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Interacting proteins tend to have similar functions, influencing the same organismal traits. Interaction networks can be used to expand the list of candidate trait-associated genes from genome-wide association studies. Here, we performed network-based expansion of trait-associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share an underlying genetic and biological process. We identified 73 pleiotropic gene modules linked to multiple traits, enriched in genes involved in processes such as protein ubiquitination and RNA processing. In contrast to gene deletion studies, pleiotropy as defined here captures specifically multicellular-related processes. We show examples of modules linked to human diseases enriched in genes with known pathogenic variants that can be used to map targets of approved drugs for repurposing. Finally, we illustrate the use of network expansion scores to study genes at inflammatory bowel disease genome-wide association study loci, and implicate inflammatory bowel disease-relevant genes with strong functional and genetic support., (© 2023. The Author(s).)

Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body while at the same time raising the need to formalize this new knowledge. Here, we discuss current efforts to harmonize and integrate different sources of annotations of cell types and states into a reference cell ontology. We illustrate with examples how a unified ontology can consolidate and advance our understanding of cell types across scientific communities and biological domains., (© 2021. Springer Nature Limited.)

The Human Reference Atlas (HRA) aims to map all of the cells of the human body to advance biomedical research and clinical practice. This Perspective presents collaborative work by members of 16 international consortia on two essential and interlinked parts of the HRA: (1) three-dimensional representations of anatomy that are linked to (2) tables that name and interlink major anatomical structures, cell types, plus biomarkers (ASCT+B). We discuss four examples that demonstrate the practical utility of the HRA., (© 2021. Springer Nature Limited.)

The performance of light microscopes is limited by the stochastic nature of light, which exists in discrete packets of energy known as photons. Randomness in the times that photons are detected introduces shot noise, which fundamentally constrains sensitivity, resolution and speed 1 . Although the long-established solution to this problem is to increase the intensity of the illumination light, this is not always possible when investigating living systems, because bright lasers can severely disturb biological processes 2-4 . Theory predicts that biological imaging may be improved without increasing light intensity by using quantum photon correlations 1,5 . Here we experimentally show that quantum correlations allow a signal-to-noise ratio beyond the photodamage limit of conventional microscopy. Our microscope is a coherent Raman microscope that offers subwavelength resolution and incorporates bright quantum correlated illumination. The correlations allow imaging of molecular bonds within a cell with a 35 per cent improved signal-to-noise ratio compared with conventional microscopy, corresponding to a 14 per cent improvement in concentration sensitivity. This enables the observation of biological structures that would not otherwise be resolved. Coherent Raman microscopes allow highly selective biomolecular fingerprinting in unlabelled specimens 6,7 , but photodamage is a major roadblock for many applications 8,9 . By showing that the photodamage limit can be overcome, our work will enable order-of-magnitude improvements in the signal-to-noise ratio and the imaging speed.

Cells interpret cues from and interact with fibrous microenvironments through the body based on the mechanics and organization of these environments and the phenotypic state of the cell. This in turn regulates mechanoactive pathways, such as the localization of mechanosensitive factors. Here, we leverage the microscale heterogeneity inherent to engineered fiber microenvironments to produce a large morphologic data set, across multiple cells types, while simultaneously measuring mechanobiological response (YAP/TAZ nuclear localization) at the single cell level. This dataset describing a large dynamic range of cell morphologies and responses was coupled with a machine learning approach to predict the mechanobiological state of individual cells from multiple lineages. We also noted that certain cells (e.g., invasive cancer cells) or biochemical perturbations (e.g., modulating contractility) can limit the predictability of cells in a universal context. Leveraging this finding, we developed further models that incorporate biochemical cues for single cell prediction or identify individual cells that do not follow the established rules. The models developed here provide a tool for connecting cell morphology and signaling, incorporating biochemical cues in predictive models, and identifying aberrant cell behavior at the single cell level.

This review is dedicated to the role of nitration of proteins by tyrosine residues in physiological and pathological conditions. First of all, we analyze the biochemical evidence of peroxynitrite formation and reactions that lead to its formation, types of posttranslational modifications (PTMs) induced by reactive nitrogen species, as well as three biological pathways of tyrosine nitration. Then, we describe two possible mechanisms of protein nitration that are involved in intracellular signal transduction, as well as its interconnection with phosphorylation/dephosphorylation of tyrosine. Next part of the review is dedicated to the role of proteins nitration in different pathological conditions. In this section, special attention is devoted to the role of nitration in changes of functional properties of actin-protein that undergoes PTMs both in normal and pathological conditions. Overall, this review is devoted to the main features of protein nitration by tyrosine residue and the role of this process in intracellular signal transduction in basal and pathological conditions., (© 2020 International Federation for Cell Biology.)

Mucopolysaccharidoses (MPS) are a group of genetic disorders belonging to lysosomal storage diseases. They are caused by genetic defects leading to a lack or severe deficiency of activity of one of lysosomal hydrolases involved in degradation of glycosaminoglycans (GAGs). Partially degraded GAGs accumulate in lysosomes, which results in dysfunctions of cells, tissues, and organs. Until recently, it was assumed that GAG accumulation in cells is the major, if not the only, mechanism of pathogenesis in MPS, as GAGs may be a physical ballast for lysosomes causing inefficiency of cells due to a large amount of a stored material. However, recent reports suggest that in MPS cells there are changes in many different processes, which might be even more important for pathogenesis than lysosomal accumulation of GAGs per se. Moreover, there are many recently published results indicating that lysosomes not only are responsible for degradation of various macromolecules, but also play crucial roles in the regulation of cellular metabolism. Therefore, it appears plausible that previous failures in treatment of MPS (i.e., possibility to correct only some symptoms and slowing down of the disease rather than fully effective management of MPS) might be caused by underestimation of changes in cellular processes and concentration solely on decreasing GAG levels in cells., (© 2019 International Federation for Cell Biology.)

Immunotherapy has changed the treatment paradigm in multiple solid and hematologic malignancies. However, response remains limited in a significant number of cases, with tumors developing innate or acquired resistance to checkpoint inhibition. Certain "hot" or "immune-sensitive" tumors become "cold" or "immune-resistant", with resultant tumor growth and disease progression. Multiple factors are at play both at the cellular and host levels. The tumor microenvironment (TME) contributes the most to immune-resistance, with nutrient deficiency, hypoxia, acidity and different secreted inflammatory markers, all contributing to modulation of immune-metabolism and reprogramming of immune cells towards pro- or anti-inflammatory phenotypes. Both the tumor and surrounding immune cells require high amounts of glucose, amino acids and fatty acids to fulfill their energy demands. Thus, both compete over one pool of nutrients that falls short on needs, obliging cells to resort to alternative adaptive metabolic mechanisms that take part in shaping their inflammatory phenotypes. Aerobic or anaerobic glycolysis, oxidative phosphorylation, tryptophan catabolism, glutaminolysis, fatty acid synthesis or fatty acid oxidation, etc. are all mechanisms that contribute to immune modulation. Different pathways are triggered leading to genetic and epigenetic modulation with consequent reprogramming of immune cells such as T-cells (effector, memory or regulatory), tumor-associated macrophages (TAMs) (M1 or M2), natural killers (NK) cells (active or senescent), and dendritic cells (DC) (effector or tolerogenic), etc. Even host factors such as inflammatory conditions, obesity, caloric deficit, gender, infections, microbiota and smoking status, may be as well contributory to immune modulation, anti-tumor immunity and response to immune checkpoint inhibition. Given the complex and delicate metabolic networks within the tumor microenvironment controlling immune response, targeting key metabolic modulators may represent a valid therapeutic option to be combined with checkpoint inhibitors in an attempt to regain immune function.

Mitochondria are autonomous and dynamic cellular organelles orchestrating a diverse range of cellular activities. Numerous cell-signaling pathways target these organelles and Ca 2+ is one of the most significant. Mitochondria are able to rapidly and transiently take up Ca 2+ , thanks to the mitochondrial Ca 2+ uniporter complex, as well as to extrude it through the Na + /Ca 2+ and H + /Ca 2+ exchangers. The transient accumulation of Ca 2+ in the mitochondrial matrix impacts on mitochondrial functions and cell pathophysiology. Here we summarize the role of mitochondrial Ca 2+ signaling in both physiological (yang) and pathological (yin) processes and the methods that can be used to investigate mitochondrial Ca 2+ homeostasis. As an example of the pivotal role of mitochondria in pathology, we described the state of the art of mitochondrial Ca 2+ alterations in different pathological conditions, with a special focus on Alzheimer's disease., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Facioscapulohumeral muscular dystrophy (FSHD) is one of the most common forms of muscular dystrophy and presents with weakness of the facial, scapular and humeral muscles, which frequently progresses to the lower limbs and truncal areas, causing profound disability. Myopathy results from epigenetic de-repression of the D4Z4 microsatellite repeat array on chromosome 4, which allows misexpression of the developmentally regulated DUX4 gene. DUX4 is toxic when misexpressed in skeletal muscle and disrupts several cellular pathways, including myogenic differentiation and fusion, which likely underpins pathology. DUX4 and the D4Z4 array are strongly conserved only in primates, making FSHD modeling in non-primate animals difficult. Additionally, its cytotoxicity and unusual mosaic expression pattern further complicate the generation of in vitro and in vivo models of FSHD. However, the pressing need to develop systems to test therapeutic approaches has led to the creation of multiple engineered FSHD models. Owing to the complex genetic, epigenetic and molecular factors underlying FSHD, it is difficult to engineer a system that accurately recapitulates every aspect of the human disease. Nevertheless, the past several years have seen the development of many new disease models, each with their own associated strengths that emphasize different aspects of the disease. Here, we review the wide range of FSHD models, including several in vitro cellular models, and an array of transgenic and xenograft in vivo models, with particular attention to newly developed systems and how they are being used to deepen our understanding of FSHD pathology and to test the efficacy of drug candidates., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2020. Published by The Company of Biologists Ltd.)

The dynamic response of cells when subjected to mechanical impact has become increasingly relevant for accurate assessment of potential blunt injuries and elucidating underlying injury mechanisms. When exposed to mechanical impact, a biological system such as the human skin, brain, or liver is rapidly accelerated, which could result in blunt injuries. For this reason, an acceleration of greater than > 150 g is the most commonly used criteria for head injury. To understand the main mechanism(s) of blunt injury under such extreme dynamic threats, we have developed an innovative experimental method that applies a well-characterized and -controlled mechanical impact to live cells cultured in a custom-built in vitro setup compatible with live cell microscopy. Our studies using fibroblast cells as a model indicate that input acceleration ([Formula: see text]) alone, even when it is much greater than the typical injury criteria, e.g., [Formula: see text] g, does not result in cell damage. On the contrary, we have observed a material-dependent critical pressure value above which a sudden decrease in cell population and cell membrane damage have been observed. We have unambiguously shown that (1) this critical pressure is associated with the onset of cavitation bubbles in a cell culture chamber and (2) the dynamics of cavitation bubbles in the chamber induces localized compressive/tensile pressure cycles, with an amplitude that is considerably greater than the acceleration-induced pressure, to cells. More importantly, the rate of pressure change with time for cavitation-induced pressure is significantly faster (more than ten times) than acceleration-induced pressure. Our in vitro study on the dynamic response of biological systems due to mechanical impact is a crucial step towards understanding potential mechanism(s) of blunt injury and implementing novel therapeutic strategies post-trauma.

Necrosis plays vital roles in living organisms which is related closely with various diseases. Non-invasively necrotic imaging can be of great values in clinical decision-making, evaluation of individualized treatment responses, and prediction of patient prognosis. This narrative review will demonstrate how the evolution of quinones for necrotic imaging has been promoted by searching for their active centers. In this review, we summarized the recent developments of various quinones with the continuous simplified π-conjugated cores in necrotic imaging and speculated their possible molecular mechanisms might be attributed to their intercalations with exposed DNA in necrotic tissues. We discussed their clinical challenges of necrotic imaging with quinones and their future translation studies deserved to be explored in personalized patient treatment.

E-cigarettes have a liquid that may contain flavors, solvents, and nicotine. Heating this liquid generates an aerosol that is inhaled into the lungs in a process commonly referred to as vaping. E-cigarette devices can also contain cannabis-based products including tetrahydrocannabinol (THC), the psychoactive component of cannabis (marijuana). E-cigarette use has rapidly increased among current and former smokers as well as youth who have never smoked. The long-term health effects are unknown, and emerging preclinical and clinical studies suggest that e-cigarettes may not be harmless and can cause cellular alterations analogous to traditional tobacco smoke. Here, we review the historical context and the components of e-cigarettes and discuss toxicological similarities and differences between cigarette smoke and e-cigarette aerosol, with specific reference to adverse respiratory outcomes. Finally, we outline possible clinical disorders associated with vaping on pulmonary health and the recent escalation of acute lung injuries, which led to the declaration of the vaping product use-associated lung injury (EVALI) outbreak. It is clear there is much about vaping that is not understood. Consequently, until more is known about the health effects of vaping, individual factors that need to be taken into consideration include age, current and prior use of combustible tobacco products, and whether the user has preexisting lung conditions such as asthma and chronic obstructive pulmonary disease (COPD)., Competing Interests: The authors declare no conflict of interest. The funders had no role in the writing of the manuscript.

The epithelial-mesenchymal transition (EMT) is a process by which cells lose epithelial traits, such as cell-cell adhesion and apico-basal polarity, and acquire migratory and invasive traits. EMT is crucial to embryonic development and wound healing. Misregulated EMT has been implicated in processes associated with cancer aggressiveness, including metastasis. Recent experimental advances such as single-cell analysis and temporal phenotypic characterization have established that EMT is a multistable process wherein cells exhibit and switch among multiple phenotypic states. This is in contrast to the classical perception of EMT as leading to a binary choice. Mathematical modeling has been at the forefront of this transformation for the field, not only providing a conceptual framework to integrate and analyze experimental data, but also making testable predictions. In this article, we review the key features and characteristics of EMT dynamics, with a focus on the mathematical modeling approaches that have been instrumental to obtaining various useful insights.

A sensing methodology that combines Au, tobacco mosaic virus (TMV), and folic acid for selective, sensitive, and colorimetric detection of tumor cells based on the peroxidase-like activity was reported in this study. Gold nanowires with a high aspect ratio were synthesized using TMV as a template. Au@TMV nanowire (AT) complex was obtained with diameter of 4 nm and length between 200 and 300 nm. In addition, since TMV was biocompatible and had many amino and carboxyl groups on its surface, AT was conjugated by folate to form a folic acid (FA)-conjugated AT composite (ATF) and tested by FTIR measurements. Furthermore, the peroxidase-like properties were studied and the optimal conditions for mimic enzyme activity were optimized. Finally, HeLa and other tumor cells expressed excessive receptors of folate on the surface, which can specifically bind to folic acid. As the specific binding of ATF with HeLa cells, the peroxidase properties of ATF were used for detection of cancer cells (Scheme 1). The cancer cells were detected not only qualitatively but also quantitatively. In this study, as low as 2000 cancer cells/mL could be detected using the current method.

Our common knowledge on oxidative stress has evolved substantially over the years, being focused mostly on the fundamental chemical reactions and the most relevant chemical species involved in human pathophysiology of oxidative stress-associated diseases. Thus, reactive oxygen species and reactive nitrogen species (ROS and RNS) were identified as key players in initiating, mediating, and regulating the cellular and biochemical complexity of oxidative stress either as physiological (acting pro-hormetic) or as pathogenic (causing destructive vicious circles) processes. The papers published in this particular Special Issue of Cells show an impressive range on the pathophysiological relevance of ROS and RNS, including the relevance of second messengers of free radicals like 4-hydroxynonenal, allowing us to assume that the future will reveal even more detailed mechanisms of their positive and negative effects that might improve the monitoring of major modern diseases, and aid the development of advanced integrative biomedical treatments.

Autophagy, a main intracellular catabolic process, is induced in response to a variety of cellular stresses to promptly degrade harmful agents and to coordinate the activity of prosurvival and prodeath processes in order to determine the fate of the injured cells. While the main components of the autophagy machinery are well characterized, the molecular mechanisms that confer selectivity to this process both in terms of stress detection and cargo engulfment have only been partly elucidated. Here, we discuss the emerging role played by the E3 ubiquitin ligases of the TRIM family in regulating autophagy in physiological and pathological conditions, such as inflammation, infection, tumorigenesis, and muscle atrophy. TRIM proteins employ different strategies to regulate the activity of the core autophagy machinery, acting either as scaffold proteins or via ubiquitin-mediated mechanisms. Moreover, they confer high selectivity to the autophagy-mediated degradation as described for the innate immune response, where TRIM proteins mediate both the engulfment of pathogens within autophagosomes and modulate the immune response by controlling the stability of signaling regulators. Importantly, the elucidation of the molecular mechanisms underlying the regulation of autophagy by TRIMs is providing important insights into how selective types of autophagy are altered under pathological conditions, as recently shown in cancer and muscular dystrophy.

Background: Microscopic image analysis based on image processing is required for quantitative evaluation of decellularization. Existing methods are not widely used because of expensive commercial software, and machine learning-based techniques lack generality for decellularization because many high-resolution image data has to be processed., Objective: In this study, we developed an image processing algorithm for quantitative analysis of tissues and cells in a general microscopic image., Methods: The proposed method extracts the color images obtained by the microscope into reference images consisting of grayscale, red (R), green (G), and blue (B) information and transforms each into a binary image. The transformed images were extracted by separating the cells and tissues through outlier noise elimination, logical multiplication and labeling. In order to verify the method, decellularization of porcine arotic valve was performed by the electrical method. Slice samples were obtained by time and the proposed method was applied., Results: The experimental results show that the segmentation of cells and tissues, and quantitative analysis of the number of cells and changes in tissue area during the decellularization process was possible., Conclusions: The proposed method shows that cell and tissue extraction and quantitative numerical analysis were possible in different brightness of microscopic images.

Medical conditions due to acute cell injury, such as stroke and heart attack, are of tremendous impact and have attracted huge amounts of research effort. The biomedical research that seeks cures for these conditions has been dominated by a qualitative, inductive mind-set. Although the inductive approach has not been effective in developing medical treatments, it has amassed enough information to allow construction of quantitative, deductive models of acute cell injury. In this work we develop a modeling approach by extending an autonomous nonlinear dynamic theory of acute cell injury that offered new ways to conceptualize cell injury but possessed limitations that decrease its effectiveness. Here we study the global dynamics of the cell injury theory using a nonautonomous formulation. Different from the standard scenario in nonlinear dynamics that is determined by the steady state and fixed points of the model equations, in this nonautonomous model with a trivial fixed point, the system property is dominated by the transient states and the corresponding dynamic processes. The model gives rise to four qualitative types of dynamical patterns that can be mapped to the behavior of cells after clinical acute injuries. The nonautonomous theory predicts the existence of a latent stress response capacity (LSRC) possessed by injured cells. The LSRC provides a theoretical explanation of how therapies, such as hypothermia, can prevent cell death after lethal injuries. The nonautonomous theory of acute cell injury provides an improved quantitative framework for understanding cell death and recovery and lays a foundation for developing effective therapeutics for acute injury.

Cells are dynamic structures that must respond to complex physical and chemical signals from their surrounding environment. The cytoskeleton is a key mediator of a cell's response to the signals of both the extracellular matrix and other cells present in the local microenvironment and allows it to tune its own mechanical properties in response to these cues. A growing body of evidence suggests that altered cellular viscoelasticity is a strong indicator of disease state; including cancer, laminopathy (genetic disorders of the nuclear lamina), infection, and aging. Here, we review recent work on the characterization of cell mechanics in disease and discuss the implications of altered viscoelasticity in regulation of immune responses. Finally, we provide an overview of techniques for measuring the mechanical properties of cells deeply embedded within tissues., (Copyright © 2018. Published by Elsevier Ltd.)

Autophagy is a conserved catabolic process that delivers cytoplasmic components and organelles to lysosomes for degradation and recycling. This pathway serves to degrade nonfunctional organelles and aggregate-prone proteins, as well as to produce substrates for energy production and biosynthesis. Autophagy is especially important for the maintenance of stem cells, and for the survival and homeostasis of post-mitotic cells like neurons. Functional autophagy promotes longevity in several model organisms. Autophagy regulates immunity and inflammation at several levels and has both anti- and pro-tumorigenic roles in cancer. This review provides a concise overview of autophagy and its importance in cellular and organismal homeostasis, with emphasis on aging, stem cells, neuronal cells, immunity, inflammation, and cancer., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The tumour microenvironment (TME) has recently drawn much attention due to its profound impact on tumour development, drug resistance and patient outcome. There is an increasing interest in new therapies that target the TME. Nonetheless, most established in vitro models fail to include essential cues of the TME. Microfluidics can be used to reproduce the TME in vitro and hence provide valuable insight on tumour evolution and drug sensitivity. However, microfluidics remains far from well-established mainstream molecular and cell biology methods. Therefore, we have developed a quick and straightforward collagenase-based enzymatic method to recover cells embedded in a 3D hydrogel in a microfluidic device with no impact on cell viability. We demonstrate the validity of this method on two different cell lines in a TME microfluidic model. Cells were successfully retrieved with high viability, and we characterised the different cell death mechanisms via AMNIS image cytometry in our model.

The phosphoinositide 3-kinases (PI3Ks) are a family of lipid kinases that phosphorylate inositol phospholipids, thereby controlling membrane lipid composition and regulating a wide range of intracellular processes, including vesicular trafficking and signal transduction. Despite the vast knowledge on class I PI3Ks, recent studies are only now revealing the importance of class II PI3Ks in cell proliferation, survival, and migration. Increasing evidence suggests that the three class II PI3Ks isoforms (PI3K-C2α, PI3K-C2β, and PI3K-C2γ) have distinct and non-overlapping cellular roles. Here, we focus on the cellular functions of class II PI3Ks in different cell systems and underline the emerging importance of these enzymes in various physiological and pathological contexts., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Phosphoinositides (PIs) are recognized as major signaling molecules in many different functions of eukaryotic cells. PIs can be dephosphorylated by multiple phosphatase activities at the 5-, 4-, and 3- positions. Human PI 5-phosphatases belong to a family of 10 members. Except for inositol polyphosphate 5-phosphatase A, they all catalyze the dephosphorylation of PI(4,5)P 2 and/or PI(3,4,5)P 3 at the 5- position. PI 5-phosphatases thus directly control the levels of PI(3,4,5)P 3 and participate in the fine-tuning regulatory mechanisms of PI(3,4)P 2 and PI(4,5)P 2 Second messenger functions have been demonstrated for PI(3,4)P 2 in invadopodium maturation and lamellipodia formation. PI 5-phosphatases can use several substrates on isolated enzymes, and it has been challenging to establish their real substrate in vivo. PI(4,5)P 2 has multiple functions in signaling, including interacting with scaffold proteins, ion channels, and cytoskeleton proteins. PI 5-phosphatase isoenzymes have been individually implicated in human diseases, such as the oculocerebrorenal syndrome of Lowe, through mechanisms that include lipid control. Oncogenic and tumor-suppressive functions of PI 5-phosphatases have also been reported in different cell contexts. The mechanisms responsible for genetic diseases and for oncogenic or tumor-suppressive functions are not fully understood. The regulation of PI 5-phosphatases is thus crucial in understanding cell functions., (Copyright © 2019 Ramos et al.)

A single change in DNA, RNA, proteins or cellular images can be useful as a biomarker of disease onset or progression. With high-throughput molecular phenotyping of single cells, it is now conceivable that the molecular changes occurring across thousands, or tens of thousands, of individual cells could additionally be considered as a disease biomarker. Transition to a disease state would then be reflected by the shifts in cell numbers and locations across a multidimensional space that is defined by the molecular content of cells. Realising this ambition requires a robust formulation of such a multidimensional 'cell space' . This is one of the goals of the recently launched Human Cell Atlas project. A second goal is to populate this 'cell space' with all cell types in the human body. Here, I consider the potential of the Human Cell Atlas project for improving our description and understanding of the cell-type specificity of disease., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2019. Published by The Company of Biologists Ltd.)

The incidence of cancers has increased year by year and the early diagnosis for cancer is important to prevent cancers. Due to abnormal metabolism of tumor cells, the acidification of extracellular environment is an important feature for cancer cells. To achieve efficient and accurate pH measurement, this paper proposes a new detection method based on the fluorescence characteristics of CdSe quantum dots detection, which uses CdSe QDs with unique optical properties as sensitive substances for pH detection. PBS buffer (0.2 mol/L) with different pH has been used to simulate cell metabolites at different pH values and use 365 nm laser source to excite mixture liquid. The emitted light was detected by a fluorescence detection device. A mathematical model between fluorescence intensity and pH was finally obtained. The experimental results show the method show better effectiveness than other pH detection methods and has a high resolution of 0.037 within a pH range of 6.1-7.8 and a measurement resolution of 0.037 pH units. This method has high temperature stability and short testing time, which shows high potential for pH detection. This method has its superiority in all the testing methods. It exhibits a new way of generalizing to pH detection.

Ultrafast time-stretch imaging technique recently attracts an increasing interest for applications in cell classification due to high throughput and high sensitivity. A novel imaging modality of time-stretch imaging technique for edge detection is proposed. Edge detection based on the directional derivative is realized using differential detection. As the image processing is mainly implemented in the physical layer, the computation complexity of edge extraction is significantly reduced. An imaging system for edge detection with the scan rate of 77.76 MHz is experimentally demonstrated. Resolution target is first measured to verify the feasibility of the edge extraction. Furthermore, various cells, including red blood cells, lung cancer cells and breast cancer cells, are detected. The edges of cancerous cells present in a completely different form. The imaging system for edge detection would be a good candidate for high-throughput cell classification., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We present a theoretical framework based on an extension of dynamical density-functional theory (DDFT) for describing the structure and dynamics of cells in living tissues and tumors. DDFT is a microscopic statistical mechanical theory for the time evolution of the density distribution of interacting many-particle systems. The theory accounts for cell-pair interactions, different cell types, phenotypes, and cell birth and death processes (including cell division), to provide a biophysically consistent description of processes bridging across the scales, including describing the tissue structure down to the level of the individual cells. Analysis of the model is presented for single-species and two-species cases, the latter aimed at describing competition between tumor and healthy cells. In suitable parameter regimes, model results are consistent with biological observations. Of particular note, divergent tumor growth behavior, mirroring metastatic and benign growth characteristics, are shown to be dependent on the cell-pair-interaction parameters.

The study was carried out to evaluate enzyme activities and histopathological changes due to the effect of acute and chronic definitive toxicity of selenium (Se) on the post larvae (PL) of giant tiger shrimp (Penaeus monodon), and green mussel (Perna viridis). The 96-h Median Lethal concentration (LC 50 ) for the PL of shrimp was 3.36 mg L -1 and the chronic value for the long-term survival endpoint in a 21-d exposure was 0.10 mg L -1 . The green mussel 96-h LC 50 was 28.41 mg L -1 and the chronic value for the long-term survival endpoint in a 30-d exposure was 3.06 mg L -1 . Native polyacrylamide gel electrophoresis revealed altered diverse isoforms of esterase, superoxide dismutase and malate dehydrogenase activities in the PL of shrimp and green mussel exposed to sublethal concentration of Se. Cellular anomalies such as deformation and fusion of corneal cells, detachment of corneal cells from cornea facet and increased space between ommatidia were observed in the compound eye of PL of shrimp exposed to Se for 21-d. Shrinkage and clumping of mucous gland, degenerative changes in phenol gland, and ciliated epithelium were observed in the foot of green mussel exposed to Se for 30-d. This study shows that cellular anomalies in the compound eye of PL of P. monodon and foot tissues of P. viridis described would affect the vision of shrimp and byssus thread formation in green mussel., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

In this paper, we review some of the recent advances in cellular heterogeneity and single-cell analysis methods. In modern research of cellular heterogeneity, there are four major approaches: analysis of pooled samples, single-cell analysis, high-throughput single-cell analysis, and lately integrated analysis of cellular population at a single-cell level. Recently developed high-throughput single-cell genetic analysis methods such as RNA-Seq require purification step and destruction of an analyzed cell often are providing a snapshot of the investigated cell without spatiotemporal context. Correlative analysis of multiparameter morphological, functional, and molecular information is important for differentiation of more uniform groups in the spectrum of different cell types. Simplified distributions (histograms and 2D plots) can underrepresent biologically significant subpopulations. Future directions may include the development of nondestructive methods for dissecting molecular events in intact cells, simultaneous correlative cellular analysis of phenotypic and molecular features by hybrid technologies such as imaging flow cytometry, and further progress in supervised and non-supervised statistical analysis algorithms.

Background: Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development., Results: In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable., Conclusions: Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.

Solid tumours are often first diagnosed by palpation, suggesting that the tumour is more rigid than its surrounding environment. Paradoxically, individual cancer cells appear to be softer than their healthy counterparts. In this review, we first list the physiological reasons indicating that cancer cells may be more deformable than normal cells. Next, we describe the biophysical tools that have been developed in recent years to characterise and model cancer cell mechanics. By reviewing the experimental studies that compared the mechanics of individual normal and cancer cells, we argue that cancer cells can indeed be considered as softer than normal cells. We then focus on the intracellular elements that could be responsible for the softening of cancer cells. Finally, we ask whether the mechanical differences between normal and cancer cells can be used as diagnostic or prognostic markers of cancer progression., (© 2017 Société Française des Microscopies and Société de Biologie Cellulaire de France. Published by John Wiley & Sons Ltd.)

Activation of the NLRP3-inflammasome pathway and production of the inflammatory cytokine IL-1B after cellular damage caused by infarct or infection is a key process in several diseases such as acute myocardial infarction and inflammatory bowel disease. However, while the molecular triggers of the NLRP3-pathway after cellular damage are well known, the mechanisms that sustain or confine its activity are currently under investigation. We present here an Ordinary Differential Equation-based model that investigates the mechanisms of inflammasome activation and regulation in monocytes to predict IL-1β activation kinetics upon a two-step activation by Damage-Associate-Molecular-Particles (DAMP) and extracellular ATP. Assuming both activation signals to be concomitantly present or present with a delay of 12h, the model predicted a transient IL-1β activation at different concentration levels dependent on signal synchronisation. Introducing a positive feedback loop mediated by active IL-1β resulted in a sustained IL-1β activation, hence arguing for a paracrine signalling between inflammatory cells to guarantee a temporally stable inflammatory response. We then investigate mechanisms that control termination of inflammation using two recently identified molecular intervention points in the inflammasome pathway. We found that a more upstream regulation, by attenuating production of the IL-1β-proform, was more potent in attenuating active IL-1β production than direct inhibition of the NLRP3-inflammasome. Interestingly, ablating this upstream negative feedback led to a high variability of IL-1β production in monocytes from different subjects, consistent with a recent pre-clinical study. We finally discuss the relevance and implications of our findings in disease models of acute myocardial infarction and spontaneous colitis., (Copyright © 2016. Published by Elsevier Ltd.)

The coordination of cell proliferation and differentiation is central to the development and maintenance of tissues, while its dysregulation underlies the transition to diseased states. By combining lineage tracing with transcriptional profiling and marker-based assays, statistical methods are delivering insights into the dynamics of stem cells and their developmental precursors. These studies have provided evidence for molecular heterogeneity and fate priming, and have revealed a role for stochasticity in stem cell fate, refocusing the search for regulatory mechanisms. At the same time, they present a quantitative platform to study the initiation and progression of disease. Here, we review how quantitative lineage tracing strategies are shaping our understanding of the cellular mechanisms of tissue development, maintenance and disease., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Bacterial pathogens encode a wide variety of effectors and toxins that hijack host cell structure and function. Of particular importance are virulence factors that target actin cytoskeleton dynamics critical for cell shape, stability, motility, phagocytosis, and division. In addition, many bacteria target organelles of the general secretory pathway (e.g., the endoplasmic reticulum and the Golgi complex) and recycling pathways (e.g., the endolysosomal system) to establish and maintain an intracellular replicative niche. Recent research on the biochemistry and structural biology of bacterial effector proteins and toxins has begun to shed light on the molecular underpinnings of these host-pathogen interactions. This exciting work is revealing how pathogens gain control of the complex and dynamic host cellular environments, which impacts our understanding of microbial infectious disease, immunology, and human cell biology.

FTIR spectroscopic imaging is a label-free, non-destructive and chemically specific technique that can be utilised to study a wide range of biomedical applications such as imaging of biopsy tissues, fixed cells and live cells, including cancer cells. In particular, the use of FTIR imaging in attenuated total reflection (ATR) mode has attracted much attention because of the small, but well controlled, depth of penetration and corresponding path length of infrared light into the sample. This has enabled the study of samples containing large amounts of water, as well as achieving an increased spatial resolution provided by the high refractive index of the micro-ATR element. This review is focused on discussing the recent developments in FTIR spectroscopic imaging, particularly in ATR sampling mode, and its applications in the biomedical science field as well as discussing the future opportunities possible as the imaging technology continues to advance.

Vibrational spectroscopy, both infrared absorption and Raman spectroscopy, have attracted increasing attention for biomedical applications, from in vivo and ex vivo disease diagnostics and screening, to in vitro screening of therapeutics. There remain, however, many challenges related to the accuracy of analysis of physically and chemically inhomogeneous samples, across heterogeneous sample sets. Data preprocessing is required to deal with variations in instrumental responses and intrinsic spectral backgrounds and distortions in order to extract reliable spectral data. Data postprocessing is required to extract the most reliable information from the sample sets, based on often very subtle changes in spectra associated with the targeted pathology or biochemical process. This review presents the current understanding of the factors influencing the quality of spectra recorded and the pre-processing steps commonly employed to improve on spectral quality. It further explores some of the most common techniques which have emerged for classification and analysis of the spectral data for biomedical applications. The importance of sample presentation and measurement conditions to yield the highest quality spectra in the first place is emphasised, as is the potential of model simulated datasets to validate both pre- and post-processing protocols.

Mammalian cells have been widely used in bioreactors to produce biological products such as pharmaceutical materials. The productivity of such bioreactors is vastly affected by flow-induced cell damage in complicated flow environments, such as agitation-driven turbulence and oxygen bubble bursting at the interface between the culturing medium and air. However, there is no systematic approach to diagnose the cell damage caused by the hydrodynamic stress. In this work, we propose a novel microfluidic method to accurately assess the mechanical cell damage under a controlled extensional stress field, generated in a microfluidic cross-slot geometry. The cell damage in the extensional field is related to the oxygen bubble bursting process. We employed viscoelasticity-induced particle focusing to align the cells along the shear-free channel centerline, so that all the cells experience a similar extensional stress field, which also precludes the cell damage due to wall shear stress. We applied our novel microfluidic sensor to find the critical extensional stress to damage Chinese hamster ovary (CHO) cells; the critical stress is found to be ∼250 Pa. Our current results are relevant in the design of practical bioreactors, as our results clearly demonstrate that the control of the bubble bursting process is critical in minimizing cell damage in bioreactor applications. Further, our results will provide useful information on the biophysical cell properties under fluid flow environments.

We are witnessing the emergence of a novel type of biological regulation, namely, the communication between cells via their secreted substances, the secretome. This brief overview is based on the available published data and our own experience. We discuss three vignettes illustrating the importance of communication via the secretome: (1) the secretome of stem cells and its effects in sepsis and systemic inflammatory response; (2) the profibrotic secretomes partially responsible for development of fibrotic complications; and (3) the contribution of senescence-associated secretory products to the propagation of the senescence phenotype. Considering the richness of secretomes of different cells under diverse conditions, it becomes imperative to gain insights into their individual components in an attempt to harness cell secretomes for therapeutic purposes., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: The stromal compartment in several organs seems to play an important role in the initiation, growth, and progression of certain neoplasms. The tumor-stroma ratio (TSR) has been found to be an independent factor for prognosis of several types of carcinomas, but the effect of the TSR on hepatocellular carcinoma (HCC) has not been explored yet. The objective of the study is to evaluate the prognostic importance of TSR in HCC patients after liver resection or transplantation., Methods: A total of 300 patients with HCC who underwent liver resection or transplantation were included in this study. TSR was assessed on hematoxylin and eosin-stained sections by 2 independent investigators. Patients were divided into 2 groups: a stroma-rich group (stroma ≥ 50%) and a stroma-poor group (stroma < 50%). Chi-square test, Kaplan-Meier method, and Cox univariable and multivariable regression were used in to analyze the data., Results: Among the post liver resection patients, the TSR was associated with overall survival (OS) in univariate and multivariate analyses (hazard ratio [HR], 4.35 [95% CI, 2.54-7.47] and HR, 2.55 [95% CI, 1.44-4.52], respectively). Among the post liver transplant patients, the TSR was also associated with OS in univariate and multivariate analyses (HR, 2.92 [95% CI, 1.63-5.23] and HR, 2.76 [95% CI, 1.47-4.85], respectively), and TSR with recurrence-free survival (RFS) in univariate and multivariate analyses (HR, 2.63 [95% CI, 1.42-4.86] and HR, 1.93 [95% CI, 1.03-3.62], respectively). Patients with stroma-poor tumor and who were within the Milan criteria or the UCSF criteria had a better in OS and RFS., Conclusion: We show for the first time that TSR is an independent prognostic factor for HCC patients after liver resection or transplantation. TSR may enable better identification of patients at risk for recurrence in HCC patients after curative treatment and may aid in patient management and development of individualized medicine for treatment of HCC., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The importance of pyrimidines lies in the fact that they are structural components of a broad spectrum of key molecules that participate in diverse cellular functions, such as synthesis of DNA, RNA, lipids, and carbohydrates. Pyrimidine metabolism encompasses all enzymes involved in the synthesis, degradation, salvage, interconversion and transport of these molecules. In this review, we summarize recent publications that document how pyrimidine metabolism changes under a variety of conditions, including, when possible, those studies based on techniques of genomics, transcriptomics, proteomics, and metabolomics. First, we briefly look at the dynamics of pyrimidine metabolism during nonpathogenic cellular events. We then focus on changes that pathogen infections cause in the pyrimidine metabolism of their host. Next, we discuss the effects of antimetabolites and inhibitors, and finally we consider the consequences of genetic manipulations, such as knock-downs, knock-outs, and knock-ins, of pyrimidine enzymes on pyrimidine metabolism in the cell., (Copyright © 2015 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Ltd. All rights reserved.)

The post-translational modification of proteins with ubiquitin represents a complex signalling system that co-ordinates essential cellular functions, including proteolysis, DNA repair, receptor signalling and cell communication. DUBs (deubiquitinases), the enzymes that disassemble ubiquitin chains and remove ubiquitin from proteins, are central to this system. Reflecting the complexity and versatility of ubiquitin signalling, DUB activity is controlled in multiple ways. Although several lines of evidence indicate that aberrant DUB function may promote human disease, the underlying molecular mechanisms are often unclear. Notwithstanding, considerable interest in DUBs as potential drug targets has emerged over the past years. The future success of DUB-based therapy development will require connecting the basic science of DUB function and enzymology with drug discovery. In the present review, we discuss new insights into DUB activity regulation and their links to disease, focusing on the role of DUBs as regulators of cell identity and differentiation, and discuss their potential as emerging drug targets.

Transfer RNA (tRNA) is traditionally considered to be an adaptor molecule that helps ribosomes to decode messenger RNA (mRNA) and synthesize protein. Recent studies have demonstrated that tRNAs also serve as a major source of small non-coding RNAs that possess distinct and varied functions. These tRNA fragments are heterogeneous in size, nucleotide composition, biogenesis and function. Here we describe multiple roles that tRNA fragments play in cell physiology and discuss their relevance to human health and disease., (Copyright © 2014. Published by Elsevier B.V.)

In these days, there are many various diseases, whose diagnosis is very hardly. Breast cancer is one of these type diseases. In this paper, accuracy diagnosis of normal, benign, and malign breast cancer cell were found by combining mean success rates Jensen Shannon, Hellinger, and Triangle measure which connected with each other. In this article, an diagnostic method based on feature extraction Discrete Wavelet Entropy Energy (DWEE) and Jensen Shannon, Hellinger, Triangle Measure (JHT) Classifier for diagnosis of breast cancer. This diagnosis method is called as DWEE--JHT this paper. With this diagnosis method have found optimal feature subset using discrete wavelet transform feature extraction. Then these convenient features are given to Jensen Shannon, Hellinger, Triangle Measure (JHT) classifier. Then, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, breast cancer cells are classified using Jensen Shannon, Hellinger, and Triangle distance. Mean success rate of 16 feature vector with Jensen Shannon classifier is found % 97.81. Mean success rate of 16 feature vector with Hellinger classifier is found % 97.75. Mean success rate of 16 feature vector with Triangle classifier is found % 97.87. By averaging of results obtained from these 3 classifiers are found as 97.81 % average of accuracy diagnosis.