Understanding cell decision-making using near-infrared quantum dots: One cell, one molecule at a time

Le, Phuong B.

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https://hdl.handle.net/2142/106482 Copy

Description

Title

Understanding cell decision-making using near-infrared quantum dots: One cell, one molecule at a time

Author(s)

Le, Phuong B.

Issue Date

2019-12-06

Director of Research (if dissertation) or Advisor (if thesis)

Smith, Andrew M.

Doctoral Committee Chair(s)

Smith, Andrew M.

Committee Member(s)

• Kilian, Kristopher A.
• Selvin, Paul R.
• Perez-Pinera, Pablo

Department of Study

Bioengineering

Discipline

Bioengineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• quantum dots
• EGF
• TNFα
• single molecule
• near-infrared
• cell decision
• NFκB
• cytokine
• growth factor
• transcription factor

Abstract

Cell receives input signals such growth factors from the environment and decides an appropriate output such as migration, division, differentiation, and apoptosis. This decision making process is tightly controlled, and when misregulated leads to pathogenesis such as cancer, immune disease, and neurodegenerative diseases. Hence, understanding how cell decision making will help us better understand pathogenesis processes and aid in developing treatments that are more effective. Thus far, studies on cell decision-making process focus on intracellular events such as transcription factor activation and gene expression. However, a void exists in experimental techniques to measure how cellular decision-making processes derive from extracellular biochemical input signals, such as peptide growth factors and cytokines, which are the primary initiating factors that influencing cell decision and cannot be measured at the single-cell level. Most studies infer cell stimulation from response and apply input factors at stimulation extremes (zero and near saturation), whereas physiologically relevant tissue concentrations are in intermediate ranges (c ~ 1 – 100 pM) over which cells exhibit sensitive, heterogeneous dose-response relationships (EC50 ~ 1 – 100 pM). At these concentrations, relevant tissue microdomain volumes (~10 pL) contain just tens to hundreds of factors, such that signal stimulation is temporally and spatially stochastic. Accurate quantification of initiating signals is therefore very challenging and requires single-molecule sensitivity. My thesis research focuses on developing a new technology platform to accurately count signaling molecules acting on individual cells and correlating this input stimulation with downstream signaling. I will focus on epidermal growth factor (EGF) and tumor necrosis factor alpha (TNFα) signaling pathways as model system. As single-molecule detection and counting requires probes with extremely high signal intensity that are homogeneous and stable, I utilized quantum dots (QDs). QDs are fluorescent nanocrystals that are exceptional probes for single-molecule imaging and quantification due to their intense brightness (~10-100 fold brighter than organic dyes), tunable emission wavelength (400 nm to the infrared), and extreme stability. In the first part of my thesis, I optimized the bioconjugation, size, and emission wavelength of QDs to achieve a compact and highly specific probe to detect single molecules in cells. In the second part of my thesis, I developed an intensity-calibrated method using QDs and 3D deconvolution microscopy to accurately count EGF acting on cells across three orders of magnitude in concentrations. I applied this method to human MDA-MB-231 breast cancer cells, and found that the amount of EGF binding is proportional to the degree of EGF receptor activation, measured by EGF internalization, and inversely proportional to the effect of pharmacological inhibition of EGF receptor. This finding implicates that signaling molecules present in tumor—a place where signaling molecules are often misregulated—can enhance the cancer cells’ resistance to pharmaceutical agents. Finally, I investigated the correlation between TNFα binding and the activation of its downstream transcription factor, NFκB, which plays a critical role in a wide range of pathologies including cancer, inflammatory disease, chronic infection, and autoimmune disease. I combined the technological platform developed in the first two parts of my thesis with automated live-cell imaging and tracking, automated cell segmentation, and applied to cells expressing fluorescent fusion protein generated via CRISPR/Cas9 system. So far, I have found that a single molecule of TNFα is sufficient to translocate NFκB to the nucleus and currently examine on the spatial activation of TNFα. This thesis provides the first ability to count growth factors and cytokines and connecting discrete signal initiating events to comprehensive cell response and population distributions. We expect that this toolbox can be applied to any peptide ligand and used broadly to provide a new understanding of how input signals control cell decision and influence heterogeneity within cell populations and drug effect variability.

Graduation Semester

2019-12

Type of Resource

text

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http://hdl.handle.net/2142/106482

Copyright and License Information

Copyright 2019 Phuong Le

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