Single cell proteomics microchip to profile immune function, with applications in stem cell biology, translational disease mechanism study and clinical therapeutics monitoring

In response to infection or tissue dysfunction, immune cells develop into highly heterogeneous repertoires with diverse functions. Capturing the full spectrum of these functions requires analysis of large numbers of effector molecules from single cells. However, currently only 3-5 functional proteins can be measured from single cells. We developed a single cell functional proteomics approach that integrates a microchip platform with multiplex cell purification. This approach can quantitate 20 proteins from >5,000 phenotypically pure single cells simultaneously. With a 1-million fold miniaturization, the system can detect down to ~100 molecules and requires only ~104 cells. Single cell functional proteomic analysis finds broad applications in basic, translational and clinical studies. In the three studies conducted, it yielded critical insights for understanding clinical cancer immunotherapy, inflammatory bowel disease (IBD) mechanism and hematopoietic stem cell (HSC) biology.

To study phenotypically defined cell populations, single cell barcode microchips were coupled with upstream multiplex cell purification based on up to 11 parameters. Statistical algorithms were developed to process and model the high dimensional readouts. This analysis evaluates rare cells and is versatile for various cells and proteins. (1) We conducted an immune monitoring study of a phase 2 cancer cellular immunotherapy clinical trial that used T-cell receptor (TCR) transgenic T cells as major therapeutics to treat metastatic melanoma. We evaluated the functional proteome of 4 antigen-specific, phenotypically defined T cell populations from peripheral blood of 3 patients across 8 time points. (2) Natural killer (NK) cells can play a protective role in chronic inflammation and their surface receptor – killer immunoglobulin-like receptor (KIR) – has been identified as a risk factor of IBD. We compared the functional behavior of NK cells that had differential KIR expressions. These NK cells were retrieved from the blood of 12 patients with different genetic backgrounds. (3) HSCs are the progenitors of immune cells and are thought to have no immediate functional capacity against pathogen. However, recent studies identified expression of Toll-like receptors (TLRs) on HSCs. We studied the functional capacity of HSCs upon TLR activation. The comparison of HSCs from wild-type mice against those from genetics knock-out mouse models elucidates the responding signaling pathway.

In all three cases, we observed profound functional heterogeneity within phenotypically defined cells. Polyfunctional cells that conduct multiple functions also produce those proteins in large amounts. They dominate the immune response. In the cancer immunotherapy, the strong cytotoxic and antitumor functions from transgenic TCR T cells contributed to a ~30% tumor reduction immediately after the therapy. However, this infused immune response disappeared within 2-3 weeks. Later on, some patients gained a second antitumor response, consisted of the emergence of endogenous antitumor cytotoxic T cells and their production of multiple antitumor functions. These patients showed more effective long-term tumor control. In the IBD mechanism study, we noticed that, compared with others, NK cells expressing KIR2DL3 receptor secreted a large array of effector proteins, such as TNF-α, CCLs and CXCLs. The functions from these cells regulated disease-contributing cells and protected host tissues. Their existence correlated with IBD disease susceptibility. In the HSC study, the HSCs exhibited functional capacity by producing TNF-α, IL-6 and GM-CSF. TLR stimulation activated the NF-κB signaling in HSCs. Single cell functional proteome contains rich information that is independent from the genome and transcriptome. In all three cases, functional proteomic evaluation uncovered critical biological insights that would not be resolved otherwise. The integrated single cell functional proteomic analysis constructed a detail kinetic picture of the immune response that took place during the clinical cancer immunotherapy. It revealed concrete functional evidence that connected genetics to IBD disease susceptibility. Further, it provided predictors that correlated with clinical responses and pathogenic outcomes.

Spinal cord injury therapy through active learning

Therapy employing epidural electrostimulation holds great potential for improving therapy for patients with spinal cord injury (SCI) (Harkema et al., 2011). Further promising results from combined therapies using electrostimulation have also been recently obtained (e.g., van den Brand et al., 2012). The devices being developed to deliver the stimulation are highly flexible, capable of delivering any individual stimulus among a combinatorially large set of stimuli (Gad et al., 2013). While this extreme flexibility is very useful for ensuring that the device can deliver an appropriate stimulus, the challenge of choosing good stimuli is quite substantial, even for expert human experimenters. To develop a fully implantable, autonomous device which can provide useful therapy, it is necessary to design an algorithmic method for choosing the stimulus parameters. Such a method can be used in a clinical setting, by caregivers who are not experts in the neurostimulator's use, and to allow the system to adapt autonomously between visits to the clinic. To create such an algorithm, this dissertation pursues the general class of active learning algorithms that includes Gaussian Process Upper Confidence Bound (GP-UCB, Srinivas et al., 2010), developing the Gaussian Process Batch Upper Confidence Bound (GP-BUCB, Desautels et al., 2012) and Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) algorithms. This dissertation develops new theoretical bounds for the performance of these and similar algorithms, empirically assesses these algorithms against a number of competitors in simulation, and applies a variant of the GP-BUCB algorithm in closed-loop to control SCI therapy via epidural electrostimulation in four live rats. The algorithm was tasked with maximizing the amplitude of evoked potentials in the rats' left tibialis anterior muscle. These experiments show that the algorithm is capable of directing these experiments sensibly, finding effective stimuli in all four animals. Further, in direct competition with an expert human experimenter, the algorithm produced superior performance in terms of average reward and comparable or superior performance in terms of maximum reward. These results indicate that variants of GP-BUCB may be suitable for autonomously directing SCI therapy.

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.

Development of a Cationic Mucic Acid Polymer-Based Nanoparticle siRNA Delivery System

Cancer chemotherapy has advanced from highly toxic drugs to more targeted treatments in the last 70 years. Chapter 1 opens with an introduction to targeted therapy for cancer. The benefits of using a nanoparticle to deliver therapeutics are discussed. We move on to siRNA in particular, and why it would be advantageous as a therapy. Specific to siRNA delivery are some challenges, such as nuclease degradation, quick clearance from circulation, needing to enter cells, and getting to the cytosol. We propose the development of a nanoparticle delivery system to tackle these challenges so that siRNA can be effective.

Chapter 2 of this thesis discusses the synthesis and analysis of a cationic mucic acid polymer (cMAP) which condenses siRNA to form a nanoparticle. Various methods to add polyethylene glycol (PEG) for stabilizing the nanoparticle in physiologic solutions, including using a boronic acid binding to diols on mucic acid, forming a copolymer of cMAP with PEG, and creating a triblock with mPEG on both ends of cMAP. The goal of these various pegylation strategies was to increase the circulation time of the siRNA nanoparticle in the bloodstream to allow more of the nanoparticle to reach tumor tissue by the enhanced permeation and retention effect. We found that the triblock mPEG-cMAP-PEGm polymer condensed siRNA to form very stable 30-40 nm particles that circulated for the longest time – almost 10% of the formulation remained in the bloodstream of mice 1 h after intravenous injection.

Chapter 3 explores the use of an antibody as a targeting agent for nanoparticles. Some antibodies of the IgG1 subtype are able to recruit natural killer cells that effect antibody dependent cellular cytotoxicity (ADCC) to kill the targeted cell to which the antibody is bound. There is evidence that the ADCC effect remains in antibody-drug conjugates, so we wanted to know whether the ADCC effect is preserved when the antibody is bound to a nanoparticle, which is a much larger and complex entity. We utilized antibodies against epidermal growth factor receptor with similar binding and pharmacokinetics, cetuximab and panitumumab, which differ in that cetuximab is an IgG1 and panitumumab is an IgG2 (which does not cause ADCC). Although a natural killer cell culture model showed that gold nanoparticles with a full antibody targeting agent can elicit target cell lysis, we found that this effect was not preserved in vivo. Whether this is due to the antibody not being accessible to immune cells or whether the natural killer cells are inactivated in a tumor xenograft remains unknown. It is possible that using a full antibody still has value if there are immune functions which are altered in a complex in vivo environment that are intact in an in vitro system, so the value of using a full antibody as a targeting agent versus using an antibody fragment or a protein such as transferrin is still open to further exploration.

In chapter 4, nanoparticle targeting and endosomal escape are further discussed with respect to the cMAP nanoparticle system. A diboronic acid entity, which gives an order of magnitude greater binding (than boronic acid) to cMAP due to the vicinal diols in mucic acid, was synthesized, attached to 5kD or 10kD PEG, and conjugated to either transferrin or cetuximab. A histidine was incorporated into the triblock polymer between cMAP and the PEG blocks to allow for siRNA endosomal escape. Nanoparticle size remained 30-40 nm with a slightly negative ca. -3 mV zeta potential with the triblock polymer containing histidine and when targeting agents were added. Greater mRNA knockdown was seen with the endosomal escape mechanism than without. The nanoparticle formulations were able to knock down the targeted mRNA in vitro. Mixed effects suggesting function were seen in vivo.

Chapter 5 summarizes the project and provides an outlook on siRNA delivery as well as targeted combination therapies for the future of personalized medicine in cancer treatment.