A novel thin plate spline methodology to model tissue surfaces and quantify tumor cell invasion in organ-on-chip models.

Organ-on-chip (OOC) models can be useful tools for cancer drug discovery. Advances in OOC technology have led to the development of more complex assays, yet analysis of these systems does not always account for these advancements, resulting in technical challenges. A challenging task in the analysis of these two-channel microfluidic models is to define the boundary between the channels so objects moving within and between channels can be quantified. We propose a novel imaging-based application of a thin plate spline method - a generalized cubic spline that can be used to model coordinate transformations - to model a tissue boundary and define compartments for quantification of invaded objects, representing the early steps in cancer metastasis. To evaluate its performance, we applied our analytical approach to an adapted OOC developed by Emulate, Inc., utilizing a two-channel system with endothelial cells in the bottom channel and colorectal cancer (CRC) patient-derived organoids (PDOs) in the top channel. Initial application and visualization of this method revealed boundary variations due to microscope stage tilt and ridge and valley-like contours in the endothelial tissue surface. The method was functionalized into a reproducible analytical process and web tool - the Chip Invasion and Contour Analysis (ChICA) - to model the endothelial surface and quantify invading tumor cells across multiple chips. To illustrate applicability of the analytical method, we applied the tool to CRC organoid-chips seeded with two different endothelial cell types and measured distinct variations in endothelial surfaces and tumor cell invasion dynamics. Since ChICA utilizes only positional data output from imaging software, the method is applicable to and agnostic of the imaging tool and image analysis system used. The novel thin plate spline method developed in ChICA can account for variation introduced in OOC manufacturing or during the experimental workflow, can quickly and accurately measure tumor cell invasion, and can be used to explore biological mechanisms in drug discovery.

A novel thin plate spline methodology to model tissue surfaces and quantify tumor cell invasion in organ-on-chip models.

Organ-on-chip (OOC) models can be useful tools for cancer drug discovery. Advances in OOC technology have led to the development of more complex assays, yet analysis of these systems does not always account for these advancements, resulting in technical challenges. A challenging task in the analysis of these two-channel microfluidic models is to define the boundary between the channels so objects moving within and between channels can be quantified. We propose a novel imaging-based application of a thin plate spline method - a generalized cubic spline that can be used to model coordinate transformations - to model a tissue boundary and define compartments for quantification of invaded objects, representing the early steps in cancer metastasis. To evaluate its performance, we applied our analytical approach to an adapted OOC developed by Emulate, Inc., utilizing a two-channel system with endothelial cells in the bottom channel and colorectal cancer (CRC) patient-derived organoids (PDOs) in the top channel. Initial application and visualization of this method revealed boundary variations due to microscope stage tilt and ridge and valley-like contours in the endothelial tissue surface. The method was functionalized into a reproducible analytical process and web tool - the Chip Invasion and Contour Analysis (ChICA) - to model the endothelial surface and quantify invading tumor cells across multiple chips. To illustrate applicability of the analytical method, we applied the tool to CRC organoid-chips seeded with two different endothelial cell types and measured distinct variations in endothelial surfaces and tumor cell invasion dynamics. Since ChICA utilizes only positional data output from imaging software, the method is applicable to and agnostic of the imaging tool and image analysis system used. The novel thin plate spline method developed in ChICA can account for variation introduced in OOC manufacturing or during the experimental workflow, can quickly and accurately measure tumor cell invasion, and can be used to explore biological mechanisms in drug discovery.

Immune biomarkers associated with COVID-19 disease severity in an urban, hospitalized population.

Objectives: We sought to identify immune biomarkers associated with severe Coronavirus disease 2019 (COVID-19) in patients admitted to a large urban hospital during the early phase of the SARS-CoV-2 pandemic. Design: The study population consisted of SARS-CoV-2 positive subjects admitted for COVID-19 (n = 58) or controls (n = 14) at the Los Angeles County University of Southern California Medical Center between April 2020 through December 2020. Immunologic markers including chemokine/cytokines (IL-6, IL-8, IL-10, IP-10, MCP-1, TNF-α) and serologic markers against SARS-CoV-2 antigens (including spike subunits S1 and S2, receptor binding domain, and nucleocapsid) were assessed in serum collected on the day of admission using bead-based multiplex immunoassay panels. Results: We observed that body mass index (BMI) and SARS-CoV-2 antibodies were significantly elevated in patients with the highest COVID-19 disease severity. IP-10 was significantly elevated in COVID-19 patients and was associated with increased SARS-CoV-2 antibodies. Interactions among all available variables on COVID-19 disease severity were explored using a linear support vector machine model which supported the importance of BMI and SARS-CoV-2 antibodies. Conclusions: Our results confirm the known adverse association of BMI on COVID-19 severity and suggest that IP-10 and SARS-CoV-2 antibodies could be useful to identify patients most likely to experience the most severe forms of the disease.