Elastic Light Scattering (ELS) is an innovative technique to identify bacterial pathogens directly on culture plates. Compelling results have already been reported for agri-food applications. Here, we have developed ELS for clinical diagnosis, starting with Staphylococcus aureus early screening. Our goal is to bring a result (positive/negative) after only 6 h of growth to fight surgical-site infections. The method starts with the acquisition of the scattering pattern arising from the interaction between a laser beam and a single bacterial colony growing on a culture medium. Then, the resulting image, considered as the bacterial species signature, is analyzed using statistical learning techniques. We present a custom optical setup able to target bacterial colonies with various sizes (30-500 microns). This system was used to collect a reference dataset of 38 strains of S. aureus and other Staphyloccocus species (5459 images) on ChromIDSAID/ MRSA bi-plates. A validation set from 20 patients has then been acquired and clinically-validated according to chromogenic enzymatic tests. The best correct-identification rate between S. aureus and S. non-aureus (94.7%) has been obtained using a support vector machine classifier trained on a combination of Fourier-Bessel moments and Local- Binary-Patterns extracted features. This statistical model applied to the validation set provided a sensitivity and a specificity of 90.0% and 56.9%, or alternatively, a positive predictive value of 47% and a negative predictive value of 93%. From a clinical point of view, the results head in the right direction and pave the way toward the WHO’s requirements for rapid, low-cost, and automated diagnosis tools.
We report here on the ability of elastic light scattering in discriminating Gram+, Gram- and yeasts at an early stage of growth (6h). Our technique is non-invasive, low cost and does require neither skilled operators nor reagents. Therefore it is compatible with automation. It is based on the analysis of the scattering pattern (scatterogram) generated by a bacterial microcolony growing on agar, when placed in the path of a laser beam. Measurements are directly performed on closed Petri dishes. <p> </p>The characteristic features of a given scatterogram are first computed by projecting the pattern onto the Zernike orthogonal basis. Then the obtained data are compared to a database so that machine learning can yield identification result. A 10-fold cross-validation was performed on a database over 8 species (15 strains, 1906 scatterograms), at 6h of incubation. It yielded a 94% correct classification rate between Gram+, Gram- and yeasts. Results can be improved by using a more relevant function basis for projections, such as Fourier-Bessel functions. A fully integrated instrument has been installed at the Grenoble hospital’s laboratory of bacteriology and a validation campaign has been started for the early screening of MSSA and MRSA (<i>Staphylococcus aureus</i>, methicillin-resistant <i>S. aureus</i>) carriers.<p> </p> Up to now, all the published studies about elastic scattering were performed in a forward mode, which is restricted to transparent media. However, in clinical diagnostics, most of media are opaque, such as blood-supplemented agar. That is why we propose a novel scheme capable of collecting back-scattered light which provides comparable results.
We report on our recent results on robust identification of single bacterial cells embedded in various environments using Spontaneous Raman Scattering. Five species of bacteria were considered, two of which (B. Subtilis and E. Coli) were grown under various conditions, or embedded in two real-world matrices. We recorded the Raman spectra of single cells with a confocal instrument developed in our lab, and performed identification at the species level. Our system integrates a Lensfree imaging module that allows fast detection of bacteria over a large Field-Of-View. Identification rates comparable to those obtained on lab cultures were possible using a comprehensive database containing spectra from bacteria in all environments. In addition, B. Subtilis was correctly identified in 95.5% of the cases using a database composed exclusively of spectra obtained in standard conditions. This is very promising for pathogen threat detection where the construction of an exhaustive database may be challenging.
We report on rapid identification of single bacteria using a low-cost, compact, Raman spectroscope. We demonstrate that a 60-s procedure is sufficient to acquire a comprehensive Raman spectrum in the range of 600 to 3300 cm−1. This time includes localization of small bacteria aggregates, alignment on a single individual, and spontaneous Raman scattering signal collection. Fast localization of small bacteria aggregates, typically composed of less than a dozen individuals, is achieved by lensfree imaging over a large field of view of 24 mm2. The lensfree image also allows precise alignment of a single bacteria with the probing beam without the need for a standard microscope. Raman scattered light from a 34-mW continuous laser at 532 nm was fed to a customized spectrometer (prototype Tornado Spectral Systems). Owing to the high light throughput of this spectrometer, integration times as low as 10 s were found acceptable. We have recorded a total of 1200 spectra over seven bacterial species. Using this database and an optimized preprocessing, classification rates of ∼90% were obtained. The speed and sensitivity of our Raman spectrometer pave the way for high-throughput and nondestructive real-time bacteria identification assays. This compact and low-cost technology can benefit biomedical, clinical diagnostic, and environmental applications.
In this paper we present a longitudinal study of bacteria metabolism performed with a novel Raman spectrometer system.
Longitudinal study is possible with our Raman setup since the overall procedure to localize a single bacterium and
collect a Raman spectrum lasts only 1 minute. Localization and detection of single bacteria are performed by means of
lensfree imaging, whereas Raman signal (from 600 to 3200 cm<sup>-1</sup>) is collected into a prototype spectrometer that allows
high light throughput (HTVS technology, Tornado Spectral System). Accomplishing time-lapse Raman spectrometry
during growth of bacteria, we observed variation in the net intensities for some band groups, e.g. amides and proteins.
The obtained results on two different bacteria species, i.e. Escherichia coli and Bacillus subtilis clearly indicate that
growth affects the Raman chemical signature. We performed a first analysis to check spectral differences and
similarities. It allows distinguishing between lag, exponential and stationary growth phases. And the assignment of
interest bands to vibration modes of covalent bonds enables the monitoring of metabolic changes in bacteria caused by
growth and aging. Following the spectra analysis, a SVM (support vector machine) classification of the different growth
phases is presented.
In sum this longitudinal study by means of a compact and low-cost Raman setup is a proof of principle for routine
analysis of bacteria, in a real-time and non-destructive way. Real-time Raman studies on metabolism and viability of
bacteria pave the way for future antibiotic susceptibility testing.
In this paper we present results on single bacteria rapid identification obtained with a low-cost and compact Raman
spectrometer. At present, we demonstrate that a 1 minute procedure, including the localization of single bacterium, is
sufficient to acquire comprehensive Raman spectrum in the range of 600 to 3300 cm<sup>-1</sup>. Localization and detection of single bacteria is performed by means of lensfree imaging over a large field of view of 24 mm<sup>2</sup>. An excitation source of 532 nm and 30 mW illuminates single bacteria to collect Raman signal into a Tornado Spectral Systems prototype
spectrometer (HTVS technology). The acquisition time to record a single bacterium spectrum is as low as 10 s owing to
the high light throughput of this spectrometer. The spectra processing features different steps for cosmic spikes removal,
background subtraction, and gain normalization to correct the residual inducted fluorescence and substrate fluctuations.
This allows obtaining a fine chemical fingerprint analysis. We have recorded a total of 1200 spectra over 7 bacterial
species (<i>E. coli, Bacillus species, S. epidermis, M. luteus, S. marcescens</i>). The analysis of this database results in a high
classification score of almost 90 %. Hence we can conclude that our setup enables automatic recognition of bacteria
species among 7 different species. The speed and the sensitivity (<30 minutes for localization and spectra collection of
30 single bacteria) of our Raman spectrometer pave the way for high-throughput and non-destructive real-time bacteria
identification assays. This compact and low-cost technology can benefit biomedical, clinical diagnostic and
In this paper, we report on a compact prototype capable both of lensfree imaging, Raman spectrometry and scattering
microscopy from bacteria samples. This instrument allows high-throughput real-time characterization without the need
of markers, making it potentially suitable to field label-free biomedical and environmental applications.
Samples are illuminated from above with a focused-collimated 532nm laser beam and can be x-y-z scanned. The bacteria
detection is based on emerging lensfree imaging technology able to localize cells of interest over a large field-of-view of
Raman signal and scattered light are then collected by separate measurement arms simultaneously. In the first arm the
emission light is fed by a fiber into a prototype spectrometer, developed by Tornado Spectral System based on Tornado’s
High Throughput Virtual Slit (HTVS) novel technology. The enhanced light throughput in the spectral region of interest
(500-1800 cm<sup>-1</sup>) reduces Raman acquisition time down to few seconds, thus facilitating experimental protocols and
avoiding the bacteria deterioration induced by laser thermal heating. Scattered light impinging in the second arm is
collected onto a charge-coupled-device. The reconstructed image allows studying the single bacteria diffraction pattern
and their specific structural features.
The characterization and identification of different bacteria have been performed to validate and optimize the acquisition
system and the component setup.
The results obtained demonstrate the benefits of these three techniques combination by providing the precise bacteria
localization, their chemical composition and a morphology description. The procedure for a rapid identification of
particular pathogen bacteria in a sample is illustrated.