Shashanka Ashili

Method for physiologic phenotype characterization at the single-cell level in non-interacting and interacting cells

Intercellular heterogeneity is a key factor in a variety of core cellular processes including proliferation, stimulus response, carcinogenesis, and drug resistance. However, cell-to-cell variability studies at the single-cell level have been hampered by the lack of enabling experimental techniques. We present a measurement platform that features the capability to quantify oxygen consumption rates of individual, non-interacting and interacting cells under normoxic and hypoxic conditions. It is based on real-time concentration measurements of metabolites of interest by means of extracellular optical sensors in cell-isolating microwells of subnanoliter volume. We present the results of a series of measurements of oxygen consumption rates (OCRs) of individual non-interacting and interacting human epithelial cells. We measured the effects of cell-to-cell interactions by using the systems capability to isolate two and three cells in a single well. The major advantages of the approach are: 1. ratiometric, intensity-based characterization of the metabolic phenotype at the single-cell level, 2. minimal invasiveness due to the distant positioning of sensors, and 3. ability to study the effects of cell-cell interactions on cellular respiration rates.

A statistical framework for multiparameter analysis at the single-cell level

Phenotypic characterization of individual cells provides crucial insights into intercellular heterogeneity and enables access to information that is unavailable from ensemble averaged, bulk cell analyses. Single-cell studies have attracted significant interest in recent years and spurred the development of a variety of commercially available and research-grade technologies. To quantify cell-to-cell variability of cell populations, we have developed an experimental platform for real-time measurements of oxygen consumption (OC) kinetics at the single-cell level. Unique challenges inherent to these single-cell measurements arise, and no existing data analysis methodology is available to address them. Here we present a data processing and analysis method that addresses challenges encountered with this unique type of data in order to extract biologically relevant information. We applied the method to analyze OC profiles obtained with single cells of two different cell lines derived from metaplastic and dysplastic human Barretts esophageal epithelium. In terms of method development, three main challenges were considered for this heterogeneous dynamic system: (i) high levels of noise, (ii) the lack of a priori knowledge of single-cell dynamics, and (iii) the role of intercellular variability within and across cell types. Several strategies and solutions to address each of these three challenges are presented. The features such as slopes, intercepts, breakpoint or change-point were extracted for every OC profile and compared across individual cells and cell types. The results demonstrated that the extracted features facilitated exposition of subtle differences between individual cells and their responses to cell-cell interactions. With minor modifications, this method can be used to process and analyze data from other acquisition and experimental modalities at the single-cell level, providing a valuable statistical framework for single-cell analysis.

Automated platform for multiparameter stimulus response studies of metabolic activity at the single-cell level

We have developed a fully automated platform for multiparameter characterization of physiological response of individual and small numbers of interacting cells. The platform allows for minimally invasive monitoring of cell phenotypes while administering a variety of physiological insults and stimuli by means of precisely controlled microfluidic subsystems. It features the capability to integrate a variety of sensitive intra- and extra-cellular fluorescent probes for monitoring minute intra- and extra-cellular physiological changes. The platform allows for performance of other, post- measurement analyses of individual cells such as transcriptomics. Our method is based on the measurement of extracellular metabolite concentrations in hermetically sealed ~200-pL microchambers, each containing a single cell or a small number of cells. The major components of the system are a) a confocal laser scan head to excite and detect with single photon sensitivity the emitted photons from sensors; b) a microfluidic cassette to confine and incubate individual cells, providing for dynamic application of external stimuli, and c) an integration module consisting of software and hardware for automated cassette manipulation, environmental control and data collection. The custom-built confocal scan head allows for fluorescence intensity detection with high sensitivity and spatial confinement of the excitation light to individual pixels of the sensor area, thus minimizing any phototoxic effects. The platform is designed to permit incorporation of multiple optical sensors for simultaneous detection of various metabolites of interest. The modular detector structure allows for several imaging modalities, including high resolution intracellular probe imaging and extracellular sensor readout. The integrated system allows for simulation of physiologically relevant microenvironmental stimuli and simultaneous measurement of the elicited phenotypes. We present details of system design, system characterization and metabolic response analysis of individual eukaryotic cells.

Platform for combined analysis of functional and biomolecular phenotypes of the same cell

Functional and molecular cell-to-cell variability is pivotal at the cellular, tissue and whole-organism levels. Yet, the ultimate goal of directly correlating the function of the individual cell with its biomolecular profile remains elusive. We present a platform for integrated analysis of functional and transcriptional phenotypes in the same single cells. We investigated changes in the cellular respiration and gene expression diversity resulting from adaptation to repeated episodes of acute hypoxia in a premalignant progression model. We find differential, progression stage-specific alterations in phenotypic heterogeneity and identify cells with aberrant phenotypes. To our knowledge, this study is the first demonstration of an integrated approach to elucidate how heterogeneity at the transcriptional level manifests in the physiologic profile of individual cells in the context of disease progression.

A novel method for multiparameter physiological phenotype characterization at the single-cell level

Non-genetic intercellular heterogeneity has been increasingly recognized as one of the key factors in a variety of core cellular processes including proliferation, stimulus response, carcinogenesis and drug resistance. Many diseases, including cancer, originate in a single or a few cells. Early detection and characterization of these abnormal cells can provide new insights into the pathogenesis and serve as a tool for better disease diagnosis and treatment. We report on a novel technology for multiparameter physiological phenotype characterization at the single-cell level. It is based on real-time measurements of concentrations of several metabolites by means of extracellular optical sensors in microchambers of sub-nL volume containing single cells. In its current configuration, the measurement platform features the capability to detect oxygen consumption rate and pH changes under normoxic and hypoxic conditions at the single-cell level. We have conceived, designed and developed a semi-automated method for single-cell manipulation and loading into microwells utilizing custom, high-precision fluid handling at the nanoliter scale. We present the results of a series of measurements of oxygen consumption rates (OCRs) of single human metaplastic esophageal epithelial cells. In addition, to assess the effects of cell-to-cell interactions, we have measured OCRs of two and three cells placed in a single well. The major advantages of the approach are a) multiplexed characterization of cell phenotype at the single-cell level, b) minimal invasiveness due to the distant positioning of sensors, and c) flexibility in terms of accommodating measurements of other metabolites or biomolecules of interest.

Abstract LB-027: Single-cell analysis of functional heterogeneity dynamics in premalignant progression revealed by combined interrogation of functional and transcriptional phenotypes

Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA Intercellular heterogeneity is pivotal in carcinogenesis and tumor development and plays a central role in advanced metastatic disease, therapeutic resistance, and recurrence at the cellular, tissue and whole organism levels. Rapid advances in single-cell analysis technologies have enabled unprecedented insights into cellular machinery. Yet, the ultimate goal of directly correlating the cellular function with the transcriptional profile of a single cell for comprehensive interrogation of the dynamics of cellular variability remains elusive. We present a study on alterations in intercellular diversity in terms of functional and transcriptional phenotypes in the context of selective pressure based on integrated, multiplexed single-cell measurements of cellular respiration and gene transcription levels in the same single cells. Utilizing a Barretts esophagus premalignant progression model, we investigated alterations in the intercellular diversity resulting from adaptation to the episodes of acute hypoxia that generated stringent selective pressure on a panel of human esophageal epithelial cell lines representing different stages (metaplasia and dysplasia) in the progression. We correlated alterations in cellular respiration with the expression levels of genes involved in the glycolysis and hypoxia response pathways in the same individual cells. Using dimensionality reduction approaches combined with multiparameter analysis we found evidence that population-level functional heterogeneity can be recapitulated and maintained by a small (∼10%) sub-population of cells that survive selective pressure. By combining functional and molecular profiling of the same individual cells we identified unique errant cellular functional phenotypes of individual cells that would remain hidden otherwise, but may harbor clues to phenotypic cancer progression dynamics. To our knowledge, this study is the first successful demonstration of an integrated, multiparameter approach to elucidate how heterogeneity at the transcriptional level manifests in the physiologic profile of individual cells at different stages of pre-malignant progression. Citation Format: Laimonas Kelbauskas, Shashanka Ashili, Jia Zeng, Aida Mohammadreza, Kristen Lee, Dmitry Derkach, Benjamin Ueberroth, Weimin Gao, Thomas Paulson, Hong Wang, Yanqing Tian, Dean Smith, Brian Reid, Deirdre Meldrum. Single-cell analysis of functional heterogeneity dynamics in premalignant progression revealed by combined interrogation of functional and transcriptional phenotypes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-027.

Multiple sensor arrays for single cell metabolic analysis

We present the design, fabrication and characterization of multiple micro-pocket lid arrays used in live single cell metabolic analysis. In previous work we reported a platform for quantifying single cell oxygen consumption rates realized using a fused silica deep wet etching process. Here we extend that work to a dual-depth wet etching process for microfabrication of multiple sensor trapping (MST) lid arrays. Each lid comprises multiple micro-pockets. Oxygen, pH, other extra-cellular sensors, and reference dye were deposited in the pockets. In order to achieve simultaneous monitoring of multiple metabolic parameters, the lid array serves to hermetically seal arrays of microwells, each containing a single cell. The dual-depth etching process we developed can be easily applied to other glass-based microfabrication purposes requiring dual- or multiple-depth microstructures.