Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues.

The capability to spatially explore RNA biology in formalin-fixed paraffin-embedded (FFPE) tissues holds transformative potential for histopathology research. Here, we present pathology-compatible deterministic barcoding in tissue (Patho-DBiT) by combining in situ polyadenylation and computational innovation for spatial whole transcriptome sequencing, tailored to probe the diverse RNA species 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 5 years. Furthermore, genome-wide single-nucleotide RNA variants can be captured to distinguish malignant subclones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis. Single-cell level Patho-DBiT dissects the spatiotemporal cellular dynamics driving tumor clonal architecture and progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to aid in clinical pathology evaluation.

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.

Density regression and uncertainty quantification with Bayesian deep noise neural networks.

Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of applications. However, it remains a challenging task to accurately quantify the uncertainty in DNN predictions, especially those of continuous outcomes. To this end, we propose the Bayesian deep noise neural network (B-DeepNoise), which generalizes standard Bayesian DNNs by extending the random noise variable from the output layer to all hidden layers. Our model is capable of approximating highly complex predictive density functions and fully learn the possible random variation in the outcome variables. For posterior computation, we provide a closed-form Gibbs sampling algorithm that circumvents tuning-intensive Metropolis-Hastings methods. We establish a recursive representation of the predictive density and perform theoretical analysis on the predictive variance. Through extensive experiments, we demonstrate the superiority of B-DeepNoise over existing methods in terms of density estimation and uncertainty quantification accuracy. A neuroimaging application is included to show our model's usefulness in scientific studies.

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.