# Spatial transcriptomics

Spatial transcriptomics models usually exchange representations only between cells or spots, with gene information arriving through the attached expression profile. SpaAxFormer keeps every cell–gene entry as its own state, first letting genes within a cell reference each other, then exchanging the same gene's state across spatial neighbors, with a separate cell-level summary. On six DLPFC slices its mean L1 imputation distance was lower than Impeller's, for example 0.239 versus 0.254 on slice 151510; on Stereo-seq and Slide-seq V2, Impeller had lower mean L1 and RMSE.

Spatial transcriptomics models usually exchange representations only between cells or spots, with gene information arriving through the attached expression profile. SpaAxFormer keeps every cell–gene entry as its own state, first letting genes within a cell reference each other, then exchanging the same gene's state across spatial neighbors, with a separate cell-level summary. On six DLPFC slices its mean L1 imputation distance was lower than Impeller's, for example 0.239 versus 0.254 on slice 151510; on Stereo-seq and Slide-seq V2, Impeller had lower mean L1 and RMSE.

Frozen pathology foundation-model embeddings transfer poorly to single-cell spatial gene expression prediction from H&E: on the 5K gene panel, mean per-gene Pearson correlation falls from 0.375 at pseudo-spot resolution to 0.065 at single-cell resolution. A set of single-cell-oriented lightweight adaptations, namely cell-localized token pooling, 40x field-of-view inputs, Poisson count supervision and LoRA fine-tuning, raises the same panel to 0.174 and beats SpatialEx on the 5K panel with AUROC 0.800 versus 0.642. Gains concentrate in spatially variable and disease-relevant genes, while absolute correlation across the full panel stays low.

Frozen pathology foundation-model embeddings transfer poorly to single-cell spatial gene expression prediction from H&E: on the 5K gene panel, mean per-gene Pearson correlation falls from 0.375 at pseudo-spot resolution to 0.065 at single-cell resolution. A set of single-cell-oriented lightweight adaptations, namely cell-localized token pooling, 40x field-of-view inputs, Poisson count supervision and LoRA fine-tuning, raises the same panel to 0.174 and beats SpatialEx on the 5K panel with AUROC 0.800 versus 0.642. Gains concentrate in spatially variable and disease-relevant genes, while absolute correlation across the full panel stays low.

Multimodal cell foundation models usually freeze each modality's encoder and train only a fusion interface, yet whether fusion adds information beyond the best single modality has lacked a test. Researchers introduce the Synergistic Information Score (SIS), which measures accessible synergy as the gain of a fused representation over the strongest unimodal one under a linear probe. Across lung, thymus and breast spatial transcriptomics, unimodal baselines suffice in redundancy-dominated tasks, while non-spectral fusion reaches higher SIS in the resolution-mismatched thymus task.

Multimodal cell foundation models usually freeze each modality's encoder and train only a fusion interface, yet whether fusion adds information beyond the best single modality has lacked a test. Researchers introduce the Synergistic Information Score (SIS), which measures accessible synergy as the gain of a fused representation over the strongest unimodal one under a linear probe. Across lung, thymus and breast spatial transcriptomics, unimodal baselines suffice in redundancy-dominated tasks, while non-spectral fusion reaches higher SIS in the resolution-mismatched thymus task.

Spatial transcriptomics sequencing is destructive, so each time point yields unaligned cell snapshots and the intermediate trajectories and cross-scale intercellular interactions stay unobserved. stMIND decomposes each cell's expression drift into a cell-intrinsic program, a local graph-difference operator over relative expression differences and spatial geometry within a radius, and a mass-weighted nonlocal mean-field attention operator outside it, with neighborhoods co-evolving as cells migrate. On axolotl telencephalon regeneration and zebrafish embryogenesis benchmarks it reaches the highest gene-expression accuracy, anatomical consistency and spatial coherence; ablations show both operators are needed, and the learned fields locate a regenerative transition at 10–15 days post-injury.

Spatial transcriptomics sequencing is destructive, so each time point yields unaligned cell snapshots and the intermediate trajectories and cross-scale intercellular interactions stay unobserved. stMIND decomposes each cell's expression drift into a cell-intrinsic program, a local graph-difference operator over relative expression differences and spatial geometry within a radius, and a mass-weighted nonlocal mean-field attention operator outside it, with neighborhoods co-evolving as cells migrate. On axolotl telencephalon regeneration and zebrafish embryogenesis benchmarks it reaches the highest gene-expression accuracy, anatomical consistency and spatial coherence; ablations show both operators are needed, and the learned fields locate a regenerative transition at 10–15 days post-injury.

Spatial transcriptomics alignment lacked an auditable way to evaluate AI agents, so Paper2Bench turned two published alignment benchmark studies into 121 executable tasks, had agents solve them in containers with ground truth withheld, and re-ran 16 published baselines under one scorer. Across the 121 tasks, the best agents (GPT-6-Astra and Gemini-4-Argon) matched or outperformed all baselines on 80% of tasks; distilling agent runs into a standalone program beat 87% of baselines on held-out tasks.

Spatial transcriptomics alignment lacked an auditable way to evaluate AI agents, so Paper2Bench turned two published alignment benchmark studies into 121 executable tasks, had agents solve them in containers with ground truth withheld, and re-ran 16 published baselines under one scorer. Across the 121 tasks, the best agents (GPT-6-Astra and Gemini-4-Argon) matched or outperformed all baselines on 80% of tasks; distilling agent runs into a standalone program beat 87% of baselines on held-out tasks.

Conventional 3′-end single-cell and spatial transcriptomes were built to count gene expression, yet a small subset of reads carrying untemplated poly(A) tails marks where RNAs actually end. scPolyA uses those tail reads to discover polyadenylation sites de novo, then sets UMI-counting intervals from total-read coverage and reports per-cell or per-spot terminal-site usage, 3′ UTR length and intronic polyadenylation. Across eight single-cell datasets, 63.3% of its sites fell within 25 nt of PolyA_DB 3, above polyApipe, scAPAtrap, Sierra and SCAPE and close to DeepPASS-filtered SCAPTURE.

Conventional 3′-end single-cell and spatial transcriptomes were built to count gene expression, yet a small subset of reads carrying untemplated poly(A) tails marks where RNAs actually end. scPolyA uses those tail reads to discover polyadenylation sites de novo, then sets UMI-counting intervals from total-read coverage and reports per-cell or per-spot terminal-site usage, 3′ UTR length and intronic polyadenylation. Across eight single-cell datasets, 63.3% of its sites fell within 25 nt of PolyA_DB 3, above polyApipe, scAPAtrap, Sierra and SCAPE and close to DeepPASS-filtered SCAPTURE.

ALHENA distills spatial transcriptomic information into a routine H&E encoder that reads only images at inference. Rather than regressing individual genes, it trains the H&E representation to match the sample-to-sample similarity structure of two frozen transcriptomic models at cell and neighborhood scales. Across 110 cancer-type and signature combinations in TCGA, mean Spearman correlation was 0.341 versus 0.161 for unadapted UNI2-h; on the Thunder pathology benchmark the median change was −0.004.

ALHENA distills spatial transcriptomic information into a routine H&E encoder that reads only images at inference. Rather than regressing individual genes, it trains the H&E representation to match the sample-to-sample similarity structure of two frozen transcriptomic models at cell and neighborhood scales. Across 110 cancer-type and signature combinations in TCGA, mean Spearman correlation was 0.341 versus 0.161 for unadapted UNI2-h; on the Thunder pathology benchmark the median change was −0.004.

Time-series spatial transcriptomics measures each tissue section once, so how cells migrate, proliferate and influence one another can only be inferred indirectly. CytoBridge learns continuous population evolution in a joint space of transcriptomic state and spatial coordinates, splitting velocity into an interaction term estimated from spatial neighborhoods and ligand–receptor priors and an intrinsic-context term, while allowing cell numbers to grow or shrink. Across five datasets on three spatial transcriptomics platforms it generates unmeasured intermediate stages; in a leave-one-stage-out benchmark it had the lowest Sliced-W2 in 12 of 33 combinations, more than any other method or control.

Time-series spatial transcriptomics measures each tissue section once, so how cells migrate, proliferate and influence one another can only be inferred indirectly. CytoBridge learns continuous population evolution in a joint space of transcriptomic state and spatial coordinates, splitting velocity into an interaction term estimated from spatial neighborhoods and ligand–receptor priors and an intrinsic-context term, while allowing cell numbers to grow or shrink. Across five datasets on three spatial transcriptomics platforms it generates unmeasured intermediate stages; in a leave-one-stage-out benchmark it had the lowest Sliced-W2 in 12 of 33 combinations, more than any other method or control.