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Background Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. Results An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0 % to 3.0 % gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. Conclusions Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.

Heterogeneous organ‐specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single‐cell profiling using formalin‐fixed paraffin‐embedded (FFPE) materials. In this study, single‐cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ‐specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single‐cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ‐specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ‐specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM‐3. This study originally deciphers the single‐cell landscape of the organ‐specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ‐specific treatment approaches. The mechanism for organ‐specific responses of lung cancer to immunotherapy remains unclear. By using both snRNA‐sequencing and imaging mass cytometry, this study deciphered the heterogeneous single‐cell landscape of organ‐specific tumor microenvironment at both cellular and spatial levels in lung cancer patients and provided potential treatment paradigms for metastases resistant to immunotherapy.

The nucleus of mammalian cells displays a distinct spatial segregation of active euchromatic and inactive heterochromatic regions of the genome 1 , 2 . In conventional nuclei, microscopy shows that euchromatin is localized in the nuclear interior and heterochromatin at the nuclear periphery 1 , 2 . Genome-wide chromosome conformation capture (Hi-C) analyses show this segregation as a plaid pattern of contact enrichment within euchromatin and heterochromatin compartments 3 , and depletion between them. Many mechanisms for the formation of compartments have been proposed, such as attraction of heterochromatin to the nuclear lamina 2 , 4 , preferential attraction of similar chromatin to each other 1 , 4 – 12 , higher levels of chromatin mobility in active chromatin 13 – 15 and transcription-related clustering of euchromatin 16 , 17 . However, these hypotheses have remained inconclusive, owing to the difficulty of disentangling intra-chromatin and chromatin–lamina interactions in conventional nuclei 18 . The marked reorganization of interphase chromosomes in the inverted nuclei of rods in nocturnal mammals 19 , 20 provides an opportunity to elucidate the mechanisms that underlie spatial compartmentalization. Here we combine Hi-C analysis of inverted rod nuclei with microscopy and polymer simulations. We find that attractions between heterochromatic regions are crucial for establishing both compartmentalization and the concentric shells of pericentromeric heterochromatin, facultative heterochromatin and euchromatin in the inverted nucleus. When interactions between heterochromatin and the lamina are added, the same model recreates the conventional nuclear organization. In addition, our models allow us to rule out mechanisms of compartmentalization that involve strong euchromatin interactions. Together, our experiments and modelling suggest that attractions between heterochromatic regions are essential for the phase separation of the active and inactive genome in inverted and conventional nuclei, whereas interactions of the chromatin with the lamina are necessary to build the conventional architecture from these segregated phases. Attractions between heterochromatic regions are essential for phase separation of the active and inactive genome in inverted and conventional nuclei, whereas chromatin–lamina interactions are necessary to build the conventional genomic architecture from these segregated phases.

Cell nuclei segmentation is a challenging task, especially in real applications, when the target images significantly differ between them. This task is also challenging for methods based on convolutional neural networks (CNNs), which have recently boosted the performance of cell nuclei segmentation systems. However, when training data are scarce or not representative of deployment scenarios, they may suffer from overfitting to a different extent, and may hardly generalise to images that differ from the ones used for training. In this work, we focus on real-world, challenging application scenarios when no annotated images from a given dataset are available, or when few images (even unlabelled) of the same domain are available to perform domain adaptation. To simulate this scenario, we performed extensive cross-dataset experiments on several CNN-based state-of-the-art cell nuclei segmentation methods. Our results show that some of the existing CNN-based approaches are capable of generalising to target images which resemble the ones used for training. In contrast, their effectiveness considerably degrades when target and source significantly differ in colours and scale.

Gene regulation requires the dynamic coordination of hundreds of regulatory factors at precise genomic and RNA targets. Although many regulatory factors have specific affinity for their nucleic acid targets, molecular diffusion and affinity models alone cannot explain many of the quantitative features of gene regulation in the nucleus. One emerging explanation for these quantitative properties is that DNA, RNA and proteins organize within precise, 3D compartments in the nucleus to concentrate groups of functionally related molecules. Recently, nucleic acids and proteins involved in many important nuclear processes have been shown to engage in cooperative interactions, which lead to the formation of condensates that partition the nucleus. In this Review, we discuss an emerging perspective of gene regulation, which moves away from classic models of stoichiometric interactions towards an understanding of how spatial compartmentalization can lead to non-stoichiometric molecular interactions and non-linear regulatory behaviours. We describe key mechanisms of nuclear compartment formation, including emerging roles for non-coding RNAs in facilitating their formation, and discuss the functional role of nuclear compartments in transcription regulation, co-transcriptional and post-transcriptional RNA processing, and higher-order chromatin regulation. More generally, we discuss how compartmentalization may explain important quantitative aspects of gene regulation.An emerging model of gene regulation posits that DNA, RNA and proteins form condensate nuclear compartments that facilitate cooperative interactions. This Review discusses how compartmentalization can lead to non-stoichiometric molecular interactions and behaviours in transcription, co-transcriptional and post-transcriptional RNA processing, and higher-order chromatin regulation.

This protocol describes how to sequence the transcriptome from a single nucleus. It is particularly suited to cell types that are difficult to isolate as intact whole cells, such as neurons. A protocol is described for sequencing the transcriptome of a cell nucleus. Nuclei are isolated from specimens and sorted by FACS, cDNA libraries are constructed and RNA-seq is performed, followed by data analysis. Some steps follow published methods (Smart-seq2 for cDNA synthesis and Nextera XT barcoded library preparation) and are not described in detail here. Previous single-cell approaches for RNA-seq from tissues include cell dissociation using protease treatment at 30 °C, which is known to alter the transcriptome. We isolate nuclei at 4 °C from tissue homogenates, which cause minimal damage. Nuclear transcriptomes can be obtained from postmortem human brain tissue stored at −80 °C, making brain archives accessible for RNA-seq from individual neurons. The method also allows investigation of biological features unique to nuclei, such as enrichment of certain transcripts and precursors of some noncoding RNAs. By following this procedure, it takes about 4 d to construct cDNA libraries that are ready for sequencing.

With the exception of lamina-associated domains, the radial organization of chromatin in mammalian cells remains largely unexplored. Here we describe genomic loci positioning by sequencing (GPSeq), a genome-wide method for inferring distances to the nuclear lamina all along the nuclear radius. GPSeq relies on gradual restriction digestion of chromatin from the nuclear lamina toward the nucleus center, followed by sequencing of the generated cut sites. Using GPSeq, we mapped the radial organization of the human genome at 100-kb resolution, which revealed radial patterns of genomic and epigenomic features and gene expression, as well as A and B subcompartments. By combining radial information with chromosome contact frequencies measured by Hi-C, we substantially improved the accuracy of whole-genome structure modeling. Finally, we charted the radial topography of DNA double-strand breaks, germline variants and cancer mutations and found that they have distinctive radial arrangements in A and B subcompartments. We conclude that GPSeq can reveal fundamental aspects of genome architecture. The location of genetic and epigenetic elements in mammalian nuclei is measured by gradual DNA fragmentation.

This paper describes automated methods for the accurate segmentation and tracking of tens of thousands of nuclei in time-lapse imaging data of developing embryos. The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for the segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (i) generality by reconstructing cell lineages in four-dimensional, terabyte-sized image data sets of fruit fly, zebrafish and mouse embryos acquired with three types of fluorescence microscopes, (ii) scalability by analyzing advanced stages of development with up to 20,000 cells per time point at 26,000 cells min −1 on a single computer workstation and (iii) ease of use by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0% linkage accuracy across all species and imaging modalities. Using our system, we performed the first cell lineage reconstruction of early Drosophila melanogaster nervous system development, revealing neuroblast dynamics throughout an entire embryo.

Burridge and colleagues demonstrate that isolated nuclei respond to force by increasing their stiffness, and that this mechanical adaptation is mediated by emerin phosphorylation. Mechanical forces influence many aspects of cell behaviour. Forces are detected and transduced into biochemical signals by force-bearing molecular elements located at the cell surface, in adhesion complexes or in cytoskeletal structures 1 . The nucleus is physically connected to the cell surface through the cytoskeleton and the linker of nucleoskeleton and cytoskeleton (LINC) complex, allowing rapid mechanical stress transmission from adhesions to the nucleus 2 . Although it has been demonstrated that nuclei experience force 3 , the direct effect of force on the nucleus is not known. Here we show that isolated nuclei are able to respond to force by adjusting their stiffness to resist the applied tension. Using magnetic tweezers, we found that applying force on nesprin-1 triggers nuclear stiffening that does not involve chromatin or nuclear actin, but requires an intact nuclear lamina and emerin, a protein of the inner nuclear membrane. Emerin becomes tyrosine phosphorylated in response to force and mediates the nuclear mechanical response to tension. Our results demonstrate that mechanotransduction is not restricted to cell surface receptors and adhesions but can occur in the nucleus.

In eukaryotes, the cell volume is observed to be strongly correlated with the nuclear volume. The slope of this correlation depends on the cell type, growth condition, and the physical environment of the cell. We develop a computational model of cell growth and proteome increase, incorporating the kinetics of amino acid import, protein/ribosome synthesis and degradation, and active transport of proteins between the cytoplasm and the nucleoplasm. We also include a simple model of ribosome biogenesis and assembly. Results show that the cell volume is tightly correlated with the nuclear volume, and the cytoplasm-nucleoplasm transport rates strongly influence the cell growth rate as well as the cell/nucleus volume ratio (C/N ratio). Ribosome assembly and the ratio of ribosomal proteins to mature ribosomes also influence the cell volume and the cell growth rate. We find that in order to regulate the cell growth rate and the cell/nucleus volume ratio, the cell must optimally control groups of kinetic and transport parameters together, which could explain the quantitative roles of canonical growth pathways. Finally, although not explicitly demonstrated in this work, we point out that it is possible to construct a detailed proteome distribution using our model and RNAseq data, provided that a quantitative cell division mechanism is known.