Adipose tissue is a critical energy reservoir in animals and humans, with multifaceted roles in endocrine regulation, immune response, and providing mechanical protection. Based on anatomical location and functional characteristics, adipose tissue can be categorized into distinct types, including white adipose tissue (WAT), brown adipose tissue (BAT), beige adipose tissue, and pink adipose tissue. Traditionally, adipose tissue research has centered on its morphological and functional properties as a whole. However, with the advent of single-cell transcriptomics, a new level of complexity in adipose tissue has been unveiled, showing that even under identical conditions, cells of the same type may exhibit significant variation in morphology, structure, function, and gene expression——phenomena collectively referred to as cellular heterogeneity. Single-cell transcriptomics, including techniques like single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), enables in-depth analysis of the diversity and heterogeneity of adipocytes at the single-cell level. This high-resolution approach has not only deepened our understanding of adipocyte functionality but also facilitated the discovery of previously unidentified cell types and gene expression patterns that may play key roles in adipose tissue function. This review delves into the latest advances in the application of single-cell transcriptomics in elucidating the heterogeneity and diversity within adipose tissue, highlighting how these findings have redefined the understanding of cell subpopulations within different adipose depots. Moreover, the review explores how single-cell transcriptomic technologies have enabled the study of cellular communication pathways and differentiation trajectories among adipose cell subgroups. By mapping these interactions and differentiation processes, researchers gain insights into how distinct cellular subpopulations coordinate within adipose tissues, which is crucial for maintaining tissue homeostasis and function. Understanding these mechanisms is essential, as dysregulation in adipose cell interactions and differentiation underlies a range of metabolic disorders, including obesity and diabetes mellitus type 2. Furthermore, single-cell transcriptomics holds promising implications for identifying therapeutic targets; by pinpointing specific cell types and gene pathways involved in adipose tissue dysfunction, these technologies pave the way for developing targeted interventions aimed at modulating specific adipose subpopulations. In summary, this review provides a comprehensive analysis of the role of single-cell transcriptomic technologies in uncovering the heterogeneity and functional diversity of adipose tissues.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

ObjectiveThis study proposes a novel single-cell protein localization method based on a class perception graph convolutional network (CP-GCN) to overcome several critical challenges in protein microscopic image analysis, including the scarcity of cell-level annotations, inadequate feature extraction, and the difficulty in achieving precise protein localization within individual cells. The methodology involves multiple innovative components designed to enhance both feature extraction and localization accuracy. MethodsFirst, a class perception module (CPM) is developed to effectively capture and distinguish semantic features across different subcellular categories, enabling more discriminative feature representation. Building upon this, the CP-GCN network is designed to explore global features of subcellular proteins in multicellular environments. This network incorporates a category feature-aware module to extract protein semantic features aligned with label dimensions and establishes a subcellular relationship mining module to model correlations between different subcellular structures. By doing so, it generates co-occurrence embedding features that encode spatial and contextual relationships among subcellular locations, thereby improving feature representation. To further refine localization, a multi-scale feature analysis approach is employed using the K-means clustering algorithm, which classifies multi-scale features within each subcellular category and generates multi-cell class activation maps (CAMs). These CAMs highlight discriminative regions associated with specific subcellular locations, facilitating more accurate protein localization. Additionally, a pseudo-label generation strategy is introduced to address the lack of annotated single-cell data. This strategy segments multicellular images into single-cell images and assigns reliable pseudo-labels based on the CAM-predicted regions, ensuring high-quality training data for single-cell analysis. Under a transfer learning framework, the model is trained to achieve precise single-cell-level protein localization, leveraging both the extracted features and pseudo-labels for robust performance. ResultsExperimental validation on multiple single-cell test datasets demonstrates that the proposed method significantly outperforms existing approaches in terms of robustness and localization accuracy. Specifically, on the Kaggle 2021 dataset, the method achieves superior mean average precision (mAP) metrics across 18 subcellular categories, highlighting its effectiveness in diverse protein localization tasks. Visualization of the generated CAM results further confirms the model’s capability to accurately localize subcellular proteins within individual cells, even in complex multicellular environments. ConclusionThe integration of the CP-GCN network with a pseudo-labeling strategy enables the proposed method to effectively capture heterogeneous cellular features in protein images and achieve precise single-cell protein localization. This advancement not only addresses key limitations in current protein image analysis but also provides a scalable and accurate solution for subcellular protein studies, with potential applications in biomedical research and diagnostic imaging. The success of this method underscores the importance of combining advanced deep learning architectures with innovative training strategies to overcome data scarcity and improve localization performance in biological image analysis. Future work could explore the extension of this framework to other types of microscopic imaging and its application in large-scale protein interaction studies.

Background::The conversion of adenosine (A) to inosine (I) through deamination is the prevailing form of RNA editing, impacting numerous nuclear and cytoplasmic transcripts across various eukaryotic species. Millions of high-confidence RNA editing sites have been identified and integrated into various RNA databases, providing a convenient platform for the rapid identification of key drivers of cancer and potential therapeutic targets. However, the available database for integration of RNA editing in hematopoietic cells and hematopoietic malignancies is still lacking.Methods::We downloaded RNA sequencing (RNA-seq) data of 29 leukemia patients and 19 healthy donors from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, and RNA-seq data of 12 mouse hematopoietic cell populations obtained from our previous research were also used. We performed sequence alignment, identified RNA editing sites, and obtained characteristic editing sites related to normal hematopoietic development and abnormal editing sites associated with hematologic diseases.Results::We established a new database, "REDH", represents RNA editome in hematopoietic differentiation and malignancy. REDH is a curated database of associations between RNA editome and hematopoiesis. REDH integrates 30,796 editing sites from 12 murine adult hematopoietic cell populations and systematically characterizes more than 400,000 edited events in malignant hematopoietic samples from 48 cohorts (human). Through the Differentiation, Disease, Enrichment, and knowledge modules, each A-to-I editing site is systematically integrated, including its distribution throughout the genome, its clinical information (human sample), and functional editing sites under physiological and pathological conditions. Furthermore, REDH compares the similarities and differences of editing sites between different hematologic malignancies and healthy control.Conclusions::REDH is accessible at http://www.redhdatabase.com/. This user-friendly database would aid in understanding the mechanisms of RNA editing in hematopoietic differentiation and malignancies. It provides a set of data related to the maintenance of hematopoietic homeostasis and identifying potential therapeutic targets in malignancies.

Objective To explore the clinical significance of epigenetic modification factors and metabolites in the early diagnosis and differential diagnosis of pancreatic cancer by detecting the levels of circulating nu-cleosomes,lactic acid and pyruvate in the serum of patients with pancreatic ductal adenocarcinoma,patients with other pancreatic tumors and healthy people.Methods A total of 26 patients with pancreatic ductal ade-nocarcinoma admitted to Peking University Third Hospital from January 2022 to April 2023 were selected as the experimental group.The other pancreatic tumor group included 8 cases of solid pseudopapillary tumor of the pancreas,11 cases of serous cystadenoma of the pancreas,and 9 cases of neuroendocrine tumor of the pan-creas.Another 26 healthy individuals who underwent outpatient examinations during the same period were se-lected as the health control group.Enzyme linked immunosorbent assay and targeted metabolomics were used to detect the expression levels of circulating nucleosomes,lactate,and pyruvate in serum,respectively.SPSS26.0 statistical software was used for multivariable statistical analysis to compare whether the difference in serum expression in the experimental group,other pancreatic tumor group and the health control group was statistically significant,and the metabonomics results of pancreatic cancer patients at different stages in the public database were analyzed by bioinformatics.Results There were statistically significant differences in the expression levels of lactate(P＜0.001),pyruvate(P＜0.001),and circulating nucleosomes(P＜0.001)be-tween the experimental group and the health control group,suggesting that the three had high sensitivity and specificity for the diagnosis of pancreatic ductal adenocarcinoma.Moreover,the expression levels of the three in the serum of the experimental group were significantly higher than those in the serum of other pancreatic tumor group,suggesting that these could be used to distinguish the types of pancreatic tumors.The serum ex-pression levels of the three in other pancreatic tumor group were significantly higher than those in the health control group,which were expected to become new risk indicators for pancreatic tumors.Among them,pyruvic acid showed the most significant difference in the combined model.Bioinformatics analysis indicated that pyru-vate and lactate were differential metabolites in patients with pancreatic cancer at different stages.Conclusion Circu-lating nucleosomes,lactic acid and pyruvate have high sensitivity and specificity among the three groups,which could be used as new markers of pancreatic cancer.

Objective To compare clinical efficacy of robot-assisted(RA)and remote sensing navigation alignment(RSNA)system-assisted total knee arthroplasty(TKA).Methods From March 2023 to June 2023,60 patients who underwent the first unilateral TKA due to severe knee osteoarthritis(KOA)were admitted and divided into RSNA group and RA group according to different treatment methods,with 30 patients in each group.There were 5 males and 25 females in RSNA group,aged from 56 to 81 years old with an average of(66.33±7.16)years old;body mass index(BM1)ranged from 19.87 to 38.54 kg·m-2 with an average of(28.40±6.18)kg·m-2;the courses of disease ranged from 5 to 36 months with an average of(18.20±8.98)months;RSNA system was used to assist the positioning of osteotomy.There were 7 males and 23 females in RA group,aged from 55 to 82 years old with an average of(67.83±8.61)years old;BMI ranged from 19.67 to 37.25 kg·m-2 with an aver-age of(28.01±4.89)kg·m-2;the courses of disease ranged from 3 to 33 months with an average of(17.93±9.20)months;RA was performed.Operation time,incision length,latent blood loss at 2 weeks after operation and incidence of lower extremity thrombosis were compared between two groups.Hip-knee ankle angle(HKAA),HKAA deviation,lateral distal femoral angle(LDFA),medial proximal tibial angle(MPTA)and posterior tibial slope(PTS)were compared between two groups;Western Ontario McMaster Universities Osteoarthritis Index(WOMAC)and Knee Society score(KSS)were used to evaluate functional recovery before operation,3 and 6 months after operation.Results The operation was performed successfully in both groups,and there were no serious complications such as vascular and nerve injury during operation.The wound healed well at stage Ⅰafter operation,and the follow-up time was 6 months.The operation time,latent blood loss at 2 weeks after operation and inci-sion length in RSNA group were(94.35±5.75)min,(130.54±17.53)mland(14.73±2.14)cm,respectively;while(102.57±6.88)min,(146.33±19.47)ml and(16.78±2.32)cm in RA group,respectively.RSNA group was better than RA group(P＜0.05).No deep vein thrombosis occurred in both groups at 2 weeks after operation,5 patients occurred intermuscular vein throm-bosisin in RSNA group and 8 patients in RA group,the difference was not statistically significant(P＞0.05).In RSNA group,HKAA,LDFA and MPTA were(173.00±5.54)°,(86.96±3.45)°,(82.79±3.35)° before operation,and(178.34±1.85)°,(89.92±0.42)°,(89.84±0.73)° at 1 week after operation,respectively.In RA group,HKAA,LDFA and MPTA were(173.31±6.48)°,(87.15±3.40)° and(82.99±3.05)° before operation,and(178.52±1.79)°,(90.03±0.39)° and(90.15±0.47)° at 1 week after operation,respectively.HKAA,LDFA and MPTA were significantly improved in both groups at 1 week after oper-ation(P＜0.05).There were no significant difference in HKAA,LDFA,MPTA and PTS between two groups before operation and 1 week after operation(P＞0.05).There was no significant difference in deviation distribution of HKAA at 1 week after op-eration(x2=2.61 1,P=0.456).There were no significant difference in WOMAC and KSS between two groups before operation,3 and 6 months after operation(P＞0.05),and postoperative WOMAC and KSS at 3 and 6 months between two groups were im-proved compared with those before operation(P＜0.05).Conclusion Both RA and RSNA system assisted TKA could obtain ac-curate osteotomy,RA has higher surgical accuracy,RSNA system assisted operation has less trauma,and operation is simpler.

Objective To explore clinical accuracy of remote sensing navigation alignment(RSNA)system in total knee arthroplasty(TKA)and its influence on postoperative clinical efficacy.Methods From May 2021 to May 2022,60 knee os-teoarthritis(KOA)patients with Kellgren-Lawrence(K-L)grade Ⅲ to Ⅳ treated by unilateral primary TKA were selected and divided into RSNA group and traditional operation group according to treatment methods,and 30 patients in each group.There were 6 males and 24 females in RSNA group,aged from 55 to 86 years old with an average of(68.06±8.23)years old;body mass index(BMI)ranged from 22.15 to 34.58 kg·m-2 with an average of(28.20±3.01)kg·m-2;the courses of disease ranged from 2 to 60 months with an average of(18.80±14.80)months;13 patients with grade Ⅲ and 17 patients with grade Ⅳaccording to K-L grading.In traditional operation group,there were 8 males and 22 females,aged from 57 to 85 years old with an average of(67.26±6.32)years old;BMI ranged from 23.94 to 34.55 kg·m-2 with an average of(27.49±2.32)kg·m-2;the courses of disease ranged from 3 to 60 months with an average of(21.30±16.44)months;14 patients with grade Ⅲ and 16 pa-tients with grade Ⅳ according to K-L grading.Western Ontario and McMaster Universities(WOMAC)osteoarthritis index and Knee Society score(KSS)were used to evaluate functional recovery of patients.Hip-knee-ankle angle(HKAA),distal femoral valgus angle(FVA)and distal fermoral flexion angle(DFFA)were measured before operation.HKAA and HKAA deviation angle were measured at 1 week after operation,and defective rate of lower limb force line,femur prosthesis valgus angle(FP-VA)and femoral prosthesis flexion angle(FPFA),respectively,were calculated.Results There were no serious complications such as vascular and nerve injury during operation,and wound healed at stage Ⅰ.Both groups were followed up for 6 months.There were no significant difference in WOMAC index,KSS,HKAA,FVA and DFFA between two groups before operation(P＞0.05).The force line defect rate,HKAA,HKAA deviation angle,FPVA deviation angle and FPFA of RSNA group were 6.7％,(178.74±1.56)°,(1.25±1.56)°,(1.84±0.16)° and(4.85±2.46)°,respectively;while in traditional operation group were 20％,(176.73±3.46)°,(3.27±3.46)°,(2.44±0.26)°,(6.60±1.86)°;the difference between two groups were statistically sig-nificant(P＜0.05).There were no significant difference in WOMAC index and KSS between two groups at 3 and 6 months after operation(P＞0.05).Conclusion RSNA system could reduce defective rate of lower limb force line,FPVA deviation angle and FPFA after TKA,which is more accurate and easy to operate than traditional intramedullary localization surgery while ensuring postoperative efficacy.

AIM To investigate the purification process for esculin,fraxin,esculetin and fraxetin in Fraxini Cortex by macroporous resin.METHODS Static adsorption experiment was applied to screening resin model,single factor test was adopted in the optimization of purification process,UPLC-QTOF-MS/MS was used for identifying main components,after which heatmap was drawn.RESULTS The optimal resin model was ADS-5.The optimal purification process was determined to be 1.1 BV for loading amount,0.75 g/mL for loading concentration,2 BV pure water for washing impurity,and 4 BV 25%ethanol for eluting effective constituents,coumarins demonstrated the total transfer rate,purity and yield of 84.42%,53.28%and 4.79%,respectively.Total 37 constituents were identified,among which coumarins and phenylethanol glycosides were mainly concentrated in 25%ethanol eluent,organic acids,iridoids and flavonoids were mainly concentrated in 95%ethanol eluent.CONCLUSION This stable,feasible and accurate method can characterize the distribution patterns of coumarins in Fraxini Cortex in different eluents of macroporous resin,which provides guidance for further related pharmaceutical research.