Cytometric routes to single cell transcriptomics
A touchstone for cytometry and the article by Samadder et al. (this issue, page 430) is an appreciation of both the nature and consequences of heterogeneity while grappling with the stochastic attributes of biology. Although flow cytometry is not a single cell analysis technology in its strictest sense, an enduring attraction is its ability to identify and indeed isolate the needles in the biological haystacks that are complex cellular systems. It offers the most accessible visualization of population heterogeneity that allows for a disassembly of complexity (1) while providing insights into the dynamics operating within cellular micro communities (2) A recurring challenge is how to avoid the masking of biological complexity and subtlety when conventional transcriptomics employs bulk tissues or even mixtures of sub populations often defined by specific protein expression. It is apparent that a revolution in cellular measurement technology is under way and cytometry is well placed to participate in this ongoing revolution (3) Cytometry in its various incarnations frequently requires well designed "link" methodologies to enable its platform technology to access advances in a range of disciplines not least in oncology. Consider how multi parameter flow cytometry has previously reached into histopathology through the processing of paraffin embedded tissues from human tumors or how high resolution flow cytometry imaging can be applied to critical cellular subsets such as circulating tumor cells. Further the creation of tractable model systems most obvious being Genetically Engineered Mouse (GEM) models calls upon cytometry to deliver readily deployable link methodologies. The article by Samadder et al. (this issue, page 430) does not seek to introduce a radical new approach, rather the authors describe and validate a pragmatic step forward in allowing cytometry to extract information from GEM models at the single nuclei level. The motivation is the challenge of detecting early indicators of different disease states within a dominating background and potentially behaviorally dynamic stromal background. The approach provides a key methodological link that permits the collection of nuclei from tissues for a new horizon in transcriptomics. Specifically, the authors describe how they have undertaken to adapt fluorescence activated nuclear sorting (FANS) for use in GEM models. The importance of isolation of fluorescently tagged nuclei in cells in any linear methodology for single cell transcriptomics has been noted previously (4) The methodology borrows from experience in the flow cytometric methods for the analysis of nuclear targeted GFP studies in plants and more recent advances in single cell genomic analysis including RNA sequencing of single nuclei (5) The aspirations relate to the generation of driver cell lines as a resource and also wider options for the targeted localization of fluorescent proteins in well defined model systems to explore organ development and pathology. The advantages and indeed the labor involved in the establishment and preparation of GEM models mammalian development are widely appreciated. Enhanced Green Fluorescent Protein (EGFP) was the first fluorescent protein expressed in transgenic mice (6,7) and remains in widespread use today (4) Together with its spectral variants and high performance monomeric red fluorescent proteins these genetically pliable fluorescent tags play beautifully into the flow cytometry platform for the identification, quantification, and
Cytometry. Part A the journal of the International Society for Analytical Cytology