Speaker
Description
Joint analysis of different data modalities is a promising approach in developmental biology which allows to study the connection between cell-type specific gene expression and cell phenotype. Normally, using analysis methods in correlative manner poses a lot of limitations; however, studying a stereotypic model organism gives a unique opportunity to jointly analyze data obtained from different individual animals. In this work we show how 3D smFISH spatial transcriptomics data can be used to make a link between high-resolution electron microscopy volume and scRNAseq atlas of 6-days post fertilization young Platynereis worm consisting of several thousands of cells organised into distinct cell types. To enable systematic mapping of non-spatial scRNAseq clusters to EM volume we developed a deep learning-based, fully automated pipeline for registration of smFISH volumes to EM volumes. We demonstrate visualisation of multimodal data in MoBIE and explore signal quantification in 3D smFISH data. Deep-learning based registration enables large-scale integration of different modalities aiding in the interpretation of both data types and validation of biological hypotheses.
| Authors | Elena Buglakova*, Luca Santangeli, Zülfiye Gülce Serka, Adam Phillip Oel, Detlev Arendt, Anna Kreshuk |
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| Keywords | registration, segmentation, electron microscopy, confocal microscopy, multimodal analysis |
