MultiSero: An Open-Source Multiplex-ELISA Platform for Measuring Antibody Responses to Infection.

A multiplexed enzyme-linked immunosorbent assay (ELISA) that simultaneously measures antibody binding to multiple antigens can extend the impact of serosurveillance studies, particularly if the assay approaches the simplicity, robustness, and accuracy of a conventional single-antigen ELISA. Here, we report on the development of multiSero, an open-source multiplex ELISA platform for measuring antibody responses to viral infection. Our assay consists of three parts: (1) an ELISA against an array of proteins in a 96-well format; (2) automated imaging of each well of the ELISA array using an open-source plate reader; and (3) automated measurement of optical densities for each protein within the array using an open-source analysis pipeline. We validated the platform by comparing antibody binding to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antigens in 217 human sera samples, showing high sensitivity (0.978), specificity (0.977), positive predictive value (0.978), and negative predictive value (0.977) for classifying seropositivity, a high correlation of multiSero determined antibody titers with commercially available SARS-CoV-2 antibody tests, and antigen-specific changes in antibody titer dynamics upon vaccination. The open-source format and accessibility of our multiSero platform can contribute to the adoption of multiplexed ELISA arrays for serosurveillance studies, for SARS-CoV-2 and other pathogens of significance.

multiSero: open multiplex-ELISA platform for analyzing antibody responses to SARS-CoV-2 infection.

Serology has provided valuable diagnostic and epidemiological data on antibody responses to SARS-CoV-2 in diverse patient cohorts. Deployment of high content, multiplex serology platforms across the world, including in low and medium income countries, can accelerate longitudinal epidemiological surveys. Here we report multiSero, an open platform to enable multiplex serology with up to 48 antigens in a 96-well format. The platform consists of three components: ELISA-array of printed proteins, a commercial or home-built plate reader, and modular python software for automated analysis (pysero). We validate the platform by comparing antibody titers against the SARS-CoV-2 Spike, receptor binding domain (RBD), and nucleocapsid (N) in 114 sera from COVID-19 positive individuals and 87 pre-pandemic COVID-19 negative sera. We report data with both a commercial plate reader and an inexpensive, open plate reader (nautilus). Receiver operating characteristic (ROC) analysis of classification with single antigens shows that Spike and RBD classify positive and negative sera with the highest sensitivity at a given specificity. The platform distinguished positive sera from negative sera when the reactivity of the sera was equivalent to the binding of 1 ng mL âˆ'1 RBD-specific monoclonal antibody. We developed normalization and classification methods to pool antibody responses from multiple antigens and multiple experiments. Our results demonstrate a performant and accessible pipeline for multiplexed ELISA ready for multiple applications, including serosurveillance, identification of viral proteins that elicit antibody responses, differential diagnosis of circulating pathogens, and immune responses to vaccines.

Revealing architectural order with quantitative label-free imaging and deep learning.

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.

Microscopy is central to biological research and has enabled scientist to study the structure and dynamics of cells and their components within. Often, fluorescent dyes or trackers are used that can be detected under the microscope. However, this procedure can sometimes interfere with the biological processes being studied. Now, Guo, Yeh, Folkesson et al. have developed a new approach to examine structures within tissues and cells without the need for a fluorescent label. The technique, called QLIPP, uses the phase and polarization of the light passing through the sample to get information about its makeup. A computational model was used to decode the characteristics of the light and to provide information about the density and orientation of molecules in live cells and brain tissue samples of mice and human. This way, Guo et al. were able to reveal details that conventional microscopy would have missed. Then, a type of machine learning, known as 'deep learning', was used to translate the density and orientation images into fluorescence images, which enabled the researchers to predict specific structures in human brain tissue sections. QLIPP can be added as a module to a microscope and its software is available open source. Guo et al. hope that this approach can be used across many fields of biology, for example, to map the connectivity of nerve cells in the human brain or to identify how cells respond to infection. However, further work in automating other aspects, such as sample preparation and analysis, will be needed to realize the full benefits.

Generation of an atlas of the proximal femur and its application to trabecular bone analysis.

Automatic placement of anatomically corresponding volumes of interest and comparison of parameters against a standard of reference are essential components in studies of trabecular bone. Only recently, in vivo MR images of the proximal femur, an important fracture site, could be acquired with high-spatial resolution. The purpose of this MRI trabecular bone study was two-fold: (1) to generate an atlas of the proximal femur to automatically place anatomically corresponding volumes of interest in a population study and (2) to demonstrate how mean models of geodesic topological analysis parameters can be generated to be used as potential standard of reference. Ten females were used to generate the atlas and geodesic topological analysis models, and 10 females were used to demonstrate the atlas-based trabecular bone analysis. All alignments were based on three-dimensional (3D) multiresolution affine transformations followed by 3D multiresolution free-form deformations. Mean distances less than 1 mm between aligned femora, and sharp edges in the atlas and in fused gray-level images of registered femora indicated that the anatomical variability was well accommodated and explained by the free-form deformations.

Longitudinal evaluation of the effects of alendronate on MRI bone microarchitecture in postmenopausal osteopenic women.

UNLABELLED: We evaluated longitudinal effects of alendronate on MRI-based trabecular bone structure parameters derived from dual thresholding and fuzzy clustering (BE-FCM) trabecular bone segmentation. Treatment effects were observed in the distal tibia after 24 months. The BE-FCM method increased correlations to HR-pQCT-based parameters. INTRODUCTION: High-resolution magnetic resonance imaging (MRI) allows for non-invasive bone microarchitecture analysis. The goal of this study was to examine the potential of MRI-based trabecular bone structure parameters to monitor effects of alendronate in humans in vivo, and to compare the results to HR-pQCT and DXA measurements. MATERIALS AND METHODS: Postmenopausal osteopenic women were divided into alendronate treatment and control groups, and imaged at baseline, 12 months, and 24 months (n = 52 at baseline) using 3T MRI, HR-pQCT, and DXA. Image acquisition sites included distal tibia (MRI and HR-pQCT), distal radius (MRI, DXA, and HR-pQCT), and the proximal femur (MRI and DXA). Two different regions of interest were evaluated. One contained the trabecular bone region within the entire MRI acquisition, and the second contained a subregion matched to the region contained in the HR-pQCT acquisition. The trabecular bone was segmented using two different methods; dual thresholding and BE-FCM. Trabecular bone structure parameters included bone volume fraction (BV/TV), number (Tb.N), spacing (Tb.Sp), and thickness (Tb.Th), along with seven geodesic topological analysis (GTA) parameters. Longitudinal changes and correlations to HR-pQCT and DXA measurements were evaluated. RESULTS: Apparent Tb.N and four GTA parameters showed treatment effects (p < 0.05) in the distal tibia after 24 months in the entire MRI region using BE-FCM, as well as Tb.N using dual thresholding. No treatment effects after 24 months were observed in the HR-pQCT or in MRI analysis for the HR-pQCT-matched regions. Apparent BV/TV and Tb.N from BE-FCM had significantly higher correlations to HR-pQCT values compared to those derived from thresholding. CONCLUSIONS: This study demonstrates the influence of computational methods and region of interest definitions on measurements of trabecular bone structure, and the feasibility of MRI-based quantification of longitudinal changes in bone microarchitecture due to bisphosphonate therapy. The results suggest that there may be a need to reevaluate the current standard HR-pQCT region definition for increased treatment sensitivity.

Local bone enhancement fuzzy clustering for segmentation of MR trabecular bone images.

PURPOSE: Segmentation of trabecular bone from magnetic resonance (MR) images is a challenging task due to spatial resolution limitations, signal-to-noise ratio constraints, and signal intensity inhomogeneities. This article examines an alternative approach to trabecular bone segmentation using partial membership segmentation termed fuzzy C-means clustering incorporating local second order features for bone enhancement (BE-FCM) at multiple scales. This approach is meant to allow for a soft segmentation that accounts for partial volume effects while suppressing the influence of noise. METHODS: A soft segmentation method was developed and evaluated on three different sets of data; interscan reproducibility was evaluated on six test-retest in vivo MR scans of the proximal femur, correlation between MR and HR-pQCT measurements was evaluated on 49 in vivo scans from the distal tibia, and the potential for fracture discrimination was evaluated using MR scans of calcaneus specimens from 15 participants with and 15 participants without vertebral fracture. The algorithm was compared to fuzzy clustering using the intensity as the only feature (I-FCM) and a dual thresholding algorithm. The metric evaluated was bone volume over total volume (BV/TV) within user-defined regions of interest. RESULTS: BE-FCM had a higher interscan reproducibility (rms CV: 2.0%) compared to I-FCM (5.6%) and thresholding (4.2%), and expressed higher correlation to HR-pQCT data (r = 0.79, p < 10(-11)) compared to I-FCM (r = 0.74, p < 10(-8)) and thresholding (r = 0.70, p < 10(-6)). BE-FCM was also the method that was best able to differentiate between a control and a vertebral fracture group at a 95% significance level. CONCLUSIONS: The results suggest that trabecular bone segmentation by BE-FCM can provide a precise BV/TV measurement that is sensitive to pathology. The segmentation method may become useful in MR imaging-based quantification of bone microarchitecture.

Identification of progressors in osteoarthritis by combining biochemical and MRI-based markers.

INTRODUCTION: At present, no disease-modifying osteoarthritis drugs (DMOADS) are approved by the FDA (US Food and Drug Administration); possibly partly due to inadequate trial design since efficacy demonstration requires disease progression in the placebo group. We investigated whether combinations of biochemical and magnetic resonance imaging (MRI)-based markers provided effective diagnostic and prognostic tools for identifying subjects with high risk of progression. Specifically, we investigated aggregate cartilage longevity markers combining markers of breakdown, quantity, and quality. METHODS: The study included healthy individuals and subjects with radiographic osteoarthritis. In total, 159 subjects (48% female, age 56.0 +/- 15.9 years, body mass index 26.1 +/- 4.2 kg/m2) were recruited. At baseline and after 21 months, biochemical (urinary collagen type II C-telopeptide fragment, CTX-II) and MRI-based markers were quantified. MRI markers included cartilage volume, thickness, area, roughness, homogeneity, and curvature in the medial tibio-femoral compartment. Joint space width was measured from radiographs and at 21 months to assess progression of joint damage. RESULTS: Cartilage roughness had the highest diagnostic accuracy quantified as the area under the receiver-operator characteristics curve (AUC) of 0.80 (95% confidence interval: 0.69 to 0.91) among the individual markers (higher than all others, P < 0.05) to distinguish subjects with radiographic osteoarthritis from healthy controls. Diagnostically, cartilage longevity scored AUC 0.84 (0.77 to 0.92, higher than roughness: P = 0.03). For prediction of longitudinal radiographic progression based on baseline marker values, the individual prognostic marker with highest AUC was homogeneity at 0.71 (0.56 to 0.81). Prognostically, cartilage longevity scored AUC 0.77 (0.62 to 0.90, borderline higher than homogeneity: P = 0.12). When comparing patients in the highest quartile for the longevity score to lowest quartile, the odds ratio of progression was 20.0 (95% confidence interval: 6.4 to 62.1). CONCLUSIONS: Combination of biochemical and MRI-based biomarkers improved diagnosis and prognosis of knee osteoarthritis and may be useful to select high-risk patients for inclusion in DMOAD clinical trials.

Evaluation of correction methods for coil-induced intensity inhomogeneities and their influence on trabecular bone structure parameters from MR images.

Magnetic resonance (MR) imaging-based quantitative trabecular bone structure analysis has gained increasing interest in osteoporotic fracture risk assessment and treatment evaluation related to osteoporosis. In vivo MR images of anatomic regions such as the proximal femur and distal tibia are generally acquired with a surface coil in order to obtain sufficient sensitivity and resolution for quantification of the trabeculae. However, these coils introduce intensity inhomogeneities which affect the trabecular bone structure analysis. This work evaluates the applicability of a fully automatic coil correction by nonparametric nonuniform intensity normalization (N3) in the analysis of trabecular bone parameters. The ability to correct for coil-induced intensity inhomogeneity was evaluated ex vivo on proximal femur specimens scanned with both a surface coil and a volume coil, which allowed for a direct evaluation of the performance of the coil correction methods without any major confounding factors. In addition, trabecular bone parameter values were correlated with values from high-resolution peripheral computed tomography (HR-pQCT) scans, and the reproducibility of trabecular bone parameters was evaluated in an in vivo study of repeat hip MR scans. The trabecular bone parameters determined from MR surface coil scans processed with the N3 coil correction method showed significant correlation (p < 0.05) with corresponding values from homogeneous intensity data in the ex vivo study. This can be compared to the correlation without coil correction (p < 0.5), and coil correction using low-pass filtering (LPF) (p < 0.53). The in vivo interscan variability was reduced from 8.9% to 12.8% using LPF-based to 3.6%-8.4% (CV) using N3 coil correction; hence the results showed that N3 is advantageous to LPF-based coil correction. No significant differences in correlation to HR-pQCT data were found for the coil correction methods. The significant correlations with volume coil data and high reproducibility of the N3 processed data imply that N3 coil correction preserve image information while accurately correcting for coil-induced intensity inhomogeneities, which makes it suitable for quantitative analysis of trabecular bone structure from MR images acquired with surface coils.

Unifying statistical classification and geodesic active regions for segmentation of cardiac MRI.

This paper presents a segmentation method that extends geodesic active region methods by the incorporation of a statistical classifier trained using feature selection. The classifier provides class probability maps based on class representative local features, and the geodesic active region formulation enables the partitioning of the image according to the region information. We demonstrate automatic segmentation results of the myocardium in cardiac late gadolinium-enhanced magnetic resonance imaging (CE-MRI) data using coupled level set curve evolutions, in which the classifier is incorporated both from a region term and from a shape term from particle filtering. The results show potential for clinical studies of scar tissue in late CE-MRI data.

Automatic quantification of local and global articular cartilage surface curvature: biomarkers for osteoarthritis?

The objective of this study was to quantitatively assess the surface curvature of the articular cartilage from low-field magnetic resonance imaging (MRI) data, and to investigate its role in populations with varying radiographic signs of osteoarthritis (OA), cross-sectionally and longitudinally. The curvature of the articular surface of the medial tibial compartment was estimated both on fine and coarse scales using two different automatic methods which are both developed from an automatic 3D segmentation algorithm. Cross-sectionally (n=288), the surface curvature for both the fine- and coarse-scale estimates were significantly higher in the OA population compared with the healthy population, with P<0.001 and P<<0.001, respectively. For the longitudinal study (n=245), there was a significant increase in fine-scale curvature for healthy and borderline OA populations (P<0.001), and in coarse-scale curvature for severe OA populations (P<0.05). Fine-scale curvature could predict progressors using the estimates of those healthy at baseline (P<0.001). The inter-scan precision was 2.2 and 6.5 (mean CV) for the fine- and coarse scale curvature measures, respectively. The results showed that quantitative curvature estimates from low-field MRI at different scales could potentially become biomarkers targeted at different stages of OA.

Accuracy evaluation of automatic quantification of the articular cartilage surface curvature from MRI.

RATIONALE AND OBJECTIVES: To study the articular cartilage surface curvature determined automatically from magnetic resonance (MR) knee scans, evaluate accuracy of the curvature estimates on digital phantoms, and an evaluation of their potential as disease markers for different stages of osteoarthritis (OA). MATERIALS AND METHODS: Knee MR data were acquired using a low-field 0.18T scanner, along with posteroanterior x-rays for evaluation of radiographic signs of OA according to the Kellgren-Lawrence index (KL). Scans from a total of 114 knees from test subjects with KL 0-3, 59% females, ages 21-79 years were evaluated. The surface curvature for the medial tibial compartment was estimated automatically on a range of scales by two different methods: Euclidean shortening flow and boundary normal comparison on a cartilage shape model. The curvature estimates were normalized for joint size for intersubject comparisons. Digital phantoms were created to establish the accuracy of the curvature estimation methods. RESULTS: A comparison of the two curvature estimation methods to ground truth yielded absolute pairwise differences of 1.1%, and 4.8%, respectively. The interscan reproducibility for the two methods were 2.3% and 6.4% (mean coefficient of variation), respectively. The surface curvature was significantly higher in the OA population (KL > 0) compared with the healthy population (KLi = 0) for both curvature estimates, with P values of .000004 and .000006, respectively. The shape model based curvature estimate could also separate healthy from borderline OA (KL = 1) populations (P = .005). CONCLUSION: The phantom study showed that the shape model method was more accurate for a coarse-scale analysis, whereas the shortening flow estimated fine scales better. Both the fine- and the coarse-scale curvature estimates distinguished between healthy and OA populations, and the coarse-scale curvature could even distinguish between healthy and borderline OA populations. The highly significant differences between populations demonstrate the potential of cartilage curvature as a disease marker for OA.

Segmenting articular cartilage automatically using a voxel classification approach.

We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies.

Automatic segmentation of the articular cartilage in knee MRI using a hierarchical multi-class classification scheme.

Osteoarthritis is characterized by the degeneration of the articular cartilage in joints. We have developed a fully automatic method for segmenting the articular cartilage in knee MR scans based on supervised learning. A binary approximate kNN classifier first roughly separates cartilage from background voxels, then a three-class classifier assigns one of three classes to each voxel that is classified as cartilage by the binary classifier. The resulting sensitivity and specificity are 90.0% and 99.8% respectively for the medial cartilage compartments. We show that an accurate automatic cartilage segmentation is achievable using a low-field MR scanner.