Microbial Profiling via Multimodal Single Cell Biosensors
Open Access
- Author:
- Lee, Jyong Huei
- Graduate Program:
- Bioengineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- February 01, 2024
- Committee Members:
- Yuguo Lei, Professor in Charge/Director of Graduate Studies
Pak Kin Wong, Chair & Dissertation Advisor
Scott Medina, Major Field Member
Justin Pritchard, Major Field Member
Nanyin Zhang, Major Field Member
Darrell Cockburn, Outside Unit & Field Member - Keywords:
- Biosensors
Single Cell
Microbial Profiling - Abstract:
- The human microbiota, consisting of trillions of microorganisms inhabiting various regions of our bodies, is integral to numerous physiological processes such as digestion, immune response, and hormone regulation. Microbial imbalances, or dysbiosis, have been implicated in a plethora of health conditions. Exploiting the microbiota's potential can revolutionize the development of diagnostic and therapeutic strategies aimed at enhancing treatment outcomes, reducing complications, and preventing disease recurrence. A significant barrier to clinical application lies in the absence of rapid microbiota analysis methods that can produce clinically relevant data. This thesis introduces innovative multimodal biosensors designed for precise single-cell microbiota profiling. These biosensors enable the detection and functional assessment of microbial populations at the single-cell level, thus facilitating a holistic understanding of the microbiome's role in health and disease. The proposed biosensors provide a comprehensive analysis toolkit, capable of determining absolute microbial abundance, viability, spatial distribution, and gene expression profiles. Furthermore, we apply these multimodal biosensors to conduct microbial community profiling on clinical samples, with a particular focus on exploring the gut microbiome variations in mouse models of familial Alzheimer's disease. This research bridges a critical gap in microbial diagnostics and paves the way for integrating microbiome analysis into personalized medicine and clinical care.