Level Of Individual Cells Research Articles

Nuclear deformation and cell division of single cell on elongated micropatterned substrates fabricated by DMD lithography

Cells sense mechanical signals from the surrounding environment and transmit them to the nucleus through mechanotransduction to regulate cellular behavior. Microcontact printing, which utilizes elastomer stamps, is an effective method for simulating the cellular microenvironment and manipulating cell morphology. However, the conventional fabrication process of silicon masters and elastomer stamps requires complex procedures and specialized equipment, which restricts the widespread application of micropatterning in cell biology and hinders the investigation of the role of cell geometry in regulating cell behavior. In this study, we present an innovative method for convenient resin stamp microfabrication based on digital micromirror device planar lithography. Using this method, we generated a series of patterns ranging from millimeter to micrometer scales and validated their effectiveness in controlling adhesion at both collective and individual cell levels. Additionally, we investigated mechanotransduction and cell behavior on elongated micropatterned substrates. We then examined the effects of cell elongation on cytoskeleton organization, nuclear deformation, focal adhesion formation, traction force generation, nuclear mechanics, and the growth of HeLa cells. Our findings reveal a positive correlation between cell length and mechanotransduction. Interestingly, HeLa cells with moderate length exhibit the highest cell division and proliferation rates. These results highlight the regulatory role of cell elongation in mechanotransduction and its significant impact on cancer cell growth. Furthermore, our methodology for controlling cell adhesion holds the potential for addressing fundamental questions in both cell biology and biomedical engineering.

FOVEAL PHENOTYPES IN CHOROIDEREMIA ON ADAPTIVE OPTICS SCANNING LIGHT OPHTHALMOSCOPY.

Choroideremia is an X-linked inherited retinal degeneration involving the choriocapillaris, retinal pigment epithelium, and photoreceptors. Adaptive optics scanning light ophthalmoscopy allows visualization of retinal structure at the level of individual cells and is well poised to provide insight into the pathophysiologic mechanisms underpinning the retinal degeneration in choroideremia. Foveal adaptive optics scanning light ophthalmoscopy images of 102 eyes of 54 individuals with choroideremia were analyzed. Measures were compared with those from standard clinical imaging. Visual acuity was also measured and compared with quantitative foveal metrics. The 3 distinct phenotypes observed were: relatively normal (5 eyes, 4 individuals), spiderweb (9 eyes, 7 individuals), and salt and pepper (87 eyes, 47 individuals). Peak cone density (86 eyes of 51 individuals) was significantly lower in choroideremia than in healthy retinas (P < 0.0001, range: 29,382-157,717 cones/mm2). Peak cone density was significantly related to extent of retained ellipsoid zone on en face optical coherence tomography (r2 = 0.47, P = 0.0009) and inversely related to visual acuity (r2 = 0.20, P = 0.001). Distinct phenotypes can be observed on adaptive optics scanning light ophthalmoscopy imaging in choroideremia that cannot always be discerned on standard clinical imaging. Quantitative measures on adaptive optics imaging are related to the structural and functional severity of disease.

Real-time monitoring of replication errors’ fate reveals the origin and dynamics of spontaneous mutations

The efficiency of replication error repair is a critical factor governing the emergence of mutations. However, it has so far been impossible to study this efficiency at the level of individual cells and to investigate if it varies within isogenic cell populations. In addition, why some errors escape repair remains unknown. Here we apply a combination of fluorescent labelling of the Escherichia coli Mismatch Repair (MMR) complex, microfluidics, and time-lapse microscopy, to monitor in real-time the fate of >20000 replication errors. We show that i) many mutations result from errors that are detected by MMR but inefficiently repaired ii) this limited repair efficiency is due to a temporal constraint imposed by the transient nature of the DNA strand discrimination signal, a constraint that is likely conserved across organisms, and iii) repair capacity varies from cell to cell, resulting in a subpopulation of cells with higher mutation rate. Such variations could influence the fitness and adaptability of populations, accelerating for instance the emergence of antibiotic resistance.

AM-DMF-SCP: Integrated Single-Cell Proteomics Analysis on an Active Matrix Digital Microfluidic Chip.

Single-cell proteomics offers unparalleled insights into cellular diversity and molecular mechanisms, enabling a deeper understanding of complex biological processes at the individual cell level. Here, we develop an integrated sample processing on an active-matrix digital microfluidic chip for single-cell proteomics (AM-DMF-SCP). Employing the AM-DMF-SCP approach and data-independent acquisition (DIA), we identify an average of 2258 protein groups in single HeLa cells within 15 min of the liquid chromatography gradient. We performed comparative analyses of three tumor cell lines: HeLa, A549, and HepG2, and machine learning was utilized to identify the unique features of these cell lines. Applying the AM-DMF-SCP to characterize the proteomes of a third-generation EGFR inhibitor, ASK120067-resistant cells (67R) and their parental NCI-H1975 cells, we observed a potential correlation between elevated VIM expression and 67R resistance, which is consistent with the findings from bulk sample analyses. These results suggest that AM-DMF-SCP is an automated, robust, and sensitive platform for single-cell proteomics and demonstrate the potential for providing valuable insights into cellular mechanisms.

Involvement of Escherichia coli YbeX/CorC in ribosomal metabolism.

YbeX of Escherichia coli, a member of the CorC protein family, is encoded in the same operon with ribosome-associated proteins YbeY and YbeZ. Here, we report the involvement of YbeX in ribosomal metabolism. The ΔybeX cells accumulate distinct 16S rRNA degradation intermediates in the 30S particles and the 70S ribosomes. E. coli lacking ybeX has a lengthened lag phase upon outgrowth from the stationary phase. This growth phenotype is heterogeneous at the individual cell level and especially prominent under low extracellular magnesium levels. The ΔybeX strain is sensitive to elevated growth temperatures and to several ribosome-targeting antibiotics that have in common the ability to induce the cold shock response in E. coli. Although generally milder, the phenotypes of the ΔybeX mutant overlap with those caused by ybeY deletion. A genetic screen revealed partial compensation of the ΔybeX growth phenotype by the overexpression of YbeY. These findings indicate an interconnectedness among the ybeZYX operon genes, highlighting their roles in ribosomal assembly and/or degradation.

“Patchiness” in mechanical stiffness across a tumor as an early-stage marker for malignancy

Mechanical phenotyping of tumors, either at an individual cell level or tumor cell population level is gaining traction as a diagnostic tool. However, the extent of diagnostic and prognostic information that can be gained through these measurements is still unclear. In this work, we focus on the heterogeneity in mechanical properties of cells obtained from a single source such as a tissue or tumor as a potential novel biomarker. We believe that this heterogeneity is a conventionally overlooked source of information in mechanical phenotyping data. We use mechanics-based in-silico models of cell-cell interactions and cell population dynamics within 3D environments to probe how heterogeneity in cell mechanics drives tissue and tumor dynamics. Our simulations show that the initial heterogeneity in the mechanical properties of individual cells and the arrangement of these heterogenous sub-populations within the environment can dictate overall cell population dynamics and cause a shift towards the growth of malignant cell phenotypes within healthy tissue environments. The overall heterogeneity in the cellular mechanotype and their spatial distributions is quantified by a “patchiness” index, which is the ratio of the global to local heterogeneity in cell populations. We observe that there exists a threshold value of the patchiness index beyond which an overall healthy population of cells will show a steady shift towards a more malignant phenotype. Based on these results, we propose that the “patchiness” of a tumor or tissue sample, can be an early indicator for malignant transformation and cancer occurrence in benign tumors or healthy tissues. Additionally, we suggest that tissue patchiness, measured either by biochemical or biophysical markers, can become an important metric in predicting tissue health and disease likelihood just as landscape patchiness is an important metric in ecology.

Insights into the Precision Medicine of Pancreatic Cancer: Single-Cell Analysis

The rising incidence of pancreatic cancer (PC) is a global challenge of paramount importance. Understanding the complex nature of this disease is crucial for addressing various issues within the field, including early recurrence, metastasis, and the formidable resistance to chemotherapy and radiotherapy. The complexity of PC arises from the diverse biological phenotypes present in tumor cells, creating a mosaic-like diversity. The lack of comprehensive insights into the functional aspects of cellular and molecular components further complicates treatment, leading to suboptimal outcomes. To tackle these intricacies, researchers must explore the cellular and molecular foundations of this multifaceted disease. Profiling cellular transcriptomes becomes a powerful tool for understanding the subtle heterogeneity in complex cellular populations within tissues. This approach allows for the deconstruction of molecular processes at the individual cell level, revolutionizing our understanding of cellular identity and function. By carefully examining differences in single-cell genomes, transcriptomes, and epigenetic profiles among tumor cells, both primary and metastatic, collected from patient samples, we gain insights into the diverse landscape of tumor heterogeneity. This perspective has significant implications for precision oncology in the context of pancreatic cancer. The current study provides a comprehensive assessment of the empirical basis for RNA sequencing technologies, the methodologies for single-cell isolation, and the conceptual framework for integrating these elements into prognostic assessment, genomic accessibility, transcriptional dynamics, and proteomic landscapes. By combining these components, the study aims to establish a coherent framework that facilitates the development of precision medicine approaches tailored to the unique challenges presented by pancreatic cancer.

Plant biotechnology research with single-cell transcriptome: recent advancements and prospects.

Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research. Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.

A single cell RNAseq benchmark experiment embedding “controlled” cancer heterogeneity

Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.

Single-cell RNA-seq mapping of chicken peripheral blood leukocytes

BackgroundSingle-cell transcriptomics provides means to study cell populations at the level of individual cells. In leukocyte biology this approach could potentially aid the identification of subpopulations and functions without the need to develop species-specific reagents. The present study aimed to evaluate single-cell RNA-seq as a tool for identification of chicken peripheral blood leukocytes. For this purpose, purified and thrombocyte depleted leukocytes from 4 clinically healthy hens were subjected to single-cell 3′ RNA-seq. Bioinformatic analysis of data comprised unsupervised clustering of the cells, and annotation of clusters based on expression profiles. Immunofluorescence phenotyping of the cell preparations used was also performed.ResultsComputational analysis identified 31 initial cell clusters and based on expression of defined marker genes 28 cluster were identified as comprising mainly B-cells, T-cells, monocytes, thrombocytes and red blood cells. Of the remaining clusters, two were putatively identified as basophils and eosinophils, and one as proliferating cells of mixed origin. In depth analysis on gene expression profiles within and between the initial cell clusters allowed further identification of cell identity and possible functions for some of them. For example, analysis of the group of monocyte clusters revealed subclusters comprising heterophils, as well as putative monocyte subtypes. Also, novel aspects of TCRγ/δ + T-cell subpopulations could be inferred such as evidence of at least two subtypes based on e.g., different expression of transcription factors MAF, SOX13 and GATA3. Moreover, a novel subpopulation of chicken peripheral B-cells with high SOX5 expression was identified. An overall good correlation between mRNA and cell surface phenotypic cell identification was shown.ConclusionsTaken together, we were able to identify and infer functional aspects of both previously well known as well as novel chicken leukocyte populations although some cell types. e.g., T-cell subtypes, proved more challenging to decipher. Although this methodology to some extent is limited by incomplete annotation of the chicken genome, it definitively has benefits in chicken immunology by expanding the options to distinguish identity and functions of immune cells also without access to species specific reagents.

DeePhys: A machine learning–assisted platform for electrophysiological phenotyping of human neuronal networks

Reproducible functional assays to study invitro neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce DeePhys, a MATLAB-based analysis tool for data-driven functional phenotyping of invitro neuronal cultures recorded by high-density microelectrode arrays. DeePhys is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, DeePhys incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply DeePhys to human induced pluripotent stem cell-derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how DeePhys enables phenotypic screenings.

A Computational Approach to Individual Cell-Based Decision Algorithms Involved in Bone Remodeling

This work is concerned with bone remodeling, an intriguing and efficient biological process that ensures the optimal compliance of the human skeleton by screening and replacing any single piece of it on a recursive basis. We propose here that a class of algorithms, which are simple enough to be implemented at an individual cell level, suffices to account for the two main features of such homeostatic process: thorough screening of the whole skeleton on the one hand and destruction and subsequent replacement of any single bone piece on the other. This last process is accomplished at a microscopic scale by special groups of cells, assembled for that purpose, called Bone Multicellular Units (BMUs). Moreover, it is shown that the algorithms proposed are robust, i.e, they remain functional in a wide range of biomechanical environments, thus allowing for different remodeling rates at different places.

Motility behavior and physiological response mechanisms of aerobic denitrifier, Enterobacter cloacae strain HNR under high salt stress: Insights from individual cells to populations

The motility behaviors at the individual-cell level and the collective physiological responsive behaviors of aerobic denitrifier, Enterobacter cloacae strain HNR under high salt stress were investigated. The results revealed that as salinity increased, electron transport activity and adenosine triphosphate content decreased from 15.75 μg O2/g/min and 593.51 mM/L to 3.27 μg O2/g/min and 5.34 mM/L, respectively, at 40 g/L, leading to a reduction in the rotation velocity and vibration amplitude of strain HNR. High salinity stress (40 g/L) down-regulated genes involved in ABC transporters (amino acids, sugars, metal ions, and inorganic ions) and activated the biofilm-related motility regulation mechanism in strain HNR, resulting in a further decrease in flagellar motility capacity and an increase in extracellular polymeric substances secretion (4.08 mg/g cell of PS and 40.03 mg/g cell of PN at 40 g/L). These responses facilitated biofilm formation and proved effective in countering elevated salt stress in strain HNR. Moreover, the genetic diversity associated with biofilm-related motility regulation in strain HNR enhanced the adaptability and stability of the strain HNR populations to salinity stress. This study enables a deeper understanding of the response mechanism of aerobic denitrifiers to high salt stress.

Microscopic and nanoscale mechanical properties of tonkin cane bamboo measured by advanced AFM methods

Bamboo is a natural composite material with a high strength-to-weight ratio traditionally used in various consumer products as well as in building and modern advanced and sustainable manufacturing technology. While its macroscopic mechanical properties are well known, nanoscale studies of the mechanical properties of the hierarchical structure of bamboo at the level of individual cells and cell walls are lacking. Here we use different AFM-based methods to analyse the mechanical properties of individual bamboo fibres that are the foundation of the hierarchical structure at the nanoscale. The elastic modulus of the fiber in the outer region varies from 7.5 to 8.9 GPa, while that of the inner fiber ranges between 4.1 and 6.1 GPa. The results demonstrate that the fibre stiffness varies in nanoscale regions between fibre interior and the fibre wall and strongly depends on the position of the individual bamboo fibre within the culm. Outer fibres with high cellulose fibril density and low lignin level show low adhesion interaction force (13.5%) in the interface between cellulose and lignin/hemicellulose matrix, hence, resulting in low plastic deformation energy release during mechanical deformation. The implications of the interrelation of plasticity, lignin level, and adhesion force are discussed for the application of bamboo as a high-performance, renewable, and sustainable material.

The accumulation of methylated porphyrins in dormant cells of Mycolicibacterium smegmatis is accompanied by a decrease in membrane fluidity and an impede of the functioning of the respiratory chain

Transition of Mycolicibacterium smegmatis (Msm) and Mycobacterium tuberculosis to dormancy in vitro is accompanied by an accumulation of free methylated forms of porphyrins (tetramethyl coproporphyrin – TMC) localized in the cell wall of dormant bacteria. A study of the fluorescence anisotropy of BODIPY based fluorescent probes on individual cell level using confocal microscope revealed significant changes in this parameter for BODIPY FL C16 from 0.05 to 0.22 for vegetative and dormant Msm cells correspondingly. Similarly, the increase of TMC concentration in vegetative Msm cells grown in the presence of 5-aminolevulinic acid (a known inducer of porphyrin synthesis) resulted in an increase of BODIPY FL C16 anisotropy. These changes in TMC concentration and membrane fluidity were accompanied by an inhibition of the activity of the respiratory chain measured by oxygen consumption and a reduction of the DCPIP redox acceptor. During the first 8 h of the reactivation of the dormant Msm cells, the porphyrin content and probe fluorescent anisotropy returned to the level for vegetative bacteria. We suggested that upon transition to dormancy, an accumulation of TMC in membranes leads to a decrease in membrane fluidity, resulting in an inhibition of the respiratory chain activity. However, direct interactions of TMC with membrane bound enzymes cannot also be excluded. This, in turn, may result in the down regulation of many metabolic energy-dependent reactions as a part of mechanisms accompanying the transition to a hypometabolic state of mycobacteria.

Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications

This paper discusses and evaluates a novel multi-channel real-time architecture aimed at monitoring a Proton Exchange Membrane (PEM) electrolyzer, both at the individual cell and stack levels. The proposed solution includes two primary subsystems: a hardware subsystem dedicated to data acquisition (DAQ) and a software subsystem focused on monitoring purposes. The DAQ subsystem utilizes an Arduino platform, being an affordable and open-source solution. The real-time monitoring data can be encoded in JSON format, widely used as a light-weight inter-exchange data format between a variety of IoT applications. They are also available to be transferred to Excel. Indeed, and to enhance convenience, the proposed system integrates graphs displaying a template based on Excel spreadsheets, which are commonly used in industrial environments. The current, voltage, temperature, and pressure data of both individual cells and stacks were monitored and collected, being configurable under a variety of ranges. As a case study, the validation of the system involved static and dynamic operational modes using a 1.2 kW PEM electrolyzer prototype (100 A, 1 A/cm2). The results successfully provided the monitored variables across individual cells and within the stack. The proposed approach exhibits relevant key characteristics such as scalability, flexibility, user-friendliness, versatility, and affordability and are suitable to monitor PEM electrolyzers in real-time at both the cell and stack levels.

Single Cell Sequencing Technology and Its Application in Alzheimer's Disease.

Alzheimer's disease (AD) involves degeneration of cells in the brain. Due to insidious onset and slow progression, AD is often not diagnosed until it gets progressed to a more severe stage. The diagnosis and treatment of AD has been a challenge. In recent years, high-throughput sequencing technologies have exhibited advantages in exploring the pathogenesis of diseases. However, the types of cells of the central nervous system are complex and traditional bulk sequencing cannot reflect their heterogeneity. Single-cell sequencing technology enables study at the individual cell level and has an irreplaceable advantage in the study of complex diseases. In recent years, this field has expanded rapidly and several types of single-cell sequencing technologies have emerged, including transcriptomics, epigenomics, genomics and proteomics. This review article provides an overview of these single-cell sequencing technologies and their application in AD.

ScMAE: a masked autoencoder for single-cell RNA-seq clustering.

Single-cell RNA sequencing has emerged as a powerful technology for studying gene expression at the individual cell level. Clustering individual cells into distinct subpopulations is fundamental in scRNA-seq data analysis, facilitating the identification of cell types and exploration of cellular heterogeneity. Despite the recent development of many deep learning-based single-cell clustering methods, few have effectively exploited the correlations among genes, resulting in suboptimal clustering outcomes. Here, we propose a novel masked autoencoder-based method, scMAE, for cell clustering. scMAE perturbs gene expression and employs a masked autoencoder to reconstruct the original data, learning robust and informative cell representations. The masked autoencoder introduces a masking predictor, which captures relationships among genes by predicting whether gene expression values are masked. By integrating this masking mechanism, scMAE effectively captures latent structures and dependencies in the data, enhancing clustering performance. We conducted extensive comparative experiments using various clustering evaluation metrics on 15 scRNA-seq datasets from different sequencing platforms. Experimental results indicate that scMAE outperforms other state-of-the-art methods on these datasets. In addition, scMAE accurately identifies rare cell types, which are challenging to detect due to their low abundance. Furthermore, biological analyses confirm the biological significance of the identified cell subpopulations. The source code of scMAE is available at: https://zenodo.org/records/10465991.

Osnovni principi otkrivanja oštećenja DNK pomoću alkalnog komet testa

The Comet assay is a highly sensitive and rapid method for detecting DNA damage at the level of individual cells. It measures single-stranded and double-stranded DNA breaks, alkali-labile, incomplete excision repair and cross-linking sites. A major advantage of the Comet assay is its applicability to both proliferating and non-proliferating cells, and it can be performed on all types of eukaryotic cells. The basic principle of the alkaline Comet assay is to lyse the biological membranes to release proteins bound to the DNA, followed by a short-term electrophoresis at a pH above 13. Through this process, the DNA fragments migrate and form a comet-like shape, which is visualized by staining with a DNA-binding fluorescent dye. The extent of electrophoretic migration is proportional to the amount of DNA damage. Key advantages of the Comet assay include sensitivity-the assay can detect low levels of DNA damage, versatility-it can be used with a small amount of test substance, speed-results are usually available within 24 to 48 hours, and broad applicability-the method is suitable for numerous purposes, including molecular epidemiology, occupational exposure studies, environmental biomonitoring, antigenotoxicity assessments and clinical research. Overall, the Comet assay provides valuable data for a range of scientific and clinical fields, making it a versatile and powerful tool for assessing DNA damage.

Лабораторные методы оценки функционирования цитокинов в ветеринарной практике

This review paper analyzes modern laboratory methods for assessing the functioning of cytokines in veterinary practice for the prevention, prevention and development of treatment plans for pathologies of various etiologies. Purpose of the work: to analyze laboratory methods for assessing the functioning of cytokines in veterinary practice. Materials and methods. For the study, we used the resources of the Elibrary and PubMed search engines on this topic. Research results. A brief description of cytokines and their biological activity is presented. Laboratory methods for assessing the functioning of the cytokine system include: genetic analysis of mutations in the genes of cytokine receptors and proteins of intracellular signaling systems that trigger synthesis and signal transmission; analysis of cytokine gene polymorphism; study of cytokine gene expression; studying the level of cytokine production by cells in culture; determination of cytokine concentrations in biological fluids; study of cytokine synthesis at the level of individual cells; study of cytokine synthesis in tissues.