Mapping the transcriptome: Realizing the full potential of spatial data analysis

Zormpas, Eleftherios; Queen, Rachel; Comber, Alexis; Cockell, Simon J.

doi:10.1016/j.cell.2023.11.003

Cited by 12 publications

(5 citation statements)

References 81 publications

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“…We conducted 1000 replications for both strategies, with each replication sampling 50 FOVs (50 steps). The final outcome for both strategies was an array of shape [1000, 50,2], representing 1000 random sampling strategy replications, each consisting of 50 steps, and each step containing the central point coordinates for the rectangular FOVs on the slices.…”

Section: Sampling Strategy Comparisonmentioning

confidence: 99%

“…Spatial omics technologies have revolutionized the study of tissue spatial biology 1 . These cutting-edge technologies can accurately detect dozens of proteins and hundreds of RNAs at a cellular spatial resolution, enabling in-depth spatial characterization of both healthy and diseased tissues 2,3 . To address specific research questions, scientists must carefully design their experiments.…”

Section: Introductionmentioning

confidence: 99%

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SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

Li,

Wu,

Cui

et al. 2024

Preprint

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Spatial omics technologies enable the precise detection of proteins and RNAs at high spatial resolution. Designing spatial omics experiments requires careful consideration of “what” targets to measure and “where” to position the field of views (FOVs). Current FOV sampling strategies often involve acquiring densely sampled FOVs and stitching them together, which is time-consuming, resource-intensive, and sometimes impossible. To optimize FOV sampling strategies, we developed SOFisher, a reinforcement learning-based framework that harnesses the knowledge gained from the sequence of previously sampled FOVs to guide the selection of the next FOV position, to improve the efficiency of capturing more regions of interest. We rigorously evaluated SOFisher’s performance using comprehensive simulations based on real spatial datasets, and our results clearly demonstrated that SOFisher consistently outperformed the conventional approach across various metrics. SOFisher’s robustness and generalizability were further validated through cross-domain generalization tests and its adaptability to varying FOV sizes. On a real Alzheimer’s Disease (AD) dataset, SOFisher successfully guided the selection of FOVs containing neurofibrillary tangles and amyloid-β plaques in both single and dual target tissue landmark scenarios. Remarkably, SOFisher-guided experiment design of spatial single-omics on limited tissue areas yielded insights into AD-related cell states, subtypes, and gene programs previously obtained through extensive spatial multi-omics experiments. SOFisher has the potential to revolutionize the experiment design of spatial biology.

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Section: Sampling Strategy Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

Li,

Wu,

Cui

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In-depth exploration of transcriptomic information is crucial for interpreting interactions within gene regulatory networks. Single-cell RNA sequencing (scRNA-seq) refers to the high-throughput sequencing of mRNA at a single-cell level, based on the characteristics of single cells and high throughput [17,18]. It solves the problems of cell heterogeneity and small cell volume that prevent conventional high-throughput sequencing in the study of cellular molecular mechanisms [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Single-Cell Transcriptome Sequencing Reveals Molecular Expression Differences and Marker Genes in Testes during the Sexual Maturation of Mongolian Horses

Liu,

Du,

et al. 2024

Animals

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This study aimed to investigate differences in testicular tissue morphology, gene expression, and marker genes between sexually immature (1-year-old) and sexually mature (10-year-old) Mongolian horses. The purposes of our research were to provide insights into the reproductive physiology of male Mongolian horses and to identify potential markers for sexual maturity. The methods we applied included the transcriptomic profiling of testicular cells using single-cell sequencing techniques. Our results revealed significant differences in tissue morphology and gene expression patterns between the two age groups. Specifically, 25 cell clusters and 10 cell types were identified, including spermatogonial and somatic cells. Differential gene expression analysis highlighted distinct patterns related to cellular infrastructure in sexually immature horses and spermatogenesis in sexually mature horses. Marker genes specific to each stage were also identified, including APOA1, AMH, TAC3, INHA, SPARC, and SOX9 for the sexually immature stage, and PRM1, PRM2, LOC100051500, PRSS37, HMGB4, and H1-9 for the sexually mature stage. These findings contribute to a deeper understanding of testicular development and spermatogenesis in Mongolian horses and have potential applications in equine reproductive biology and breeding programs. In conclusion, this study provides valuable insights into the molecular mechanisms underlying sexual maturity in Mongolian horses.

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“…These technologies generate highly multiplexed spatial maps of transcriptomics 2,3 , proteins 4,5 , and/or metabolic features 6,7 at up to single-cell resolution 8 . Spatial omics technologies surpass traditional single-cell omics by maintaining the spatial context, which is essential for unraveling the complexities of biological processes such as development and disease 9–12 .…”

Section: Introductionmentioning

confidence: 99%

Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi

Cui,

Yuan

2024

Preprint

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Tissues are composed of heterogeneous cell niches, which can be investigated using spatial omics technologies. Large consortia have accumulated vast amounts of spatially resolved data, which typically assign slice-level condition labels without considering intra-slice heterogeneity, particularly differential cell niches that respond to certain perturbations. Here, we present Taichi, an efficient and scalable method for condition-relevant cell niche analysis that does not rely on pre-defined discrete spatial clustering. Taichi utilizes a scalable spatial co-embedding approach that effectively accounts for batch effects, incorporating advanced label refinement and graph heat diffusion techniques to explore condition-relevant cell niches across extensive multi-slice and multi-condition spatial omics datasets. Comprehensive benchmarks demonstrate Taichi’s ability to precisely identify condition-relevant niches under various levels of perturbations. We showcase Taichi’s effectiveness in accurately delineating major shifts in cell niches in a mouse model of diabetic kidney disease compared to a normal group, revealing disease-specific cell-cell interactions and spatial gene expression patterns. Furthermore, Taichi can identify key subtype-relevant niches between colorectal cancer patient groups with significantly different survival outcomes. Moreover, we demonstrate that Taichi can help discover more fine-grained clinical properties within the originally coarse-defined patient groups in large-scale tumor spatial atlases, reflecting intra-group heterogeneity obscured previously. Additionally, we combine Taichi and tensor decomposition to discover higher-order biomarkers relevant to the immunotherapy response of triple-negative breast cancer. Finally, we highlight Taichi’s speed and scalability by confirming its unique applicability in large-scale scenarios containing up to 16 million cells in ∼ 12 minutes. Taichi provides a powerful tool for mining disease-relevant spatially resolved insights in the era of big data in spatial biology.

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Mapping the transcriptome: Realizing the full potential of spatial data analysis

Cited by 12 publications

References 81 publications

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

Single-Cell Transcriptome Sequencing Reveals Molecular Expression Differences and Marker Genes in Testes during the Sexual Maturation of Mongolian Horses

Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi

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