Representatives of colony formations, for the heatmap of Fig 4, for each cell line that is fixed one a specific date.

<p>The scale bar is 10 microns.</p

BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models

<div><p>BioSig3D is a computational platform for high-content screening of three-dimensional (3D) cell culture models that are imaged in full 3D volume. It provides an end-to-end solution for designing high content screening assays, based on colony organization that is derived from segmentation of nuclei in each colony. BioSig3D also enables visualization of raw and processed 3D volumetric data for quality control, and integrates advanced bioinformatics analysis. The system consists of multiple computational and annotation modules that are coupled together with a strong use of controlled vocabularies to reduce ambiguities between different users. It is a web-based system that allows users to: design an experiment by defining experimental variables, upload a large set of volumetric images into the system, analyze and visualize the dataset, and either display computed indices as a heatmap, or phenotypic subtypes for heterogeneity analysis, or download computed indices for statistical analysis or integrative biology. BioSig3D has been used to profile baseline colony formations with two experiments: (i) morphogenesis of a panel of human mammary epithelial cell lines (HMEC), and (ii) heterogeneity in colony formation using an immortalized non-transformed cell line. These experiments reveal intrinsic growth properties of well-characterized cell lines that are routinely used for biological studies. BioSig3D is being released with seed datasets and video-based documentation.</p></div

Comparison of BioSig3D with other systems.

<p>Comparison of BioSig3D with other systems.</p

BioSig3D consists of six modules.

<p>These include resource manager, experimental design, data loader, visualization and 3D rendering, image analysis, and bioinformatics. These modules are tightly integrated with the backend database.</p

Architecture of BioSig3D is based on open source components Apache Tomcat, OME Image Server, ParaviewWeb from Kitware, and PostgreSQL database.

<p>Architecture of BioSig3D is based on open source components Apache Tomcat, OME Image Server, ParaviewWeb from Kitware, and PostgreSQL database.</p

Colony formation is heterogeneous under normal growth condition for MCF10A (a non-transformed HMEC cell line).

<p>(a) a representative of colonies shown as thumbnails with the middle section of the 3D volume displayed, (b) client can select from a list of computed indices (e.g., colony size) for subtyping, (c) two subtypes, shown as a similarity matrix with two dominant blocks, are inferred for the computed index corresponding to the number of cells per colony, (d) the client queries for a representative population to view each subtype in its own column, and (e-f) rendered representative colonies for each of the subtype clearly indicates that one subtype has more cells than the other.</p

Population analysis for the heatmap of Fig 4 indicates stability of computed indices.

<p>In some cases, flatness of colony is referred to as “Indian file pattern” in clinical pathology.</p

CBP outperforms other methods in nuclear segmentation.

<p>CBP outperforms other methods in nuclear segmentation.</p

Table_8_Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening.docx

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.</p

Table_3_Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening.docx

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.</p