Publications by authors named "Shravanthi Rajasekar"

Diabetic kidney disease (DKD) is the main cause of chronic kidney disease (CKD) and progresses faster in males than in females. We identify sex-based differences in kidney metabolism and in the blood metabolome of male and female individuals with diabetes. Primary human proximal tubular epithelial cells (PTECs) from healthy males displayed increased mitochondrial respiration, oxidative stress, apoptosis, and greater injury when exposed to high glucose compared with PTECs from healthy females.

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The direction and pattern of fluid flow affect vascular structure and function, in which vessel-lining endothelial cells exhibit variable cellular morphologies and vessel remodeling by mechanosensing. To recapitulate this microenvironment, some approaches have been reported to successfully apply unidirectional flow on endothelial cells in organ-on-a-chip systems. However, these platforms encounter drawbacks such as the dependency on pumps or confinement to closed microfluidic channels.

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Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop crypt-like structures similar to those found in the native colon. While analyzing the structural development of organoids can be a valuable readout, using traditional image analysis tools makes it challenging because of the heterogeneities and the abstract nature of organoid morphologies.

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Organ-on-a-chip systems that recapitulate tissue-level functions have been proposed to improve - correlation in drug development. Significant progress has been made to control the cellular microenvironment with mechanical stimulation and fluid flow. However, it has been challenging to introduce complex 3D tissue structures due to the physical constraints of microfluidic channels or membranes in organ-on-a-chip systems.

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Vasculature is a key component of many biological tissues and helps to regulate a wide range of biological processes. Modeling vascular networks or the vascular interface in organ-on-a-chip systems is an essential aspect of this technology. In many organ-on-a-chip devices, however, the engineered vasculatures are usually designed to be encapsulated inside closed microfluidic channels, making it difficult to physically access or extract the tissues for downstream applications and analysis.

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Correction for 'Deep-LUMEN assay - human lung epithelial spheroid classification from brightfield images using deep learning' by Lyan Abdul et al., Lab Chip, 2020, DOI: .

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Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay.

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Despite the complexity and structural sophistication that 3D organoid models provide, their lack of vascularization and perfusion limit the capability of these models to recapitulate organ physiology effectively. A microfluidic platform named IFlowPlate is engineered, which can be used to culture up to 128 independently perfused and vascularized colon organoids in vitro. Unlike traditional microfluidic devices, the vascularized organoid-on-chip device with an "open-well" design does not require any external pumping systems and allows tissue extraction for downstream analyses, such as histochemistry or even in vivo transplantation.

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