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Droplet-based microfluidics for screening and single-cell analysis


Droplet-based microfluidics for screening and single-cell analysis

Ahmadi, Fatemeh ORCID: https://orcid.org/0000-0002-0353-8856 (2023) Droplet-based microfluidics for screening and single-cell analysis. PhD thesis, Concordia University.

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The field of biology and biochemistry relies heavily on efficient screening procedures to discover and develop new entities or optimize existing processes. However, traditional screening methods are time-consuming, labor-intensive, and expensive, requiring thousands to millions of experiments. Microfluidic and automation technologies offer a promising solution to this problem, enabling high-throughput screening (thousands of droplets in a few seconds) in a much faster and less expensive manner. In addition to accelerating the screening processes, microfluidic technologies can reduce reagent consumption and improve precision and control through automation and miniaturization of experimentation. However, droplet-in-channel microfluidic systems are limited in terms of fluidic operations as they manipulate droplets only by pressure-based flows. In contrast, digital microfluidics provides greater programmability by manipulating droplets using integrated electrodes. However, this precise control and manipulation significantly decreases the system throughput.
Therefore, this thesis aims to integrate droplet and digital microfluidic systems to offer valuable insights into the potential of microfluidic technologies for enhancing the efficiency of screening procedures in the field of single-cell analysis and biochemical synthesis. By integrating droplet and digital microfluidic systems, we propose new methods for single-cell studies, such as mammalian cells gene editing and monoclonal antibody discovery. Furthermore, the developed high throughput screening system can be used to screen large libraries of potential therapeutics and diagnostics, such as radiotracers for bioimaging applications. We also propose to leverage design-of-experiment methodologies and machine learning algorithms to enhance the efficiency of digital microfluidics for optimization of biochemical synthesis reactions. These works involve the development of new hardware and software, and integration of biological assays and biochemical reactions on these platforms. These systems can expand and improve the application of microfluidic and automation systems for biotechnology industries.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Concordia University > Research Units > Centre for Structural and Functional Genomics
Item Type:Thesis (PhD)
Authors:Ahmadi, Fatemeh
Institution:Concordia University
Degree Name:Ph. D.
Program:Electrical and Computer Engineering
Date:2 May 2023
Thesis Supervisor(s):Shih, Steve
ID Code:992361
Deposited By: Fatemeh Ahmadi
Deposited On:15 Nov 2023 15:28
Last Modified:15 Nov 2023 15:28
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