Path2ST: Hierarchical Cell-Tissue Grounded Cross-Modal Translation for Spatial Transcriptomics
Authors: Ruochen Liu, Wei Lou
Organizations: Faculty of Science and Engineering University of Liverpool Liverpool, United Kingdom · College of Mathematical Medicine Zhejiang Normal University Jinhua, China
Predicting spatial gene expression from hematoxylin and eosin (H&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST). However, existing methods treat H&E images as generic visual inputs and ignore their intrinsic biological hierarchy, where spatially organized cell types collectively form functional tissue microenvironments that govern local gene expression programs. To bridge this gap, we formulate H&E-to-ST prediction as a cross-modal semantic translation task and propose Path2ST, a hierarchically grounded autoregressive framework featuring three key components: (i) a Hierarchical Cell-Tissue Conditioning mechanism that fuses explicit and implicit cellular features with tissue-level semantic representations to construct hierarchical conditioning signals; (ii) a Scale-Adaptive Autoregressive Generation process over a hierarchical semantic vocabulary, enabling coarse-to-fine, biologically consistent expression synthesis; and (iii) SpectraLoss, a full-spectrum objective that jointly enforces ordinal fidelity, models transcriptional bursts, and aligns semantic structures with cell types. Extensive experiments on three datasets demonstrate state-of-the-art performance, validating that Path2ST generates highly accurate and spatially coherent transcriptomic profiles. The related code is released at https://github.com/RuochenLiu23/Path2ST.
Figures & tables
Figure 1. Pathological images exhibit an intrinsic hierarchical structure highly analogous to natural language, framing spatial transcriptomics generation as a translation task facilitates comprehensive modeling of biologically meaningful hierarchical associations, grounding the generation process in authentic biological semantics.
Figure 2. Overview of the Path2ST framework. The proposed framework comprises three key components: Hierarchical Cell-Tissue Conditioning for constructing biologically grounded generation conditions, Scale-Adaptive Autoregressive Generation for coarse-to-fine multi-scale gene expression generation, and SpectraLoss, a composite objective combining GKL, ZINB, and semantic contrastive losses.
Figure 3. The Hierarchical Cell-Tissue Conditioning module integrates cellular and tissue information via two components: a Hybrid Explicit-Implicit Cross-Attention that captures tissue-context-aware cellular semantic features, and a Dynamic Gating mechanism that adaptively controls the injection strength of fused cellular signals.
Method
PRAD
HER2ST
Healthy Mouse Brain
PCC-10 ↑
PCC-50 ↑
PCC-200 ↑
MSE ↓
MAE ↓
PCC-10 ↑
PCC-50 ↑
PCC-200 ↑
MSE ↓
MAE ↓
PCC-10 ↑
PCC-50 ↑
PCC-200 ↑
MSE ↓
MAE ↓
BLEEP
0.580
0.510
0.316
2.475
1.091
0.773
0.714
0.565
1.243
0.833
0.342
0.280
0.156
1.591
0.987
TRIPLEX
0.620
0.544
0.423
1.319
0.836
0.783
0.714
0.586
1.212
0.857
0.501
0.445
0.312
1.157
0.822
M2OST
0.602
0.551
0.442
1.290
0.862
0.810
0.759
0.660
1.151
0.820
0.456
0.387
0.231
1.148
0.861
STEM
0.636
0.555
0.403
1.457
0.857
0.831
0.770
0.625
1.199
0.787
0.526
0.452
0.331
1.235
0.864
GenAR
0.702
0.650
0.512
1.191
0.771
0.842
0.784
0.663
1.082
0.745
0.568
0.503
0.367
1.138
0.805
Table 1. Comparison with state-of-the-art methods across PRAD, HER2ST, and Healthy Mouse Brain datasets. ↑ indicates higher is better, ↓ indicates lower is better. Best results are in bold .
Method
PCC-10 ↑
PCC-50 ↑
PCC-200 ↑
MSE ↓
MAE ↓
Baseline
0.736
0.659
0.519
1.148
0.753
+ Hierarchical Conditioning
0.748
0.673
0.549
1.080
0.738
+ Scale-Adaptive Autoregressive Generation
0.752
0.679
0.554
1.069
0.736
+ ZINB Loss
0.756
0.686
0.558
1.058
0.733
+ Soft-Positive Semantic Contrastive Loss (Ours)
0.767
0.701
0.578
1.005
0.725
Table 2. Ablation study on PRAD dataset.
Query
PCC-10 ↑
PCC-50 ↑
PCC-200 ↑
MSE ↓
MAE ↓
1
0.767
0.701
0.578
1.005
0.725
2
0.757
0.685
0.553
1.048
0.733
3
0.751
0.671
0.535
1.069
0.765
4
0.765
0.689
0.557
1.068
0.739
5
0.753
0.668
0.514
1.096
0.771
Table 3. Hyperparameter analysis on the query number Nq on PRAD dataset.
Figure 4. Visualization of cell classification and neoplastic cell clustering on the MEND145 sample from PRAD, alongside spatial expression maps of cancer biomarker genes SORD, TSPAN1, and TMPRSS2, where darker colors indicate lower gene expression at the corresponding spot.