A Method for Identifying Cellular Heterogeneity

Cell specification and cell phenotype heterogeneity are key determinants of many biological functions, such as differentiation, reprogramming of somatic cells into pluripotent state, cell transformation during cancerogenesis and cell changes after viral infection. Using single-molecule localization microscopy methods (SMLM), and specifically, stochastic optical reconstruction microscopy (STORM), some cellular features not recognisable with conventional microscopy, such as nanoscale arrangement of the chromatin fibres, and nucleosome group arrangements (clutches), and distribution of RNA Pol II as well as nascent RNA in the nucleus, may be recognised. These features can be the key determinants of the somatic or stem cell state. This invention relates to a method of identifying cells and cellular features. In particular, the invention relates to a method of applying artificial neural networks to identify cells and cellular features associated with cellular heterogeneity.