A framework for analytical characterization of monoclonal antibodies based on reactivity profiles in different tissues

Regulatory T Cells in Melanoma Revisited by a Computational Clustering of FOXP3+ T Cell Subpopulations

CD4⁺ T cells that express the transcription factor FOXP3 (FOXP3⁺ T cells) are commonly regarded as immunosuppressive regulatory T cells (Tregs). FOXP3⁺ T cells are reported to be increased in tumor-bearing patients or animals and are considered to suppress antitumor immunity, but the evidence is often contradictory. In addition, accumulating evidence indicates that FOXP3 is induced by antigenic stimulation and that some non-Treg FOXP3⁺ T cells, especially memory-phenotype FOXP3^low cells, produce proinflammatory cytokines. Accordingly, the subclassification of FOXP3⁺ T cells is fundamental for revealing the significance of FOXP3⁺ T cells in tumor immunity, but the arbitrariness and complexity of manual gating have complicated the issue. In this article, we report a computational method to automatically identify and classify FOXP3⁺ T cells into subsets using clustering algorithms. By analyzing flow cytometric data of melanoma patients, the proposed method showed that the FOXP3⁺ subpopulation that had relatively high FOXP3, CD45RO, and CD25 expressions was increased in melanoma patients, whereas manual gating did not produce significant results on the FOXP3⁺ subpopulations. Interestingly, the computationally identified FOXP3⁺ subpopulation included not only classical FOXP3^high Tregs, but also memory-phenotype FOXP3^low cells by manual gating. Furthermore, the proposed method successfully analyzed an independent data set, showing that the same FOXP3⁺ subpopulation was increased in melanoma patients, validating the method. Collectively, the proposed method successfully captured an important feature of melanoma without relying on the existing criteria of FOXP3⁺ T cells, revealing a hidden association between the T cell profile and melanoma, and providing new insights into FOXP3⁺ T cells and Tregs.

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

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.

Laser microdissection of the alveolar duct enables single-cell genomic analysis

Complex tissues such as the lung are composed of structural hierarchies such as alveoli, alveolar ducts, and lobules. Some structural units, such as the alveolar duct, appear to participate in tissue repair as well as the development of bronchioalveolar carcinoma. Here, we demonstrate an approach to conduct laser microdissection of the lung alveolar duct for single-cell PCR analysis. Our approach involved three steps. (1) The initial preparation used mechanical sectioning of the lung tissue with sufficient thickness to encompass the structure of interest. In the case of the alveolar duct, the precision-cut lung slices were 200 μm thick; the slices were processed using near-physiologic conditions to preserve the state of viable cells. (2) The lung slices were examined by transmission light microscopy to target the alveolar duct. The air-filled lung was sufficiently accessible by light microscopy that counterstains or fluorescent labels were unnecessary to identify the alveolar duct. (3) The enzymatic and microfluidic isolation of single cells allowed for the harvest of as few as several thousand cells for PCR analysis. Microfluidics based arrays were used to measure the expression of selected marker genes in individual cells to characterize different cell populations. Preliminary work suggests the unique value of this approach to understand the intra- and intercellular interactions within the regenerating alveolar duct.

Parametric modeling of cellular state transitions as measured with flow cytometry

Background

Gradual or sudden transitions among different states as exhibited by cell populations in a biological sample under particular conditions or stimuli can be detected and profiled by flow cytometric time course data. Often such temporal profiles contain features due to transient states that present unique modeling challenges. These could range from asymmetric non-Gaussian distributions to outliers and tail subpopulations, which need to be modeled with precision and rigor.

Results

To ensure precision and rigor, we propose a parametric modeling framework StateProfiler based on finite mixtures of skew t-Normal distributions that are robust against non-Gaussian features caused by asymmetry and outliers in data. Further, we present in StateProfiler a new greedy EM algorithm for fast and optimal model selection. The parsimonious approach of our greedy algorithm allows us to detect the genuine dynamic variation in the key features as and when they appear in time course data. We also present a procedure to construct a well-fitted profile by merging any redundant model components in a way that minimizes change in entropy of the resulting model. This allows precise profiling of unusually shaped distributions and less well-separated features that may appear due to cellular heterogeneity even within clonal populations.

Conclusions

By modeling flow cytometric data measured over time course and marker space with StateProfiler, specific parametric characteristics of cellular states can be identified. The parameters are then tested statistically for learning global and local patterns of spatio-temporal change. We applied StateProfiler to identify the temporal features of yeast cell cycle progression based on knockout of S-phase triggering cyclins Clb5 and Clb6, and then compared the S-phase delay phenotypes due to differential regulation of the two cyclins. We also used StateProfiler to construct the temporal profile of clonal divergence underlying lineage selection in mammalian hematopoietic progenitor cells.