Creating Open-Source software packages for Raman spectrum processing and analysis

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Development of a Spatial Offset Raman Spectroscopy (SORS) Platform for Biological Tissue Interrogation

La spectroscopie Raman (RS) devient un outil de plus en plus populaire pour le diagnostic du cancer. Mesurant la diffusion inélastique de la lumière associée aux modes de vibration des molécules, le signal Raman est spécifique à la composition moléculaire d’un échantillon. Lorsqu’il est mesuré sur un échantillon biologique, le signal Raman peut être utilisé pour la caractérisation et l’identification de celui-ci, et ainsi mener à un diagnostic. De plus, le signal Raman est entièrement intrinsèque et peut être couplé à un algorithme d’apprentissage automatique permettant la classification de tissus en temps réel. Au cours de la dernière décennie, une technique baptisée spectroscopie Raman à décalage spatial (SORS) a été utilisée afin d’augmenter la profondeur de pénétration du signal Raman dans les tissus biologiques. Cependant, aucun modèle empirique n’a été établi permettant de relier le décalage spatial et la profondeur de pénétration pour une large gamme de propriétés optiques d’échantillons. De plus, l’effet de l’ouverture numérique (NA) des fibres optiques sur le signal SORS acquis reste inexploré. Ce projet consiste en la conception et la validation d’une plateforme de caractérisation SORS pour l’interrogation de tissus biologiques. Tout d’abord, un système d’acquisition Raman existant a été optimisé et modernisé pour être utilisable avec une plate-forme de caractérisation SORS. En remplaçant les contrôleurs laser et en reprogrammant le logiciel d’acquisition, le contrôle de puissance laser a été amélioré et le temps d’acquisition a été réduit. Deuxièmement, les outils logiciel de traitement et de classification des signaux Raman existants ont été mis à jour afin d’inclure des algorithmes d’extraction d’information et de permettre aux modèles de classification d’être exportés et validés sur de nouvelles données. Enfin, une plate-forme de caractérisation SORS a été conçue et validée sur des fantômes optique nylon-PDMS à deux couches. En procédant à par enduction centrifuge du PDMS sur les disques de nylon, il a été possible d’obtenir de manière répétable une couche supérieure de 500 ± 50 um de PDMS. Du TiO2 a été ajouté au mélange de PDMS avant l’enduction ce qui a permis de modifier le coefficient de diffusion du PDMS. Les coefficients de diffusion des fantômes ont été mesurés de 0 cm−1 à 30 cm−1.----------Abstract Raman spectroscopy (RS) is becoming an increasingly popular tool for cancer diagnosis. Because RS measures inelastic light scattering based on the vibrational modes of molecules, Raman signal is specific to a sample’s molecular composition. Thus, when performed on tissue, spectroscopic signal can be used for characterization and identification leading to a diagnosis. In addition, Raman signal is entirely label-free and can be coupled with machine learning algorithms for real-time tissue classification. In the past decade, a technique referred as Spatially Offset Raman Spectroscopy (SORS) has been used to increase the Raman signal penetration depth in biological tissue. However, no empirical model has been established to link spatial offset and penetration depth for a large range of sample optical properties. In addition, the effects of the optical fibers numerical aperture (NA) on SORS acquired signals remains unexplored. This project presents the design and validation of a SORS characterization platform for biological tissue interrogation. First, an existing Raman acquisition system has been optimized and modernized to be usable in conjunction with a SORS characterization platform. By replacing the laser controllers and by reprogramming the control software, the laser power control has been improved and the acquisition time has been reduced. Then, existing Raman signal processing and classification toolboxes have been upgraded to include feature engineering algorithms and to allow trained classification models to be exported and cross validated on unseen data. Finally, a SORS characterization platform has been designed and validated on two-layer nylon-PDMS based optical phantoms. By spin coating PDMS on nylon disks, it was possible to a uniform 500 ± 50 um PDMS top layer. TiO2 was added to the PDMS mix prior to spin coating to modify the PDMS diffusion coefficient. Phantoms diffusion coefficient were measured between 0 cm−1 to 30 cm−1

Wide-field optical spectroscopy system integrating reflectance and spatial frequency domain imaging to measure attenuation-corrected intrinsic tissue fluorescence in radical prostatectomy specimens

The development of a multimodal optical imaging system is presented that integrates endogenous fluorescence and diffuse reflectance spectroscopy with single-wavelength spatial frequency domain imaging (SFDI) and surface profilometry. The system images specimens at visible wavelengths with a spatial resolution of 70 microm, a field of view of 25 cm(2) and a depth of field of approximately 1.5 cm. The results of phantom experiments are presented demonstrating the system retrieves absorption and reduced scattering coefficient maps using SFDI with <6% reconstruction errors. A phase-shifting profilometry technique is implemented and the resulting 3-D surface used to compute a geometric correction ensuring optical properties reconstruction errors are maintained to <6% in curved media with height variations <20 mm. Combining SFDI-computed optical properties with data from diffuse reflectance spectra is shown to correct fluorescence using a model based on light transport in tissue theory. The system is used to image a human prostate, demonstrating its ability to distinguish prostatic tissue (anterior stroma, hyperplasia, peripheral zone) from extra-prostatic tissue (urethra, ejaculatory ducts, peri-prostatic tissue). These techniques could be integrated in robotic-assisted surgical systems to enhance information provided to surgeons and improve procedural accuracy by minimizing the risk of damage to extra-prostatic tissue during radical prostatectomy procedures and eventually detect residual cancer

Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements

Significance: Ensuring spectral quality is prerequisite to Raman spectroscopy applied to surgery. This is because the inclusion of poor-quality spectra in the training phase of Raman-based pathology detection models can compromise prediction robustness and generalizability to new data. Currently, there exists no quantitative spectral quality assessment technique that can be used to either reject low-quality data points in existing Raman datasets based on spectral morphology or, perhaps more importantly, to optimize the in vivo data acquisition process to ensure minimal spectral quality standards are met. Aim: To develop a quantitative method evaluating Raman signal quality based on the variance associated with stochastic noise in important tissue bands, including C─C stretch, CH2  /  CH3 deformation, and the amide bands. Approach: A single-point hand-held Raman spectroscopy probe system was used to acquire 315 spectra from 44 brain cancer patients. All measurements were classified as either high or low quality based on visual assessment (qualitative) and using a quantitative quality factor (QF) metric. Receiver-operator-characteristic (ROC) analyses were performed to evaluate the performance of the quantitative metric to assess spectral quality and improve cancer detection accuracy. Results: The method can separate high- and low-quality spectra with a sensitivity of 89% and a specificity of 90% which is shown to increase cancer detection sensitivity and specificity by up to 20% and 12%, respectively. Conclusions: The QF threshold is effective in stratifying spectra in terms of spectral quality and the observed false negatives and false positives can be linked to limitations of qualitative spectral quality assessment

Macroscopic-Imaging Technique for Subsurface Quantification of Near-Infrared Markers During Surgery

Obtaining accurate quantitative information on the concentration and distribution of fluorescent markers lying at a depth below the surface of optically turbid media, such as tissue, is a significant challenge. Here, we introduce a fluorescence reconstruction technique based on a diffusion light transport model that can be used during surgery, including guiding resection of brain tumors, for depth-resolved quantitative imaging of near-infrared fluorescent markers. Hyperspectral fluorescence images are used to compute a topographic map of the fluorophore distribution, which yields structural and optical constraints for a three-dimensional subsequent hyperspectral diffuse fluorescence reconstruction algorithm. Using the model fluorophore Alexa Fluor 647 and brain-like tissue phantoms, the technique yielded estimates of fluorophore concentration within ±25% of the true value to depths of 5 to 9 mm, depending on the concentration. The approach is practical for integration into a neurosurgical fluorescence microscope and has potential to further extend fluorescence-guided resection using objective and quantified metrics of the presence of residual tumor tissue

Experimental validation of a spectroscopic Monte Carlo light transport simulation technique and Raman scattering depth sensing analysis in biological tissue

ABSTRACT: Significance: Raman spectroscopy (RS) applied to surgical guidance is attracting attention among scientists in biomedical optics. Offering a computational platform for studying depthresolved RS and probing molecular specificity of different tissue layers is of crucial importance to increase the precision of these techniques and facilitate their clinical adoption. Aim: The aim of this work was to present a rigorous analysis of inelastic scattering depth sampling and elucidate the relationship between sensing depth of the Raman effect and optical properties of the tissue under interrogation. Approach: A new Monte Carlo (MC) package was developed to simulate absorption, fluorescence, elastic, and inelastic scattering of light in tissue. The validity of the MC algorithm was demonstrated by comparison with experimental Raman spectra in phantoms of known optical properties using nylon and polydimethylsiloxane as Raman-active compounds. A series of MC simulations were performed to study the effects of optical properties on Raman sensing depth for an imaging geometry consistent with single-point detection using a handheld fiber optics probe system. Results: The MC code was used to estimate the Raman sensing depth of a handheld fiber optics system. For absorption and reduced scattering coefficients of 0.001 and 1 mm−1, the sensing depth varied from 105 to 225 μm for a range of Raman probabilities from 10−6 to 10−3. Further, for a realistic Raman probability of 10−6, the sensing depth ranged between 10 and 600 μm for the range of absorption coefficients 0.001 to 1.4 mm−1 and reduced scattering coefficients of 0.5 to 30 mm−1. Conclusions: A spectroscopic MC light transport simulation platform was developed and validated against experimental measurements in tissue phantoms and used to predict depth sensing in tissue. It is hoped that the current package and reported results provide the research community with an effective simulating tool to improve the development of clinical applications of RS

Data consistency and classification model transferability across biomedical Raman spectroscopy systems

Surgical guidance applications using Raman spectroscopy are being developed at a rapid pace in oncology to ensure safe and complete tumor resection during surgery. Clinical translation of these approaches relies on the acquisition of large spectral and histopathological data sets to train classification models. Data calibration must ensure compatibility across Raman systems and predictive model transferability to allow multi-centric studies to be conducted. This paper addresses issues relating to Raman measurement standardization by first comparing Raman spectral measurements made on an optical phantom and acquired with nine distinct point probe systems and one wide-field imaging instrument. Data standardization method led to normalized root-mean-square deviations between instruments of 2%. A classification model discriminating between white and gray matter was trained with one point probe system. When used to classify independent data sets acquired with the other systems, model predictions led to >95% accuracy, preliminarily demonstrating model transferability across different biomedical Raman spectroscopy instruments

HIV-1 Vif binds to APOBEC3G mRNA and inhibits its translation

The HIV-1 viral infectivity factor (Vif) allows productive infection of non-permissive cells (including most natural HIV-1 targets) by counteracting the cellular cytosine deaminases APOBEC-3G (hA3G) and hA3F. The Vif-induced degradation of these restriction factors by the proteasome has been extensively studied, but little is known about the translational repression of hA3G and hA3F by Vif, which has also been proposed to participate in Vif function. Here, we studied Vif binding to hA3G mRNA and its role in translational repression. Filter binding assays and fluorescence titration curves revealed that Vif tightly binds to hA3G mRNA. Vif overall binding affinity was higher for the 3′UTR than for the 5′UTR, even though this region contained at least one high affinity Vif binding site (apparent Kd = 27 ± 6 nM). Several Vif binding sites were identified in 5′ and 3′UTRs using RNase footprinting. In vitro translation evidenced that Vif inhibited hA3G translation by two mechanisms: a main time-independent process requiring the 5′UTR and an additional time-dependent, UTR-independent process. Results using a Vif protein mutated in the multimerization domain suggested that the molecular mechanism of translational control is more complicated than a simple physical blockage of scanning ribosomes

Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates

ABSTRACT: Objectives Raman spectroscopy as a diagnostic tool for biofluid applications is limited by low inelastic scattering contributions compared to the fluorescence background from biomolecules. Surface-enhanced Raman spectroscopy (SERS) can increase Raman scattering signals, thereby offering the potential to reduce imaging times. We aimed to evaluate the enhancement related to the plasmonic effect and quantify the improvements in terms of spectral quality associated with SERS measurements in human saliva. Methods Dried human saliva was characterized using spontaneous Raman spectroscopy and SERS. A fabrication protocol was implemented leading to the production of silver (Ag) nanopillar substrates by glancing angle deposition. Two different imaging systems were used to interrogate saliva from 161 healthy donors: a custom single-point macroscopic system and a Raman micro-spectroscopy instrument. Quantitative metrics were established to compare spontaneous RS and SERS measurements: the Raman spectroscopy quality factor (QF), the photonic count rate (PR), the signal-to-background ratio (SBR). Results SERS measurements acquired with an excitation energy four times smaller than with spontaneous RS resulted in improved QF, PR values an order of magnitude larger and a SBR twice as large. The SERS enhancement reached 100×, depending on which Raman bands were considered. Conclusions Single-point measurement of dried saliva with silver nanopillars substrates led to reproducible SERS measurements, paving the way to real-time tools of diagnosis in human biofluids

Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

ABSTRACT: 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 and reagents may become less specific to the virus. AIM: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents. APPROACH: We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique-Raman spectroscopy-to detect changes in the molecular profile of saliva associated with COVID-19 infection. RESULTS: We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 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