Registration of Multi-modal Volumetric Images by Establishing Cell Correspondence

Manan Lalit, Mette Handberg-Thorsager, Yu-Wen Hsieh, Florian Jug, Pavel Tomancak

Abstract: Early development of an animal from an egg involves a rapid increase in cell number and several cell fate specification events accompanied by dynamic morphogenetic changes. In order to correlate the morphological changes with the genetic events, one typically needs to monitor the living system with several imaging modalities offering different spatial and temporal resolution. Live imaging allows monitoring the embryo at a high temporal resolution and observing the morphological changes. On the other hand, confocal images of specimens fixed and stained for the expression of certain genes enable observing the transcription states of an embryo at specific time points during development with high spatial resolution. The two imaging modalities cannot, by definition, be applied to the same specimen and thus, separately obtained images of different specimens need to be registered. Biologically, the most meaningful way to register the images is by identifying cellular correspondences between these two imaging modalities. In this way, one can bring the two sources of information into a single domain and combine dynamic information on morphogenesis with static gene expression data. Here we propose a new computational pipeline for identifying cell-to-cell correspondences between images from multiple modalities and for using these correspondences to register 3D images within and across imaging modalities. We demonstrate this pipeline by combining four-dimensional recording of embryogenesis of Spiralian annelid ragworm Platynereis dumerilii with three-dimensional scans of fixed Platynereis dumerilii embryos stained for the expression of a variety of important developmental genes. We compare our approach with methods for aligning point clouds and show that we match the accuracy of these state-of-the-art registration pipelines on synthetic data. We show that our approach outperforms these methods on real biological imaging datasets. Importantly, our approach uniquely provides, in addition to the registration, also the non-redundant matching of corresponding, biologically meaningful entities within the registered specimen which is the prerequisite for generating biological insights from the combined datasets. The complete pipeline is available for public use through a Fiji plugin.