Fig 1.
(a) Preprocessing. Raw data from scRNA-seq dataset and ST slices are preprocessed and taken as iSORT’s inputs. They then go through several pre-processing steps of normalization, log-transformation and selection of highly variable genes. Different samples could have diverse distributions of gene expressions. (b) Training. iSORT estimates the weights of each ST slice based on the scRNA-seq data and subsequently trains a mapping f = g ∘ h from gene expression x to spatial location y, where z = h(x) co-embeds data into the feature space in unified scale and y = g(z) constructs a neural network combining slice-specific weights and density ratios. During training, each sample is estimated a weight based on the density ratio w(z) of the scRNA-seq data to the ST data. (c) Downstream analysis. By the mapping f, iSORT can reconstruct spatial organization of tissues at single-cell resolution, reveal spatial expressive patterns of genes, identify SOGs, and visualize SpaRNA velocity in the physical space.
Fig 2.
Spatial organization reconstruction by iSORT on the DLPFC, spatially resolved mouse embryo, and mouse brain datasets.
(a) Ground truth and spatial reconstruction results for ID151674 in DLPFC dataset by eleven algorithms: iSORT, scSpace, Tangram, novoSpaRc, CeLEry, CellTrek, RCTD, CARD, Redeconve, CytoSPACE, and Celloc, where Tangram, RCTD, CARD, Redeconve, CytoSPACE, and Celloc are designed for spot deconvolution, whereas novoSpaRc is designed for imputing the undetected genes. Different colors represent different cortical regions. (b) Bar charts to compare the performance of eleven algorithms based on four indicators: intra-layer similarity (), normalized density distribution (
), aggregative volume index (
) and aggregative perimeter index (
) of each layer. The indicators of iSORT (the red filled bars) are above 0.875 in average, larger than the other ten algorithms. (c) Spatial reconstruction of the spatially resolved mouse embryo dataset with iSORT. Different colors represent different cell types. iSORT can reorganize single cells based on their gene expressions in a continuous space. (d) Spatial reconstruction of the mouse brain dataset with iSORT. iSORT can recover the stratified structures in the mouse brain.
Fig 3.
Robustness of iSORT in reconstructing human DLPFC slice ID151674 with different ST references.
(a) iSORT’s results with different ST references. Case I: Reconstruction using one homologous slice (Sample ID151675). Case II: Reconstruction using one heterologous slice (Sample ID151671). Case III: Reconstruction using three homologous slices (Sample ID151673, ID151675, and ID151676). Case IV: Reconstruction using three heterologous slices (Sample ID 151675, ID151607, and ID151671). Case V: Reconstruction using three rotated homologous slices (Sample ID151673, ID151675, and ID151676). Case VI: Reconstruction using three rotated heterologous slices (Sample ID151675, ID151671, and ID151507). (b) Violin plots of X coordinates across different reconstruction scenarios, depicting the distributions of cells on the X-axis. Case II’: Reconstruction using one heterologous slice (Sample ID151570). (c) Violin plots of Y coordinates across different reconstruction scenarios, depicting the distributions of cells on the Y-axis. Case IV’: Reconstruction using three heterologous slices (Sample ID151675, ID151507, and ID151508). Case IV”: Reconstruction using three heterologous slices (Sample ID151675, ID151670, and ID151671) (d) The accuracy of iSORT across different cases. Reconstruction using a single heterogeneous slice will be slightly less effective compared to homogeneous slices, but if multiple heterogeneous slices are used, the reconstruction results are better than single slice in the sense of reconstruction accuracy.
Fig 4.
Revealing the spatial pattern of the ftz gene from Drosophila embryo data using iSORT.
(a) Visualization of ftz’s spatial pattern in the original ST slice. (b) Visualization of ftz’s expression in the simulated coarse-grained ST reference, demonstrating the disruption of gene patterns in the low-resolution ST spots. (c) Visualization of ftz’s spatial pattern reconstructed from the scRNA-seq data by iSORT. Despite the fact that reference has lost the seven-stripe pattern, iSORT successfully restored the spatial distribution of the ftz seven stripes. (d) Reconstructed spatial patterns by scSpace, Tangram, novoSpaRc, and CeLEry. (e) Density plot contrasting the true (blue) and predicted (red) spatial locations of cells. The spatial density distribution of the iSORT reconstruction results is consistent with the ground truth. (f) Marginal densities for the true (blue) and predicted (red) spatial distributions of the ftz gene. The errors between the true and predicted densities are shown by the yellow line. The seven stripes along the x-axis are recovered by iSORT.
Fig 5.
In-silico gene knockout experiments on DLPFC dataset and analysis of the human artery dataset.
(a) Reconstruction of DLPFC by iSORT without gene knockout. (b) Reconstruction of DLPFC with the top-20 SOGs knocked out. Knocking out the first 20 SOGs disrupts the structure of the cerebral cortex. (c) Reconstruction of DLPFC with the top-300 SOGs knocked out. The structural disruption in the cerebral cortex is intensified, with increased mixing of cells across cortical layers. (d) Curve of the mean squared error (MSE) in reconstruction with increasing SOGs knocked out. The more genes that are knocked out, the worse the reconstruction is in the sense of MSE. (e) Reconstruction of DLPFC with the top-20 Moran’s I SVGs knocked out. (f) Reconstruction of DLPFC with the top-20 SpatialDE SVGs knocked out. (g) Schematic diagram of artery structure illustrating layered composition: the innermost layer is lined with endothelial cells (ECs), followed by smooth muscle cells (SMCs), and the outermost layer composed of fibroblasts. (h) Hematoxylin and Eosin staining and the reconstruction results of the normal artery and the diseased artery with atherosclerosis (AS): Panel I: Histology image of a normal artery. Panel II: Histology image of an artery with AS. Panel III: Reconstruction result for a normal artery, showcasing ECs, SMCs, and fibroblasts. Panel IV: Reconstruction result for a diseased artery with AS, showcasing ECs, SMCs, and fibroblasts. The reconstruction results by iSORT distinguish the hierarchical structure of the three cell types.
Fig 6.
Visualization of SpaRNA velocity on the DLPFC, human developing heart and mouse embryo datasets.
(a) Sample ID151674 shown in UMAP-reduction gene expression space annotated by different layers. In the low-dimensional gene space, the hierarchical organization of the cerebral cortex is arranged differently from the normal sequence. (b) Pseudo-time of sample ID151674 inferred by scTour shown in UMAP-reduction gene expression space. (c) RNA velocity of sample ID151674 inferred by scTour shown in UMAP-reduction gene expression space. Due to the misaligned hierarchical organization in the low-dimensional gene space, RNA velocity shows an incorrect trajectory, moving directly from layer 6 to layer 1 and continuing its evolution from layer 1. (d) Sample ID151674 displayed in physical space, annotated by different layers, illustrates the true hierarchical organization of the cerebral cortex. (e) Pseudo-time of sample ID151674 shown in physical space. (f) SpaRNA velocity of sample ID151674 inferred by iSORT shown in physical space. (g) Spatial transcriptomics of a human developing heart at 9 post-conception week. Different colors represent different cell types. (h) SpaRNA velocity on the human developing heart visualized by iSORT, characterizing the sequential appearance of different cell types in the human heart during development. (i) SpaRNA velocity inferred by iSORT on the mouse embryo dataset. iSORT successfully predicted the spatial differentiation trajectories of different cell types, revealing the patterns of spatial development.