The multimodality cell segmentation challenge: toward universal solutions.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

Semaphorin-3A regulates liver sinusoidal endothelial cell porosity and promotes hepatic steatosis.

Prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease, increases worldwide and associates with type 2 diabetes and other cardiometabolic diseases. Here we demonstrate that Sema3a is elevated in liver sinusoidal endothelial cells of animal models for obesity, type 2 diabetes and MASLD. In primary human liver sinusoidal endothelial cells, saturated fatty acids induce expression of SEMA3A, and loss of a single allele is sufficient to reduce hepatic fat content in diet-induced obese mice. We show that semaphorin-3A regulates the number of fenestrae through a signaling cascade that involves neuropilin-1 and phosphorylation of cofilin-1 by LIM domain kinase 1. Finally, inducible vascular deletion of Sema3a in adult diet-induced obese mice reduces hepatic fat content and elevates very low-density lipoprotein secretion. Thus, we identified a molecular pathway linking hyperlipidemia to microvascular defenestration and early development of MASLD.

Contour proposal networks for biomedical instance segmentation.

We present a conceptually simple framework for object instance segmentation, called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using a fixed-size representation based on Fourier Descriptors. The CPN can incorporate state-of-the-art object detection architectures as backbone networks into a single-stage instance segmentation model that can be trained end-to-end. We construct CPN models with different backbone networks and apply them to instance segmentation of cells in datasets from different modalities. In our experiments, CPNs outperform U-Net, Mask R-CNN and StarDist in instance segmentation accuracy. We present variants with execution times suitable for real-time applications. The trained models generalize well across different domains of cell types. Since the main assumption of the framework is closed object contours, it is applicable to a wide range of detection problems also beyond the biomedical domain. An implementation of the model architecture in PyTorch is freely available.