Biology subjects

Griffith, L. G.

Publications and source records attributed to Griffith, L. G..

3 recordsLinked to original sources

bioRxiv2018

OrgaQuant: Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks

Organoid cultures are proving to be powerful in vitro models that closely mimic the cellular constituents of their native tissue. The organoids are typically expanded and cultured in a 3D environment using either naturally derived or synthetic extracellular matrices. Assessing the morphology and growth characteristics of these cultures has been difficult due to the many imaging artifacts that accompany the corresponding images. Unlike single cell cultures, there are no reliable segmentation techniques that allow for the localization and quantification of organoids in their 3D culture environment. Here we describe OrgaQuant, a deep convolutional neural network implementation that can locate and quantify the size distribution of intestinal organoids in brightfield images. OrgaQuant is an end-to-end trained neural network that requires no parameter tweaking, thus it can be fully automated to analyze thousands of images with no user intervention. To develop OrgaQuant we created a unique dataset of manually annotated intestinal organoid images and trained an object detection pipeline using TensorFlow. We have made the dataset, trained model and inference scripts publically available along with detailed usage instructions.

bioengineering↗

bioRxiv2017

Closed-loop feedback control for microfluidic systems through automated capacitive fluid height sensing

Precise fluid height sensing in open-channel microfluidics has long been a desirable feature for a wide range of applications. However, performing accurate measurements of the fluid level in small-scale reservoirs (<1mL) has proven to be an elusive goal, especially if direct fluid-sensor contact needs to be avoided. In particular, gravity-driven systems used in several microfluidic applications to establish pressure gradients and impose flow remain open-loop and largely unmonitored due to these sensing limitations. Here we present an optimized self-shielded coplanar capacitive sensor design and automated control system to provide submillimeter fluid-height resolution (~250 m) and control of small-scale open reservoirs without the need for direct fluid contact. Results from testing and validation of our optimized sensor and system also suggest that accurate fluid height information can be used to robustly characterize, calibrate and dynamically control a range of microfluidic systems with complex pumping mechanisms, even in cell culture conditions. Capacitive sensing technology provides a scalable and cost-effective way to enable continuous monitoring and closed-loop feedback control of fluid volumes in small-scale gravity-dominated wells in a variety of microfluidic applications.

bioengineering↗

bioRxiv2017

MICCS: A Fully Programmable Multipurpose Integrated Cell Culture System

With the rise of research utilizing microphysiological systems (MPSs), the need for tools that enable the physiological mimicking of the relevant cellular environment is vital. The limited ability to reproduce crucial features of the microenvironment, such as surrounding fluid flow and dynamic changes in biochemical stimuli, severely limits the types of experiments that can be carried out. Current equipment to achieve this, such as syringe and peristaltic pumps, is expensive, large, difficult to program and has limited potential for scalability. Here, we present a new pumping platform that is open-source, low-cost, modular, scalable, fully-programmable and easy to assemble that can be incorporated into cell culture systems to better recapitulate physiological environments. By controlling two commercially available piezoelectric pumps using a Raspberry Pi Zero microcontroller, the system is capable of producing arbitrary dynamic flow profiles with reliable flow rates ranging from 1 to 3,000 {micro}L/min as specified by an easily programmable Python-based script. We validated the accuracy of the flow rates, the use of time-varying profiles, and the practicality of the system by creating repeatable dynamic concentration profiles using a 3D-printed static micromixer.

bioengineering↗