person-to-person variation (Figure 1 ). Raman spectroscopy is sensitive to all Raman-active molecules within a sample and so minimizing exogenous particles is critical. We had previously found that the Raman spectrum of chromophores was present in volunteers who donated saliva whilst wearing lipstick (Supplementary Figure   S1 ). We therefore provided volunteers with lipstick removal wipes if necessary and implemented a mouth washing step to remove food debris. After a waiting time of 5 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted  minutes, whole saliva was collected from 550 volunteers at a COVID-19 testing site and processed in a containment level 2 facility (clinical characteristics listed in Table 1 ).

Saliva was centrifuged to remove further food particles and the supernatant stored at -80C. The supernatant was allowed to thaw at room temperature. It was then vortexed, dried on an aluminum slide, and analyzed using a Raman microspectrometer (InVia, Renishaw, UK). Spectra obtained from aqueous phase saliva supernatant were primarily fluorescence with no visible Raman peaks, but upon drying we could obtain Raman spectra with clearly distinguishable Raman peaks. The methodology is described in the materials and methods section in more detail. Clinical characteristics of the volunteers are reported in Table 1 . The viral loads of our samples ranged from a cycle threshold of Table S1 ). perpetuity.

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Female 225 (44) 18 (47) 18 (55) Male 206 (40) 20 (53) 15 (45) Prefer not to say 82 (16) 0 (0) 0 (0) 

Raman microspectrometers are spectrometers coupled to microscopes to yield Raman spectra with a spatial resolution that can be modulated by the use of objectives with different magnifications. A single Raman spectrum can be obtained from a discrete point within a sample and the area of excitation in these experiments was 3 µm by 8 µm, using a 50x objective. In brightfield images (5x and 50x) taken with the system, we observed that drops of human saliva perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint supernatant most frequently dried with a translucent crystalline region in the center and a slightly more opaque peak around the edge (Figure 2A-C) . This is a common phenomenon observed in dried water-based droplets due to the "coffee-ring effect" in which suspended particles accumulate at the edge of the droplet. In some saliva droplets, there was also a region between the crystalline center and edge where there were no crystals and Raman signal was minimal which could be attributable to a lower concentration of Raman-active molecules.

In order to determine which part of the droplet should be interrogated for discrimination between COVID-negative and COVID-positive samples, we took measurements from both the center ( Figure 2B ) and the edge ( Figure 2C ) of the dried droplet. The central region was divided into "on crystal"where measurements were taken from visible crystals -and "off crystal"where measurements were taken from the milieu in-between crystals. Spectra were taken at 10 points in each of the "edge", "on crystal" and "off crystal" regions with 8-10 successive acquisitions taken from each point to increase signal-to-noise ratio (SNR) by averaging the spectra ( Figure 2D ). In the "on crystal" region of human saliva, we observed strong peaks at 1003 and 1045 cm -1 as well as 853 and 930 cm -1 in some volunteers. In the edge region, we observed strong peaks at 1003, 1449 and 1655 cm -1 (Supplementary Table S3 ). These different Raman signatures indicate that the molecular content of the center and the edge of dried saliva supernatant is different. As a droplet dries, certain molecules accumulate at the edge and others in the center. The strong peak at 1003 cm -1 is common in Raman studies of biological materials as it corresponds to the ring breathing mode of phenylalanine and is often one of the strongest peak in proteins 44 .

To confidently assign Raman peaks to donor spectra, we used a saliva model adapted from Sarkar et al. 45 containing salts, bovine serum albumin, mucin and other metabolites. The precise composition of the model is listed in Supplementary Table S2 . We dried the droplet in All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Furthermore, the Raman peaks from the center and the edge of human saliva supernatant ( Figure 3D ) resembled those of model saliva, suggesting a similar molecular composition. We crystalline region (50x) and (C) edge (50x) with acquisition points shown with different symbols: diamonds ("on crystal"), crosses ("off crystal") and triangles (edge). (D) Average spectra from one saliva sample for "on crystal" (top), "off crystal" (middle), and "edge" (bottom) regions respectively with shaded areas representing the interspectral variance within the specimen. Each spectrum is an average of multiple acquisitions obtained with a 785 nm laser using a Renishaw InVia Raman microscope. The scale bar in A is 1 mm in length, whilst the scale bar in B and C are 0.1 mm long. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint took spectra of the pure constituents of the model saliva to determine which molecules give rise to peaks in the model saliva and whether these peaks were also present in human saliva supernatant ( Supplementary Figures S3-S6) . By comparing spectra of human saliva supernatant to model saliva, we could identify Raman peaks due to phosphate, nitrate, proteins and nucleic acids (Supplementary Table S3 ). We found that peaks due to salts (nitrate and phosphate) were strong in spectra taken from the central crystalline region of both model saliva and real human saliva supernatant. Meanwhile, the spectra from the edge of the drop were primarily composed of peaks due to proteins (phenylalanine, amide II, amide III). This is consistent with findings in serum and other biofluidsproteins move towards the edge of the droplet in the drying process whilst salts may be spread throughout the droplet 4647 .

To fully explore the potential of all regions of the dried saliva supernatant matrix for detection of COVID-19, we developed predictive models based on "edge", "on crystal" and "off crystal" regions. The large number of COVID-negative saliva samples allowed us to match each COVID-positive sample with a negative sample having approximately the same characteristics in terms of sex at birth, age, COVID symptoms, body mass index (BMI) and prescription drugs taken (Table 1 ). This ensured that the clinical characteristics of the samples analyzed were approximately the same between the 33 positive (15 males, 18 females) and 38 negative (20 males, 18 females) samples. Patient matching is done to reduce the impact of potential confounding factors. However, there was a slightly higher percentage of analyzed samples from volunteers with comorbidities in the COVID negative group. Comorbidities included optic neuritis, cancer, hypertension, diabetes, anxiety, allergies and migraines.

All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Machine learning (ML) algorithms were trained using spectra from 33 COVID positive and 38 COVID negative samples. The area of laser excitation was small (24 µm 2 ) relative to the area of the droplet (approximately 12 mm 2 ). Due to the crystallization process and dilution of biomolecules within saliva, not all spectra necessarily carry molecular information relevant to the COVID status of the patient. As a result, a ML approach needed to be adapted whereby each spectrum acquired from a saliva droplet from a volunteer could be treated independently ( Figure 4) . The ML approaches that were employed are based on multiple instance learning (MIL); specifically, MIL via embedded instance selection (MILES) and MIL with discriminative bag mapping (MILDM). The implementation details of techniques are presented Figure 4 : Machine learning schematic workflow. The ML workflow consists of a 5-fold cross-validation (CV) embedded in a 5-time repeat loop creating different splitting of the training and validation sets. Feature selection, bagging, mapping, and training steps are repeated for each fold. Raman spectra are represented by spectral peaks and fitted peaks. Each instance is represented by the relevant features which are selected using a combination of a variance-based algorithm, acting as a broad skimmer, and a random forest (RF). Each bag is then mapped from a multiple instance representation to a single instance representation through an instance similarity measure; the mapping function being different for MILES and MILDM. A linear Support vector machine (SVM) algorithm is used to train each model and output a classification probability for patients in the validation set. After each CV procedure, a receiver operating ROC curve is computed with a corresponding AUC value to assess the model performance. The final output is the ROC and AUC averaged over the 5 repetitions, ensuring further stability. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in 

As sex hormones can affect metabolism and immune responses and thus the molecular content of saliva, we separated samples based on sex at birth. In males, we could discriminate between COVID positive and negative saliva supernatant samples with a receiver operating characteristic (ROC) curve AUC of 0.80 using machine learning on Raman spectra taken from the "edge" region ( Figure 5A&B ). This predictive model was built using MILDM (n = 35, 15 COVID positive and 20 COVID negative samples), yielding a sensitivity of 79% and a specificity of 75%. Key features used in model building included peaks that can be assigned to carbohydrates, carotenoids, proteins and nucleic acids ( Figure 5C ). In males, models built using the "on crystal" region had a lower AUC than the edge models (0.72 with MILDM, 15 COVID positive and 20 COVID negative samples) ( Figure 5D -F).

All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint For females, using "on crystal" spectra, we could discriminate between COVID positive and negative saliva samples with an AUC of 0.80 ( Figure 6D&E ). This model was built using MILES (n = 36, 18 COVID-positive and 18 COVID-negative), with a sensitivity of 84% and a specificity of 65%. Key features included peaks that can be assigned to lipids, proteins and nucleic acids ( Figure 6F ). In females, models built using the edge region had a lower AUC than the "on crystal" models (0.67 with MILES, n = 36, 16 COVID-positive, 18 COVID-negative) ( Figure 6A-C) . This is reflected by the observation that the mean edge spectra of COVIDpositive and COVID-negative females have much greater overlap than the mean edge spectra from males, suggesting the molecular composition of the protein-rich edge region of saliva is not altered as severely in females as it is in males.

All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint

The best model in males (built using the "edge") only shared 1203-1206 cm -1 and 1603-1605 cm -1 with the best model in females only. All other features were different.

Amongst all confounding factors including sex at birth, we could discriminate between COVID positive and negative saliva supernatant samples using machine learning on Raman spectra taken from the edge region with an AUC of 0.76 ( Figure 7A&B ). This predictive model was built using MILDM (n = 71, 33 COVID-positive and 38 COVID-negative), yielding a sensitivity of 73% and a specificity of 71%. This reduced the AUC by 0.04 relative to the model built in males alone using the same part of the droplet. Half of the top molecular features are shared between the two models ( Figure 5C and 7C) . We could discriminate between COVID-19 positive and negative "on crystal" regions with an AUC of 0.69 using MILES (n = 69, 31 COVID-positive and 38 COVID-negative) ( Figure 7D&E ). This resulted in a reduction in AUC of 0.11 compared to the model built in females only using the same part of the droplet ( Figure   6E ). More than 65% of the top features are shared between the two models ( Figures 5C and   7C ). We could not build a reliable model using spectra from the "off crystal" region (AUC = 0.57). This was true for all models built, so the "off crystal" region was removed from the analyzes.

Key features used in the edge model for females and males together ( Figure 7C ) included peaks corresponding to mucin (1146 and 1248-1249 cm -1 ), carotenoids (1146, 1170, 1580 cm -1 ) as well as multiple bands that corresponded to the amide I and III regions in proteins and nucleic acids. In the "on crystal" region, the top features used to produce the classification model included peaks associated with lipids (1123-1124, 1265-1267, 1447-1449, 1661-1663 cm -1 ), proteins (all peaks except 1643 cm -1 ), nucleic acids and urea or uric acid (1643 cm -1 ). One of perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in 

Building separate models for females and males resulted in higher predictive accuracy than models including both sexes, so we investigated whether we could discriminate between samples based on sex at birth in COVID-negative samples only. The predictive model produced an ROC with an AUC of 0.70 on edge (n = 20 males, n = 18 females, MILDM) yielding 69% sensitivity, 70% specificity and 0.81 on crystal yielding 82% sensitivity, 69% specificity (MILES) (Supplementary Figure S7D-F) .

We had hypothesized that Raman spectroscopy could be used to discriminate between samples from volunteers with respiratory symptoms compared to those without respiratory symptoms, perhaps as the mucus content of saliva could differ between the two. However, we found that we could not build models with high predictive accuracy to discriminate between samples based on whether reported symptoms were considered "respiratory symptoms" or not (Supplementary Figure S7A-C) . We used samples from all volunteers and grouped "nonrespiratory" symptomatics and asymptomatic samples together into a "non-respiratory" group and compared the corresponding spectra to those from "respiratory" symptometics (n = 44).

Using spectra taken from all volunteers, the AUC was 0.57 in models built using "edge" spectra (n = 44 respiratory, n = 23 non-respiratory) and the AUC was 0.56 using discriminative MILES on "on crystal" spectra (n = 43 respiratory, n = 23 non-respiratory).

We were able to discriminate between COVID-positive and COVID-negative saliva samples in a primer-free, label-free way using Raman spectroscopy and machine learning. We achieved a sensitivity of 79% and specificity of 75% in detecting COVID infection in non-hospitalized males with a range of symptoms, ages, medical conditions and viral load. We achieved a All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint sensitivity of 84% and specificity of 65% in detecting COVID in non-hospitalized females with similar clinical characteristics. We found that for males, the best classification model was built using the "edge" region using features associated with carbohydrates, carotenoids, proteins and nucleic acids whilst for females, the best classification model was built using the "on crystal" region using features associated with lipids, proteins and nucleic acids. The "off crystal" region -the central region of the saliva droplet outside of the crystals -was not useful, suggesting that compounds associated with COVID-19 were perhaps less concentrated in this region than within the crystals themselves or the edge region.

We have found that the Raman spectrum of the "edge" region of dried saliva supernatant differs from that of the central region, with the central region containing peaks due to salts and proteins, whilst the edge region is primarily composed of peaks due to proteins. This is in agreement with electron microscopy studies of dried solutions of lysozyme and salt mixtures in which lysozyme was shown to accumulate at the edge of dried droplets 48 . Although many studies claim to use Raman spectroscopy of saliva to diagnose diseases, this is the first published instance to our knowledge in which both "edge" and center regions (both "oncrystal" "off-crystal") have been used in a single study, yielding complementary information in terms of interrogated biomolecular vibrational modes. Moreover, we could not find any other publications in which a saliva model was used to confidently assign molecular features in a Raman spectrum.

The observation that the best model for males is built using different features and spectra from different parts of the droplet compared to females suggests that COVID-19 may elicit different changes in the biomolecular profile of saliva between the sexes. Studies have shown that there are differences in the immune response of males and females to COVID, with females All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; mounting a more robust T cell response whilst males had higher cytokine levels and are more severely impacted by COVID-19 49 . Furthermore, ACE2 is expressed in lower levels in liver and lung tissue of women compared to men, which could result in different impacts on metabolism 50 .

We found that sex at birth was a confounding factor in saliva-based COVID-19 detection using label-free Raman spectroscopy as the AUC was reduced in models built from males and females together compared to those built from males and females separately. We could also discriminate between saliva supernatant samples based on sex at birth from COVID-negative females and males with an AUC of 0.81 using the "on crystal" region. This is consistent with a study by Muro et al. in which Raman spectroscopy was used to discriminate between 60 samples of whole saliva from males and females 51 . Moreover, the biomolecular composition of saliva has been shown to be different in females and males by nuclear magnetic resonance (NMR) spectroscopy 52 . Levels of glycine, lactic acid and acetate were all higher in saliva taken from males.

It seems we cannot discriminate between samples from volunteers with respiratory symptoms compared to volunteers without respiratory symptoms with high sensitivity or specificity using Raman spectroscopy. This may be because the majority of mucus was pelleted along with food debris during the saliva centrifugation process or because mucus content does not differ significantly in saliva supernatant between "respiratory" and "non-respiratory" samples.

However, as this study occurred during December 2020-February 2021, some volunteers may have exaggerated their symptoms during reporting to obtain a PCR test for non-essential reasons e.g. travel or meeting family members during the holiday season. Unreliable reporting of symptoms is a key factor to be taken into account during infectious disease research and All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted  testing. With larger sample sizes, we may be able to determine whether the sensitivity and specificity of our test is affected by whether the volunteer has respiratory symptoms, nonrespiratory symptoms or no symptoms.

In conclusion, we showed that Raman spectroscopy can be used to detect biomolecular changes between COVID-positive and COVID-negative saliva supernatant and that accounting for the sex of the saliva donor can increase the accuracy of predictive models. However, limitations of our Raman microspectroscopy approach include the fact that it was only possible to sample less than 1% of the full sample area and imaging times were too long for rapid COVID screening (2-3 hours per drop). It is likely that in this study, we may not have captured the full molecular profile of every saliva drop. Therefore, we have developed in parallel a rapid singlepoint Raman spectroscopy platform similar to the probe we have already developed and commercialized for detecting brain cancer 20, 53, 54 . This can image a whole droplet within a few seconds and is portable, affordable and suitable for high throughput screening. The platform could also be used for detection of other infectious diseases or COVID variants simply by retraining the machine learning algorithms on new samples.

Moreover, saliva is a complex medium and multiple factors can affect the Raman spectrum.

However, even whilst taking into account sex at birth, the maximum AUC in our study was 0.80. This suggests that there are further confounding factors. We are currently expanding our analysis to image the remaining 475 COVID-negative samples to take such factors into account using the single point platform. We will be thoroughly assessing the effects of more confounding factors such as age, diet, smoking and comorbidities on the salivary Raman All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ; https://doi.org/10.1101/2021.09.21.21262619 doi: medRxiv preprint fingerprint and evaluating whether these variables can impact the accuracy of detecting COVID-19. We will also look further into factors that could affect hormone levels such as pregnancy, medical conditions, prescription medications and surgeries, and assess their impact on the Raman spectrum of saliva.

6 Notes and references 6 perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in 

There are no conflicts to declare.

We would like to acknowledge Julie Dionne and Axel Bergman from the TransMedTech team.

We would like to thank Laurence Knafo for biohazard training and access to containment level 2 facilities. We would like to thank the following personnel for assistance with questionnaire data entry: Gabriel Beaudoin, Jérémie Kerouac, Thomas Regouffre, Jean-Francois Martin. We All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 23, 2021. ;

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would also like to thank all staff at the Pointe St-Charles testing center, especially Jacynthe, David and Samia.

The authors acknowledge funding from the Canada First Research Excellence Fund