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Abstract Cell-cell communication (CCC) occurs across different biological scales, ranging from interactions between large groups of cells to interactions between individual cells, forming a hierarchical structure. Globally, CCC may exist between clusters or only subgroups of a cluster with varying size, while locally, a group of cells as sender or receiver may exhibit distinct signaling properties. Current existing methods infer CCC from single-cell RNA-seq or Spatial Transcriptomics only between predefined cell groups, neglecting the existing hierarchical structure within CCC that are determined by signaling molecules, in particular, ligands and receptors. Here, we develop CrossChat, a novel computational framework designed to infer and analyze the hierarchical cell-cell communication structures using two complementary approaches: a global hierarchical structure using a multi-resolution clustering method, and multiple local hierarchical structures using a tree detection method. This framework provides a comprehensive approach to understand the hierarchical relationships within CCC that govern complex tissue functions. By applying our method to two nonspatial scRNA-seq datasets sampled from COVID-19 patients and mouse embryonic skin, and two spatial transcriptomics datasets generated from Stereo-seq of mouse embryo and 10x Visium of mouse wounded skin, we showcase CrossChat’s functionalities for analyzing both global and local hierarchical structures within cell-cell communication.

Abstract Rosacea is a chronic inflammatory skin disorder, whose underlying cellular and molecular mechanisms remain obscure. Here, we generate a single-cell atlas of facial skin from female rosacea patients and healthy individuals. Among keratinocytes, a subpopulation characterized by IFNγ-mediated barrier function damage is found to be unique to rosacea lesions. Blocking IFNγ signaling alleviates rosacea-like phenotypes and skin barrier damage in mice. The papulopustular rosacea is featured by expansion of pro-inflammatory fibroblasts, Schwann, endothelial and macrophage/dendritic cells. The frequencies of type 1/17 and skin-resident memory T cells are increased, and vascular mural cells are characterized by activation of inflammatory pathways and impaired muscle contraction function in rosacea. Most importantly, fibroblasts are identified as the leading cell type producing pro-inflammatory and vasodilative signals in rosacea. Depletion of fibroblasts or knockdown of PTGDS, a gene specifically upregulated in fibroblasts, blocks rosacea development in mice. Our study provides a comprehensive understanding of the aberrant alterations of skin-resident cell populations and identifies fibroblasts as a key determinant in rosacea development.

Abstract Background Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). Results We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. Conclusions New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations.

Abstract Rabbit meat is tender, high in protein, low in fat and cholesterol, and offers several nutritional benefits. However, it has a stronger taste and can be challenging to cook. The global rabbit industry has been developing steadily, and China's total rabbit meat production has exceeded half of the world's total production in 2021. Along with the progress of the production process, the meat of the nutrition and taste at the same time, to improve smell also can get better control, solved the problems met in rabbit meat production. This article discusses the attributes of rabbit meat and outlines the deodorization, texture adjustment, and water retention techniques currently used in rabbit meat processing, aiming to offer a theoretical foundation for the advancement of rabbit meat processing technology.

Abstract Biomaterial wound dressings, such as hydrogels, interact with host cells to regulate tissue repair. This study investigates how crosslinking of gelatin-based hydrogels influences immune and stromal cell behavior and wound healing in female mice. We observe that softer, lightly crosslinked hydrogels promote greater cellular infiltration and result in smaller scars compared to stiffer, heavily crosslinked hydrogels. Using single-cell RNA sequencing, we further show that heavily crosslinked hydrogels increase inflammation and lead to the formation of a distinct macrophage subpopulation exhibiting signs of oxidative activity and cell fusion. Conversely, lightly crosslinked hydrogels are more readily taken up by macrophages and integrated within the tissue. The physical properties differentially affect macrophage and fibroblast interactions, with heavily crosslinked hydrogels promoting pro-fibrotic fibroblast activity that drives macrophage fusion through RANKL signaling. These findings suggest that tuning the physical properties of hydrogels can guide cellular responses and improve healing, offering insights for designing better biomaterials for wound treatment.

The purpose of this paper is to study the response of aggregate and sectoral employment in the United States to shocks and uncertainties in the oil and macro markets. To estimate the actual dataset, we employ a VAR model with 12 variables using U.S. data during 1986Q1-2021Q4. With alternative oil and macroeconomic uncertainties and different monetary authorities, we find that employment is significantly affected by both types of uncertainty when zero lower bound (ZLB) binds. Total employment and oil-related industries employment show a significant increase in the presence of the ZLB and the propagation of uncertainty shock from the oil market. These findings highlight the empirical relevance of oil prices and macroeconomic uncertainty on U.S. labor market dynamics.

Vitrification is the safest and most effective method for treating solid waste incineration residues, and the utilization of vitrified slag can promote recovery of waste resources. By using the response surface methodology to comprehensively consider the impact of three factors – cement substitution ratio, water-binder ratio, and sand rate – on the strength of concrete, the optimal process parameters for predicting the maximum 28-day compressive strength of concrete specimens are determined as follows: 5% cement substitution ratio, 0.69 water-binder ratio, and 40% sand rate. The experiments demonstrate that concrete specimens prepared by partially replacing cement with molten glass slag exhibit excellent mechanical and physicochemical properties. As the curing age increases, the compressive strength of the specimens also enhances. Leaching test results showed that the concrete test block met groundwater Class III quality standards, and the addition of vitrified slag powder reduced the content of calcium hydroxide and improved concrete performance.

IntroductionThe incorporation of Staphylococcus xylosus in sausage production is hypothesized to affect various physicochemical properties and flavor profiles of sausages. This study aimed to evaluate the simulation of these features in a sausage model and establish its applicability for in vitro studies.MethodsBoth a control and an experimental model, inclusive of Staphylococcus xylosus, were assessed for changes in physicochemical indexes (pH and water activity, Aw) and the concentration of flavoring components (esters and aldehydes). Thiobarbituric acid reactive substances (TBARS) values were also measured to evaluate lipid oxidation.ResultsThe introduction of Staphylococcus xylosus resulted in no significant changes in pH and Aw between the sausage and the model. However, there was a considerable increase in the content of volatile flavor compounds, specifically esters and aldehydes, in the experimental groups compared to the control. Additionally, the TBARS values in experimental groups were significantly lower than those in the control group at the end of the testing period.DiscussionThe findings indicate that Staphylococcus xylosus plays a critical role in enhancing the flavor profile of sausages through the increased synthesis of volatile compounds and inhibiting fat oxidation. The sausage model effectively simulated the physicochemical and flavor index responses, demonstrating its potential utility for further in vitro research on sausage fermentation and preservation techniques.

Abstract Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots—regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.

LMNA-related dilated cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C (LMNA) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. The molecular mechanisms of the disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA-related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA-mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (four from Patients and eight from Controls) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for cardiac progenitors to cardiomyocytes (CMs) and epicardium-derived cells (EPDCs). Data integration and comparative analyses of Patient and Control cells found cell type and lineage-specific differentially expressed genes (DEGs) with enrichment, supporting pathway dysregulation. Top DEGs and enriched pathways included 10 ZNF genes and RNA polymerase II transcription in pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CMs; LMNA and epigenetic regulation, as well as DDIT4 and mTORC1 signaling in EPDCs. Top DEGs also included XIST and other X-linked genes, six imprinted genes (SNRPN, PWAR6, NDN, PEG10, MEG3, MEG8), and enriched gene sets related to metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs, as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.

The distinctive taste of Sichuan sauce-flavored sausage comes from an intricate microbial metabolism. The correlation between microbial composition and distinct flavor components has not been researched. The study used headspace solid-phase microextraction action with gas chromatography mass spectrometry to find flavor components and high-throughput sequencing of 16S rRNA to look at the diversity and succession of microbial communities. The correlation network model forecasted the connection between essential bacteria and the development of flavors. The study revealed that the primary flavor compounds in Sichuan sauce-flavored sausages were alcohols, aldehydes, and esters. The closely related microbes were Leuconostoc, Pseudomonas, Psychrobacter, Flavobacterium, and Algoriella. The microbes aided in the production of various flavor compounds, such as 1-octen-3-ol, benzeneacetaldehyde, hexanal, (R,R)-2,3-butanediol, and ethyl caprylate. This work has enhanced our comprehension of the diverse functions that bacteria serve in flavor development during the fermentation of Sichuan sauce-flavored sausage.

Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell–cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip–Stalk cell patterning can undergo an order–disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understanding of cell plasticity underlying angiogenesis and other complex morphogenic processes.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand–receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

Utilizing a sausage model and principal component analysis (PCA), a strain of Lactobacillus plantarum that significantly improved the flavor substance of sausage was selected. It was added to the sausage, and the pH, Aw, color, peroxide value (POV), thiobarbituric acid (TBARS), and volatile flavor substances of Sichuan sausage production were measured at 0, 1, 3, 7 and 14 d, using the uninoculated group as control. A partial least squares discriminant analysis model (PLS-DA) was applied to evaluate the characteristic flavor substances in sausages with VIP (variable important in projection) > 1 and P < 0.05 as screening conditions. Results showed that, the pH of control and Lactobacillus plantarum-fermented sausages declined and then gradually rose with fermentation time. The Aw reduced dramatically, the POV and TBARS increased significantly, while there was no significant change in color (P>0.05). And the pH, Aw, POV and TBARS values were significantly lower in Lactobacillus plantarum inoculated sausages compared to the control group. In comparison to the control group, the sausages inoculated with Lactobacillus plantarum had significantly higher concentrations of alcohols, ketones, and esters, as well as significantly enhanced the concentrations of characteristic flavor substances (isovaleric acid, 3-hydroxy-2-butanone, phenylethylaldehyde and phenylethanol). In summary, Lactobacillus plantarum M-25 selected by the sausage model was effective in enhancing the quality and flavor characteristics of sausages.

Neurons communicate differently from non-neuronal cells. Here, authors present a method, NeuronChat, that utilizes scRNA-seq data and/or spatial transcriptomics to infer, visualize and analyze neural-specific cell-cell communication.

How molecular diversity in neurons links to versatile functions is elusive. Here the authors profiled embryonic spinal motor neurons with single-cell RNAseq and identified molecular subtypes targeting distinct muscle groups in different species.

Abstract Two lactic acid bacteria (LAB) strains were screened in traditional Sichuan sausage, and their nitrite‐reducing performance in different production processes was determined in this study. LAB were isolated from traditional Sichuan sausages and identified by 16S rDNA sequencing. The effects of the isolated bacteria on the bacterial community, physicochemical properties, and nitrite‐decreasing capacity during sausage fermentation were investigated by comparing nonactivated treatment and activated treatment at 30°C. The results showed that the screened two LAB strains were identified as Pediococcus pentosaceus and Leuconostoc mesenteroides, which adapted well to the new habitat of fermented sausage, reaching a maximum of 7.00 and 7.32 lg CFU g−1 in the nonactivated treatment, 7.65 and 7.52 lg CFU g−1 in the activated treatment, and the two strains had strong acidizing potential, which could inhibit the growth of saprophytic bacteria. Otherwise, the activated treatment was better than the traditional process for nitrite degradation and fermentation time, except for color. Both strains contribute to nitrite consumption while reducing fermentation time and sausage production cycles. In conclusion, these two strains are expected to be the starting point for the improvement of biological safety and nitrite residue control in traditional Sichuan sausage production.

Abstract Extracting dynamical information from single‐cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory‐oriented, bottom‐up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single‐cell RNA sequencing (scRNA‐seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state‐specific gene–gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial–mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre‐existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.

Saccharomyces cerevisiae (S. cerevisiae) and Kluyveromyces marxianus (K. marxianus) are often used as fermenters in yogurt and alcohol, and have been less studied within meat products. The yeasts were added to sauce meat, and the uninoculated group served as a control in this study to examine and compare the changing patterns of physicochemical and flavor characteristics of S. cerevisiae and K. marxianus on sauce meat during storage. The changes in moisture content, aw, pH, thiobarbituric acid reactive substances (TBARS), and other flavor characteristics were measured in sauce meat during the first, second, fourth, and sixth months after production. The following factors were examined: moisture content, aw, pH, TBARS, peroxide value (POV), acid value (AV), soluble protein (SP), free amino acid (FAA), and volatile flavoring compounds. With VIP > 1 and p < 0.05 as the screening conditions, the partial least squares model (PLS-DA) was used to assess the distinctive flavor components in the sausages. The findings demonstrated that the three groups’ changes in sauce meat were comparable during the first two months of storage but differed significantly between the 4th and 6th months. The moisture content, water activity, and pH of the sauce meat decreased gradually with the storage time; TBARS, AV, and FAA increased significantly; SP decreased significantly from 2.61 to 1.72, while POV increased to 0.03 and then decreased to 0.02. The POV and TBARS values of the yeast-infected meat were substantially lower than those of the control group, and the POV and TBARS values of the meat inoculated with S. cerevisiae were particularly decreased (p < 0.05). The POV and TBARS values of SC (S. cerevisiae group) decreased by 49.09% and 40.15%, respectively, compared to CK (the control group) at the time of storage until June. The experimental group (KM: K. marxianus group) significantly increased the SP and FAA values of the sauce meat (p < 0.05) by 32.4% and 29.84% compared to the CK group, respectively. Esters and olefins as well as alcohols and esters were much greater in meat that had been supplemented with S. cerevisiae and K. marxianus than in meat from the control group. In conclusion, inoculating sauce meat with S. cerevisiae can significantly enhance the quality and flavor of sauce meat while it is being stored.

A critical task in spatial transcriptomics analysis is to understand inherently spatial relationships among cells. Here, the authors present a deep learning framework to integrate spatial and transcriptional information, spatially extending pseudotime and revealing spatiotemporal organization of cells.

Summary: Several methods for generating human-skin-equivalent (HSE) organoid cultures are in use to study skin biology; however, few studies thoroughly characterize these systems. To fill this gap, we use single-cell transcriptomics to compare in vitro HSEs, xenograft HSEs, and in vivo epidermis. By combining differential gene expression, pseudotime analyses, and spatial localization, we reconstruct HSE keratinocyte differentiation trajectories that recapitulate known in vivo epidermal differentiation pathways and show that HSEs contain major in vivo cellular states. However, HSEs also develop unique keratinocyte states, an expanded basal stem cell program, and disrupted terminal differentiation. Cell-cell communication modeling shows aberrant epithelial-to-mesenchymal transition (EMT)-associated signaling pathways that alter upon epidermal growth factor (EGF) supplementation. Last, xenograft HSEs at early time points post transplantation significantly rescue many in vitro deficits while undergoing a hypoxic response that drives an alternative differentiation lineage. This study highlights the strengths and limitations of organoid cultures and identifies areas for potential innovation.

Abstract The effects of adjuvants for increasing the immunogenicity of influenza vaccines are well known. However, the effect of adjuvants on increasing the breadth of cross-reactivity is less well understood. In this study we have performed a systematic screen of different toll-like receptor (TLR) agonists, with and without a squalene-in-water emulsion on the immunogenicity of a recombinant trimerized hemagglutinin (HA) vaccine in mice after single-dose administration. Antibody (Ab) cross-reactivity for other variants within and outside the immunizing subtype (homosubtypic and heterosubtypic cross-reactivity, respectively) was assessed using a protein microarray approach. Most adjuvants induced broad IgG profiles, although the response to a combination of CpG, MPLA and AddaVax (termed ‘IVAX-1’) appeared more quickly and reached a greater magnitude than the other formulations tested. Antigen-specific plasma cell labeling experiments show the components of IVAX-1 are synergistic. This adjuvant preferentially stimulates CD4 T cells to produce Th1>Th2 type (IgG2c>IgG1) antibodies and cytokine responses. Moreover, IVAX-1 induces identical homo- and heterosubtypic IgG and IgA cross-reactivity profiles when administered intranasally. Consistent with these observations, a single-cell transcriptomics analysis demonstrated significant increases in expression of IgG1, IgG2b and IgG2c genes of B cells in H5/IVAX-1 immunized mice relative to naïve mice, as well as significant increases in expression of the IFNγ gene of both CD4 and CD8 T cells. These data support the use of adjuvants for enhancing the breath and durability of antibody responses of influenza virus vaccines.

Abstract Background Bladder cancer is one of the most common malignancies but the corresponding diagnostic methods are either invasive or limited in specificity and/or sensitivity. This study aimed to develop a urine-based methylation panel for bladder cancer detection by improving published panels and validate performance of the new panel with clinical samples. Methods Related researches were reviewed and 19 potential panels were selected. RRBS was performed on a cohort with 45 samples to reassess these panels and a new panel inherited best markers was developed. The new panel was applied with qMSP platform to 33 samples from the RRBS cohort and the results were compared to those of RRBS. Lastly, another larger cohort with 207 samples was used to validate new panel performance with qMSP. Results Three biomarkers (PCDH17, POU4F2 and PENK) were selected to construct a new panel P3. P3 panel achieved 100% specificity and 71% sensitivity with RRBS in corresponding cohort and then showed a better performance of 100% specificity and 84% sensitivity with qMSP platforms in a balanced cohort. When validated with 207-sample cohort, P3 with qMSP showed a performance of 97% specificity and 87% sensitivity which was modestly improved compared to the panels it derided from. Conclusions Overall, the P3 panel achieved relatively high sensitivity and accuracy in bladder cancer detection.

Walker et al. review methods used to analyse spatial transcriptomics data and discuss open questions and areas of future development.

Abstract Bladder cancer (BC) is one of the most frequent cancer in the world, and its incidence is rising worldwide, especially in developed countries. Urine metabolomics is a powerful approach to discover potential biomarkers for cancer diagnosis. In this study, we applied an ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) method to profile the metabolites in urine from 29 bladder cancer patients and 15 healthy controls. The differential metabolites were extracted and analyzed by univariate and multivariate analysis methods. Together, 19 metabolites were discovered as differently expressed biomarkers in the two groups, which mainly related to the pathways of phenylacetate metabolism, propanoate metabolism, fatty acid metabolism, pyruvate metabolism, arginine and proline metabolism, glycine and serine metabolism, and bile acid biosynthesis. In addition, a subset of 11 metabolites of those 19 ones were further filtered as potential biomarkers for BC diagnosis by using logistic regression model. The results revealed that the area under the curve (AUC) value, sensitivity and specificity of receiving operator characteristic (ROC) curve were 0.983, 95.3% and 100%, respectively, indicating an excellent discrimination power for BC patients from healthy controls. It was the first time to reveal the potential diagnostic markers of BC by metabolomics, and this will provide a new sight for exploring the biomarkers of the other disease in the future work.

Abstract At present, more than one cell death pathways have been found, one of which is ferroptosis. Ferroptosis was discovered in 2012 and described as an iron-dependent and lipid peroxidation-driven regulated cell death pathway. In the past few years, ferroptosis has been shown to induce tumor cell death, providing new ideas for tumor treatment. In this article, we summarize the latest advances in ferroptosis-induced tumor therapy at the intersection of tumor biology, molecular biology, redox biology, and materials chemistry. First, we state the characteristics of ferroptosis in cells, then introduce the key molecular mechanism of ferroptosis, and describes the relationship between ferroptosis and oxidative stress signaling pathways. Finally, we focused on several types of ferroptosis inducers discovered by scholars, and the application of ferroptosis in systemic chemotherapy, radiotherapy, immunotherapy and nanomedicine, in the hope that ferroptosis can exert its potential in the treatment of tumors.

Abstract Cancer-associated fibroblasts (CAFs), the most abundant cells in the tumor microenvironment, play an indispensable role in cancer initiation, progression, metastasis, and metabolism. The limitations of traditional treatments can be partly attributed to the lack of understanding of the role of the tumor stroma. For this reason, CAF targeting is gradually gaining attention, and many studies are trying to overcome the limitations of tumor treatment with CAF as a breakthrough. Glutamine (GLN) has been called a “nitrogen reservoir” for cancer cells because of its role in supporting anabolic processes such as fuel proliferation and nucleotide synthesis, but ammonia is a byproduct of the metabolism of GLN and other nitrogenous compounds. Moreover, in some studies, GLN has been reported as a fundamental nitrogen source that can support tumor biomass. In this review, we discuss the latest findings on the role of GLN and ammonia in the crosstalk between CAFs and cancer cells as well as the potential therapeutic implications of nitrogen metabolism.

How to infer transient cells and cell-fate transitions from snap-shot single cell transcriptome dataset remains a major challenge. Here the authors present a multiscale approach to construct single-cell dynamical manifold, quantify cell stability, and compute transition trajectory and probability between cell states.

Bergman, Karikomi et al. propose a multiscale evolutionary model to describe tumor-immune-EMT interactions and their impact on epithelial cancer progression, which highlights a controllable region that maximizes invasion-free survival. Using EMT-inflammation associated TCGA data, the authors find EMT association worsens invasion-free survival and identify genes that influence outcomes in bladder and uterine cancer.

Cell–cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focus on direct comparisons between individual CCI networks of individual samples from patients, rather than “group-level” comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease statuses, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease statuses. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network level. By applying these analysis to a large-scale single-cell RNA-sequencing dataset of coronavirus disease 2019 (COVID-19), we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19.

Semiconductor manufacturing is one of the most complex manufacturing systems, of which wafer fabrication is the core part. Dual-armed cluster tool is widely used in the wafer manufacturing industry with its efficient production characteristics. The scheduling process of the dual-armed cluster tool has to consider many constraints; wafer residency time constraints, activity time fluctuations, etc. are relatively basic requirements. With the improvement of wafer processing accuracy in the market, green scheduling and processing efficiency are emerging as key issues for research, the scheduling of dual-armed cluster tool considering the above constraints is an NP-hard problem. Based on this study, the clean scenario is divided, a scheduling model that can realize low-carbon production is constructed, and the scheduling idea of the dual-armed cluster tool under clean transient is analyzed to ensure that the clustering tool is in green scheduling and restored to a steady state as soon as possible during the scheduling process.

Vitiligo is an autoimmune skin disease characterized by the destruction of melanocytes by autoreactive CD8+ T cells. Melanocyte destruction in active vitiligo is mediated by CD8+ T cells, but the persistence of white patches in stable disease is poorly understood. The interaction between immune cells, melanocytes, and keratinocytes in situ in human skin has been difficult to study due to the lack of proper tools. We combine noninvasive multiphoton microscopy (MPM) imaging and single-cell RNA-Seq (scRNA-Seq) to identify subpopulations of keratinocytes in stable vitiligo patients. We show that, compared with nonlesional skin, some keratinocyte subpopulations are enriched in lesional vitiligo skin and shift their energy utilization toward oxidative phosphorylation. Systematic investigation of cell-to-cell communication networks show that this small population of keratinocyte secrete CXCL9 and CXCL10 to potentially drive vitiligo persistence. Pseudotemporal dynamics analyses predict an alternative differentiation trajectory that generates this new population of keratinocytes in vitiligo skin. Further MPM imaging of patients undergoing punch grafting treatment showed that keratinocytes favoring oxidative phosphorylation persist in nonresponders but normalize in responders. In summary, we couple advanced imaging with transcriptomics and bioinformatics to discover cell-to-cell communication networks and keratinocyte cell states that can perpetuate inflammation and prevent repigmentation.

Abstract Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer ( http://www.cellchat.org/ ) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.

Abstract Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.

The interfollicular epidermis (IFE) is part of the epidermis but how it forms is unclear. Here, the authors perform scRNA sequencing at various developmental stages on the mouse epidermis and on deletion of Grhl3, showing IFE differentiation is gradual and that Grhl3 is needed for commitment to differentiation by regulating Wnt signalling pathway.

The mechanisms regulating stem cells to give rise to human interfollicular epidermis are unclear. Here, the authors use single cell RNA sequencing to identify heterogeneity within the human neonatal interfollicular epidermis and distinct spatial positioning of at least four basal stem cell populations.

Abstract Background Diabetic nephropathy (DN) is one of the most common and serious complications of diabetes, and is the most important cause of death for diabetic patients. Baicalin (BAI) has anti-oxidative, anti-inflammatory and anti-apoptotic activities, which play a role in attenuating insulin resistance and protecting the kidney. Moreover, cell-specific targeting of renal tubular cells is an approach to enhance drug accumulation in the kidney. Methods Forty-five Sprague-Dawley rats were divided into four groups. A diabetes model was created using streptozotocin (STZ) intraperitoneally injection. The four groups included: Control group (n = 10), DN (n = 15), BAI treatment (BAI; n = 10) and BAI-LZM treatment (BAI-LZM; n = 10) groups. In the current study, the renoprotection and anti-fibrotic effects of BAI-lysozyme (LZM) conjugate were further investigated in rats with DN induced by STZ compared with BAI treatment alone. Results The results suggest that BAI-LZM better ameliorates renal impairment, metabolic disorder and renal fibrosis than BAI alone in rats with DN, and the potential regulatory mechanism likely involves inhibiting inflammation via the nuclear factor-κB signaling pathway, inhibiting extracellular matrix accumulation via the transforming growth factor-β/Smad3 pathway and regulating cell proliferation via the insulin-like growth factor (IGF)-1/IGF-1 receptor/p38 Mitogen-activated protein kinase (MAPK) pathway. BAI and the kidney-targeted BAI-LZM can utilize the body’s cytoprotective pathways to reactivate autophagy (as indicated by the autophagy markers mechanistic target of rapamycin and sirtuin 1 to ameliorate DN outcomes. Conclusions Our data support the traditional use of S. baicalensis as an important anti-DN traditional chinese medicine (TCM), and BAI, above all BAI-LZM, is a promising source for the identification of molecules with anti-DN effects.

Dissociation of tissues allows high-throughput expression profiling of single cells, but spatial information is lost. Here the authors apply an unbalanced and structured optimal transport method to infer spatial and signalling relationships between cells from scRNA-seq data by integrating it with spatial imaging data.

Abstract Background Emerging data has suggested that Tregs, Th17, Th1 and Th2 are correlated with early immune mechanisms by controlling Plasmodium infection. Plasmodium infection appeared to impair the antigen presentation and maturation of DCs, leading to attenuation of specific cellular immune response ultimately. Hence, in this study, we aim to evaluate the relevance between DCs and Tregs/Th17 populations in the process and outcomes of infection with Plasmodium yoelii 17XL (P.y17XL). Methods DCs detection/analysis dynamically was performed by Tregs depletion or Th17 neutralization in P.y17XL infected BALB/c mice via flow cytometry. Then the levels of cytokines production were detected using enzyme-linked mmunosorbent assay (ELISA). Results Our results indicated that Tregs depletion or Th17 neutralization in BALB/c mice infected with P.y17XL significantly up-regulated the percentages of mDC and pDC, increased the expressions of major histocompatibility complex (MHC) class II, CD80, CD86 on DCs and the levels of IL-10/IL-12 secreted by DCs, indicating that abnormal amplification of Tregs or Th17 may damage the maturation and function of DCs during the early stage of malaria infection. Interestingly, we also found that the abnormal amplification of Th17, as well as Tregs, could inhibit the maturation of DCs. Conclusions Tregs skewing or Th17 amplification during the early stage of malaria infection may inhibit the maturation and function of DCs by modifying the subsets of DCs, expressions of surface molecules on DCs and secretion mode of cytokines.

Abstract Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms.

Immune checkpoint therapies such as PD-1 blockade have vastly improved the treatment of numerous cancers, including basal cell carcinoma (BCC). However, patients afflicted with pancreatic ductal carcinoma (PDAC), one of the deadliest malignancies, overwhelmingly exhibit negative responses to checkpoint therapy. We sought to combine data analysis and machine learning to differentiate the putative mechanisms of BCC and PDAC non-response. We discover that increased MHC-I expression in malignant cells and suppression of MHC and PD-1/PD-L expression in CD8+ T cells is associated with nonresponse to treatment. Furthermore, we leverage machine learning to predict response to PD-1 blockade on a cellular level. We confirm divergent resistance mechanisms between BCC, PDAC, and melanoma and highlight the potential for rapid and affordable testing of gene expression in BCC patients to accurately predict response to checkpoint therapies. Our findings present an optimistic outlook for the use of quantitative cross-cancer analyses in characterizing immune responses and predicting immunotherapy outcomes.

Data-based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering, and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross-map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

Aging and metabolic disorders feedback and promote each other and are closely related to the occurrence and development of cardiovascular disease, type 2 diabetes, neurodegeneration and other degenerative diseases. Liupao tea is a geographical indication product of Chinese dark tea, with a “red, concentrated, aged and mellow” flavor quality. In this study, the aqueous extract of aged Liupao tea (ALPT) administered by continuous gavage significantly inhibited the increase of visceral fat and damage to the intestinal–liver–microbial axis in high-fat modeling of SAMP8 (P8+HFD) mice. Its potential mechanism is that ALPT significantly inhibited the inflammation and aggregation formation pathway caused by P8+HFD, increased the abundance of short-chain fatty acid producing bacteria Alistipes, Alloprevotella and Bacteroides, and had a calorie restriction effect. The results of the whole target metabolome network pharmacological analysis showed that there were 139 potential active components in the ALPT aqueous extract, and the core targets of their actions were SRC, TP53, AKT1, MAPK3, VEGFA, EP300, EGFR, HSP90AA1, CASP3, etc. These target genes were mainly enriched in cancer, neurodegenerative diseases, glucose and lipid metabolism and other pathways of degenerative changes. Molecular docking further verified the reliability of network pharmacology. The above results indicate that Liupao tea can effectively delay the body’s degenerative changes through various mechanisms and multi-target effects. This study revealed that dark tea such as Liupao tea has significant drinking value in a modern and aging society.

Summary: Bivalent chromatin is characterized by occupation of both activating and repressive histone modifications. Here, we develop a mathematical model that involves antagonistic histone modifications H3K4me3 and H3K27me3 to capture the key features of bivalent chromatin. Three necessary conditions for the emergence of bivalent chromatin are identified, including advantageous methylating activity over demethylating activity, frequent noise conversions of modifications, and sufficient nonlinearity. The first condition is further confirmed by analyzing the existing experimental data. Investigation of the composition of bivalent chromatin reveals that bivalent nucleosomes carrying both H3K4me3 and H3K27me3 account for no more than half of nucleosomes at the bivalent chromatin domain. We identify that bivalent chromatin not only allows transitions to multiple states but also serves as a stepping stone to facilitate a stepwise transition between repressive chromatin state and activating chromatin state and thus elucidate crucial roles of bivalent chromatin in mediating phenotypical plasticity during cell fate determination.

The diversity of fibroblasts contributing to wound healing is unclear. Here, the authors use single-cell RNA-sequencing to identify heterogeneity among murine fibroblasts in the wound and find that recruited myeloid cells contribute to adipocyte regeneration during healing.

The vertebrate hindbrain is segmented into rhombomeres (r) initially defined by distinct domains of gene expression. Previous studies have shown that noise-induced gene regulation and cell sorting are critical for the sharpening of rhombomere boundaries, which start out rough in the forming neural plate (NP) and sharpen over time. However, the mechanisms controlling simultaneous formation of multiple rhombomeres and accuracy in their sizes are unclear. We have developed a stochastic multiscale cell-based model that explicitly incorporates dynamic morphogenetic changes (i.e. convergent-extension of the NP), multiple morphogens, and gene regulatory networks to investigate the formation of rhombomeres and their corresponding boundaries in the zebrafish hindbrain. During pattern initiation, the short-range signal, fibroblast growth factor (FGF), works together with the longer-range morphogen, retinoic acid (RA), to specify all of these boundaries and maintain accurately sized segments with sharp boundaries. At later stages of patterning, we show a nonlinear change in the shape of rhombomeres with rapid left-right narrowing of the NP followed by slower dynamics. Rapid initial convergence improves boundary sharpness and segment size by regulating cell sorting and cell fate both independently and coordinately. Overall, multiple morphogens and tissue dynamics synergize to regulate the sizes and boundaries of multiple segments during development.

Single-cell RNA sequencing (scRNA-seq) data provides unprecedented information on cell fate decisions; however, the spatial arrangement of cells is often lost. Several recent computational methods have been developed to impute spatial information onto a scRNA-seq dataset through analyzing known spatial expression patterns of a small subset of genes known as a reference atlas. However, there is a lack of comprehensive analysis of the accuracy, precision, and robustness of the mappings, along with the generalizability of these methods, which are often designed for specific systems. We present a system-adaptive deep learning-based method (DEEPsc) to impute spatial information onto a scRNA-seq dataset from a given spatial reference atlas. By introducing a comprehensive set of metrics that evaluate the spatial mapping methods, we compare DEEPsc with four existing methods on four biological systems. We find that while DEEPsc has comparable accuracy to other methods, an improved balance between precision and robustness is achieved. DEEPsc provides a data-adaptive tool to connect scRNA-seq datasets and spatial imaging datasets to analyze cell fate decisions. Our implementation with a uniform API can serve as a portal with access to all the methods investigated in this work for spatial exploration of cell fate decisions in scRNA-seq data. All methods evaluated in this work are implemented as an open-source software with a uniform interface.

During early mammalian embryo development, a small number of cells make robust fate decisions at particular spatial locations in a tight time window to form inner cell mass (ICM), and later epiblast (Epi) and primitive endoderm (PE). While recent single-cell transcriptomics data allows scrutinization of heterogeneity of individual cells, consistent spatial and temporal mechanisms the early embryo utilize to robustly form the Epi/PE layers from ICM remain elusive. Here we build a multiscale three-dimensional model for mammalian embryo to recapitulate the observed patterning process from zygote to late blastocyst. By integrating the spatiotemporal information reconstructed from multiple single-cell transcriptomic datasets, the data-informed modeling analysis suggests two major processes critical to the formation of Epi/PE layers: a selective cell-cell adhesion mechanism (via EphA4/EphrinB2) for fate-location coordination and a temporal attenuation mechanism of cell signaling (via Fgf). Spatial imaging data and distinct subsets of single-cell gene expression data are then used to validate the predictions. Together, our study provides a multiscale framework that incorporates single-cell gene expression datasets to analyze gene regulations, cell-cell communications, and physical interactions among cells in complex geometries at single-cell resolution, with direct application to late-stage development of embryogenesis.

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.

Epithelial-to-mesenchymal transition (EMT) plays an important role in many biological processes during development and cancer. The advent of single-cell transcriptome sequencing techniques allows the dissection of dynamical details underlying EMT with unprecedented resolution. Despite several single-cell data analysis on EMT, how cell communicates and regulates dynamics along the EMT trajectory remains elusive. Using single-cell transcriptomic datasets, here we infer the cell–cell communications and the multilayer gene–gene regulation networks to analyze and visualize the complex cellular crosstalk and the underlying gene regulatory dynamics along EMT. Combining with trajectory analysis, our approach reveals the existence of multiple intermediate cell states (ICSs) with hybrid epithelial and mesenchymal features. Analyses on the time-series datasets from cancer cell lines with different inducing factors show that the induced EMTs are context-specific: the EMT induced by transforming growth factor B1 (TGFB1) is synchronous, whereas the EMTs induced by epidermal growth factor and tumor necrosis factor are asynchronous, and the responses of TGF-β pathway in terms of gene expression regulations are heterogeneous under different treatments or among various cell states. Meanwhile, network topology analysis suggests that the ICSs during EMT serve as the signaling in cellular communication under different conditions. Interestingly, our analysis of a mouse skin squamous cell carcinoma dataset also suggests regardless of the significant discrepancy in concrete genes between in vitro and in vivo EMT systems, the ICSs play dominant role in the TGF-β signaling crosstalk. Overall, our approach reveals the multiscale mechanisms coupling cell–cell communications and gene–gene regulations responsible for complex cell-state transitions.