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

Ashili, Shashaanka; Kelbauskas, Laimonas; Houkal, Jeff; Smith, Dean; Tian, Yanqing; Youngbull, Cody; Zhu, Hua; Anis, Yasser H.; Hupp, Michael; Lee, Kristen B.; Kumar, Ashok; Vela, Juan; Shabilla, Andrew; Holl, Mark R.; Meldrum, Deirdre R.

doi:10.1117/12.875438

Cited by 2 publications

(5 citation statements)

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“…A great deal of interest is found in the scientific community to understand the role of heterogeneity in cellular homeostasis and pathogenesis. 28,38 In recent years, innovative technologies have been developed to perform biological studies at the single-cell level, [24][25][26][27][28] including single-cell oxygen consumption measurements. Despite the availability of these technologies, their real potential can only be exploited utilizing effective analytical methods capable of performing robust de-noising and feature extraction steps on the novel type of information.…”

Section: Discussionmentioning

confidence: 99%

“…During measurement, individual cells are hermetically isolated in sub-nanolitre volume chambers which results in a limited amount of oxygen being available for consumption by cells. 27,28 Data collected after the oxygen concentration in the chambers reaches zero are not useful for OC kinetics analysis and can be discarded as extraneous or redundant. During an experiment, OC kinetics of nine individual cells was recorded simultaneously.…”

Section: Feature Extractionmentioning

confidence: 99%

“…As a first step in acquiring and analyzing multiparameter data, our center has developed an experimental platform for metabolic phenotype characterization, including oxygen consumption, at the single-cell level. 27,28 Single-cell oxygen consumption rates are on a scale of fmoles min À1 cell À1 . Because oxygen sensing is based on the dynamic quenching of sensor luminescence by oxygen, the signal-to-noise ratio of the measurement varies as a function of oxygen concentration in the microchamber.…”

Section: Datasetmentioning

confidence: 99%

“…The sensor emission intensity was collected as a function of time until oxygen concentration in the microchamber reached zero. 27 These variable importance scores are calculated based on the average over all trees of a scoring measure. This scoring measure is computed as the difference of correctly classified cases when the feature matrix values are evaluated onto the grown tree minus correctly misclassified items when the variable to be scored is permuted prior tree model evaluation.…”

Section: Datasetmentioning

confidence: 99%

“…The cell metabolic analysis method entails manipulation 24 and isolation of single cells 25 and determination of their oxygen consumption (OC) kinetics in real-time. [26][27][28] The data obtained from these measurements exhibit much higher levels of noise compared to bulk-cell experiments. The lack of a priori knowledge of single-cell dynamics makes it difficult to define characteristic features in these datasets, posing challenges in the extraction of biologically information and its proper biological interpretation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2012

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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 Barrett's 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.

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Section: Discussionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2012

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Single-Cell Analysis Reveals Early Manifestation of Cancerous Phenotype in Pre-Malignant Esophageal Cells

et al. 2013

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Cellular heterogeneity plays a pivotal role in a variety of functional processes in vivo including carcinogenesis. However, our knowledge about cell-to-cell diversity and how differences in individual cells manifest in alterations at the population level remains very limited mainly due to the lack of appropriate tools enabling studies at the single-cell level. We present a study on changes in cellular heterogeneity in the context of pre-malignant progression in response to hypoxic stress. Utilizing pre-malignant progression of Barrett’s esophagus (BE) as a disease model system we studied molecular mechanisms underlying the progression from metaplastic to dysplastic (pre-cancerous) stage. We used newly developed methods enabling measurements of cell-to-cell differences in copy numbers of mitochondrial DNA, expression levels of a set of mitochondrial and nuclear genes involved in hypoxia response pathways, and mitochondrial membrane potential. In contrast to bulk cell studies reported earlier, our study shows significant differences between metaplastic and dysplastic BE cells in both average values and single-cell parameter distributions of mtDNA copy numbers, mitochondrial function, and mRNA expression levels of studied genes. Based on single-cell data analysis, we propose that mitochondria may be one of the key factors in pre-malignant progression in BE.

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

Cited by 2 publications

References 12 publications

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

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

Single-Cell Analysis Reveals Early Manifestation of Cancerous Phenotype in Pre-Malignant Esophageal Cells

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