Ferroptosis plays an important role in the development of acute-on-chronic liver failure (ACLF). The present project aimed to identify and validate the potential ferroptosis-related genes in ACLF by bioinformatics analysis and experimental verification.The GSE139602 dataset was obtained from the Gene Expression Omnibus database and intersected with ferroptosis genes. Ferroptosis-related differentially expressed genes (DEGs) between the ACLF tissue and healthy group were analyzed using bioinformatics methods. Analysis of enrichment, protein‒protein interactions, and hub genes was conducted. Potential drugs targeting these hub genes were retrieved from the DrugBank database. Finally, we performed real-time quantitative PCR (RT-qPCR) to validate the expression of the hub genes.A total of 35 ferroptosis-related DEGs were screened, which were enriched in the biosynthesis of amino acids, peroxisomes, fluid shear stress and atherosclerosis. PPI network analysis indicated five ferroptosis-related hub genes, namely, HRAS, TXNRD1, NQO1, PSAT1, and SQSTM1. The experimental validation indicated that the expression levels of HRAS, TXNRD1, NQO1, and SQSTM1 were lower, while the expression level of PSAT1 was higher in ACLF model rats than in healthy rats.Our findings reveal that PSAT1, TXNRD1, HRAS, SQSTM1 and NQO1 may affect the development of ACLF by regulating ferroptotic events. These results provide a valid reference for potential mechanisms and identification in ACLF.The online version contains supplementary material available at 10.1186/s12920-023-01480-4. Acute-on-chronic liver failure (ACLF) is an acutely exacerbated chronic liver disease, accompanied by the rapid progression of multiple organ failure and high short-term mortality. With the expansion of liver disease and hepatocirrhosis worldwide, the incidence of ACLF is rising yearly. However, effective medical treatment has yet to be discovered. Obviously, effective treatment for the disease has become an important medical issue.Effectively preventing liver cell death in patients with ACLF may be the key to treatment. Recently, a novel type of cell death has been discovered—ferroptosis, which is characterized by iron ion accumulation in the cell, imbalanced redox reactions, and enhanced lipid peroxidation, eventually leading to cell membrane rupture and cell death . These chttps://www.ncbi.nlm.nih.gov/). We downloaded the GSE139602 dataset derived on the GPL13667 platform (Affymetrix Human Genome U219 Array). In this database, transcriptome analysis of liver biopsies from patients at different liver disease stages, including 5 fibrosis patients, 8 compensated cirrhosis patients, 12 decompensated cirrhosis patients, 8 ACLF patients, and 6 control healthy individuals, was performed [Patients with ACLF were obtained from the NCBI Gene Expression Omnibus  [http://www.datjar.com:40013/bt2104/) and compared it with the GSE139602 dataset to screen for ferroptosis-related DEGs. Using Venny2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html), we created a Venn diagram of the DEGs. The R software “ggplots” and “pheatmap” packages were used to plot the volcano map and heatmap, respectively.The GSE139602 dataset was first normalized using the normalized between arrays function in the R software limma package  to avoidhttp://www.webgestalt.org/), DAVID 6.8 (https://david.ncifcrf.gov), and the ClusterProfiler package in R. We uploaded the ferroptosis-related DEGs to the Gene Set Enrichment Analysis (GSEA) tool in WebGestalt for further analysis. Initially, GSEA filtered the gene sets according to the number of genes contained, with a minimum number of three genes and a maximum number of 2,000 genes per gene set. The Kyoto Encyclopedia of Genes and Genomes (KEGG) [http://bioconductor.org/packages/release/bioc/html/clusterProfer.html) [P < 0.1 and count ≥ 2 were chosen as the cutoff values for identifying ferroptosis-related DEGs in terms of biological processes (BPs), cellular components (CCs), and molecular functions (MFs). After that, we carried out KEGG pathway enrichment analysis using P < 0.2 and count ≥ 2 as cutoff values. Gene set enrichment analysis (GSEA) was also performed using the ClusterProfiler package to investigate the biological functions of hub genes. Samples were separated into two groups based on the median values of the hub gene expression group. Genes were sorted by logFC from the highest to the lowest, and the background gene set was downloaded from MSigDB [Functional enrichment analysis of DEGs was conducted with WebGestalt (s (KEGG)  analysiser.html) . P < 0.1m MSigDB . To showhttps://cn.string-db.org/) [https://cytoscape.org/) with a confidence score > 0.15 [A protein‒protein interaction (PPI) network of ferroptosis-related DEGs was constructed using STRING  . The vise > 0.15 . We usede > 0.15 . Then, wDrugs targeting the identified hub genes were retrieved from the DrugBank database . The DruAdult male Sprague‒Dawley rats were purchased from Beijing HFK Bioscience Co., Ltd. According to protocols approved by the Animal Care and Use Committee at the Beijing HFK Bioscience CO., LTD Peking University Shenzhen Graduate School , all experimental rats were provided free access to water and food. This experiment was approved by the Animal Ethics Committee of Shenzhen Peking University Hong Kong University of Science and Technology Medical Center, China No:2022-194), and all methods were carried out in accordance with relevant guidelines and regulations. This study was carried out in compliance with the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines. The rats were separated into two groups (the control group and ACLF model group) with 6 rats in each group. With reference to Cong W , rats we94, and aRat liver tissues were lysed with RNA lysis buffer and ground with a high-speed tissue grinder . All RNA extraction was performed according to the instructions of the RNA extraction kit , and the concentration and purity of RNA were tested by a NanoDrop-2000 spectrophotometer . Then, the RNA was reverse transcribed into cDNA using Evo M-MLV RT Premix for qPCR . Finally, RT‒qPCR experiments were performed using a SYBR Green Premix Pro Taq HS qPCR Kit . The primers for HRAS, NQO1, TXNRD1, PSAT1, SQSTM1, and beta-actin were supplied by Tsingke Biotechnology Co., Ltd. . Information on the primers is listed in Table P < 0.05, **P < 0.01, or ***P < 0.001.Both the Statistical analysis and the charts were generated and drawn with GraphPad Prism 8.0 , respectively. Data are expressed as the mean ± standard deviation. Student’s t test was used to compare the significant difference between the means of the two groups, and the analysis results were considered to be statistically significant when *The GSE139602 dataset was obtained from the GEO database, which included 8 ACLF and 6 control healthy livers. We found 699 upregulated DEGs and 625 downregulated DEGs in the GSE139602 dataset  is an important component of the cytochrome P450 pathway that promotes ferroptosis by increasing the peroxidation of saturated phospholipids in the cell membrane , 35. WheAdditionally, drugs targeting the above genes were retrieved from DrugBank, which consisted of six approved drugs. Flavin adenine dinucleotide (DB03147) is a coenzyme form of vitamin B2 used in clinical conditions associated with vitamin B2 deficiency, such as keratitis and blepharitis. Arsenic trioxide (DB01169) is a chemotherapeutic agent used in the treatment of refractory or relapsed acute promyelocytic leukemia in patients with prior retinoid and anthracycline chemotherapy. It is suspected that arsenic trisulfide induces cancer cells to undergo apoptosis. The enzyme thioredoxin reductase has recently been identified as a target for arsenic trioxide. Pyridoxal phosphate (DB00114) is the active form of vitamin B6, which serves as a coenzyme for the synthesis of amino acids and aminolevulinic acid. Dicoumarol (DB00266) is a coumarin-like compound found in sweet clover. It is used as an oral anticoagulant and acts by inhibiting the hepatic synthesis of vitamin K-dependent coagulation factors –42. MenaThe RT‒qPCR results showed that the expression levels of HRAS, TXNRD1, NQO1, and SQSTM1 were clearly lower, while the expression level of PSAT1 was higher in the ACLF group than in the control group, confirming the reliability of the bioinformatics analysis. Among them, PSAT1, TXNRD1, and HRAS expression affect cellular amino acid biosynthesis. PSAT1 is a critical regulator in the serine synthesis pathway (SSP). A study found that PSAT1, a member of the class-V family of pyridyl aldehyde phosphate-dependent aminotransferases, catalyzes the second step of SSP . SpecifiIn addition, the present study reveals that SQSTM1 and NQO1 may mediate ferroptosis by targeting the NRF2 pathway. SQSTM1, also known as p62, is an autophagy receptor . A studyThere are some limitations to our study. The sample size in the GSE139602 dataset was small, which may lead to bias in the final gene validation results. The human liver tissue gene expression data are relatively few. In this regard, the mechanisms and specific signaling pathways of ferroptosis in ACLF can be further investigated by collecting data with a larger sample size. Moreover, there is a lack of validation of external datasets and clinical trials. To improve the prevention and treatment of ACLF, more ACLF human liver tissue expression profiles will be needed for further analysis.In conclusion, we explored the gene expression profiles of ACLF patient samples, identified 35 ferroptosis-related DEGs, and identified their participating biological processes and pathways. We also identified and validated 5 pivotal genes related to ferroptosis in ACLF, which are mainly involved in the amino acid biosynthesis pathway and NRF2 pathway. Targeting these genes and pathways in combination with a pharmacological inhibitor of ferroptosis may be a potential therapy for ACLF.Additional file1. Figure S1: The appearance of liver tissues in control and model groups.Additional file2. Table S1: Top10 ferroptosis-related DEGs by 10 topological analysis methods of CytoHubba.


Cardiac mortality in patients with heart failure (HF) is likely to be aggravated by malnutrition, assessed by serum cholinesterase (ChE) level, as well as by kidney dysfunction or impairment of cardiac sympathetic denervation. Their prognostic interactions, however, have not been determined.A total of 991 systolic HF patients were enrolled in our HF database following clinical evaluation including evaluation of the nutrition state and assessment of standardized heart-to-mediastinum ratio (sHMR) of iodine-123-labeled meta-iodobenzylguanidine activity. Patients were followed up for an average of 43 months with the primary endpoint of fatal cardiac events (CEs).2 and late sHMR < 1.90 had significantly and incrementally increased CE rates compared to those in HF patients with none or only one of the prognostic variables.The CE patient group had a lower level of ChE, lower estimated glomerular filtration rate (eGFR) and lower late sHMR than those in the non-CE patient group. A five-parameter model with the addition of serum ChE selected in the multivariate logistic analysis (model 2) significantly increased the AUC predicting risk of cardiac events compared with a four-parameter model without serum ChE (model 1), and net reclassification analysis also suggested that the model with the addition of serum cholinesterase significantly improved cardiac event prediction. Moreover, in overall multivariate Cox hazard analysis, serum ChE, eGFR and late sHMR were identified to be significant prognostic determinants. HF patients with two or all of the prognostic variables of serum ChE < 230 U/L, eGFR < 48.8 ml/min/1.73 mDecreases in cholinesterase level and kidney function further increase cardiac mortality risk in HF patients with impairment of cardiac sympathetic innervation. Obesity is well known to be an independent risk factor for metabolic, endocrine and cardiovascular diseases . On the With reference to previous studies on the prognosis of HF –13, the 123I-labeled meta-iodobenzylguanidine (MIBG) for prognostic evaluation after compensatory and optimal medical therapy were retrospectively enrolled in our HF database.A total of 991 consecutive patients with symptomatic HF and echocardiographic LVEF <50% who were admitted to our hospital between April 2010 and December 2016 and underwent myocardial scintigraphy of 2 (m2) × 22] = 14.89 × serum albumin (g/dl) + 41.7 × BMI/22. Plasma BNP levels were measured in 598 patients (60.3%) and NT-pro BNP levels were measured in the remaining 393 patients (39.6%) before discharge when heart failure was stable. To evaluate BNP and NT-pro BNP in an integrated manner, all of the HF patients were classified into four groups according to the following values based on ESC HF guidelines: 0–40 pg/ml and 0–125 pg/ml for stage 1, 41–100 pg/ml and 126–400 pg/ml for stage 2, 101–200 pg/ml and 401–900 pg/ml for stage 3, and 201 pg/ml and 901 pg/ml for stage 4, respectively.The inclusion criteria for this retrospective study were symptomatic HF requiring hospitalization, LVEF <50% determined by echocardiography, and age >20 years. Patients who refused resuscitation, patients who had obvious malignancy or hemorrhagic disease, and patients who were younger than 20 years of age were excluded. The patients included 738 males (74.5%). The mean age of the patients was 67.5 ± 12.9 years and the mean LVEF was 32.7 ± 11.3%. The diagnosis of HF was made by the Framingham criteria including typical symptoms of jugular venous distention, peripheral edema, pulmonary rales, S3 or S4 gallop sounds, and tachycardia. A chest x-ray was taken and two-dimensional echocardiography was performed to corroborate the diagnosis and rule out other diseases with similar symptoms and signs. In addition to a history of myocardial infarction or coronary revascularization, the etiology of HF, such as ischemic or nonischemic, was differentiated by electrocardiography, echocardiography, scintigraphy, or a combination of these when necessary as well as by coronary vascular information obtained by computed tomography, magnetic resonance imaging, and invasive selective coronary angiography. Just before discharge, blood tests were performed to measure the levels of hemoglobin (Hb), albumin, lipid profiles, glutamic-oxaloacetic transferase (GOT), glutamic-pyruvic transferase (GPT), lactate dehydrogenase (LDH), serum ChE, creatinine and brain natriuretic peptide (BNP). Renal function was evaluated by estimated glomerular filtration rate (eGFR) using the standard formula. The baseline geriatric nutrition risk index (GNRI) was calculated from serum albumin and BMI using the following formula: GNRI = 14.89 × serum albumin (g/dl) + 41.7 × present body weight/[heightE/e’ , left ventricular end-diastolic diameter , LVEF (%) calculated using the biplane modified Simpson's method, left ventricular volume at end-diastole , left ventricular volume at end-systole  and septal E/e’ , 15.123I-labeled MIBG of 111 MBq was quantitatively assessed in patients with compensated heart failure in a fasting and resting state by cardiac imaging using a gamma camera with a low-energy generic collimator at 15–30 min (early images) and 4 h (late images) after an intravenous tracer injection, as previously described (Cardiac sympathetic innervation by escribed –11.123I-MIBG activity was measured as heart-to-mediastinum ratio (HMR) using dedicated MIBG software  operated by experienced nuclear medicine technicians with no knowledge of the patients' clinical data, automatically setting the region of interest to the upper mediastinum and the entire heart on a planar anterior image. 123I-MIBG washout kinetics from the left ventricle were calculated from early and late cardiac 123I-MIBG activities as washout rate (WR). The HMR of cardiac 123I-MIBG was measured using a late image and standardized as sHMR for medium energy collimator conditions by a mathematical method established in a cross-calibration phantom experiment  that were considered as the primary endpoints of the present study were as follows: fatal CEs such as sudden cardiac death, death due to refractory and progressive pump failure, fatal sustained ventricular tachyarrhythmias, including ventricular tachycardia and ventricular fibrillation, and appropriate implantable cardioverter defibrillator (ICD) therapy against fatal ventricular arrhythmias. Clinical findings were confirmed by reviewing the patients' medical records and outcome analysis was retrospectively performed. In HF patients enrolled in the present study, sudden cardiac death was defined as witnessed cardiac arrest and death within one hour of acute onset or unexpected death in patients who had survived for the previous 24 h. The present study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by our institutional ethics committee for enrollment in our database and use of data for clinical research. The present study was approved by the Ethics Committee of Obihiro Kosei Hospital. Its approval number was 2016-015. Due to the retrospective, non-interventional, observational nature of the study as well as the fact that the clinical research was publicly disclosed on the institution's Web site and the fact that comprehensive consent was determined by the institution's ethics committee, the need for each patient's written informed consent was waived.t-test and categorical variables were compared by the χ2 test. New parameters were added to the conventional risk factors for patients with HF, and area under the curve (AUC) and net reclassification index (NRI) were used to assess whether the prognostic evaluation was significantly improved. Reclassification was additionally used to re-evaluate the results.Statistical values are presented as means ± SD. Means of values in the two groups were compared by the unpaired P values less than 0.05 were considered significant.A time-dependent cumulative event-free curve was generated by Kaplan-Meier analysis using the key parameters identified in the present study, and log-rank tests were also used to compare the curves as needed. The number of significant variables identified in the univariate analysis was statistically appropriate for the number of cardiac events in the present study, and multivariate analysis with the Cox proportional hazards model was performed to calculate hazard ratios and 95% confidence intervals (CIs) for the significant variables. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-off values for the independent significant parameters of CEs. Statistical analysis in the present study was performed using the computer software SAS for Windows  and Mathematica software for Windows . Following measurement of serum ChE and calculation of eGFR, cardiac sympathetic innervation was quantified as standardized HMR of MIBG activity as shown by two typical cases . Despite2, P < 0.0001), lower BMI  and lower serum ChE  than those in patients in the group without CEs , ESV , eGFR  and late sHMR . The ROC AUC of model 1 was 0.751. In the logistic regression analysis with serum ChE (model 2), significant variables were NYHA functional class , ESV , eGFR , late sHMR , and serum ChE  (P < 0.0001).In the multivariate logistic regression analysis excluding serum ChE (model 1), significant variables were NYHA functional class  . The ROCP = 0.0015). Of the 677 patients who did not die of a cardiac event, 14 were reclassified upward and 248 patients were reclassified downward, with a net gain in reclassification of −57.4% (P < 0.0001). The NRI in all subjects was 62.7% (P < 0.0001) [bintercep, bNYHA, bESV, beGFR, blate sHMR, and bChE derived from the parameter estimates of the model were 1.875, 2.655, 0.003, −0.012, −1.060, and −0.003 , respectively.Net reclassification analysis was performed using the four-parameter logistic model created by the combination of NYHA functional class, ESV, eGFR and late sHMR (model 1) and by the five-parameter model that included ChE (model 2) . Of the  0.0001) . Thus, mIn the present formula, NYHA functional class  was a categorical variable, and ESV, eGFR, late sHMR and ChE were continuous variables.P = 0.0034), 0.994, 0.988–0.999 (P = 0.0250) and 0.532, 0.398–0.709 (P < 0.0001), respectively.Among the significant univariate variables , serum C2 or late sHMR of less than 1.90 had significantly lower cardiac event-free rates than did their counterparts (2 and late sHMR < 1.90) was demonstrated to increase CE risks in HF patients , defined as a depletion of protein energy sources, has been used to define malnutrition in patients with renal dysfunction. The causes of PEW include inadequate nutritional intake, systemic inflammatory response syndrome due to a cytokine storm including HF, uremia, endocrine abnormalities , and metabolic acidosis , 19.The vicious cycle leads to hypoproteinemia and albuminemia.In addition, hyperglycemia and increased insulin resistance in patients with renal dysfunction lead to abnormal glucose metabolism. In the liver, this leads to accelerated glycogen breakdown, increased glycogenesis, and inhibition of sugar and amino acid oxidation . FurtherIn the present study, the synthesis of lipids and proteins was also suppressed in the CE group compared to that in the non-CE group, with significantly lower blood levels of lipid profile and protein/albumin in the CE group. Serum ChE level, a surrogate parameter for lipid and protein-albumin synthesis in the liver, was also significantly lower in the CE group. Serum ChE was an independent prognostic factor, as was eGFR, a surrogate parameter for renal dysfunction, suggesting that the combination of these two parameters might enable synergistic risk stratification of HF patients.When hypoperfusion is sustained due to HF, the renin-angiotensin-aldosterone system, cardiac sympathetic nerve function, arginine vasopressin, endothelin I, and atrial natriuretic peptide are constantly activated . Long-laDecreased GFR leads to anemia due to impaired erythropoietin production and other causes. Anemia worsens heart failure, increases cardiac sympathetic function and leads to refractory HF , 20.This study also showed significant cardiac sympathetic dysfunction and renal dysfunction in the CE group compared to those in the non-CE group. Moreover, cardiac sympathetic denervation and renal dysfunction were selected as independent prognostic factors, indicating that the combination of these two parameters might enable clearer risk stratification in HF patients.The present study was designed as an observational and non-interventional cohort study of patients with irreversible systolic HF at a single facility.A large multicenter intervention study based on the results of our study could contribute to the development of better prevention and treatment strategies, including diet and cardiac rehabilitation, using appropriate indications for malnutrition in HF patients with comorbid cardiac cachexia who are at high risk of death. In the present study, there was no evaluation of the Prognostic Nutrition Index (PNI) or Controlling Nutritional Status (CONUT), which have been reported to have a prognostic value for malnutrition in HF patients , 22.In HF patients at risk for nutritional impairment, approaches to improving nutritional status have been reaffirmed as part of the role of cardiac rehabilitation. Improvement of nutritional status is expected to be effective for improving exercise capacity. It has also been reported that cardiac rehabilitation increases muscle mass, prevents muscle hypercatabolism, and improves nutritional status , 24.There are two main types of therapy for HF patients: pharmacological and non-pharmacological. The use of percutaneous coronary intervention, catheter ablation, ICDs and biventricular pacemakers, transcatheter aortic valve implantation, MitraClip ® implantation, and other non-pharmacologic therapies have received attention, and the importance of nutritional management, including diet and exercise rehabilitation, should be further discussed.A lower level of serum ChE can independently and synergistically predict the risk of cardiac mortality in systolic HF patients with cardiac sympathetic innervation and renal dysfunction.


Microbial communities in freshwater lake sediments exhibit a distinct depth-dependent variability. Further exploration is required to understand their biodiversity pattern and microbial interactions in vertical sediments. In this study, sediment cores from two freshwater lakes, Mugecuo (MGC) and Cuopu (CP), on the Tibetan plateau were sampled and subsequently sliced into layers at a depth of every centimeter or half a centimeter. Amplicon sequencing was used to analyze the composition, diversity, and interaction of microbial communities. Results showed that sediment samples of both lakes could be clustered into two groups at a sediment depth of about 20 cm, with obvious shifts in microbial community compositions. In lake MGC, the richness component dominated β-diversity and increased with depth, indicating that the microbial communities in the deep layer of MGC was selected from the surface layer. Conversely, the replacement component dominated β-diversity in CP, implying a high turnover rate in the surface layer and inactive seed banks with a high variety in the deep layer. A co-occurrence network analysis showed that negative microbial interactions were prevalent in the surface layers with high nutrient concentrations, while positive microbial interactions were more common in the deep layers with low nutrient concentrations, suggesting that microbial interactions are influenced by nutrient conditions in the vertical sediments. Additionally, the results highlight the significant contributions of abundant and rare taxa to microbial interactions and vertical fluctuations of β-diversity, respectively. Overall, this work deepens our understanding of patterns of microbial interactions and vertical fluctuation in β-diversity in lake sediment columns, particularly in freshwater lake sediments from the Tibetan plateau. Lake sediment is an essential global organic carbon sink , formed The β-diversity, as the extent of change in community composition, was first proposed in 1960 . Previouin situ habitats  and Cuopu Lake  are freshwater lakes located on the eastern Tibetan plateau . MGC andctively, . In Octo4+-N), nitrate nitrogen (NO3−-N), and inorganic phosphorous (IP). The TOC was detected using a TOC analyzer  with the dry combustion method. The TN was measured using an ultraviolet spectrophotometer  with the sulfate digestion method. The NH4+-N and nitrate nitrogen NO3−-N were measured using a continuous flow analyzer . The IP was measured following the Standards Measurements and Testing (SMT) protocol , total nitrogen (TN), ammonia nitrogen (NHprotocol .226Ra and 210Pb was measured using a high-resolution high purity Germanium (HPGe) γ-spectrometer . The age of the sediment deposition was calculated using the constant rate of supply (CRS) of the 210Pb model , hA is the 210Pbex activity of each sedimentary layer, and 0A is the total activity of 210Pb in the sedimentary core.Where To minimize the risk of DNA contamination, all the molecular experiments were conducted in a sterile environment using 75% ethanol-cleaned and UV sterilized laboratory tools. The total DNA of lake sediments was extracted using the FastDNA SPIN Kit , and its concentration was quantified using a Nanodrop ultraviolet spectrophotometer . The extracted DNA was stored at −20°C until further analysis.R2 and amplification efficiency of the standard curve were 0.996 and 99%, respectively.The abundance of 16S rRNA gene in the sediment samples was detected by quantitative PCR (qPCR) using 515F (5′-GTGCCAG CMGCCGCGGTAA-3′) and 907R (5′-CCGYCAATTYMTTTRA GTTT-3′) primer pairs . The qPCPCR amplicon was carried out targeting 16S rRNA gene V4–V5 region using primer pairs 515F and 907R . The PCRThe 16S rRNA raw data were processed using USEARCH (v10.0) and Mothur (v1.45.2) to perform the sequencing analysis. Firstly, the paired reads of raw sequences were merged using USEARCH’s-fastq_mergepairs command, and the forward and reverse primers were removed. After filtering quality, sequences with a maximum expected error greater than one were excluded. The Operational taxonomic units (OTUs) were generated by clustering the sequence at 97% sequence similarity using USEARCH’s-cluster_otus command. The obtained OTU table was then classified using the command Mothur’s classify. Seqs command, using the SILVA 138 reference database. The alpha diversity indices, including Chao1, Shannon, Simpson, and Richness, were calculated in USEARCH using the-alpha_div command based on the normalized OTU table. All the 16S rRNA sequences have been deposited in the SRA database of the national center for biotechnology information (NCBI) with the accession number PRJNA876099.http://www.r-project.org).To assess the microbial community by considering subcommunities with different relative abundance, we defined the “abundant” OTUs as the OTUs with relative abundance >0.1% in more than 50% of the samples and present in more than 80% of the samples. “Rare” OTUs refer to OTUs with an average relative abundance of <0.1% in all samples. “Moderate” OTUs refer to other OTUs except for rare and abundant OTUs . All furrepl) and richness difference (βrich) components were calculated based on the Jaccard dissimilarity via the “adespatial” package in R  was conducted. To construct the heatmap, representative OTUs were selected with the highest loadings of PC1, PC2, and PC3. The heatmap was created based on Manhattan distance and the clustering algorithm of ward.D using the function “heatmap.2” of the “gplots” package in R. Nonmetric multidimensional scaling (NMDS) was constructed to display differences in microbial communities among different lakes and depths and was plotted using the “phyloseq” package based on Bray–Curtis distance.The normalized stochasticity ratio (NST) was used to estimate the determinacy and stochasticity of the microbial assembly processes in two vertical lake sediments . The NSTρ > 0.7 and ρ < −0.7, p-value <0.05) in the network. The network was performed by the “igraph” package in R  In lake MGC, and positively correlated with TN and IP (p < 0.05). The Simpson index in lake MGC showed an opposite relationship with environmental factors. In lake CP, the correlation between the four alpha diversity indices and environmental factors was not significant (p > 0.05).Moreover, Spearman correlation analysis showed the association between alpha diversity and environmental factors , with ChProteobacteria was the most abundant phylum in both lakes, accounting for 62.00 and 38.42% of bacteria in the MGC and CP lakes, respectively. Gammaproteobacteria was the largest bacterial class in both lakes, accounting for 57.99 and 30.07% in MGC and CP, respectively. In MGC, Chloroflexi (9.74%) was the second most abundant phylum, followed by Planctomycetes (4.35%) and Bacteroidetes (3.10%). In contrast, Cyanobacteria (15.79%) was the second most abundant phylum in lake CP, followed by Chloroflexi (7.23%) and Bacteroidetes (6.49%).Operational taxonomic units were classified into 65 bacterial phyla in sediments. The phyla with relative abundance higher than 1% in the sediments of both lakes were regarded as the abundant phyla . ProteobGammaproteobacteria increased with depth (p < 0.001). Cyanobacteria, Bacteroidetes, and Deltaproteobacteria were enriched in shallow layers, and their relative abundance declined with depth (p < 0.001). In lake CP, the relative abundance of Cyanobacteria was much higher than that in lake MGC, even accounting for 8.30% (average relative abundance) below 25 cm in CP lake.In lake MGC, the relative abundance of Proteobacteria in lake MGC (37.74%) and CP (33.44%). The relative abundance of the abundant OTUs in total reads was high, exceeding 60% in each lake  generally decreased with depth, and richness differences (βrich) increased with depth (repl) was consistently high and increased with depth (35–45 cm) in the deep layer, and richness differences (βrich) were the opposite, which remained at a lower level and decreased with depth at 35–45 cm (repl and βrich) of the rare OTUs was consistent with the entire community in each lake. In contrast, the β-diversity and its components (βrepl and βrich) of abundant OTUs remained stable and were consistently low in both lakes.The total β-diversity increased with depth, between 0.62–0.90 and 0.57–0.85 in MGC and CP lakes, respectively, . In laketh depth . Convers35–45 cm . The treGammaproteobacteria were abundant in both shallow and deep layers of MGC. However, the different OTUs in Gammaproteobacteria have different depth-related distributions. OTU4, OTU469, and OTU14 were only abundant in the deep layer, while OTU2, OTU17, and OTU18 were enriched in both shallow and deep layers. The depth distribution of Gammaproteobacteria OTUs in lake CP was consistent with that in lake MGC. In addition, OTUs belonging to Cyanobacteria, which include OTU7 and OTU43, have a higher relative abundance in the shallow layers (1–22 cm) of lake CP.The heatmap analysis based on representative OTUs showed that the microbial communities in each lake had a depth-related trend , with saR = 0.72, p = 0.001). Furthermore, depth-related trends of microbial communities were observed in these two lakes .Moreover, NMDS revealed the difference in microbial community structure among samples with different lakes and depths . There wPotential function prediction was calculated by FAPROTAX analysis. A total of 1,184 OTUs  were assigned to 59 functional groups. The results of the predictions showed that the potential functions varied and changed with the sediment depth .The functions involved in the carbon cycle were the primary and abundant functions. In lake MGC, photoautotrophy was abundant in surface sediments (1–5 cm), while chloroplast was most abundant in shallow sediments (1–25 cm) of lake CP. Chemoheterotrophy, aerobic chemoheterotrophy, and ureolysis were the main functions in deeper sediments of lake MGC (15–38.5 cm) and lake CP (30–45 cm). In the sediments below 10 cm, the functional groups involved in methanogenesis were more abundant. In addition, for the functions involved in the nitrogen cycle, nitrification was active in the surface sediments (1–5 cm). In comparison, nitrate reduction was more abundant in the deep sediments below 35 cm in lake MGC. In the sediments above 25 cm in lake CP, the functional groups involved in the sulfur cycle, such as the respiration of sulfur compounds and sulfite respiration, were more abundant .The CAP was performed to illustrate the influence of environmental factors on microbial communities and vertical β-diversity . All envp < 0.001). In MGC, the second crucial variable was IP contents, which explained 53.83% of the variation (p < 0.001), while in CP, depositional age and TOC were the second and third crucial variables, explaining 46.17 and 36.92% of the variation, respectively. In terms of the relationship between replacement and richness with environmental factors, the results differ between MGC and CP. In MGC, replacement increased with IP but decreased with depth and NO− 3-N, while richness showed the opposite trend. In CP, replacement increased with depth but decreased with TOC, TN, NO− 3-N, and IP, and richness had a weak correlation with these environmental factors.The CAP results showed that environmental factors accounted for 68.05 and 81.93% of the total variations in β-diversity in MGC and CP, respectively, . Depth iChloroflexi and Nanoarchaeaeota. The microbial community of module 3 was enriched in deep sediments (18–38.5 cm) and mainly consisted of Chloroflexi and Acidobacteria. Besides, in modules 1 and 4, the abundant class Gammaproteobacteria was enriched in deep sediments (18–38.5 cm). In the network of lake CP, there are 1,173 nodes and 47,402 links. Modules 1 and 4 had higher abundance in deep sediments (23–45 cm). Gammaproteobacteria was abundant in module 1, while Euryarchaea were enriched in module 4. Module 2 mainly consisted of the phylum Cyanobacteria, which was enriched in shallow sediments (1–22 cm).Networks were built based on all samples in each lake to explore co-occurrence patterns among the microbial community . The netp < 0.001, Kruskal–Wallis test). The positive and negative interactions among OTUs were also calculated, with a decrease in the proportion of negative interactions observed with increasing depth. Furthermore, negative interactions mainly existed in moderate/rare OTUs or between rare and moderate OTUs  can provide insights into the processes driving the vertical variation of microbial communities. In this study, the richness effect constantly increased with depth while the replacement effect decreased with depth in lake MGC . This trIn this study, the proportion of negative correlation among OTUs in the network decreased with sediment depth in both MGC and CP . ResourcOur analysis also revealed that the number of rare OTUs accounted for a higher proportion of total nodes, and that most negative correlations existed among moderate or rare OTUs. This suggested that rare and moderate OTUs have more overlapping ecological niches. Besides, abundant OTUs had higher degree and betweenness centrality levels, which aligns with previous findings , indicatCyanobacteria being more prevalent in lake CP. Additionally, we observed varying β-diversity patterns correlated with environmental factors. Our results indicate that the pattern of microbial interactions varied with depth, with evidence suggesting that higher nutrient levels may lead to negative interactions among microbes, while positive interactions are more prevalent in low-nutrient environments. These results broaden our understanding of patterns of β-diversity partitioning and microbial interactions in vertical sediment samples on the Tibetan plateau and provide new insights into mechanisms driving microbial community variation.In this study, we conducted a comprehensive analysis of the microbial community composition, diversity, and interaction in sediment samples collected from two lakes on the Tibetan plateau. Our findings demonstrate the significance of both abundant and rare taxa in shaping the β-diversity and microbial interactions. Our results reveal similarities in microbial community compositions at the phylum level between the two lakes, with https://www.ncbi.nlm.nih.gov/, PRJNA876099.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: YD, TH, and CheH designed the study. TH and ZZ sampled the sediments. TH and CheH were responsible for microbial lab work. XZ and YD analyzed the data, prepared figures, and wrote the initial manuscript. LC, KL, YL, and ChaH reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.This research was funded by the National Natural Science Foundation of China (Grant No. 41971077) and the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0503).The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Portunus trituberculatus, is one of the main aquaculture species in Chinese coastal regions due to its palatability and high economic value. To obtain a better understanding of the genetic diversity of P. trituberculatus in the Bohai Sea, the present study used 40 SSR loci to investigate the genetic diversity and population structure of 420 P. trituberculatus individuals collected from seven populations in the Bohai Sea. Genetic parameters revealed a low level of genetic diversity in the cultured population  in comparison with wild populations . The genetic differentiation index (Fst) and gene flow (Nm) ranged from 0.001 to 0.060 (mean: 0.022) and 3.917 to 249.750 (mean: 31.289) respectively, showing a low differentiation among the seven populations of P. trituberculatus. Population structure analysis, phylogenetic tree, and principal component analysis (PCA) demonstrated that the seven groups of P. trituberculatus were divided into four subpopulations (K = 4), but the correlation between genetic structure and geographical distribution was not obvious. These results are expected to provide useful information for the fishery management of wild swimming crabs.The swimming crab,  Portunus trituberculatus, is one of the important economic crabs in the Chinese marine fisheries and mariculture industry. It has a wide distribution in the coastal areas of South-East Asia and has been farmed for more than 30 years3. Over the past few decades, the consumption of swimming crab has gradually increased due to the delicious taste and versatile nutrients4. Among the main producers, China ranked first with an annual production of 559,796 tons according to the China Fisheries Statistical Yearbook (2022) published by the Ministry of Agriculture, China. However, with the development of intensive farming and marine fishing industry in recent years, germplasm resources of P. trituberculatus have dramatically declined due to over-exploitation and environmental deterioration6. In addition, the heavy demand for wild parents from artificial propagation resulted in the decline of the genetic diversity of the natural populations7. Such episodes emphasize the vital nature of monitoring the genetic diversity of P. trituberculatus populations to protect germplasm resources and facilitate molecular marker-assisted breeding (MAS).The swimming crab, 8. A high level of genetic diversity indicates strong biological survivability and environmental adaptation, which is required for sustained genetic improvement and stable inheritance of desirable traits9. Conversely, low genetic diversity can lead to reduced adaptability and viability, and ultimately to the degradation of species10. In aquaculture, genetic diversity constitutes a fundamental resource to improve the quality of stock11. However, for breeding populations of P. trituberculatus, long-term artificial directional selection eventually leads to a decline in genetic diversity12. Moreover, it is difficult to recover the declining genetic diversity caused by overfishing13. To formulate an effective conservation strategy, it is necessary to evaluate the genetic diversity and population structure of P. trituberculatus. In our previous study, SNP markers determined by genotyping-by-sequencing (GBS) revealed a low level of genetic diversity in P. trituberculatus along the coastal waters of China14. To evaluate the impact of the massive releases on natural populations, the researchers monitored the temporal variations in genetic diversity and structure in Panjin and Yingkou using microsatellite markers, which suggested that the large-scale stock enhancement of P. trituberculatus presented potential genetic risks to wild populations16. However, hatchery stock enhancements resulted in no reduction in genetic diversity for wild populations of P. trituberculatus in the Yangtze Estuary17.Investigating the genetic diversity of species is a prerequisite for the effective exploration and utilization of germplasms20. At present, RNA-seq has become a popular high-throughput sequencing technology that enables the development of SSR markers due to its characteristics of wide dynamic range, high accuracy, and strong sensitivity21. In addition, compared with genomic-derived SSRs, transcriptome-derived SSRs are characterized by high efficiency, strong transferability, and correlation with potential genes22. Cao et al.23 first analyzed the transcriptome of Crassadoma gigantean using RNA-seq technology, identified 12 polymorphic SSRs, and found several genes related to the growth and immunity of C. gigantean. These results would facilitate future studies of population structure and conservation genetics in this species. In aquatic crustaceans, Zhang et al.24 conducted transcriptome sequencing on the male and female gonads of Portunus sanguinolentus and detected 93,196 SSR loci. In Pachygrapsus marmoratus, 43,915 SSRs were excavated by RNA-seq, providing a reliable resource for investigating biological responses to pollution in intertidal and marine populations25. Lv et al.6 identified 22,673 SSRs with transcriptome analysis of P. trituberculatus, which provided a material basis for genetic linkage and quantitative trait loci analyses. The objective of the current study is to evaluate the genetic diversity and population structure of P. trituberculatus in the Bohai Sea with transcriptomic SSRs. The findings will contribute to understanding the population genetic structure of P. trituberculatus in the Bohai Sea and be useful in improving management and conservation strategies for this species.The development of high-throughput sequencing technologies provides great convenience for the identification of DNA molecular markers in genetic research. Among known DNA molecular markers, simple sequence repeat (SSR) shows the advantages of co-dominant inheritance, highly polymorphic, and wide distribution throughout the genomeA total of seven populations were collected from the Bohai Sea Fig. , Table 126 , followed by 35 cycles of denaturation (30 s at 94 ℃), annealing (30 s), extension (30 s at 72 °C) and additional extension (10 min at 72 °C). After amplification, PCR products were diluted 10 times with sterile water. The pooled sample was composed of 20 μL Hi-Di formamide and 0.2 μL GeneScan 500 ROX size standard. An ABI 3730XL Genetic Analyzer  was used to conduct capillary electrophoresis (CE) following the manufacturer's instructions. Each CE sample contained 1μL diluted PCR product and 15 μL pooled sample. Allele sizes (in base pairs) were determined with GeneMarker®Fragment Analysis Software  on the comparison of the position of the internal size standard in each lane with the position of the peak value of each sample.Forty pairs of SSR primers were obtained from the transcriptome data in our previous studyP. trituberculatus populations was estimated by determining genetic parameters, including the number of alleles (Na), the effective number of alleles (Ne), Shannon’s diversity index (SI), observed heterozygosity (Ho) and expected heterozygosity (He) using POPGENE version 1.327. Based on allele frequency, polymorphism information content (PIC) was estimated by PIC-CALC software28. Null allele frequencies (Fna) for SSR loci were calculated using GenePOP29. P values were calculated for determining Hardy–Weinberg equilibrium (HWE) at each locus with POPGENE version 1.3. Genetic differentiation and variation were inferred using Nei's genetic distance (D)30 and genetic identity (I) calculated by POPGENE version 1.3 and F-statistics  calculated by analysis of molecular variance (AMOVA) with software GenAlEx 6.531 through 999 permutations. Gene flow (Nm) was inferred from the formula of Nm = (1 − Fst)/4Fst32.Genetic diversity within 33. Principal component analysis (PCA) was carried out using Canoco 4.5 to elucidate genetic relationships within and among P. trituberculatus populations. Based on the 40 polymorphic SSR loci, Bayesian model-based population genetic structure was inferred using STRUCTURE version 2.3.434. The putative number of populations (K) was set from 1 to 10 with 3 replicate simulations for each K value using 100,000 MCMC (Markov Chain Monte Carlo) iterations after an initial 100,000 burn-in period. With the log probability of data (LnP(D)) and an ad hoc statistic ΔK based on the rate of change in LnP(D) between successive K-values, the structure output was entered into Structure Harvester36 to determine the optimum K value. The best K value was analyzed by CLUMPP37 and visualized with Distruct 1.1 software38.The phylogenetic tree was constructed based on Nei’s genetic distance and used to test population grouping as implemented in MEGA7Ne) ranged from 1.785 to 10.271 with a mean of 4.264. Shannon’s diversity index (SI), observed heterozygosity (Ho) and expected heterozygosity (He) ranged from 0.885 to 2.404 (mean: 1.482), 0.405 to 0.950 (mean: 0.639) and 0.440 to 0.903 (mean: 0.725), respectively. PIC values ranged from 0.415 (TRAN1) to 0.895 (TRAN20) with an average of 0.685. Five SSRs  showed moderate polymorphism (0.25 < PIC < 0.5), and the remaining 35 SSRs showed high polymorphism (PIC > 0.5). Null allele frequencies (Fna) and fixation index (Fis) varied from 0.029 (DX19) to 0.564 (TRAN13) and -0.207 (DX19) to 0.478 (TRAN21) respectively, indicating the existence of null alleles and heterozygosity deficit. Additionally, nine SSR loci fitted with HWE (P > 0.05), and the remaining 7 and 24 loci deviated from HWE at P < 0.05 and P < 0.01 levels, respectively.In this study, all parameters of the 40 SSR loci were calculated and presented in Table Na, Ne, SI, Ho, He, and PIC of seven P. trituberculatus populations ranged from 5.225 to 5.375, 3.794 to 4.103, 1.374 to 1.449, 0.624 to 0.654, 0.687 to 0.714, and 0.643 to 0.673, respectively  in comparison with wild populations .The mean values of P. trituberculatus individuals was performed to infer the optimal K value with the ΔK method. When the highest ΔK value was observed, the optimal K value was 4  and 3.68% (PC2) of total variation and could distinguish cultivated individuals from wild populations  and high gene flow (Nm = 249.750) were observed between the PL and QHD populations, and the high differentiation (Fst = 0.060) and low gene flow (Nm = 3.917) was observed between the HC and DY populations (Table D) and genetic identity (I) showed similar results between HC and DY populations  and PL and QHD populations  , Shannon’s diversity index (SI), and heterozygosity (H). However, expected heterozygosity (He) could better reflect the genetic diversity of species than observed heterozygosity (Ho)41.Genetic diversity is a crucial criterion in estimating the adaptability of species to changing environments, hence a better understanding of the genetic diversity of species is vital for evaluating population structure and evolutionary dynamicsPIC values of 40 SSR loci of 0.415 ~ 0.895, indicating the polymorphic nature of the loci and their suitability for assessing genetic diversity in the seven P. trituberculatus populations. Genetic analysis revealed that the genetic diversity of the wild populations (He ≥ 0.692) was higher than that of the cultivated population (He = 0.687), which was consistent with our previous report42. A similar result was found in E. sinensis43. In general, genetic drift, selection, and inbreeding resulted in low genetic variability in farmed stocks44. In addition, many SSR loci significantly deviated from HWE (P < 0.05), which might be attributed to null allele and heterozygote deficiency (Fis > 0). Null alleles might be accounted for insufficient sampling45 and variation of microsatellite flanking sequence46. Loss of heterozygosity might be accounted for migration, artificial selection, and inbreeding48, which was common in marine species such as Scylla paramamosain51, Pinctada margaritifera52, and Hypophthalmichthys nobilis53. Chen et al.54 used ten SSRs to investigate the effect of artificial selection on the genetic structure of two abalone lines and found a loss of heterozygosity (Ho = 0.650 < He = 0.711). These studies indicated the negative impact of heterozygote deficiency on population genetic diversity. Therefore, it is necessary to maintain a high level of genetic diversity in aquatic animals to reduce heterozygous loss and prevent germplasm degradation.The current study reported P. trituberculatus in the Bohai Sea (He = 0.725) than that in the Yellow Sea47 (He = 0.814) and the East China Sea55 (He = 0.916), which was consistent with the results revealed by SNP markers14. It has been shown that when conducting genetic diversity analysis on aquatic animals, the number of SSR loci should be greater than 20 and the sample size should be greater than 4556. The number of loci and sample size in this study meet this standard, indicating the reliable result of low genetic diversity of swimming crabs in the Bohai Sea. Bohai Sea is a semi-enclosed and shallow body of water that limits the dispersal of P. trituberculatus, leading to a decline in genetic diversity47. In the SSR investigation of Exopalaemon carinicauda, Zhang et al.57 suggested that the Binzhou population in the Bohai Sea had the lowest level of genetic diversity, which illustrated that the Bohai Sea might hinder the gene flow. Moreover, marine pollution, aquaculture pollution, and reclamation also reduced genetic diversity58. Therefore, it is necessary to carry out long-term genetic monitoring of P. trituberculatus in the Bohai Sea for full protection and utilization of the germplasm resources of this species.In terms of expected heterozygosity, this study showed lower genetic diversity of P. trituberculatus, genetic monitoring of population structure is essential for the development of effective management strategies13. The results of the current study established that all P. trituberculatus individuals were divided into four subpopulations , an essential gauge of genetic differentiation among populations, is crucial to understand genetic relationships. 0 < Fst < 0.05, 0.05 < Fst < 0.15, 0.15 < Fst < 0.25, and Fst > 0.25 showed negligible, moderate, high, and strong genetic differentiation respectively61. In this study, HC and DY populations were medium differentiation (Fst = 0.060 > 0.05), which might be related to the geographical location of the two groups. Huanghua and Dongying were located at Bohai Bay and Laizhou Bay on both sides of the Yellow River estuary, respectively. The ecological environment, species distribution, and organic pollution in the Yellow River estuary led to the geographical differences between the two different sea areas63, which led to the differences in activity scope and habitat preference of P. trituberculatus, and ultimately resulted in high genetic differentiation between the HC and DY populations. In addition, geographic isolation also leads to low gene exchange between cultivated and wild populations compared to wild populations in the open sea, which can be proven by the genetic differentiation index. The average value of Fst between the HC and wild populations was 0.031, and between wild populations was 0.017 (Table Nm = 31.289), genetic distance (D = 0.08), and genetic identity (I = 0.924) also demonstrated low genetic differentiation and strong genetic admixture among the seven P. trituberculatus populations.The genetic differentiation index (P. trituberculatus throughout the coastal areas of the Bohai Sea. Forty microsatellite loci revealed a low level of genetic diversity in the seven P. trituberculatus populations in the Bohai Sea. A low level of genetic differentiation and frequent gene flow among these seven populations were revealed, suggesting high genetic connectivity. The structure analysis illustrated four subpopulations, but the clustering pattern was not related to geographical location. To increase the genetic diversity of P. trituberculatus, practical and effective protective measures are expected to be taken to prevent the degeneration of germplasm resources. This study also provides a theoretical basis for selecting parents from different geographical populations during the artificial breeding programs.In summary, this study provided useful insights into the population structure of


The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5–12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight. The authors created a comprehensive developmental cell atlas for spatiotemporal gene expression of the human spinal cord, revealed species-specific regulation during development and used the atlas to infer novel markers for pediatric ependymomas. Various transcription factors (TFs) along the dorsal–ventral (DV) axis are then activated, resulting in spatially segregated progenitor domains. In rodents, domain-specific NPCs first generate neurons and then glia, and these differentiated neural cells migrate to their final locations in the spinal cord and form distinct circuits1.The spinal cord comprises the caudal region of the central nervous system (CNS) and is responsible for conveying and processing motor and sensory information between the brain and the periphery. During spinal cord development, gradients of dorsal and ventral morphogens2 or little differentiation3, suggesting that more information about the genetic regulation of cell fate commitment in hNPCs is necessary. Furthermore, this information may provide insight into pediatric tumorigenesis and neurodevelopmental disease.It is, however, not known to what extent this knowledge can be extended to humans. It is generally thought that, during the first trimester of pregnancy, most of the human NPCs (hNPCs) are highly proliferative in preparation for neurogenesis and gliogenesis. Recent studies, however, showed that hNPCs derived from early development exhibit either robust glial differentiation4. Furthermore, a high-throughput and multiplex in situ hybridization method, called hybridization-based in situ sequencing (HybISS), has recently been developed for single RNA molecule localization of large gene panels with single-cell resolution within human tissue for data validation5. Combining these methods can help overcome the limitations of individual techniques, facilitate unbiased cell type annotation and allow high-resolution spatiotemporal mapping of the developing human spinal cord. Two recent studies used scRNA-seq on human developing spinal cord and revealed the appearance of different neural cell types7. However, the genetic regulation of the commitment of homogenous hNPCs to heterogenous neuronal and glial fates in vivo is still unclear. Furthermore, although neural patterning during human development is well described8, how neural patterning directs regional neuronal and glial differentiation is still not well studied in human.Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have provided high-throughput and spatially resolved analysis of gene expression during human prenatal developmentIn this study, we analyzed 16 human embryonic and fetal spinal cord samples, with ages spanning across the first trimester, from post-conceptional week (W) 5–12, using scRNA-seq, ST and HybISS, and we integrated these datasets with previously reported mouse and human spinal cord datasets. Here we provide a comprehensive developmental cell atlas of the human spinal cord, reveal spatiotemporal gene expression and regulation of cell fate commitment, highlight the major differences of cellular and molecular events in human and rodent spinal cord development and report the discovery of novel molecular targets and genetic regulation of pediatric spinal cancer stem cells (CSCs).10. We performed scRNA-seq, ST and HybISS to create a developmental cell atlas of the human spinal cord with detailed spatiotemporal gene expression and validation  axis of representative ages . Seventy-six sections from ST resulted in 23 clusters (12 major cell types) along the RC and DV axis  were the major cell population at W5 and were highly proliferative (MKI67+TOP2A+) but were restricted to the ventricular zone from W8 , IbNs (SCGZ+ or NRXN3+) and ChNs (ISL1+ and/or SLC5A7+), appeared as early as W5 and were widely distributed throughout the gray matter  at W8 , IbNs (PAX2+) and ChNs (ISL1+) , we observed that all glial cell markers were expressed at W5, in which ASCs (MSX1+GFAP+) were derived from the dorsal ventricular zone; EPCs (FOXJ1+RFX4+) were derived from the ventral ventricular zone; and OPCs (OLIG1+OLIG2+) were derived from the pMN domain  and EPCs (RFX4+FOXJ1+) in the dorsal and ventral area of the spinal cord, respectively, in agreement with HybISS results  until W8  in the ventral spinal cord  and OPCs_3 (EN2+)   substantially decreased in hNPCs, and hNPCs_10 even disappeared from W9 , neuronal lineage-associated genes  and neuronal markers  , PAX2 (for IbNs) and SLC5A7 (for ChNs), exhibited a biased DV expression in ST analysis, which correlated with the localization of differentiated neuron types   Fig. . SpatialCs) Fig. . IntegraCs) Fig. . We thensed Fig. . The spa17 in the scRNA-seq dataset. The analysis showed the top regulons for human spinal cord development as well as the gene expression of the top TFs   along the DV axispes Fig.  but overpes Fig. . IHC conpes Fig. , but thepes Fig. . Howeverpes Fig. . Under tpes Fig. . As vari–8) Fig. . We also–8) Fig. .Fig. 4ThWe next performed a co-localization analysis using the proportion estimates obtained from stereoscope to visualize the initiation of cell fate transition locally before migration starts. The ratio of NPCs decreased markedly during development Fig. . The maj1, we found that more than half of the NPC clusters expressed low levels of S or G-to-M phase cell cycle genes  were highly proliferative at least up to embryonic day (E) 13.5   also showed higher expression during embryonic and fetal stages in human compared to mouse, in which high expression took place postnatally  Fig. . The NPClly Fig. . In agreent Fig. .Fig. 5Sp6. Our HybISS showed that glial lineage-associated genes were expressed as early as W5 , equivalent to E11 in mouse development, earlier than the first appearance of mouse ASCs, OPCs and EPCs at E16.5, E12.5 and E15.5, respectively25, indicating an earlier onset of gliogenesis in human. Interestingly, Msx1 is a key regulator of EPC differentiation during mouse spinal cord development26, and we found that MSX1 is both a cell marker and a lineage-associated gene for human ASCs  and mouse (E16) spinal cord sections at comparable timepoints validated that MSX1 expression in humans was mainly located in the dorsal ventricular zone, correlated with the marker gene expression GFAP in ASCs and FOXJ1 in EPCs  in the developing human spinal cordCs Figs.  and 3e,fSCs Fig. . In contPCs Fig. . Howeverent Fig. . In addient Fig.  and idenent Fig. , such asPCs Fig. . ST conford Fig. . Altoget28, especially in children29. Pediatric ependymoma development recapitulates neurodevelopment28, but previous scRNAseq studies lacked proper normal human neurodevelopment datasets as control28. We first used our data to gain insight into the molecular signature and differentiation of drug-resistant CSCs in pediatric ependymomas. We obtained genes related to spinal cord tumor (HP:0010302) from the Human Phenotype Ontology (HPO) database and plotted the module on ST data. We observed broad but no regionally specific gene module expression of spinal cord tumor in all ST sections  in the ST dataset from normal spinal cord but did not find any expression in the sections, confirming that these genes are tumor specific.To demonstrate how our developmental cell atlas can be used for disease studies, we focused on ependymomas, an aggressive CNS tumor group with high recurrence rateons Fig. , suggest 28 Fig. . Despite 28 Fig. , neurona 28 Fig. , whereas 28 Fig. . The nonmas Fig. . Moreovemas Fig.  and sharVIM Fig. . After iVIM Fig. , we uncoSCs Fig. . We also28. To further investigate the molecular differences between hNPC and CSC differentiation, our trajectory analysis showed that EPC-related TFs RFX2 and RFX4 were highly associated with EPC differentiation , respectively .To create a spinal cord cell atlas across species, timepoints and technologies, we integrated our human scRNA-seq data with all publicly available scRNA-seq datasets of spinal cord samples as of June 2022 for 1.8 million cells Fig. , includiIn this study, using multi-omics and data integration to study the developing human spinal cord, we (1) created a developmental cell atlas of the human spinal cord throughout the first trimester of development; (2) revealed spatiotemporal regulation of human spinal cord neurogenesis and gliogenesis; (3) presented major differences of cell and molecular regulation between rodent and human spinal cord development; and (4) discovered unique markers and regulation of CSC differentiation in human ependymomas.7. In this study, we acquired human prenatal spinal cords over the first trimester for scRNA-seq and spatial techniques, integrated the multi-omics datasets and validated the results, which gave new insights into the spatiotemporal gene expression of the developing human spinal cord.The dynamics and molecular regulation of the human spinal cord development are still understudied. Although two recent studies explored the developing human spinal cord by scRNA-seq and showed neural patterning and neurogenesis in identified clusters, they did not elucidate how NPCs are committed to multiple neural cell lineages or how the spatiotemporal gene expression is involved in neurogenesis and gliogenesis1. However, we found that many hNPCs throughout the ventricular zone did not proliferate even at the early embryonic stage. The proliferative NPCs lose their proliferation during the first trimester in humans, much earlier than in rodents. The loss of active NPCs after fetal development limits regeneration in the mammalian adult spinal cord—for example, after spinal cord injury24. The loss of active NPCs during first trimester development in humans partly explains the extremely low regenerative potential in human spinal cord.NPCs are thought to proliferate vividly during fetal development37). Second, whereas rodent astrocytes migrate horizontally during development to the mantle zone and the future lateral white matter37, we showed that human astrocytes were first restricted to the dorsal region of the spinal cord and spatiotemporally regulated by MSX1, a TF shown to specifically regulate ependymal cell development in rodents26. Third, we conclude that human EPCs exhibit a longer developmental period than expected. Mouse spinal cord EPCs are derived from mid-late fetal stage (E15.5) and are fully developed within 1 week in vivo24. However, whereas human EPCs are derived ventrally from W5, dorsal EPCs found at adulthood38 were still missing at W12, suggesting a second wave of gliogenesis during the second trimester. Future studies involving scRNA-seq and spatial techniques are needed to fully describe gliogenesis in the human spinal cord. Notably, between human and mouse development, many regulons and genes are only present in human spinal cord but not in mice, suggesting that neurodevelopment is regulated differently between species. Notably, although most studies on neurogenesis and gliogenesis have focused only on the temporal gene expression, we developed a method to demonstrate that neural patterning and positioning of neural cells are the results of the spatially biased expression in addition to temporal gene expression.Because lineage tracing techniques cannot be applied in humans, it is unclear how neurogenesis and gliogenesis in human spinal cord are regulated in a spatiotemporal manner. With integration of multi-omics data, we highlighted some unique developmental events in the human developing spinal cord. First, we found that hNPCs were committed to glial fates as early as W5, whereas previous studies on active regulons and marker expression showed that this occurred at W8–10  under sterile conditions within 1–2 h after the abortion. Specific information can be found in Supplementary Table All mouse experiments were conducted in accordance with the guidelines of the Swedish Board of Agriculture  and were approved by the Karolinska Institutet Animal Care Committee.Sixteen samples of human prenatal spinal cord tissue were used in the study , representing W5–12. In the present study, W5–8 is referred to as early stages (embryonic), and W9–12 is referred to as later first trimester stages . Post-conception age was determined by information from the clinical ultrasound, by time from last menstrual period and by identifying age-dependent anatomical landmarks with true crown-rump-length (CRL), taking into account that post-conception age and clinical age differs by 1.5–2 weeks.Mouse fetal spinal cords were dissected quickly on ice-cold PBS after dams were sacrificed. Tissues were fresh frozen in OCT and stored for future experiments.16, with modification for Ca2Cl2 (1 mM) and MgCl2 (2 mM). The aCSF was oxygenated with 95% O2:5% CO2 for 20 min at 4 °C. The samples were then digested at 37 °C in aCSF. Papain solution  and DNase I  were added to the aCSF to dissociate the tissue. Incubation time was adjusted based on developmental stage, ranging from 15 min to 25 min. The spinal cords were subsequently dissociated manually with fire-polished glass pipettes. When most of the tissue was dissociated into single cells, the solution was filtered using a 30-µm cell strainer  and collected in a 15-ml Falcon tube. The digestion solution was diluted with 7.5 ml of aCSF and centrifuged at 300g for 5 min at 4 °C. The pellets were resuspended in aCSF and transferred to Eppendorf tubes pre-coated with 30% BSA . After cell counting, the single-cell solution was diluted to a concentration of 800–1,200 cells per microliter and kept on ice for immediate chip loading.W5–7 spinal cord tissues were used as one piece, whereas W8–12 spinal cords were divided into three pieces  before dissociation. The dorsal root ganglia were removed by cutting the roots. Each piece of tissue was minced into smaller pieces using sterile blades and scissors. Artificial cerebrospinal (aCSF) was prepared as previously describedHuman spinal cord tissues at W5, W8, W9 and W12 were embedded in Tissue-Tek (OCT) and snap frozen using an isopentane/dry ice slurry. W8–12 samples were first divided into cervical, thoracic and lumbar. To enable spatial protein and gene expression analyses, the spinal cords were cryosectioned at 16-μm thickness and alternatingly placed on Superfrost microscope glass slides (Thermo Fisher Scientific) and Visium spatial gene expression slides (10x Genomics), after which they were stored at −80 °C for no more than 14 days before being used.24. In brief, tissue sections were rehydrated by 1× PBS for 5 min, and then primary antibodies diluted in blocking solution  were applied to the sections and incubated at room temperature overnight. Secondary antibodies were applied to sections after two times wash with 1× PBS. DAPI was applied on sections for 1 min. Sections were mounted after washing and ready for confocal imaging by Zeiss LSM 700.IHC was performed as previously describedDroplet-based scRNA-seq was performed using the 10x Genomics Chromium Single Cell Kit v3. Single-cell suspensions concentrated at 800–1,200 cells per milliliter were mixed with master mix and nuclease-free water according to the Chromium manual, targeting 5,000 cells per reaction. The library preparation and sequencing were done according to the Chromium v3 standard protocol. Sequencing was performed using the Illumina NovaSeq 6000.https://support.10xgenomics.com/spatial-gene-expression). Sections were fixed for 30 min in methanol, stained with hematoxylin and eosin and imaged using the Metafer Slide Scanning system (MetaSystems). Optimal permeabilization time for spinal cord sections was determined to be 20 min using the 10x Genomics Visium Tissue Optimization Kit. In total, Visium Spatial Gene Expression libraries from 76 spinal cord sections were prepared by following the manufacturer’s protocol. Libraries were sequenced using Illumina platform (NovaSeq 6000 and NextSeq 2000). The number of cycles for read 1 was 28 bp and 120 bp for read 2.Spatial gene expression libraries were generated using the Visium Spatial Gene Expression Kit from 10x Genomics . Probe sequences are included in Supplementary Tables HybISS was performed as reported by Gyllborg et al.z-stack planes with 0.5 μm spacing and 10% overlap between FOVs.Imaging was performed with a Leica DMi8 epifluorescence microscope equipped with an LED light source (Lumencor SPECTRA X), sCMOS camera (Leica DFC9000GTC) and ×20 objective . Each field of view (FOV) was imaged with 24 Single-cell sequencing data were processed using the CellRanger pipeline . Reads were mapped against the human genome  and annotated with GENCODE gene annotations for the GRCh38-3.0.0 genome assembly (GENCODE release 32). Using the BAM files from CellRanger, molecules were mapped into spliced and unspliced transcripts using velocyto (0.17.17) into which loom files were generated for each sample.Sequenced ST libraries were processed using the Space Ranger version 1.0.0 pipeline (10x Genomics). Reads were aligned to the human reference genome  and annotated using GRCh38-3.0.0 to obtain expression matrixes.The single-cell count matrix was first enriched for protein-coding RNA and long intergenic non-coding RNA (lincRNA) gene types. Cells with fewer than 500 genes and genes expressed in fewer than 15 cells were excluded from the analysis. Cells with over 25% mitochondrial gene expression were also excluded.MALAT1 and mitochondrial and ribosomal protein-coding genes. Spots with fewer than 500 genes and genes expressed in fewer than five spots were excluded from analysis of the three post-conception timepoints.In total, 76 tissue sections were analyzed, resulting in 20,835 spots used for data analysis. The count matrix was enriched for protein-coding and lincRNA genes. Count matrix was filtered for all hemoglobin-related genes, 38, and the top 6,000 genes with high dispersion were selected using the FindVariableGenes function. Cell cycle activity, number of genes and mitochondrial content across the data were regressed out using the ScaleData function. Principal component analysis (PCA) was performed on the 50 most significant components as determined by the PCElbowPlot function, showing the standard deviation of the principal components. Cells in different cycling stages were identified by gene sets called ‘S.Score’ and ‘G2M.Score’ within the Seurat package. Clusters were identified using the FindClusters function by using Louvain resolution 1.2 for scRNA-seq.Normalization, dimensionality reduction and clustering of scRNA-seq data were performed using the Seurat package (Seurat version 4.0.4)Analysis, including data normalization, dimensionality reduction and clustering, of ST data was performed jointly using the Seurat and STUtility packages. Normalization was conducted using variance stabilizing transformation (SCTransform). PCA was used for selection of significant components; a total of 50 principal components were used in downstream analysis; and 30 principal components were used for ST analysis. To integrate ST sections, the Harmony  function was used. Spots were clustered using the shared nearest neighbor algorithm implemented in the Seurat package as FindNeighbors and FindClusters (Louvain resolution 0.7).P values below 0.01 were considered significant. For integration of scRNA-seq data and ST data, we used stereoscope11, which performs guided decomposition of the mixed expression data collected from each spatial capture location, using profiles learned from scRNA-seq data as a reference. In the stereoscope analysis, a batch size of 2,048 and 50,000 epochs was used for both the parameter estimation step and the proportion inference process. Cell types with fewer than 25 cells were excluded from the analysis, and we randomly selected 500 cells from cell types with more than 500 members. For cell types with more than 25 members and fewer than 500 members, all cells were included. In the analysis, 2,000 highly variable genes were used. These genes were extracted by applying the function scanpy.pp.highly_variable_genes with n_top_genes = 2,000 from the scanpy (version 1.8.0.dev78 + gc488909a) suite, after having normalized ) and log-transformed (scanpy.pp.log1p(…)) the data. Cell type decomposition of ST spots was then saved as an assay for downstream analysis.Uniform manifold approximation and projection (UMAP) was used to create a two-dimensional (2D) embedding of cell or spot transcription profiles for visualization purposes (RunUMAP). Identification of DEGs among clusters was done using the FindAllMarkers function from the Seurat package, where genes with log fold changes (FCs) above 0.2 and 39 for all DEGs with an average logFC value above 0 and an adjusted P value below 0.01. The compareCluster function was used with a pvalueCutoff = 0.05. Analysis of genes belonging to Wnt, Shh or Notch pathways as well as Human Spinal Cord development were done using the KEGG database and Phenotype Orthologs (HPO), respectively.GO characteristics of gene clusters were determined using the clusterProfiler package (version 3.8.1)For previously published scRNAseq data used in this study, data sources are listed below in the ‘Data availability’ section. All these datasets were processed the same way as their publication stated.After pre-processing and clustering analysis, each cluster (for both scRNA-seq and ST) was manually annotated based on previous knowledge and recent atlas resources. After annotating each cluster, clusters with the same major cell type names were merged, and DEG analysis on these major cell types was performed in an unsupervised manner. These DEG results confirmed the accuracy of annotation. In addition, all available spinal cord scRNA-seq datasets (by June 2022) were integrated, and correlation analysis for annotations was performed, which showed high correlation between our dataset annotation and previous studies.15 was used to analyze neurogenesis and gliogenesis, respectively. For neurogenesis, the NPC cluster close to INPs, all the INPs and all differentiated neurons were selected. For gliogenesis, we selected all glial cells and all NPCs that were connected to the trajectory. For each branch, clusters in the upstream and downstream were selected for pseudotime analysis. Lineage-associated genes were calculated by the R package TradeSeq (version 1.4.0)40.The R package slingshot (version 1.8.0)14 was used to build differentiation trajectories during development. In the neurogenesis and gliogenesis analysis, a population of cells that were sampled from W5, clustered as NPCs_10, and with higher expression of TOP2A and SOX2 was identified and used as root in the URD trajectory reconstruction. The tips of each lineage were identified based on the Louvain clusters. After 350,000 simulated random walks were performed per tip, the divergence method ‘preference’ was used to build the tree, with minimum.visits = 2, cells.per.pseudotime.bin = 25, bins.per.pseudotime.window = 8, p.thresh = 0.05 and min.cells.per.segment =10.The R package URD (version 1.1.1)In the inference of hNPC development trajectory, the same population of NPCs was used as root, and the NPCs with later pseudotime estimated by scVelo and closer to neuronal and glial lineages on UMAP were identified as tips, respectively. The divergence method ‘preference’ was also used for tree building, with cells.per.pseudotime.bin = 25, bins.per.pseudotime.window = 8, p.thresh = 0.001 and other parameters default.13 and projected onto the UMAP embedding as streamlines. To show the connectivity between different clusters, the transition probabilities of cell-to-cell transitions were estimated and projected onto the same UMAP embedding.The transcriptional dynamics of splicing kinetics were modeled stochastically with scVelo (version 0.2.4)17 was used to infer TF activities in human and mouse neural cells separately. In the human dataset, 10% of cells in each subtype were randomly sampled and combined to infer gene regulatory network with the GRNBoost2 algorithm. Then, all neural cells were used to predict candidate regulons (cisTarget) and to estimate the cellular enrichment of the predicted regulons (AUCell). The top five regulons with the highest specificity in each cell type were selected using the regulon_specificity_scores function implemented in Python. For each regulon, its activity in all cells was fitted and binarized to determine the ‘on’ or ‘off’ state and further used to compute the ‘percent activated’ in the dot plots yi = f(xi), where yi is the feature value of observation i, and xi is the position of said observation on the DV axis. Below we describe in detail how we obtained the values yi and xi as well as the character of the non-parametric function f.To assess how certain feature values  vary along the DV axis, we designed a method to cast the 2D data into a different and more informative one-dimensional (1D) representation relating to the aforementioned axis. More specifically, we sought to model the feature value as a function of the position along the DV axis—that is, δs according to:First, to determine each observation’s position along the DV axis, we had to define the DV axis in each sample. Thus, we manually annotated all observations (spots) as belonging to either the ventral or the dorsal region. We denoted the  sets of spots in the dorsal and ventral regions as D and V, respectively; we also let |·| represent the cardinality operator. Then, we selected a subset of observations (D′ and V′) of size min and min, respectively, from each set and computed the ‘DV-difference vectors’ a be defined as the normalized (to unit norm) average, across all observations within the sample, DV-difference vector. We then proceeded to project each observation’s spatial coordinates (in 2D space) onto the (1D) axis vector a, as to obtain its position along the DV axis (ps); for this, standard orthogonal projection is used:us is an observation’s original coordinates in the 2D plane; the final equality holds true because a has unit norm. For each sample, we then normalized the axis projections using min–max scaling . For computational reasons, we assign each observation s  to one bin (bi) of nbins different bins, according to:Finally, we let the axis vector bi, we compute the average axis value (xi) and average feature value (yi) as follows:νs is the feature value associated with observation s. In the last step, we aim to relate the feature values to the axis positions via a function f. The character of f is determined by loess regression , implemented with geom_smooth from the R package ggplot2 and visualized as a 1D plot—generating the plots similar to those shown (for example) in Fig. Next, for each bin https://github.com/almaan/axis-projection) as a package that can be installed and used in a standard R environment.We implemented this method in R, and all code is available at GitHub .After imaging, Leica LAS X software was used to maximum intensity project each FOV to obtain a flattened 2D image. Imaging data were then analyzed with in-house custom software that handles image processing and gene calling based on the Python package Starfish. Each 2D FOV was exported and pre-processed by alignment between cycles and stitched together using the MIST algorithmhttps://github.com/acycliq/pciSeq and is described in Qian et al.12. In short, pciSeq works by assigning genes to cells and then cells to cell types, and this assignment is done using a probabilistic framework based on scRNA-seq data12. Owing to the density of nuclei in the tissue, nuclear segmentation could not be done; instead, a compartment-based approach was employed in which each compartment was defined as a 40 × 40 pixel grid (roughly 13 × 13 µm).Probabilistic cell maps were created using probabilistic cell typing by in situ sequencing (pciSeq). The pciSeq pipeline can be found at P < 0.001 were selected as significantly differentially expressed genes. Significantly differentially expressed gene lists were ordered and filtered by smallest P value and largest change of logFC.Significance of scRNA-seq and ST analysis for differential gene expression were carried out using Wilcox. Genes with Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41593-023-01312-9.Supplementary InformationSupplementary Figs. 1–7 and Supplementary Tables 1–4Reporting SummarySupplementary TableTable 1. Sample information of human prenatal spinal cords used in this study. Table 2. Reagents and resources used in this study. Table 3. Summary for HybISS oligo information for major cell type validation. Table 4. Summary for HybISS oligo information for subtype validation.