Biological Research Research Articles

Deep learning for polymer scaffold bioimage analysis: Opportunities and challenges

Significant efforts have been made to advance bioprinted scaffold research in cell biology, tissue engineering, and drug screening studies. Ideal scaffolds should demonstrate suitable mechanical properties, excellent biocompatibility, and bioactivities. However, the design and preparation of such scaffolds are challenging. Imaging modalities including magnetic resonance imaging (MRI), micro-computed tomography (Micro-CT), ultrasound imaging (UI), optical coherence tomography (OCT), and confocal laser scanning microscopy (CLSM) are commonly used to visualize the interior architecture of bioprinted scaffolds as well as the surrounding cells and tissues. The obtained bioimages provide the first-hand information of scaffold biological functionalities, while the interpretation of such images can readily give rise to divergent viewpoints and even arguments. This study is to review and explore deep learning (DL) methods in those image analysis through restoration, segmentation, and classification. Firstly, the current DL methods for biological image processing are summarized, such as convolutional neural network, U-Net and generative adversarial network. The corresponding outcomes of these methods can reveal the cell-scaffold and tissue-scaffold interactions and guide scaffold design in each specific task. Last but not least, the challenges and limitations of DL applications in this area are highlighted, such as building DL models using smaller bioimage datasets, interpreting DL models, vision-language model guided bioimage analysis, and developing intelligent analysis platforms. These studies would mark a paradigm shift in polymer scaffold designs and the associated performance.

Boosting Lipofection Efficiency Through Enhanced Membrane Fusion Mechanisms

Gene transfection is a fundamental technique in the fields of biological research and therapeutic innovation. Due to their biocompatibility and membrane-mimetic properties, lipid vectors serve as essential tools in transfection. The successful delivery of genetic material into the cytoplasm is contingent upon the fusion of the vector and cellular membranes, which enables hydrophilic polynucleic acids to traverse the hydrophobic barriers of two intervening membranes. This review examines the critical role of membrane fusion in lipofection efficiency, with a particular focus on the molecular mechanisms that govern lipoplex–membrane interactions. This analysis will examine the key challenges inherent to the fusion process, from achieving initial membrane proximity to facilitating final content release through membrane remodeling. In contrast to viral vectors, which utilize specialized fusion proteins, lipid vectors necessitate a strategic formulation and environmental optimization to enhance their fusogenicity. This review discusses recent advances in vector design and fusion-promoting strategies, emphasizing their potential to improve gene delivery yield. It highlights the importance of understanding lipoplex–membrane fusion mechanisms for developing next-generation delivery systems and emphasizes the need for continued fundamental research to advance lipid-mediated transfection technology.

Engineering a robust Cas12i3 variant-mediated wheat genome editing system.

Wheat (Triticum aestivum L., 2n = 6x = 42, AABBDD) is one of the most important food crops in the world. CRISPR/Cas12i3, which belongs to the type V-I Cas system, has attracted extensive attention recently due to its smaller protein size and its less-restricted canonical 'TTN' protospacer adjacent motif (PAM). However, due to its relatively lower editing efficacy in plants and the hexaploidy complex nature of wheat, Cas12i3/Cas12i3-5M-mediated genome editing in wheat has not been documented yet. Here, we report the engineering of a robust Cas12i3-5M-mediated genome editing system in wheat through the fusion of T5 exonuclease (T5E) in combination with an optimised crRNA expression strategy (Opt). We first showed that fusion of T5E, rather than ExoI, to Cas12i3-5M increased the gene editing efficiencies by up to 1.34-fold and 3.87-fold, compared to Cas12i3-5M and Cas12i3 in HEK293Tcells, respectively. However, its editing efficiency remains low in wheat. We then optimised the crRNA expression strategy and demonstrated that Opt-T5E-Cas12i3-5M could enhance the editing efficiency by 1.20- to 1.33-fold and 4.05- to 7.95-fold in wheat stable lines compared to Opt-Cas12i3-5M and Opt-Cas12i3, respectively, due to progressive 5'-end resection of the DNA strand at the cleavage site with increased deletion size. The Opt-T5E-Cas12i3-5M enabled an editing efficiency ranging from 60.71% to 90.00% across four endogenous target genes in stable lines of three elite Chinese wheat varieties. Together, the developed robust Opt-T5E-Cas12i3-5M system enriches wheat genome editing toolkits for either biological research or genetic improvement and may be extended to other important polyploidy crop species.

Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases

Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people’s health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.

Updates implemented in version 4 of the GlyCosmos Glycoscience Portal.

Glycosylation, characterized by its complexity and diversity, is a common system across all domains of life. The glycosylation of proteins or lipids imparts them with structural and functional roles, ranging from development to infectious or Mendelian disease. The high-throughput-based omics data has revealed that glycans are involved in important cellular processes. Comprehensive knowledge of glycosylation has contributed not only to the fundamental concepts in glycoscience but also to its applications, including the development of molecular markers for diagnosis and therapeutic tools for treating diseases. The GlyCosmos Glycoscience Portal (GlyCosmos) has undergone significant updates to better support the scientific community in studying glycosylation-related phenomena. Key enhancements include the integration of expanded datasets linking glycans to other omics fields, improved tools for glycan structure prediction and analysis, and upgraded visualization capabilities to streamline data interpretation. A strengthened focus on data standardization has also been introduced, fostering interoperability between glycoscience resources and external databases. Since its release in 2019, the portal has seen a fivefold increase in user engagement, reflecting its growing relevance. These recent advancements aim to provide researchers with a more comprehensive and user-friendly platform, enabling deeper insights into glycan roles in cellular processes and disease mechanisms. GlyCosmos will continue to evolve, prioritizing community needs and advancing the integration of glycoscience with broader biological and biomedical research.

Liquid–liquid phase separation in neurodegenerative diseases: An updated understanding

Liquid–liquid phase separation (LLPS), once understood merely as a physicochemical phenomenon, has emerged over the past decade as a critical player in life processes. LLPS provides a novel framework for understanding the structure, function, and spatiotemporal regulation of intracellular biomolecules. Through LLPS, biomolecules within cells can spontaneously assemble into membraneless compartments, which allow precise regulation of biochemical reactions and influence critical cellular processes such as signal transduction and gene expression. Despite its recognized significance in basic biological research, the role of LLPS in human disease is still an area worthy of continued exploration. Currently, the most studied disorders in relation to LLPS are neurodegenerative diseases and cancer. In neurodegenerative diseases, LLPS is closely linked to protein misfolding and aggregation, processes that can lead to the formation of toxic assemblies, ultimately causing neuronal damage and death. In cancer, aberrant LLPS may contribute to the dysregulation of signaling pathways, promoting uncontrolled cell proliferation and metastasis. This review highlights recent advances regarding the role of LLPS in the pathogenesis of neurodegenerative diseases, discussing its function in these pathological conditions and proposing directions for future research. As research progresses, the potential role of LLPS in other human diseases will likely be uncovered, offering new avenues for diagnosis and therapy. Therefore, further investigation into the mechanisms of LLPS and its involvement in disease pathology will be crucial for advancing our understanding of human health and disease.

Editorial for Special Issue “Cellular Senescence: Recent Cellular Advances and Discoveries”

Cellular senescence has emerged as a fascinating frontier in biological research and now presents profound implications across diverse fields, from aging research to cancer therapy [...]

Differential Regulation of Hyaluronan Synthesis by Three Isoforms of Hyaluronan Synthases in Mammalian Cells

Hyaluronan (HA) is one of the crucial components of the extracellular matrix in vertebrates and is synthesized by three hyaluronan synthases (HASs), namely HAS1, HAS2, and HAS3. The low expression level of HASs in normal keratinocytes and other various types of cells presents a recognized challenge, impeding biological and pathological research on their localization. In this study, the human proteins HAS1, HAS2, and HAS3 with fused maltose-binding protein (MBP) tags were successfully expressed at high levels and purified for the first time in HEK293F cells. The enzymatic properties of the three HAS proteins were further characterized and compared. A pulse-field gel electrophoresis analysis of the size distribution of the hyaluronan generated in vitro by the membrane proteins demonstrated that the three HAS isoforms generate HA polymer chains at different molecular masses. Kinetic studies demonstrated that the three HAS proteins differed in their catalytic efficiency and apparent Km values for the two substrates, UDP-GlcA and UDP-GlcNAc. Furthermore, the cellular hyaluronan secretion by the three isoenzymes was evaluated and quantified in the HEK 293T cells transfected with GFP-tagged HAS1-GFP, HAS2-GFP, and HAS3-GFP using an ELISA assay. These findings enhance our understanding of the membrane protein HASs in mammalian cells.

Advancing public understanding of epigenetics: the EPIBOOST project’s video science outreach

ABSTRACT The public’s growing interest in epigenetics, particularly its applications in human health, underscores the significance of epigenetic mechanisms in regulating gene activity and promoting phenotypic diversity without altering DNA sequences. Within the framework of the Extended Evolutionary Synthesis, epigenetics is regarded as a non-genetic contributor to evolution. The EPIBOOST project, “BOOSting excellence in environmental EPIgenetics,” investigates epigenetic changes induced by contaminants in aquatic organisms and aims to enhance public understanding of epigenetics through science outreach videos. A multidisciplinary team comprising Biology, Education, and Design researchers produced the video “What is Epigenetics?” using a design-based research methodology and a design thinking approach. This process involved storyboard design, audio recording, image production, video editing, testing, refinement, and public dissemination. The interactive development of the storyboard and script aimed to efficiently communicate the significance of epigenetics, fostering public understanding of this emerging research area through an effective science communication strategy. This paper focuses on the development process for the first video, which aims to promote public awareness of epigenetics using the AEIOU framework (Awareness, Enjoyment, Interest, Opinions, and Understanding) to elucidate epigenetic concepts such as DNA methylation, gene expression, and phenotype, and their integration in environmental assessment frameworks.

MRNA-encoded fluorescent proteins enable superior organelle labeling for live cell imaging.

Live cell labeling of various organelles is of great demand in the research field of cell biology. However, current approaches often lack an optimal balance between efficiency and versatility. We took advantage of chemical modified mRNA to express various organelle located fluorescent proteins. This approach facilitated highly efficient multi-organelle labeling across diverse cell types, both invitro and invivo. The application of mRNA-based organelle markers offers faster and more efficient transfection and expression compared to plasmids. When expressing fluorescent proteins in neuronal cells, it is faster and safer compared to viral vectors. Notably, this approach excels in rapidly labeling organelles, making it highly effective for dynamic live cell imaging applications. By leveraging this tool, we unveiled the unique behaviors of mitochondria and the endoplasmic reticulum during spike protein-mediated cell fusion, a critical event in SARS-CoV-2 viral entry. Thus, our results demonstrate the potency of mRNA-encoded fluorescent proteins as powerful tools for advancing biological research.

Clinical and instrumental characteristics of mild traumatic brain injury and acubarotrauma due to the impact of an explosive shock wave

BACKGROUND: Traumatic brain injuries are widespread and need to be studied, having a serious impact on health and the economy. Every year, about 69 million people experience traumatic brain injuries, including road accidents and military conflicts. A number of studies show that up to 20% of veterans who have returned from Iraq and Afghanistan, got a mild head injury. 83% of those hospitalized between 2001 and 2018 had a mild traumatic brain injuries. Pathophysiological processes in traumatic brain injuries include axon loss and demyelination, which disrupts the functioning of neural networks and manifests itself in the form of instability, cognitive decline, and mental changes. The causes of instability can be associated with damage to the vestibular apparatus, conducting pathways and connections of the cerebellum and cortical analyzers, which can be detected by modern neuroimaging methods. Stabilometry using a power platform is a promising diagnostic method for light therapy. traumatic brain injuries, which allows you to study the equilibrium function. AIM: to determine the clinical and instrumental characteristics of mild traumatic brain injury and acubarotrauma due to the impact of a shock blast wave separately and in combination. MATERIALS AND METHODS: 66 patients were selected (n = 66) aged 18 to 56 years (38.28 ± 9.98 years) who were exposed to a shock wave. Of these, 28 (42.4%) patients with mild traumatic brain injury (group 1), 21 (31.8%) patients with isolated acubarotrauma (group 2), with a combination of mild traumatic brain injury and acubarotrauma in 17 (25.8%) patients (group 3). Age, presence of chronic diseases, neurological status, neuropsychological examination, stabilometric examination, computed tomography and magnetic resonance imaging of the brain were evaluated. The table editor (MS Excel 2019) was used for statistical data processing and the program for medical and biological research (STATISTICA 12). RESULTS: the diagnosis of mild traumatic brain injury and acubarotrauma was established based on clinical recommendations. In the neurological status of all patients in groups 1 and 3, scattered organic symptoms were detected. In group 2, patients had a few isolated unstable neurological signs. According to neuropsychological testing, there was a deterioration in the results in group 3 compared to the group 1 and 2 (p 0.05). A stabilometric study showed that in group 3 significantly worse indicators of the speed of the general center of pressure, the area of the statokinesiogram with both open and closed eyes were noted compared to the other groups (p 0.05). According to the data of magnetic resonance imaging of the brain, including using SWI/SWAN sequences, diffuse micro-hemorrhages in the brain substance were not detected in the study groups. CONCLUSION: the main characteristics of mild traumatic brain injury in combination with acubarotrauma include pronounced postural instability, deterioration of stabilometric parameters and cognitive functions.

Prospects for applying the chronic social conflict model in medical and biological research

Long-term studies (1987–2023) have shown that the model of chronic social conflict with the original name «sensory contact model» can be used to model various pathological conditions that develop in mice under the influence of chronic social stress, which makes it possible to study neurophysiological and neuromolecular mechanisms at different stages of disease development, in particular, increased anxiety, depression-like and psychosis-like states in mice of the C57BL/6 strain under repeated agonistic interactions. In pharmacological experiments in mice with different pathological symptoms, it becomes possible to study: the therapeutic and protective effects of drugs at different stages of disease development, the effectiveness of treatment, and methods for prevention of relapses of the disease. The model makes it possible to develop approaches to pharmacogenomic therapy, as well as search for peripheral markers of pathological conditions.

Roles and regulatory patterns of protein isoforms in plant adaptation and development.

Protein isoforms (PIs) play pivotal roles in regulating plant growth and development that confer adaptability to diverse environmental conditions. PIs are widely present in plants and generated through alternative splicing (AS), alternative polyadenylation (APA), alternative initiation (AI), and ribosomal frameshifting (RF) events. The widespread presence of PIs not only significantly increases the complexity of genomic information but also greatly enriches regulatory networks and enhances their flexibility. PIs may also play important roles in phenotypic diversity, ecological niche differentiation, and speciation, thereby increasing the dimensions of research in molecular ecology. However, PIs pose new challenges for the quantitative analysis, annotation, and identification of genetic regulatory mechanisms. Thus, focus on PIs make genomic and epigenomic studies both more powerful and more challenging. This review summarizes the origins, functions, regulatory patterns of isoforms, and the challenges they present for future research in molecular ecology and molecular biology.

State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues

Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g., noise), which can compromise the accuracy of traditional methods. The emergence of deep learning (DL) has catalyzed substantial advancements in addressing these issues. This systematic literature review (SLR) provides a comprehensive overview of state-of-the-art DL methods developed over the past six years for the segmentation of microscopic images. We critically analyze key contributions, emphasizing how these methods specifically tackle challenges in cell, nucleus, and tissue segmentation. Additionally, we evaluate the datasets and performance metrics employed in these studies. By synthesizing current advancements and identifying gaps in existing approaches, this review not only highlights the transformative potential of DL in enhancing diagnostic accuracy and research efficiency but also suggests directions for future research. The findings of this study have significant implications for improving methodologies in medical and biological applications, ultimately fostering better patient outcomes and advancing scientific understanding.

Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset

This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm. The models underwent comprehensive evaluation, considering accuracy, precision, and recall as key parameters. Notably, YOLOv8 demonstrated superior performance over Mask R-CNN, particularly in accurately calculating stomata pore dimensions. Beyond this comparative study, the implications of our findings extend across diverse biological research, providing a robust foundation for advancing our understanding of plant physiology. Furthermore, the preprocessing enhancements offer valuable insights for refining image analysis techniques, showcasing the potential for broader applications in scientific domains. This research marks a significant stride in unraveling the complexities of plant structures, offering both theoretical insights and practical applications in scientific research.

A Review of Liquid Chromatography-Mass Spectrometry and its Applications in Chemical Analysis

Liquid Chromatography-Mass Spectrometry (LC-MS) has emerged as a powerful analytical technique combining the separation capabilities of liquid chromatography with the high sensitivity and selectivity of mass spectrometry. This sophisticated instrumentation enables precise identification and quantification of complex chemical mixtures across diverse fields including pharmaceuticals, environmental monitoring, food safety, and biological research. Modern LC-MS systems offer enhanced resolution, improved ionization methods, and sophisticated mass analyzers that facilitate accurate molecular weight determination and structural elucidation of compounds. Recent technological advancements have led to the development of ultra-high-performance liquid chromatography (UHPLC) systems coupled with high-resolution mass spectrometers, enabling faster analysis times and improved detection limits. The versatility of LC-MS is demonstrated through its applications in proteomics, metabolomics, drug development, and quality control processes. Integration of artificial intelligence and machine learning algorithms has further enhanced data processing capabilities, allowing for automated peak identification and complex mixture analysis. The continuous evolution of LC-MS technology has addressed previous limitations in ionization efficiency, matrix effects, and quantification accuracy. This analytical technique has revolutionized chemical analysis by providing comprehensive molecular information, making it an indispensable tool in modern analytical laboratories.

Unlocking New Capabilities in the Analysis of GC × GC‐TOFMS Data With Shift‐Invariant Multi‐Linearity

ABSTRACTThis paper introduces a novel deconvolution algorithm, shift‐invariant multi‐linearity (SIML), which significantly enhances the analysis of data from two‐dimensional gas chromatography instruments coupled to a time‐of‐flight mass spectrometer (GC × GC‐TOFMS). Designed to address the challenges posed by retention time shifts and high noise levels, SIML incorporates wavelet‐based smoothing and Fourier‐transform based shift‐correction within the multivariate curve resolution‐alternating least squares (MCR‐ALS) framework. We benchmarked the SIML algorithm against non‐negativity constrained MCR‐ALS and parallel factor analysis 2 with flexible coupling (PARAFAC2 × N) using both simulated and real GC × GC‐TOFMS datasets. Our results demonstrate that SIML provides unique solutions with significantly improved robustness, particularly in low signal‐to‐noise ratio scenarios, where it maintains high accuracy in estimating mass spectra and concentrations. The enhanced reliability of quantitative analyses afforded by SIML underscores its potential for broad application in complex matrix analyses across environmental science, food science, and biological research.

Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer.

Virtual staining for digital pathology has great potential to enable spatial biology research, improve efficiency and reliability in the clinical workflow, as well as conserve tissue samples in a non-destructive manner. In this study, we demonstrate the feasibility of generating virtual stains for hematoxylin and eosin (H&E) and a multiplex immunofluorescence (mIF) immuno-oncology panel (DAPI, PanCK, PD-L1, CD3, CD8) from autofluorescence images of unstained non-small cell lung cancer tissue by combining high-throughput hyperspectral fluorescence microscopy and machine learning. Using domain-specific computational methods, we evaluated the accuracy of virtual H&E for histologic subtyping and virtual mIF for cell segmentation-based measurements, including clinically-relevant measurements such as tumor area, T cell density, and PD-L1 expression (tumor proportion score and combined positive score). The virtual stains reproduce key morphologic features and protein biomarker expressions at both tissue and cell levels compared to real stains, enable the identification of key immune phenotypes important for immuno-oncology, and show moderate to good performance across various evaluation metrics. This study extends our previous work on virtual staining from autofluorescence in liver disease and prostate cancer, further demonstrating the generalizability of this deep learning technique to a different disease (lung cancer) and stain modality (mIF).

A new symbiotic, holistic and gradualist model proposal for the concept of "living organism".

In biology, the concept of "living organism" has traditionally been based on the smallest level of organization comprising all the necessary and essential characteristics of life: the cell. Today, this concept is being challenged by the analysis of ambiguous biological entities, located both below and above the level of the living cell, which exhibit some of the characteristics of living organisms. This situation has given rise to an epistemological pluralism of the concepts of "organism", "individual" and "living", for which no clear and unanimous definition has yet been accepted. The aim of this manuscript is to explore new ideas and perspectives for defining the concept of "living organism", in order to eliminate a certain level of pluralism that could generate confusion, particularly in the pragmatic context of biological research. First, I expose the dualism of the concepts of "organism" and "individual" and suggest a fusion of these concepts in order to eliminate a certain level of pluralism. In doing so, I develop a symbiotic and holistic definition of the concept of "living organism", which includes different structural levels of the organism: molecular, cellular and ecosystems. Second, I present the epistemological problem of the concept of "living", which is closely related to the concepts of "organism" and "individual", by analyzing the list and gradational types of definition. In doing so, I propose a new symbiotic, holistic and gradualist model of the concept of "living organism", using a gradation of several properties of the living applied to the different structural levels of the organism developed previously (molecular, cellular, ecosystems).

DNA Barcodes and Morphology Reveal Two New Species of the Genus Prochas Walkley, 1959 (Ichneumonidae, Campopleginae), from China

DNA barcoding is an effective modern tool in taxonomy, evolutionary biology, and biodiversity research. Many new species have been discovered and described with DNA barcodes as part of their diagnostic features. We combined morphological examination and molecular species delimitation of the mitochondrial cytochrome c oxidase 1 (COI) gene using the automatic barcode gap discovery (ABGD) to investigate species boundaries. The genus Prochas Walkley (Hymenoptera, Ichneumonidae, Campopleginae) was first reported from China and is new for the Oriental and Eastern Palearctic regions. Using an integrative taxonomy method, two new species P. rugipunctata sp. nov. and P. striata sp. nov. are hereby described and illustrated. A key to the world species and a distribution map are provided.