An official website of the United States government
Most eukaryotes and cyanobacterial species have a biological clock that allows adaptation to the daily light/dark cycle of the planet. A central problem in the study of the biological clock is understanding the synchro-nization of the stochastic oscillators in different cells and tissues, but this problem is largely unstudied, particularly in the context of circadian rhythms. We developed a novel microfluidic platform to make high-throughput and high-precision measurements of biological clocks on a controlled number of Neurospora crassa (N. crassa) cells. Single cell measurements in this platform enabled us to test whether clocks of individual cells are able to communicate.
more »
« less
Light entrainment of single cell circadian oscillator measured by a high-throughput microfluidic droplet platform.
This paper reports the measurement on light entrainment of single cell circadian oscillator of a model fungal system, Neurospora crassa (N. crassa), through a high-throughput microfluidic droplet platform [1]. The results demonstrated for the first time that single cell circadian oscillators could be entrained by light.
more »
« less
- Award ID(s):
- 1713746
- PAR ID:
- 10057819
- Date Published:
- Journal Name:
- Proc. of the 21st International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS). Savannah, GA October 22-26, 2017.
- Volume:
- 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Mathematical models have a long and influential history in the study of human circadian rhythms. Accurate predictive models for the human circadian light response have been used to study the impact of a host of light exposures on the circadian system. However, generally, these models do not account for the physiological basis of these rhythms. We illustrate a new paradigm for deriving models of the human circadian light response. Beginning from a high-dimensional model of the circadian neural network, we systematically derive low-dimensional models using an approach motivated by experimental measurements of circadian neurons. This systematic reduction allows for the variables and parameters of the derived model to be interpreted in a physiological context. We fit and validate the resulting models to a library of experimental measurements. Finally, we compare model predictions for experimental measurements of light levels and discuss the differences between our model’s predictions and previous models. Our modeling paradigm allows for the integration of experimental measurements across the single-cell, tissue, and behavioral scales, thereby enabling the development of accurate low-dimensional models for human circadian rhythms.more » « less
-
Abstract An autonomous, environmentally-synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription–translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue–dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNAseq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single cell RNAseq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain, and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient, reliable strategy to detect circadian genes in the single–cell setting.more » « less
-
Abstract As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher’s performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.more » « less
-
Four inter-related measures of phase are described to study the phase synchronization of cellular oscillators, and computation of these measures is described and illustrated on single cell fluorescence data from the model filamentous fungus, Neurospora crassa. One of these four measures is the phase shift ϕ in a sinusoid of the form x(t) = A(cos(ωt + ϕ), where t is time. The other measures arise by creating a replica of the periodic process x(t) called the Hilbert transform x̃(t), which is 90 degrees out of phase with the original process x(t). The second phase measure is the phase angle FH(t) between the replica x̃(t) and X(t), taking values between -π and π. At extreme values the Hilbert Phase is discontinuous, and a continuous form FC(t) of the Hilbert Phase is used, measuring time on the nonnegative real axis (t). The continuous Hilbert Phase FC(t) is used to define the phase MC(t1,t0) for an experiment beginning at time t0 and ending at time t1. In that phase differences at time t0 are often of ancillary interest, the Hilbert Phase FC(t0) is subtracted from FC(t1). This difference is divided by 2π to obtain the phase MC(t1,t0) in cycles. Both the Hilbert Phase FC(t) and the phase MC(t1,t0) are functions of time and useful in studying when oscillators phase-synchronize in time in signal processing and circadian rhythms in particular. The phase of cellular clocks is fundamentally different from circadian clocks at the macroscopic scale because there is an hourly cycle superimposed on the circadian cycle.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
