Transcriptional regulation, where transcription factors bind to DNA to control the level of mRNA produced, has primarily been studied using bulk biochemical assays which average signal across large numbers of cells. However, single cell studies have shown that individual cells within isogenic populations can transcribe quite differently than the average, with important consequences for biological processes such as cancer, development, and cellular differentiation. Still, the molecular basis for this single cell transcriptional variability remains mysterious. Namely, we do not know what molecules bind to the DNA in cells and determine whether or not it transcribes a locus at a particular time. This is true both upon initial induction of gene expression, where the same gene responds to the same signal at different times in different cells, as well as at steady state, where due to the pulsatile nature of transcription, the same gene fires at different times in different cells. These questions remain unanswered because current biochemical assays cannot directly associate the molecules bound to a gene promoter at a given time with its instantaneous transcriptional activity in single cells due to limits of detection efficiency. What if instead, we could sort out subpopulations of cells by transcriptional activity in large numbers, allowing us to harness utilize bulk biochemical assays to determine associated differences in molecular factor binding to promoter DNA? Here we propose to utilize novel single-molecule fluorescent signal amplification methods (clampFISH and third generation hybridization chain reaction) to separate isogenic cellular populations by whether or not they were actively transcribing at fixed time points. In our preliminary work, we showed that clampFISH is capable of sorting fixed cells by specific endogenous and even nascent transcripts. Once cells are sorted into locus-specific transcriptional subpopulations, we will be able to profile them to assess what is molecularly bound at the target promoter in the context of its activity. We have started by targeting human FKBP5 and mouse Hbb-b1 genomic loci because they are well-studied, so we have candidate factors to interrogate, and display heterogeneous transcription between cells and over time.
In Aim 1, we plan to investigate the factors that are tightly associated with transcriptional bursts by sorting out cells that do and do not contain nascent transcripts.
In Aim 2, we plan to evaluate which factors are tightly associated with the temporal heterogeneity of transcriptional response after induction by sorting out cells that have responded (i.e. contain high levels of target RNA) from those that have not at different time points after initial induction. In each of these cases, by performing ChIP as well as ATAC-seq and Capture-C on sorted populations, I will determine whether candidate factors or interactions are present either in tight association with or irrespective of transcriptional activity. Associations revealed by these analyses will guide further mechanistic examinations. Successful completion of these aims will address longstanding questions regarding the unknown biochemical underpinnings and regulation of variability in transcription.
Individual cells transcribe genes very differently than the population average with important biological consequences, such as cell fate determination during development and cell/tissue responses to medical treatment, but it is unknown which molecular factors regulate this variability. We will apply new methods developed by our lab and others to separate cells into subpopulations based on instantaneous transcriptional activity at specific time points and determine which factors are responsible for transcriptional heterogeneity at the single cell level. Results from this study will fill a longstanding critical gap in the understanding of molecular mechanisms of transcriptional regulation.