High-throughput refractive index-based microphotonic sensor for enhanced cellular discrimination

This paper presents a novel microphotonic sensor based on silicon technologies for high-throughput single cell measurements. It employs a highly sensitive Fabry-Pérot resonant cavity to extract cellular refractive index information. The integrated large cross-section rib waveguides provide a single-mode like behavior important for resonant cell sensing. Differentiated myeloid cells derived from a promyelocytic leukemia cell line were injected in a microchannel, sheathlessly focused using inertial forces and analyzed while flowing through the resonant cavity volume. Results were compared against a commercial flow cytometer and showed a substantial improvement on cellular discrimination. Thus, this sensor has the ability to discriminate cell populations, usually identified using fluorescent parameters, without any dyes and can reach measurement rate as high as 2000 cells per second. By harnessing the cell's effective volume refractive index, our device offers complementary measurements readily improving actual technologies and thus providing crucial information for research and clinical professionals

Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification.

Classification of imbalanced datasets has attracted substantial research interest over the past years. This is because imbalanced datasets are common in several domains such as health, finance and security, but learning algorithms are generally not designed to handle them. Many existing solutions focus mainly on the class distribution problem. However, a number of reports showed that class overlap had a higher negative impact on the learning process than class imbalance. This thesis thoroughly explores the impact of class overlap on the learning algorithm and demonstrates how elimination of class overlap can effectively improve the classification of imbalanced datasets. Novel undersampling approaches were developed with the main objective of enhancing the presence of minority class instances in the overlapping region. This is achieved by identifying and removing majority class instances potentially residing in such a region. Seven methods under the two different approaches were designed for the task. Extensive experiments were carried out to evaluate the methods on simulated and well-known real-world datasets. Results showed that substantial improvement in the classification accuracy of the minority class was obtained with favourable trade-offs with the majority class accuracy. Moreover, successful application of the methods in predictive diagnostics of diseases with imbalanced records is presented. These novel overlap-based approaches have several advantages over other common resampling methods. First, the undersampling amount is independent of class imbalance and proportional to the degree of overlap. This could effectively address the problem of class overlap while reducing the effect of class imbalance. Second, information loss is minimised as instance elimination is contained within the problematic region. Third, adaptive parameters enable the methods to be generalised across different problems. It is also worth pointing out that these methods provide different trade-offs, which offer more alternatives to real-world users in selecting the best fit solution to the problem

On-Chip Fabry-Pérot Microcavity for Refractive Index Cytometry and Deformability Characterization of Single Cells

Une identification correcte et précise du phénotype et des fonctions cellulaires est fondamentale pour le diagnostic de plusieurs pathologies ainsi qu’à la compréhension de phénomènes biologiques tels que la croissance, les réponses immunitaires et l’évolution de maladies. Conséquemment, le développement de technologies de pointe offrant une mesure multiparamétrique à haut débit est capital. À cet égard, la cytométrie en flux est l’étalon de référence due à sa grande spécificité, sa grande sensibilité et ses débits élevés. Ces performances sont atteintes grâce à l’évaluation précise du taux d’émission de fluorophores, conjugués à des anticorps, ciblant certains traits cellulaires spécifiques. Néanmoins, sans ce précieux étiquetage, les propriétés physiques caractérisées par la cytométrie sont limitées à la taille et la granularité des cellules. Bien que la cytométrie en flux soit fondamentalement un détecteur optique, elle ne tire pas avantage de l’indice de réfraction, un paramètre reflétant la composition interne de la cellule. Dans la littérature, l’indice de réfraction cellulaire a été utilisé comme paramètre phénotypique discriminant pour la détection de nombreux cancers, d’infections, de la malaria ou encore de l’anémie. Également, les structures fluidiques de la cytométrie sont conçues afin d’empêcher une déformation cellulaire de se produire. Cependant, les preuves que la déformabilité est un indicateur de plusieurs pathologies et d’état de santé cellulaire sont manifestes. Pour ces raisons, l’étude de l’indice de réfraction et de la déformabilité cellulaire en tant que paramètres discriminants est une avenue prometteuse pour l’identification de phénotypes cellulaires. En conséquence, de nombreux biodétecteurs qui exploitent l’une ou l’autre de ces propriétés cellulaires ont émergé au cours des dernières années. D’une part, les dispositifs microfluidiques sont des candidats idéaux pour la caractérisation mécanique de cellules individuelles. En effet, la taille des structures microfluidiques permet un contrôle rigoureux de l’écoulement ainsi que de ses attributs. D’autre part, les dispositifs microphotoniques excellent dans la détection de faibles variations d’indice de réfraction, ce qui est critique pour un phénotypage cellulaire correcte. Par conséquent, l’intégration de composants microfluidiques et microphotoniques à l’intérieur d’un dispositif unique permet d’exploiter ces propriétés cellulaires d’intérêt. Néanmoins, les dispositifs capables d’atteindre une faible limite de détection de l’indice de réfraction tels que les détecteurs à champ évanescent souffrent de faibles profondeurs de pénétration. Ces dispositifs sont donc plus adéquats pour la détection de fluides ou de molécules. De manière opposée, les détecteurs interférométriques tels que les Fabry- Pérots sont sensibles aux éléments présents à l’intérieur de leurs cavités, lesquelles peuvent mesurer jusqu’à plusieurs dizaines de micromètres.----------Abstract Accurate identification of cellular phenotype and function is fundamental to the diagnostic of many pathologies as well as to the comprehension of biological phenomena such as growth, immune responses and diseases development. Consequently, development of state-of-theart technologies offering high-throughput and multiparametric single cell measurement is crucial. Therein, flow cytometry has become the gold standard due to its high specificity and sensitivity while reaching a high-throughput. Its marked performance is a result of its ability to precisely evaluate expression levels of antibody-fluorophore complexes targeting specific cellular features. However, without this precious fluorescence labelling, characterized physical properties are limited to the size and granularity. Despite flow cytometry fundamentally being an optical sensor, it does not take full advantage of the refractive index (RI), a valuable labelfree measurand which reflects the internal composition of a cell. Notably, the cellular RI has proven to be a discriminant phenotypic parameter for various cancer, infections, malaria and anemia. Moreover, flow cytometry is designed to prevent cellular deformation but there is growing evidence that deformability is an indicator of many pathologies, cell health and state. Therefore, cellular RI and deformability are promising avenues to discriminate and identify cellular phenotypes. Novel biosensors exploiting these cellular properties have emerged in the last few years. On one hand, microfluidic devices are ideal candidates to characterize single cells mechanical properties at large rates due to their small structures and controllable flow characteristics. On the other hand, microphotonic devices can detect very small RI variations, critical for an accurate cellular phenotyping. Hence, the integration of microfluidic and microphotonic components on a single device can harness these promising cellular physical properties. However, devices achieving very small RI limit of detection (LOD) such as evanescent field sensors suffer from very short penetration depths and thus are better suited for fluid or single molecule detection. In opposition, interference sensors such as Fabry-Pérots are sensitive to the medium inside their cavity, which can be several tens of micrometers in length, and thus are ideally suited for whole-cell measurement. Still, most of these volume sensors suffer from large LOD or require out-of-plane setups not appropriate for an integrated solution. Such a complex integration of high-throughput, sensitivity and large penetration depth on-chip is an ongoing challenge. Besides, simultaneous characterization of whole-cell RI and deformability has never been reported in the literature