Contamination-resistant, rapid emulsion-based isothermal nucleic acid amplification with Mie-scatter inspired light scatter analysis for bacterial identification

An emulsion loop-mediated isothermal amplification (eLAMP) platform was developed to reduce the impact that contamination has on assay performance. Ongoing LAMP reactions within the emulsion droplets cause a decrease in interfacial tension, causing a decrease in droplet size, which results in decreased light scatter intensity due to Mie theory. Light scatter intensity was monitored via spectrophotometers and fiber optic cables placed at 30° and 60°. Light scatter intensities collected at 3 min, 30° were able to statistically differentiate 103 and 106 CFU/µL initial Escherichia coli O157:H7 concentrations compared to NTC (0 CFU/µL), while the intensity at 60° were able to statistically differentiate 106 CFU/µL initial concentrations and NTC. Control experiments were conducted to validate nucleic acid detection versus bacterial adsorption, finding that the light scatter intensities change is due specifically to ongoing LAMP amplification. After inducing contamination of bulk LAMP reagents, specificity lowered to 0% with conventional LAMP, while the eLAMP platform showed 87.5% specificity. We have demonstrated the use of angle-dependent light scatter intensity as a means of real-time monitoring of an emulsion LAMP platform and fabricated a smartphone-based monitoring system that showed similar trends as spectrophotometer light scatter data, validating the technology for a field deployable platform.

DEVELOPMENT OF OPTICAL TRANSDUCER ON FABRICS FOR THE APPLICATION OF LIQUID CLUSTERING USING REFLECTANCE SPECTROSCOPY

Recently, usage of fabrics as wearable device, along with their applications are increasing, one example being the detection of bio-analyzes such as blood or sweat. One method used to observe the properties of the material of a fabric is to use the Refcletance Spectroscopy, in which excitation of monochromatic light with a specific wavelength is given to a fabrics. Intensity value is then processed using the PCA method in order to obtain the pattern of the difference between each substrate. The proposed transducer optic system consists of 405nm blueviolet laser as the light source, biconvex lens, Adafruit AS7262 light detector, and Arduino. This system can only detect the difference in substrate content from the occurring light scatter. This system can be applied to various kinds of fabric wearable material with differing scatter intensity values depending on the kind of fabrics. Softer kind of fabric is proposed as material for the wearable device because it gives a high scatter intensity value and constant values in every repetation which results in better data reading.Keywords: clustering, optical, reflectance, spectroscopy, transducer, wearable.

Real-Time Monitoring of Microalgal Biomass in Pilot-Scale Photobioreactors Using Nephelometry

The increasing cultivation of microalgae in photobioreactors warrants efficient and non-invasive methods to quantify biomass density in real time. Nephelometric turbidity assessment, a method that measures light scatter by particles in suspension, was introduced already several decades ago but was only recently validated as a high-throughput tool to monitor microalgae biomass. The light scatter depends on the density of the suspended particles as well as on their physical properties, but so far there are hardly any accounts on how nephelometric assessment relates to classic methods such as dry weight and spectrophotometric measurement across a broad biomass density range for different microalgae species. Here, we monitored biomass density online and in real time during the semi-continuous cultivation of three commercial microalgae species Chloromonas typhlos, Microchloropsis gaditana and Porphyridium purpureum in pilot-scale photobioreactors, and relate nephelometric turbidity to dry weight and optical density. The results confirm a relatively strong (R2 = 0.87–0.93) and nonlinear relationship between turbidity and biomass density that differs among the three species. Overall, we demonstrate how nephelometry can be used to monitor microalgal biomass in photobioreactors, and provide the necessary means to estimate the biomass density of the studied species from turbidity data to facilitate automated biomass monitoring.

Visualization of Forward Light Scatter in Opacified Intraocular Lenses and Straylight Assessment

Background: Qualitative visualization of forward light scatter and quantitative straylight measurement of intraocular lenses (IOLs). Methods: We analyzed two calcified IOL-explants, the Euromaxx ALI313Y (Argonoptics GmbH) and the LS-312 MF30 (Oculentis BV), one IOL with artificially induced glistenings (PC-60AD, Hoya), and one control (CT Asphina 409MP, Carl Zeiss Meditec AG) free of any opacification. Analysis included light microscopy, qualitative light scatter visualization using ray propagation imaging technique, and quantitative straylight measurement using C-Quant (Oculus). Results: More light scattering effect—visible as increased light intensity outside the IOL’s main focus—was evident in all opacified IOLs than the control. The highest straylight levels were observed in the Euromaxx (289.71 deg2/sr), which showed extensive granular deposits throughout its optic, followed by the MF30 (78.58 deg2/sr), which only showed opacification in its center. The glistenings-IOL demonstrated numerous microvacuoles within the optic and had straylight levels of 22.6 deg2/sr, while the control showed the lowest straylight levels (1.7 deg2/sr). Conclusions: Ray propagation imaging technique allowed qualitative assessment of off-axis veils of light that result from increased forward light scattering. Straylight was increased in all opacified lenses compared to the clear control lens. The IOL opacifications are significant sources of glare.

Elastic Light Scatter Pattern Analysis for the Expedited Detection of Yersinia Species in Pork Mince: Proof of Concept

Isolation of the pathogens Yersinia enterocolitica and Yersinia pseudotuberculosis from foods typically rely on slow (10–21 day) “cold enrichment” protocols before confirmed results are obtained. We describe an approach that yields results in 39 h that combines an alternative enrichment method with culture on a non-selective medium, and subsequent identification of suspect colonies using elastic light scatter (ELS) analysis. A prototype database of ELS profiles from five Yersinia species and six other bacterial genera found in pork mince was established, and used to compare similar profiles of colonies obtained from enrichment cultures from pork mince samples seeded with representative strains of Y. enterocolitica and Y. pseudotuberculosis. The presumptive identification by ELS using computerised or visual analyses of 83/90 colonies in these experiments as the target species was confirmed by partial 16S rDNA sequencing. In addition to seeded cultures, our method recovered two naturally occurring Yersinia strains. Our results indicate that modified enrichment combined with ELS is a promising new approach for expedited detection of foodborne pathogenic yersiniae.

Machine Learning-Based Flow Cytometry Diagnostics in Myelodysplastic Syndromes: Validation in the HOVON89 Clinical Trial (EudraCT 2008-002195-10)

Introduction Flow cytometry is a recommended tool in the diagnostic work-up of cytopenic patients suspected for myelodysplastic syndromes. Currently used flow cytometry scores rely on human assessment of dysplastic features in the bone marrow. Although proven useful, these methods are labor intensive and require a high level of expertise. Therefore, we previously developed a machine learning-based workflow for flow cytometry diagnostics in MDS by combining computational cell detection and a machine learning-classifier. This workflow outperformed traditional diagnostic scores with respect to accuracy (sensitivity 85-97%, specificity 93-97%), time investment (<30 seconds) and required materials (manuscript submitted). In the present study, we validated sensitivity of the workflow in a well-characterized clinical trial cohort (HOVON89 EudraCT 2008-002195-10) of lower risk MDS patients. Method Patient inclusion and characteristics Very low to intermediate risk MDS patients enrolled in the HOVON89 clinical trial (EudraCT 2008-002195-10) were included. 53 patients met the additional inclusion criteria, concerning written consent for add-on studies and availability of required flow cytometry data. Sample preparation Bone marrow samples were processed for flow cytometry analysis according to the European Leukemia Net guidelines. This study focused on the antibody combination optimized for assessment of myeloid progenitors and erythroid dysplasia (CD45, CD34, CD117, HLA-DR, CD71, CD36, CD105, CD33, sideward light scatter (SSC) and forward light scatter (FSC)). Machine learning-based workflow The machine learning-based workflow was developed in a prior study based on a reference cohort consisting of MDS patients without excess of blasts(n=67) and non-MDS cases (n=81) (Figure 1). MDS patients were diagnosed based on (cyto)morphology, cytogenetics and clinical follow-up. Non-MDS cases were patients with confirmed non-neoplastic cytopenias (n=69) and age-matched healthy individuals (n=12). Results In the validation cohort, the machine learning-based diagnostic workflow classified 49 out of 53 patients correctly, reaching a sensitivity of 92%. The workflow outperformed two currently used diagnostic tools for MDS flow cytometry, the Ogata score and integrated flow cytometry score (iFS). The former obtained 72% sensitivity (McNemar: p = 0.001) and the latter 83% sensitivity (McNemar: p = 0.06) in the validation cohort. Per patient, time required for automated analysis was less than 30 seconds. All four MDS patients that classified false negatively had a normal karyotype and (very) low risk disease according to the IPSS-r. In three out of four patients, no mutations or MDS-associated immunophenotypic features were detected. One patients was diagnosed as MDS-MLD and three patients as MDS-RS-SLD according to the WHO 2016 classification. The ten most relevant cellular features that discriminated between MDS and non-MDS patients in the reference data were confirmed in the current validation cohort. All ten features of MDS patients in the validation cohort were significantly different from non-MDS patients of the reference cohort (all features, p < 0.00001) (Figure 2). Seven out of ten features were similar in MDS patients of the validation cohort compared to those of the MDS patients of the reference cohort (p>0.05) (Figure 2). Conclusion In this validation study, we confirmed accuracy of machine learning-based flow cytometry diagnostics in lower risk MDS. The workflow obtained 92% sensitivity, which is in accordance with results from our previous study (85-97%), and outperformed currently used diagnostic flow cytometry scores for MDS (i.e. Ogata score and iFS). In our previous study specificity was 95% in both reference and test cohorts. Cellular features, most discriminative for diagnosis, were confirmed in the validation cohort, emphasizing robustness of the method. Additional benefits of this approach are the reduction in analysis time to less than thirty seconds per patient, reduction of required antibodies and increased reproducibility. Disclosures van de Loosdrecht: celgene: Honoraria; novartis: Honoraria.