Noise transmission during the dynamic pattern formation in fly embryos

An integrated microfluidic device for multiplexed imaging of spatial gene expression patterns of Drosophila embryos

To reveal the underlying mechanism of the biological function of multicellular systems, it is important to obtain comprehensive spatial gene expression profiles. Among the emerging single-cell spatial-omics techniques, immunofluorescence (IF)-based iterative multiplexed imaging is a promising approach. However, the conventional method is usually costly, time-consuming, labor-intensive, and has low throughput. Moreover, it has yet to be demonstrated in intact multicellular organisms. Here, we developed an integrated microfluidic system to overcome these challenges for quantitatively measuring multiple protein profiles sequentially in situ in the same Drosophila embryo. We designed an array of hydrodynamic trapping sites to automatically capture over ten Drosophila embryos with orientation selectivity at more than 90% trapping rates. We also optimized the geometry of confinement and the on-chip IF protocol to achieve the same high signal-to-noise ratio as the off-chip traditional IF experiments. Moreover, we developed an efficient de-staining protocol by combining on-chip antibody stripping and fluorophore bleaching. Using the same secondary antibody to sequentially stain different genes, we confirmed that the de-stained genes have no detectable interference with the subsequently stained genes, and the gene expression profiles are preserved after multiple cycles of staining and de-staining processes. This preliminary test shows that our newly developed integrated microfluidic system can be a powerful tool for multiplexed imaging of Drosophila embryos. Our work opens a new avenue to design microfluidic chips for multicellular organisms and single-cell spatial-omics techniques.

Single-cell Transcriptomes Reveal Characteristics of MicroRNA in Gene Expression Noise Reduction

Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene expression. High levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell behaviors. In this work, we showed evidence from single-cell RNA sequencing data analysis that microRNAs (miRNAs) can reduce gene expression noise at the mRNA level in mouse cells. We identified that the miRNA expression level, number of targets, target pool abundance, and miRNA–target interaction strength are the key features contributing to noise repression. miRNAs tend to work together in cooperative subnetworks to repress target noise synergistically in a cell type-specific manner. By building a physical model of post-transcriptional regulation and observing in synthetic gene circuits, we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic fluctuations. Together, through the integrated analysis of single-cell RNA and miRNA expression profiles, we demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

Quantifying Temperature Compensation of Bicoid Gradients with a Fast T-Tunable Microfluidic Device

As a reaction-diffusion system strongly affected by temperature, early fly embryos surprisingly show highly reproducible and accurate developmental patterns during embryogenesis under temperature perturbations. To reveal the underlying temperature compensation mechanism, it is important to overcome the challenge in quantitative imaging on fly embryos under temperature perturbations. Inspired by microfluidics generating temperature steps on fly embryos, here we design a microfluidic device capable of ensuring the normal development of multiple fly embryos as well as achieving real-time temperature control and fast temperature switches for quantitative live imaging with a home-built two-photon microscope. We apply this system to quantify the temperature compensation of the morphogen Bicoid (Bcd) gradient in fly embryos. The length constant of the exponential Bcd gradient reaches the maximum at 25°C within the measured temperatures of 18-29°C and gradually adapts to the corresponding value at new temperatures upon a fast temperature switch. The relaxation time of such an adaptation becomes longer if the temperature is switched in a later developmental stage. This age-dependent temperature compensation could be explained if the traditional synthesis-diffusion-degradation model is extended to incorporate the dynamic change of the parameters controlling the formation of Bcd gradients.

The dynamic transmission of positional information in stau- mutants during Drosophila embryogenesis

It has been suggested that Staufen (Stau) is key in controlling the variability of the posterior boundary of the Hb anterior domain (xHb). However, the mechanism that underlies this control is elusive. Here, we quantified the dynamic 3D expression of segmentation genes in Drosophila embryos. With improved control of measurement errors, we show that the xHb of stau- mutants reproducibly moves posteriorly by 10% of the embryo length (EL) to the wild type (WT) position in the nuclear cycle (nc) 14, and that its variability over short time windows is comparable to that of the WT. Moreover, for stau- mutants, the upstream Bicoid (Bcd) gradients show equivalent relative intensity noise to that of the WT in nc12-nc14, and the downstream Even-skipped (Eve) and cephalic furrow (CF) show the same positional errors as these factors in WT. Our results indicate that threshold-dependent activation and self-organized filtering are not mutually exclusive and could both be implemented in early Drosophila embryogenesis.

Noise model estimation with application to gene expression

Algorithms for the estimation of noise level and the detection of noise model are proposed. They are applied to gene expression data for Drosophila embryos. The 2D data on gene expression and the extracted 1D profiles are considered. Since the 1D data contain processing errors, an algorithm for separation of these processing errors is constructed to estimate the biological noise level. An approach to discrimination between the additive and multiplicative models is suggested for the 1D and 2D cases. Singular spectrum analysis and its 2D extension are exploited for the pattern extraction. The algorithms are tested on artificial data similar to the real data. Comparison of the results, which are obtained by the 1D and 2D methods, is performed for Krüppel and giant genes.