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Background The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. However, the lack of benchmarking complicates the selection of a suitable method. Results Here we systematically evaluate a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. We address questions including whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. Conclusions Our study evaluates the performance of each method from multiple aspects and highlights the discrepancy among different methods when calling statistically significant SVGs across diverse datasets. Overall, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of related methods.

Normalization of spatial transcriptomics data is challenging due to spatial association between region-specific library size and biology. We develop SpaNorm, the first spatially-aware normalization method that concurrently models library size effects and the underlying biology, segregates these effects, and thereby removes library size effects without removing biological information. Using 27 tissue samples from 6 datasets spanning 4 technological platforms, SpaNorm outperforms commonly used single-cell normalization approaches while retaining spatial domain information and detecting spatially variable genes. SpaNorm is versatile and works equally well for multicellular and subcellular spatial transcriptomics data with relatively robust performance under different segmentation methods.

A poem is presented.

Retinoic acid (RA) is a key morphogen in human retinal development, activating transcriptional programs that drive retinal progenitor differentiation and photoreceptor development, ensuring proper spatial organisation within the neural retina, essential for vision. Despite its well-established role in retinal patterning, the concentration-dependent effects of RA on human retinal cell fate specification and the regional definition of the primate macula and peripheral retina remain poorly understood. Here, we show that temporal and dosage-dependant modulation of RA during human retinal organoid differentiation induces distinct changes in retinal cell abundance, maturation, and organisation. Single-cell transcriptomics and protein analysis reveal that RA dosage influences the relative abundance and maturation of photoreceptors and retinal interneurons. Spatial transcriptomics analyses demonstrates that low RA levels biases retinal organoids toward a macular-like regional identity, whereas high RA levels promotes peripheral-like development. Collectively, our findings emphasise the critical role of RA signalling in retinal maturation and regional specification. This study elucidates mechanisms involved in human retinal development and we anticipate that controlled RA modulation in retinal organoids provides a strategy to refine disease modelling of inherited retinal disorders and enhance the specific generation of photoreceptors suitable for transplantation and regenerative therapies.

Research has shown that chronic inflammation is associated with many diseases such as cardiovascular disease, diabetes, cancer, and asthma. This chronic inflammation is caused by the failure of proper resolution of the inflammation overtime. Therefore, this dissertation explores the biosynthesis of lipoxygenase-derived products from DHA, drug discovery, and site-directed mutagenesis to determine the substrate and inhibitors binding to the lipoxygenase active site in order to understand how these pro-resolving molecules are made and help to guide for chronic disease therapy. The first chapter probes the electrostatic and steric requirements for substrate binding in human 12-LOX. The second chapter investigates the biosynthetic pathway of NPD1 and PDX by human lipoxygenase isozymes. The third chapter determines the dimeric interface of human 12-LOX by site-directed mutagenesis. The fourth and fifth chapters discover potent and selective inhibitors against human 12-LOX and 15-LOX-2 for thrombosis and atherosclerosis therapy.

The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-specific gene statistics of differentially stable genes from scRNA-seq data to define cell identity. When applied to multiple datasets, Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells and lineage inference of single cells.

The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. The availability of multiple methods for detecting SVGs bears questions such as whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. In this study, we address the above questions by systematically evaluating a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. Our results shed light on the performance of each method from multiple aspects and highlight the discrepancy among different methods especially on calling statistically significant SVGs across datasets. Taken together, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of such methods.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Included additional SVG detection methods and tested on additional spatial transcriptomic data

Clustering is a fundamental task in single-cell omics data analysis and can significantly impact downstream analyses and biological interpretations. The standard approach involves grouping cells based on their gene expression profiles, followed by annotating each cluster to a cell type using marker genes. However, the number of cell types detected by different clustering methods can vary substantially due to several factors, including the dimension reduction method used and the choice of parameters of the chosen clustering algorithm. These discrepancies can lead to subjective interpretations in downstream analyses, particularly in manual cell type annotation. To address these challenges, we propose CLUEY, a knowledge-guided framework for cell type detection and clustering of single-cell omics data. CLUEY integrates prior biological knowledge into the clustering process, providing guidance on the optimal number of clusters and enhancing the interpretability of results. We apply CLUEY to both unimodal (e.g. scRNA-seq, scATAC-seq) and multimodal datasets (e.g. CITE-seq, SHARE-seq) and demonstrate its effectiveness in providing biologically meaningful clustering outcomes. These results highlight CLUEY on providing the much-needed guidance in clustering analyses of single-cell omics data. CLUEY package is available from https://github.com/SydneyBioX/CLUEY.Competing Interest StatementThe authors have declared no competing interest.

Many midbrain dopaminergic neuron (mDA) differentiation protocols aimed at Parkinson’s disease (PD) modeling and cell replacement therapy have been developed. However, comprehensive evaluations of the transcriptomic fidelity of these protocols at the single-cell level against a common in vivo reference have been lacking. To this end, we constructed an integrated human fetal whole-brain atlas and a midbrain subatlas to use as a standard of comparison. From the whole-brain atlas, we observed distinct brain-region-specific gene expression in most neural cell types, emphasizing the need to first evaluate in vitro protocols at the whole-brain level to identify midbrain-associated cells. These cells are then mapped to the midbrain subatlas for more refined neuronal subtype specification and trajectory analysis specific to the midbrain. We surveyed all publicly available single-cell datasets of human midbrain culture models and performed the two-tier mapping. Using this biologically-driven multi-resolution mapping strategy which we termed BrainSTEM (brain Single-cell Two tiEr Mapping), we confirmed the presence of multiple midbrain cell types (‘on-target’), but also a substantial proportion of cells associated with non-midbrain regions and subtypes (‘off-target’). This leads to an overall ‘inflation’ of mDA presence, stemming from non-midbrain-associated cells, across all published protocols. BrainSTEM thus offers an unbiased framework for understanding the current state of midbrain models and aids the improvement of midbrain differentiation protocols for PD studies.

Library size normalisation is necessary to enable comparisons between observations in transcriptomic datasets. Numerous methods have been developed to normalise these effects with sample and gene specific adjustments. However, in spatial transcriptomics data, normalisation is complicated by the fact that spatial region-specific library size confounds biology. The most popular approach of adapting methods developed for single-cell RNA-seq data has been shown to excessively remove biological signals associated with spatial domains and thus results in poorer downstream domain identification. To this end, we propose the first spatially-aware normalisation method, SpaNorm. SpaNorm concurrently models spatial library size effects and the underlying smooth biology, to tease apart these effects, and thereby remove library size effects without removing biology. This is achieved through optimal decomposition of spatially smooth variation into those related and unrelated to library size and the use of location-specific scaling factors. Using 27 tissue samples from 6 datasets spanning 4 spatial platforms, we show that SpaNorm outperforms current state of the art methods at retaining biological information in the form of spatial domains and spatially variable genes (SVGs) better than 4 commonly used single-cell normalisation approaches. SpaNorm is versatile and it can be used for both spot-based and subcellular spatial transcriptomics data. Notably, the benefit of using SpaNorm is more pronounced for the latter data such as those from Xenium, STOmics and CosMx platforms for which the proportion of genes exhibiting region-specific library size effect is higher. SpaNorm works equally well with segmented cell-level data and spot-based data where each spot contains multiple cells.