scRNA-seq transcriptomic profiling of irradiated mouse skin reveals altered cell types,pathways,and cell-cell interactions

Objective:To investigate the substantial changes in cell types,pathways,and cell-cell interactions occurring in the irradiation-induced alopecia and dermatitis(IRIAD)mouse model and to identify potential targets for patients experiencing skin adverse reactions to radiotherapy.Methods:Mice were irradiated at 15 Gy,targeting the head and neck region.After a 14-day interval,living cells were extracted from both wild-type(WT)mice and irradiated mice for single-cell RNA sequencing(scRNA-seq).The scRNA-seq data,retrieved from the GEO database(GSE201447),underwent stringent quality control using the Seurat(v4.3.0)R package.Cell type annotation relied on previously reported typical markers and CellMarker 2.0.Differentially expressed genes were calculated to perform gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses.Cell-cell interactions were evaluated using the Cellchat R package.Results:The application of single-cell RNA sequencing(scRNA-seq)enabled a comprehensive characterization of the intricate cellular composition of both wild-type(WT)and irradiated mice skin.Remarkably,cells within irradiated mice skin exhibited a significant alteration in the intensity of cell-cell interactions compared to their wild-type counterparts.This change in interaction intensity was observed across various cell types,including fibroblast cells,endothelial cells,and dendritic cells.Importantly,these"interacting cells"shared common signaling pathways,notably the upregulation of the IL-17 pathway following irradiation.Conclusions:The modification of intercellular communication induced by irradiation primarily involves fibroblast cells,endothelial cells,and various types of immune cells.This investigation provides a novel perspective on potential targets and holds promise for enhancing the clinical management of IRIAD.

Deciphering molecular response of cell-cell interactions at the single-cell level by precise on-demand cell assembly

Multicellular systems rely on the interactions between cells to coordinate cell signaling and regulate cell functions [1]. Understanding the mechanism of cell–cell interactions (CCIs) is critical to many physiological and pathological processes, such as embryogenesis,differentiation, cancer metastasis, and immunological interactions.

Cell-cell interactions and communication dynamics in lung fibrosis

Cell-cell interactions are essential components of coordinated cell function in lung homeostasis.Lung diseases involve altered cell-cell interactions and communication between different cell types,as well as between subsets of cells of the same type.The identification and understanding of intercellular signaling in lung fibrosis offer insights into the molecular mechanisms underlying these interactions and their implications in the development and progression of lung fibrosis.A comprehensive cell atlas of the human lung,established with the facilita-tion of single-cell RNA transcriptomic analysis,has enabled the inference of intercellular communications using ligand-receptor databases.In this review,we provide a comprehensive overview of the modified cell-cell commu-nications in lung fibrosis.We highlight the intricate interactions among the major cell types within the lung and their contributions to fibrogenesis.The insights presented in this review will contribute to a better understand-ing of the molecular mechanisms underlying lung fibrosis and may guide future research efforts in developing targeted therapies for this debilitating disease.

Microfluidics for cell-cell interactions： A review

Micro fluidic chip has been applied in various biological fields owing to its low-consumption of reagents, high throughput, fuidic controllability and integrity. The well-designed microscale intermediary is also ideal for the study of cell biology. Particularly, microfluidic chip is helpful for better understanding cell-cell interactions. A general survey of recent publications would help to generalize the designs of the co-culture chips with different features. With ingenious and combinational utilization, the chips facilitate the implementation of some special coculture models that are highly concerned in a different spatial and temporal way.

Influence of Cell-Cell Interactions on the Population Growth Rate in a Tumor

The understanding of the macroscopic phenomenological models of the population growth at a microscopic level is important to predict the population behaviors emerged from the interactions between the individuals. In this work, we consider the influence of the population growth rate R on the cell-cell interaction in a tumor system and show that, in most cases especially small proliferative probabilities, the regulative role of the interaction will be strengthened with the decline of the intrinsic proliferative probabilities. For the high replication rates of an individual and the cooperative interactions, the proliferative probability almost has no effect. We compute the dependences of R on the interactions between the cells under the approximation of the nearest neighbor in the rim of an avascular tumor. Our results are helpful to qualitatively understand the influence of the interactions between the individuals on the growth rate in population systems.

Tailoring Light–Matter Interactions in Overcoupled Resonator for Biomolecule Recognition and Detection

Plasmonic nanoantennas provide unique opportunities for precise control of light–matter coupling in surface-enhanced infrared absorption(SEIRA)spectroscopy,but most of the resonant systems realized so far suffer from the obstacles of low sensitivity,narrow bandwidth,and asymmetric Fano resonance perturbations.Here,we demonstrated an overcoupled resonator with a high plasmon-molecule coupling coefficient(μ)(OC-Hμresonator)by precisely controlling the radiation loss channel,the resonator-oscillator coupling channel,and the frequency detuning channel.We observed a strong dependence of the sensing performance on the coupling state,and demonstrated that OC-Hμresonator has excellent sensing properties of ultra-sensitive(7.25%nm^(−1)),ultra-broadband(3–10μm),and immune asymmetric Fano lineshapes.These characteristics represent a breakthrough in SEIRA technology and lay the foundation for specific recognition of biomolecules,trace detection,and protein secondary structure analysis using a single array(array size is 100×100μm^(2)).In addition,with the assistance of machine learning,mixture classification,concentration prediction and spectral reconstruction were achieved with the highest accuracy of 100%.Finally,we demonstrated the potential of OC-Hμresonator for SARS-CoV-2 detection.These findings will promote the wider application of SEIRA technology,while providing new ideas for other enhanced spectroscopy technologies,quantum photonics and studying light–matter interactions.

Catalyst–Support Interaction in Polyaniline‑Supported Ni_(3)Fe Oxide to Boost Oxygen Evolution Activities for Rechargeable Zn‑Air Batteries

Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3)Fe oxide/PANI)with a robust hetero-interface,which significantly improves oxygen evolution activities with an overpotential of 270 mV at 10 mA cm^(-2)and specific activity of 2.08 mA cm_(ECSA)^(-2)at overpotential of 300 mV,3.84-fold that of Ni_(3)Fe oxide.It is revealed that the catalyst–support interaction between Ni_(3)Fe oxide and PANI support enhances the Ni–O covalency via the interfacial Ni–N bond,thus promoting the charge and mass transfer on Ni_(3)Fe oxide.Considering the excellent activity and stability,rechargeable Zn-air batteries with optimum Ni_(3)Fe oxide/PANI are assembled,delivering a low charge voltage of 1.95 V to cycle for 400 h at 10 mA cm^(-2).The regulation of the effect of catalyst–support interaction on catalytic activity provides new possibilities for the future design of highly efficient OER catalysts.

T cell interactions with microglia in immune-inflammatory processes of ischemic stroke

The primary mechanism of secondary injury after cerebral ischemia may be the brain inflammation that emerges after an ischemic stroke,which promotes neuronal death and inhibits nerve tissue regeneration.As the first immune cells to be activated after an ischemic stroke,microglia play an important immunomodulatory role in the progression of the condition.After an ischemic stroke,peripheral blood immune cells(mainly T cells)are recruited to the central nervous system by chemokines secreted by immune cells in the brain,where they interact with central nervous system cells(mainly microglia)to trigger a secondary neuroimmune response.This review summarizes the interactions between T cells and microglia in the immune-inflammatory processes of ischemic stroke.We found that,during ischemic stroke,T cells and microglia demonstrate a more pronounced synergistic effect.Th1,Th17,and M1 microglia can co-secrete proinflammatory factors,such as interferon-γ,tumor necrosis factor-α,and interleukin-1β,to promote neuroinflammation and exacerbate brain injury.Th2,Treg,and M2 microglia jointly secrete anti-inflammatory factors,such as interleukin-4,interleukin-10,and transforming growth factor-β,to inhibit the progression of neuroinflammation,as well as growth factors such as brain-derived neurotrophic factor to promote nerve regeneration and repair brain injury.Immune interactions between microglia and T cells influence the direction of the subsequent neuroinflammation,which in turn determines the prognosis of ischemic stroke patients.Clinical trials have been conducted on the ways to modulate the interactions between T cells and microglia toward anti-inflammatory communication using the immunosuppressant fingolimod or overdosing with Treg cells to promote neural tissue repair and reduce the damage caused by ischemic stroke.However,such studies have been relatively infrequent,and clinical experience is still insufficient.In summary,in ischemic stroke,T cell subsets and activated microglia act synergistically to regulate inflammatory progression,mainly by secreting inflammatory factors.In the future,a key research direction for ischemic stroke treatment could be rooted in the enhancement of anti-inflammatory factor secretion by promoting the generation of Th2 and Treg cells,along with the activation of M2-type microglia.These approaches may alleviate neuroinflammation and facilitate the repair of neural tissues.

Biological Interaction and Imaging of Ultrasmall Gold Nanoparticles

Ultrasmall gold nanoparticles(AuNPs)typically includes atomically precise gold nanoclusters(AuNCs)and AuNPs with a core size below 3 nm.Serving as a bridge between small molecules and traditional inorganic nanoparticles,the ultrasmall AuNPs show the unique advantages of both small molecules(e.g.,rapid distribution,renal clearance,low non-specific organ accumulation)and nanoparticles(e.g.,long blood circulation and enhanced permeability and retention effect).The emergence of ultrasmall AuNPs creates significant opportunities to address many challenges in the health field including disease diagnosis,monitoring and treatment.Since the nano–bio interaction dictates the overall biological applications of the ultrasmall AuNPs,this review elucidates the recent advances in the biological interactions and imaging of ultrasmall AuNPs.We begin with the introduction of the factors that influence the cellular interactions of ultrasmall AuNPs.We then discuss the organ interactions,especially focus on the interactions of the liver and kidneys.We further present the recent advances in the tumor interactions of ultrasmall AuNPs.In addition,the imaging performance of the ultrasmall AuNPs is summarized and discussed.Finally,we summarize this review and provide some perspective on the future research direction of the ultrasmall AuNPs,aiming to accelerate their clinical translation.

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.

Strong metal–support interaction boosts the electrocatalytic hydrogen evolution capability of Ru nanoparticles supported on titanium nitride

Ruthenium(Ru)has been regarded as one of the most promising alternatives to substitute Pt for catalyzing alkaline hydrogen evolution reaction(HER),owing to its inherent high activity and being the cheapest platinum-group metal.Herein,based on the idea of strong metal–support interaction(SMSI)regulation,Ru/TiN catalysts with different degrees of TiN overlayer over Ru nanoparticles were fabricated,which were applied to the alkaline electrolytic water.Characterizations reveal that the TiN overlayer would gradually encapsulate the Ru nanoparticles and induce more electron transfer from Ru nanoparticles to TiN support by the Ru–N–Ti bond as the SMSI degree increased.Further study shows that the exposed Ru–TiN interfaces greatly promote the H_(2) desorption capacity.Thus,the Ru/TiN-300 with a moderate SMSI degree exhibits excellent HER performance,with an overpotential of 38 mV at 10 mA cm^(−2).Also,due to the encapsulation role of TiN overlayer on Ru nanoparticles,it displays super long-term stability with a very slight potential change after 24 h.This study provides a deep insight into the influence of the SMSI effect between Ru and TiN on HER and offers a novel approach for preparing efficient and stable HER electrocatalysts through SMSI engineering.

Enhanced Ion-Selective Diffusion Achieved by Supramolecular Interaction for High Thermovoltage and Thermal Stability

Thermoelectric(TE)generators capable of converting thermal energy into applicable electricity have gained great popularity among emerging energy conversion technologies.Biopolymer-based ionic thermoelectric(i-TE)materials are promising candidates for energy conversion systems because of their wide sources,innocuity,and low manufacturing cost.However,common physically crosslinked biopolymer gels induced by single hydrogen bonding or hydrophobic interaction suffer from low differential thermal voltage and poor thermodynamic stability.Here,we develop a novel i-TE gel with supramolecular structures through multiple noncovalent interactions between ionic liquids(ILs)and gelatin molecular chains.The thermopower and thermoelectric power factor of the ionic gels are as high as 2.83 mV K-1 and 18.33μW m^(-1)K^(-2),respectively.The quasi-solid-state gelatin-[EMIM]DCA i-TE cells achieve ultrahigh 2 h output energy density(E_(2h)=9.9 mJ m^(-2))under an optimal temperature range.Meanwhile,the remarkable stability of the supramolecular structure provides the i-TE hydrogels with a thermal stability of up to 80℃.It breaks the limitation that biopolymer-based i-TE gels can only be applied in the low temperature range and enables biopolymer-based i-TE materials to pursue better performance in a higher temperature range.

Electrostatic Interaction-directed Construction of Hierarchical Nanostructured Carbon Composite with Dual Electrical Conductive Networks for Zinc-ion Hybrid Capacitors with Ultrastability

Metal-organic framework(MOF)-derived carbon composites have been considered as the promising materials for energy storage.However,the construction of MOF-based composites with highly controllable mode via the liquid-liquid synthesis method has a great challenge because of the simultaneous heterogeneous nucleation on substrates and the self-nucleation of individual MOF nanocrystals in the liquid phase.Herein,we report a bidirectional electrostatic generated self-assembly strategy to achieve the precisely controlled coatings of single-layer nanoscale MOFs on a range of substrates,including carbon nanotubes(CNTs),graphene oxide(GO),MXene,layered double hydroxides(LDHs),MOFs,and SiO_(2).The obtained MOF-based nanostructured carbon composite exhibits the hierarchical porosity(V_(meso)/V_(micro)∶2.4),ultrahigh N content of 12.4 at.%and"dual electrical conductive networks."The assembled aqueous zinc-ion hybrid capacitor(ZIC)with the prepared nanocarbon composite as a cathode shows a high specific capacitance of 236 F g^(-1)at 0.5 A g^(-1),great rate performance of 98 F g^(-1)at 100 A g^(-1),and especially,an ultralong cycling stability up to 230000 cycles with the capacitance retention of 90.1%.This work develops a repeatable and general method for the controlled construction of MOF coatings on various functional substrates and further fabricates carbon composites for ZICs with ultrastability.

Meiotic transcriptional reprogramming mediated by cell-cell communications in humans and mice revealed by scATACseq and scRNA-seq

Meiosis is a highly complex process significantly influenced by transcriptional regulation.However,studies on the mechanisms that govern transcriptomic changes during meiosis,especially in prophase I,are limited.Here,we performed single-cell ATAC-seq of human testis tissues and observed reprogramming during the transition from zygotene to pachytene spermatocytes.This event,conserved in mice,involved the deactivation of genes associated with meiosis after reprogramming and the activation of those related to spermatogenesis before their functional onset.Furthermore,we identified 282 transcriptional regulators(TRs)that underwent activation or deactivation subsequent to this process.Evidence suggested that physical contact signals from Sertoli cells may regulate these TRs in spermatocytes,while secreted ENHO signals may alter metabolic patterns in these cells.Our results further indicated that defective transcriptional reprogramming may be associated with non-obstructive azoospermia(NOA).This study revealed the importance of both physical contact and secreted signals between Sertoli cells and germ cells in meiotic progression.

Prediction of cell-cell communication patierns of dorsal root ganglion cells:single-cell RNA sequencing data analysis

Dorsal root ganglion neurons transmit peripheral somatic information to the central nervous system,and dorsal root ganglion neuron excitability affects pain perception.Dorsal root ganglion stimulation is a new approach for managing pain sensation.Knowledge of the cell-cell communication among dorsal root ganglion cells may help in the development of new pain and itch management strategies.Here,we used the single-cell RNA-sequencing(scRNA-seq)database to investigate intercellular communication networks among dorsal root ganglion cells.We collected scRNA-seq data from six samples from three studies,yielding data on a total of 17,766 cells.Based on genetic profiles,we identified satellite glial cells,Schwann cells,neurons,vascular endothelial cells,immune cells,fibroblasts,and vascular smooth muscle cells.Further analysis revealed that eight types of dorsal root ganglion neurons mediated proprioceptive,itch,touch,mechanical,heat,and cold sensations.Moreover,we predicted several distinct forms of intercellular communication among dorsal root ganglion cells,including cell-cell contact,secreted signals,extracellular matrix,and neurotransmitter-mediated signals.The data mining predicted that Mrgpra3-positive neurons robustly express the genes encoding the adenosine Adora2b(A2B)receptor and glial cell line-derived neurotrophic factor family receptor alpha 1(GFRα-1).Our immunohistochemistry results confirmed the coexpression of the A2B receptor and GFRα-1.Intrathecal injection of the A2B receptor antagonist PSB-603 effectively prevented histamine-induced scratching behaviour in a dose-dependent manner.Our results demonstrate the involvement of the A2B receptor in the modulation of itch sensation.Furthermore,our findings provide insight into dorsal root ganglion cell-cell communication patterns and mechanisms.Our results should contribute to the development of new strategies for the regulation of dorsal root ganglion excitability.

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

BACKGROUND Liver cancer is one of the deadliest malignant tumors worldwide.Immunotherapy has provided hope to patients with advanced liver cancer,but only a small fraction of patients benefit from this treatment due to individual differences.Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies,thereby improving patient survival rates.Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer,the impact of cell-cell interactions in the tumor microenvir-onment has not been adequately considered.AIM To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy.METHODS Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways.Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells.The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features,and a least absolute shrinkage and selection operator(LASSO)regression model was constructed to screen for diagnostic-related features.Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model.Finally,3 genes(stathmin 1,cofilin 1,and C-C chemokine ligand 5)significantly associated with survival were identified and used to construct an immune-related gene signature.RESULTS The immune-related gene signature composed of stathmin 1,cofilin 1,and C-C chemokine ligand 5 was identified through cell-cell communication.The effectiveness of the identified gene signature was validated based on experi-mental results of predictive immunotherapy response,tumor mutation burden analysis,immune cell infiltration analysis,survival analysis,and expression analysis.CONCLUSION The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment,providing insights for personalized treatment strategies.

Plume-lithosphere interaction in the Comei Large Igneous Province: Evidence from two types of mafic dykes in Gyangze, south Tibet, China

Two suites of mafic dykes,T1193-A and T1194-A,outcrop in Gyangze area,southeast Tibet.They are in the area of Comei LIP and have indistinguishable field occurrences with two other dykes in Gyangze,T0902 dyke with 137.7±1.3 Ma zircon age and T0907 dyke with 142±1.4 Ma zircon age reported by Wang YY et al.(2016),indicating coeval formation time.Taking all the four diabase dykes into consideration,two different types,OIB-type and weak enriched-type,can be summarized.The“OIB-type”samples,including T1193-A and T0907 dykes,show OIB-like geochemical features and have initial Sr-Nd isotopic values similar with most mafic products in Comei Large Igneous Provinces(LIP),suggesting that they represent melts directly generated from the Kerguelen mantle plume.The“weak enriched-type”samples,including T1194-A and T0902 dykes,have REEs and trace element patterns showing withinplate affinity but have obvious Nb-Ta-Ti negative anomalies.They show uniform lowerεNd(t)values(−6‒−2)and higher 87Sr/86Sr(t)values(0.706‒0.709)independent of their MgO variation,indicating one enriched mantle source.Considering their closely spatial and temporal relationship with the widespread Comei LIP magmatic products in Tethyan Himalaya,these“weak enriched-type”samples are consistent with mixing of melts from mantle plume and the above ancient Tethyan Himalaya subcontinental lithospheric mantle(SCLM)in different proportions.These weak enriched mafic rocks in Comei LIP form one special rock group and most likely suggest large scale hot mantle plume-continental lithosphere interaction.This process may lead to strong modification of the Tethyan Himalaya lithosphere in the Early Cretaceous.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Study of the Relationship Between New Ionic Interaction Parameters and Salt Solubility in Electrolyte Solutions Based on Molecular Dynamics Simulation

Studying the relationship between ionic interactions and salt solubility in seawater has implications for seawater desalination and mineral extraction.In this paper,a new method of expressing ion-to-ion interaction is proposed by using molecular dynamics simulation,and the relationship between ion-to-ion interaction and salt solubility in a simulated seawater water-salt system is investigated.By analyzing the variation of distance and contact time between ions in an electrolyte solution,from both spatial and temporal perspectives,new parameters were proposed to describe the interaction between ions:interaction distance(ID),and interaction time ratio(ITR).The best correlation between characteristic time ratio and solubility was found for a molar ratio of salt-to-water of 10:100 with a correlation coefficient of 0.96.For the same salt,a positive correlation was found between CTR and the molar ratio of salt and water.For type 1-1,type 2-1,type 1-2,and type 2-2 salts,the correlation coefficients between CTR and solubility were 0.93,0.96,0.92,and 0.98 for a salt-to-water molar ratio of 10:100,respectively.The solubility of multiple salts was predicted by simulations and compared with experimental values,yielding an average relative deviation of 12.4%.The new ion-interaction parameters offer significant advantages in describing strongly correlated and strongly hydrated electrolyte solutions.

Interatomic Interaction Models for Magnetic Materials:Recent Advances

Atomistic modeling is a widely employed theoretical method of computational materials science.It has found particular utility in the study of magnetic materials.Initially,magnetic empirical interatomic potentials or spinpolarized density functional theory(DFT)served as the primary models for describing interatomic interactions in atomistic simulations of magnetic systems.Furthermore,in recent years,a new class of interatomic potentials known as magnetic machine-learning interatomic potentials(magnetic MLIPs)has emerged.These MLIPs combine the computational efficiency,in terms of CPU time,of empirical potentials with the accuracy of DFT calculations.In this review,our focus lies on providing a comprehensive summary of the interatomic interaction models developed specifically for investigating magnetic materials.We also delve into the various problem classes to which these models can be applied.Finally,we offer insights into the future prospects of interatomic interaction model development for the exploration of magnetic materials.