Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues.

Spatial transcriptomics has emerged as a powerful tool for dissecting spatial cellular heterogeneity but as of today is largely limited to gene expression analysis. Yet, the life of RNA molecules is multifaceted and dynamic, requiring spatial profiling of different RNA species throughout the life cycle to delve into the intricate RNA biology in complex tissues. Human disease-relevant tissues are commonly preserved as formalin-fixed and paraffin-embedded (FFPE) blocks, representing an important resource for human tissue specimens. The capability to spatially explore RNA biology in FFPE tissues holds transformative potential for human biology research and clinical histopathology. Here, we present Patho-DBiT combining in situ polyadenylation and deterministic barcoding for spatial full coverage transcriptome sequencing, tailored for probing the diverse landscape of RNA species even in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for five years. Furthermore, genome-wide single nucleotide RNA variants can be captured to distinguish different malignant clones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA-mRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis trajectory. High resolution Patho-DBiT at the cellular level reveals a spatial neighborhood and traces the spatiotemporal kinetics driving tumor progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to study human tissue biology and aid in clinical pathology evaluation.

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies.

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Ultrafast laser-induced plasma anisotropy in pristine and surface pre-structured zinc telluride, probed by terahertz pulses.

We use THz probe pulses to detect and analyze the dynamics of charge transport anisotropies generated by ultrafast laser two-photon absorption in Zinc Telluride (ZnTe) semi-insulating crystal showing smooth and laser structured surfaces. The detected anisotropy consists in a modulation of the THz transmission as a function of the orientation of the <001 > axis of ZnTe. The change in THz transmission after pump excitation is attributed to free carrier absorption of the THz field in the laser-induced electron-hole plasma. Pre-structuring the surface sample with laser-induced periodic surface structures (ripples) has strong influence on free carrier THz transmission and its associated anisotropic oscillation. Within the relaxation dynamics of the laser-induced free carriers, two relaxation times have to be considered in order to correctly describe the dynamics, a fast relaxation, of about 50 picoseconds in pristine sample (90 picoseconds in sample pre-structured with ripples), and a slow one, of about 1.5 nanoseconds. A theoretical model based on classical Drude theory and on the dependence of the two-photon absorption coefficient with the crystal orientation and with the laser polarization is used to fit the experimental results.

Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA.

Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.

Adolescence is a sensitive period for acrylamide-induced sex hormone disruption: Evidence from NHANES populations and experimental mice.

Acrylamide (AA) is widely contaminated in environment and diet. However, the association of AA and sex hormones has rarely been investigated, especially in adolescents, a period of particular susceptibility to sex hormone disruption. In this study, survey-weighted multivariate linear regression models were conducted to determine the association between AA Hb biomarkers [HbAA and glycidamide (HbGA)] and sex hormones [total testosterone (TT) and estradiol (E2)] in a total of 3268 subjects from National Health and Nutrition Examination Survey (NHANES) 2013-2016 waves. Additionally, adult and pubertal mice were treated with AA to assess the effect of AA on sex hormones and to explore the potential mechanisms. Among all the subjects, significant negative patterns for HbGA and sex hormones were identified only in youths (6-19 years old), with the lowest ß being - 0.53 (95% CI: -0.80 to -0.26) for TT in males and - 0.58 (95% CI: -0.93 to -0.23) for E2 in females. Stratified analysis further revealed significant negative associations between HbGA and sex hormones in adolescents, with the lowest ß being - 0.58 (95% CI: -1.02 to -0.14) for TT in males and - 0.54 (95% CI: -1.03 to -0.04) for E2 in females, while there were no significant differences between children or late adolescents. In mice, the levels of TT and E2 were dramatically reduced in AA-treated pubertal mice but not in adult mice. AA disturbed the expression of genes in the hypothalamic-pituitary-gonadal (HPG) axis, induced apoptosis of hypothalamus-produced gonadotropin-releasing hormone (GnRH) neurons in the hypothalamus and reduced serum and hypothalamic GnRH levels in pubertal mice. Our study indicates AA could reduce TT and E2 levels by injuring GnRH neurons and disrupting the HPG axis in puberty, which manifested as severe endocrine disruption on adolescents. Our findings reinforce the idea that adolescence is a vulnerable stage in AA-induced sex hormone disruption.

Image response regression via deep neural networks.

Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex association patterns. We establish estimation and selection consistency and derive asymptotic error bounds. We demonstrate the method's advantages through intensive simulations and analyses of two functional magnetic resonance imaging data sets.

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States.

Background: During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns. Methods: COVID-19-related tweets from March 5th, 2020, to January 31st, 2021, were collected through Twitter streaming API using keywords (i.e., "corona," "covid19," and "covid"). By further filtering using keywords (i.e., "depress," "failure," and "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic. Results: We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home," "death poll," and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns. Conclusions: The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males and White) were more likely to have mental health concerns during the COVID-19 pandemic.

Thiamine functions as a key activator for modulating plant health and broad-spectrum tolerance in cotton.

Global climate changes cause an increase of abiotic and biotic stresses that tremendously threaten the world's crop security. However, studies on broad-spectrum response pathways involved in biotic and abiotic stresses are relatively rare. Here, by comparing the time-dependent transcriptional changes and co-expression analysis of cotton (Gossypium hirsutum) root tissues under abiotic and biotic stress conditions, we discovered the common stress-responsive genes and stress metabolism pathways under different stresses, which included the circadian rhythm, thiamine and galactose metabolism, carotenoid, phenylpropanoid, flavonoid, and zeatin biosynthesis, and the mitogen-activated protein kinase signaling pathway. We found that thiamine metabolism was an important intersection between abiotic and biotic stresses; the key thiamine synthesis genes, GhTHIC and GhTHI1, were highly induced at the early stage of stresses. We confirmed that thiamine was crucial and necessary for cotton growth and development, and its deficiency could be recovered by exogenous thiamine supplement. Furthermore, we revealed that exogenous thiamine enhanced stress tolerance in cotton via increasing calcium signal transduction and activating downstream stress-responsive genes. Overall, our studies demonstrated that thiamine played a crucial role in the tradeoff between plant health and stress resistance. The thiamine deficiency caused by stresses could transiently induce upregulation of thiamine biosynthetic genes in vivo, while it could be totally salvaged by exogenous thiamine application, which could significantly improve cotton broad-spectrum stress tolerance and enhance plant growth and development.

Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci.

Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.

On cross-ancestry cancer polygenic risk scores.

Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.

Fast and robust ancestry prediction using principal component analysis.

MOTIVATION: Population stratification (PS) is a major confounder in genome-wide association studies (GWAS) and can lead to false-positive associations. To adjust for PS, principal component analysis (PCA)-based ancestry prediction has been widely used. Simple projection (SP) based on principal component loadings and the recently developed data augmentation, decomposition and Procrustes (ADP) transformation, such as LASER and TRACE, are popular methods for predicting PC scores. However, the predicted PC scores from SP can be biased toward NULL. On the other hand, ADP has a high computation cost because it requires running PCA separately for each study sample on the augmented dataset. RESULTS: We develop and propose two alternative approaches: bias-adjusted projection (AP) and online ADP (OADP). Using random matrix theory, AP asymptotically estimates and adjusts for the bias of SP. OADP uses a computationally efficient online singular value decomposition algorithm, which can greatly reduce the computation cost of ADP. We carried out extensive simulation studies to show that these alternative approaches are unbiased and the computation speed can be 16-16 000 times faster than ADP. We applied our approaches to the UK Biobank data of 488 366 study samples with 2492 samples from the 1000 Genomes data as the reference. AP and OADP required 0.82 and 21 CPU hours, respectively, while the projected computation time of ADP was 1628 CPU hours. Furthermore, when inferring sub-European ancestry, SP clearly showed bias, unlike the proposed approaches. AVAILABILITY AND IMPLEMENTATION: The OADP and AP methods, as well as SP and ADP, have been implemented in the open-source Python software FRAPOSA, available at github.com/daviddaiweizhang/fraposa. CONTACT: leeshawn@umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phenolic group on A-ring is key for dracoflavan B as a selective noncompetitive inhibitor of α-amylase.

A high throughput assay was applied to guide the isolation of a new pancreatic α-amylase inhibitor, dracoflavan B, from the dragon's blood resin from Daemonorops draco. Applying C18 column, we successfully isolated both diastereomers and their structures verified by (1)H NMR spectra in comparison with the literature values. Their activity in inhibition of pancreatic α-amylase with comparable IC50 values of 23µM (A type) and 27µM (B type) that are similar to that of acarbose. Dracoflavan B shows much weaker activity in inhibiting bacterial α-amylase and no activity towards fungal α-amylase. Moreover, both isomers show no inhibitory activity towards mammalian α-glucosidase. Kinetic analysis revealed that using starch as the substrate, dracoflavan B was a non-competitive α-amylase inhibitor with a Ki value of 11.7µM. Lack of α-amylase inhibition for proanthocyanidin A2 dimer demonstrated that dracoflavan B hydrophobic nature of the B, A', C' and B' rings are important for its α-amylase inhibition. In addition, selective chemical modification studies revealed that the phenolic group is also vital to dracoflavan B for its pancreatic α-amylase inhibition activity. Without the A ring phenolic hydrogen bond donor, the derivatives of dracoflavan B showed detrimental α-amylase inhibition. On the contrary, galloylation on the A ring phenolic OH group enhanced the activity as shown by the low IC50 (12µM) against α-amylase which is 56% more potent as compared to dracoflavan B.

Effects of silencing cyclooxygenase-2 expression via RNA interference on the tumorigenicity of the SMMC-7721 human hepatocarcinoma cell line.

We constructed a vector carrying a shRNA sequence against cyclooxygenase-2 (COX-2) that was subsequently transfected into the human hepatocarcinoma cell line SMMC­7721. Furthermore, we established a COX-2-deficient stable cell line and a model of tumor-shRNA transplantation in nude mice. Negative shRNA was used as the control. The tumor volume in the experimental group was smaller compared to that in the control group. Hematoxylin and eosin staining indicated that the cells in the experimental group differentiated better than those in the control group. The COX-2 mRNA level in the tumor tissues injected with SMMC-7721/COX-2i was markedly downregulated compared to that in the tumor tissues injected with SMMC-7721/negative shRNA. The inhibition rate reached 68.6%. Immunohistological study showed a significantly strong COX-2 expression in the control group tumor cells, whereas the experimental group exhibited moderate expression, indicating the inhibition of COX-2 expression after transfection of cells with shRNA against COX-2. Western blot analysis further proved the inhibition of COX-2 expression. In conclusion, RNAi-mediated regulation of COX-2 expression could efficiently inhibit liver-transplanted tumor growth in BALB/c nude mice.

Further evaluation of the characteristics of Treponema pallidum-specific IgM antibody in syphilis serofast reaction patients.

Syphilis serofast reaction (SSR) is common in clinical work. From June 2005 to May 2009, 1208 syphilis patients were chosen for research by the Xiamen Center of Clinical Laboratory in China. Serologic tests were performed with toluidine red unheated serum test (TRUST) and Treponema pallidum particle agglutination (TPPA). Then, T. pallidum-specific IgM antibody (TP-IgM) was detected with fluorescent treponemal antibody absorption (FTA-Abs) and TPPA. In this study, patients were divided into the following experimental groups according to the results of TRUST and TPPA: (1) the SSR group consisted of 411 cases with (+) TRUST and (+) TPPA, and without clinical manifestations after 1 year of recommended syphilis treatment; (2) the serum cure group, which was further subdivided into group A consisting of 251cases with (-) TRUST and (+) TPPA; (3) group B consisting of 546 cases with (-) TRUST and (-) TPPA; and (4) the blood donor control group which consisted of 100 cases. We demonstrated that a total of 136 cases (33.09%) of 411 SSR patients were TP-IgM positive by TPPA, and this percentage was markedly higher than that in serum cure group A (9.16%). FTA-Abs analyses revealed similar results. All samples in serum cure group B and the control group were TP-IgM negative, which is identical to our previous report. The present study also indicated that the TP-IgM positive rate was not significantly different among patients with different ages, genders, and clinical phases after 1 year of recommended therapy. From the total of 1208 syphilis patients, 289 were randomly selected for TP-DNA detection by fluorescence quantitative polymerase chain reaction, and the positive rate of TP-DNA was 32.53%, which was slightly higher than that of FTA-Abs TP-IgM, and no statistically significant difference by chi-square tests, indicating the TP-DNA result is preferably consistent with FTA-Abs and supporting our deduction that TP-IgM could be used as a serologic marker for the relapse and infection of syphilis.