Mapping normal and cancer cell signalling networks: towards single-cell proteomics

Irish, Jonathan M.; Kotecha, Nikesh; Nolan, Garry P.

doi:10.1038/nrc1804

Cited by 295 publications

(233 citation statements)

References 87 publications

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“…In cancer patients, the possibility of detecting minimal residual disease (MRD) by sensitive molecular markers (for example, quantitative PCR) or other measures 93 is an important aspect of disease management. However, depending on the type of neoplasm, persistent MRD may not necessarily argue against long-term disease-free survival.…”

Section: Evaluation Of Csc Eradicationmentioning

confidence: 99%

Cancer stem cell definitions and terminology: the devil is in the details

Valent

Bonnet

Maria³

et al. 2012

Nat Rev Cancer

625

564

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| The cancer stem cell (CSC) concept has important therapeutic implications, but its investigation has been hampered both by a lack of consistency in the terms used for these cells and by how they are defined. Evidence of their heterogeneous origins, frequencies and their genomic, as well as their phenotypic and functional, properties has added to the confusion and has fuelled new ideas and controversies. Participants in The Year 2011 Working Conference on CSCs met to review these issues and to propose a conceptual and practical framework for CSC terminology. More precise reporting of the parameters that are used to identify CSCs and to attribute responses to them is also recommended as key to accelerating an understanding of their biology and developing more effective methods for their eradication in patients. PERSPECTIVES NATURE REVIEWS | CANCER VOLUME 12 | NOVEMBER 2012 | 767

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Section: Evaluation Of Csc Eradicationmentioning

confidence: 99%

Cancer stem cell definitions and terminology: the devil is in the details

Valent

Bonnet

Maria³

et al. 2012

Nat Rev Cancer

625

564

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“…Although flow cytometry initially allowed the investigation of only a single fluorophore, recent advances allow close to 20 parallel channels for monitoring different determinants (1)(2)(3)(4). These advances have now surpassed our ability to interpret manually the resulting high-dimensional data and have led to growing interest and recent activity in the development of new computational tools and approaches (5)(6)(7)(8).…”

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confidence: 99%

Automated High-Dimensional Flow Cytometric Data Analysis

Pyne

Wang

et al. 2010

Lecture Notes in Computer Science

114

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Flow cytometric analysis allows rapid single cell interrogation of surface and intracellular determinants by measuring fluorescence intensity of fluorophore-conjugated reagents. The availability of new platforms, allowing detection of increasing numbers of cell surface markers, has challenged the traditional technique of identifying cell populations by manual gating and resulted in a growing need for the development of automated, high-dimensional analytical methods. We present a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation. We demonstrate its ability to detect rare populations, to model robustly in the presence of outliers and skew, and to perform the critical task of matching cell populations across samples that enables downstream analysis. This advance will facilitate the application of flow cytometry to new, complex biological and clinical problems.finite mixture model ͉ flow cytometry ͉ multivariate skew distribution F low cytometry transformed clinical immunology and hematology over 2 decades ago by allowing the rapid interrogation of cell surface determinants and, more recently, by enabling the analysis of intracellular events using fluorophore-conjugated antibodies or markers. Although flow cytometry initially allowed the investigation of only a single fluorophore, recent advances allow close to 20 parallel channels for monitoring different determinants (1-4). These advances have now surpassed our ability to interpret manually the resulting high-dimensional data and have led to growing interest and recent activity in the development of new computational tools and approaches (5-8).The difficulty in data analysis arises from the traditional technique of identifying discrete cell populations by manual gating, which is a labor-intensive process and varies by user experience. The initial computational packages for flow cytometric analyses focused largely on different preprocessing tasks such as data acquisition, normalization, and live cell gating. Besides visualization and transformation of flow cytometric data, useful tools such as Flowjo (www.flowjo.com) and the packages in BioConductor (www.bioconductor.org) (such as prada, flowCore, flowViz, flowUtils, and rflowcyt) allow some form of software-assisted gating and extraction of populations of interest. The operator subjectively demarcates a cell population while moving through successive 2-or 3-dimensional projections of the data. This process limits the reproducibility of data processing. A more fundamental problem is that this lower dimensional visualization hinders the identification of higher-dimensional features. Furthermore, current methods extract only a limited number of sample parameters, such as the mean fluorescence intensity of a cell population, which can lead to loss of critical information in defining the properties of a cell population....

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“…Further technical details on this experiment and the processing of samples can be found in the Supporting Information of (19). It was observed in (20) that the FL patients can be stratified into two classes that have distinctly different survival outcomes, which is linked to the presence or absence of a subpopulation of B-cells known as the lymphoma negative prognostic (LNP) cells. For our illustration, the dataset is randomly partitioned into a training and test set with equal number of samples in each set.…”

Section: Overview Of the Datasetsmentioning

confidence: 99%

Modeling of inter‐sample variation in flow cytometric data with the joint clustering and matching procedure

2015

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We present an algorithm for modeling flow cytometry data in the presence of large inter-sample variation. Large-scale cytometry datasets often exhibit some within-class variation due to technical effects such as instrumental differences and variations in data acquisition, as well as subtle biological heterogeneity within the class of samples. Failure to account for such variations in the model may lead to inaccurate matching of populations across a batch of samples and poor performance in classification of unlabeled samples. In this paper, we describe the Joint Clustering and Matching (JCM) procedure for simultaneous segmentation and alignment of cell populations across multiple samples. Under the JCM framework, a multivariate mixture distribution is used to model the distribution of the expressions of a fixed set of markers for each cell in a sample such that the components in the mixture model may correspond to the various populations of cells, which have similar expressions of markers (that is, clusters), in the composition of the sample. For each class of samples, an overall class template is formed by the adoption of random-effects terms to model the inter-sample variation within a class. The construction of a parametric template for each class allows for direct quantification of the differences between the template and each sample, and also between each pair of samples, both within or between classes. The classification of a new unclassified sample is then undertaken by assigning the unclassified sample to the class that minimizes the distance between its fitted mixture density and each class density as provided by the class templates. For illustration, we use a symmetric form of the Kullback-Leibler divergence as a distance measure between two densities, but other distance measures can also be applied. We show and demonstrate on four real datasets how the JCM procedure can be used to carry out the tasks of automated clustering and alignment of cell populations, and supervised classification of samples. V C 2015International Society for Advancement of Cytometry Key terms flow cytometry; classification; class template; inter-sample variation; clustering; matching; skew mixture models; EM algorithm; JCM FLOW cytometry (FCM) is a powerful tool in clinical diagnosis of health disorders, in particular, immunological diseases. It offers rapid high-throughput measurements of multiple characteristics on every cell in a sample, enabling biologists to study a variety of biological processes at the cellular level. A critical component in the pipeline of FCM data analysis is the identification of cell populations from the multidimensional FCM dataset, currently performed manually by visually separating regions or gates of interest on a series of sequential bivariate projections of the data, a process known as gating. However, this approach has many problems and limitations, including variability between different analysts, non-standardization and nonreproducibility of results, an unrealistic assumption that bi...

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Mapping normal and cancer cell signalling networks: towards single-cell proteomics

Cited by 295 publications

References 87 publications

Cancer stem cell definitions and terminology: the devil is in the details

Cancer stem cell definitions and terminology: the devil is in the details

Automated High-Dimensional Flow Cytometric Data Analysis

Modeling of inter‐sample variation in flow cytometric data with the joint clustering and matching procedure

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