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Illuminating the cycle of life
Published in Raquel Seruca, Jasjit S. Suri, João M. Sanches, Fluorescence Imaging and Biological Quantification, 2017
Anabela Ferro, Patrícia Carneiro, Maria Sofia Fernandes, Tânia Mestre, Ivan Sahumbaiev, João M. Sanches, Raquel Seruca
Despite the tremendous possibilities awarded by microscopy and other cytometry methods applied to the study of cell-cycle dynamics, each has its own set of limitations. Allowing the preservation of the cell’s natural architecture, microscopy is the gold standard technology to study unperturbed systems of ex vivo, in vivo, and processed samples. Despite the advantages of traditional fluorescence microscopy, the improvement of fluorescent dyes and probes, and the massive development of resolution-enhanced microscopes, the high throughputness of microscopy still falls behind the needs to address many biological questions [131,132]. As such, multiparametric analyses in thousands of cells, testing several cellular parameters simultaneously, are not easily manageable by microscopy. On the other hand, flow cytometry technology has underwent an amazing evolution throughout the past decades, boosting its high-throughput properties, and it is now possible to routinely perform polychromatic analyses of cell samples tagged and emitting eight or more colors. The multidimensional improvement/evolution witnessed on every aspect of cytometry, such as available reagents, light sources, detectors, and analysis software, eased-up the analysis of data retrieved from high-content experiments. Nowadays it is possible to quantitatively analyze millions of cells in few minutes, while focusing on each cell individually. However, the fundamental principle of classical flow cytometry and imaging cytometry, where one must use single cell-enriched samples to be analyzed in a hydrodynamic flow stream, does not provide information on unperturbed cellular systems. Samples have to be disintegrated, hence cellular responses and 3D information derived from the physiological tissue microenvironment is lost. Laser-scanning cytometry suppressed this limitation, while empowering cytometry with imaging, quantification, and the possibility to continuously reanalyze individual cells in tissues in situ. The development of acoustical focusing next-generation cytometers and the combination of mass spectrometry with cytometry (CyTOF) set the high-throughput and high-content analysis to unprecedented levels. Acoustic-based cytometry is particularly fascinating for rare event analysis, such as circulating tumor cells, minimal residual disease, and stem cell identification, given that it allows faster analyses of higher sample volumes. Mass cytometry allows next-generation multiparametric analysis with minimal background noise derived from signal overlap or endogenous cellular content. The possibility to use rare metal-tagged antibodies ensures a scalable multiparameterization of analysis thus enabling the study of functionally complex and heterogeneous biological systems, in a single celled manner. The major drawback of mass cytometry is its incompatibility with live-cell sorting. The need for more validated metal-tagged antibodies and the improvement of DNA-binding stoichiometry is yet to be tackled. These will be the next-generation cytometric technologies; however, their current pricing, both due to the machines it selves and required reagents, is a serious bottleneck issue for their availability to academic and clinical settings.
Modelling acute myeloid leukaemia in a continuum of differentiation states
Published in Letters in Biomathematics, 2018
H. Cho, K. Ayers, L. de Pills, Y.-H. Kuo, J. Park, A. Radunskaya, R. C. Rockne
In van Unen et al. (2017), a new technique for examining high-dimensional mass cytometry data, known as HSNE is presented. Mass cytometry allows for the examination of several cellular markers on samples made up of vast quantities of cells. These data sets are truly ‘big’ in the sense that they are very large (a sample for each cell) as well has high-dimensional. Therefore, pre-existing dimension reduction techniques are not optimal for mass cytometry data. HSNE, as suggested by its name, is hierarchical by nature, allowing for refinement in the level of detail. HSNE ultimately constructs a hierarchy of subsets of the dataset X: The hierarchy begins with the data set itself (). A weighted k-nearest neighbour (kNN) graph is constructed on the data set, and individual points, or ‘landmarks’, are selected from each node on the graph to represent the data set at the next, coarser, level, . This process is repeated as desired. These subsets can each be embedded in lower dimensional space. This hierarchical embedding scheme allows the user to view the data at different resolutions, from a broad overview (level ) to a more refined understanding of cell types associated with markers (intermediate levels). Starting with a certain subset , the user is able to ‘drill in’ to the data by selecting a subset . Thus, HSNE is an approach that is useful for data that require different levels of detail at different scales. An illuminating graphical representation of the HSNE process can be found in van Unen et al. (2017) (Figure 1).