Image Cytometry

Abstract: Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.

Abstract: Flow cytometry has become a valuable tool in aquatic and environmental microbiology that combines direct and rapid assays to determine numbers, cell size distribution and additional biochemical and physiological characteristics of individual cells, revealing the heterogeneity present in a population or community. Flow cytometry exhibits three unique technical properties of high potential to study the microbiology of aquatic systems: (i) its tremendous velocity to obtain and process data; (ii) the sorting capacity of some cytometers, which allows the transfer of specific populations or even single cells to a determined location, thus allowing further physical, chemical, biological or molecular analysis; and (iii) high-speed multiparametric data acquisition and multivariate data analysis. Flow cytometry is now commonly used in aquatic microbiology, although the application of cell sorting to microbial ecology and quantification of heterotrophic nanoflagellates and viruses is still under development. The recent development of laser scanning cytometry also provides a new way to further analyse sorted cells or cells recovered on filter membranes or slides. The main infrastructure limitations of flow cytometry are: cost, need for skilled and well-trained operators, and adequate refrigeration systems for high-powered lasers and cell sorters. The selection and obtaining of the optimal fluorochromes, control microorganisms and validations for a specific application may sometimes be difficult to accomplish.

Abstract: An apoptosing cell demonstrates multitude of characteristic morphological and biochemical features, which vary depending on the stimuli and the cell type. The gross majority of classical apoptotic hallmarks can be rapidly examined by flow and image cytometry. Cytometry thus became a technology of choice in diverse studies of cellular demise. A large variety of cytometric methods designed to identify apoptotic cells and probe mechanisms associated with this mode of cell demise have been developed during the past two decades. In the present chapter, we outline a handful of commonly used methods that are based on the assessment of: mitochondrial transmembrane potential, activation of caspases, plasma membrane alterations and DNA fragmentation.

Abstract: Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.