Statistical Design And Imaging Of Position-Encoded 3D Microarrays

We propose a three-dimensional microarray device with microspheres having controllable positions for error-free target identification. Here targets: such as mRNAs, proteins, antibodies, and cells) are captured by the microspheres on one side, and are tagged by nanospheres embedded with quantum-dots: QDs) on the other. We use the lights emitted by these QDs to quantify the target concentrations. The imaging is performed using a fluorescence microscope and a sensor. We conduct a statistical design analysis to select the optimal distance between the microspheres as well as the optimal temperature. Our design simplifies the imaging and ensures a desired statistical performance for a given sensor cost. Specifically, we compute the posterior Cram&eacuter-Rao bound on the errors in estimating the unknown target concentrations. We use this performance bound to compute the optimal design variables. We discuss both uniform and sparse concentration levels of targets. The uniform distributions correspond to cases where the target concentration is high or the time period of the sensing is sufficiently long. The sparse distributions correspond to low target concentrations or short sensing durations. We illustrate our design concept using numerical examples. We replace the photon-conversion factor of the image sensor and its background noise variance with their maximum likelihood: ML) estimates. We estimate these parameters using images of multiple target-free microspheres embedded with QDs and placed randomly on a substrate. We obtain the photon-conversion factor using a method-of-moments estimation, where we replace the QD light-intensity levels and locations of the imaged microspheres with their ML estimates. The proposed microarray has high sensitivity, efficient packing, and guaranteed imaging performance. It simplifies the imaging analysis significantly by identifying targets based on the known positions of the microspheres. Potential applications include molecular recognition, specificity of targeting molecules, protein-protein dimerization, high throughput screening assays for enzyme inhibitors, drug discovery, and gene sequencing

SNR-based adaptive acquisition method for fast Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a computational imaging technique with both high resolution and large field-of-view. However, the effective numerical aperture (NA) achievable with a typical LED panel is ambiguous and usually relies on the repeated tests of different illumination NAs. The imaging quality of each raw image usually depends on the visual assessments, which is subjective and inaccurate especially for those dark field images. Moreover, the acquisition process is really time-consuming.In this paper, we propose a SNR-based adaptive acquisition method for quantitative evaluation and adaptive collection of each raw image according to the signal-to-noise ration (SNR) value, to improve the FPM's acquisition efficiency and automatically obtain the maximum achievable NA, reducing the time of collection, storage and subsequent calculation. The widely used EPRY-FPM algorithm is applied without adding any algorithm complexity and computational burden. The performance has been demonstrated in both USAF targets and biological samples with different imaging sensors respectively, which have either Poisson or Gaussian noises model. Further combined with the sparse LEDs strategy, the number of collection images can be shorten to around 25 frames while the former needs 361 images, the reduction ratio can reach over 90%. This method will make FPM more practical and automatic, and can also be used in different configurations of FPM.Comment: 11 pages, 6 figure

High-throughput Single-Entity Analysis Methods: From Single-Cell Segmentation to Single-Molecule Force Measurements

This work is focused on the development of new microscopy-based analysis methods with single-entity resolution and high-throughput capabilities from the cellular to the molecular level to study biomembrane-associated interactions. Currently, there is a variety of methods available for obtaining quantitative information on cellular and molecular responses to external stimuli, but many of them lack either high sensitivity or high throughput. Yet, the combination of both aspects is critical for studying the weak but often complex and multivalent interactions at the interface of biological mem-branes. These interactions include binding of pathogens such as some viruses (e.g., influenza A virus, herpes simplex virus, and SARS-CoV-2), transmembrane signaling such as ligand-based oli-gomerization processes, and transduction of mechanical forces acting on cells. The goal of this work was to overcome the shortcomings of current methods by developing and es-tablishing new methods with unprecedented levels of automation, sensitivity, and parallelization. All methods are based on the combination of optical (video) microscopy followed by highly refined data analysis to study single cellular and molecular events, allowing the detection of rare events and the identification and quantification of cellular and molecular populations that would remain hidden in ensemble-averaging approaches. This work comprises four different projects. At the cellular level, two methods have been developed for single-cell segmentation and cell-by-cell readout of fluorescence reporter systems, mainly to study binding and inhibition of binding of viruses to host cells. The method developed in the first pro-ject features a high degree of automation and automatic estimation of sufficient analysis parameters (background threshold, segmentation sensitivity, and fluorescence cutoff) to reduce the manual ef-fort required for the analysis of cell-based infection assays. This method has been used for inhibition potency screening based on the IC50 value of various virus binding inhibitors. With the method used in the second project, the sensitivity of the first method is extended by providing an estimate of the number of fluorescent nanoparticles bound to the cells. The image resolution was chosen to allow many cells to be imaged in parallel. This allowed for the quantification of cell-to-cell heterogeneity of particle binding, at the expense of resolution of the individual fluorescent nanoparticles. To account for this, a new approach was developed and validated by simulations to estimate the number of fluo-rescent nanoparticles below the diffraction limit with an accuracy of about 80 to 100 %. In the third project, an approach for the analysis and refinement of two-dimensional single-particle tracking ex-periments was presented. It focused on the quality assessment of the derived tracks by providing a guide for the selection of an appropriate maximal linking distance. This tracking approach was used in the fourth project to quantify small molecule responses to hydrodynamic shear forces with sub-nm resolution. Here, the combination of TIRF microscopy, microfluidics, and single particle tracking enabled the development of a new single molecule force spectroscopy method with high resolution and parallelization capabilities. This method was validated by quantifying the mechanical response of well-defined PEG linkers and subsequently used to study the energy barriers of dissociation of mul-tivalent biotin-NeutrAvidin complexes under low (~ 1.5 to 12 pN) static forces. In summary, with this work, the repertoire of appropriate methods for high-throughput investigation of the properties and interactions of cells, nanoparticles, and molecules at single resolution is expand-ed. In the future, the methods developed here will be used to screen for additional virus binding inhib-itors, to study the oligomerization of membrane receptors on cells and model membranes, and to quantify the mechanical response of force-bearing proteins and ligand-receptor complexes under low force conditions

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Geometric compensation applied to image analysis of cell populations with morphological variability: A new role for a classical concept

Immunofluorescence is the gold standard technique to determine the level and spatial distribution of fluorescent-tagged molecules. However, quantitative analysis of fluorescence microscopy images faces crucial challenges such as morphologic variability within cells. In this work, we developed an analytical strategy to deal with cell shape and size variability that is based on an elastic geometric alignment algorithm. Firstly, synthetic images mimicking cell populations with morphological variability were used to test and optimize the algorithm, under controlled conditions. We have computed expression profiles specifically assessing cell-cell interactions (IN profiles) and profiles focusing on the distribution of a marker throughout the intracellular space of single cells (RD profiles). To experimentally validate our analytical pipeline, we have used real images of cell cultures stained for E-cadherin, tubulin and a mitochondria dye, selected as prototypes of membrane, cytoplasmic and organelle-specific markers. The results demonstrated that our algorithm is able to generate a detailed quantitative report and a faithful representation of a large panel of molecules, distributed in distinct cellular compartments, independently of cell's morphological features. This is a simple end-user method that can be widely explored in research and diagnostic labs to unravel protein regulation mechanisms or identify protein expression patterns associated with disease.This work was supported by FEDER funds through the Operational Programme for Competitiveness Factors (COMPETE) and National Funds through the Portuguese Foundation for Science and Technology (FCT), under the projects PTDC/BIM-ONC/0171/2012, PTDC/BIM-ONC/0281/2014, PTDC/BBB-IMG/0283/2014; Post-Doctoral grants SFRH/BPD/87705/2012-JF and SFRH/BPD/104208/2014-BS; and Doctoral grant SFRH/ BD/108009/2015-SM. We acknowledge the Programa IFCT (FCT Investigator) for funding JP research. We also thank to the American Association of Patients with Hereditary Gastric Cancer “No Stomach for Cancer” for funding the projects “Today’s present, tomorrow’s future on the study of germline E-cadherin missense mutations” and “Today’s Present, Tomorrow’s Future on the Study of Germline E-Cadherin Missense Mutations: A Step Forward on Providing Informed Genetic Counseling to Everyone”