介紹
單細胞RNA測序（scRNA-seq）能夠在單細胞分辨率下實現高通量轉錄組分析。固有的空間位置對于理解單細胞如何協調多細胞功能和驅動疾病至關重要。然而，在組織分離過程中，空間信息常常丟失。空間轉錄組學（ST）技術可以提供精確的空間基因表達圖譜，但其實用性受到其可測定的基因數量或在更大規模上的相關成本以及細粒度細胞類型注釋的限制。通過細胞對應學習在scRNA-seq和空間轉錄組學數據之間傳遞知識，可以恢復scRNA-seq數據集固有的空間特性。
在本研究中，我們引入了一種名為COME的對比映射學習方法，該方法可以學習ST和scRNA-seq數據之間的映射，從而恢復scRNA-seq數據的空間信息。大量的實驗表明，提出的COME方法有效地捕獲了精確的細胞-點關系，并且在恢復scRNA-seq數據的空間位置方面優于先前的方法。更重要的是，我們的方法能夠精確識別數據中有生物學意義的信息，如缺失基因的空間結構、空間層次模式和每個點的細胞類型組成。這些結果表明，提出的COME方法可以幫助理解組織環境中細胞之間的異質性和活性。
Abstract
Motivation
Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive diseases. However, spatial information is often lost during tissue dissociation. Spatial transcriptomic (ST) technologies can provide precise spatial gene expression atlas, while their practicality is constrained by the number of genes they can assay or the associated costs at a larger scale and the fine-grained cell type annotation. By transferring knowledge between scRNA-seq and spatial transcriptomics data through cell correspondence learning, it is possible to recover the spatial properties inherent in scRNA-seq datasets.
Results
In this study, we introduce COME, a COntrastive Mapping lEarning approach that learns mapping between ST and scRNA-seq data to recover the spatial information of scRNA-seq data. Extensive experiments demonstrate that the proposed COME method effectively captures precise cell-spot relationships and outperforms previous methods in recovering spatial location for scRNA-seq data. More importantly, our method is capable of precisely identifying biologically meaningful information within the data, such as the spatial structure of missing genes, spatial hierarchical patterns, and the cell-type compositions for each spot. These results indicate that the proposed COME method can help to understand the heterogeneity and activities among cells within tissue environments.
代碼
https://github.com/cindyway/COME/blob/main/Tutorial.ipynb
參考
- COME: contrastive mapping learning for spatial reconstruction of scRNA-seq data
