CROST: a comprehensive repository of spatial transcriptomics.

The development of spatial transcriptome sequencing technology has revolutionized our comprehension of complex tissues and propelled life and health sciences into an era of spatial omics. However, the current availability of databases for accessing and analyzing spatial transcriptomic data is limited. In response, we have established CROST (https://ngdc.cncb.ac.cn/crost), a comprehensive repository of spatial transcriptomics. CROST encompasses high-quality samples and houses 182 spatial transcriptomic datasets from diverse species, organs, and diseases, comprising 1033 sub-datasets and 48 043 tumor-related spatially variable genes (SVGs). Additionally, it encompasses a standardized spatial transcriptome data processing pipeline, integrates single-cell RNA sequencing deconvolution spatial transcriptomics data, and evaluates correlation, colocalization, intercellular communication, and biological function annotation analyses. Moreover, CROST integrates the transcriptome, epigenome, and genome to explore tumor-associated SVGs and provides a comprehensive understanding of their roles in cancer progression and prognosis. Furthermore, CROST provides two online tools, single-sample gene set enrichment analysis and SpatialAP, for users to annotate and analyze the uploaded spatial transcriptomics data. The user-friendly interface of CROST facilitates browsing, searching, analyzing, visualizing, and downloading desired information. Collectively, CROST offers fresh and comprehensive insights into tissue structure and a foundation for understanding multiple biological mechanisms in diseases, particularly in tumor tissues.

Overview of multi-omics research in gout.

Gout is a self-limiting inflammation disease triggered by deposition of monosodium urate with a variety of comorbidities. With the improvement of living standards, the global incidence of gout is increasing year by year, which seriously affects people's health. As an effective tool to study diseases, omics technology has been widely used to discover potential biomarkers and risk factors of gout. The identified variation sites or different-expressed products provide different dimensions of insights for the study of the pathogenesis and disease progression of gout. In this review, the application and research results of multi-omics technology in gout were analyzed and summarized through PubMed literature retrieval. Meanwhile, the recent research progress of multi-omics technology in the field of gout was reviewed to understand the specific changes of gout patients at different molecular levels, and to provide ideas and directions for further research on gout in the future.

DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression.

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.

Single-cell transcriptomic analysis of the tumor ecosystem of adenoid cystic carcinoma.

Adenoid cystic carcinoma (ACC) is a malignant tumor that originates from exocrine gland epithelial cells. We profiled the transcriptomes of 49,948 cells from paracarcinoma and carcinoma tissues of three patients using single-cell RNA sequencing. Three main types of the epithelial cells were identified into myoepithelial-like cells, intercalated duct-like cells, and duct-like cells by marker genes. And part of intercalated duct-like cells with special copy number variations which altered with MYB family gene and EN1 transcriptomes were identified as premalignant cells. Developmental pseudo-time analysis showed that the premalignant cells eventually transformed into malignant cells. Furthermore, MYB and MYBL1 were found to belong to two different gene modules and were expressed in a mutually exclusive manner. The two gene modules drove ACC progression into different directions. Our findings provide novel evidence to explain the high recurrence rate of ACC and its characteristic biological behavior.

Single-cell RNA sequencing reveals the cellular and molecular changes that contribute to the progression of lung adenocarcinoma.

Pure ground glass nodules (GGNs) and solid nodules (SNs) represent early and relatively late stages of lung adenocarcinoma (LUAD) in radiology, respectively. The cellular and molecular characteristics of pure GGNs and SNs have not been comprehensively elucidated. Additionally, the mechanism driving the progression of lung adenocarcinoma from pure GGN to SN in radiology is also elusive. In this study, by analyzing the single-cell transcriptomic profiles of 76,762 cells from four pure GGNs, four SNs, and four normal tissues, we found that anti-tumor immunity mediated by NK and CD8+T cells gradually weakened with the progression of LUAD and humoral immunity mediated by plasma B cells was more active in SNs. Additionally, the proliferation ability of some special epithelial cell increased during the progression process from pure GGN to SN. Furthermore, stromal cells and M2 macrophages could assist the progression of LUAD. Through comprehensive analyses, we revealed dynamic changes in cellular components and intercellular interactions during the progression of LUAD. These findings could facilitate our understanding of LUAD and discovery of novel therapeutic targets.

A machine learning-assisted model for renal urate underexcretion with genetic and clinical variables among Chinese men with gout.

OBJECTIVES: The objective of this study was to develop and validate a prediction model for renal urate underexcretion (RUE) in male gout patients. METHODS: Men with gout enrolled from multicenter cohorts in China were analyzed as the development and validation data sets. The RUE phenotype was defined as fractional excretion of uric acid (FEUA) <5.5%. Candidate genetic and clinical features were screened by the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Machine learning algorithms (stochastic gradient descent (SGD), logistic regression, support vector machine) were performed to construct a predictive classifier of RUE. Models were assessed by the area under the receiver operating characteristic curve (AUC) and the precision-recall curve (PRC). RESULTS: One thousand two hundred thirty-eight and two thousand twenty-three patients were enrolled as the development and validation cohorts, with 1220 and 754 randomly chosen patients genotyped, respectively. Rs3775948.GG of SLC2A9/GLUT9, rs504915.AA of NRXN2/URAT1, and 7 clinical features (age, hypertension, nephrolithiasis, blood glucose, serum urate, urea nitrogen, and creatinine) were generated by LASSO. Two additional SNP variants (rs2231142.GG of ABCG2 and rs11231463.GG of SLC22A9/OAT7) were selected based on their contributions to gout in the development cohort and their reported effects on renal urate handling. The optimized classifiers yielded AUCs of ~0.914 and PRCs of ~0.980 using these 11 variables. The SGD model was conducted in the validation cohort with an AUC of 0.899 and the PRC of 0.957. CONCLUSIONS: A prediction model for RUE composed of four SNPs and readily accessible clinical features was established with acceptable accuracy for men with gout.

A deep learning model for early risk prediction of heart failure with preserved ejection fraction by DNA methylation profiles combined with clinical features.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF), affected collectively by genetic and environmental factors, is the common subtype of chronic heart failure. Although the available risk assessment methods for HFpEF have achieved some progress, they were based on clinical or genetic features alone. Here, we have developed a deep learning framework, HFmeRisk, using both 5 clinical features and 25 DNA methylation loci to predict the early risk of HFpEF in the Framingham Heart Study Cohort. RESULTS: The framework incorporates Least Absolute Shrinkage and Selection Operator and Extreme Gradient Boosting-based feature selection, as well as a Factorization-Machine based neural network-based recommender system. Model discrimination and calibration were assessed using the AUC and Hosmer-Lemeshow test. HFmeRisk, including 25 CpGs and 5 clinical features, have achieved the AUC of 0.90 (95% confidence interval 0.88-0.92) and Hosmer-Lemeshow statistic was 6.17 (P = 0.632), which outperformed models with clinical characteristics or DNA methylation levels alone, published chronic heart failure risk prediction models and other benchmark machine learning models. Out of them, the DNA methylation levels of two CpGs were significantly correlated with the paired transcriptome levels (R < -0.3, P < 0.05). Besides, DNA methylation locus in HFmeRisk were associated with intercellular signaling and interaction, amino acid metabolism, transport and activation and the clinical variables were all related with the mechanism of occurrence of HFpEF. Together, these findings give new evidence into the HFmeRisk model. CONCLUSION: Our study proposes an early risk assessment framework for HFpEF integrating both clinical and epigenetic features, providing a promising path for clinical decision making.

Common Postzygotic Mutational Signatures in Healthy Adult Tissues Related to Embryonic Hypoxia.

Postzygotic mutations are acquired in normal tissues throughout an individual's lifetime and hold clues for identifying mutagenic factors. Here, we investigated postzygotic mutation spectra of healthy individuals using optimized ultra-deep exome sequencing of the time-series samples from the same volunteer as well as the samples from different individuals. In blood, sperm, and muscle cells, we resolved three common types of mutational signatures. Signatures A and B represent clock-like mutational processes, and the polymorphisms of epigenetic regulation genes influence the proportion of signature B in mutation profiles. Notably, signature C, characterized by C>T transitions at GpCpN sites, tends to be a feature of diverse normal tissues. Mutations of this type are likely to occur early during embryonic development, supported by their relatively high allelic frequencies, presence in multiple tissues, and decrease in occurrence with age. Almost none of the public datasets for tumors feature this signature, except for 19.6% of samples of clear cell renal cell carcinoma with increased activation of the hypoxia-inducible factor 1 (HIF-1) signaling pathway. Moreover, the accumulation of signature C in the mutation profile was accelerated in a human embryonic stem cell line with drug-induced activation of HIF-1α. Thus, embryonic hypoxia may explain this novel signature across multiple normal tissues. Our study suggests that hypoxic condition in an early stage of embryonic development is a crucial factor inducing C>T transitions at GpCpN sites; and individuals' genetic background may also influence their postzygotic mutation profiles.

DeteX: A highly accurate software for detecting SNV and InDel in single and paired NGS data in cancer research.

Background: Genetic testing is becoming more and more accepted in the auxiliary diagnosis and treatment of tumors. Due to the different performance of the existing bioinformatics software and the different analysis results, the needs of clinical diagnosis and treatment cannot be met. To this end, we combined Bayesian classification model (BC) and fisher exact test (FET), and develop an efficient software DeteX to detect SNV and InDel mutations. It can detect the somatic mutations in tumor-normal paired samples as well as mutations in a single sample. Methods: Combination of Bayesian classification model (BC) and fisher exact test (FET). Results: We detected SNVs and InDels in 11 TCGA glioma samples, 28 clinically targeted capture samples and 2 NCCL-EQA standard samples with DeteX, VarDict, Mutect, VarScan and GatkSNV. The results show that, among the three groups of samples, DeteX has higher sensitivity and precision whether it detects SNVs or InDels than other callers and the F1 value of DeteX is the highest. Especially in the detection of substitution and complex mutations, only DeteX can accurately detect these two kinds of mutations. In terms of single-sample mutation detection, DeteX is much more sensitive than the HaplotypeCaller program in Gatk. In addition, although DeteX has higher mutation detection capabilities, its running time is only .609 of VarDict, which is .704 and .343 longer than VarScan and MuTect, respectively. Conclusion: In this study, we developed DeteX to detect SNV and InDel mutations in single and paired samples. DeteX has high sensitivity and precision especially in the detection of substitution and complex mutations. In summary, DeteX from NGS data is a good SNV and InDel caller.

Omics big data and medical artificial intelligence.

With the rapid development of high-throughput sequencing technology and computer science, the amount of large omics data has increased exponentially, the advantages of multi-omics analysis have gradually emerged, and the application of artificial intelligence has become more and more extensive. In this review, we introduce the application progress of multi-omics data analysis and artificial intelligence in the medical field in recent years, and also show the cases and advantages of their combined application. Finally, we briefly explain the current challenges of multi-omics analysis and artificial intelligence in order to provide new research ideas for the medical industry and to promote the development and application of precision medicine.

Regulatory association of long noncoding RNAs and chromatin accessibility facilitates erythroid differentiation.

Erythroid differentiation is a dynamic process regulated by multiple factors, whereas the interaction between long noncoding RNAs (lncRNAs) and chromatin accessibility and its influence on erythroid differentiation remains unclear. To elucidate this interaction, we used hematopoietic stem cells, multipotent progenitor cells, common myeloid progenitor cells, megakaryocyte-erythroid progenitor cells, and erythroblasts from human cord blood as an erythroid differentiation model to explore the coordinated regulatory functions of lncRNAs and chromatin accessibility by integrating RNA-seq and ATAC-seq data. We revealed that the integrated network of chromatin accessibility and lncRNAs exhibits stage-specific changes throughout the erythroid differentiation process and that the changes at the erythroblast stage of maturation are dramatic. We identified a subset of stage-specific lncRNAs and transcription factors (TFs) that associate with chromatin accessibility during erythroid differentiation, in which lncRNAs are key regulators of terminal erythroid differentiation via an lncRNA-TF-gene network. LncRNA PCED1B-AS1 was revealed to regulate terminal erythroid differentiation by coordinating GATA1 dynamically binding to the chromatin and interacting with the cytoskeleton network during erythroid differentiation. DANCR, another lncRNA that is highly expressed at the megakaryocyte-erythroid progenitor cell stage, was verified to promote erythroid differentiation by compromising megakaryocyte differentiation and coordinating with chromatin accessibility and TFs, such as RUNX1. Overall, our results identify the associated network of lncRNAs and chromatin accessibility in erythropoiesis and provide novel insights into erythroid differentiation and abundant resources for further study.

Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells.

Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning-based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. We showed the diagnostic potential of the method by performing RNA-seq for 37 peripheral blood samples from breast cancer patients and by collecting RNA-seq data from healthy donors in Genotype-Tissue Expression project and microarray mRNA expression datasets in Gene Expression Omnibus. The framework was insensitive to experimental batch effects and data normalization, and it can be simultaneously applied to new sample prediction.

GliomaDB: A Web Server for Integrating Glioma Omics Data and Interactive Analysis.

Gliomas are one of the most common types of brain cancers. Numerous efforts have been devoted to studying the mechanisms of glioma genesis and identifying biomarkers for diagnosis and treatment. To help further investigations, we present a comprehensive database named GliomaDB. GliomaDB includes 21,086 samples from 4303 patients and integrates genomic, transcriptomic, epigenomic, clinical, and gene-drug association data regarding glioblastoma multiforme (GBM) and low-grade glioma (LGG) from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), the Memorial Sloan Kettering Cancer Center Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT), the US Food and Drug Administration (FDA), and PharmGKB. GliomaDB offers a user-friendly interface for two main types of functionalities. The first comprises queries of (i) somatic mutations, (ii) gene expression, (iii) microRNA (miRNA) expression, and (iv) DNA methylation. In addition, queries can be executed at the gene, region, and base level. Second, GliomaDB allows users to perform survival analysis, coexpression network visualization, multi-omics data visualization, and targeted drug recommendations based on personalized variations. GliomaDB bridges the gap between glioma genomics big data and the delivery of integrated information for end users, thus enabling both researchers and clinicians to effectively use publicly available data and empowering the progression of precision medicine in glioma. GliomaDB is freely accessible at http://bigd.big.ac.cn/gliomaDB.

The Trends in Global Gene Expression in Mouse Embryonic Stem Cells During Spaceflight.

The environment in space differs greatly from the environment on the ground. Spaceflight causes a number of physiological changes in astronauts, such as bone loss and immune system dysregulation. These effects threaten astronauts' space missions, and understanding the underlying cellular and molecular mechanisms is important to manage the risks of space missions. The biological effects of spaceflight on mammalian cells, especially with regards to DNA damage, have attracted much attention. Rad9 -/- mouse embryonic stem cells (mESCs) are known to be extremely sensitive to DNA damage agents. In this study, a project of the SJ-10 satellite programme, we investigated the gene expression profiles of both Rad9 -/- mESCs and Rad9 +/+ (wild-type) mESCs in space with a focus on genes critical for inducing, preventing, or repairing genomic DNA lesions. We found that spaceflight downregulated more genes than it upregulated in both wild-type and Rad9 -/- mESCs, indicating a suppressive effect of spaceflight on global gene expression. In contrast, Rad9 deletion upregulated more genes than it downregulated. Of note, spaceflight mainly affected organ development and influenced a wide range of cellular functions in mESCs, while Rad9 deletion mainly affected the development and function of the hematological system, especially the development, differentiation and function of immune cells. The patterns of gene expression in mouse embryonic stem cells in space is distinct from those in other types of cells. In addition, both spaceflight and Rad9 deletion downregulated DNA repair genes, suggesting a possibility that spaceflight has negative effects on genome for embryonic stem cells and the effects are likely worsen when the genome maintenance mechanism is defective.

Whole Genome Analyses of Chinese Population and De Novo Assembly of A Northern Han Genome.

To unravel the genetic mechanisms of disease and physiological traits, it requires comprehensive sequencing analysis of large sample size in Chinese populations. Here, we report the primary results of the Chinese Academy of Sciences Precision Medicine Initiative (CASPMI) project launched by the Chinese Academy of Sciences, including the de novo assembly of a northern Han reference genome (NH1.0) and whole genome analyses of 597 healthy people coming from most areas in China. Given the two existing reference genomes for Han Chinese (YH and HX1) were both from the south, we constructed NH1.0, a new reference genome from a northern individual, by combining the sequencing strategies of PacBio, 10× Genomics, and Bionano mapping. Using this integrated approach, we obtained an N50 scaffold size of 46.63 Mb for the NH1.0 genome and performed a comparative genome analysis of NH1.0 with YH and HX1. In order to generate a genomic variation map of Chinese populations, we performed the whole-genome sequencing of 597 participants and identified 24.85 million (M) single nucleotide variants (SNVs), 3.85 M small indels, and 106,382 structural variations. In the association analysis with collected phenotypes, we found that the T allele of rs1549293 in KAT8 significantly correlated with the waist circumference in northern Han males. Moreover, significant genetic diversity in MTHFR, TCN2, FADS1, and FADS2, which associate with circulating folate, vitamin B12, or lipid metabolism, was observed between northerners and southerners. Especially, for the homocysteine-increasing allele of rs1801133 (MTHFR 677T), we hypothesize that there exists a "comfort" zone for a high frequency of 677T between latitudes of 35-45 degree North. Taken together, our results provide a high-quality northern Han reference genome and novel population-specific data sets of genetic variants for use in the personalized and precision medicine.

Long noncoding RNA PCED1B-AS1 promotes erythroid differentiation coordinating with GATA1 and chromatin remodeling.

Erythropoiesis is a complex and sophisticated multi-stage process regulated by a variety of factors, including the transcription factor GATA1 and non-coding RNA. GATA1 is regarded as an essential transcriptional regulator promoting transcription of erythroid-specific genes-such as long non-coding RNAs (lncRNA). Here, we comprehensively screened lncRNAs that were potentially regulated by GATA1 in erythroid cells. We identified a novel lncRNA-PCED1B-AS1-and verified its role in promoting erythroid differentiation of K562 erythroid cells. We also predicted a model in which PCED1B-AS1 participates in erythroid differentiation via dynamic chromatin remodeling involving GATA1. The relationship between lncRNA and chromatin in the process of erythroid differentiation remains to be revealed, and in our study we have carried out preliminary explorations.

MicroRNA 200a inhibits erythroid differentiation by targeting PDCD4 and THRB.

Previous studies on erythropoiesis revealed that microRNAs (miRNAs) play a critical role in erythroid differentiation. Given the abundance of identified miRNAs and the limited understanding of erythroid miRNAs, additional examination is required. Here, two sets of erythroid differentiation miRNome data were analysed to screen for novel erythroid-inhibiting miRNAs. MIR200A was selected based on its pattern of downregulated expression in the miRNome datasets during induction of erythroid differentiation. Overexpression of MIR200A in K562 and TF-1 cells confirmed its inhibitory role in erythroid differentiation. Further in vivo study indicated that overexpression of mir200a inhibited primitive erythropoiesis of zebrafish. Transcriptome analyses after MIR200A overexpression in TF-1 cells indicated a significant role in regulating erythroid function and revealed potential regulation networks. Additionally, bioinformatics and experimental analyses confirmed that PDCD4 (programmed cell death 4) and THRB (thyroid hormone receptor, beta) are both targets of MIR200A-3p. Gain- and loss-of-function studies of PDCD4 and THRB revealed that the two targets were capable of promoting erythroid gene expression. Overall, our results revealed that microRNA 200a inhibits erythroid differentiation by targeting PDCD4 and THRB.

Biological Databases for Hematology Research.

With the advances of genome-wide sequencing technologies and bioinformatics approaches, a large number of datasets of normal and malignant erythropoiesis have been generated and made public to researchers around the world. Collection and integration of these datasets greatly facilitate basic research and clinical diagnosis and treatment of blood disorders. Here we provide a brief introduction of the most popular omics data resources of normal and malignant hematopoiesis, including some integrated web tools, to help users get better equipped to perform common analyses. We hope this review will promote the awareness and facilitate the usage of public database resources in the hematology research.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model.

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R(2) > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20-80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications.