Cytometry Part A

Cover image for Vol. 91 Issue 4

Edited By: Attila Tárnok

Impact Factor: 3.181

ISI Journal Citation Reports © Ranking: 2015: 24/77 (BIOCHEMICAL RESEARCH METHODS); 94/187 (Cell Biology)

Online ISSN: 1552-4930

Associated Title(s): Cytometry Part B: Clinical Cytometry

Current Special Issues - Highlights

Special Issue on Image-Based Systems Biology – Highlights

Special Issue on Computational Analysis of Flow Cytometry

Special Issue on Image – Based Systems Biology – Highlights

In this Special Issue on Image Based Systems Biology, guest editors Marc Thilo Figge and Robert F. Murphy have provided a collection of articles focusing on the growing discipline of Image-based systems biology that seeks to take full advantage of the information in images and establishes an essential connective link between experimental and theoretical examination of biological processes at a spatiotemporal level. Their insightful Editorial provides context for the collection and find below several highlights from the Special Issue.

Image based validation of dynamical models for cell reorientation

How motile cells reorient in changing gradients of an extracellular signal has long been subject of theoretical studies. Lockley, Ladds and Bretschneider provide a fresh understanding by putting three different mathematical models to the test. The models are fitted to complex time series image data of Dictyostelium cells reorienting in response to reversals of mechanical shear flow [Dalous et al., Biophys J. 2008;94:1063–74]. An intriguing result is that the very first model proposed in this area by Meinhardt [J Cell Sci 1999;112:2867–2874] not only yields an excellent fit to the data, but also provides deeper theoretical insight into the differences observed between randomly migrating cells with multiple transient fronts, and highly oriented cells with one stable front. Furthermore, the study demonstrates how advanced, readily available systems biology tools for time series data can be elegantly used to tackle image based problems equally well.
Robert Lockley, Graham Ladds and Till Bretschneider. Cytometry, 87: 471-480.

Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies

Embryonic stem cells (ESCs) self-organize in adherent aggregates and establish spatial structures that are essential for pluripotency regulation. However, these aggregates appear heterogeneous both in their morphology and in gene expression raising questions on the functional importance of these substructures. Livecell images of fluorescent cell lines uncover spatial patterns, but quantitative measures and mathematical models are required to reveal the intra- and intercellular feedback mechanisms behind them. Herberg et al. combined quantitative image analysis with their previous, multiscale modeling approaches of ESC organization to directly compare experimental data with the outcome of different model assumptions, e.g. on cell-cell adhesions or communications. They demonstrated that transcription factor related adhesions and proliferation kinetics contribute to the overall diversity and to the clustering of ESCs with high self-renewing capacities in the interior of a colony structure. The presented methodology will be extended to study dynamic process like ESC differentiation.
Maria Herberg, Thomas Zerjatke, Walter de Back, Ingmar Glauche and Ingo Roeder. Cytometry, 87: 481-490.

Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies

Setting up and evaluating image-based high-throughput and high-content screens often turns out to be laborious and time-consuming. Adjusting the experimental settings typically requires multiple test runs of experiments and imaging, and once the optimized screen has been carried out, the resulting image data needs to be analyzed accurately, objectively, and reproducibly. Harder et al. developed an image analysis approach to automate these tasks. A complex approach for single cell-based analysis of a large-scale live-cell imaging screen on Neuroblastoma cells is presented. Their approach takes into account the temporal context using tracking and a temporal model. Moreover, challenges of object classification in large heterogeneous data sets are addressed. By extracting migration and proliferation behaviors of the studied cell cultures for complete screening plates the experimental optimization is supported. Overall, this work provides a spectrum of methods for extracting complex readouts from challenging data which will be of interest for many researchers dealing with live-cell image-based screens.
Nathalie Harder, Richa Batra, Nicolle Diessl, Sina Gogolin, Roland Eils, Frank Westermann, Rainer König and Karl Rohr. Cytometry, 87: 524-540.

On comparing heterogeneity across biomarkers

Elucidating the role of cellular heterogeneity is one of the key challenges in biology today. Biomarker expression is frequently used to define distinct cellular states, and the frequencies of observing these cellular states can be used to describe and compare heterogeneous populations. Given the experimental limitations on the number of biomarkers that can be simultaneously assayed, a crucial question is how many biomarkers are really needed to describe heterogeneity. Do additional biomarkers identify new cellular states or simply re-identify the same states? Steininger et al. present a novel experimental-computational framework to compare phenotypic states identified using different biomarkers. The particular novelty of their approach is that it does not require that the biomarkers be co-stained, making it extensible to large sets of biomarkers. This approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into the complexity of the state space of biological systems.
Robert J. Steininger III, Satwik Rajaram, Luc Girard, John D. Minna, Lani F. Wu and Steven J. Altschuler. Cytometry, 87: 558–567.

Special Issue on Computational Analysis of Flow Cytometry Data

In this Special Issue on Computational Analysis of Flow Cytometry Data, guest editors Ryan R. Brinkman, Nima Aghaeepour, Greg Finak, Raphael Gottardo, Tim Mosmann and Richard H. Scheuermann have provided a collection of articles describing state-of-the-art computational tools for the high throughput analysis of high dimensional flow cytometry data. Read their Editorial for their perspective on the current state of affairs in this growing field and corresponding papers in this Special Issue.

gEM/GANN: A multivariate computational strategy for auto-characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high-dimensional flow cytometry data

The complexity of flow cytometric datasets requires new approaches that can maximize the amount and value of the information derived therefrom. This study developed a computational approach for analysing flow cytometry datasets from HIV-infected individuals that were provided via the FlowCAP-IV Challenge. The Challenge was to use data on the phenotypic profiles of unstimulated peripheral blood mononuclear cells (PBMCs) and PBMCs that had been stimulated in vitrousing HIV-derived antigens from the provided “training” samples, which came from individuals with known outcomes (i.e., non-progressor, progressor, survival time) to identify features which could predict the time to progression to AIDS and the outcomes for those individuals whose data were provided in the “test” set. The approach identified ‘feature sets’ which could discriminate between ‘progressors’ and ‘non-progressors’ in the training set, and could predict outcomes in the ‘test’ set. This new approach has a promising capacity to extract valuable diagnostic and prognostic information from complex flow cytometry datasets.
Dong Ling Tong, Graham R. Ball and A. Graham Pockley. Cytometry, 87: 616-623.

Probability State Modeling Theory

As the technology of cytometry matures, there is mounting pressure to address two major issues with data analysis. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other challenge is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these seemingly disparate issues. PSM produces easy-to-understand progression plots representing complex biological processes and easily scales to any number of correlated measurements, dramatically simplifying data interpretation. Its analysis is guided by objective functions, making it possible for different users to get exactly the same result with the same data.
C. Bruce Bagwell, Benjamin C. Hunsberger, Donald J. Herbert, Mark E. Munson, Beth L. Hill, Chris M. Bray and Frederic I. Preffer. Cytometry, 87: 646-660.

FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data

Flow cytometry data is typically analyzed using 2D scatter plots. However, for datasets with more than a few markers, this is time-consuming, gives a very limited view on the data and the order in which these scatter plots should be investigated is highly subjective. FlowSOM steps away from 2D scatter plots and opens many new visualization opportunities. FlowSOM gives a quick overview of how markers are behaving in one single figure and gives information about all cells in the dataset, without discarding any subsets. FlowSOM uses tree structures similar to SPADE, but these are calculated much faster, and display many markers in one figure using star charts. This way, a clearer overall view of the data can be obtained.
Sofie Van Gassen, Britt Callebaut, Mary J. Van Helden, Bart N. Lambrecht, Piet Demeester, Tom Dhaene and Yvan Saeys. Cytometry, 87: 636-645.

Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data

An important immune monitoring task for both vaccine development and clinical trials is the characterization and comparison of antigen-specific T-cells from subject samples under different conditions. The increasing dimensionality of cytometry technologies is posing challenges for the visualization and comparison of rare cell populations, particularly highly polyfunctional T-cell subsets. Gottardo and colleagues have developed an integrated analysis pipeline for effective identification, visualization and comparison of such rare cell subsets. They used the OpenCyto framework to perform semi-automated gating to extract antigen-specific T-cell subsets while simultaneously controlling for background from non-stimulated samples. The cells were visualized using dimensionality reduction with t-SNE. This procedure allowed them to visualize treatment-specific changes in polyfunctional, antigen-specific T-cell subpopulations in response to different doses of an experimental HIV vaccine, and inresponse to MTB-specific and non-specific peptide stimulations in MTB-infected and uninfected subjects. This analytic tool provides a rapid, qualitative, and “at-a-glance” summary of an experiment.
Lin Lin, Jacob Frelinger, Wenxin Jiang, Greg Finak, Chetan Seshadri, Pierre-Alexandre Bart, Giuseppe Pantaleo, Julie McElrath, Steve DeRosa and Raphael Gottardo. Cytometry, 87: 675-682.