From 2.5D Stacking to Continuous 3D

Reconstructing True 3D
Tissue Topology

Capturing the 3D organization of cells is essential for deciphering complex biological processes, yet severely hindered by the destructive nature of physical sectioning. DeepSpatial is a generative framework driven by Optimal Transport Flow Matching that learns a continuous dynamic vector field — enabling the direct extraction of uninterrupted, infinitely resolvable tissue states at any arbitrary spatial depth.

Get Started Documentation GitHub
import scanpy as sc
import deepspatial as ds

adatas = [sc.read_h5ad(f"slice_{i}.h5ad")
          for i in range(5)]

model = ds.DeepSpatial()
model.setup_data(adatas)
model.build_model()
model.fit()

adata_3d = model.reconstruct_full_volume(
    adatas, thickness=10)

✓ Continuous 3D Volume Generated
Infinite Z-resolution
AnnData Compatible
Unbalanced OT Cell-to-cell coupling
•
Flow Matching Probability flow ODE
•
GiT Gene Diffusion Transformer
•
39,168,380 Cells in whole-brain atlas
•
STARmap / RIBOmap 3D ground-truth validated
•
openST / IMC Cross-modality generalization
•
129 Slices Allen Brain CCFv3 atlas
•
Unbalanced OT Cell-to-cell coupling
•
Flow Matching Probability flow ODE
•
GiT Gene Diffusion Transformer
•
39,168,380 Cells in whole-brain atlas
•
STARmap / RIBOmap 3D ground-truth validated
•
openST / IMC Cross-modality generalization
•
129 Slices Allen Brain CCFv3 atlas
The Pipeline

From Discrete Slices to Continuous Volume

DeepSpatial reframes 3D reconstruction as a continuous probability density evolution. Rather than merely inserting pseudo-2D planes, it models the multi-modal transitions between physical slices as mixed continuous-discrete dynamics via a scalable Transformer architecture.

1. Input 2D Slices
Multi-layer spatial omics: coordinates, gene/protein expression, cell labels, slide thickness & interval
UOT Coupling method
2. UOT Coupling
Unbalanced Optimal Transport constructs a cost matrix balancing spatial proximity, transcriptomic similarity, and cell-type penalty to establish biologically rigorous cell-to-cell correspondences
Flow Matching method
3. Flow Matching (GiT)
Gene Diffusion Transformer with adaLN-Zero conditioning learns a shared continuous vector field over spatial, transcriptomic, and categorical modalities
3D Virtual Tissue generation
4. 3D Virtual Tissue
ODE integration with density-preserving bidirectional sampling synthesizes uninterrupted tissue at any arbitrary physical depth
Interactive Explorer

3D Cell Atlas

Explore the reconstructed MERFISH mouse hypothalamus volume. Rotate, zoom and filter across annotated cell types — each point represents a single cell in its true 3D spatial context.

merfish_3d_mesh.html
WebGL Accelerated
Applications

Downstream Analysis

By providing an infinitely resolvable 3D virtual tissue, DeepSpatial catalyzes the shift from traditional planar analyses to true volumetric quantification — enabling 3D cell-cell communication mapping, spatial domain identification, and whole-organ atlas construction.

Virtual Slide

i. Virtual Slide

Generate virtual tissue slides at arbitrary Z-depths, revealing continuous 3D microenvironments destroyed by physical sectioning.

3D Spatial Domain

ii. 3D Spatial Domain

Apply CellCharter or similar methods to delineate continuous 3D spatial domains and architectures across the reconstructed volume.

Whole-Brain 3D Atlas

iii. Whole-Brain 3D Atlas

Scalable reconstruction of a 39.2M-cell mouse whole-brain spatial atlas from 129 CCF-aligned sections.

In Silico Sectioning

iv. In Silico Sectioning

Computationally slice the volume along coronal or sagittal planes for cross-platform comparison and topological analysis.

Open Source

Built for Researchers

DeepSpatial is built on AnnData and fully compatible with the Scanpy ecosystem. GPU-accelerated PyTorch implementation enables efficient 3D manifold recovery of large-scale spatial datasets.

Quick Start

$ pip install deepspatial
# Or install from source
$ git clone https://github.com/yyh030806/DeepSpatial.git
$ cd DeepSpatial
$ pip install -e .

Tutorials & API Reference

Step-by-step guides for MERFISH Mouse Hypothalamus, Human Breast Cancer IMC, and Deep-STARmap validation — plus full API documentation.

Read the Docs

Citation (BibTeX)

If DeepSpatial is useful in your research, please cite our paper.

@article{yang2026deepspatial, title = {Reconstructing True 3D Spatial Omics at Single-Cell Resolution}, author = {Yang, Yuhang and Luo, Yiming and Zhang, Kai and others}, journal = {Nature Computational Science}, year = {2026} }