Automated analysis of the cytokinesis-block micronucleus assay for radiation biodosimetry using imaging flow cytometry

The cytokinesis-block micronucleus (CBMN) assay is employed in biological dosimetry to determine the dose of radiation to an exposed individual from the frequency of micronuclei (MN) in binucleated lymphocyte cells. The method has been partially automated for the use in mass casualty events, but it would be advantageous to further automate the method for increased throughput. Recently, automated image analysis has been successfully applied to the traditional, slide-scoring-based method of the CBMN assay. However, with the development of new technologies such as the imaging flow cytometer, it is now possible to adapt this microscope-based assay to an automated imaging flow cytometry method. The ImageStream^X is an imaging flow cytometer that has adequate sensitivity to quantify radiation doses larger than 1 Gy while adding the increased throughput of traditional flow cytometry. The protocol and analysis presented in this work adapts the CBMN assay for the use on the ImageStream^X. Ex vivo-irradiated whole blood samples cultured for CBMN were analyzed on the ImageStream^X, and preliminary results indicate that binucleated cells and MN can be identified, imaged and enumerated automatically by imaging flow cytometry. Details of the method development, gating strategy and the dose response curve generated are presented and indicate that adaptation of the CBMN assay for the use with imaging flow cytometry has potential for high-throughput analysis following a mass casualty radiological event.     

flowCore: a Bioconductor package for high throughput flow cytometry

Background  

Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.     

Cellometer image cytometry as a complementary tool to flow cytometry for verifying gated cell populations

Traditionally, many cell-based assays that analyze cell populations and functionalities have been performed using flow cytometry. However, flow cytometers remain relatively expensive and require highly trained operators for routine maintenance and data analysis. Recently, an image cytometry system has been developed by Nexcelom Bioscience (Lawrence, MA, USA) for automated cell concentration and viability measurement using bright-field and fluorescent imaging methods. Image cytometry is analogous to flow cytometry in that gating operations can be performed on the cell population based on size and fluorescent intensity. In addition, the image cytometer is capable of capturing bright-field and fluorescent images, allowing for the measurement of cellular size and fluorescence intensity data. In this study, we labeled a population of cells with an enzymatic vitality stain (calcein-AM) and a cell viability dye (propidium iodide) and compared the data generated by flow and image cytometry. We report that measuring vitality and viability using the image cytometer is as effective as flow cytometric assays and allows for visual confirmation of the sample to exclude cellular debris. Image cytometry offers a direct method for performing fluorescent cell-based assays but also may be used as a complementary tool to flow cytometers for aiding the analysis of more complex samples.     

Nozzle design parameters and their effects on rapid sample delivery in flow cytometry

BACKGROUND: Rapid kinetic and high throughput flow cytometry are emerging as valuable tools in biotechnology research applications ranging from mechanistic analysis of molecular assemblies to high throughput screening. Many of these new applications have been made possible by improved sample delivery capabilities, focusing increased attention on fluidic issues associated with rapid sample delivery. METHODS: Using basic fluidic premises, we derived a model that predicted the effect of nozzle parameters during rapid sample delivery. We tested the model using the rapid mix flow cytometer and modifications were made to the equipment to optimize performance. RESULTS: The model predicted that shorter nozzles with wide exit orifices decrease the delay before initial particle analysis and the fluidic stabilization time. Experimental results confirmed this prediction and model-based modifications allowed analysis of particles within 55 ms or 600 ms after mixing, with or without electronic gating, respectively. CONCLUSIONS: The model along with modifications to commercial equipment will allow rapid mix flow cytometry to analyze reactions in time frames threefold shorter than previously possible. The model allows for nozzle design predictions that should allow for analysis in the millisecond time frame. Furthermore, these findings are general for all rapid delivery applications, including high throughput flow cytometry.     

OpenCyto: An Open Source Infrastructure for Scalable,Robust, Reproducible,and Automated,End-to-End Flow Cytometry Data Analysis

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

This is a PLOS Computational Biology Software Article.

     

Analyzing cellular internalization of nanoparticles and bacteria by multi-spectral imaging flow cytometry

Nanoparticulate systems have emerged as valuable tools in vaccine delivery through their ability to efficiently deliver cargo, including proteins, to antigen presenting cells. Internalization of nanoparticles (NP) by antigen presenting cells is a critical step in generating an effective immune response to the encapsulated antigen. To determine how changes in nanoparticle formulation impact function, we sought to develop a high throughput, quantitative experimental protocol that was compatible with detecting internalized nanoparticles as well as bacteria. To date, two independent techniques, microscopy and flow cytometry, have been the methods used to study the phagocytosis of nanoparticles. The high throughput nature of flow cytometry generates robust statistical data. However, due to low resolution, it fails to accurately quantify internalized versus cell bound nanoparticles. Microscopy generates images with high spatial resolution; however, it is time consuming and involves small sample sizes. Multi-spectral imaging flow cytometry (MIFC) is a new technology that incorporates aspects of both microscopy and flow cytometry that performs multi-color spectral fluorescence and bright field imaging simultaneously through a laminar core. This capability provides an accurate analysis of fluorescent signal intensities and spatial relationships between different structures and cellular features at high speed. Herein, we describe a method utilizing MIFC to characterize the cell populations that have internalized polyanhydride nanoparticles or Salmonella enterica serovar Typhimurium. We also describe the preparation of nanoparticle suspensions, cell labeling, acquisition on an ImageStream(X) system and analysis of the data using the IDEAS application. We also demonstrate the application of a technique that can be used to differentiate the internalization pathways for nanoparticles and bacteria by using cytochalasin-D as an inhibitor of actin-mediated phagocytosis.     

FuGEFlow: data model and markup language for flow cytometry

Background  

Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.     

Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data

Flow cytometry is a valuable tool in research and diagnostics including minimal residual disease (MRD) monitoring of hematologic malignancies. However, its gradual advancement toward increasing numbers of fluorescent parameters leads to information rich datasets, which are challenging to analyze by standard gating and do not reflect the multidimensionality of the data. We have developed a novel method to analyze complex flow cytometry data, based on hierarchical clustering analysis (HCA) but with a new underlying algorithm, using Mahalanobis distance measure. HCA is scalable to analyze complex multiparameter datasets (here demonstrated on up to 12 color flow cytometry and on a 20-parameter synthetic dataset). We have validated this method by comparison with standard gating approaches when performed independently by expert cytometrists. Acute lymphoblastic leukemia blast populations were analyzed in diagnostic and follow-up datasets (n = 123) from three centers. HCA results correlated very well (Passing-Bablok correlation coefficient = 0.992, slope = 1, intercept = -0.01) with standard gating data obtained by the I-BFM FLOW-MRD study group. To further improve the performance in follow-up samples with low MRD levels and to automate MRD detection, we combined HCA with support vector machine (SVM) learning. HCA in combination with SVM provides a novel diagnostic tool that not only allows analysis of increasingly complex flow cytometry data but also is less observer-dependent compared with classical gating and has potential for automation.     

Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring

Background  

Identification of minor cell populations, e.g. leukemic blasts within blood samples, has become increasingly important in therapeutic disease monitoring. Modern flow cytometers enable researchers to reliably measure six and more variables, describing cellular size, granularity and expression of cell-surface and intracellular proteins, for thousands of cells per second. Currently, analysis of cytometry readouts relies on visual inspection and manual gating of one- or two-dimensional projections of the data. This procedure, however, is labor-intensive and misses potential characteristic patterns in higher dimensions.     

Data quality assessment of ungated flow cytometry data in high throughput experiments.

BACKGROUND: The recent development of semiautomated techniques for staining and analyzing flow cytometry samples has presented new challenges. Quality control and quality assessment are critical when developing new high throughput technologies and their associated information services. Our experience suggests that significant bottlenecks remain in the development of high throughput flow cytometry methods for data analysis and display. Especially, data quality control and quality assessment are crucial steps in processing and analyzing high throughput flow cytometry data. METHODS: We propose a variety of graphical exploratory data analytic tools for exploring ungated flow cytometry data. We have implemented a number of specialized functions and methods in the Bioconductor package rflowcyt. We demonstrate the use of these approaches by investigating two independent sets of high throughput flow cytometry data. RESULTS: We found that graphical representations can reveal substantial nonbiological differences in samples. Empirical Cumulative Distribution Function and summary scatterplots were especially useful in the rapid identification of problems not identified by manual review. CONCLUSIONS: Graphical exploratory data analytic tools are quick and useful means of assessing data quality. We propose that the described visualizations should be used as quality assessment tools and where possible, be used for quality control.     

An innovation in flow cytometry data collection and analysis producing a correlated multiple sample analysis in a single file

The problems associated with rapid analysis and interpretation of data from multicolor immunofluorescence panels have been a formidable barrier to their routine use. Using present flow cytometry concepts, a panel of 11 tubes each containing multiple phenotypic markers or controls requires postdata acquisition manipulation of many multiparameter histogram and listmode files. We have developed a method that compresses all of the information from such a panel into a single listmode data file during run time. A single data file is used to record the entire phenotypic analysis for a particular patient or series within an experiment. This is accomplished by the incorporation of a tube identifier parameter (TIP) as well as the fluorescence and light scatter parameters normally collected. The TIP can then be used for gating discrimination of any tube or set of tubes within a panel. When the TIP is correlated with the PRISM parameter the entire patient phenotypic image can be represented within a single two-parameter histogram we have called a phenogram. This phenogram can be generated in real time, providing on-line preprocessing of a complex multicolor experiment. By examining the image created by the phenogram it is possible to rapidly flag abnormalities such as incorrect gating. This procedure was carried out on an EPICS Elite flow cytometer in its standard configuration with the addition of hardware to provide an input for the TIP.     

Quadratic variance models for adaptively preprocessing SELDI-TOF mass spectrometry data

Background  

Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI) is a proteomics tool for biomarker discovery and other high throughput applications. Previous studies have identified various areas for improvement in preprocessing algorithms used for protein peak detection. Bottom-up approaches to preprocessing that emphasize modeling SELDI data acquisition are promising avenues of research to find the needed improvements in reproducibility.     

flowClust: a Bioconductor package for automated gating of flow cytometry data

Background  

As a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.     

One-dimensional acoustic standing waves in rectangular channels for flow cytometry

Flow cytometry has become a powerful analytical tool for applications ranging from blood diagnostics to high throughput screening of molecular assemblies on microsphere arrays. However, instrument size, expense, throughput, and consumable use limit its use in resource poor areas of the world, as a component in environmental monitoring, and for detection of very rare cell populations. For these reasons, new technologies to improve the size and cost-to-performance ratio of flow cytometry are required. One such technology is the use of acoustic standing waves that efficiently concentrate cells and particles to the center of flow channels for analysis. The simplest form of this method uses one-dimensional acoustic standing waves to focus particles in rectangular channels. We have developed one-dimensional acoustic focusing flow channels that can be fabricated in simple capillary devices or easily microfabricated using photolithography and deep reactive ion etching. Image and video analysis demonstrates that these channels precisely focus single flowing streams of particles and cells for traditional flow cytometry analysis. Additionally, use of standing waves with increasing harmonics and in parallel microfabricated channels is shown to effectively create many parallel focused streams. Furthermore, we present the fabrication of an inexpensive optical platform for flow cytometry in rectangular channels and use of the system to provide precise analysis. The simplicity and low-cost of the acoustic focusing devices developed here promise to be effective for flow cytometers that have reduced size, cost, and consumable use. Finally, the straightforward path to parallel flow streams using one-dimensional multinode acoustic focusing, indicates that simple acoustic focusing in rectangular channels may also have a prominent role in high-throughput flow cytometry.     

GenePattern flow cytometry suite

Background

Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.

Results

In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.

Conclusions

GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.

     

<Emphasis Type="Italic">μ</Emphasis>-CS: An extension of the TM4 platform to manage Affymetrix binary data

Background  

A main goal in understanding cell mechanisms is to explain the relationship among genes and related molecular processes through the combined use of technological platforms and bioinformatics analysis. High throughput platforms, such as microarrays, enable the investigation of the whole genome in a single experiment. There exist different kind of microarray platforms, that produce different types of binary data (images and raw data). Moreover, also considering a single vendor, different chips are available. The analysis of microarray data requires an initial preprocessing phase (i.e. normalization and summarization) of raw data that makes them suitable for use on existing platforms, such as the TIGR M4 Suite. Nevertheless, the annotations of data with additional information such as gene function, is needed to perform more powerful analysis. Raw data preprocessing and annotation is often performed in a manual and error prone way. Moreover, many available preprocessing tools do not support annotation. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of microarray data are needed.     

Profiling of toxicity and identification of distinct apoptosis profiles using a 384-well high-throughput flow cytometry screening platform

Methods and techniques used to detect apoptosis have benefited from advances in technologies such as flow cytometry. With a large arsenal of lasers, fluorescent labels, and readily accessible biological targets, it is possible to detect multiple targets with unique combinations of fluorescent spectral signatures from a single sample. Traditional flow cytometry has been limited as a screening tool as the sample throughput has been low, whereas the data analysis and generation of screening relevant results have been complex. The HTFC Screening System running ForeCyt software is an instrument platform designed to perform high-throughput, multiplexed screening with seamless transformation of flow cytometry data into screening hits. We report the results of a screen that simultaneously quantified caspase 3/7 activation, annexin V binding, cell viability, and mitochondrial integrity. Assay performance over 5 days demonstrated robustness, reliability, and performance of the assay. This system is high throughput in that a 384-well plate can be read and fully analyzed within 30 min and is sensitive with an assay window of at least 10-fold for all parameters and a Z' factor of ≥0.75 for all endpoints and time points. From a screen of 231 compounds, 11 representative toxicity profiles highlighting differential activation of apoptotic pathways were identified.     

Circulating Tumor Cells: Clinically Relevant Molecular Access Based on a Novel CTC Flow Cell

Background

Contemporary cancer diagnostics are becoming increasing reliant upon sophisticated new molecular methods for analyzing genetic information. Limiting the scope of these new technologies is the lack of adequate solid tumor tissue samples. Patients may present with tumors that are not accessible to biopsy or adequate for longitudinal monitoring. One attractive alternate source is cancer cells in the peripheral blood. These rare circulating tumor cells (CTC) require enrichment and isolation before molecular analysis can be performed. Current CTC platforms lack either the throughput or reliability to use in a clinical setting or they provide CTC samples at purities that restrict molecular access by limiting the molecular tools available.

Methodology/Principal Findings

Recent advances in magetophoresis and microfluidics have been employed to produce an automated platform called LiquidBiopsy®. This platform uses high throughput sheath flow microfluidics for the positive selection of CTC populations. Furthermore the platform quantitatively isolates cells useful for molecular methods such as detection of mutations. CTC recovery was characterized and validated with an accuracy (<20% error) and a precision (CV<25%) down to at least 9 CTC/ml. Using anti-EpCAM antibodies as the capture agent, the platform recovers 78% of MCF7 cells within the linear range. Non specific recovery of background cells is independent of target cell density and averages 55 cells/mL. 10% purity can be achieved with as low as 6 CTCs/mL and better than 1% purity can be achieved with 1 CTC/mL.

Conclusions/Significance

The LiquidBiopsy platform is an automated validated platform that provides high throughput molecular access to the CTC population. It can be validated and integrated into the lab flow enabling CTC enumeration as well as recovery of consistently high purity samples for molecular analysis such as quantitative PCR and Next Generation Sequencing. This tool opens the way for clinically relevant genetic profiling of CTCs.