The nucleolus, a membraneless organelle crucial for ribosome production, has a unique nanoscale structure whose organization is responsive to cell signals and disease progression. Here, we highlight t Show more
The nucleolus, a membraneless organelle crucial for ribosome production, has a unique nanoscale structure whose organization is responsive to cell signals and disease progression. Here, we highlight the potential of Expansion Microscopy (ExM) for capturing intricate spatial and functional information about membraneless organelles such as the nucleolus and nuclear foci. We apply dual protein Expansion Microscopy (dual-proExM) in combination with click Expansion Microscopy (click-ExM) to capture images at the highest resolution reported for the nucleolus of âŒ45 ± 2 nm. Inhibition of nucleolar processes triggers a nucleolar stress response, causing distinct structural rearrangements whose molecular basis is an area of active investigation. We investigate time-dependent changes in nucleolar structure and function under nucleolar stress induced by oxaliplatin, actinomycin D, and other platinum-based compounds. Our findings reveal new stages that occur prior to the complete sequestration of RNA Pol I into nucleolar caps, shedding light on the early mechanisms of the nucleolar stress response. RNA transcription is linked to nanoscale protein rearrangements using a combination of click-ExM and pro-ExM, revealing locations of active transcripts during the early stages of nucleolar stress reorganization. With prolonged stress, fibrillarin and NPM1 segregate from the nucleolus into nucleoplasmic foci that are for the first time imaged at nanometer resolution. In addition to revealing new morphological information about the nucleolus, this study demonstrates the potential of ExM for imaging membraneless organelles with nanometer-scale precision. Show less
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation â and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less