Multimodal single mouse and human cell ‘Omics: Is variability distinct across cellular modalities?

With the advent of single cell analyses many unanticipated cell biological phenomena have been discovered including, new classes of RNA, identification of RNA targeting sequences and perhaps most surprisingly the discovery of cell‐to‐cell variation in transcriptome abundances even in presumptively identical cells. There is large‐scale single cell RNA variability for different cell types that can't be explained as simple molecular or technical noise. These data have led to the hypothesis that there is a many‐to‐one relationship between transcriptome states and a cell's phenotype. In this relationship the functional molecular ratios of the RNA are determined by the cell systems' stoichiometric constraints, which underdetermine the transcriptome state. Because a broad set of multi‐genic combinations support a particular phenotype, changes in the transcriptome state do not necessarily lead to changes in the phenotype. By analogy cellular phenotype should not be defined based upon the expression of individual RNAs but rather as dynamical changes in subsets of RNAs comprising selected RNA systems where the system‐associated RNAs are balanced with each other to produce the associated cellular function. Data to support these observations will be presented from live mouse and human cells, fixed tissue analysis and subcellular regional analysis. This idea provides a framework for understanding cellular heterogeneity in phenotypic response to various stimuli and provides a means for rethinking how to manipulate cellular responses so that desired outcomes are obtained including identification of molecules showing therapeutic efficacy. Support or Funding Information NIH U01MH098953, NIH GM110005, NIH MH 110180, The Brain Research Foundation. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .