Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope.

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.

Passive Soil Arching Effect in Aeolian Sand Backfills for Grillage Foundation.

The passive soil arching effect exists in many soil-grille interaction systems. Increasing mental grillage foundations are used for transmission lines in aeolian sand areas; thus, exploring the evolution mechanism of passive soil arching is crucial. This study investigates the evolution and influencing factors of passive soil arching through a series of tests using a trapdoor device and particle image velocimetry (PIV). The test results show that the evolution of the arching structure causes the aeolian sand deformation to gradually extend to the backfill surface and stationary zone, generating two triangular arching surfaces between the movable beams and sliding surface at the junction of the active and stationary zones. Cracks in the arching and sliding surfaces were connected to form a W-shaped shear band. The development of the soil pressure was divided into four arching structure stages. The different stages of the inner and outer arches of the bearing characteristics had strong differences. Taking the appearance of the first arch surface as the time point, the soil pressure changes abruptly and the inner and outer arches alternate to bear the as a major role. The beam spacing significantly affected the arching evolution. A smaller beam spacing formed an initial bending configuration with an inconspicuous arching structure and incomplete shear band. As the beam spacing increased, the arching shape changed from triangular to parabolic, sudden changes in the soil pressure were more pronounced, and the arch height increased. The relative density and water content had little impact on the arch shape and shear zone but significantly affected the arching strength, soil pressure transfer, and arching height. The medium and high relative densities and low water contents resulted in a stronger arching structure and greater arching height, while low relative densities and high water contents weakened the soil pressure transfer. The range values for the optimum beam spacing, relative density, and water contents are given based on the variation characteristics of the evaluated parameters (E, n) under different conditions.

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.

Applicatoin of chitosan-based hydrogel in oral tissue engineering. / å£³èšç³–åŸºæ°´å‡èƒ¶åœ¨å£è ”ç»„ç»‡å·¥ç¨‹ä¸­çš„åº”ç”¨.

Pulpitis, periodontitis, jaw bone defect, and temporomandibular joint damage are common oral and maxillofacial diseases in clinic, but traditional treatments are unable to restore the structure and function of the injured tissues. Due to their good biocompatibility, biodegradability, antioxidant effect, anti-inflammatory activity, and broad-spectrum antimicrobial property, chitosan-based hydrogels have shown broad applicable prospects in the field of oral tissue engineering. Quaternization, carboxymethylation, and sulfonation are common chemical modification strategies to improve the physicochemical properties and biological functions of chitosan-based hydrogels, while the construction of hydrogel composite systems via carrying porous microspheres or nanoparticles can achieve local sequential delivery of diverse drugs or bioactive factors, laying a solid foundation for the well-organized regeneration of defective tissues. Chemical cross-linking is commonly employed to fabricate irreversible permanent chitosan gels, and physical cross-linking enables the formation of reversible gel networks. Representing suitable scaffold biomaterials, several chitosan-based hydrogels transplanted with stem cells, growth factors or exosomes have been used in an attempt to regenerate oral soft and hard tissues. Currently, remarkable advances have been made in promoting the regeneration of pulp-dentin complex, cementum-periodontium-alveolar bone complex, jaw bone, and cartilage. However, the clinical translation of chitosan-based hydrogels still encounters multiple challenges. In future, more in vivo clinical exploration under the conditions of oral complex microenvironments should be performed, and the combined application of chitosan-based hydrogels and a variety of bioactive factors, biomaterials, and state-of-the-art biotechnologies can be pursued in order to realize multifaceted complete regeneration of oral tissue.

PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations.

MOTIVATION: The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet. RESULTS: To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation-maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/YangLabHKUST/PALM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Leveraging the local genetic structure for trans-ancestry association mapping.

Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.

Total variation and spatial iteration-based 3D structured illumination microscopy.

Three-dimensional structured illumination microscopy (3D-SIM) plays an essential role in biological volumetric imaging with the capabilities of improving lateral and axial resolution. However, the traditional linear 3D algorithm is sensitive to noise and generates artifacts, while the low temporal resolution hinders live-cell imaging. In this paper, we propose a novel 3D-SIM algorithm based on total variation (TV) and fast iterative shrinkage threshold algorithm (FISTA), termed TV-FISTA-SIM. Compared to conventional algorithms, TV-FISTA-SIM achieves higher reconstruction fidelity with the least artifacts, even when the signal-to-noise ratio (SNR) is as low as 5 dB, and a faster reconstruction rate. Through simulation, we have verified that TV-FISTA-SIM can effectively reduce the amount of required data with less deterioration. Moreover, we demonstrate TV-FISTA-SIM for high-quality multi-color 3D super-resolution imaging, which can be potentially applied to live-cell imaging applications.

XPXP: improving polygenic prediction by cross-population and cross-phenotype analysis.

MOTIVATION: As increasing sample sizes from genome-wide association studies (GWASs), polygenic risk scores (PRSs) have shown great potential in personalized medicine with disease risk prediction, prevention and treatment. However, the PRS constructed using European samples becomes less accurate when it is applied to individuals from non-European populations. It is an urgent task to improve the accuracy of PRSs in under-represented populations, such as African populations and East Asian populations. RESULTS: In this article, we propose a cross-population and cross-phenotype (XPXP) method for construction of PRSs in under-represented populations. XPXP can construct accurate PRSs by leveraging biobank-scale datasets in European populations and multiple GWASs of genetically correlated phenotypes. XPXP also allows to incorporate population-specific and phenotype-specific effects, and thus further improves the accuracy of PRS. Through comprehensive simulation studies and real data analysis, we demonstrated that our XPXP outperformed existing PRS approaches. We showed that the height PRSs constructed by XPXP achieved 9% and 18% improvement over the runner-up method in terms of predicted R2 in East Asian and African populations, respectively. We also showed that XPXP substantially improved the stratification ability in identifying individuals at high genetic risk of type 2 diabetes. AVAILABILITY AND IMPLEMENTATION: The XPXP software and all analysis code are available at github.com/YangLabHKUST/XPXP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association between ADRB2 regulatory region polymorphisms and susceptibility to childhood asthma. / ADRB2åŸºå› è°ƒæŽ§åŒºå¤šæ€æ€§ä¸Žå„¿ç«¥å“®å–˜æ˜“æ„Ÿæ€§çš„ç›¸å ³æ€§ç ”ç©¶.

OBJECTIVES: To study the association of ß2-drenergic receptor (ADRB2) regulatory region single nucleotides polymorphism (SNP)/haplotypes at rs11168070, rs17108803, rs2053044, rs12654778, rs11959427, and rs2895795 loci with childhood asthma. METHODS: A total of 143 children with asthma who attended the hospital from October 2016 to October 2020 were enrolled as the asthma group, among whom 61 children had mild symptoms (mild group) and 82 children had moderate-to-severe symptoms (moderate-to-severe group). A total of 137 healthy children were enrolled as the control group. Peripheral venous blood samples were collected from the two groups. The SNaPshot SNP technique was used to analyze the SNP and haplotypes of the ADRB2 regulatory region at rs11168070, rs17108803, rs2053044, rs12654778, rs11959427, and rs2895795 loci in all children. The asthma group and the control group were compared in terms of the association of ADRB2 regulatory region SNP and haplotypes at the above six loci with susceptibility to asthma and severity of asthma. RESULTS: Polymorphisms were observed in the ADRB2 regulation region at the above six loci in both the asthma group and the control group, with significant differences between the two groups in the distribution of genotype and allele frequencies at rs2895795 (-1429T /A), rs2053044(-1023G/A), and rs12654778 (-654G/A) loci (P<0.05). Linkage disequilibrium of SNP was observed at the six loci of the ADRB2 regulatory region.The haplotypes of TATGCT, TATGGC, and AGTGCT were associated with susceptibility to childhood asthma, among which TATGCT and TATGGC were risk factors for childhood asthma (OR=1.792 and 1.946 respectively, P<0.05), while AGTGCT was a protective factor (OR=0.523, P<0.05). CONCLUSIONS: SNP/haplotype of the ADRB2 regulatory region is associated with the susceptibility to childhood asthma. The haplotypes of TATGCT and TATGGC formed by such SNP/haplotype are risk factors for childhood asthma, while AGTGCT is a protective factor.

3D super-resolution microscopy based on nonlinear gradient descent structured illumination.

Three-dimensional structured illumination microscopy (3D-SIM) is an essential tool for volumetric fluorescence imaging, which improves both axial and lateral resolution by down-modulating high-frequency information of the sample into the passband of optical transfer function (OTF). And when combining with the 4Pi structure, the performance of 3D-SIM can be further improved. The reconstruction results of generally used linear 3D algorithm, however, are lack of high-fidelity and proneess to generate artifacts. In this paper, we proposed a novel iterative algorithm based on gradient descent combined with a nonlinear optimizer, which can be applied to all 3D-SIM setups (including I5S setup). We verified through simulation that the proposed solution, termed as nonlinear gradient descent structured illumination microscopy (NGD-SIM), achieves more fidelity results which can reach the limitation of theoretical resolution improvement of SIM. Moreover, it can be firmly validated on simulation that this algorithm can effectively reduce the amount of raw data in the case of sinusoidal-pattern illumination, i.e., the algorithm doesn't need five-step phase shifting; data with any number of phases can theoretically be reconstructed. Our method also provides the possibility to extend the application of sinusoidal-pattern illumination to any kind of interference fringe, which is generated by diversified types of illumination mode.

A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits.

The development of polygenic risk scores (PRSs) has proved useful to stratify the general European population into different risk groups. However, PRSs are less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ancestry genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. Our framework can also incorporate population-specific effects to further improve construction of PRS. With innovations in data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient, and robust PRS construction across a range of genetic architectures. In a Chinese cohort, our methods achieved 7.3%-198.0% accuracy gain for height and 19.5%-313.3% accuracy gain for body mass index (BMI) in terms of predictive R2 compared to existing PRS approaches. We also show that XPA and XPASS can achieve substantial improvement for construction of height PRSs in the African population, suggesting the generality of our framework across global populations.

IGREX for quantifying the impact of genetically regulated expression on phenotypes.

By leveraging existing GWAS and eQTL resources, transcriptome-wide association studies (TWAS) have achieved many successes in identifying trait-associations of genetically regulated expression (GREX) levels. TWAS analysis relies on the shared GREX variation across GWAS and the reference eQTL data, which depends on the cellular conditions of the eQTL data. Considering the increasing availability of eQTL data from different conditions and the often unknown trait-relevant cell/tissue-types, we propose a method and tool, IGREX, for precisely quantifying the proportion of phenotypic variation attributed to the GREX component. IGREX takes as input a reference eQTL panel and individual-level or summary-level GWAS data. Using eQTL data of 48 tissue types from the GTEx project as a reference panel, we evaluated the tissue-specific IGREX impact on a wide spectrum of phenotypes. We observed strong GREX effects on immune-related protein biomarkers. By incorporating trans-eQTLs and analyzing genetically regulated alternative splicing events, we evaluated new potential directions for TWAS analysis.

LSMM: a statistical approach to integrating functional annotations with genome-wide association studies.

Motivation: Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still two major challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, accumulating evidence from GWAS suggests the polygenicity of complex traits, i.e. a complex trait is often affected by many variants with small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. Results: The availability of functional annotation data enables us to address the above challenges. In this study, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWAS of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. Availability and implementation: The LSMM software is available at https://github.com/mingjingsi/LSMM. Supplementary information: Supplementary data are available at Bioinformatics online.

Spectroscopic characterization of DOM and the nitrogen removal mechanism during wastewater reclamation plant.

The performance of the Sha-he wastewater reclamation plant was evaluated in this study. To remove residual nitrogen after Anaerobic-Anoxic-Oxic (A2O) treatment, three multistage Anoxic-Oxic (A/O) were added to investigate the nitrogen removal efficiency and its mechanism. In addition, the constituents and evolution of dissolved organic matter (DOM) during wastewater reclamation was also investigated using a method combining fluorescence spectroscopy with fluorescence regional integration (FRI). The results suggested that multistage A/O treatment can effectively improve the nitrogen removal ability under low concentrations of carbon sources. The total nitrogen (TN) exhibits significantly positive correlation with fulvic acid-like materials and humic acid-like materials. The correlation coefficient for TN and fulvic acid-like substances (R2 = 0.810, P < 0.01) removal was greater than that of humic acid-like substances (R2 = 0.636, P < 0.05). The results indicate that nitrogen removal may be achieved with the fulvic-like and humic-like substances, and the removal effects were higher by fulvic acid-like substances than humic-like substances, mostly due to that the latter were relatively more difficult to be utilized as carbon source during the nitrogen removal process. The effluent water quality of biological treatment reached the first grade A standard of "Cities sewage treatment plant pollutant discharge standard" (GB18918-2002). In addition, the effluent from the membrane bioreactor reached the "Standards of reclaimed water quality" (SL368-2006).

IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies.

MOTIVATION: Results from genome-wide association studies (GWAS) suggest that a complex phenotype is often affected by many variants with small effects, known as 'polygenicity'. Tens of thousands of samples are often required to ensure statistical power of identifying these variants with small effects. However, it is often the case that a research group can only get approval for the access to individual-level genotype data with a limited sample size (e.g. a few hundreds or thousands). Meanwhile, summary statistics generated using single-variant-based analysis are becoming publicly available. The sample sizes associated with the summary statistics datasets are usually quite large. How to make the most efficient use of existing abundant data resources largely remains an open question. RESULTS: In this study, we propose a statistical approach, IGESS, to increasing statistical power of identifying risk variants and improving accuracy of risk prediction by i ntegrating individual level ge notype data and s ummary s tatistics. An efficient algorithm based on variational inference is developed to handle the genome-wide analysis. Through comprehensive simulation studies, we demonstrated the advantages of IGESS over the methods which take either individual-level data or summary statistics data as input. We applied IGESS to perform integrative analysis of Crohns Disease from WTCCC and summary statistics from other studies. IGESS was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.2% ( ±0.4% ) to 69.4% ( ±0.1% ) using about 240 000 variants. AVAILABILITY AND IMPLEMENTATION: The IGESS software is available at https://github.com/daviddaigithub/IGESS . CONTACT: zbxu@xjtu.edu.cn or xwan@comp.hkbu.edu.hk or eeyang@hkbu.edu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.