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Cell nuclei prepared from rat liver were alkylated in vitro with ethylnitrosourea; the nuclear DNA was found to lose O6-ethylguanine and 7-ethylguanine during a subsequent incubation at 37 degrees C. The rate of O6-ethylguanine loss is comparable to that observed in vivo, indicating that no cytoplasmic component is needed for the repair; no free O6-ethylguanine was found in the incubation medium of the ethylated nuclei. The rate of 7-ethylguanine loss is higher than the spontaneous depurination in vitro and an amount of free 7-ethylguanine equivalent to that lost by the nuclear DNA was found in the incubation medium; these results suggest that this DNA lesion is excised by a DNA glycosylase. The proteins of the chromatin prepared from the isolated nuclei induced the disappearance of O6-ethylguanine from an added ethylated DNA. No free O6-ethylguanine was released indicating that the repair is not catalyzed by a DNA glycosylase; no oligonucleotides enriched in O6-ethylguanine were released either, indicating that the disappearance of O6-ethylguanine from DNA is not the result of the cooperative action of a specific endonuclease and an exonuclease. Activities capable of removing O6-ethylguanine from DNA were found in other cell compartments; most of it, however, is in the nucleus where the main location is chromatin. A pretreatment of the rats with daily low doses of diethylnitrosamine during 3 or 4 weeks increased 2-3-times the repair activity of the chromatin proteins.

Further studies have been performed to define the kinetic characteristics of nuclear triiodothyronine (T(3)) binding sites in rat liver (J. Clin. Endocrinol. Metab. 1972. 35: 330). Sequential determination of labeled T(3) associated with nuclei and cytoplasm over a 4-h period allowed analysis of the relationship of T(3) in nuclear and cytoplasmic compartments. A rapid interchange of hormone between nuclei and cytoplasm was demonstrated, and in vitro incubation experiments with nuclei yielded no evidence favoring metabolic transformation of T(3) by the nuclei. In vivo displacement experiments were performed by subcellular fractionation of liver (1/2) h after injection of [(125)I]T(3) with increasing quantities of unlabeled T(3). The nuclear binding capacity for T(3) could be defined (0.52 ng/mg DNA). Analysis of these experiments also allowed an estimation of the association constant of nuclear sites for T(3) (4.7 x 10(11)M(-1)). The affinity of these sites for T(3) was estimated to be 20-40 fold greater than for thyroxine (T(4)). Chromatographic analysis of the nuclear radioactivity after injection of labeled T(4) indicated that the binding of T(4) by the nucleus could not be attributed to in vivo conversion of T(4) to T(3) but reflected intrinsic cross-reactivity of the two iodothyronines at the nuclear binding sites.

By an improved method of hydroxylapatite chromatography, the reassociated sequences of chick nuclear DNA were isolated, and their base composition analysed. By increasing the amount of reassociation, the G + C content of the renatured sequences decreased progressively to reach a mean value corresponding to that of the total DNA. In order to study the distribution of the families, or group of families having different amount of reassociation, DNA was fractionated by CsC1 density gradient centrifugation. Fractions having different G + C content were obtained, and their reassociation rates analysed. At high C(o)t value of renaturation (C(o)t=50) the amount of reassociated sequences included in the high or in the low buoyant density DNA fractions was approximately the same, but their G + C content was as expected different. At lower C(o)t values of renaturation (between C(o)t of 0.2 and the C(o)t of 10), the results indicated an heterogeneity of the repeated sequences in the A + T rich DNA fractions, as compared to the G + C rich ones.

Isolated hepatic nuclei from euthyroid rats were incubated with tracer (125I)L-triiodothyronine (T3) and increasing doses of nonradioactive T3 for 30 min at 37degrees C. The T3 bound specifically to nuclear sites increased with increasing T3 doses to a plateau, which represented the nuclear binding capacity, M. Addition of 1 mM KCN, NaF, dinitrophenol, oriodoacetate did not affect nuclear binding, indicating that active metabolism was not required. Kinetic studies showed that the nuclear sites were equilibrated with T3 within 30 min of incubation (one-half maximal binding at 3 min) and that the rate of release of T3 in vitro (0.058 min-1) was the same for endogenous T3 or for T3 bound to nuclei in vitro. Nuclear T3 resisted extraction with 0.14 M NaC1 buffered at pH 7.5, but both endogenous hormone and T3 bound in vitro were readily extracted by 0.4 M KC1 at pH 8.0. The elution profiles of endogenous and in vitro-bound T3 from Sephadex G-100 columns showed a common protein peak with a molecular weight of 60-65,000, assuming a globular protein. Scatchard analysis of in vitro displacement studies showed a single class of binding sites. Mean M equals 0.23 times 10-9 M or 0.85 ng T3 for nuclei isolated from 1 g of liver. Mean M closely corresponded to that anticipated from reported in vivo studies. The apparent association constant Ka for the nuclear sites, 5.55 times 108 M-1, was lower than in studies in vivo, probably attributable to the different ionic milieu of nuclei in the incubation buffer and in the intact cell. Thus, the identity of the nuclear T3 binding sites studied in vitro to those reported for endogenous hormone is demonstrated by similar binding capacities, release rates, analogue binding affinities (previously reported), and localization to chromatin nonhistone proteins of comparable molecular weight. The role of cytosol protein in nuclear binding was assessed by comparing binding parameters for extensively washed nuclei and nuclei incubated either with contaminating or added cytosol. No difference in Ka or M was found. Moreover, it was unlikely that specific cytosol proteins were already present in nuclei and functioned during incubation as a shuttle for T3, since Ka and M for nuclei obtained from athyreotic rats were similar to Ka and M for nuclei from euthyroid animals. Thus, an initial interaction between T3 and specific cytosol proteins does not appear to be a prerequisite for translocation of T3 to nuclear sites.

Evidence that 32 S nRNA contains 5.8 S rRNA was provided by studies on specific oligonucleotide sequences of these RNA species. Purified 32P-labeled 5.8 and 28 S rRNA and 32 S RNA were digested with T-1 ribonuclease, and the products were fractionated according to chain length by chromatography on DEAE-Sephadex A-25 at neutral pH. The oligonucleotides in Peak 8 were treated with alkaline phosphatase and the products were separated by two-dimensional electrophoresis on cellulose acetate at pH 3.5 and DEAE-paper in 7% formic acid. Seven unique oligonucleotide markers for 5.8 S rRNA including the methylated octanucleotide A-A-U-U-Gm-G-A-Gp were present in 32 S RNA but were not found in 28 S rRNA, indicating that 5.8 S rRNA is directly derived from the 32 S nucleolar precursor. These studies confirm a maturation pathway for rRNA species in which 32 S nucleolar RNA is a precursor of 5.8 S rRNA as well as 28 S rRNA.

This report demonstrates that the chick oviduct, a specific target organ for progesterone, contains both cytoplasmic and nuclear macromolecules which bind progestins. These binding molecules can be clearly distinguished from transcortin by centrifugation through sucrose gradients of low ionic strength and by agarose gel filtration. The cytoplasmic progesterone-binding molecules also bind 5-alpha-pregnane-3,20-dione, but have significantly lower affinity for cortisol, estrone, or aldosterone. They are absent from blood and nontarget organs such as lung and spleen. The tissue-specific binding components appear to be heat-labile proteins with an average dissociation constant for progesterone of about 8 x 10(-10) M at 2 degrees C. These results are consistent with the identification of the progesterone-binding molecules as the functional hormone receptors. In further support of this concept is the finding that treatment of the chicks with estrogen coordinately induces a 20-fold increase in the number of progesterone-binding molecules and enhances the capacity of progesterone to induce avidin synthesis.A progesterone-"receptor" complex can be detected in both the cytoplasm and nuclei of oviduct tissue after an injection of [(3)H]progesterone to estrogen-treated chicks. By contrast, incubation of oviduct tissue with [(3)H]progesterone in vitro at 2 degrees C for 5 min leads to labeling of the cytoplasmic "receptor" only. Transfer of the "receptor"-steroid complex into the nucleus then appears to occur upon subsequent incubation in vitro at 37 degrees C. This observation suggests that the transfer of bound progesterone across the nuclear membrane may be an energy-requiring enzymatic process.

Enzymes involved in the expression of the mitochondrial genome in Neurospora crassa are induced by chloramphenicol and ethidium bromide, which block transcription and translation of mitochondrial DNA. It is concluded that most, if not all, proteins of the mitochondrial genetic apparatus are coded by nuclear genes, synthesized on cytoplasmic ribosomes, and controlled by a repressor-like mitochondrial gene product. A model explaining the coordination of nuclear and mitochondrial division cycles by repressor control is discussed.

Nuclei isolated from rat liver, when incubated with methyl-labeled S-adenosylmethionine, incorporated label into 2'-O-methyladenosine, 2'-O-methylguanosine, 2'-O-methyluridine, and 2'-O-methylcytidine of endogenous nuclear RNA. Addition of ribosomal RNA from wheat germ to the reaction markedly stimulated 2'-O-methylation of total RNA. The relative incorporation of label into the four different 2'-O-methyl ribonucleosides was greatly changed by the addition of wheat germ RNA. There was much more 2'-O-methylation of the purine ribonucleosides, relative to the pyrimidine ribonucleosides, in the reaction stimulated by wheat germ RNA.

Ultralow temperature radioautography, suitable for the quantitative localization of diffusible solutes, was used to study the permeability of the nuclear envelope in the intact amphibian oocyte Sucrose-(3)H solutions were injected into mature oocytes, in volumes of 0 016-0 14% of that of the cell, and the subsequent movement of the solute was recorded. The resultant radioautographs show diffusion gradients in the cytoplasm and nucleus, and concentration gradients across the nuclear envelope Analysis of these gradients discloses that the nuclear envelope is as permeable as a comparable structure composed of cytoplasm, and is about 10(8) times more permeable than the oocyte plasma membrane The diffusion coefficient of sucrose in cytoplasm is 2 x 10(-6) cm(2)/sec, or about one-third its diffusivity in pure water. This reduction can probably be accounted for by an effective lengthening of the diffusional path because of obstruction by cytoplasmic inclusions. The nuclear: cytoplasmic sucrose concentration ratio at diffusional equilibrium is about 3 05, or 1.6 times as great as expected from the water content of the two compartments This asymmetry is attributed to an unavailability of 36% of the cytoplasmic water as solvent Finally, sucrose entry into oocytes from a bathing solution was monitored by whole cell analysis and radioautography. These and the microinjection results are consistent with a model in which sucrose entry into the cell is entirely limited by the permeability of the plasma membrane. The results are inconsistent with cell models that hypothesize a short-circuit transport route from the extracellular compartment to the nucleus, and with models in which cytoplasmic diffusion is viewed as limiting the rate of solute permeation.

Clinical trials evaluating cardiac progenitor cells (CPC) demonstrated feasibility and safety, but no clear functional benefits. Therefore a deeper understanding of CPC biology is warranted to inform strategies capable to enhance their therapeutic potential. Here we have defined, using a label-free proteomic approach, the differential cytoplasmic and nuclear compartments of human CPC (hCPC). Global analysis of cytoplasmic repertoire in hCPC suggested an important hypoxia response capacity and active collagen metabolism. In addition, comparative analysis of the nuclear protein compartment identified a significant regulation of a small number of proteins in hCPC versus human mesenchymal stem cells (hMSC). Two proteins significantly upregulated in the hCPC nuclear compartment, IL1A and IMP3, showed also a parallel increase in mRNA expression in hCPC versus hMSC, and were studied further. IL1A, subjected to an important post-transcriptional regulation, was demonstrated to act as a dual-function cytokine with a plausible role in apoptosis regulation. The knockdown of the mRNA binding protein (IMP3) did not negatively impact hCPC viability, but reduced their proliferation and migration capacity. Analysis of a panel of putative candidate genes identified HMGA2 and PTPRF as IMP3 targets in hCPC. Therefore, they are potentially involved in hCPC proliferation/migration regulation., (© 2022. The Author(s).)

Background: 6 - 4 photoproducts are the second most common UV-induced DNA lesions after cyclobutane pyrimidine dimers. In plants, they are mainly repaired by photolyases in a process called photoreactivation. While pyrimidine dimers can be deleterious, leading to mutagenesis or even cell death, 6 - 4 photoproducts can activate specific signaling pathways. Therefore, their removal is particularly important, especially for plants exposed to high UV intensities due to their sessile nature. Although photoreactivation in nuclear DNA is well-known, its role in plant organelles remains unclear. In this paper we analyzed the activity and localization of GFP-tagged AtUVR3, the 6 - 4 photoproduct specific photolyase., Results: Using transgenic Arabidopsis with different expression levels of AtUVR3, we confirmed a positive trend between these levels and the rate of 6 - 4 photoproduct removal under blue light. Measurements of 6 - 4 photoproduct levels in chloroplast and nuclear DNA of wild type, photolyase mutants, and transgenic plants overexpressing AtUVR3 showed that the photoreactivation is the main repair pathway responsible for the removal of these lesions in both organelles. The GFP-tagged AtUVR3 was predominantly located in nuclei with a small fraction present in chloroplasts and mitochondria of transgenic Arabidopsis thaliana and Nicotiana tabacum lines. In chloroplasts, this photolyase co-localized with the nucleoid marked by plastid envelope DNA binding protein., Conclusions: Photolyases are mainly localized in plant nuclei, with only a small fraction present in chloroplasts and mitochondria. Despite this unbalanced distribution, photoreactivation is the primary mechanism responsible for the removal of 6 - 4 photoproducts from nuclear and chloroplast DNA in adult leaves. The amount of the AtUVR3 photolyase is the limiting factor influencing the photoreactivation rate of 6 - 4 photoproducts. The efficient photoreactivation of 6 - 4 photoproducts in 35S: AtUVR3-GFP Arabidopsis and Nicotiana tabacum is a promising starting point to evaluate whether transgenic crops overproducing this photolyase are more tolerant to high UV irradiation and how they respond to other abiotic and biotic stresses under field conditions., (© 2024. The Author(s).)

Focal adhesions (FAs) are nanoscale complexes containing clustered integrin receptors and intracellular structural and signaling proteins that function as principal sites of mechanotransduction in part via promoting the nuclear translocation and activation of the transcriptional coactivator yes-associated protein (YAP). Knockdown of FA proteins such as focal adhesion kinase (FAK), talin, and vinculin can prevent YAP nuclear localization. However, the mechanism(s) of action remain poorly understood. Herein, we investigated the role of different functional domains in vinculin, talin, and FAK in regulating YAP nuclear localization. Using genetic or pharmacological inhibition of fibroblasts and human mesenchymal stem cells (hMSCs) adhering to deformable substrates, we find that disruption of vinculin-talin binding versus talin-FAK binding reduces YAP nuclear localization and transcriptional activity via different mechanisms. Disruption of vinculin-talin binding or knockdown of talin-1 reduces nuclear size, traction forces, and YAP nuclear localization. In contrast, disruption of the talin binding site on FAK or elimination of FAK catalytic activity did not alter nuclear size yet still prevented YAP nuclear localization and activity. These data support both nuclear tension-dependent and independent models for matrix stiffness-regulated YAP nuclear localization. Our results highlight the importance of vinculin-talin-FAK interactions at FAs of adherent cells, controlling YAP nuclear localization and activity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Bazooka/Par-3 (Baz) is an evolutionarily conserved scaffold protein that functions as a master regulator for the establishment and maintenance of cell polarity in many different cell types. In the vast majority of published research papers Baz has been reported to localize at the cell cortex and at intercellular junctions. However, there have also been several reports showing localization and function of Baz at additional subcellular sites, in particular the nuclear envelope and the neuromuscular junction. In this study we have re-assessed the localization of Baz to these subcellular sites in a systematic manner. We used antibodies raised in different host animals against different epitopes of Baz for confocal imaging of Drosophila tissues. We tested the specificity of these antisera by mosaic analysis with null mutant baz alleles and tissue-specific RNAi against baz. In addition, we used a GFP-tagged gene trap line for Baz and a bacterial artificial chromosome (BAC) expressing GFP-tagged Baz under control of its endogenous promoter in a baz mutant background to compare the subcellular localization of the GFP-Baz fusion proteins to the staining with anti-Baz antisera. Together, these experiments did not provide evidence for specific localization of Baz to the nucleus or the neuromuscular junction., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of the wide existence and large variances in nuclei morphometry in histopathology images. However, manual annotation of tens of thousands of nuclei is tedious and time-consuming, which requires significant amount of human effort and domain-specific expertise. To alleviate this problem, in this paper, we propose a weakly-supervised nuclei segmentation method that only requires partial point labels of nuclei. Specifically, we propose a novel boundary mining framework for nuclei segmentation, named BoNuS, which simultaneously learns nuclei interior and boundary information from the point labels. To achieve this goal, we propose a novel boundary mining loss, which guides the model to learn the boundary information by exploring the pairwise pixel affinity in a multiple-instance learning manner. Then, we consider a more challenging problem, i.e., partial point label, where we propose a nuclei detection module with curriculum learning to detect the missing nuclei with prior morphological knowledge. The proposed method is validated on three public datasets, MoNuSeg, CPM, and CoNIC datasets. Experimental results demonstrate the superior performance of our method to the state-of-the-art weakly-supervised nuclei segmentation methods. Code: https://github.com/hust-linyi/bonus.

Nuclear protein trafficking requires the soluble transport factor RanBP1. The subcellular distribution of RanBP1 is dynamic, as the protein shuttles between the nucleus and cytoplasm. To date, the signaling pathways regulating RanBP1 subcellular localization are poorly understood. During interphase, RanBP1 resides mostly in the cytoplasm. We show here that oxidative stress concentrates RanBP1 in the nucleus, and our study defines the underlying mechanisms. Specifically, RanBP1's cysteine residues are not essential for its oxidant-induced relocation. Furthermore, our pharmacological approaches uncover that signaling mediated by epidermal growth factor receptor (EGFR) and protein kinase A (PKA) control RanBP1 localization during stress. In particular, pharmacological inhibitors of EGFR or PKA diminish the oxidant-dependent relocation of RanBP1. Mutant analysis identified serine 60 and tyrosine 103 as regulators of RanBP1 nuclear accumulation during oxidant exposure. Taken together, our results define RanBP1 as a target of oxidative stress and a downstream effector of EGFR and PKA signaling routes. This positions RanBP1 at the intersection of important cellular signaling circuits., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier GmbH.. All rights reserved.)

The best-known function of the essential GPN-loop GTPase Gpn3 is to contribute to RNA polymerase II assembly, a prerequisite for its nuclear targeting. Although this process occurs in the cytoplasm, we have previously shown that Gpn3 enters the cell nucleus before being polyubiquitinated. Here, we show that inhibiting Crm1-mediated nuclear export with leptomycin B, or the proteasome with MG132, caused the nuclear accumulation of recombinant and endogenous Gpn3 in MCF-12A cells. When added simultaneously, leptomycin B and MG132 had an additive effect. Analysis of Gpn3 primary sequence revealed the presence of at least five nuclear export sequence (NES) motifs, with some having a higher exposure to the solvent in the GTP-bound than GDP-bound state in a Gpn3 structural model. Inactivation of any of these NESes led to some degree of Gpn3 nuclear accumulation, although mutating NES1 or NES3 had the more robust effect. MCF-12A cells expressing exclusively a NES-deficient version of Gpn3R-Flag proliferated slower than cells expressing Gpn3R-Flag wt, indicating that nuclear export is important for Gpn3 function. Next, we searched for physiological conditions regulating Gpn3 nucleocytoplasmic shuttling. Interestingly, whereas Gpn3R-Flag was both nuclear and cytoplasmic in low-density growing MCF-12A cells, it was exclusively cytoplasmic in high-density areas. Furthermore, Gpn3R-Flag was cytoplasmic, mostly perinuclear, in sparse but starved MCF-12A cells, and serum-stimulation caused a rapid, although transient, Gpn3R-Flag nuclear accumulation. We conclude that Gpn3 nucleocytoplasmic shuttling is regulated by cell density and growth factors, and propose that Gpn3 has an unknown nuclear function positively linked to cell growth and/or proliferation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Despite their superior performance, deep-learning methods often suffer from the disadvantage of needing large-scale well-annotated training data. In response, recent literature has seen a proliferation of efforts aimed at reducing the annotation burden. This paper focuses on a weakly-supervised training setting for single-cell segmentation models, where the only available training label is the rough locations of individual cells. The specific problem is of practical interest due to the widely available nuclei counter-stain data in biomedical literature, from which the cell locations can be derived programmatically. Of more general interest is a proposed self-learning method called collaborative knowledge sharing, which is related to but distinct from the more well-known consistency learning methods. This strategy achieves self-learning by sharing knowledge between a principal model and a very light-weight collaborator model. Importantly, the two models are entirely different in their architectures, capacities, and model outputs: In our case, the principal model approaches the segmentation problem from an object-detection perspective, whereas the collaborator model a sematic segmentation perspective. We assessed the effectiveness of this strategy by conducting experiments on LIVECell, a large single-cell segmentation dataset of bright-field images, and on A431 dataset, a fluorescence image dataset in which the location labels are generated automatically from nuclei counter-stain data. Implementing code is available at https://github.com/jiyuuchc/lacss.

Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for the high nuclei density. To alleviate the annotation burden, we seek to solve the problem through image-level weakly supervised learning, which is underexplored for nuclei instance segmentation. Compared with most existing methods using other weak annotations (scribble, point, etc.) for nuclei instance segmentation, our method is more labor-saving. The obstacle to using image-level annotations in nuclei instance segmentation is the lack of adequate location information, leading to severe nuclei omission or overlaps. In this paper, we propose a novel image-level weakly supervised method, called cyclic learning, to solve this problem. Cyclic learning comprises a front-end classification task and a back-end semi-supervised instance segmentation task to benefit from multi-task learning (MTL). We utilize a deep learning classifier with interpretability as the front-end to convert image-level labels to sets of high-confidence pseudo masks and establish a semi-supervised architecture as the back-end to conduct nuclei instance segmentation under the supervision of these pseudo masks. Most importantly, cyclic learning is designed to circularly share knowledge between the front-end classifier and the back-end semi-supervised part, which allows the whole system to fully extract the underlying information from image-level labels and converge to a better optimum. Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods.

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segmentation Network (NISNet3D) that directly segments 3D volumes through the use of a modified 3D U-Net, 3D marker-controlled watershed transform, and a nuclei instance segmentation system for separating touching nuclei. NISNet3D is unique in that it provides accurate segmentation of even challenging image volumes using a network trained on large amounts of synthetic nuclei derived from relatively few annotated volumes, or on synthetic data obtained without annotated volumes. We present a quantitative comparison of results obtained from NISNet3D with results obtained from a variety of existing nuclei segmentation techniques. We also examine the performance of the methods when no ground truth is available and only synthetic volumes were used for training., (© 2023. The Author(s).)

The morphology of the nuclei represents most of the clinical pathological information, and nuclei segmentation is a vital step in current automated histopathological image analysis. Supervised machine learning-based segmentation models have already achieved outstanding performance with sufficiently precise human annotations. Nevertheless, outlining such labels on numerous nuclei is extremely professional needing and time consuming. Automatic nuclei segmentation with minimal manual interventions is highly needed to promote the effectiveness of clinical pathological researches. Semi-supervised learning greatly reduces the dependence on labeled samples while ensuring sufficient accuracy. In this paper, we propose a Multi-Edge Feature Fusion Attention Network (MEFFA-Net) with three feature inputs including image, pseudo-mask and edge, which enhances its learning ability by considering multiple features. Only a few labeled nuclei boundaries are used to train annotations on the remaining mostly unlabeled data. The MEFFA-Net creates more precise boundary masks for nucleus segmentation based on pseudo-masks, which greatly reduces the dependence on manual labeling. The MEFFA-Block focuses on the nuclei outline and selects features conducive to segment, making full use of the multiple features in segmentation. Experimental results on public multi-organ databases including MoNuSeg, CPM-17 and CoNSeP show that the proposed model has the mean IoU segmentation evaluations of 0.706, 0.751, and 0.722, respectively. The model also achieves better results than some cutting-edge methods while the labeling work is reduced to 1/8 of common supervised strategies. Our method provides a more efficient and accurate basis for nuclei segmentations and further quantifications in pathological researches., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised and semi-supervised learning methods have been studied for nuclei segmentation, very few works have delved into the selective labeling of samples to reduce the workload of annotation. Thus, in this paper, we propose a novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentation in a semi-supervised manner. In the proposed framework, we first develop a novel consistency-based patch selection method to determine which image patches are the most beneficial to the training. Then we introduce a conditional single-image GAN with a component-wise discriminator, to synthesize more training samples. Lastly, our proposed framework trains an existing segmentation model with the above augmented samples. The experimental results show that our proposed method could obtain the same-level performance as a fully-supervised baseline by annotating less than 5% pixels on some benchmarks.

Accurate segmentation is a basic and crucial step for medical image processing and analysis. In the last few years, U-Net, and its variants, have become widely adopted models in medical image segmentation tasks. However, the multiple training parameters of these models determines high computation complexity, which is impractical for further applications. In this paper, by introducing depthwise separable convolution and attention mechanism into U-shaped architecture, we propose a novel lightweight neural network (DSCA-Net) for medical image segmentation. Three attention modules are created to improve its segmentation performance. Firstly, Pooling Attention (PA) module is utilized to reduce the loss of consecutive down-sampling operations. Secondly, for capturing critical context information, based on attention mechanism and convolution operation, we propose Context Attention (CA) module instead of concatenation operations. Finally, Multiscale Edge Attention (MEA) module is used to emphasize multi-level representative scale edge features for final prediction. The number of parameters in our network is 2.2 M, which is 71.6% less than U-Net. Experiment results across four public datasets show the potential and the dice coefficients are improved by 5.49% for ISIC 2018, 4.28% for thyroid, 1.61% for lung and 9.31% for nuclei compared with U-Net.

Automated nuclei detection is crucial prerequisites for a number of histopathology related image analysis such as cancer diagnosis. Although existing deep learning based nuclei detection methods have achieved promising results, they cannot effectively deal with domain shift problem caused by different staining procedures and organ specific nuclear morphology. To handle this problem, in this paper a novel adversarial feature alignment method is proposed for domain adaptive nuclei detection, which includes both global alignment and local attentional alignment components to transfer the knowledge from source domain to target domain. Specifically, in local attentional alignment component, by using nuclei locations as guidance we extract local features and perform adversarial alignment. Furthermore, to address the issue that these local features from nuclei regions often contain insufficient information because of the small size of nuclei, we introduce an efficient location-aware self-attention (LocSA) module to refine local features by utilizing cues from all nuclei for obtaining discriminative features to perform successful feature alignment. Extensive experimental results are provided on two adaptation scenarios and our method demonstrates favorable performance against existing domain adaptation methods, which highlights the effectiveness of the proposed method for domain adaptive nuclei detection., Competing Interests: Declaration of competing interest None., (Copyright © 2022. Published by Elsevier B.V.)

Cryoelectron tomography of the cell nucleus using scanning transmission electron microscopy and deconvolution processing technology has highlighted a large-scale, 100- to 300-nm interphase chromosome structure, which is present throughout the nucleus. This study further documents and analyzes these chromosome structures. The paper is divided into four parts: 1) evidence (preliminary) for a unified interphase chromosome structure; 2) a proposed unified interphase chromosome architecture; 3) organization as chromosome territories (e.g., fitting the 46 human chromosomes into a 10-μm-diameter nucleus); and 4) structure unification into a polytene chromosome architecture and lampbrush chromosomes. Finally, the paper concludes with a living light microscopy cell study showing that the G1 nucleus contains very similar structures throughout. The main finding is that this chromosome structure appears to coil the 11-nm nucleosome fiber into a defined hollow structure, analogous to a Slinky helical spring [https://en.wikipedia.org/wiki/Slinky; motif used in Bowerman et al. , eLife 10, e65587 (2021)]. This Slinky architecture can be used to build chromosome territories, extended to the polytene chromosome structure, as well as to the structure of lampbrush chromosomes.

This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on GitHub and the method is available as a service through the BisQue portal.

Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g., glomeruli), which play an essential role in renal pathology. In this paper, we propose a simple circle representation for medical object detection and introduce CircleNet, an anchor-free detection framework. Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance and be more rotation-invariant, compared with the bounding box. The code has been made publicly available: https://github.com/hrlblab/CircleNet.

Nucleus segmentation of fluorescence microscopy is a critical step in quantifying measurements in cell biology. Automatic and accurate nucleus segmentation has powerful applications in analyzing intrinsic characterization in nucleus morphology. However, existing methods have limited capacity to perform accurate segmentation in challenging samples, such as noisy images and clumped nuclei. In this paper, inspired by the idea of cascaded U-Net (or W-Net) and its remarkable performance improvement in medical image segmentation, we proposed a novel framework called Attention-enhanced Simplified W-Net (ASW-Net), in which a cascade-like structure with between-net connections was used. Results showed that this lightweight model could reach remarkable segmentation performance in the BBBC039 testing set (aggregated Jaccard index, 0.90). In addition, our proposed framework performed better than the state-of-the-art methods in terms of segmentation performance. Moreover, we further explored the effectiveness of our designed network by visualizing the deep features from the network. Notably, our proposed framework is open source.

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.

Tumour progression within the tissue microenvironment is accompanied by complex biomechanical alterations of the extracellular environment. While histopathology images provide robust biochemical markers for tumor progression in clinical settings, a quantitative single cell score using nuclear morphology and chromatin organization integrated with the long range mechanical coupling within the tumor microenvironment is missing. We propose that the spatial chromatin organization in individual nuclei characterises the cell state and their alterations during tumor progression. In this paper, we first built an image analysis pipeline and implemented it to classify nuclei from patient derived breast tissue biopsies of various cancer stages based on their nuclear and chromatin features. Replacing H&E with DNA binding dyes such as Hoescht stained tissue biopsies, we improved the classification accuracy. Using the nuclear morphology and chromatin organization features, we constructed a pseudo-time model to identify the chromatin state changes that occur during tumour progression. This enabled us to build a single-cell mechano-genomic score that characterises the cell state during tumor progression from a normal to a metastatic state. To gain further insights into the alterations in the local tissue microenvironments, we also used the nuclear orientations to identify spatial neighbourhoods that have been posited to drive tumor progression. Collectively, we demonstrate that image-based single cell chromatin and nuclear features are important single cell biomarkers for phenotypic mapping of tumor progression., (© 2021. The Author(s).)

Organelles possess critical biological effects in cellular processes. However, the relationship between organelle targeting and antitumour activity is a challenging issue. In this paper, a number of amide/acylhydrazine modified naphthalimide derivatives were designed and synthesized. Interestingly, amide modified naphthalimide derivatives NI-A-NH and NI-C-NH with (R)-piperdine and (S)-pyrrolidine functionalization exhibited enhanced cytotoxicity compared with acylhydrazine modified derivatives NI-A-2NH and NI-C-2NH. However, acylhydrazine modified derivatives NI-B-2NH and NI-D-2NH with (S)-piperdine and achiral piperdine conjugates possessed better cytotoxicity than NI-B-NH and NI-D-NH with amide modifications. Fluorescence imaging, DNA binding interactions and cell cycle analyses were further completed to clarify that the nucleus-targeting effects showed enhanced cytotoxic activity, strong DNA binding and the blocking of cells in S phase. These results provide a preliminary theoretical basis for the further design of organelle-targeting antitumour drugs., (Copyright © 2021. Published by Elsevier Inc.)