From WSIs to Bulk, Spatial and Single-Cell Transcriptomics via Multi-Scale Graph Neural Networks

Miguel Andrade; José Almeida; Carlos Santiago; Catarina Barata

cs.LGOct 30, 2026

From WSIs to Bulk, Spatial and Single-Cell Transcriptomics via Multi-Scale Graph Neural Networks

Authors: Miguel Andrade, José Almeida, Carlos Santiago, Catarina Barata

Organizations: Departamento de Engenharia Electrotécnica e de Computadores, Instituto Superior Técnico, Lisboa, Portugal · Champalimaud Foundation, Lisbon, Portugal

Abstract

Understanding the spatial distribution of gene expression is key to studying complex diseases like cancer. While Spatial Transcriptomics (ST) and Single-Cell Transcriptomics (scT) provide high-resolution insights, they remain expensive and scarce. In contrast, standard histopathology Whole-Slide Images (WSIs) paired with bulk RNA sequencing are widely available. This work introduces a Multi-Scale Graph Neural Network (GNN) capable of simultaneously predicting bulk, spatial, and single-cell transcriptomics directly from WSIs. By extracting morphological features using a Pathology Foundation Model (PFM) (UNI2-h) and constructing spatial graphs at both patch and cell levels, the model learns the hierarchical relationships within the tissue. The network is trained on a combined dataset of The Cancer Genome Atlas Program (TCGA), Visium, and Xenium samples across lung, kidney, and breast tissues. A hybrid training strategy is employed, leveraging direct supervision alongside weakly supervised consistency losses to ensure predictions align across biological scales. Results show the model accurately predicts gene expression at the slide level and moderately at the patch level (strongest on Xenium), and achieves moderate correlation at the challenging single-cell level. Furthermore, downstream tasks explore the clinical relevance of these predictions, demonstrating their utility in survival analysis, spatial hazard mapping, and tumor classification via molecular signatures.

Figures & tables

Figure 1: Proposed model’s overall pipeline architecture.

Metric	TCGA $N=1724$	Visium $N=105$	Xenium $N=47$
Bulk PCC	$0.881\pm 0.010$	$0.774\pm 0.146$	$0.797\pm 0.057$
Patch PCC	–	$0.509\pm 0.026$	$0.786\pm 0.041$
Cell PCC	–	–	$0.396\pm 0.021$
Patch AUROC	–	$0.554\pm 0.004$	$0.703\pm 0.011$
Cell AUROC	–	–	$0.649\pm 0.011$

Table 1: Proposed model’s metrics across datasets. Metrics are reported as the mean and standard deviations across five cross-validation folds.

Figure 2: Multi-Scale GNN results. A ) Gene-wise mean PCC in each dataset against the number of top-predictive genes in the proposed model. B ) Gene-wise mean PCC in each model against the number of top-predictive genes. C ) Visium spot-level evaluation for slide INT22 (kidney), gene IGKC . Left to right: WSI, true spot expression, predicted spot expression, predicted cell expression. D ) Xenium spot-level evaluation for slide TENX200 (breast), gene FBLN1 . Left to right: WSI, true spot expression, predicted spot expression, true cell expression, predicted cell expression.

C-index	TCGA-LUAD $N=393$	TCGA-KIRC $N=413$	TCGA-BRCA $N=834$
Real bulk	$0.544$	$0.748$	$0.639$
Predicted bulk	$0.497$	$0.665$	$0.610$
Visual features	$0.555$	$0.688$	$0.730$

Table 2: Comparison of Bulk C-index metrics across cohorts.

Figure 3: Spatial PCC and standard deviation across paired Xenium slides of the cell-level signature maps for all hallmark gene sets, for the proposed model and DeepSpot2Cell.

Signature	Proposed Model	DeepSpot2Cell
Proliferation	$0.443\pm 0.211$	$0.309\pm 0.181$
Hypoxia	$0.465\pm 0.218$	$0.387\pm 0.211$
Apoptosis	$0.358\pm 0.219$	$0.318\pm 0.229$
Angiogenesis	$0.299\pm 0.210$	$0.118\pm 0.208$

Table 3: Average spatial PCC (mean

\pm

standard deviation across slides) across the analyzed signatures in the proposed model and in DeepSpot2Cell.

Figure 4: Downstream tasks using predicted gene expression. A) WSI with tumor annotation, predicted proliferation expression, and spatial hazard heat map in a TCGA-KIRC sample. B) Predicted and ground-truth hypoxia signature maps in a Xenium sample, after spatial binning and Gaussian smoothing ( $r=0.840$ ). C) Tumor annotation and predicted spatial proliferation expression ( $\text{AUROC}=0.921$ ).

Cohort / signature	$N$	AUROC	TPR
LUAD: Prolif.	431	0.711	1.412
LUAD: Hypoxia	431	0.593	1.131
LUAD: Apopt.	431	0.525	0.987
LUAD: Angio.	431	0.517	0.992
KIRC: Prolif.	416	0.648	1.193
KIRC: Hypoxia	416	0.776	1.444

Table 4: Tumor annotation and predicted molecular signature metrics by cohort. LUAD, lung adenocarcinoma; KIRC, kidney renal clear cell carcinoma; BRCA, breast invasive carcinoma.

Cohort	Input	Bal. Acc.	F1-score	AUROC
LUAD	50 signatures	0.707	0.707	0.771
	Full transcript.	0.684	0.679	0.742
	Patch embeddings	0.741	0.740	0.822
KIRC	50 signatures	0.857	0.861	0.932
	Full transcript.	0.851	0.850	0.912
	Patch embeddings	0.862	0.861	0.942

Table 5: Tumor classification performance by input type and TCGA cohort.

Cohort	Total $N$	Bulk	Patch	Feat. Recon.	PS Cons.	CP Cons.	Patch	Signif. Genes (Patch)
Cohort	Total $N$	PCC	PCC	Cos. Sim.	PCC	PCC	AUROC	(FDR < 0.05)
Overall	105	$0.774\pm 0.146$	$0.509\pm 0.026$	$0.634\pm 0.009$	$0.777\pm 0.142$	$0.896\pm 0.016$	$0.554\pm 0.004$	$37.7\%\pm 5.9\%$
Breast	7	$0.752\pm 0.080$	$0.583\pm 0.053$	$0.481\pm 0.068$	$0.816\pm 0.082$	$0.892\pm 0.028$	$0.567\pm 0.014$	$-$
Kidney	62	$0.742\pm 0.216$	$0.544\pm 0.024$	$0.672\pm 0.040$	$0.753\pm 0.216$	$0.907\pm 0.014$	$0.536\pm 0.005$	$14.0\%\pm 9.7\%$
Lung	36	$0.844\pm 0.071$	$0.430\pm 0.035$	$0.609\pm 0.020$	$0.819\pm 0.042$	$0.874\pm 0.031$	$0.586\pm 0.018$	$13.8\%\pm 13.8\%$

Table S1: Proposed model’s metrics for the Visium Dataset

Cohort / Subtype	Total $N$	Bulk	Patch	Cell	Feat. Recon.	PS Cons.	CP Cons.	Patch	Signif. Genes (Patch)	Cell
Cohort / Subtype	Total $N$	PCC	PCC	PCC	Cos. Sim.	PCC	PCC	AUROC	(FDR < 0.05)	AUROC
Overall	47	$0.797\pm 0.057$	$0.786\pm 0.041$	$0.396\pm 0.021$	$0.566\pm 0.044$	$0.858\pm 0.044$	$0.779\pm 0.029$	$0.703\pm 0.011$	$8.0\%\pm 14.0\%$	$0.649\pm 0.011$
Connective	-	-	-	$0.367\pm 0.019$	-	-	-	-	-	$0.616\pm 0.003$
Dead	-	-	-	$0.330\pm 0.019$	-	-	-	-	-	$0.613\pm 0.018$
Epithelial	-	-	-	$0.406\pm 0.023$	-	-	-	-	-	$0.626\pm 0.022$
Inflammatory	-	-	-	$0.340\pm 0.017$	-	-	-	-	-	$0.596\pm 0.012$

Table S2: Proposed model’s metrics for the Xenium Dataset

Cohort	Total $N$	Bulk	Feat. Recon.	PS Cons.	Bulk	Signif. Genes (Bulk)
Cohort	Total $N$	PCC	Cos. Sim.	PCC	AUROC	(FDR < 0.05)
Overall	$1{,}724$	$0.881\pm 0.010$	$0.637\pm 0.014$	$0.893\pm 0.015$	$0.661\pm 0.003$	$72.6\%\pm 16.0\%$
Breast	$866$	$0.875\pm 0.012$	$0.615\pm 0.015$	$0.891\pm 0.018$	$0.608\pm 0.007$	$53.1\%\pm 12.3\%$
Kidney	$423$	$0.886\pm 0.025$	$0.682\pm 0.014$	$0.881\pm 0.030$	$0.586\pm 0.015$	$31.0\%\pm 20.0\%$
Lung	$435$	$0.888\pm 0.004$	$0.637\pm 0.016$	$0.907\pm 0.008$	$0.602\pm 0.005$	$42.2\%\pm 4.8\%$

Table S3: Proposed model’s metrics for the TCGA Dataset

Figure S1: Left: Harmony corrected t-SNE of clustered patch features. Right: Harmony corrected t-SNE of gene expression profiles, colored according to the cluster assignment of the corresponding patch features (across $150{,}000$ randomly selected patches).

Figure S2: Harmony corrected t-SNEs of patches’ gene expression profiles colored by percentage of each cell type (neoplastic, connective, inflammatory, epithelial, dead) (across $250{,}000$ randomly selected patches).

Figure S3: Harmony corrected t-SNEs of cells’ gene expression profiles highlighting cell type (neoplastic, connective, inflammatory, epithelial, dead) (across $250{,}000$ randomly selected cells).

Figure S4: Gene-wise mean PCC in each dataset against the number of top Highly Variable Genes (HVGs) for the proposed model.

Figure S5: Histograms of gene-wise AUROC in each dataset for the proposed model.

Panel of Figure 18 — Figure S6: Log hazard ratios for each molecular signature to predict DSS time in TCGA cohorts.

Test Set	Average Accuracy	Average F1-score	Average AUROC
Visium true spot’s gene expression	$0.980\pm 0.025$	$0.975\pm 0.028$	$0.994\pm 0.009$
Visium predicted spot’s gene expression	$0.859\pm 0.068$	$0.804\pm 0.101$	$0.942\pm 0.056$
TCGA predicted spot’s gene expression	$0.815\pm 0.029$	$0.774\pm 0.038$	$0.910\pm 0.022$

Table S4: MLP’s classification metrics across the true and predicted spot’s gene expression.

Test Set	Accuracy	F1-score	AUROC
Original spot’s embedding	$0.924\pm 0.007$	$0.915\pm 0.008$	$0.984\pm 0.005$
Reconstructed spot’s embedding	$0.902\pm 0.009$	$0.893\pm 0.011$	$0.981\pm 0.005$

Table S5: MLP’s classification metrics across the original and reconstructed spot’s embedding.

Figure S7: Gene-wise mean PCC in each model against the number of top HVGs.

Figure S8: ST and scT predictions for TCGA slide AK-3465, gene MYL9 . Left to right: WSI, predicted spot expression, predicted cell expression.

Figure S9: Comparison between the tumor annotations and the predicted tumor probability by each feature set in TCGA slide AK-3458.

Model	Total $N$	Bulk	Patch	Feat. Recon.	PS Cons.	CP Cons.	Patch	Signif. Genes (Patch)
Model	Total $N$	PCC	PCC	Cos. Sim.	PCC	PCC	AUROC	(FDR < 0.05)
Proposed Model (Full)	$105$	$0.774\pm 0.146$	$0.509\pm 0.026$	$0.634\pm 0.009$	$0.777\pm 0.142$	$0.896\pm 0.016$	$0.554\pm 0.004$	$37.7\%\pm 5.9\%$
Proposed Model (w/o Consistency)	$105$	$0.726\pm 0.178$	$0.512\pm 0.027$	$0.606\pm 0.036$	$0.740\pm 0.183$	$0.222\pm 0.007$	$0.555\pm 0.004$	$43.5\%\pm 9.3\%$
Proposed Model (w/o Feat. Recon.)	$105$	$0.744\pm 0.159$	$0.501\pm 0.030$	$-$	$0.757\pm 0.168$	$0.881\pm 0.029$	$0.548\pm 0.002$	$34.6\%\pm 12.5\%$
Proposed Model (w/o TCGA)	$105$	$0.875\pm 0.017$	$0.510\pm 0.025$	$0.522\pm 0.033$	$0.924\pm 0.019$	$0.867\pm 0.030$	$0.549\pm 0.011$	$36.4\%\pm 16.0\%$
Proposed Model (w/o TCGA cells)	$105$	$0.794\pm 0.085$	$0.506\pm 0.028$	$0.559\pm 0.063$	$0.814\pm 0.090$	$0.850\pm 0.027$	$0.548\pm 0.005$	$37.3\%\pm 4.7\%$

Table S6: Metrics for the Visium Dataset (Overall) across different models.

Model	Total $N$	Bulk	Patch	Cell	Feat. Recon.	PS Cons.	CP Cons.	Patch	Signif. Genes (Patch)	Cell
Model	Total $N$	PCC	PCC	PCC	Cos. Sim.	PCC	PCC	AUROC		AUROC
Proposed Model (Full)	$47$	$0.797\pm 0.057$	$0.786\pm 0.041$	$0.396\pm 0.021$	$0.566\pm 0.044$	$0.858\pm 0.044$	$0.779\pm 0.029$	$0.703\pm 0.011$	$8.0\%\pm 14.0\%$	$0.649\pm 0.011$
Proposed Model (w/o Consistency)	$47$	$0.779\pm 0.022$	$0.770\pm 0.054$	$0.390\pm 0.029$	$0.564\pm 0.078$	$0.837\pm 0.032$	$0.734\pm 0.032$	$0.691\pm 0.027$	$8.7\%\pm 15.5\%$	$0.638\pm 0.014$
Proposed Model (w/o Feat. Recon.)	$47$	$0.810\pm 0.045$	$0.777\pm 0.049$	$0.384\pm 0.032$	$-$	$0.863\pm 0.044$	$0.741\pm 0.056$	$0.686\pm 0.015$	$8.0\%\pm 15.1\%$	$0.637\pm 0.013$
Proposed Model (w/o TCGA)	$47$	$0.831\pm 0.036$	$0.771\pm 0.045$	$0.398\pm 0.028$	$0.453\pm 0.056$	$0.924\pm 0.009$	$0.780\pm 0.042$	$0.669\pm 0.031$	$7.8\%\pm 15.1\%$	$0.648\pm 0.015$
Proposed Model (w/o TCGA cells)	$47$	$0.764\pm 0.079$	$0.759\pm 0.041$	$0.391\pm 0.020$	$0.519\pm 0.096$	$0.817\pm 0.115$	$0.739\pm 0.035$	$0.674\pm 0.022$	$7.3\%\pm 13.0\%$	$0.640\pm 0.009$

Table S7: Metrics for the Xenium Dataset (Overall) across different models.

Model	Total $N$	Bulk	Feat. Recon.	PS Cons.	Bulk	Signif. Genes (Bulk)
Model	Total $N$	PCC	Cos. Sim.	PCC	AUROC	(FDR $<$ 0.05)
Proposed Model (Full)	$1{,}724$	$0.881\pm 0.010$	$0.637\pm 0.014$	$0.893\pm 0.015$	$0.661\pm 0.003$	$72.6\%\pm 16.0\%$
Proposed Model (w/o Consistency)	$1{,}724$	$0.883\pm 0.014$	$0.586\pm 0.047$	$0.691\pm 0.034$	$0.651\pm 0.007$	$69.6\%\pm 14.9\%$
Proposed Model (w/o Feat. Recon.)	$1{,}724$	$0.888\pm 0.010$	$-$	$0.881\pm 0.007$	$0.649\pm 0.008$	$83.4\%\pm 15.0\%$
Proposed Model (w/o TCGA)	$-$	$-$	$-$	$-$	$-$	$-$
Proposed Model (w/o TCGA cells)	$1{,}724$	$0.882\pm 0.017$	$0.541\pm 0.061$	$0.865\pm 0.026$	$0.647\pm 0.006$	$79.3\%\pm 12.6\%$

Table S8: Metrics for the TCGA Dataset (Overall) across different models.

	CellViT	Classpose
Cell type	PQ / DQ	PQ / DQ
Neoplastic	$0.581$ / $0.71$	$0.600$ / $0.71$
Inflammatory	$0.417$ / $0.59$	$0.544$ / $0.65$
Connective	$0.423$ / $0.53$	$0.441$ / $0.55$
Dead	$0.149$ / $0.39$	$0.325$ / $0.428$
Epithelial	$0.583$ / $0.73$	$0.586$ / $0.73$

Table S9: Average 3-fold performance (panoptic and detection quality; PQ and DQ, respectively) comparison between Classpose and CellViT models trained on PanNuke * . The best performing metrics are in bold.

Figure S10: Architecture of the Multi-Scale Graph Neural Network (GNN).

Spatial Gene Expression Prediction

Spatial gene expression prediction infers tissue-resolved molecular activity from histology and spatial context.

See all 42 papers

Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference
Mar 24, 2026Zhiceng Shi, Changmiao Wang, Jun Wan +1Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images
Yunnan University, Kunming 650500, China · Shenzhen Research Institute of Big Data, Shenzhen 518172, China · Zhongnan University of Economics and Law, Wuhan 430073, China
Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images
Sep 29, 2026Zijun Gao, Chunbin Gu, Jinxi Xiang +2Spatial Transcriptomics Spatial Attention
The Chinese University of Hong Kong · Stanford University School of Medicine
Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis
Feb 26, 2026Bin Xu, Yufei Zhou, Boling Song +6Hierarchical GNNs Cancer Survival Prediction
Nanjing University of Information Science and Technology, Nanjing, China · Case Western Reserve University, Cleveland, OH, USA · Georgia Institute of Technology and Emory University, Atlanta, GA, USA +4

Mar 24, 2026J/K move · Enter open · S save

Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference

Zhiceng Shi, Changmiao Wang, Jun Wan, Wenwen Min

Yunnan University, Kunming 650500, China · Shenzhen Research Institute of Big Data, Shenzhen 518172, China · Zhongnan University of Economics and Law, Wuhan 430073, China

Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images Spatial Transcriptomics Heterogeneous GNNs Graph Contrastive Learning

Open paper AskPDF arXiv

While spatial transcriptomics (ST) has advanced our understanding of gene expression in tissue context, its high experimental cost limits its large-scale application. Predicting ST from pathology images is a promising, cost-effective alternative, but existing methods struggle to capture complex cross-slide spatial relationships. To address the challenge, we propose SpaHGC, a multi-modal heterogeneous graph-based model that captures both intra-slice and inter-slice spot-spot relationships from histology images. It integrates local spatial context within the target slide and cross-slide similarities computed from image embeddings extracted by a pathology foundation model. These embeddings enable inter-slice knowledge transfer, and SpaHGC further incorporates Masked Graph Contrastive Learning to enhance feature representation and transfer spatial gene expression knowledge from reference to target slides, enabling it to model complex spatial dependencies and significantly improve prediction accuracy. We conducted comprehensive benchmarking on seven matched histology-ST datasets from different platforms, tissues, and cancer subtypes. The results demonstrate that SpaHGC significantly outperforms the existing nine state-of-the-art methods across all evaluation metrics. Additionally, the predictions are significantly enriched in multiple cancer-related pathways, thereby highlighting its strong biological relevance and application potential.

Abstract

Spatial Gene Expression Prediction

Explore similar work

Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference

Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images

Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis