Machine-guided design of synthetic cell type-specific cis-regulatory elements.

Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose in vivo across vertebrates.

ANISOTROPIC PLATE DIFFUSION FILTERING FOR DETECTION OF CELL MEMBRANES IN 3D MICROSCOPY IMAGES.

We propose an anisotropic diffusion method to denoise and aid the reconstruction of planar objects in three-dimensional images. The contribution of this paper is the development of a planarity function characterizing plate-like structures using an image Hessian's eigensystem. We then construct a diffusion tensor for anisotropically smoothing plates and satisfying necessary scale-space properties. Our method finds applications in improving the fidelity of highly noisy cell membrane images from confocal microscopy. In dense cellular regions, cell membranes assume linear shapes (planar) between neighbors. The imaging process makes cell membranes appear as diffuse structures owing to the non-uniform fluorescent marker distribution, point-spread function of the optics, and anisotropic voxel resolution which make automatic cell segmentation difficult. We apply diffusion filtering to identify and enhance membranes. We demonstrate the use of our methods on 3D cell membrane images of a zebrafish embryo acquired using fluorescent microscopy and quantify the improvement in image quality.

VARIATIONAL LEVEL-SET WITH GAUSSIAN SHAPE MODEL FOR CELL SEGMENTATION.

In analysis of microscopy based images, a major challenge lies in splitting apart cells that appear to overlap because they are too densely packed. This task is complicated by the physics of the image acquisition that causes large variations in pixel intensities. Each image typically contains thousands of cells with each cell having a different orientation, size and intensity histogram. In this paper, a spatial intensity model of a nucleus is incorporated into [1] to aid cell segmentation from microscopy datasets. An energy functional is defined and with it the spatial intensity distribution of a nuclei is modeled as a Gaussian distribution with constant intensity background. Experimental results on a variety of microscopic data validate its effectiveness.