Imaging cytometry: Automated morphology and feature extraction

FOR many years cell biologists have been locked in a “parameter space” race driven by the need to make as many informative measurements on as many cells as possible toward the goal of identifying every single cell type and transition state. The recent Human Cell Atlas (HCA) initiative (1) has given new purpose to this noble idea and has very much been driven by the recent advances in genomic and transcriptomic capabilities at the single cell level. The first published output from this initiative recently described the elegant use of cytometry in combination with scRNA Seq to redefine the human blood dendritic cells and monocytes lineages (2). However, prior to the HCA initiative and the birth of single cell genomics/transcriptomics, cytometry has played a fundamental role in how we have elucidated cellular heterogeneity at the single cell level within complex biological systems as highlighted by the “Human Cytome Project” (3). Indeed, the word “cytometry” stems from two Greek words, “Kytos” meaning “hollow basket” or “cell” and “metria” meaning to “measure. ” There are essentially three tenets of cytometry: (i) the ability to make controlled, (semi) quantitative measurements on single cells to, therefore, allow unbiased comparisons to be made between each individual cell; (ii) to make these measurements in a relatively high throughput manner enabling the identification and analysis of rare cell types and short-lived cellular transition states; and (iii) the ability to make several measurements per cell either simultaneously or in a way that sequential measurements can be correlated back to single cell data. Often conventional fluorescence-based flow cytometry is used as the paradigm of these three tenets but the lack of spatial resolution can be a limitation. Although spatially resolved parameters are not obtained with conventional fluorescence-based flow cytometry, which collects light intensity in the form of peak, width, and area from waveforms created by cells transiting focused laser beams, the race is on course to exceed 20-parameters per single cell measurement (4). The parameter space race is also evident with instruments that leverage unique analytical and intensity-independent approaches. For example, mass spectrometry combined with cytometry moves away from fluorescence-based detection entirely by using antibodies tagged with rare earth metals, and in doing so easily achieves over 40 measured parameters per cell (5). Similarly, full spectral flow cytometry is pervasive, and ways in which fluorescence dynamics (i.e., fluorescence lifetimes) can be measured has gained acceptance in recent years (6–8). Again, not spatially dependent, these optical measurements add to the information content per-cell in ways that alleviate issues with conventional systems. Yet when exceedingly high numbers of parameters are taken from single cells using non-image based cytometers, the parameter space remains “zero-resolution. ” That is, the set of parameters per channel for each cell measured contain no multipixel or multivoxel information that can identify how targeted treatments affect location-dependent features such as