Subcellular Structures Research Articles

Holographic tomography of the diatom Skeletonema pseudocostatum used as a bioindicator of heavy metal-polluted waters.

Heavy metal contamination in aquatic environments poses a significant threat to microbial communities, yet the subcellular responses of phytoplankton to metal stress remain poorly understood. In particular, the effects of heavy metal exposure on the structural and physiological properties of diatoms require further investigation. Here, we analyze the impact of cadmium (Cd) and copper (Cu) exposure on the subcellular structures of the diatom Skeletonema pseudocostatum using holographic tomography. This imaging technique enables detailed visualization and quantitative analysis of diatom subcomponents, including frustules, protoplasm, vacuoles, and chloroplasts, under varying metal concentrations. The study aims to understand the changes in the mean refractive index (RI) and concentration (e.g., the ratio among cell dry mass and its biovolume) as indicators of cellular response to metal stress and to infer if such diatom can be used as sentinel species of heavy metal pollution. Findings indicate that diatoms exhibit significant variations in RI and internal cell density when exposed to different metal concentrations. Lower RI values observed at higher metal concentrations, can be considered as a sign of stress due to cytoplasm extrusion and/or vacuolization. The results highlight the potential of using S. pseudocostatum as a bioindicator for monitoring water metal pollution. Moreover, the results show that holographic tomography as useful tool for non-invasive, high-resolution cellular imaging of phytoplankton in environmental studies.

MEHP Impairs Male Reproductive Function Through the Lipid Metabolism Pathway: Evidence from in vitro Human Immature Testis Culture.

MEHP Impairs Male Reproductive Function Through the Lipid Metabolism Pathway: Evidence from in vitro Human Immature Testis Culture.

Non-DNA radiosensitive targets that initiate persistent behavioral deficits in rats exposed to space radiation.

Non-DNA radiosensitive targets that initiate persistent behavioral deficits in rats exposed to space radiation.

Nanoplastics drive toxicity under co-exposure with perfluorooctanesulfonic acid in human intestinal cells

Abstract Per- and polyfluoroalkyl substances and nanoplastics frequently co-occur in environmental matrices, yet the effects of co-exposure on cellular responses upon ingestion are poorly understood. Here, we exposed human intestinal Caco-2 cells to perfluorooctanesulfonic acid, nanoplastics, and their combination. Cell painting-based phenomics was used to map phenotypic alterations across subcellular structures, and untargeted metabolomics using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry was employed to assess metabolic changes. Results show that perfluorooctanesulfonic acid predominantly affected the actin cytoskeleton, Golgi apparatus, and plasma membrane, while nanoplastics primarily targeted mitochondria. Combined exposure disrupted the endoplasmic reticulum, RNA, and mitochondria. Perfluorooctanesulfonic acid reduced levels of carnitines, free fatty acids, nucleotides, and sugars, whereas nanoplastics inhibited ceramides, triglycerides, sphingomyelins, and additional free fatty acids. Combined exposure produced a metabolic profile resembling that of nanoplastics, with specific differences attributed to perfluorooctanesulfonic acid. Overall, nanoplastics appear as the main drivers of the co-exposure effects.

Boosting High‐Fidelity Isotropic Super‐Resolution via Image Interference Structured Illumination Microscopy with Spatial‐Spectral Optimization

AbstractSpatial resolution is crucial for imaging subcellular structures. The advent of three‐dimensional structured illumination microscopy (3D‐SIM) greatly benefits the biology community, providing a powerful tool for imaging organelles with a twofold resolution enhancement in all three dimensions. However, the axial resolution of 3D‐SIM is limited to around 300 nm, which is inferior to its lateral resolution. Here, a novel method called image interference SIM () is reported, which utilizes two oppositely positioned objectives to detect fluorescence emission interference under three‐beam excitation. By incorporating spectral modulation and spatial domain Frobenius‐Hessian optimization, achieves an axial resolution approximately twice that of 3D‐SIM, reaching around 130 nm. Furthermore, the potential of for imaging subcellular structures is demonstrated on various biological samples, including microtubules, actin filaments, and mitochondrial outer membranes. The enhanced optical sectioning capability can be utilized to resolve axial structures that are challenging to discern using ordinary 3D‐SIM.

Single-nucleus RNA sequencing and spatial transcriptomics reveal the mechanism by which Xiaozhiling injection treats internal hemorrhoids.

Hemorrhoids, a prevalent chronic condition globally, significantly impact patients' quality of life. While various surgical interventions, such as external stripping and internal ligation, procedure for prolapse and hemorrhoids, and tissue selecting technique, are employed for treatment, they are often associated with postoperative complications, including unsatisfactory defecation, bleeding, and anal stenosis. In contrast, Xiaozhiling injection, a traditional Chinese medicine-based therapy, has emerged as a minimally invasive and effective alternative for internal hemorrhoids. This treatment offers distinct advantages, such as reduced dietary restrictions, broad applicability, and minimal induction of systemic inflammatory responses. Additionally, Xiaozhiling injection effectively eliminates hemorrhoid nuclei, prevents local tissue necrosis, preserves anal cushion integrity, and mitigates postoperative complications, including bleeding and prolapse. Despite its clinical efficacy, the molecular mechanisms underlying its therapeutic effects remain poorly understood, warranting further investigation. To investigate the molecular mechanism underlying the therapeutic effect of Xiaozhiling injection in the treatment of internal hemorrhoids. An internal hemorrhoid model was established in rats, and the rats were randomly divided into a modeling group [control group (CK group)] and a treatment group. One week after injection, Stereo-seq and electron microscopy were used to study the changes in gene expression and subcellular structures in fibroblasts. Single-cell sequencing revealed differences in the expression and transcript levels of the genes collagen 3 alpha 1, decorin, and actin alpha 2 in fibroblasts between the CK group and the treatment group. Spatial transcriptome analysis revealed that genes of the sphingosine kinase 1 (Sphk1)/sphingosine-1-phosphate (S1P) pathway spatially overlapped with key genes of the transforming growth factor beta 1 pathway, namely, Sphk1, S1P receptor, and transforming growth factor beta 1, in the treatment group. The proportion of fibroblasts was lower in the treatment group than in the CK group, and Xiaozhiling treatment had a significant effect on the proportion of fibroblasts in hemorrhoidal tissue. Immunohistochemistry revealed a significant increase in the expression of a fibroblast marker. Electron microscopy showed that the endoplasmic reticulum of fibroblasts contained a large amount of glycogen, indicating cell activation. Fibroblast activation and the expression of key genes of the Sphk1-S1P pathway could be observed at the injection site, suggesting that after Xiaozhiling intervention, the Sphk1-S1P pathway could be activated to promote fibrosis. Xiaozhiling injection exerts its therapeutic effects on internal hemorrhoids by promoting collagen synthesis and secretion in fibroblasts. After Xiaozhiling intervention, the Sphk1-S1P pathway can be activated to promote fibrosis.

Single Molecule Localization Super-resolution Dataset for Deep Learning with Paired Low-resolution Images

Deep learning super-resolution microscopy has advanced rapidly in recent years. Super-resolution images acquired by single molecule localization microscopy (SMLM) are ideal sources for high-quality datasets. However, the scarcity of public datasets limits the development of deep learning methods. Here, we describe a biological image dataset, DL-SMLM, which provides paired low-resolution fluorescence images and super-resolution SMLM data for training super-resolution models. DL-SMLM consists of six different subcellular structures, including microtubules, lumen and membrane of endoplasmic reticulum (ER), Clathrin coated pits (CCPs), outer membrane of mitochondria (OMM) and inner membrane of mitochondria (IMM). There are 188 sets of raw SMLM data and 100 signal levels for each low-resolution image. This allows software developers to generate thousands of training pairs through data segmentation. The performance of the imaging system was further evaluated using DNA origami samples. Finally, we demonstrated examples of super-resolution models trained using data from DL-SMLM, highlighting the effectiveness of DL-SMLM for developing deep learning super-resolution microscopy.

Abstract 7448: Developing virtual staining algorithm of colon cancer organoids using holotomography and deep learning

Abstract Colon cancer organoids hold great potential for studying the development of colon cancer. To assess their physiological and developmental status, both morphological parameters (e.g., cell shape and volume) and functional parameters (e.g., presence of proliferating cells or pluripotent stem cells) are crucial. However, current fluorescence labeling methods are invasive, potentially damaging or altering the organoids, making them disposable. To overcome this limitation, it is essential to develop a non-invasive method for monitoring organoids for long-term. We propose a solution that integrates holotomography and deep learning. Holotomography is a label-free imaging technique that measures the 3D refractive index distribution of samples without requiring any exogenous labeling. While this technique operates within a label-free framework, the resulting images provide only structural information and lack functional information. This limitation makes it challenging for researchers to distinguish cellular and subcellular structures with specific function. We address this challenge by leveraging deep learning to provide both structural and functional information through virtual staining of label-free images. Specifically, we construct a paired dataset using correlative holotomography, which captures both label-free images and their corresponding fluorescence images. Using supervised learning, we train a neural network to map label-free images to their corresponding fluorescence counterparts. Once trained, the neural network can automatically predict virtual fluorescence images from label-free inputs without exposing the organoids to any exogenous chemicals. To validate the accuracy of the trained neural network, we first acquire label-free images of the organoids and input them into the network. After obtaining the network-generated virtual fluorescence images, we chemically stain the same organoids. Finally, we compare the chemically stained images with the virtual images produced by the neural network to evaluate its performance. By combining the safety of label-free imaging with the precision of artificial intelligence, our technology eliminates critical barriers to organoid research and translation. This breakthrough accelerates the development of scalable and safe organoid therapies, bringing us closer to a future where lab-grown organoids revolutionize regenerative medicine and save lives. Citation Format: Juyeon Park, Geon Kim, YongKeun Park. Developing virtual staining algorithm of colon cancer organoids using holotomography and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7448.

Catalpol reduces liver toxicity of triptolide in mice by inhibiting hepatocyte ferroptosis through the SLC7A11/GPX4 pathway: testing the Fuzheng Zhidu theory for detoxification

To investigate the protective effect of catalpol against triptolide-induced liver injury and explore its mechanism to test the Fuzheng Zhidu theory for detoxification. C57BL/6J mice were randomized into blank control group, catalpol group, triptolide group and triptolide+catalpol group. After 13 days of treatment with the agents by gavage, the mice were examined for liver tissue pathology, liver function, hepatocyte subcellular structure, lipid peroxidation, ferrous ion deposition and expressions of ferroptosis-related proteins in the liver. In a liver cell line HL7702, the effect of catalpol or the ferroptosis inhibitor Fer-1 on triptolide-induced cytotoxicity was tested by examining cell functions, Fe2+ concentration, lipid peroxidation, ROS level and the ferroptosis-related proteins. In C57BL/6J mice, catalpol significantly alleviated triptolide-induced hepatic injury, lowered the levels of ALT, AST and LDH, and reversed the elevation of Fe2+ concentration and MDA level and the reduction of GPX level. In HL7702 cells, inhibition of ferroptosis by Fer-1 significantly reversed triptolide-induced elevation of ALT, AST and LDH levels. Western blotting and qRT-PCR demonstrated that catalpol reversed abnormalities in expressions of SLC7A11, FTH1 and GPX4 at both the mRNA and protein levels in triptolide-treated HL7702 cells. The combined use of catalpol can reduce the hepatotoxicity of triptolide in mice by inhibiting excessive hepatocyte ferroptosis through the SLC7A11/GPX4 pathway.

Advancing radiation-induced mutant screening through high-throughput technology: a preliminary evaluation of mutant screening in Arabidopsis thaliana

Identifying mutant traits is essential for improving crop yield, quality, and stress resistance in plant breeding. Historically, the efficiency of breeding has been constrained by throughput and accuracy. Recent significant advancements have been made through the development of automated, high-accuracy, and high-throughput equipment. However, challenges remain in the post-processing of large-scale image data and its practical application and evaluation in breeding. This study presents a comparative analysis of human and machine recognition, with validation of a randomly selected mutant at the physiological level performed on wild-type Arabidopsis thaliana and a candidate mutant of the M3 generation, which was generated through mutagenesis with heavy ion beams (HIBs) and 60Co-γ radiation. The mutant populations were subjected to image acquisition and automated screening using the High-throughput Plant Imaging System (HTPIS), generating approximately 10 GB of data (4,635 image datasets). We performed Principal Components Analysis (PCA), scatter matrix clustering, and Logistic Growth Curve (LGC) analyses, and compared these results with those obtained from traditional manual screening based on human visual assessment, and randomly selected #197 candidate mutants for validation in terms of growth and development, chlorophyll fluorescence, and subcellular structure. Our findings demonstrate that as the confidence interval level increases from 75 to 99.9%, the accuracy of machine-based mutant identification decreases from 1 to 0.446, while the false positive rate decreases from 0.817 to 0.118, and the false negative rate increases from 0 to 0.554. Nevertheless, machine-based screening remains more accurate and efficient than human assessment. This study evaluated and validated the efficiency (greater than 80%) of high-throughput techniques for screening mutants in complex populations of radiation-induced progeny, and presented a graphical data processing procedure for high-throughput screening of mutants, providing a basis for breeding techniques utilizing HIBs and γ-ray radiation, and offering innovative approaches and methodologies for radiation-induced breeding in the context of high-throughput big data.

Subcellular localisation and identification of single atoms using quantitative scanning transmission electron microscopy.

Determining the concentration of elements in subcellular structures poses a significant challenge. By locating an elemental species at high spatial resolution and with subcellular context, and subsequently quantifying it on an absolute scale, new information about cellular function can be revealed. Such measurements have not as yet been realised with existing techniques due to limitations on spatial resolution and inherent difficultiesin detecting elements present in low concentrations. In this paper, we use scanning transmission electron microscopy (STEM) to establish a methodology for localising and quantifying high-Z elementsin a biological setting by measuring elastic electron scattering. We demonstrate platinum (Pt) deposition within neuronal cell bodies followingin vivoadministration of the Pt-based chemotherapeutic oxaliplatin to validate this novel methodology. For the first time, individual Pt atoms and nanoscale Pt clusters are shown within subcellular structures. Quantitative measurements of elastic electron scattering are used to determine absolute numbers of Pt atoms in each cluster. Cluster density iscalculated on an atoms-per-cubic-nanometre scale, and used to show clusters form with densities below that of metallic Pt. By considering STEM partial scattering cross-sections, we determine that this new approach to subcellular elemental detection may be applicable to elements as light as sodium. LAY DESCRIPTION: Heterogeneous elemental distributions drive fundamental biological processes within cells. While carbon, hydrogen, oxygen and nitrogen comprise by far the majority of living matter, concentrations and locations of more than a dozen other species must also be tightly controlled to ensure normal cell function. Oxaliplatin is a first-line and adjuvant treatment for colorectal cancer. However, pain in the body's extremities (fingers and toes) significantly impairs clinical usage as this serious and persistent side effect impacts on both patient cancer care and quality of life. Annular dark-field (ADF) imaging in the scanning transmission electron microscope (STEM) provides an image with strong atom-number contrast and is sufficient to distinguish between different cell types and different organelles within the cells of the DRG. We also show that Pt may be imaged at the single atom level and be localised at very high resolution while still preserving a degree of ultrastructural context. The intrinsic image contrast generated is sufficient to identify these features without the need for heavy metal stains and other extensive processing steps which risk disturbing native platinum distributions within the tissue. We subsequently demonstrate that by considering the total elastic scattering intensity generated by nanometre-sized Pt aggregations within the cell, the ADF STEM may be used to make a measurement of local concentration of Pt in units of atoms per cubic nanometre. We further estimate the minimum atomic number required to visualise single atoms in this setting, concluding that in similar samples it may be possible to detect species as light as sodium with atomic sensitivity.

SCCS: Deep Neural Spectral Clustering for Self-Supervised Subcellular Structure Segmentation

Subcellular structure segmentation is a fundamental task in biological imaging. Existing self-supervised representation learning combined with classical k-means clustering achieved unsupervised image segmentation, but it was constrained by time-consuming test-time pixel-wise feature extraction and clustering synchronization. This study introduces SCCS, a lightweight graph neural network-based spectral clustering framework for end-to-end subcellular structure segmentation upon superpixel graphs, greatly relieving the computational complexity in test-time numerical spectral clustering and inter-graph label inconsistency. Specifically, SCCS exploits the self-supervised masked autoencoder for representation learning and the construction of superpixel graphs (spG). Unlike per-graph scalar affinity-based spectral clustering, the proposed SCCS parameterizes the mapping from learned deep spG representations to coordinates in the spectral embedding space and the clustering assignments. The SCCS is optimized under unsupervised eigendecomposition and incremental clustering criteria, which synchronize the intra- and inter-graph spectral clustering. The proposed approach is evaluated on a publicly available volumetric electron microscopy dataset. Experiments demonstrate the effectiveness and performance gains of the proposed SCCS over the state-of-the-art in discovering a variety of subcellular structures.

Membraneless Organelles and Phase Separation in Tumours: Mechanisms and Prospects.

Membraneless organelles (MLOs) are a type of subcellular compartment structure discovered in eukaryotes in recent years. They are mainly formed through the liquid-liquid phase separation (LLPS) and aggregation of macromolecular substances such as proteins or nucleic acids in cells. When cells are stimulated, they initiate a series of stress responses including gene transcription, RNA metabolism, translation, protein modification and signal transduction to maintain homeostasis. The dysregulation of these cellular processes is a key event in the occurrence and development of cancer. This article provides an overview of the structure and function of membraneless organelles, as well as the mechanisms of phase separation, to summarise the latest research progress on phase separation in tumours. It focuses on the role and molecular mechanism of LLPS in the development of tumours, with the aim of providing new theoretical references for developing drug action targets and innovative treatment strategies.

Bright and photostable yellow fluorescent proteins for extended imaging

Fluorescent proteins are indispensable molecular tools for visualizing biological structures and processes, but their limited photostability restricts the duration of dynamic imaging experiments. Yellow fluorescent proteins (YFPs), in particular, photobleach rapidly. Here, we introduce mGold2s and mGold2t, YFPs with up to 25-fold greater photostability than mVenus and mCitrine, two commonly used YFPs, while maintaining comparable brightness. These variants were identified using a high-throughput pooled single-cell platform, simultaneously screening for high brightness and photostability. Compared with our previous benchmark, mGold, the mGold2 variants display a ~4-fold increase in photostability without sacrificing brightness. mGold2s and mGold2t extend imaging durations across diverse modalities, including widefield, total internal reflection fluorescence (TIRF), super-resolution, single-molecule, and laser-scanning confocal microscopy. When incorporated into fluorescence resonance energy transfer (FRET)-based biosensors, the proposed YFPs enable more reliable, prolonged imaging of dynamic cellular processes. Overall, the enhanced photostability of mGold2s and mGold2t enables high-sensitivity imaging of subcellular structures and cellular activity over extended periods, broadening the scope and precision of biological imaging.

Enhancing ultra-high density single-molecule localization with deep spatiotemporal networks

Single-molecule localization microscopy (SMLM) is a powerful imaging technique that surpasses the diffraction limit of light by computationally localizing individual fluorescent molecules. However, achieving sufficient spatial resolution in SMLM requires extensive frame acquisition, limiting temporal resolution. Increasing the density of fluorescent molecules is a common strategy to enhance temporal resolution, but this often results in overlapping point spread functions and computational challenges in distinguishing adjacent molecules. We developed a deep learning-driven approach, termed super-resolution spatiotemporal information integration (SRST), for ultra-high-density molecules' precise three-dimensional (3D) localization. SRST leveraged temporal information from adjacent frames and the blinking mechanism to enhance localization accuracy, demonstrating a 10% increase in the Jaccard index and a 14 nm reduction in localization error compared with the state-of-the-art methods in low signal-to-noise ratio conditions. SRST exhibited broad applicability and maintains accurate reconstruction in ultra-high-density scenarios, enhancing structural detail in 3D imaging of subcellular structures such as mitochondria and microtubules while reducing imaging artifacts and improving structural smoothness. SRST will hold substantial promise for detailed structural analysis of cellular components, providing high-resolution imaging with enhanced localization accuracy.

Application of HEMA-AAm copolymer to achieve faster optical tissue transparency for 2D/3D fluorescence imaging

Optical transparency methods can facilitate biological tissue optical imaging, which enabled accurate three-dimensional (3D) signal visualization and quantification of complex biological structures. Unfortunately, existing optical clearing approaches present a compromise between maximizing clearing capability, the preservation of fluorescent protein emission and the speed of sample processing. To address this challenge, we synthesis of a 2-hydroxyethyl methacrylate (HEMA)-acrylamide (AAm) copolymer using antipyrine (ATP) and 2,2′-thiodiethanol (TDE) as solvent, which could embed tissue samples rapidly and highly transparent, and compatible with multiple fluorescence labeling. It can enable volumetric imaging of tissue up to the scale of mice organs, shrinkage duration of the clearing and preserve emission from fluorescent proteins and dyes. This copolymer with suitable toughness and plasticity allows the tissue of interest to be sectioned into thin slices, and histological techniques provide high-resolution two-dimensional (2D) images of cells and subcellular structures. Furthermore, HEMA-AAm copolymer -tissue transparent could distinguish cell structures between healthy and diabetic disease in dye-labeled liver tissues, which provides new insights into pathological diagnosis and analysis. Copolymer provides an environment to facilitate high-resolution 3D/2D fluorescence imaging, which enables the study of cellular and tissue morphology in experimental and clinical conditions of interest.

Functional Microendoscopy Reveals Calcium Responses of Single Cells in Tracheal Tuft Cells and Kidney Podocytes.

Microendoscopy, a crucial technology for minimally invasive investigations of organs, facilitates studies within confined cavities. However, conventional microendoscopy is often limited by probe size and the constraint of using a single excitation wavelength. In response to these constraints, a multichannel microendoscope with a slender profile of only 360µm is engineered. Functional signals both in situ and in vivo are successfully captured from individual single cells, employing a specially developed software suite for image processing, and exhibiting an effective resolution of 4.6µm, allowing for the resolution of subcellular neuronal structures. This system enabled the first examination of calcium dynamics in vivo in murine tracheal tuft cells (formerly named brush cells) and in situ in kidney podocytes. Additionally, it recorded ratiometric redox reactions in various biological settings, including intact explanted organs and pancreatic islet cultures. The flexibility and streamlined operation of the microendoscopic technique open new avenues for conducting in vivo research, allowing for studies of tissue and organ function at cellular resolution.

Super-resolution algorithms for imaging FCS enhancement: A comparative study.

Super-resolution algorithms for imaging FCS enhancement: A comparative study.

HuR prevents amyloid beta-induced phase separation of miRNA-bound Ago2 to RNA-processing bodies.

HuR prevents amyloid beta-induced phase separation of miRNA-bound Ago2 to RNA-processing bodies.

Human oocyte quality and reproductive health.

Human oocyte quality and reproductive health.