DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing.

The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.

Understanding metabolic alterations after SARS-CoV-2 infection: insights from the patients' oral microenvironmental metabolites.

BACKGROUND: Coronavirus disease 2019 is a type of acute infectious pneumonia and frequently confused with influenza since the initial symptoms. When the virus colonized the patient's mouth, it will cause changes of the oral microenvironment. However, few studies on the alterations of metabolism of the oral microenvironment affected by SARS-CoV-2 infection have been reported. In this study, we explored metabolic alterations of oral microenvironment after SARS-CoV-2 infection. METHODS: Untargeted metabolomics (UPLC-MS) was used to investigate the metabolic changes between oral secretion samples of 25 COVID-19 and 30 control participants. To obtain the specific metabolic changes of COVID-19, we selected 25 influenza patients to exclude the metabolic changes caused by the stress response of the immune system to the virus. Multivariate analysis (PCA and PLS-DA plots) and univariate analysis (students' t-test) were used to compare the differences between COVID-19 patients and the controls. Online hiplot tool was used to perform heatmap analysis. Metabolic pathway analysis was conducted by using the MetaboAnalyst 5.0 web application. RESULTS: PLS-DA plots showed significant separation of COVID-19 patients and the controls. A total of 45 differential metabolites between COVID-19 and control group were identified. Among them, 35 metabolites were defined as SARS-CoV-2 specific differential metabolites. Especially, the levels of cis-5,8,11,14,17-eicosapentaenoic acid and hexanoic acid changed dramatically based on the FC values. Pathway enrichment found the most significant pathways were tyrosine-related metabolism. Further, we found 10 differential metabolites caused by the virus indicating the body's metabolism changes after viral stimulation. Moreover, adenine and adenosine were defined as influenza virus-specific differential metabolites. CONCLUSIONS: This study revealed that 35 metabolites and tyrosine-related metabolism pathways were significantly changed after SARS-CoV-2 infection. The metabolic alterations of oral microenvironment in COVID-19 provided new insights into its molecular mechanisms for research and prognostic treatment.

Equalization equal gain combining for a single-input to multiple-output underwater wireless optical communication system under a Gaussian beam.

In this study, we examined the performance of an underwater wireless optical communication (UWOC) system employing a single-input to multiple-output (SIMO) scheme and proposed an equalization equal gain combining (EEGC) algorithm for it under Gaussian beam conditions. Furthermore, based on a Yue spectrum with the instability of oceanic water stratification and a finite outer scale, we derived the closed analytical formulas for the scintillation index and spatial coherence radius in weak oceanic turbulence for a Gaussian beam, from which we could obtain the threshold of the detector spacing and the strength of oceanic turbulence. We then derived the closed-form formula for the upper bound average bit error rate of the EEGC SIMO system with ON-OFF keying modulation by using the hyperbolic tangent distribution function. Our simulations demonstrate two issues if oceanic water stratification is treated as a steady state: the performance of the diversity receiver system will be significantly underestimated in salinity-dominated weak oceanic turbulence channels and will be significantly overestimated in temperature-dominated weak oceanic turbulence channels. Additionally, the SIMO system performance improvement using the proposed EEGC algorithm was more evident with increasing detector spacing, and the EEGC algorithm reduced the impact of the layout of the avalanche photodiode arrays on the UWOC system performance, in contrast to the equal gain combining algorithm.

Dexmedetomidine exerts cerebral protective effects against cerebral ischemic injury by promoting the polarization of M2 microglia via the Nrf2/HO-1/NLRP3 pathway.

INTRODUCTION: Cerebral ischemic injury is associated with long-term disability. Dexmedetomidine (Dex) can exert neuroprotective effects on cerebral ischemic/reperfusion injury. The present study explored the mechanism of Dex in cerebral ischemic injury. MATERIALS AND METHODS: To this end, the permanent middle cerebral artery occlusion (p-MCAO) mouse model was established and treated with Dex or/and Nrf2 inhibitor ML385. Subsequently, microglia were subjected to oxygen-glucose deprivation (OGD) in sugar-free environment and thereafter treated with Dex, Nrf2 inhibitor, and NLRP3 lentiviral overexpression vector, respectively. RESULTS: Dex alleviated the neurobehavioral deficit of p-MCAO mice, reduced brain water content, relieved pathological changes, and reduced cerebral infarction size. Dex promoted the polarization of microglia from M1 to M2, thus ameliorating oxidative stress and inflammatory responses. Our results showed that Dex promoted M2-polarization of microglia in vivo and in vitro by promoting HO-1 expression via Nrf2 nuclear import. Moreover, the Nrf2/HO-1 axis inhibited the activation of NLRP2 inflammasome and NLRP3 overexpression reversed the effect of Dex. CONCLUSION: In conclusion, Dex promoted M2-polarization of microglia and attenuated oxidative stress and inflammation, and thus protected against cerebral ischemic injury by activating the Nrf2/HO-1 pathway and inhibiting NLRP3 inflammasome.

Comprehensive analysis of TCR repertoire in COVID-19 using single cell sequencing.

T-cell receptor (TCR) is crucial in T cell-mediated virus clearance. To date, TCR bias has been observed in various diseases. However, studies on the TCR repertoire of COVID-19 patients are lacking. Here, we used single-cell V(D)J sequencing to conduct comparative analyses of TCR repertoire between 12 COVID-19 patients and 6 healthy controls, as well as other virus-infected samples. We observed distinct T cell clonal expansion in COVID-19. Further analysis of VJ gene combination revealed 6 VJ pairs significantly increased, while 139 pairs significantly decreased in COVID-19 patients. When considering the VJ combination of α and ß chains at the same time, the combination with the highest frequency on COVID-19 was TRAV12-2-J27-TRBV7-9-J2-3. Besides, preferential usage of V and J gene segments was also observed in samples infected by different viruses. Our study provides novel insights on TCR in COVID-19, which contribute to our understanding of the immune response induced by SARS-CoV-2.

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma.

BACKGROUD: Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal the relationship between stemness and cancer. However, we found that existing methods do not perform well in assessing the stemness of KIRC patients, and they overlooked the impact of alternative splicing. Alternative splicing not only progresses during the differentiation of stem cells, but also changes during the acquisition of the stemness features of cancer stem cells. There is an urgent need for a new method to predict KIRC-specific stemness more accurately, so as to provide help in selecting treatment options. METHODS: The corresponding RNA-Seq data were obtained from the The Cancer Genome Atlas (TCGA) data portal. We also downloaded stem cell RNA sequence data from the Progenitor Cell Biology Consortium (PCBC) Synapse Portal. Independent validation sets with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database. we constructed a KIRC-specific stemness prediction model using an algorithm called one-class logistic regression based on the expression and alternative splicing data to predict stemness indices of KIRC patients, and the model was externally validated. We identify stemness-associated alternative splicing events (SASEs) by analyzing different alternative splicing event between high- and low- stemness groups. Univariate Cox and multivariable logistic regression analysisw as carried out to detect the prognosis-related SASEs respectively. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of our model. RESULTS: Here, we constructed a KIRC-specific stemness prediction model with an AUC of 0.968,and to provide a user-friendly interface of our model for KIRC stemness analysis, we have developed KIRC Stemness Calculator and Visualization (KSCV), hosted on the Shiny server, can most easily be accessed via web browser and the url https://jiang-lab.shinyapps.io/kscv/ . When applied to 605 KIRC patients, our stemness indices had a higher correlation with the gender, smoking history and metastasis of the patients than the previous stemness indices, and revealed intratumor heterogeneity at the stemness level. We identified 77 novel SASEs by dividing patients into high- and low- stemness groups with significantly different outcome and they had significant correlations with expression of 17 experimentally validated splicing factors. Both univariate and multivariate survival analysis demonstrated that SASEs closely correlated with the overall survival of patients. CONCLUSIONS: Basing on the stemness indices, we found that not only immune infiltration but also alternative splicing events showed significant different at the stemness level. More importantly, we highlight the critical role of these differential alternative splicing events in poor prognosis, and we believe in the potential for their further translation into targets for immunotherapy.

A sweet spot for macrophages: Focusing on polarization.

Macrophages are a type of functionally plastic cells that can create a pro-/anti-inflammatory microenvironment for organs by producing different kinds of cytokines, chemokines, and growth factors to regulate immunity and inflammatory responses. In addition, they can also be induced to adopt different phenotypes in response to extracellular and intracellular signals, a process defined as M1/M2 polarization. Growing evidence indicates that glycobiology is closely associated with this polarization process. In this research, we review studies of the roles of glycosylation, glucose metabolism, and key lectins in the regulation of macrophages function and polarization to provide a new perspective for immunotherapies for multiple diseases.

A network pharmacology-based approach to explore the effects of Chaihu Shugan powder on a non-alcoholic fatty liver rat model through nuclear receptors.

The pathogenesis of non-alcoholic fatty liver disease (NAFLD) is still not fully understood, and currently, no effective pharmacotherapy is available. Nuclear receptors (NRs) are important biological participants in NAFLD that exhibit great therapeutic potential. Chaihu Shugan powder (CSP) is a traditional Chinese medicine (TCM) formula that has a wide therapeutic spectrum including NAFLD, but the effective components and functional mechanisms of CSP are unclear. We adopted a network pharmacology approach using multiple databases for Gene Ontology (GO) enrichment analysis and the molecular complex detection (MCODE) method for a protein-protein interaction (PPI) analysis, and we used molecular docking method to screen the NR targets and determine the corresponding CSP components. The screening results were validated through a NAFLD rat model that was used to explain the possible relationship between CSP and NAFLD. Finally, we screened PPARγ, FXR, PPARα, RARα and PPARδ as target genes and quercetin, kaempferol, naringenin, isorhamnetin and nobiletin as target compounds. The five components were detected through high-performance liquid chromatography-mass spectrometry (HPLC-MS), the results of which aligned with the docking experiments of PPARγ, PPARα and PPARδ. After CSP intervention, the NAFLD rat model showed ameliorated effects in terms of bodyweight, hepatic histopathology, and serum and liver lipids, and the mRNA levels of PPARγ, FXR, PPARα and RARα were significantly changed. The results from this study indicate that CSP exhibits healing effects in an NAFLD model and that the network pharmacology approach to screening NR targets and determining the corresponding CSP components is a practical strategy for explaining the mechanism by which CSP ameliorates NAFLD.

MARVELD1 interacting with catalase regulates reactive oxygen species metabolism and mediates the sensitivity to chemotherapeutic drugs in epithelial tumors of the reproductive system.

Previous investigations have found that MARVEL domain-containing 1 (MARVELD1) could inhibit tumor cell proliferation and enhance the sensitivity to chemotherapeutic drugs in hepatocellular carcinoma. Hence, it may be a valuable therapeutic target. In the study, we analyzed the responsive changes of MARVELD1 to 25 stress factors and expression of MARVELD1 in epithelial tumors of the reproductive system. We found that MARVELD1 was transferred to the cytoplasm and mitochondria under cell stress. And under cellular stress, the reactive oxygen species (ROS) levels decreased in MARVELD1 expressed cells while increased in the cells of MARVELD1-specific siRNA treatment. Meanwhile, MARVELD1 overexpression significantly promoted the inhibition of tumor cell proliferation under cellular stress via affecting ROS metabolism, not cell cycle. In xenograft tumor tissues with MARVELD1 expression, the tumor growth was inhibited and accompanied by the lower ROS levels. Furthermore, we identified that MARVELD1 could interact with catalase (CAT) to enhance latter activity and maintain stability. And the enhanced sensitivity to chemotherapeutic drugs clearly depended on the ability of MARVELD1 scavenge the ROS in carcinoma cells of the reproductive system. Our findings clearly explain that MARVELD1 may regulate tumor cell proliferation and sensitivity to chemotherapeutic drugs via reducing the exorbitant ROS. The mechanism was that MARVELD1 interacted with CAT to maintain latter stability, and then ensure continuous ROS scavenge.

Metabolic profiling analysis upon acylcarnitines in tissues of hepatocellular carcinoma revealed the inhibited carnitine shuttle system caused by the downregulated carnitine palmitoyltransferase 2.

The carnitine shuttle system (CSS) plays a crucial role in the transportation of fatty acyls during fatty acid ß-oxidation for energy supplementation, especially in cases of high energy demand, such as in cancer. In this study, to systematically characterize alterations of the CSS in hepatocellular carcinoma (HCC), acylcarnitine metabolic profiling was carried out on 80 pairs of HCC tissues and adjacent noncancerous tissues (ANTs) by using ultra-performance liquid chromatography coupled to mass spectrometry. Twenty-four acylcarnitines classified into five categories were identified and characterized between HCCs and ANTs. Notably, increased saturated long-chain acylcarnitines (LCACs) and decreased short- and medium-chain acylcarnitines (S/MCACs) were simultaneously observed in HCC samples. Subsequent correlation network and heatmap analysis indicated low correlations between LCACs and S/MCACs. The mRNA and protein expressions of carnitine palmitoyltransferase 2 (CPT2) was significantly downregulated in HCC samples, whereas CPT1A expression was not significantly changed. Correspondingly, the relative levels of S/MCACs were reduced and those of LCACs were increased in BEL-7402/CPT2-knockdown cells compared to negative controls. Both results suggested that decreased shuttling efficiency in HCC might be associated with downregulation of CPT2. In addition, decreases in the mRNA expression of acetyl-CoA acyltransferase 2 were also observed in HCC tissues and BEL-7402/CPT2-knockdown cells, suggesting potential low ß-oxidation efficiency, which was consistent with the increased expression of stearoyl-CoA desaturase 1 in both samples. The systematic strategy applied in our study illustrated decreased shuttling efficiency of the carnitine shuttle system in HCC and can provide biologists with an in-depth understanding of ß-oxidation in HCC.

Lipidomic Analysis of the Protective Effects of Shenling Baizhu San on Non-Alcoholic Fatty Liver Disease in Rats.

Shenling Baizhu San (SLBZS), a famous traditional Chinese medicine, has been demonstrated to exert protective effects against non-alcoholic fatty liver disease (NAFLD), but its exact mechanisms have not been well understood. The aim of this study was to investigate the mechanisms underlying the protective effects of SLBZS in a rat model of NAFLD using lipidomics and to evaluate the role of Sirtuin 1 (SIRT1) in the mechanism of SLBZS against NAFLD. The rat model of NAFLD was induced by high-fat feeding. An ultra-performance liquid chromatography-mass spectrometry (UHPLC-MS)-based untargeted lipidomics approach was applied to analyze hepatic lipid alterations, and the SIRT1-selective inhibitor EX 527 was used to inhibit SIRT expression in the liver. The results of body and biochemical parameters, as well as histological changes, indicated that SLBZS administration exerted protective effects against NAFLD. Lipidomic analysis showed that 30 lipid species were effectively regulated by SLBZS administration in rats fed a high-fat diet. Pathway analysis indicated that glycerophospholipid metabolism and glycerolipid metabolism were potential target pathways closely involved in the mechanism of SLBZS against NAFLD. Moreover, the beneficial effects of SLBZS on hepatic steatosis, some biochemical parameters and hepatic lipid species were partly diminished by SIRT1 inhibition. In conclusion, our results suggested that SLBZS administration could effectively alter some hepatic lipid species in rats fed a high-fat diet, which was mainly associated with the regulation of glycerophospholipid and glycerolipid metabolism. Furthermore, the beneficial effects of SLBZS on hepatic lipid metabolism may be at least partly attributed to SIRT1 activation in the liver.

Recent approaches for directly profiling cell surface sialoform.

Sialic acids (SAs) are nine-carbon monosaccharides existing at the terminal location of glycan structures on the cell surface and secreted glycoconjugates. The expression levels and linkages of SAs on cells and tissues, collectively known as sialoform, present the hallmark of the cells and tissues of different systems and conditions. Accordingly, detecting or profiling cell surface sialoforms is very critical for understanding the function of cell surface glycans and glycoconjugates and even the molecular mechanisms of their underlying biological processes. Further, it may provide therapeutic and diagnostic applications for different diseases. In the past decades, several kinds of SA-specific binding molecules have been developed for detecting and profiling specific sialoforms of cells and tissues; the experimental materials have expanded from frozen tissue to living cells; and the analytical technologies have advanced from histochemistry to fluorescent imaging, flow cytometry and microarrays. This review summarizes the recent bioaffinity approaches for directly detecting and profiling specific SAs or sialylglycans, and their modifications of different cells and tissues.

Sialylation and desialylation dynamics of monocytes upon differentiation and polarization to macrophages.

Sialic acids (SAs) often exist as the terminal sugars of glycan structures of cell surface glycoproteins and glycolipids. The level and linkages of cell surface SAs, which are controlled by both sialylation and desialylation processes and environment cues, can dramatically impact cell properties and represent different cellular status. In this study, we systematically examined the sialylation and desialylation profiles of THP-1 monocytes after differentiation to M0 macrophages, and polarization to M1 and M2 macrophages by the combination of LC-MS/MS, flow cytometry and confocal microscopy. Interestingly, both α2-3- and α2-6-linked SAs on the cell surface decreased after monocytes were differentiated to macrophages, which was in accordance with the increased level of free SA in the cell culture medium and the elevated activity of endogenous Neu1 sialidase. Meanwhile, the siaoglycoconjugates inside the cells increased as confirmed by confocal microscopy and the total SA inside the cells increased as determined by LC-MS/MS. Western blot analysis showed higher expression levels of sialyltransferases, including ST3Gal-I, ST3Gal-V, ST6Gal-I and ST6GalNAc-II. Further, upon polarization, the cell surface sialylation levels of M1 and M2 macrophages remained the same as M0 macrophages, while a slight decrease of cellular SAs in the M1 macrophages but increase in the M2 macrophages were confirmed by LC-MS/MS.

Globally profiling sialylation status of macrophages upon statin treatment.

Sialic acids (SAs) are widely expressed on immune cells and their levels and linkages named as sialylation status vary upon cellular environment changes related to both physiological and pathological processes. In this study, we performed a global profiling of the sialylation status of macrophages and their release of SAs in the cell culture medium by using flow cytometry, confocal microscopy and liquid chromatography tandem mass spectrometry (LC-MS/MS). Both flow cytometry and confocal microscopy results showed that cell surface α-2,3-linked SAs were predominant in the normal culture condition and changed slightly upon treatment with atorvastatin for 24 h, whereas α-2,6-linked SAs were negligible in the normal culture condition but significantly increased after treatment. Meanwhile, the amount of total cellular SAs increased about three times (from 369 ± 29 to 1080 ± 50 ng/mL) upon treatment as determined by the LC-MS/MS method. On the other hand, there was no significant change for secreted free SAs and conjugated SAs in the medium. These results indicated that the cell surface α-2,6 sialylation status of macrophages changes distinctly upon atorvastatin stimulation, which may reflect on the biological functions of the cells.

Multi-dimensional glycan microarrays with glyco-macroligands.

Glycan microarray has become a powerful high-throughput tool for examining binding interactions of carbohydrates with the carbohydrate binding biomolecules like proteins, enzymes, antibodies etc. It has shown great potential for biomedical research and applications, such as antibody detection and profiling, vaccine development, biomarker discovery, and drug screening. Most glycan microarrays were made with monovalent glycans immobilized directly onto the array surface via either covalent or non-covalent bond, which afford a multivalent glycans in two dimensional (2D) displaying. A variety of glyco-macroligands have been developed to mimic multivalent carbohydrate-protein interactions for studying carbohydrate-protein interactions and biomedical research and applications. Recently, a number of glyco-macroligands have been explored for glycan microarray fabrication, in particular to mimick the three dimensional (3D) multivalent display of cell surface carbohydrates. This review highlights these recent developments of glyco-macroligand-based microarrays, predominantly, novel glycan microarrays with glyco-macroligands like glycodendrimers, glycopolymers, glycoliposomes, neoglycoproteins, and glyconanoparticles with the effort in controlling the density and orientation of glycans on the array surface, which facilitate both their binding specificity and affinity and thus the high performance of glycan microarrays.

BSA-boronic acid conjugate as lectin mimetics.

We report bovine serum albumin (BSA)-boronic acid (BA) conjugates as lectin mimetics and their glyco-capturing capacity. The BSA-BA conjugates were synthesized by amidation of carboxylic acid groups in BSA with aminophenyl boronic acid in the presence of EDC, and were characterized by Alizarin Red S (ARS) assay and SDS-PAGE gel. The BSA-BA conjugates were immobilized onto maleimide-functionalized silica beads and their sugar capturing capacity and specificity were confirmed by ARS displacement assay. Further, surface plasmon resonance (SPR) analysis of the glyco-capturing activity of the BSA-BA conjugates was conducted by immobilizing BSA-BA onto SPR gold chip. Overall, we demonstrated a BSA-BA-based lectin mimetics for glyco-capturing applications. These lectin mimetics are expected to provide an important tool for glycomics and biosensor research and applications.

Neoadjuvant therapy for non-small cell lung cancer and esophageal cancer.

As the major malignant tumors in the chest, non-small cell lung cancer (NSCLC) and esophageal cancer (EC) bring huge health burden to human beings worldwide. Currently, surgery is still the mainstay for comprehensive treatment for NSCLC and EC, but the prognosis is still poor as the results of cancer recurrence and distant metastasis. Neoadjuvant therapy refers to a single or combined treatment before surgery, aiming to improve the therapeutic effects of the traditional therapies. Unfortunately, the clinical outcomes and effects of neoadjuvant therapy are still controversial due to its apparent advantages and disadvantages, and different patients may respond differentially to the same scheme of neoadjuvant therapy, which makes it urgent and necessary to develop personalized scheme of neoadjuvant therapy for different individuals. Therefore, this review summarizes the novel schemes and strategies of neoadjuvant therapy, which may help to significantly improve of life quality of patients suffering from chest-related malignancies.

Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network.

The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.