Visualizing Early Frog Development with Motion-Sensitive 3-D Optical Coherence Microscopy

A motion-sensitive en-face-scanning 3-D optical coherence microscope (OCM) has been designed and constructed to study critical events in the early development of plants and animals. We describe the OCM instrument and present time-lapse movies of frog gastrulation, an early developmental event in which three distinct tissue layers are established that later give rise to all major organ systems. OCM images constructed with fringe-amplitude data show the mesendoderm migrating up along the blastocoel roof, thus forming the inner two tissue layers. Motion-sigma data, measuring the random motion of scatterers, is used to construct complementary images that indicate the presence of Brownian motion in the yolk cells of the endoderm. This random motion provides additional intrinsic contrast that helps to distinguish different tissue types. Depth penetration at 850 nm is sufficient for studies of the outer ectoderm layer, but is not quite adequate for detailed study of the blastocoel floor, about 500 to 800 μm deep into the embryo. However, we measure the optical attenuation of these embryos to be about 35% less at 1310 nm. 2-D OCT images at 1310 nm are presented that promise sufficient depth penetration to test current models of cell movement near the blastocoel floor during gastrulation

Accurate classification of RNA structures using topological fingerprints

While RNAs are well known to possess complex structures, functionally similar RNAs often have little sequence similarity. While the exact size and spacing of base-paired regions vary, functionally similar RNAs have pronounced similarity in the arrangement, or topology, of base-paired stems. Furthermore, predicted RNA structures often lack pseudoknots (a crucial aspect of biological activity), and are only partially correct, or incomplete. A topological approach addresses all of these difficulties. In this work we describe each RNA structure as a graph that can be converted to a topological spectrum (RNA fingerprint). The set of subgraphs in an RNA structure, its RNA fingerprint, can be compared with the fingerprints of other RNA structures to identify and correctly classify functionally related RNAs. Topologically similar RNAs can be identified even when a large fraction, up to 30%, of the stems are omitted, indicating that highly accurate structures are not necessary. We investigate the performance of the RNA fingerprint approach on a set of eight highly curated RNA families, with diverse sizes and functions, containing pseudoknots, and with little sequence similarity–an especially difficult test set. In spite of the difficult test set, the RNA fingerprint approach is very successful (ROC AUC \u3e 0.95). Due to the inclusion of pseudoknots, the RNA fingerprint approach both covers a wider range of possible structures than methods based only on secondary structure, and its tolerance for incomplete structures suggests that it can be applied even to predicted structures. Source code is freely available at https://github.rcac.purdue.edu/mgribsko/XIOS_RNA_fingerprint

Deciphering the universe of RNA structures and trans RNA-RNA interactions of transcriptomes in vivo: from experimental protocols to computational analyses

The last few years have seen an explosion of experimental and computational methods for investigating RNA structures of entire transcriptomes in vivo. Very recent experimental protocols now also allow trans RNA–RNA interactions to be probed in a transcriptome-wide manner. All of the experimental strategies require comprehensive computational pipelines for analysing the raw data and converting it back into actual RNA structure features or trans RNA–RNA interactions. The overall performance of these methods thus strongly depends on the experimental and the computational protocols employed. In order to get the best out of both worlds, both aspects need to be optimised simultaneously. This review introduced the methods and proposes ideas how they could be further improved