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.

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.

Publicly-accessible resources have promoted the advance of scientific discovery. The era of genomics and big data has brought the need for collaboration and data sharing in order to make effective use of this new knowledge. Here, we describe the web resources for cancer genomics research and rate them on the basis of the diversity of cancer types, sample size, omics data comprehensiveness, and user experience. The resources reviewed include data repository and analysis tools; and we hope such introduction will promote the awareness and facilitate the usage of these resources in the cancer research community.

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.

Background: Mapping of DNase I hypersensitive sites (DHSs) is a powerful tool to experimentally identify cis-regulatory elements (CREs). Among CREs, enhancers are abundant and predominantly act in driving cell-specific gene expression. Krüppel-like factors (KLFs) are a family of eukaryotic transcription factors. Several KLFs have been demonstrated to play important roles in hematopoiesis. However, transcriptional regulation of KLFs via CREs, particularly enhancers, in erythroid cells has been poorly understood. Results: In this study, 23 erythroid-specific or putative erythroid-specific DHSs were identified by DNase-seq in the genomic regions of 17 human KLFs, and their enhancer activities were evaluated using dual-luciferase reporter (DLR) assay. Of the 23 erythroid-specific DHSs, the enhancer activities of 15 DHSs were comparable to that of the classical enhancer HS2 in driving minimal promoter (minP). Fifteen DHSs, some overlapping those that increased minP activities, acted as enhancers when driving the corresponding KLF promoters (KLF-Ps) in erythroid cells; of these, 10 DHSs were finally characterized as erythroid-specific KLF enhancers. These 10 erythroid-specific KLF enhancers were further confirmed using chromatin immunoprecipitation coupled to sequencing (ChIP-seq) data-based bioinformatic and biochemical analyses. Conclusion: Our present findings provide a feasible strategy to extensively identify gene- and cell-specific enhancers from DHSs obtained by high-throughput sequencing, which will help reveal the transcriptional regulation and biological functions of genes in some specific cells.

Non-sentinel lymph node (NSLN) status prediction with molecular biomarkers may make some sentinel lymph node (SLN) positive breast cancer patients avoid the axillary lymph node dissection, but the available markers remain limited. SLN positive patients with and without NSLN invasion were selected, and genes differentially expressed or fused in SLN metastasis were screened by next-generation RNA sequencing. Six candidates (all ER/PR+, HER2−, Ki-67 <20 %) with metastatic SLNs selected from 305 patients were equally categorized as NSLN negative and positive. We identified 103 specifically expressed genes in the NSLN negative group and 47 in the NSLN positive group. Among them, FABP1 (negative group) and CYP2A13 (positive group) were the only 2 protein-encoding genes with expression levels in the 8th to 10th deciles. Using a false discovery rate threshold of <0.05, 62 up-regulated genes and 98 down-regulated genes were discovered in the NSLN positive group. Furthermore, 10 gene fusions were identified in this group with the most frequently fused gene being IGLL5. The biomarkers screened in present study may broaden our understanding of the mechanisms of breast cancer metastasis to the lymph nodes and contribute to the axillary surgery selection for SLN positive patients.