Nuclear structure in cancer cells

PML Nuclear bodies: the cancer connection and beyond

Promyelocytic leukemia (PML) nuclear bodies, membrane-less organelles in the nucleus, play a crucial role in cellular homeostasis. These dynamic structures result from the assembly of scaffolding PML proteins and various partners. Recent crystal structure analyses revealed essential self-interacting domains, while liquid-liquid phase separation contributes to their formation. PML bodies orchestrate post-translational modifications, particularly stress-induced SUMOylation, impacting target protein functions. Serving as hubs in multiple signaling pathways, they influence cellular processes like senescence. Dysregulation of PML expression contributes to diseases, including cancer, highlighting their significance. Therapeutically, PML bodies are promising targets, exemplified by successful acute promyelocytic leukemia treatment with arsenic trioxide and retinoic acid restoring PML bodies. Understanding their functions illuminates both normal and pathological cellular physiology, guiding potential therapies. This review explores recent advancements in PML body biogenesis, biochemical activity, and their evolving biological roles.

Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS

Ductal carcinoma in situ (DCIS) is a pre-invasive tumor that can progress to invasive breast cancer, a leading cause of cancer death. We generate a large-scale tissue microarray dataset of chromatin images, from 560 samples from 122 female patients in 3 disease stages and 11 phenotypic categories. Using representation learning on chromatin images alone, without multiplexed staining or high-throughput sequencing, we identify eight morphological cell states and tissue features marking DCIS. All cell states are observed in all disease stages with different proportions, indicating that cell states enriched in invasive cancer exist in small fractions in normal breast tissue. Tissue-level analysis reveals significant changes in the spatial organization of cell states across disease stages, which is predictive of disease stage and phenotypic category. Taken together, we show that chromatin imaging represents a powerful measure of cell state and disease stage of DCIS, providing a simple and effective tumor biomarker.

Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning

BACKGROUND

The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application.

METHODS

We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations.

RESULTS

The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration.

CONCLUSIONS

We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.

Nucleolin acute degradation reveals novel functions in cell cycle progression and division in TNBC

Nucleoli are large nuclear sub-compartments where vital processes, such as ribosome assembly, take place. Technical obstacles still limit our understanding of the biological functions of nucleolar proteins in cell homeostasis and cancer pathogenesis. Since most nucleolar proteins are essential, their abrogation cannot be achieved through conventional approaches. Additionally, the biological activities of many nucleolar proteins are connected to their physiological concentration. Thus, artificial overexpression might not fully recapitulate their endogenous functions. Proteolysis-based approaches, such as the Auxin Inducible Degron (AID) system paired with CRISPR/Cas9 knock-in gene-editing, have the potential to overcome these limitations, providing unprecedented characterization of the biological activities of endogenous nucleolar proteins. We applied this system to endogenous nucleolin (NCL), one of the most abundant nucleolar proteins, and characterized the impact of its acute depletion on Triple-Negative Breast Cancer (TNBC) cell behavior. Abrogation of endogenous NCL reduced proliferation and caused defective cytokinesis, resulting in bi-nucleated tetraploid cells. Bioinformatic analysis of patient data, and quantitative proteomics using our experimental NCL-depleted model, indicated that NCL levels are correlated with the abundance of proteins involved in chromosomal segregation. In conjunction with its effects on sister chromatid dynamics, NCL abrogation enhanced the anti-proliferative effects of chemical inhibitors of mitotic modulators such as the Anaphase Promoting Complex. In summary, using the AID system in combination with CRISPR/Cas9 for endogenous gene editing, our findings indicate a novel role for NCL in supporting the completion of the cell division in TNBC models, and that its abrogation could enhance the therapeutic activity of mitotic-progression inhibitors.

Coaching ribosome biogenesis from the nuclear periphery

Severe invagination of the nuclear envelope is a hallmark of cancers, aging, neurodegeneration, and infections. However, the outcomes of nuclear invagination remain unclear. This work identified a new function of nuclear invagination: regulating ribosome biogenesis. With expansion microscopy, we observed frequent physical contact between nuclear invaginations and nucleoli. Surprisingly, the higher the invagination curvature, the more ribosomal RNA and pre-ribosomes are made in the contacted nucleolus. By growing cells on nanopillars that generate nuclear invaginations with desired curvatures, we can increase and decrease ribosome biogenesis. Based on this causation, we repressed the ribosome levels in breast cancer and progeria cells by growing cells on low-curvature nanopillars, indicating that overactivated ribosome biogenesis can be rescued by reshaping nuclei. Mechanistically, high-curvature nuclear invaginations reduce heterochromatin and enrich nuclear pore complexes, which promote ribosome biogenesis. We anticipate that our findings will serve as a foundation for further studies on nuclear deformation.

NIR-emissive carbon nanodots as a tool to mark ribosomal RNA and nucleolus components using super-resolution microscopy

Ribosomal RNA (rRNA) plays a key role in protein synthesis and ribosomal biogenesis. The exclusively used commercial dye for RNA staining is SYTO RNASelect, which works in fixed cells only. To overcome this constraint, we synthesized NIR-emissive, highly photostable, and biocompatible carbon nanodots (CNDs) as a fluorescent biomarker for rRNA. The synthesized CNDs could stain rRNA in both live and fixed cells. We were able to visualize rRNA at different sites in eukaryotic cells using super-resolution microscopy (SRM). The CNDs localized rRNA in the dense fibrillar components (DFCs) of the nucleolus, nuclear membrane, and rough endoplasmic reticulum (RER). The super-resolved hollow ring-structured DFC with an FWHM of 140 nm, nuclear membrane with an FWHM of 120 nm, and ER with an FWHM of 115 nm were observed. We further found a marked contrast between the pre-RNA synthesized in cancer cells and normal cells. We believe that these CNDs have great potential in rRNA imaging and comprehending the complex relationships between rRNA dynamics and basic biological processes, disease development, or drug interactions.

AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

Nuclear matrix protein 22 in bladder cancer

Bladder cancer (BC) is ranked as the ninth most common malignancy worldwide, with approximately 570,000 new cases reported annually and over 200,000 deaths. Cystoscopy remains the gold standard for the diagnosis of BC, however, its invasiveness, cost, and discomfort have driven the demand for the development of non-invasive, cost-effective alternatives. Nuclear matrix protein 22 (NMP22) is a promising non-invasive diagnostic tool, having received FDA approval. Traditional methods for detecting NMP22 require a laboratory environment equipped with specialized equipment and trained personnel, thus, the development of NMP22 detection devices holds substantial potential for application. In this review, we evaluate the NMP22 sensors developed over the past decade, including electrochemical, colorimetric, and fluorescence biosensors. These sensors have enhanced detection sensitivity and overcome the limitations of existing diagnostic methods. However, many emerging devices exhibit deficiencies that limit their potential clinical use, therefore, we propose how sensor design can be optimized to enhance the likelihood of clinical translation and discuss the future applications of NMP22 as a legacy biomarker, providing insights for the design of new sensors.

Size Scaling of Condensates in Multicomponent Phase Separation

Constant proportionalities between cells and their intracellular organelles have been widely observed in various types of cells, known as intracellular size scaling. However, the mechanism underlying the size scaling and its modulation by environmental factors in multicomponent systems remain poorly understood. Here, we study the size scaling of membrane-less condensates using microdroplet-encapsulated minimalistic condensates formed by droplet microfluidics and mean-field theory. We demonstrate that the size scaling of condensates is an inherent characteristic of liquid-liquid phase separation. This concept is supported by experiments showing the occurrence of size scaling phenomena in various condensate systems and a generic lever rule acquired from mean-field theory. Moreover, it is found that the condensate-to-microdroplet scaling ratio can be affected by the solute and salt concentrations, with good agreement between experiments and predictions by theory. Notably, we identify a noise buffering mechanism whereby condensates composed of large macromolecules effectively maintain constant volumes and counteract concentration fluctuations of small molecules. This mechanism is achieved through the dynamic rearrangement of small molecules in and out of membrane-free interfaces. Our work provides crucial insights into understanding mechanistic principles that govern the size of cells and intracellular organelles as well as associated biological functions.

Polymer physics view of peripheral chromatin: de Gennes' self-similar carpet

Using scaling arguments to model peripheral chromatin localized near the inner surface of the nuclear envelope (NE) as a flexible polymer chain, we discuss the structural properties of the peripheral chromatin composed of alternating lamin-associated domains (LADs) and inter-LADs. Modeling the attraction of LADs to NE by de Gennes' self-similar carpet, which treats the chromatin layer as a polymer fractal, explains two major experimental observations. (i) The high density of chromatin close to the nuclear periphery decays to a constant density as the distance to the periphery increases. (ii) Due to the decreasing mesh size towards the nuclear periphery, the chromatin carpet inside NE excludes molecules (via nonspecific interactions) above a threshold size that depends on the distance from the nuclear periphery.

Regulation of Chromatin Modifications through Coordination of Nucleus Size and Epithelial Cell Morphology Heterogeneity

Cell morphology heterogeneity within epithelial collectives is a pervasive phenomenon intertwined with tissue mechanical properties. Despite its widespread occurrence, the underlying mechanisms driving cell morphology heterogeneity and its consequential biological ramifications remain elusive. Here, we investigate the dynamic evolution of epithelial cell morphology and nucleus morphology during crowding, unveiling a consistent correlation between the two. Our investigation reveals a persistent log-normal probability distribution characterizing both cell and nucleus areas across diverse crowding stages and epithelial model systems. We showed that this morphological diversity arises from asymmetric partitioning during cell division and is perpetuated through actomyosin-mediated regulation of cell-nucleus size coordination. Moreover, we provide insights into the impact of nucleus morphology on chromatin dynamics, demonstrating that constraining nucleus area leads to downregulation of the euchromatic mark H3K9ac and upregulation of the heterochromatic mark H3K27me3 through modulation of histone demethylase UTX expression. These findings under-score the significance of cell morphology heterogeneity as a driver of chromatin state diversity, shaping functional variability within epithelial tissues.

Nucleolar stress caused by arginine-rich peptides triggers a ribosomopathy and accelerates aging in mice

Nucleolar stress (NS) has been associated with age-related diseases such as cancer or neurodegeneration. To investigate how NS triggers toxicity, we used (PR)n arginine-rich peptides present in some neurodegenerative diseases as inducers of this perturbation. We here reveal that whereas (PR)n expression leads to a decrease in translation, this occurs concomitant with an accumulation of free ribosomal (r) proteins. Conversely, (PR)n-resistant cells have lower rates of r-protein synthesis, and targeting ribosome biogenesis by mTOR inhibition or MYC depletion alleviates (PR)n toxicity in vitro. In mice, systemic expression of (PR)97 drives widespread NS and accelerated aging, which is alleviated by rapamycin. Notably, the generalized accumulation of orphan r-proteins is a common outcome of chemical or genetic perturbations that induce NS. Together, our study presents a general model to explain how NS induces cellular toxicity and provides in vivo evidence supporting a role for NS as a driver of aging in mammals.

Alterations in the chromatin packaging, driven by transcriptional activity, revealed by AFM

BACKGROUND

The gene expression differs in the nuclei of normal and malignant mammalian cells, and transcription is a critical initial step, which defines the difference. The mechanical properties of transcriptionally active chromatin are still poorly understood. Recently we have probed transcriptionally active chromatin of the nuclei subjected to mechanical stress, by Atomic Force Microscopy (AFM) [1]. Nonetheless, a systematic study of the phenomenon is needed.

METHODS

Nuclei were deformed and studied by AFM. Non-deformed nuclei were studied by fluorescence confocal microscopy. Their transcriptional activity was studied by RNA electrophoresis.

RESULTS

The malignant nuclei under the study were stable to deformation and assembled of 100-300 nm beads-like units, while normal cell nuclei were prone to deformation. The difference in stability to deformation of the nuclei correlated with DNA supercoiling, and transcription-depended units were responsive to supercoils breakage. The inhibitors of the topoisomerases I and II disrupted supercoiling and made the malignant nucleus prone to deformation. Cell nuclei treatment with histone deacetylase inhibitors (HDACIs) preserved the mechanical stability of deformed malignant nuclei and, at the same time, made it possible to observe chromatin decondensation up to 20-60 nm units. The AFM results were supplemented with confocal microscopy and RNA electrophoresis data.

CONCLUSIONS

Self-assembly of transcriptionally active chromatin and its decondensation, driven by DNA supercoiling-dependent rigidity, was visualized by AFM in the mechanically deformed nuclei.

GENERAL SIGNIFICANCE

We demonstrated that supercoiled DNA defines the transcription mechanics, and hypothesized the nuclear mechanics in vivo should depend on the chromatin architecture.

Live Cell Imaging and Real-Time Monitoring of Nucleolus Morphology and Mitophagy with a Red Fluorescent and Photostable rRNA-Specific Probe in Human Cancer Cells

rRNAs are prevalent in living organisms. They are produced in nucleolus and mitochondria and play essential cellular functions. In addition to the primary biofunction in protein synthesis, rRNAs have been recognized as the emerging signaling molecule and drug target for studies on nucleolus morphology, mitochondrial autophagy, and tumor cell malignancy. Currently, only a few rRNA-selective probes have been developed, and most of them encounter the drawbacks of low water solubility, poor nuclear membrane permeability, short emission wavelength, low stability against photobleaching, and high cytotoxicity. These unfavorable properties of rRNA probes limit their potential applications. In the present study, we reported a new rRNA-selective and near-infrared fluorescent turn-on probe, 4MPS-TO, capable of tracking rRNA in live human cancer cells. The real-time monitoring performance in nucleolus morphology and mitochondrial autophagy is demonstrated in HeLa cells. The probe shows great application potential for being used as a rRNA-selective, sensitive, and photostable imaging tool in chemical biology study and drug screening.

Super-Resolution Microscopy Unveils Synergistic Structural Changes of Organelles Upon Point Mutation

Ethyl methanesulphonate (EMS), is a widely used chemical mutagen that causes high-frequency germline null mutation by inserting an alkyl group into the nucleotide guanine in eukaryotic cells. The effect of EMS on the dynamics of the aneuploid genome, increased cellular instability, and carcinogenicity in relation to benign and malignant tumors are reported, but the molecular level understanding of morphological changes of higher-order chromatin structure has poorly been understood. This is due to a lack of sufficient resolution in conventional microscopic techniques to see small structures below the diffraction limit. Here, using super-resolution radial fluctuation, a largely fragmented, decompaction, and less dense heterochromatin structure upon EMS treatment to HEK 293A cells without any change in nuclear DNA domains is observed. This result suggests an early stage of carcinogenicity happened due to the point mutation. In addition, the distinct structural changes with an elongated morphology of lysosomes are also observed. On the other hand, fragmented and increased heterogeneous populations with an increased cytoplasmic occupancy of mitochondria are observed.

Cancer-Specific Alterations in Nuclear Matrix Proteins Determined by Multi-omics Analyses of Ductal Carcinoma in Situ

Breast cancer (BC) is the most common cancer affecting women in the United States. Ductal carcinoma in situ (DCIS) is the earliest identifiable pre-invasive BC lesion. Estimates show that 14 to 50% of DCIS cases progress to invasive BC. Our objective was to identify nuclear matrix proteins (NMP) with specifically altered expression in DCIS and later stages of BC compared to non-diseased breast reduction mammoplasty and a contralateral breast explant using mass spectrometry and RNA sequencing to accurately identify aggressive DCIS. Sixty NMPs were significantly differentially expressed between the DCIS and non-diseased breast epithelium in an isogenic contralateral pair of patient-derived extended explants. Ten of the sixty showed significant mRNA expression level differences that matched the protein expression. These 10 proteins were similarly expressed in non-diseased breast reduction cells. Three NMPs (RPL7A, RPL11, RPL31) were significantly upregulated in DCIS and all other BC stages compared to the matching contralateral breast culture and an unrelated non-diseased breast reduction culture. RNA sequencing analyses showed that these three genes were upregulated increasingly with BC progression. Finally, we identified three NMPs (AHNAK, CDC37 and DNAJB1) that were significantly downregulated in DCIS and all other BC stages compared to the isogenically matched contralateral culture and the non-diseased breast reduction culture using both proteomics and RNA sequencing techniques.

Reprogramming nucleolar size by genetic perturbation of the extranuclear Rab GTPases Ypt6 and Ypt32

Reprogramming organelle size has been proposed as a potential therapeutic approach. However, there have been few reports of nucleolar size reprogramming. We addressed this question in Saccharomyces cerevisiae by studying mutants having opposite effects on the nucleolar size. Mutations in genes involved in nuclear functions (KAR3, CIN8, and PRP45) led to enlarged nuclei/nucleoli, whereas mutations in secretory pathway family genes, namely the Rab-GTPases YPT6 and YPT32, reduced nucleolar size. When combined with mutations leading to enlarged nuclei/nucleoli, the YPT6 or YPT32 mutants can effectively reprogram the nuclear/nucleolar size almost back to normal. Our results further indicate that null mutation of YPT6 causes secretory stress that indirectly influences nuclear localization of Maf1, the negative regulator of RNA Polymerase III, which might reduce the nucleolar size by inhibiting nucleolar transcript enrichment.

Electromechanical interactions between cell membrane and nuclear envelope: Beyond the standard Schwan's model of biological cells

We investigate little-appreciated features of the hierarchical core-shell (CS) models of the electrical, mechanical, and electromechanical interactions between the cell membrane (CM) and nuclear envelope (NE). We first consider a simple model of an individual cell based on a coupled resistor-capacitor (Schwan model (SM)) network and show that the CM, when exposed to ac electric fields, acts as a low pass filter while the NE acts as a wide and asymmetric bandpass filter. We provide a simplified calculation for characteristic time associated with the capacitive charging of the NE and parameterize its range of behavior. We furthermore observe several new features dealing with mechanical analogs of the SM based on elementary spring-damper combinations. The chief merit of these models is that they can predict creep compliance responses of an individual cell under static stress and their effective retardation time constants. Next, we use an alternative and a more accurate CS physical model solved by finite element simulations for which geometrical cell reshaping under electromechanical stress (electrodeformation (ED)) is included in a continuum approach with spatial resolution. We show that under an electric field excitation, the elongated nucleus scales differently compared to the electrodeformed cell.

Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca2+ imaging and electrophysiology

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.

Coordinated in confined migration: crosstalk between the nucleus and ion channel-mediated mechanosensation

Cell surface and intracellular mechanosensors enable cells to perceive different geometric, topographical, and physical cues. Mechanosensitive ion channels (MICs) localized at the cell surface and on the nuclear envelope (NE) are among the first to sense and transduce these signals. Beyond compartmentalizing the genome of the cell and its transcription, the nucleus also serves as a mechanical gauge of different physical and topographical features of the tissue microenvironment. In this review, we delve into the intricate mechanisms by which the nucleus and different ion channels regulate cell migration in confinement. We review evidence suggesting an interplay between macromolecular nuclear-cytoplasmic transport (NCT) and ionic transport across the cell membrane during confined migration. We also discuss the roles of the nucleus and ion channel-mediated mechanosensation, whether acting independently or in tandem, in orchestrating migratory mechanoresponses. Understanding nuclear and ion channel sensing, and their crosstalk, is critical to advancing our knowledge of cell migration in health and disease.

"Light-On" Fluorescent Nanoprobes for Monitoring Dynamic Distribution of Cellular Nucleolin During Pyroptosis

Nucleolin (NCL) is a multifunctional nuclear protein that plays significant roles in regulating physiological activities of the cells. However, it remains a challenge to monitor the dynamic distribution and expression of nucleolin within living cells during cell stress processes directly. Here, we designed "turn-on" fluorescent nanoprobes composed of specific AS1411 aptamer and nucleus-targeting peptide on gold nanoparticles (AuNPs) to effectively capture and track the NCL distribution and expression during pyroptosis triggered by electrical stimulation (ES). The distribution of nucleolin in the cell membrane and nucleus can be easily observed by simply changing the particle size of the nanoprobes. The present strategy exhibits obvious advantages such as simple operation, low cost, time saving, and suitability for living cell imaging. The ES can induce cancer cell pyroptosis controllably and selectively, with less harm to the viability of normal cells. The palpable cell nuclear stress responses of cancerous cells, including nucleus wrinkling and nucleolus fusion after ES at 1.0 V were obviously observed. Compared with normal cells (MCF-10A), NCL is overexpressed within cancerous cells (MCF-7 cells) using the as-designed nanoprobes, and the ES can effectively inhibit NCL expression within cancerous cells. The developed NCL sensing platform and ES-based methods hold great potential for cellular studies of cancer-related diseases.

Deep-learning-assisted snapshot optical tomography for microscopic volume prediction: a simulation study

In this simulation study, we demonstrate fast-yet-accurate volume measurement of microscopic objects by combining snapshot optical tomography and deep learning. Snapshot optical tomography simultaneously collects a multitude of projection images and thus can perform 3D imaging in a single snapshot. However, as with other wide-field microscopy techniques, it suffers from the missing-cone problem, which can seriously degrade the quality of 3D reconstruction. We use deep learning to generate a volume prediction from 2D projection images bypassing the 3D reconstruction.

Nuclear growth and import can be uncoupled

What drives nuclear growth? Studying nuclei assembled in Xenopus egg extract and focusing on importin α/β-mediated nuclear import, we show that, while import is required for nuclear growth, nuclear growth and import can be uncoupled when chromatin structure is manipulated. Nuclei treated with micrococcal nuclease to fragment DNA grew slowly despite exhibiting little to no change in import rates. Nuclei assembled around axolotl chromatin with 20-fold more DNA than Xenopus grew larger but imported more slowly. Treating nuclei with reagents known to alter histone methylation or acetylation caused nuclei to grow less while still importing to a similar extent or to grow larger without significantly increasing import. Nuclear growth but not import was increased in live sea urchin embryos treated with the DNA methylator N-nitrosodimethylamine. These data suggest that nuclear import is not the primary driving force for nuclear growth. Instead, we observed that nuclear blebs expanded preferentially at sites of high chromatin density and lamin addition, whereas small Benzonase-treated nuclei lacking DNA exhibited reduced lamin incorporation into the nuclear envelope. In summary, we report experimental conditions where nuclear import is not sufficient to drive nuclear growth, hypothesizing that this uncoupling is a result of altered chromatin structure.

Specific Polo-Like Kinase 1 Expression in Nodular Lymphocyte-Predominant Hodgkin Lymphoma Suggests an Intact Immune Surveillance Program

Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare and relatively indolent B-cell lymphoma. Characteristically, the [lymphocyte-predominant (LP)] tumor cells are embedded in a microenvironment enriched in lymphocytes. More aggressive variants of mature B-cell and peripheral T-cell lymphomas exhibit nuclear expression of the polo-like kinase 1 (PLK1) protein, stabilizing MYC (alias c-myc) and associated with worse clinical outcomes. This study demonstrated expression of PLK1 in the LP cells in 100% of NLPHL cases (n = 76). In contrast, <5% of classic Hodgkin lymphoma cases (n = 70) showed PLK1 expression within the tumor cells. Loss-of-function approaches demonstrated that the expression of PLK1 promoted cell proliferation and increased MYC stability in NLPHL cell lines. Correlation with clinical parameters revealed that the increased expression of PLK1 was associated with advanced-stage disease in patients with NLPHL. A multiplex immunofluorescence panel coupled with artificial intelligence algorithms was used to correlate the composition of the tumor microenvironment with the proliferative stage of LP cells. The results showed that LP cells with PLK1 (high) expression were associated with increased numbers of cytotoxic and T-regulatory T cells. Overall, the findings demonstrate that PLK1 signaling increases NLPHL proliferation and constitutes a potential vulnerability that can be targeted with PLK1 inhibitors. An active immune surveillance program in NLPHL may be a critical mechanism limiting PLK1-dependent tumor growth.

Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning

Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations

Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task.However, analyzing them is now a bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, and semi-supervised learning as well as the next steps for the mitigation of the manual segmentation bottleneck.

AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma

BACKGROUND

Locoregional recurrence of nasopharyngeal carcinoma (NPC) occurs in 10% to 50% of cases following primary treatment. However, the current main prognostic markers for NPC, both stage and plasma Epstein-Barr virus DNA, are not sensitive to locoregional recurrence.

METHODS

We gathered 385 whole-slide images (WSIs) from haematoxylin and eosin (H&E)-stained NPC sections (n = 367 cases), which were collected from Sun Yat-sen University Cancer Centre. We developed a deep learning algorithm to detect tumour nuclei and lymphocyte nuclei in WSIs, followed by density-based clustering to quantify the tumour-infiltrating lymphocytes (TILs) into 12 scores. The Random Survival Forest model was then trained on the TILs to generate risk score.

RESULTS

Based on Kaplan-Meier analysis, the proposed methods were able to stratify low- and high-risk NPC cases in a validation set of locoregional recurrence with a statically significant result (p < 0.001). This finding was also found in distant metastasis-free survival (p < 0.001), progression-free survival (p < 0.001), and regional recurrence-free survival (p < 0.05). Furthermore, in both univariate analysis (HR: 1.58, CI: 1.13-2.19, p < 0.05) and multivariate analysis (HR:1.59, CI: 1.11-2.28, p < 0.05), we also found that our methods demonstrated a strong prognostic value for locoregional recurrence.

CONCLUSION

The proposed novel digital markers could potentially be utilised to assist treatment decisions in cases of NPC.

Beyond the surface: Investigation of tumorsphere morphology using volume electron microscopy

The advent of volume electron microscopy (vEM) has provided unprecedented insights into cellular and subcellular organization, revolutionizing our understanding of cancer biology. This study presents a previously unexplored comparative analysis of the ultrastructural disparities between cancer cells cultured as monolayers and tumorspheres. By integrating a robust workflow that incorporates high-pressure freezing followed by freeze substitution (HPF/FS), serial block face scanning electron microscopy (SBF-SEM), manual and deep learning-based segmentation, and statistical analysis, we have successfully generated three-dimensional (3D) reconstructions of monolayer and tumorsphere cells, including their subcellular organelles. Our findings reveal a significant degree of variation in cellular morphology in tumorspheres. We observed the increased prevalence of nuclear envelope invaginations in tumorsphere cells compared to monolayers. Furthermore, we detected a diverse range of mitochondrial morphologies exclusively in tumorsphere cells, as well as intricate cellular interconnectivity within the tumorsphere architecture. These remarkable ultrastructural differences emphasize the use of tumorspheres as a superior model for cancer research due to their relevance to in vivo conditions. Our results strongly advocate for the utilization of tumorsphere cells in cancer research studies, enhancing the precision and relevance of experimental outcomes, and ultimately accelerating therapeutic advancements.

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

Genetically heterogenous cell lines from laboratory mice are promising tools for population-based screening as they offer power for genetic mapping, and potentially, predictive value for in vivo experimentation in genetically matched individuals. To explore this further, we derived a panel of fibroblast lines from a genetic reference population of laboratory mice (the Diversity Outbred, DO). We then used high-content imaging to capture hundreds of cell morphology traits in cells exposed to the oxidative stress-inducing arsenic metabolite monomethylarsonous acid (MMAIII). We employed dose-response modeling to capture latent parameters of response and we then used these parameters to identify several hundred cell morphology quantitative trait loci (cmQTL). Response cmQTL encompass genes with established associations with cellular responses to arsenic exposure, including Abcc4 and Txnrd1, as well as novel gene candidates like Xrcc2. Moreover, baseline trait cmQTL highlight the influence of natural variation on fundamental aspects of nuclear morphology. We show that the natural variants influencing response include both coding and non-coding variation, and that cmQTL haplotypes can be used to predict response in orthogonal cell lines. Our study sheds light on the major molecular initiating events of oxidative stress that are under genetic regulation, including the NRF2-mediated antioxidant response, cellular detoxification pathways, DNA damage repair response, and cell death trajectories.

Super-resolution microscopy reveals the nanoscale cluster architecture of the DEK protein cancer biomarker

DEK protein, a key chromatin regulator, is strongly overexpressed in various forms of cancer. While conventional microscopy revealed DEK as uniformly distributed within the cell nucleus, advanced super-resolution techniques uncovered cluster-like structures. However, a comprehensive understanding of DEK's cellular distribution and its implications in cancer and cell growth remained elusive. To bridge this gap, we employed single-molecule localization microscopy (SMLM) to dissect DEK's nanoscale organization in both normal-like and aggressive breast cancer cell lines. Our investigation included characteristics such as localizations per cluster, cluster areas, and intra-cluster localization densities (ICLDs). We elucidated how cluster features align with different breast cell types and how chromatin decompaction influences DEK clusters in these contexts. Our results indicate that DEK's intra-cluster localization density and nano-organization remain preserved and not significantly influenced by protein overexpression or chromatin compaction changes. This study advances the understanding of DEK's role in cancer and underscores its stable nanoscale behavior.

EGFR Targeted Redox Sensitive Chitosan Nanoparticles of Cabazitaxel: Dual-Targeted Cancer Therapy, Lung Distribution, and Targeting Studies by Photoacoustic and Optical Imaging

In this research, we have modified tocopheryl polyethylene glycol succinate (TPGS) to a redox-sensitive material, denoted as TPGS-SH, and employed the same to develop dual-receptor-targeted nanoparticles of chitosan loaded with cabazitaxel (CZT). The physicochemical properties and morphological characteristics of all nanoparticle formulations were assessed. Dual-receptor targeting redox-sensitive nanoparticles of CZT (F-CTX-CZT-CS-SH-NPs) were developed by a combination of pre- and postconjugation techniques by incorporating synthesized chitosan-folate (F) and TPGS-SH during nanoparticle synthesis and further postconjugated with cetuximab (CTX) for epidermal growth factor receptor (EGFR) targeting. The in vitro release of the drug was seemingly higher in the redox-sensitive buffer media (GSH, 20 mM) compared to that in physiological buffer. However, the extent of cellular uptake of dual-targeted nanoparticles was significantly higher in A549 cells than other control nanoparticles. The IC50 values of F-CTX-CZT-CS-SH-NPs against A549 cells was 0.26 ± 0.12 μg/mL, indicating a 6.3-fold and 60-fold enhancement in cytotoxicity relative to that of dual-receptor targeted, nonredox sensitive nanoparticles and CZT clinical injection, respectively. Furthermore, F-CTX-CZT-CS-SH-NPs demonstrated improved anticancer activity in the benzo(a)pyrene lung cancer model with a higher survival rate. Due to the synergistic combination of enhanced permeability and retention (EPR) effect of small-sized nanoparticles, the innovative and redox sensitive TPGS-SH moiety and the dual folate and EGFR mediated augmented endocytosis have all together significantly enhanced their biodistribution and targeting exclusively to the lung which is evident from their ultrasound/photoacoustic and in vivo imaging system (IVIS) studies.

2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer

Nuclear atypia, including altered nuclear size, contour, and chromatin organization, is ubiquitous in cancer cells. Atypical primary nuclei and micronuclei can rupture during interphase; however, the frequency, causes, and consequences of nuclear rupture are unknown in most cancers. We demonstrate that nuclear envelope rupture is surprisingly common in many human cancers, particularly glioblastoma. Using highly-multiplexed 2D and super-resolution 3D-imaging of glioblastoma tissues and patient-derived xenografts and cells, we link primary nuclear rupture with reduced lamin A/C and micronuclear rupture with reduced lamin B1. Moreover, ruptured glioblastoma cells activate cGAS-STING-signaling involved in innate immunity. We observe that local patterning of cell states influences tumor spatial organization and is linked to both lamin expression and rupture frequency, with neural-progenitor-cell-like states exhibiting the lowest lamin A/C levels and greatest susceptibility to primary nuclear rupture. Our study reveals that nuclear instability is a core feature of cancer, and links nuclear integrity, cell state, and immune signaling.

The transportosome system as a model for the retrotransport of soluble proteins

The classic model of action of the glucocorticoid receptor (GR) sustains that its associated heat-shock protein of 90-kDa (HSP90) favours the cytoplasmic retention of the unliganded GR, whereas the binding of steroid triggers the dissociation of HSP90 allowing the passive nuclear accumulation of GR. In recent years, it was described a molecular machinery called transportosome that is responsible for the active retrograde transport of GR. The transportosome heterocomplex includes a dimer of HSP90, the stabilizer co-chaperone p23, and FKBP52 (FK506-binding protein of 52-kDa), an immunophilin that binds dynein/dynactin motor proteins. The model shows that upon steroid binding, FKBP52 is recruited to the GR allowing its active retrograde transport on cytoskeletal tracks. Then, the entire GR heterocomplex translocates through the nuclear pore complex. The HSP90-based heterocomplex is released in the nucleoplasm followed by receptor dimerization. Subsequent findings demonstrated that the transportosome is also responsible for the retrotransport of other soluble proteins. Importantly, the disruption of this molecular oligomer leads to several diseases. In this article, we discuss the relevance of this transport machinery in health and disease.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations

Electron microscopy (EM) enables imaging at nanometer resolution and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task; however, analyzing them is now the bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, semi-supervised learning as well as next steps for the mitigation of the manual segmentation bottleneck.

Biophysics in tumor growth and progression: from single mechano-sensitive molecules to mechanomedicine

Evidence from physical sciences in oncology increasingly suggests that the interplay between the biophysical tumor microenvironment and genetic regulation has significant impact on tumor progression. Especially, tumor cells and the associated stromal cells not only alter their own cytoskeleton and physical properties but also remodel the microenvironment with anomalous physical properties. Together, these altered mechano-omics of tumor tissues and their constituents fundamentally shift the mechanotransduction paradigms in tumorous and stromal cells and activate oncogenic signaling within the neoplastic niche to facilitate tumor progression. However, current findings on tumor biophysics are limited, scattered, and often contradictory in multiple contexts. Systematic understanding of how biophysical cues influence tumor pathophysiology is still lacking. This review discusses recent different schools of findings in tumor biophysics that have arisen from multi-scale mechanobiology and the cutting-edge technologies. These findings range from the molecular and cellular to the whole tissue level and feature functional crosstalk between mechanotransduction and oncogenic signaling. We highlight the potential of these anomalous physical alterations as new therapeutic targets for cancer mechanomedicine. This framework reconciles opposing opinions in the field, proposes new directions for future cancer research, and conceptualizes novel mechanomedicine landscape to overcome the inherent shortcomings of conventional cancer diagnosis and therapies.

DNA damage induces nuclear envelope rupture through ATR-mediated phosphorylation of lamin A/C

The integrity of the nuclear envelope (NE) is essential for maintaining the structural stability of the nucleus. Rupture of the NE has been frequently observed in cancer cells, especially in the context of mechanical challenges, such as physical confinement and migration. However, spontaneous NE rupture events, without any obvious physical challenges to the cell, have also been described. The molecular mechanism(s) of these spontaneous NE rupture events remain to be explored. Here, we show that DNA damage and subsequent ATR activation leads to NE rupture. Upon DNA damage, lamin A/C is phosphorylated in an ATR-dependent manner, leading to changes in lamina assembly and, ultimately, NE rupture. In addition, we show that cancer cells with intrinsic DNA repair defects undergo frequent events of DNA-damage-induced NE rupture, which renders them extremely sensitive to further NE perturbations. Exploiting this NE vulnerability could provide a new angle to complement traditional, DNA-damage-based chemotherapy.

SUN1/2 controls macrophage polarization via modulating nuclear size and stiffness

Alteration of the size and stiffness of the nucleus triggered by environmental cues are thought to be important for eukaryotic cell fate and function. However, it remains unclear how context-dependent nuclear remodeling occurs and reprograms gene expression. Here we identify the nuclear envelope proteins SUN1/2 as mechano-regulators of the nucleus during M1 polarization of the macrophage. Specifically, we show that LPS treatment decreases the protein levels of SUN1/2 in a CK2-βTrCP-dependent manner to shrink and soften the nucleus, therefore altering the chromatin accessibility for M1-associated gene expression. Notably, the transmembrane helix of SUN1/2 is solely required and sufficient for the nuclear mechano-remodeling. Consistently, SUN1/2 depletion in macrophages facilitates their phagocytosis, tissue infiltration, and proinflammatory cytokine production, thereby boosting the antitumor immunity in mice. Thus, our study demonstrates that, in response to inflammatory cues, SUN1/2 proteins act as mechano-regulators to remodel the nucleus and chromatin for M1 polarization of the macrophage.

Modulation of the Tumor Microenvironment by Ellagic Acid in Rat Model for Hepatocellular Carcinoma: A Potential Target against Hepatic Cancer Stem Cells

The resistance to therapy and relapse in hepatocellular carcinoma (HCC) is highly attributed to hepatic cancer stem cells (HCSCs). HCSCs are under microenvironment control. This work aimed to assess the systemic effect of ellagic acid (EA) on the HCC microenvironment to decline HCSCs. Fifty Wistar rats were divided into six groups: negative control (CON), groups 2 and 3 for solvents (DMSO), and (OVO). Group 4 was administered EA only. The (HCC-M) group, utilized as an HCC model, administered CCL4 (0.5 mL/kg in OVO) 1:1 v/v, i.p) for 16 weeks. HCC-M rats were treated orally with EA (EA + HCC) 50 mg/kg bw for five weeks. Biochemical, morphological, histopathological, and immunohistochemical studies, and gene analysis using qRT-PCR were applied. Results revealed elevated liver injury biomarkers ALT, AST, ALP, and tumor biomarkers AFP and GGT, and marked nodularity of livers of HCC-M. EA effectively reduced the biomarkers and restored the altered structure of the livers. At the mRNA level, EA downregulated the expression of TGF-α, TGF-β, and VEGF, and restored p53 expression. This induced an increase in apoptotic cells immunostained with caspase3 and decreased the CD44 immunostained HCSCs. EA could modulate the tumor microenvironment in the HCC rat model and ultimately target the HCSCs.

Flexible dynamic quantitative phase imaging based on division of focal plane polarization imaging technique

This paper proposes a flexible and accurate dynamic quantitative phase imaging (QPI) method using single-shot transport of intensity equation (TIE) phase retrieval achieved by division of focal plane (DoFP) polarization imaging technique. By exploiting the polarization property of the liquid crystal spatial light modulator (LC-SLM), two intensity images of different defocus distances contained in orthogonal polarization directions can be generated simultaneously. Then, with the help of the DoFP polarization imaging, these images can be captured with single exposure, enabling accurate dynamic QPI by solving the TIE. In addition, our approach gains great flexibility in defocus distance adjustment by adjusting the pattern loaded on the LC-SLM. Experiments on microlens array, phase plate, and living human gastric cancer cells demonstrate the accuracy, flexibility, and dynamic measurement performance for various objects. The proposed method provides a simple, flexible, and accurate approach for real-time QPI without sacrificing the field of view.

Alzheimer's disease and related tauopathies: disorders of disrupted neuronal identity

Postmitotic neurons require persistently active controls to maintain terminal differentiation. Unlike dividing cells, aberrant cell cycle activation in mature neurons causes apoptosis rather than transformation. In Alzheimer's disease (AD) and related tauopathies, evidence suggests that pathogenic forms of tau drive neurodegeneration via neuronal cell cycle re-entry. Multiple interconnected mechanisms linking tau to cell cycle activation have been identified, including, but not limited to, tau-induced overstabilization of the actin cytoskeleton, consequent changes to nuclear architecture, and disruption of heterochromatin-mediated gene silencing. Cancer- and development-associated pathways are upregulated in human and cellular models of tauopathy, and many tau-induced cellular phenotypes are also present in various cancers and progenitor/stem cells. In this review, I delve into mechanistic parallels between tauopathies, cancer, and development, and highlight the role of tau in cancer and in the developing brain. Based on these studies, I put forth a model by which pathogenic forms of tau disrupt the program that maintains terminal neuronal differentiation, driving cell cycle re-entry and consequent neuronal death. This framework presents tauopathies as conditions involving the profound toxic disruption of neuronal identity.

Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model

BACKGROUND

Breast cancer subtyping is a crucial step in determining therapeutic options, but the molecular examination based on immunohistochemical staining is expensive and time-consuming. Deep learning opens up the possibility to predict the subtypes based on the morphological information from hematoxylin and eosin staining, a much cheaper and faster alternative. However, training the predictive model conventionally requires a large number of histology images, which is challenging to collect by a single institute.

OBJECTIVE

We aimed to develop a data-efficient computational pathology platform, 3DHistoNet, which is capable of learning from z-stacked histology images to accurately predict breast cancer subtypes with a small sample size.

METHODS

We retrospectively examined 401 cases of patients with primary breast carcinoma diagnosed between 2018 and 2020 at the Department of Pathology, National Cancer Center, South Korea. Pathology slides of the patients with breast carcinoma were prepared according to the standard protocols. Age, gender, histologic grade, hormone receptor (estrogen receptor [ER], progesterone receptor [PR], and androgen receptor [AR]) status, erb-B2 receptor tyrosine kinase 2 (HER2) status, and Ki-67 index were evaluated by reviewing medical charts and pathological records.

RESULTS

The area under the receiver operating characteristic curve and decision curve were analyzed to evaluate the performance of our 3DHistoNet platform for predicting the ER, PR, AR, HER2, and Ki67 subtype biomarkers with 5-fold cross-validation. We demonstrated that 3DHistoNet can predict all clinically important biomarkers (ER, PR, AR, HER2, and Ki67) with performance exceeding the conventional multiple instance learning models by a considerable margin (area under the receiver operating characteristic curve: 0.75-0.91 vs 0.67-0.8). We further showed that our z-stack histology scanning method can make up for insufficient training data sets without any additional cost incurred. Finally, 3DHistoNet offered an additional capability to generate attention maps that reveal correlations between Ki67 and histomorphological features, which renders the hematoxylin and eosin image in higher fidelity to the pathologist.

CONCLUSIONS

Our stand-alone, data-efficient pathology platform that can both generate z-stacked images and predict key biomarkers is an appealing tool for breast cancer diagnosis. Its development would encourage morphology-based diagnosis, which is faster, cheaper, and less error-prone compared to the protein quantification method based on immunohistochemical staining.

Functional Materials for Subcellular Targeting Strategies in Cancer Therapy: Progress and Prospects

Neoadjuvant and adjuvant therapies have made significant progress in cancer treatment. However, tumor adjuvant therapy still faces challenges due to the intrinsic heterogeneity of cancer, genomic instability, and the formation of an immunosuppressive tumor microenvironment. Functional materials possess unique biological properties such as long circulation times, tumor-specific targeting, and immunomodulation. The combination of functional materials with natural substances and nanotechnology has led to the development of smart biomaterials with multiple functions, high biocompatibilities, and negligible immunogenicities, which can be used for precise cancer treatment. Recently, subcellular structure-targeting functional materials have received particular attention in various biomedical applications including the diagnosis, sensing, and imaging of tumors and drug delivery. Subcellular organelle-targeting materials can precisely accumulate therapeutic agents in organelles, considerably reduce the threshold dosages of therapeutic agents, and minimize drug-related side effects. This review provides a systematic and comprehensive overview of the research progress in subcellular organelle-targeted cancer therapy based on functional nanomaterials. Moreover, it explains the challenges and prospects of subcellular organelle-targeting functional materials in precision oncology. The review will serve as an excellent cutting-edge guide for researchers in the field of subcellular organelle-targeted cancer therapy.

Reprogramming of glucose metabolism via PFKFB4 is critical in FGF16-driven invasion of breast cancer cells

Fibroblast growth factors (FGFs) are expressed in both developing and adult tissues and play important roles in embryogenesis, tissue homeostasis, angiogenesis, and neoplastic transformation. Here, we report the elevated expression of FGF16 in human breast tumor and investigate its potential involvement in breast cancer progression. The onset of epithelial-mesenchymal transition (EMT), a prerequisite for cancer metastasis, was observed in human mammary epithelial cell-line MCF10A by FGF16. Further study unveiled that FGF16 alters mRNA expression of a set of extracellular matrix genes to promote cellular invasion. Cancer cells undergoing EMT often show metabolic alteration to sustain their continuous proliferation and energy-intensive migration. Similarly, FGF16 induced a significant metabolic shift toward aerobic glycolysis. At the molecular level, FGF16 enhanced GLUT3 expression to facilitate glucose transport into cells, which through aerobic glycolysis generates lactate. The bi-functional protein, 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase 4 (PFKFB4) was found to be a mediator in FGF16-driven glycolysis and subsequent invasion. Furthermore, PFKFB4 was found to play a critical role in promoting lactate-induced cell invasion since silencing PFKFB4 decreased lactate level and rendered the cells less invasive. These findings support potential clinical intervention of any of the members of FGF16-GLUT3-PFKFB4 axis to control the invasion of breast cancer cells.

Consequences of gaining an extra chromosome

Mistakes in chromosome segregation leading to aneuploidy are the primary cause of miscarriages in humans. Excluding sex chromosomes, viable aneuploidies in humans include trisomies of chromosomes 21, 18, or 13, which cause Down, Edwards, or Patau syndromes, respectively. While individuals with trisomy 18 or 13 die soon after birth, people with Down syndrome live to adulthood but have intellectual disabilities and are prone to multiple diseases. At the cellular level, mistakes in the segregation of a single chromosome leading to a cell losing a chromosome are lethal. In contrast, the cell that gains a chromosome can survive. Several studies support the hypothesis that gaining an extra copy of a chromosome causes gene-specific phenotypes and phenotypes independent of the identity of the genes encoded within that chromosome. The latter, referred to as aneuploidy-associated phenotypes, are the focus of this review. Among the conserved aneuploidy-associated phenotypes observed in yeast and human cells are lower viability, increased gene expression, increased protein synthesis and turnover, abnormal nuclear morphology, and altered metabolism. Notably, abnormal nuclear morphology of aneuploid cells is associated with increased metabolic demand for de novo synthesis of sphingolipids. These findings reveal important insights into the possible pathological role of aneuploidy in Down syndrome. Despite the adverse effects on cell physiology, aneuploidy is a hallmark of cancer cells. Understanding how aneuploidy affects cell physiology can reveal insights into the selective pressure that aneuploid cancer cells must overcome to support unlimited proliferation.

Three-dimensional structural analysis of papillary thyroid carcinoma nuclei with serial block-face scanning electron microscopy (SBF-SEM)

Nuclear morphology of carcinoma cells is critical for the pathological diagnosis of papillary thyroid carcinoma (PTC). However, three-dimensional architecture of PTC nuclei is still elusive. In this study, we analyzed the three-dimensional ultrastructure of PTC nuclei using serial block-face scanning electron microscopy which takes advantage of the high-throughput acquisition of serial electron microscopic images and three-dimensional reconstruction of subcellular structures. En bloc-stained and resin-embedded specimens were prepared from surgically removed PTCs and normal thyroid tissues. We acquired two-dimensional images from serial block-face scanning electron microscopy and reconstructed three-dimensional nuclear structures. Quantitative comparisons showed that the nuclei of carcinoma cells were larger and more complex than those of normal follicular cells. The three-dimensional reconstruction of carcinoma nuclei divided intranuclear cytoplasmic inclusions into "open intranuclear cytoplasmic inclusions" connecting to cytoplasm outside the nucleus and "closed intranuclear cytoplasmic inclusions" without that connection. Cytoplasm with abundant organelles was observed in open inclusions, but closed inclusions contained fewer organelles with or without degeneration. Granules with a dense core were only observed in closed inclusions. Our observations suggested that open inclusions originate from nuclear invaginations, and disconnection from cytoplasm leads to closed inclusions.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

A genome-wide single-cell 3D genome atlas of lung cancer progression

Alterations in three-dimensional (3D) genome structures are associated with cancer1-5. However, how genome folding evolves and diversifies during subclonal cancer progression in the native tissue environment remains unknown. Here, we leveraged a genome-wide chromatin tracing technology to directly visualize 3D genome folding in situ in a faithful Kras-driven mouse model of lung adenocarcinoma (LUAD)6, generating the first single-cell 3D genome atlas of any cancer. We discovered stereotypical 3D genome alterations during cancer development, including a striking structural bottleneck in preinvasive adenomas prior to progression to LUAD, indicating a stringent selection on the 3D genome early in cancer progression. We further showed that the 3D genome precisely encodes cancer states in single cells, despite considerable cell-to-cell heterogeneity. Finally, evolutionary changes in 3D genome compartmentalization - partially regulated by polycomb group protein Rnf2 through its ubiquitin ligase-independent activity - reveal novel genetic drivers and suppressors of LUAD progression. Our results demonstrate the importance of mapping the single-cell cancer 3D genome and the potential to identify new diagnostic and therapeutic biomarkers from 3D genomic architectures.

Heritable transcriptional defects from aberrations of nuclear architecture

Transcriptional heterogeneity due to plasticity of the epigenetic state of chromatin contributes to tumour evolution, metastasis and drug resistance1-3. However, the mechanisms that cause this epigenetic variation are incompletely understood. Here we identify micronuclei and chromosome bridges, aberrations in the nucleus common in cancer4,5, as sources of heritable transcriptional suppression. Using a combination of approaches, including long-term live-cell imaging and same-cell single-cell RNA sequencing (Look-Seq2), we identified reductions in gene expression in chromosomes from micronuclei. With heterogeneous penetrance, these changes in gene expression can be heritable even after the chromosome from the micronucleus has been re-incorporated into a normal daughter cell nucleus. Concomitantly, micronuclear chromosomes acquire aberrant epigenetic chromatin marks. These defects may persist as variably reduced chromatin accessibility and reduced gene expression after clonal expansion from single cells. Persistent transcriptional repression is strongly associated with, and may be explained by, markedly long-lived DNA damage. Epigenetic alterations in transcription may therefore be inherently coupled to chromosomal instability and aberrations in nuclear architecture.

AKT2-mediated nuclear deformation leads to genome instability during epithelial-mesenchymal transition

Nuclear deformation has been observed in some cancer cells for decades, but its underlying mechanism and biological significance remain elusive. To address these questions, we employed human lung cancer A549 cell line as a model in context with transforming growth factor β (TGFβ)-induced epithelial-mesenchymal transition. Here, we report that nuclear deformation induced by TGFβ is concomitant with increased phosphorylation of lamin A at Ser390, defective nuclear lamina and genome instability. AKT2 and Smad3 serve as the downstream effectors for TGFβ to induce nuclear deformation. AKT2 directly phosphorylates lamin A at Ser390, whereas Smad3 is required for AKT2 activation upon TGFβ stimulation. Expression of the lamin A mutant with a substitution of Ser390 to Ala or suppression of AKT2 or Smad3 prevents nuclear deformation and genome instability induced by TGFβ. These findings reveal a molecular mechanism for TGFβ-induced nuclear deformation and establish a role of nuclear deformation in genome instability during epithelial-mesenchymal transition.

A Yeast Mitotic Tale for the Nucleus and the Vacuoles to Embrace

The morphology of the nucleus is roughly spherical in most eukaryotic cells. However, this organelle shape needs to change as the cell travels through narrow intercellular spaces during cell migration and during cell division in organisms that undergo closed mitosis, i.e., without dismantling the nuclear envelope, such as yeast. In addition, the nuclear morphology is often modified under stress and in pathological conditions, being a hallmark of cancer and senescent cells. Thus, understanding nuclear morphological dynamics is of uttermost importance, as pathways and proteins involved in nuclear shaping can be targeted in anticancer, antiaging, and antifungal therapies. Here, we review how and why the nuclear shape changes during mitotic blocks in yeast, introducing novel data that associate these changes with both the nucleolus and the vacuole. Altogether, these findings suggest a close relationship between the nucleolar domain of the nucleus and the autophagic organelle, which we also discuss here. Encouragingly, recent evidence in tumor cell lines has linked aberrant nuclear morphology to defects in lysosomal function.