Quantitative single cell dynamics of signaling and transcriptional response in mammalian cells

Cells live in ever-changing environments, thereby facing a variety of dynamic environmental signals. Environmental stimuli elicit intracellular responses through signaling pathways, which converge on transcriptional activation or repression of target genes. Despite intensive research, dissecting the complex interactions between pathway components that modulate mRNA and protein production still remains a difficult task. To this end, single-cell approaches provide unique insights into intracellular processes and cell responses to environmental stimuli, otherwise inaccessible with traditional bulk studies. Single-cell measurements have revealed that isogenic cells sharing the same environment display substantial heterogeneity in both transcript and protein level. As the initial step in gene expression, transcription is an important source of such variability in eukaryotic cells due to low number of molecules involved, transcription factor dynamics and the discrete nature of biochemical reactions. Notably, production of mRNA during transcription occurs in short periods of activity, termed as transcriptional bursts, followed by longer periods of inactivity. Despite widespread observations of transcriptional dynamics in mammals, upstream molecular mechanisms shaping the eukaryotic transcription remained elusive. In this study, we quantitatively linked upstream factors and transcriptional kinetics by developing an experimental system to temporally monitor, simultaneously, inputs (transcription factors) and outputs (target gene expression) in mammalian cells, in response to stimulus. We established two Tet-On inducible stable cell lines each expressing a fusion protein of a luminescence (Nanoluciferase) reporter to either SMAD4 or SMAD2, the two main transcrip- tion factors downstream of the TGF-β pathway. These cell lines that also contain a short-lived firefly luciferase reporter for the expression of the target endogenous connective tissue growth factor (ctgf ) gene allowed us to quantitatively link nuclear accumulation of SMADs upon TGF-β stimulation to the target gene expression in real-time single cell measurements. Time- lapse luminescence microscopy and image analysis with the custom developed CAST (Cell Automated Segmentation and Tracking platform) platform provided quantitative single-cell data to link the upstream transcription factor profile to its target gene activity. The data revealed weak single-cell correlations between translocation level and the target gene expression response which suggests a mechanism in which the translocation of SMADs initiates the transcriptional response but affects the response amplitude minimally. However, SMAD4 expression levels influenced the target gene response dynamics such that cells with high SMAD4 abundance favored to respond in a more sustained and even oscillatory manner. This suggests a mechanism consisting of a dynamic interplay between the transcriptional activators and feedback mechanisms in TGF-β signaling. We explored further the effect of different factors such as ligand concentration, ligand type on both signaling and target gene response. Taken together, this study proposes an experimental and quantitative framework that dissect mechanisms underlying transcriptional response to stimulus

CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

Fluorescence and bioluminescence time-lapse imaging allows to investigate a vast range of cellular processes at single-cell or even subcellular resolution. In particular, time-lapse imaging can provide uniquely detailed information on the fine kinetics of transcription, as well as on biological oscillations such as the circadian and cell cycles. However, we face a paucity of automated methods to quantify time-lapse imaging data with single-cell precision, notably throughout multiple cell cycles. We developed CAST (Cell Automated Segmentation and Tracking platform) to automatically and robustly detect the position and size of cells or nuclei, quantify the corresponding light signals, while taking into account both cell divisions (lineage tracking) and migration events. We present here how CAST analyzes bioluminescence data from a short-lived transcriptional luciferase reporter. However, our flexible and modular implementation makes it easily adaptable to a wide variety of time-lapse recordings. We exemplify how CAST efficiently quantifies single-cell gene expression over multiple cell cycles using mouse NIH3T3 culture cells with a luminescence expression driven by the Bmal1 promoter, a central gene of the circadian oscillator. We further illustrate how such data can be used to quantify transcriptional bursting in conditions of lengthened circadian period, revealing thereby remarkably similar bursting signature compared to the endogenous circadian condition despite marked period lengthening. In summary, we establish CAST as novel tool for the efficient segmentation, signal quantification, and tracking of time-lapse images from mammalian cell culture

Quantitative relationships between SMAD dynamics and target gene activation kinetics in single live cells

The transduction of extracellular signals through signaling pathways that culminate in a transcriptional response is central to many biological processes. However, quantitative relationships between activities of signaling pathway components and transcriptional output of target genes remain poorly explored. Here we developed a dual bioluminescence imaging strategy allowing simultaneous monitoring of nuclear translocation of the SMAD4 and SMAD2 transcriptional activators upon TGF-beta stimulation, and the transcriptional response of the endogenous connective tissue growth factor (ctgf) gene. Using cell lines allowing to vary exogenous SMAD4/2 expression levels, we performed quantitative measurements of the temporal profiles of SMAD4/2 translocation and ctgf transcription kinetics in hundreds of individual cells at high temporal resolution. We found that while nuclear translocation efficiency had little impact on initial ctgf transcriptional activation, high total cellular SMAD4 but not SMAD2 levels increased the probability of cells to exhibit a sustained ctgf transcriptional response. The approach we present here allows time-resolved single cell quantification of transcription factor dynamics and transcriptional responses and thereby sheds light on the quantitative relationship between SMADs and target gene responses