Single-cell analysis of the physiology of mechanosensation in bacteria

Escherichia coli is one of the best studied living organisms and a model system for many biophysical investigations. Despite countless discoveries of the details of its physiology, we still lack a holistic understanding of how these bacteria react to changes in their environment. One of the most important examples is their response to osmotic shock. One of the mechanistic elements protecting cell integrity upon exposure to sudden changes of osmolarity is the presence of mechanosensitive channels in the cell membrane. These channels are believed to act as tension release valves protecting the inner membrane from rupturing. This thesis presents an experimental study of various aspects of mechanosensation in bacteria. We examine cell survival after osmotic shock and how the number of MscL (Mechanosensitive channel of Large conductance) channels expressed in a cell influences its physiology. We developed an assay that allows real-time monitoring of the rate of the osmotic challenge and direct observation of cell morphology during and after the exposure to osmolarity change. The work described in this thesis introduces tools that can be used to quantitatively determine at the single-cell level the number of expressed proteins (in this case MscL channels) as a function of, e.g., growth conditions. The improvement in our quantitative description of mechanosensation in bacteria allows us to address many, so far unsolved, problems, like the minimal number of channels needed for survival, and can begin to paint a clearer picture of why there are so many distinct types of mechanosensitive channels.

Mechanistic studies of reactions at the single-molecule level using microfluidics with applications in molecular diagnostics

Motivated by needs in molecular diagnostics and advances in microfabrication, researchers started to seek help from microfluidic technology, as it provides approaches to achieve high throughput, high sensitivity, and high resolution. One strategy applied in microfluidics to fulfill such requirements is to convert continuous analog signal into digitalized signal. One most commonly used example for this conversion is digital PCR, where by counting the number of reacted compartments (triggered by the presence of the target entity) out of the total number of compartments, one could use Poisson statistics to calculate the amount of input target.

However, there are still problems to be solved and assumptions to be validated before the technology is widely employed. In this dissertation, the digital quantification strategy has been examined from two angles: efficiency and robustness. The former is a critical factor for ensuring the accuracy of absolute quantification methods, and the latter is the premise for such technology to be practically implemented in diagnosis beyond the laboratory. The two angles are further framed into a “fate” and “rate” determination scheme, where the influence of different parameters is attributed to fate determination step or rate determination step. In this discussion, microfluidic platforms have been used to understand reaction mechanism at single molecule level. Although the discussion raises more challenges for digital assay development, it brings the problem to the attention of the scientific community for the first time.

This dissertation also contributes towards developing POC test in limited resource settings. On one hand, it adds ease of access to the tests by incorporating massively producible, low cost plastic material and by integrating new features that allow instant result acquisition and result feedback. On the other hand, it explores new isothermal chemistry and new strategies to address important global health concerns such as cyctatin C quantification, HIV/HCV detection and treatment monitoring as well as HCV genotyping.

Technology for Single Cell Protein Analysis in Immunology and Cancer Prognostics

The first chapter of this thesis deals with automating data gathering for single cell microfluidic tests. The programs developed saved significant amounts of time with no loss in accuracy. The technology from this chapter was applied to experiments in both Chapters 4 and 5.

The second chapter describes the use of statistical learning to prognose if an anti-angiogenic drug (Bevacizumab) would successfully treat a glioblastoma multiforme tumor. This was conducted by first measuring protein levels from 92 blood samples using the DNA-encoded antibody library platform. This allowed the measure of 35 different proteins per sample, with comparable sensitivity to ELISA. Two statistical learning models were developed in order to predict whether the treatment would succeed. The first, logistic regression, predicted with 85% accuracy and an AUC of 0.901 using a five protein panel. These five proteins were statistically significant predictors and gave insight into the mechanism behind anti-angiogenic success/failure. The second model, an ensemble model of logistic regression, kNN, and random forest, predicted with a slightly higher accuracy of 87%.

The third chapter details the development of a photocleavable conjugate that multiplexed cell surface detection in microfluidic devices. The method successfully detected streptavidin on coated beads with 92% positive predictive rate. Furthermore, chambers with 0, 1, 2, and 3+ beads were statistically distinguishable. The method was then used to detect CD3 on Jurkat T cells, yielding a positive predictive rate of 49% and false positive rate of 0%.

The fourth chapter talks about the use of measuring T cell polyfunctionality in order to predict whether a patient will succeed an adoptive T cells transfer therapy. In 15 patients, we measured 10 proteins from individual T cells (~300 cells per patient). The polyfunctional strength index was calculated, which was then correlated with the patient's progress free survival (PFS) time. 52 other parameters measured in the single cell test were correlated with the PFS. No statistical correlator has been determined, however, and more data is necessary to reach a conclusion.

Finally, the fifth chapter talks about the interactions between T cells and how that affects their protein secretion. It was observed that T cells in direct contact selectively enhance their protein secretion, in some cases by over 5 fold. This occurred for Granzyme B, Perforin, CCL4, TNFa, and IFNg. IL- 10 was shown to decrease slightly upon contact. This phenomenon held true for T cells from all patients tested (n=8). Using single cell data, the theoretical protein secretion frequency was calculated for two cells and then compared to the observed rate of secretion for both two cells not in contact, and two cells in contact. In over 90% of cases, the theoretical protein secretion rate matched that of two cells not in contact.