GAADE: identification spatially variable genes based on adaptive graph attention network.

Polychlorinated biphenyls (PCBs) are highly stable synthetic organic compounds that are present in air, water, and soil. PCBs have been identified in post-mortem human brains, indicating a possible link between environmental factors and disease risk. Research has revealed an association between PCB exposure and cognitive decline. Therefore, it is crucial to evaluate how PCB mixtures relevant to humans affect brain function and cognition. To investigate the effects of PCBs on memory and transcriptomic profiles, we exposed male C57BL/6 J mice orally to a synthetic PCB mixture daily. After seven weeks of exposure, the adult mice were assessed in a spatial object recognition task (SOR) to evaluate long-term spatial memory. Our findings showed that mice exposed to PCBs exhibited deficits in long-term spatial memory. To examine the molecular effects of PCB on the brain, we used a spatial transcriptomics technique to analyze gene expression changes in five brain regions: the hippocampus, neocortex, thalamus, caudal putamen, and fiber tracts. Our analysis of spatial gene expression revealed the molecular signatures influenced by PCB in these susceptible brain regions of mice. Network analysis suggests that these changes are associated with higher chlorinated PCBs present in the brain. Additionally, we show that PCB exposure disrupts the expression of tight junction proteins, which are crucial for maintaining the integrity of the blood-brain barrier (BBB). Thus, our results offer mechanistic insights into how PCB exposure affects brain function and cognition.

Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information. To address these issues, we introduce Impeller, a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Impeller has two unique characteristics distinct from existing approaches. First, it builds a heterogeneous graph with two types of edges representing spatial proximity and expression similarity. Therefore, Impeller can simultaneously model smooth gene expression changes across spatial dimensions and capture similar gene expression signatures of faraway cells from the same type. Moreover, Impeller incorporates both short- and long-range cell-to-cell interactions (e.g. via paracrine and endocrine) by stacking multiple GNN layers. We use a learnable path operator in Impeller to avoid the over-smoothing issue of the traditional Laplacian matrices. Extensive experiments on diverse datasets from three popular platforms and two species demonstrate the superiority of Impeller over various state-of-the-art imputation methods. The code and preprocessed data used in this study are available at https://github.com/aicb-ZhangLabs/Impeller and https://zenodo.org/records/11212604.

A toxicogenomics approach was used to qualitatively and quantitatively compare the gene expression changes in human and rat primary hepatocytes exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Hepatocytes from five individual rats and five individual humans were exposed for 24 h to 11 concentrations of TCDD ranging from 0.00001 to 100nM and a vehicle control. Gene expression changes were analyzed using whole-genome microarrays containing 13,002 orthologs. Significant changes in expression of individual orthologs at any concentration (fold change [FC] ± 1.5 and false discovery rate < 0.05) were higher in the rat (1547) compared with human hepatocytes (475). Only 158 differentially expressed orthologs were common between rats and humans. Enrichment analysis was performed on the differentially expressed orthologs in each species with 49 and 34 enriched human and rat pathways, respectively. Only 12 enriched pathways were shared between the two species. The results demonstrate significant cross-species differences in expression at both the gene and pathway level. Benchmark dose analysis of gene expression changes showed an average 18-fold cross-species difference in potency among differentially expressed orthologs with the rat more sensitive than the human. Similar cross-species differences in potency were observed for signaling pathways. Using the maximum FC in gene expression as a measure of efficacy, the human hepatocytes showed on average a 20% lower efficacy among the individual orthologs showing differential expression. The results provide evidence for divergent cross-species gene expression changes in response to TCDD and are consistent with epidemiological and clinical evidence showing humans to be less sensitive to TCDD-induced hepatotoxicity.

The homeobox gene HOXA5 encodes a transcription factor that has been shown to play important roles in embryogenesis, hematopoiesis, and tumorigenesis. In order to decipher downstream signaling pathways of HOXA5, we utilized oligonucleotide microarray analysis to identify genes that are differentially expressed in HOXA5-induced cells compared with uninduced cells. Comparative analysis of gene expression changes after 9 h of HOXA5 induction in Hs578T breast cancer cells identified 306 genes whose expression was modulated at least 2-fold. Ten of these 306 genes were also up-regulated by at least 2-fold at 6 h post-induction. The expression of all of these 10 genes was confirmed by semiquantitative reverse transcription-PCR. Among these 10 genes, which are most likely to be direct targets of HOXA5, we initiated an investigation into the pleiotrophin gene by first cloning its promoter. Transient transfection assays indicated that HOXA5 can specifically activate the pleiotrophin promoter. Promoter deletion, chromatin immunoprecipitation assay, and gel-shift assays were performed to show that HOXA5 can directly bind to one binding site on the pleiotrophin promoter. These data strongly suggest that microarray analysis can successfully identify many potential direct downstream genes of HOXA5. Further functional analysis of these targets will allow us to better understand the diverse functions of HOXA5 in embryonic development and tumorigenesis.

Spatial transcriptomics is a rapidly emerging field that allows for the study of gene expression within a tissue sample. This technology has the potential to provide valuable insights into the molecular mechanisms underlying cancer progression and could be particularly useful in identifying specific regional cancer progression. By studying the regional spatial trajectory of cancer progression, we can develop more targeted therapies and better predict the likelihood of metastasis. Additionally, analyzing gene expression patterns in specific regions of the tissue can reveal the molecular mechanisms driving cancer growth in those areas, potentially uncovering new therapeutic targets. In this study, we created a spatial trajectory analysis method, named as stLearn - PSeudo-Time-Space (PSTS) algorithm, to investigate specific regional cancer progression. Spatial trajectory analysis is a statistical method that allows for the identification of patterns of transitional gene expression over time and space within a tissue sample. In detail, this method incorporates spatial features, such as tissue sub-structure locations and gene expression changes, to model the trajectory from a single snapshot of transcriptomics data. Also, we evaluated the effectiveness of PSTS by utilizing the cancer progression model in human breast cancer samples. Using this approach, we were able to identify specific regions within the breast tissue that displayed distinct progression branching trajectories patterns of transitional gene expression associated with ductal carcinoma in situ and invasive ductal carcinoma. These regions were found to be characterized by a high level of cellular diversity and were associated with aggressive forms of breast cancer. The results of this study suggest that spatial transcriptomics and spatial trajectory analysis could be a powerful tool for identifying specific regional cancer progression and may help to inform the biomarkers for the development of targeted therapies for breast cancer. Overall, this study highlights the potential of spatial transcriptomics and spatial trajectory analysis to provide valuable insights into the molecular mechanisms underlying cancer progression and could have significant implications for the diagnosis and treatment of cancer. Citation Format: Duy Pham, Quan Nguyen. A novel spatial trajectory inference method for detecting regional breast cancer progression from spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB076.

Rationale: Identification and understanding of intercellular signaling and molecular mechanisms that initiate and drive Idiopathic Pulmonary Fibrosis (IPF) are largely unknown. In particular, the specific role of epithelial and mesenchymal cell interactions in the initiation and progression of the disease remains debated. To address this, we have developed a scaffold-based co-culture IPF organoid model that uses healthy and diseased human iPSC-derived alveolar epithelial cells and primary human lung fibroblasts to recapitulate the phenotypic and gene expression changes during progressive fibrosis. Method: We generated microbead scaffolds that structurally mimic alveolar air-sacs and coated them in a rotating bioreactor with primary lung fibroblasts and iPSC-derived type 2 alveolar cells (iAT2s). iAT2s with the surfactant protein C (SFTPC) I73T variant and the syngeneic corrected control iAT2s were cultured with primary healthy and IPF fibroblasts in different combinations. All combinations were scored daily for phenotypic changes (surface area reduction, extracellular matrix deposition) that correlate with the progression of the disease. Single-cell samples from each cell combination at each disease stage were collected, and single-cell-RNA-sequencing was performed to study epithelial-mesenchymal interactions at different stages of the disease. Result: The SFTPC-mutant iAT2s induced a fibrotic phenotype in organoids co-cultured with healthy lung fibroblasts, while the corrected iAT2s did not change the phenotype of organoids co-cultured with healthy lung fibroblasts. This fibrotic phenotype was similar to that seen with IPF lung fibroblasts co-cultured with corrected iAT2s. Single-cell-RNA-sequencing revealed that SFTPC-mutant iAT2s induced gene expression changes in healthy lung fibroblasts. These changes were viewed in Gene Ontology terms and included lung fibrosis program, ferroptosis pathway, extracellular matrices, fatty acid metabolism, and hypoxic stress responses. Time in culture of the organoids had a significant effect on both cell populations. When comparing early vs late-stage cultures, we noted the loss of iAT2 cells in co-cultures with IPF fibroblasts but no loss of iAT2 cells in co-cultures with healthy lung fibroblasts. The model also showed the distribution of key IPF biomarkers like COL1A1, CTHRC1, SFTPC, and KRT17 in fibroblast and iAT2 clusters. The gene expression changes were validated at the protein level with immunofluorescent staining. Conclusion: Our alveolar organoid model of progressive fibrosis reveals critical pathways involved in the initiation and progression of lung fibrosis. By analyzing gene expression changes across various cellular combinations and time points, we can pinpoint essential cell types and pathways in the progression of fibrosis, aiding the development of stage-specific therapeutic strategies.

We have optimized an experimental and computational pipeline to adapt spatial transcriptomics (ST) approaches based upon the Visium (10x Genomics) technology to infer cellular composition and intercellular interactions of FFPE clinical specimens. We apply this technology to deliver an approach to examine pancreatic intraepithelial neoplasia (PanIN) to identify intrinsic and extrinsic mechanisms that are associated with the progression of these pre-malignant lesions to invasive carcinoma. Currently, most pancreatic cancers are diagnosed at an advanced stage that reflects in dismal survival rates and a better understanding of PanINs biology will provide valuable insights for early therapeutic interventions. Thus, we used PanINs as our model system to implement the FFPE ST workflow. Our workflow for FFPE ST analysis facilitates sectioning of small regions (5mm in diameter) from a paraffin block that are stained and imaged with H&amp;E and concurrently measured for genome-wide transcriptional profiling. Subsequently, the image is used for automated cell annotation using an algorithm, CODA, trained to identify normal and neoplastic pancreatic cell types. CODA identified the normal pancreatic histological regions (ducts, acini, islets of Langerhans, stroma), as well as the neoplastic cells. This automated analysis enables isolation of specific spots for differential expression analysis to pinpoint the transcriptional changes that occur within neoplastic cells along ducts in PanIn and their changes between high-grade and low-grade lesions. The spatial gene expression analysis identified clusters that mapped to the cell types annotated by CODA and the marker genes of each cluster matched known markers for the correspondent cell type. Although PanINs are very small in size (&amp;lt; 1mm), we found specific clusters accurately mapped to these lesions in each sample. Overall, the spatial sequencing data presented enough depth and complexity to allow differential expression and pathway analysis. We observed a significant number of deregulated genes in PanINs compared to normal ducts. Some deregulated genes are known PanIN markers, but potential new markers were also identified. Moreover, the integration of CODA with gene expression changes enables us to verify that unique stromal regions annotated with CODA and associated with PanIns are in fact heterogeneous and formed by distinct cell subtypes. Altogether, our workflow combining automated cell annotation with STA from the same section provides a methodology to precisely examine the sample architecture while measuring heterogeneity at the transcriptional level. This combined approach can be applied to different FFPE tumor types to leverage the use of large bioarchives of samples not previously accessible to genome-wide spatial methods. Citation Format: Alexander T. Bell, Kohei Fujikura, Jacob Stern, Rena Chan, James Chell, Stephen Williams, Ashley Kiemen, Elizabeth M. Jaffee, Denis Wirtz, Laura D. Wood, Elana J. Fertig, Luciane T. Kagohara. Spatial transcriptomics for FFPE characterizes the molecular and cellular architecture of malignant changes in pancreatic pre-malignant lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 637.

The primary purpose of this study was to examine the effect of green tea on the global gene expression profile in the transgenic adenocarcinoma mouse prostate model for prostate cancer (TRAMP model). The mechanism by which green tea alters gene expression and affects an organism on the cellular level is relatively unknown. We obtained published DNA microarray data from McCarthy et al. (2007, Molecular Oncology 1:196–204). There were three groups of mice in this study: water‐fed wild type C57BL/6 mice (controls), water‐fed TRAMP mice (prostate cancer progressing), and green tea catechin‐fed TRAMP mice (prostate cancer delayed) that were sacrified at three different time points 12, 17, 24 weeks of age, with three biological replicates in each group. Labeled RNA from each mouse was hybridized to an Affymetrix Gene Chip. We reprocessed the data using the dChip software to determine differential gene expression. We then used the GenMAPP and MAPFinder programs to perform Gene Ontology (GO) term enrichment analysis to determine which GO terms were overrepresented in the data that were not reported in the original published work. We would like to gratefully acknowledge Timothy J. Yeatman, Saverio Bettuzzi, and Steve Enkemann for providing us with the complete microarray dataset.

Single-cell RNA-sequencing analysis is one of the most effective tools for understanding specific cellular states. The use of single cells or pooled cells in RNA-seq analysis requires the isolation of cells from a tissue or culture. Although trypsin or more recently cold-active protease (CAP) has been used for cell dissociation, the extent to which the gene expression changes are suppressed has not been clarified. To this end, we conducted detailed profiling of the enzyme-dependent gene expression changes in mouse skeletal muscle progenitor cells, focusing on the enzyme treatment time, amount and temperature. We found that the genes whose expression was changed by the enzyme treatment could be classified in a time-dependent manner and that there were genes whose expression was changed independently of the enzyme treatment time, amount and temperature. This study will be useful as reference data for genes that should be excluded or considered for RNA-seq analysis using enzyme isolation methods.

Spatial transcriptomics (ST) is a cutting-edge technology that provides comprehensive insights into gene expression patterns from a spatial perspective. A key research focus within this field is spatial domain identification, which is essential for exploring tissue organization, biological development, and disease mechanisms. Although methods have been developed, they still face challenges in modeling the gene expression information together with the spatial locations, resulting in suboptimal accuracy. We introduce Distribution-aware Contrastive Learning for Spatial Transcriptomics (DisConST), a novel deep learning method designed to improve spatial domain detection within ST datasets. DisConST addresses key challenges, such as the high dropout rates and the complex integration of spatial and gene expression data, by incorporating contrastive learning strategies that are aware of the underlying data distributions. It employs the zero-inflated negative binomial (ZINB) distribution, along with graph contrastive learning, to generate more informative latent representations. These representations efficiently integrate spatial positions, transcriptomic profiles, and cell-type proportions within spots. We validated DisConST across diverse ST datasets of tissues, organs, and embryos from various sequencing platforms in both normal and disease states. Our results consistently demonstrated that DisConST achieves superior spatial domain recognition accuracy compared to existing state-of-the-art methods. Furthermore, our experiments highlighted the utility of DisConST in advancing research on tissue organization, embryonic development, and tumor immune microenvironment dissection. The source code for DisConST is freely available at https://github.com/Zhenpm/DisConST/.

Gene Expression Profiles During Hepatic Stellate Cell Activation in Culture and In Vivo

Protein kinase C (PKC) isozymes are key signal transducers involved in normal physiology and disease and have been widely implicated in cancer progression. Despite our extensive knowledge of the signaling pathways regulated by PKC isozymes and their effectors, there is essentially no information on how individual members of the PKC family regulate gene transcription. Here, we report the first PKC isozyme-specific analysis of global gene expression by microarray using RNAi depletion of diacylglycerol/phorbol ester-regulated PKCs. A thorough analysis of this microarray data revealed unique patterns of gene expression controlled by PKCα, PKCδ, and PKCε, which are remarkably different in cells growing in serum or in response to phorbol ester stimulation. PKCδ is the most relevant isoform in controlling the induction of genes by phorbol ester stimulation, whereas PKCε predominantly regulates gene expression in serum. We also established that two PKCδ-regulated genes, FOSL1 and BCL2A1, mediate the apoptotic effect of phorbol esters or the chemotherapeutic agent etoposide in prostate cancer cells. Our studies offer a unique opportunity for establishing novel transcriptional effectors for PKC isozymes and may have significant functional and therapeutic implications.

Non-healing chronic ulcers are a serious complication of diabetes and are a major healthcare problem. While a host of treatments have been explored to heal or prevent these ulcers from forming, these treatments have not been found to be consistently effective in clinical trials. An understanding of the changes in gene expression in the skin of diabetic patients may provide insight into the processes and mechanisms that precede the formation of non-healing ulcers. In this study, we investigated genome wide changes in gene expression in skin between patients with type 2 diabetes and non-diabetic patients using next generation sequencing. We compared the gene expression in skin samples taken from 27 patients (13 with type 2 diabetes and 14 non-diabetic). This information may be useful in identifying the causal factors and potential therapeutic targets for the prevention and treatment of diabetic related diseases.

Expression profiling: Opportunities and pitfalls and impact on the study and management of allergic diseases

Few studies have reported on the flowering time mechanism of tropical maize under short-day conditions. Drought, another important factor that affects flowering time, has been reported to delay the silking date in tropical maize. However, due to the lack of genetic information related to flowering in maize, the mechanism by which drought delays flowering is unclear. To further understand this process, we analyzed drought-responsive genes using RNA sequencing and identified genes related to flowering time, including contigs from de novo assembly. The results revealed changes in the expression of flowering-time genes, including INDETERMINATE1 (ID1), Heading date 3a (Hd3a), CONSTANS-like genes, and ZEA MAYS CENTRORADIALES8 (ZCN8), which are known to be crucial factors in flowering. In particular, Hd3a, CONZ1, and ZCN8, which have been reported to accelerate flowering under short-day conditions, were downregulated by drought stress. Changes in gene expression appear to play an important role in changes in flowering time under drought. These expression profiles will help to further understand the flowering-time genes of tropical maize and the delayed flowering time resulting from drought.