Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH

Anca F Savulescu,Robyn Brackin,Emmanuel Bouilhol,Benjamin Dartigues,Jonathan H Warrell,Mafalda R Pimentel,Nicolas Beaume,Isabela C Fortunato,Stephane Dallongeville,Mikaël Boulle,Hayssam Soueidan,Fabrice Agou,Jan Schmoranzer,Jean-Christophe Olivo-Marin,Claudio A Franco,Edgar R Gomes,Macha Nikolski,Musa M Mhlanga

doi:10.1016/j.crmeth.2021.100068

Abstract

SummaryAdvances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers.

Highlights

The need to incorporate subcellular spatial information of central dogma molecules into traditional omics approaches has led to the call for spatially resolved omics of various kinds (Crosetto et al, 2015)
Analytical method DypFISH is designed as a library for writing analysis scripts to study subcellular spatial distributions of molecules
Each image in the hdf file is required to have primary image descriptors (Figure 1A). These include cell and nucleus masks as well as position of a landmark of interest, in our case the microtubule organizing center (MTOC), which is indicative of the cell polarity; descriptors encoding the signal; and for 3D images, the 3D volume segmentation, as well as the zero level that indicates the last Z slice within the focus of the confocal microscope

Summary

Introduction

The need to incorporate subcellular spatial information of central dogma molecules into traditional omics approaches has led to the call for spatially resolved omics of various kinds (Crosetto et al, 2015). A few studies have attempted to quantify spatial distribution of RNA (Briley et al, 2015; Park et al, 2012; Samacoits et al, 2018; Stueland et al, 2019; Yamagishi et al, 2009), the majority of studies investigating subcellular localization of numerous RNAs and proteins have been generally qualitative, lacking detailed quantitative approaches to systematically describe the positions of RNAs and proteins They have typically been limited to systems in which spatial heterogeneity is controlled and subcellular partitions are defined, such as developmental models (Macdonald and Struhl, 1988; Tautz and Pfeifle, 1989), neuronal systems (Batish et al, 2012; Buxbaum et al, 2014), and polarized cells (Martin and Ephrussi 2009; Mili et al, 2008; Clatterbuck-Soper et al, 2017). To unravel the mechanisms of RNA spatial and temporal distribution, quantitative analytical tools that probe these relationships systematically need to be developed

Methods

Results

Discussion

Conclusion