Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecular changes associated with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially resolved view of PF and establish methods that could be applied to other spatial transcriptomic studies.

The dopamine D3 receptor is a prime target for developing treatments for cocaine use disorders (CUDs). In this study, we conducted a pre-clinical investigation of the therapeutic potential of a long-acting, D3 receptor partial agonist, MC-25-41. Male rats were pre-treated with MC-25-41 (vehicle, 1.0, 3.0, 5.6, or 10 mg/kg, intraperitoneal (IP)) five minutes prior to tests of cocaine or sucrose intake on either a progressive ratio schedule of reinforcement or a variable interval 60 s multiple schedule consisting of 4, 15-min components with sucrose or cocaine available in alternating components. A separate cohort of rats was tested on a within-session, dose-reduction procedure to determine the effects of MC-25-41 on demand for cocaine using a behavioral economics analysis. Finally, rats were tested for effects of MC-25-41 on spontaneous and cocaine-induced locomotion. MC-25-41 failed to alter locomotion, but reduced reinforcement rates for both cocaine and sucrose on the low-effort, multiple schedule. However, on the higher-effort, progressive ratio schedule of cocaine reinforcement, MC-25-41 reduced infusions, and active lever presses at doses that did not alter sucrose intake. The behavioral economics analysis showed that MC-25-41 also increased cocaine demand elasticity compared to vehicle, indicating a reduction in consumption as price increases. Together, these results suggest that similar to other D3-selective antagonists and partial agonists, MC-25-41 reduces motivation for cocaine under conditions of high cost but has the added advantage of a long half-life (>10 h). These findings suggest that MC-25-41 may be a suitable pre-clinical lead compound for development of medications to treat CUDs.

The RNA-binding protein HuD (a.k.a., ELAVL4) is involved in neuronal development and synaptic plasticity mechanisms, including addiction-related processes such as cocaine conditioned-place preference (CPP) and food reward. The most studied function of this protein is mRNA stabilization; however, we have recently shown that HuD also regulates the levels of circular RNAs (circRNAs) in neurons. To examine the role of HuD in the control of coding and non-coding RNA networks associated with substance use, we identified sets of differentially expressed mRNAs, circRNAs and miRNAs in the striatum of HuD knockout (KO) mice. Our findings indicate that significantly downregulated mRNAs are enriched in biological pathways related to cell morphology and behavior. Furthermore, deletion of HuD altered the levels of 15 miRNAs associated with drug seeking. Using these sets of data, we predicted that a large number of upregulated miRNAs form competing endogenous RNA (ceRNA) networks with circRNAs and mRNAs associated with the neuronal development and synaptic plasticity proteins LSAMP and MARK3. Additionally, several downregulated miRNAs form ceRNA networks with mRNAs and circRNAs from MEF2D, PIK3R3, PTRPM and other neuronal proteins. Together, our results indicate that HuD regulates ceRNA networks controlling the levels of mRNAs associated with neuronal differentiation and synaptic physiology.

Mapping the spatial and temporal transcriptional relationships during lung development is fundamental to understanding regeneration and chronic lung disease; however, the classification of samples as control or disease is especially challenging in the setting of preterm birth (itself a lung injury). Here, we report the largest neonatal lung transcriptomic atlas to date and an analysis framework based only on gestational age and lifespan, providing a new resource for hypothesis generation to the lung community.

Image-based spatial transcriptomics (IST) technologies provide unprecedented resolution of gene expression in tissue sections, but suffer from contamination of cells’ gene expression profiles due to imperfect cell segmentation. We present DenoIST (Denoising Image-based Spatial Transcriptomics data), a new computational tool that accurately identifies and removes contaminating transcripts from IST datasets. DenoIST models the observed transcript counts using a Poisson mixture model that explicitly accounts for local neighbourhood contamination. Applied to multiple real IST datasets of varying cell densities, DenoIST restores gene expression specificity and clarifies local biological structures by identifying and filtering transcripts spilt over from neighbouring cells. The denoised data enable more consistent and interpretable cell type annotation by minimising conflicting gene expression profiles, reducing the prevalence of hybrid or ambiguous cell types, and enhancing the contrast between distinct functional compartments. Overall, we demonstrate that DenoIST can be integrated to existing IST analysis workflows to improve biological interpretability and robustness of IST data.

Image-based spatial transcriptomics deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in image-based spatial transcriptomics, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. Availability and implementation: The SpatialRNA package is freely accessible from GitLab https://gitlab.svi.edu.au/biocellgen-public/spatialrna and can be installed via pip. Python notebooks and scripts used in case studies are freely accessible at https://gitlab.svi.edu.au/biocellgen-public/case_study_ipf and https://gitlab.svi.edu.au/biocellgen-public/case_study_xenium_5k_panel .