A Caltech Library Service

Cell-Selective Proteomic Profiling in Complex Biological Systems


Liu, Xinran (2020) Cell-Selective Proteomic Profiling in Complex Biological Systems. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/p18t-5j69.


Cells within biological systems are constantly adjusting their protein synthesis in response to various environmental changes. To study the rapid cellular regulations in complex biological systems, global proteomic profiling provides important information on system-level regulations, yet physiological properties characteristic of individual cellular subpopulations could be hidden under the characterization. Instead, cell-selective proteomic profiling allows researchers to reveal the heterogeneities in biological systems with phenotypically and even genetically distinct subpopulations under different microenvironments.

Chapter 1 describes the development of bioorthogonal noncanonical amino acid tagging (BONCAT) for proteomic profiling with resolution in both space and time: its initial role is protein labeling with temporal resolution via pulse-addition of noncanonical amino acid, which could be recognized by endogenous aminoacyl tRNA-synthetase (aaRS), into systems of interest; later on, mutant aaRSs are identified through mutant synthetase library screening, which allows for efficient incorporation of various types of noncanonical amino acids that could hardly be activated by endogenous machineries. The identification and exploitation of mutant aaRSs allow sensitive cellular selectivity during protein labeling. With unprecedented spatiotemporal resolution of BONCAT, and the advancement in high-resolution mass spectrometry and computational algorithms, BONCAT is a powerful technique for selective proteomic profiling to study physiological regulations in a wide range of complex biological systems. Chapter 2 describes the application of the BONCAT method in cell-selective proteomic profiling in Pseudomonas aeruginosa biofilms. In this work, we targeted an iron-starved subpopulation in biofilms and compared its proteomic profile with that of the entire system. Key gene and pathway regulations in the subpopulation are found through the analysis of the proteomic data, which suggest that iron-starved cells shift their priority towards housing keeping pathways, adapt an energy- and resources-saving mode to cope with their harsh local environmental conditions, and get prepared to disperse for better survival. Analysis of poorly studied proteins highly upregulated in the subpopulation led to the discovery of a previously uncharacterized protein (PA14_52000) that is potentially related to iron acquisition. The transposon insertion mutant PA14_52000::tn showed significantly enhanced pyoverdine production in rich medium and reduced biofilm formation.

Chapter 3 describes the study of physiological regulations in Bacillus subtilis K-state subpopulation via BONCAT. A subset of B. subtilis cells, typically 10% - 20% of the entire population, enter K-state in a stochastic manner. With the low level of K-state entry rate and high randomness, we challenged BONCAT to specifically capture gene and pathway regulations in K-state cells and compared the proteomic profiling with that of the entire population. Regardless of the difficulties of selective protein labeling inherent in the system, our results indicate that BONCAT has high specificity and resolution in proteomic profiling for minor subpopulations and proteins with low overall absolute abundance. We found multiple pathways and genes characteristic of K-state regulated differentially from the entire population, either significantly up- or down-regulated. Proteins that are uncharacterized or previously known for functions irrelevant of K-state are highly abundant in the subpopulation, providing new insight toward their alternative functions critical for K-state cells and future investigation directions of K-state study.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Microbiology, BONCAT, cell-selective proteomics
Degree Grantor:California Institute of Technology
Division:Chemistry and Chemical Engineering
Major Option:Chemical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Tirrell, David A.
Thesis Committee:
  • Tirrell, David A.
  • Kornfield, Julia A. (chair)
  • Ismagilov, Rustem F.
  • Newman, Dianne K.
Defense Date:18 May 2020
Funding AgencyGrant Number
U.S. Army Research OfficeW911NF-09-0001
Record Number:CaltechTHESIS:05282020-112303076
Persistent URL:
Liu, Xinran0000-0003-0045-8823
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13728
Deposited By: Xinran Liu
Deposited On:01 Jun 2020 22:20
Last Modified:08 Nov 2023 00:37

Thesis Files

PDF (Thesis) - Final Version
See Usage Policy.

[img] MS Excel (Data Set A.1) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set A.2) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set A.3) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set A.4) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set A.5) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set A.6) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set A.7) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set B.1) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set B.2) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set B.3) - Supplemental Material
See Usage Policy.

[img] MS Excel (Data Set B.4) - Supplemental Material
See Usage Policy.


Repository Staff Only: item control page