Bilen, H. | Search bioRxiv

bioRxiv2024

A novel channel invariant architecture for the segmentation of cells and nuclei in multiplexed images using InstanSeg

The quantitative analysis of bioimaging data increasingly depends on the accurate segmentation of cells and nuclei, a significant challenge for the analysis of high-plex imaging data. Current deep learning-based approaches to segment cells in multiplexed images require reducing the input to a small and fixed number of input channels, discarding imaging information in the process. We present Channel Net, a novel deep learning architecture for generating three-channel representations of multiplexed images irrespective of the number or ordering of imaged biomarkers. When combined with InstanSeg, ChannelNet sets a new benchmark for the segmentation of cells and nuclei on public multiplexed imaging datasets. We provide an open implementation of our method and integrate it in open source software. Our code and models are available on https://github.com/instanseg/instanseg.

bioinformatics↗

bioRxiv2024

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics

In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous human diseases, the labour-intensive methods employed to study DNA replication have hindered large-scale analyses of its roles in pathological processes. In this study, we first demonstrate that a convolutional neural network trained to classify S-phase stages based on DAPI and EdU patterns could identify altered replication dynamics in Rif1-deficient mouse embryonic stem cells (mESCs), revealing a skewed distribution across the various S-phase stages. Given the possible practical limitations associated with a supervised framework, we proceed to show that the abnormal replication profile of Rif1-deficient mESCs could further be detected by an unsupervised approach (based on self-supervised representation learning), which could additionally reconstruct progression through S-phase. Finally, we extend our approach to a well-characterised cellular model of inducible deregulated origin firing, involving cyclin E overexpression. Through parallel EdU- and PCNA-based analyses, we demonstrate the potential applicability of our method to patient samples, offering a means to identify the contribution of deregulated DNA replication to a plethora of pathogenic processes.

cell biology↗