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Glioblastoma is the most aggressive cancer originating in the brain with an average survival of 15 months. One of the characteristics of glioblastoma is the high level of intra-tumor heterogeneity (ITH), but the composition and complexity at the single-cell level is poorly understood. Here, we aimed to assess the effects and consequences of immune checkpoint inhibitor (ICI) on the cellular and molecular heterogeneity of glioblastoma tumors at the single cell level. In collaboration with the phase I trials unit at Rigshospitalet, we performed paired molecular analysis of glioma cells from primary and relapse surgery after ICI treatment. Samples were analyzed using single-cell RNA sequencing (scRNA-seq) as well as bulk RNA sequencing and whole exome DNA sequencing. In an effort to trace cellular lineages we developed and refined methods to a identify copy number changes using scRNA-seq. To this end, we identified clonal and subclonal tumor cell populations in each sample. We found high levels of ITH prior to treatment, both with respect to the glioblastoma subtype enrichment and the cell type-specific gene expression. Using expression-based cell-type classification, we found defined recurrent cell-type populations present at both surgery time points. The immune checkpoint treatment had consequences on the cellular phenotypes and proportions of tumor cells, suggesting a level of plasticity in the neoplastic cells. Moreover, we identified examples of clonal dynamics and sweeps following ICI treatment, pointing to potential treatment response and resistance in these populations. We additionally found a recurrent pattern of a small population showing high levels of cell cycle activation and simultaneously expressing markers of stem cell potential. In summary, we pursued single cell-focused analysis of ICI treated glioblastoma patients to study the cellular and molecular heterogeneity within and between glioblastoma patients, which pointed to recurrent patterns of cellular responses following ICI treatment.

Several studies with brain signals suggested that bottom-up and top-down influences are exerted through distinct frequency bands among visual cortical areas. It has been recently shown that theta and gamma rhythms subserve feedforward, whereas the feedback influence is dominated by the alpha-beta rhythm in primates. A few theoretical models for reproducing these effects have been proposed so far. Here we show that a simple but biophysically plausible two-network motif composed of spiking-neuron models and chemical synapses can exhibit feedforward and feedback influences through distinct frequency bands. Differently from previous studies, this kind of model allows us to study directed influences not only at the population level, by using a proxy for the local field potential, but also at the cellular level, by using the neuronal spiking series.

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded. Methods We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes. The algorithm converts the gene expression matrix (GEM) into a FEM. The FEM algorithm can provide insight on the biological significance of a single cell. It can also integrate with GEM for downstream analysis. Results We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).

Classic metabolomic methods have proven to be very useful to study functional biology and variation in the chemical composition of different tissues. However, they do not provide any information in terms of spatial localization within fine structures. Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) does and reaches at best a spatial resolution of 0.25 μm depending on the laser setup, making it a very powerful tool to analyze the local complexity of biological samples at the cellular level. Here, we intend to give an overview of the diversity of the molecules and localizations analyzed using this method as well as to update on the latest adaptations made to circumvent the complexity of samples. MALDI MSI has been widely used in medical sciences and is now developing in research areas as diverse as entomology, microbiology, plant biology, and plant–microbe interactions, the rhizobia symbiosis being the most exhaustively described so far. Those are the fields of interest on which we will focus to demonstrate MALDI MSI strengths in characterizing the spatial distributions of metabolites, lipids, and peptides in relation to biological questions.

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

Mueller matrix polarimetry is regarded as a promising technique to comprehensively provide the optical and microstructural information of tissues. Especially, for pathological diagnosis, the Mueller matrix imaging can be used as a powerful tool to detect the structural features of abnormal tissue areas on pathological sections. However, a comparative study of the polarization characteristics of pathological sections with different preparation state is still needed to help decide which kinds of sections can be effectively used for polarization imaging diagnosis. In this paper, we apply the Mueller imaging polarimeter for quantitative detection of lung cancer section with the state of undewaxed, dewaxed and H-E stained at cellular-level respectively. The Mueller matrix polar decomposition parameters of the lung cancer section are calculated and analyzed. The results indicate that the polarization images of undewaxed section can reflect the morphology and arrangement of cells. The polarization images of dewaxed is more suitable for the quantitative analysis of tissue. The polarization images of stained sections are sensitive to the nucleus, which is suitable for the study of the internal structure of the nucleus.

Tumor heterogeneity is predicted to confer inferior clinical outcomes, however modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to the multitude of available treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain numerous individual organoids with the potential to recapitulate heterogeneity, though PCOs are most commonly studied in bulk ignoring any diversity in the molecular profile or treatment response. Here we demonstrate the advantage of evaluating individual PCOs in conjunction with cellular level optical metabolic imaging to characterize the largely ignored heterogeneity within these cultures to predict clinical therapeutic response, identify subclonal populations, and determine patient specific mechanisms of resistance.

At the cellular level, numerous nanocues guide the cells to adhere, interact, proliferate, differentiate, etc. Understanding and manipulating the cellular functions in vitro, necessitates the elucidation of these nanocues provided to the cells by the extracellular matrix (ECM), neighbouring cells or in the form of ligands. DNA nanotechnology is a biocompatible, flexible and a promising molecular level toolkit for mimicking cell-cell and cell-matrix interactions. In this review, we summarize various advances in cell-matrix, cell-cell and cell receptor-ligand interactions using DNA nanotechnology as a tool. We also provide a brief outlook on the current challenges and the future potentials of these DNA-based nanostructures so as to inspire novel innovations in the field.

The effects of an increase in water temperature as a direct consequence of global change on organisms living in springs and spring brooks have rarely been studied in laboratory experiments. In this study, experiments were conducted to test the response of Gammarus fossarum Koch, 1836, as an abundant representative of the European spring fauna, to changing water temperatures. The aim was to find out experimentally how G. fossarum reacts to varying and increasing water temperatures. The experiments were conducted in flow channels with spring water. In each flow channel, G. fossarum were placed in boxes with a flow-through system for 4 weeks. Two analytical methods were applied: the feeding activity of the amphipods was quantified in order to determine the reaction of G. fossarum on the level of the organism and the respiratory electron transport system (ETS) assay was conducted in order to determine changes in the test organisms on the cellular level. The results show that the feeding activity of G. fossarum increased with increasing water temperature, up to an optimum, indicating an increase in their metabolic activity. The ETS activity does not show significant differences at the different temperatures tested. A possible explanation for this is the ability of the organisms to adapt quickly to the changed environmental circumstances.

A DNAzyme-mediated target-initiated rolling circle signal amplification strategy based on a microchip platform was developed for detecting apurinic/apyrimidine endonuclease 1 (APE1) at the single-cell level. This strategy was applied to assays of lysate samples from HL-7702, HeLa and MCF-7 cells, with a detection limit of lower than 1 HeLa cell.

Lung cancer (LC) is heading up as a substantial cause of mortality worldwide. Despite enormous progress in cancer management, LC remains a crucial problem for oncologists due to the lack of early diagnosis and precise treatment. In this context, numerous early diagnosis and treatment approaches for LC at the cellular level have been developed using advanced nanomaterials in the last decades. Amongst this, graphene quantum dots (GQDs) as a novel fluorescent material overwhelmed the horizons of materials science and biomedical fields due to their multifunctional attributes. Considering the complex nature of LC, emerging diagnostic and therapeutic (Theranostics) strategies using GQDs proved to be an effective way for the current practice in LC. In this line, we have abridged various approaches used in the LC theranostics using GQDs and its surface-engineered motif. The admirable photophysical attributes of GQDs realised in photolytic therapy (PLT), hyperthermia therapy (HTT), and drug delivery have been discussed. Furthermore, we have engrossed the impasse and its effects on the use of GQDs in cancer treatments from cellular level (in vivo-in vitro) to clinical. Inclusively, this review will be an embodiment for the scientific fraternity to design and magnify their view for the theranostic application of GQDs in LC treatment.

Plants are constantly facing abiotic and biotic stresses. To continue to thrive in their environment, they have developed many sophisticated mechanisms to perceive these stresses and provide an appropriate response. There are many ways to study these stress signals in plant, and among them, protoplasts appear to provide a unique experimental system. As plant cells devoid of cell wall, protoplasts allow observations at the individual cell level. They also offer a prime access to the plasma membrane and an original view on the inside of the cell. In this regard, protoplasts are particularly useful to address essential biological questions regarding stress response, such as protein signaling, ion fluxes, ROS production, and plasma membrane dynamics. Here, the tools associated with protoplasts to comprehend plant stress signaling are overviewed and their potential to decipher plant defense mechanisms is discussed.

Recently, rapid advancement in nanomedicine has opened new horizons towards the treatment of disparate types of cancer. Nanomedicine is considered as the science of applying nanoparticles (NPs) for various diagnostic or therapeutic aims. Nanoparticles (NPs) have attracted increasing interest all over the world for the treatment of disparate types of cancer due to their noteworthy properties such as negligible toxicity and great bioactivity. The main objective of this paper is to present a comprehensive review about the potential of various NPs including silver NPs (AgNPs), gold NPs (AuNPs), selenium NPs (SeNPs), titanium oxide NPs (TiO2NPs) and iron oxide NPs (FeONPs) to treat tumoral cells, and investigate the molecular interaction at the cellular level. Moreover, different synthesis mechanisms of NPs along with their operational roles in enhancing the efficiency of conventional chemotherapeutic agents and reducing the toxicity are discussed in detail. Finally, future challenges towards the application of NPs in the field of cancer treatment are presented, and appropriate solutions to remove the ambiguities are suggested.

Significance: An increasing interest in the area of biological effects at exposure of tissues and cells to the terahertz (THz) radiation is driven by a rapid progress in THz biophotonics, observed during the past decades. Despite the attractiveness of THz technology for medical diagnosis and therapy, there is still quite limited knowledge about safe limits of THz exposure. Different modes of THz exposure of tissues and cells, including continuous-wave versus pulsed radiation, various powers, and number and duration of exposure cycles, ought to be systematically studied. Aim: We provide an overview of recent research results in the area of biological effects at exposure of tissues and cells to THz waves. Approach: We start with a brief overview of general features of the THz-wave–tissue interactions, as well as modern THz emitters, with an emphasis on those that are reliable for studying the biological effects of THz waves. Then, we consider three levels of biological system organization, at which the exposure effects are considered: (i) solutions of biological molecules; (ii) cultures of cells, individual cells, and cell structures; and (iii) entire organs or organisms; special attention is devoted to the cellular level. We distinguish thermal and nonthermal mechanisms of THz-wave–cell interactions and discuss a problem of adequate estimation of the THz biological effects’ specificity. The problem of experimental data reproducibility, caused by rareness of the THz experimental setups and an absence of unitary protocols, is also considered. Results: The summarized data demonstrate the current stage of the research activity and knowledge about the THz exposure on living objects. Conclusions: This review helps the biomedical optics community to summarize up-to-date knowledge in the area of cell exposure to THz radiation, and paves the ways for the development of THz safety standards and THz therapeutic applications.

Boron neutron capture therapy (BNCT) is a unique type of radiation therapy that enables biological targeting of cancer at the cellular level. BNCT has been used to treat cancer with a two-step proc...

Therefore, we inevitably increase tidal volume, driving pressure, and MP in individuals with the most severe lung injury such that these parameters may be associated with, but not causal to, the outcomes in acute lung injury. However, I remember all too well the assumptions that drove the paradigm in which I trained and am in favor of pushing to better understand at the tissue and even cellular level how much ‘volutrauma’ we are causing in individual patients, and testing whether or not new therapeutic ‘targets’ beyond the current ‘6 cc/kg tidal volume’ will enable us to better care for patients with ARDS and improve on the survival gains we have made in the past two decades.

The plant circadian system is based on self-sustained cellular oscillations and is utilized to adapt to daily and seasonal environmental changes. The cellular circadian clocks in the above- and belowground plant organs are subjected to diverse local environments. Individual cellular clocks are affected by other cells/tissues in plants, and the intrinsic circadian properties of individual cells remain to be elucidated. In this study, we monitored bioluminescence circadian rhythms of individual protoplast-derived cells from leaves and roots of a CCA1::LUC Arabidopsis transgenic plant. We analyzed the circadian properties of the leaf- and root-derived cells and demonstrated that the cells with no physical contact with other cells harbor a genuine circadian clock with ∼24-h periodicity, entrainability and temperature compensation of the period. The stability of rhythm was dependent on the cell density. High cell density resulted in an improved circadian rhythm of leaf-derived cells while this effect was observed irrespective of the phase relation between cellular rhythms. Quantitative and statistical analyses for individual cellular bioluminescence rhythms revealed a difference in amplitude and precision of light/dark entrainment between the leaf- and root-derived cells. Circadian systems in the leaves and roots are diversified to adapt to their local environments at the cellular level.

SummaryGastrulation movements in all animal embryos start with regulated deformations of patterned epithelial sheets. Current studies of gastrulation use a wide range of model organisms and emphasize either large-scale tissue processes or dynamics of individual cells and cell groups 1,2,11–13,3–10. Here we take a step towards bridging these complementary strategies and deconstruct early stages of gastrulation in the entire Drosophila embryo, where transcriptional patterns in the blastoderm give rise to region-specific cell behaviors. Our approach relies on an integrated computational framework for cell segmentation and tracking and on efficient algorithms for event detection. Our results reveal how thousands of cell shape changes, divisions, and intercalations drive large-scale deformations of the patterned blastoderm, setting the stage for systems-level dissection of a pivotal step in animal development.

The brain requires efficient information transfer between neurons and between large-scale brain regions. Brain connectivity follows predictable organizational principles: at the cellular level, larger supragranular pyramidal neurons have larger dendritic trees, more synapses, more complex branching and perform more complex neuronal computations; at the macro-scale, region-to-region connections are suggested to display a diverse architecture with highly connected hub-areas facilitating complex information integration and computation. Here, we explore the hypothesis that the branching structure of large-scale region-to-region connectivity follows similar organizational principles as known for the neuronal scale. We examine microscale connectivity of basal dendritic trees of supragranular pyramidal neurons (300+) across ten cortical areas in five human donor brains (1M/4F). Dendritic complexity was quantified as the number of branch points, tree length, spine count, spine density and overall branching complexity. High-resolution diffusion-weighted MRI was used to construct ‘white matter trees’ of cortico-cortical wiring. Examining the complexity of the resulting white matter trees using the same measures as for dendritic trees shows multimodal association areas to have larger, more complexly branched white matter trees than primary areas (all p

In the last decades, a wide multitude of research activity has been focused on the development of new drugs, and devoted to overcome the challenges of high cost and low efficiency in drug evaluation. The measurement of drug response at the single cell level is a quicker, more direct and more accurate way to reflect drug efficacy, which can shorten the drug development period and reduce research costs. Therefore, the single cell drug response (SCDR) measurement technology has aroused extensive attention from researchers, and has become a hot topic in the fields of drug research and cell biology. Recent years have seen the emergence of various SCDR measurement technologies that feature different working principles and different levels of measurement performance. To better examine, compare and summarize the characteristics and functions of these technologies, we select signal-to-noise ratio, throughput, content, invasion, and device complexity as the criteria to evaluate them from the drug efficacy perspective. This review aims to highlight sixteen kinds of SCDR measurement technologies, including patch-clamp technique, live-cell interferometry, capillary electrophoresis, secondary ion mass spectrometry, and more, and report widespread representative examples of SCDR measurement the recent approaches for over the past forty years. Based on their reaction principles, these technologies are classified into four categories: electrical, optical, electrochemical, and mass spectrometry, and a detailed comparison is made between them. After in-depth understanding of these technologies, it is expected to improve or integrate these technologies to propose better SCDR measurement strategies, and explore methods in new drug development and screening, as well as disease diagnosis and treatment.

Dose is one of the parameters that any pharmacologist seriously considers when studying the effects of a drug. If the necessary dose to achieve a desired pharmacological effect is in a toxic or very toxic range for human use, the drug will probably fall out from further research. The concentration that a drug can reach at its target organ or cell is a direct consequence of the administered dose and its pharmacodynamic properties. Basic researchers investigate at the cellular level or eventually with xenografts. They use different concentrations of the drug in order to determine its cellular effects. However, in many cases, these concentrations require doses that are in the toxic range or well beyond any clinically achievable level. Therefore, in these cases, research is in the realm of Toxicology rather than therapeutics. This paper will show some examples about this exercise in futility which is time and resource consuming but that pullulates the pages of many prestigious journals. Many seasoned researchers seem to have forgotten the Paracelsus Paradox.

Potent and selective ferroptosis regulators promote an intensive understanding of the regulation and mechanisms underlying ferroptosis, which is highly associated with various diseases. In this study, through a stepwise structure optimization, a potent and selective ferroptosis inducer was developed targeting to inhibit glutathione peroxidase 4 (GPX4), and the structure-activity relationship (SAR) of these compounds was uncovered. Compound 26a exhibited outstanding GPX4 inhibitory activity with a percent inhibition up to 71.7% at 1.0 μM compared to 45.9% of RSL-3. At the cellular level, 26a could significantly induce lipid peroxide (LPO) increase and effectively induce ferroptosis with satisfactory selectivity (the value of 31.5). The morphological analysis confirmed the ferroptosis induced by 26a. Furthermore, 26a significantly restrained tumor growth in a mouse 4T1 xenograft model without obvious toxicity.

Increasing evidence at the cellular level is helping to provide proximate explanations for the balance between investment in growth, reproduction and somatic maintenance in wild populations. Studies of telomere dynamics have informed researchers about the loss and gain of telomere length both on a seasonal scale and across the lifespan of individuals. In addition, telomere length and telomere rate of loss seems to have evolved differently among taxonomic groups, and relate differently to organismal diversity of lifespan. So far, the mechanisms behind telomere maintenance remain elusive, although many studies have inferred a role for telomerase, an enzyme/RNA complex known to induce telomere elongation from laboratory studies. Exciting further work is also emerging that suggests telomerase (and/or its individual component parts) has a role in fitness that goes beyond the maintenance of telomere length. Here, we review the literature on telomerase biology and examine the evidence from ecological studies for the timing and extent of telomerase activation in relation to life history events associated with telomere maintenance. We suggest that the underlying mechanism is more complicated than originally anticipated, possibly involves several complimentary pathways, and is probably associated with high energetic costs. Potential pathways for future research are numerous and we outline what we see as the most promising prospects to expand our understanding of individual differences in immunity or reproduction efficiency.

The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to “weakly-label” the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.

In multicellular organisms cells grow, divide and adopt different fates, resulting in tissues and organs with specific functions. In recent years, a number of studies have brought quantitative knowledge about how these processes are orchestrated, shed-ding new light on cells as active and central players in morphogenesis. We explore recent advances in understanding plant morphogenesis from a quantitative perspective, defining the re-search field of Computational Morphodynamics. The focus is on studies combining theoretical and experimental approaches integrating hypotheses of how molecular and mechanical regulation at the cellular level lead to tissue behaviour. Finally, we dis-cuss some of the main challenges for future work.

Photosensitized oxidations, in part responsible for the harmful effect of UV and visible radiation on biological systems, can take place through type I (generation of radicals) and type II (singlet oxygen) mechanisms. Pterins are heterocyclic compounds, widespread in living systems and involved in relevant biological functions. Pterins present a profuse and amazing photochemistry and are endogenous photosensitizers that act mainly via type I mechanism. This survey is aimed to contribute to a better understanding on the complex set of competitive pathways involved in type I photosensitization. Based on studies performed in model systems with pterins as photosensitizers, this review explores the mechanisms involved in the type I photooxidations of proteins, DNA and lipids sensitized by pterins. The generation of radicals and their subsequent reactions are described together with the analysis of the role of O2 and reactive oxygen species. The chemical modification of pterins seeking for better properties and the effect of the photochemical processes at a cellular level are also analyzed.

Simple Summary The fate of the gonad in teleost is influenced by various factors, including genetics and external factors. Several species-specific genes and environmental factors involved in sex determination and differentiation have been identified in teleost. In addition, these factors are species-specific. At the brain level, suppression of key molecule of the hypothalamus–gonadal axis affects sex determination. At the same time, pituitary hormones are required for regulating sex differentiation. However, the role of the brain during sex determination and differentiation remains elusive. In this review, we have gathered and discussed the findings on the role of prominent genes, environmental factors, and the brain in regulating sex determination and differentiation of teleost. Abstract The fish reproductive system is a complex biological system. Nonetheless, reproductive organ development is conserved, which starts with sex determination and then sex differentiation. The sex of a teleost is determined and differentiated from bipotential primordium by genetics, environmental factors, or both. These two processes are species-specific. There are several prominent genes and environmental factors involved during sex determination and differentiation. At the cellular level, most of the sex-determining genes suppress the female pathway. For environmental factors, there are temperature, density, hypoxia, pH, and social interaction. Once the sexual fate is determined, sex differentiation takes over the gonadal developmental process. Environmental factors involve activation and suppression of various male and female pathways depending on the sexual fate. Alongside these factors, the role of the brain during sex determination and differentiation remains elusive. Nonetheless, GnRH III knockout has promoted a male sex-biased population, which shows brain involvement during sex determination. During sex differentiation, LH and FSH might not affect the gonadal differentiation, but are required for regulating sex differentiation. This review discusses the role of prominent genes, environmental factors, and the brain in sex determination and differentiation across a few teleost species.

Throughout development and aging, human cells accumulate mutations, resulting in genomic mosaicism and genetic diversity at the cellular level. Mosaic mutations present in the gonads can affect both the individualand the offspring and subsequent generations. Here we explore patterns and temporal stability of clonal mosaic mutations in male gonads by sequencing ejaculated sperm. Through 300× whole-genome sequencing of blood and sperm from healthy men, we find each ejaculate carries on average 33.3 ± 12.1 (mean ± standard deviation) clonal mosaic variants, nearly all of which are detected in serial sampling, and with the majority absent from sampled somal tissues. Their temporal stability and mutational signature suggest origins during embryonic development from a largely immutable stem cell niche. Clonal mosaicism likely contributes a transmissible, predicted pathogenic exonic variant for 1 in 15 men, representing a life-long threat of transmission for these individuals, and a significant burden on human population health.

Simple Summary Magnetic hyperthermia therapy is an alternative treatment for cancer that complements traditional therapies and that has shown great promise in recent years. In this review, we assess the current applications of this therapy in order to understand why its translation from the laboratory to the clinic has been less smooth than was anticipated, identifying the possible bottlenecks and proposing solutions to the problems encountered. Abstract Hyperthermia has emerged as a promising alternative to conventional cancer therapies and in fact, traditional hyperthermia is now commonly used in combination with chemotherapy or surgery during cancer treatment. Nevertheless, non-specific application of hyperthermia generates various undesirable side-effects, such that nano-magnetic hyperthermia has arisen a possible solution to this problem. This technique to induce hyperthermia is based on the intrinsic capacity of magnetic nanoparticles to accumulate in a given target area and to respond to alternating magnetic fields (AMFs) by releasing heat, based on different principles of physics. Unfortunately, the clinical implementation of nano-magnetic hyperthermia has not been fluid and few clinical trials have been carried out. In this review, we want to demonstrate the need for more systematic and basic research in this area, as many of the sub-cellular and molecular mechanisms associated with this approach remain unclear. As such, we shall consider here the biological effects that occur and why this theoretically well-designed nano-system fails in physiological conditions. Moreover, we will offer some guidelines that may help establish successful strategies through the rational design of magnetic nanoparticles for magnetic hyperthermia.

Histologic features of diffuse-type gastric cancer indicate that the tumor microenvironment (TME) may substantially impact tumor invasiveness. However, cellular components and molecular features associated with cancer invasiveness in the TME of diffuse-type gastric cancers are poorly understood.We performed single-cell RNA-sequencing (scRNA-seq) using tissue samples from superficial and deep invasive layers of cancerous and paired normal tissues freshly harvested from five patients with diffuse-type gastric cancer. The scRNA-seq results were validated by immunohistochemistry (IHC) and duplexSeven major cell types were identified. Fibroblasts, endothelial cells, and myeloid cells were categorized as being enriched in the deep layers. Cell type-specific clustering further revealed that the superficial-to-deep layer transition is associated with enrichment in inflammatory endothelial cells and fibroblasts with upregulatedThis study reveals the spatial reprogramming of the TME that may underlie invasive tumor potential in diffuse-type gastric cancer. This TME profiling across tumor layers suggests new targets, such as CCL2, that can modify the TME to inhibit tumor progression in diffuse-type gastric cancer.

International audience; Microwave dielectric spectroscopy constitutes an attractive sensing technique for cellular analysis. One key feature is its ability to non-invasively and dielectrically monitor cellular modifications at the single cell level. The proposed microwave sensor dedicated to single cell measurement includes a mechanical trap, which permits to apply on individual cells whatever liquid-based environment. The dielectric consequences on the cell due to the application of saponine at 0.02% is recorded every two minutes, demonstrating the possibility to reach dielectric transformation kinetic of cells. This proof of concept opens the door to the non-invasive and nondestructive evaluation of biological reactions at the cellular level.

Measuring tumour cell invasiveness through three-dimensional (3D) tissues, particularly at the single cell level, can provide important mechanistic understanding and assist in identifying therapeutic targets of tumour invasion. However, current experimental approaches, including standard in vitro invasion assays, have limited physiological relevance and offer insufficient insight about the vast heterogeneity in tumour cell migration through tissues. To address these issues, here we report on the concept of optical cellular micromotion, where digital holographic microscopy (DHM) is used to map the optical thickness fluctuations at sub-micron scale within single cells. These fluctuations are driven by the dynamic movement of subcellular structures including the cytoskeleton and inherently associated with the biological processes involved in cell invasion within tissues. We experimentally demonstrate that the optical cellular micromotion correlates with tumour cells motility and invasiveness both at the population and single cell levels. In addition, the optical cellular micromotion significantly reduced upon treatment with migrastatic drugs that inhibit tumour cell invasion. These results demonstrate that micromotion measurements can rapidly and non-invasively determine the invasive behaviour of single tumour cells within tissues, yielding a new and powerful tool to assess the efficacy of approaches targeting tumour cell invasiveness.Significance StatementTumour cells invasion through tissues is a key hallmark of malignant tumour progression and its measurement is essential to unraveling biological processes and screening for new approaches targeting cell motility. To address the limitations of current approaches, we demonstrate that sub-micron scale mapping of the dynamic optical thickness fluctuations within single cells, referred to as optical cellular micromotion, correlates with their motility in ECM mimicking gel, both at the population and single cell levels. We anticipate that 3D optical micromotion measurement will provide a powerful new tool to address important biological questions and screen for new approaches targeting tumour cell invasiveness.

We performed a histological and quantitative study of iron in archaeological maize seeds from prehispanic times recovered from Tarapacá, Atacama Desert. Also, we examined iron distribution changes at the cell level in embryos from ancient versus new varieties of maize. Our results show a progressive decrease in iron concentration from the oldest maize to modern specimens. We interpret the results as an effect of prehispanic agriculture over the micronutrient composition of maize.

For most life on Earth, the rising and setting of the sun coincides with daily patterns of activity. However, biological organisms have internal clocks that orchestrate their daily rhythms even in the absence of sunlight. These circadian rhythms, which set the schedule for the sleep–wake cycle, are the reason people feel jetlag when crossing time zones. On a cellular level, circadian rhythms control patterns in metabolism and other biological processes that fluctuate throughout the course of a day. Ronald J. Konopka. This image was published in ref. 14, copyright Elsevier (2015). Seymour Benzer with a model of a fruit fly. Reprinted with permission from ref. 3, which is licensed under CC BY 4.0. Fruit fly. Image credit: Pixabay/zkrej. Locomotor activity rhythms of a normal fruit fly and three mutants recorded by Konopka and Benzer. Each vertical line represents the start of an interval of activity. Reprinted with permission from ref. 2. The existence of daily rhythms has been observed since antiquity, but early evidence that these patterns are set by an internal mechanism, rather than by sunlight alone, came from French scientist Jean-Jacques d’Ortous de Mairan in 1729 (1). After observing that mimosa plants open their leaves during the day and close them at night, d’Ortous de Mairan wondered how the plants would behave in total darkness. He found that mimosa plants kept in total darkness continued to open and close their leaves on more or less the same schedule. Before long, other researchers observed internal daily cycles in plants and animals, including humans, but the biological processes underpinning circadian rhythms would remain a mystery for the next two centuries. Current understanding of circadian rhythms, as well the field of circadian biology, owes its start to a now classic 1971 PNAS article, “Clock mutants of Drosophila …

Galium sinaicum is a wild medicinal plant in saint Catherine, Egypt. To distinguish apoptotic effect of G. sinaicum ethanol extract (GsEE), we examined the role of GsEE in inducing programmed cell death (PCD) of Allium cepa root meristematic cell (AcR). Cells was subjected to GsEE in definite concentrations (0.1,0.3, 0.5%) and duration (6, 12h), then PCD induction was assessed. Application of GsEE arrested the mitotic division of AcR with metaphase accumulation. Electron microscopy analysis demonstrated ultrastructural alterations of organelles verifying PCD hallmarks. Protein electrophoresis analysis of AcR revealed a change in protein profile of Allium cepa root, also quantitative analysis showed significant increase in nuclease activity enzymes that stimulated DNA laddering fragmentation. Additionally, cell proliferation of MCF-7 and BHK21 was arrested by GsEE. Apoptotic effect of G. sinaicum may be attributed to the presence of potent phenolic compounds such as querectin and rutin as established by HPLC phenolic fingerprint analysis.

Non-union is a taxing complication of fracture management for both the patient and their surgeon. Modern fracture fixation techniques have been developed to optimise the biomechanical environment for fracture healing but do not guarantee union. Patient biology has a critical role in achieving union and stem cell therapy has potential for improving fracture healing at a cellular level to treat or avoid non-union. This article reviews the current understanding of non-union, concepts in bone healing and the current literature on the application of stem cells in non-union.

International audience; Background and PurposeDopamine in the striatum plays a crucial role in reward processes and action selection. Dopamine signals are transduced by D1 and D2 dopamine receptors which trigger mirror effects through the cAMP/PKA signalling cascade in D1 and D2 medium-sized spiny neurones (MSNs). Phosphodiesterases (PDEs), which determine the profile of cAMP signals, are highly expressed in MSNs, but their respective roles in dopamine signal integration remain poorly understood.Experimental approachWe used genetically-encoded FRET biosensors to monitor at the single cell level the functional contribution of PDE2A, PDE4 and PDE10A in the changes of the cAMP/PKA response to transient and continuous dopamine in mouse striatal brain slices.Key ResultsWe found that PDE2A, PDE4 and PDE10A operate on the moderate to high cAMP levels elicited by D1 or A2A receptor stimulation. In contrast, only PDE10A is able to reduce cAMP down to baseline in both type of neurones, leading to the dephosphorylation of PKA substrates.Conclusion and ImplicationsIn both MSN types, PDE10A inhibition blunts the responsiveness to dopamine, whereas PDE2A or PDE4 inhibition reinforces dopamine action.

At present, the QT interval on the electrocardiographic (ECG) waveform is the most common metric for assessing an individual’s susceptibility to ventricular arrhythmias, with a long QT, or, at the cellular level, a long action potential duration (APD) considered high risk. However, the limitations of this simple approach have long been recognized. Here, we sought to improve prediction of arrhythmia susceptibility by combining mechanistic mathematical modeling with machine learning (ML). Simulations with a model of the ventricular myocyte were performed to develop a large heterogenous population of cardiomyocytes (n = 10,586), and we tested each variant’s ability to withstand three arrhythmogenic triggers: 1) block of the rapid delayed rectifier potassium current (I(Kr) Block), 2) augmentation of the L-type calcium current (I(CaL) Increase), and 3) injection of inward current (Current Injection). Eight ML algorithms were trained to predict, based on simulated AP features in preperturbed cells, whether each cell would develop arrhythmic dynamics in response to each trigger. We found that APD can accurately predict how cells respond to the simple Current Injection trigger but cannot effectively predict the response to I(Kr) Block or I(CaL) Increase. ML predictive performance could be improved by incorporating additional AP features and simulations of additional experimental protocols. Importantly, we discovered that the most relevant features and experimental protocols were trigger specific, which shed light on the mechanisms that promoted arrhythmia formation in response to the triggers. Overall, our quantitative approach provides a means to understand and predict differences between individuals in arrhythmia susceptibility.

EGFR-TKIs achieved excellent efficacy in EGFR-mutated patients. Unfortunately, most patients would inevitably develop progressive disease within a median of 10 to 14 months. Predicting the resistance probability remains a challenge. Therefore, we created an R-index model trained by single-cell RNA data with the OCLR algorithm. This model can be applied to estimate the level of EGFR-TKI resistance in cell line and xenograft mice models and predict prognosis in multiple cohorts. Comparing the high and the low R-index group, we found that the glycolysis pathway and KRAS up-regulation pathway were related to resistance, and MDSC was the leading cause of immunosuppression in the tumor microenvironment. These results are consistent with previous studies indicating that the R-index provides an insight into resistance status and a new way to explore resistance mechanisms and clinical treatment by the combination of Glucose metabolism-targeted or MDSC-targeted therapies. This is the first quantification method of EGFR-TKI resistance based on single-cell sequencing data solving the problem of the mixed resistance state of tumor cells and helping explore transcriptome characteristics of drug-resistant cell populations.

In combination with scRNA-seq, spatial transcriptomics have the potential to lead to an unprecedented view of lung architecture at the single cell level, providing original information on lung physiology and physiopathologyhttps://bit.ly/3uDYir0

Genomes have complex three-dimensional structures. High-resolution population-based biochemical studies over the last decade have painted a mostly static picture of the genome characterized by universal organizational features, such as chromatin domains and compartments. Yet, when analyzed at the single cell level, these architectural elements are highly variable. The heterogeneity in genome organization is in line with the inherent stochasticity of transcription that shows high variation between individual cells. We highlight recent findings on single-cell variability in genome organization and describe a framework for how the stochastic nature of chromatin organization may relate to transcription dynamics.

Bone marrow stromal cell antigen 2 (BST-2), also known as CD317 or tetherin, has been identified as a host restriction factor that suppresses the release of enveloped viruses from host cells by physically tethering viral particles to the cell surface, however, this host defense can be subverted by multiple viruses. For example, human immunodeficiency virus (HIV)-1 encodes a specific accessory protein, viral protein U (Vpu), to counteract BST-2 by binding to it and directing its lysosomal degradation. Thus, blocking the interaction between Vpu and BST-2 will provide a promising strategy for anti-HIV therapy. Here, we report a NanoLuc Binary Technology (NanoBiT)-based high-throughput screening assay to detect inhibitors that disrupt the Vpu-BST-2 interaction. Out of more than 1000 compounds screened, four inhibitors were identified with strong activity at nontoxic concentrations. In subsequent cell-based BST-2 degradation assays, inhibitor Y-39983 HCl restored the cell-surface and total cellular level of BST-2 in the presence of Vpu. Furthermore, the Vpu-mediated enhancement of pesudotyped viral particle production was inhibited by Y-39983 HCl. Our findings indicate that our newly developed assay can be used for the discovery of potential antiviral molecules with novel mechanisms of action.

Successfully engineering a functional, human, myocardial pump would represent a therapeutic alternative for the millions of patients with end-stage heart disease and provide an alternative to animal-based preclinical models. Although the field of cardiac tissue engineering has made tremendous advances, major challenges remain, which, if properly resolved, might allow the clinical implementation of engineered, functional, complex 3D structures in the future. In this Review, we provide an overview of state-of-the-art studies, challenges that have not yet been overcome and perspectives on cardiac tissue engineering. We begin with the most clinically relevant cell sources used in this field and discuss the use of topological, biophysical and metabolic stimuli to obtain mature phenotypes of cardiomyocytes, particularly in relation to organized cytoskeletal and contractile intracellular structures. We then move from the cellular level to engineering planar cardiac patches and discuss the need for proper vascularization and the main strategies for obtaining it. Finally, we provide an overview of several different approaches for the engineering of volumetric organs and organ parts - from whole-heart decellularization and recellularization to advanced 3D printing technologies.

Aging has largely been defined by analog measures of organ and organismal dysfunction. This has led to the characterization of aging processes at the molecular and cellular levels that underlie these gradual changes. However, current knowledge does not fully explain the growing body of data emerging from large epidemiological, systems biology, and single cell studies of entire organisms pointing to varied rates of aging between individuals (different functionality and lifespan), across lifespan (asynchronous aging), and within an organism at the tissue and organ levels (aging mosaicism). Here we consider these inhomogeneities in the broader context of the rate of aging and from the perspective of underlying cellular changes. These changes reflect genetic, environmental, and stochastic factors that cells integrate to adopt new homeostatic, albeit less functional, states, such as cellular senescence. In this sense, cellular aging can be viewed, at least in part, as a quantal process we refer to as "digital aging". Nevertheless, analog declines of tissue dysfunction and organ failure with age could be the sum of underlying digital events. Importantly, cellular aging, digital or otherwise, is not uniform across time or space within the organism or between organisms of the same species. Certain tissues may exhibit earliest signs of cellular aging, acting as drivers for organismal aging as signals from those driver cells within those tissues may accelerate the aging of other cells locally or even systemically. Advanced methodologies at the systems level and at the single cell level are likely to continue to refine our understanding to the processes of how cells and tissues age and how the integration of those processes leads to the complexities of individual, organismal aging.

Throughout life, the body is subjected to various mechanical forces on the organ, tissue, and cellular level. Mechanical stimuli are essential for organ development and function. One organ whose function depends on the tightly connected interplay between mechanical cell properties, biochemical signaling, and external forces is the lung. However, altered mechanical properties or excessive mechanical forces can also drive the onset and progression of severe pulmonary diseases. Characterizing the mechanical properties and forces that affect cell and tissue function is therefore necessary for understanding physiological and pathophysiological mechanisms. In recent years, multiple methods have been developed for cellular force measurements at multiple length scales, from subcellular forces to measuring the collective behavior of heterogeneous cellular networks. In this short review, we give a brief overview of the mechanical forces at play on the cellular level in the lung. We then focus on the technological aspects of measuring cellular forces at many length scales. We describe tools with a subcellular resolution and elaborate measurement techniques for collective multicellular units. Many of the technologies described are by no means restricted to lung research and have already been applied successfully to cells from various other tissues. However, integrating the knowledge gained from these multi-scale measurements in a unifying framework is still a major future challenge.

Single-cell RNA sequencing (scRNA-seq) technology can analyze the transcriptome expression level of cells with high-throughput from the single cell level, fully show the heterogeneity of cells, and provide a new way for the study of multicellular biological heterogeneity. Synovitis is the pathological basis of rheumatoid arthritis (RA). Synovial fibroblasts (SFs) and synovial macrophages are the core target cells of RA, which results in the destruction of articular cartilage, as well as bone. Recent scRNA-seq technology has made breakthroughs in the differentiation and development of two types of synovial cells, identification of subsets, functional analysis, and new therapeutic targets, which will bring remarkable changes in RA treatment.

The current ecosystem of single-cell RNA-seq platforms is rapidly expanding, but robust solutions for single-cell and single-molecule full-length RNA sequencing are virtually absent. A high-throughput solution that covers all aspects is necessary to study the complex life of mRNA on the single-cell level. The Nanopore platform offers long read sequencing and can be integrated with the popular single-cell sequencing method on the 10× Chromium platform. However, the high error-rate of Nanopore reads poses a challenge in downstream processing (e.g., for cell barcode assignment). We propose a solution to this particular problem by using a hybrid sequencing approach on Nanopore and Illumina platforms. Our software ScNapBar enables cell barcode assignment with high accuracy, especially if sequencing saturation is low. ScNapBar uses unique molecular identifier (UMI) or Naïve Bayes probabilistic approaches in the barcode assignment, depending on the available Illumina sequencing depth. We have benchmarked the two approaches on simulated and real Nanopore data sets. We further applied ScNapBar to pools of cells with an active or a silenced nonsense-mediated RNA decay pathway. Our Nanopore read assignment distinguishes the respective cell populations and reveals characteristic nonsense-mediated mRNA decay events depending on cell status.