Sebastian Birk

Multi-scale modeling of human tissues from spatial transcriptomics with TERRA

Spatial transcriptomics maps gene expression at cellular resolution, revealing how cells organize into multicellular niches. Yet computational analyses remain dataset-specific, without a transferable representation of tissue organization that generalizes across datasets, tasks and tissues or predicts how tissues behave under perturbation. We present TERRA, a foundation model pretrained on 112 million human cells profiled by spatial transcriptomics. From a single pretrained backbone, TERRA yields embeddings at the scale of cells, the genes they express and the neighborhoods in which they reside, and supports spatial in silico perturbation, all applied zero-shot to unseen tissues. At the cell level, in newly generated spatial data for developing pancreas, TERRA identified an islet-associated capillary state which we posit represents a developmental precursor of the mature islet microvasculature. At the gene level, in untreated kidney sections, in silico knockout of immune-checkpoint targets predicted a gene program of immune-checkpoint-blockade-associated nephrotoxicity, which we validated in treatment-exposed tissue and recovered in blood. At the neighborhood level, TERRA mapped macrophages across tissues to identify recurring cross-organ niches, which we term archetypes, including a tumor-boundary niche associated with poor prognosis in kidney cancer. Together, TERRA captures the spatial and multicellular logic of human tissue and predicts, in silico, its response to perturbation, providing a multi-scale framework for tissue biology, therapeutic development and clinical application.

G2T: Tissue Reconstruction from Gene Expression via Embedding-Distance Flow Matching

Single-cell RNA sequencing (scRNA-seq) profiles transcriptomes at high resolution but discards the spatial context of cells within a tissue—information that is essential for studying intercellular mechanisms and tissue architecture. Spatial transcriptomics (ST) retains coordinates but, depending on the assay, trades this off against gene-panel breadth, spatial resolution, or cost. We present G2T (Gene-to-Tissue), a generative deep learning model that reassembles a tissue from gene expression — its only observed input — by predicting the matrix of pairwise distances between cells in a learned embedding space. G2T uses an attention-based Transformer with an Euclidean-Distance-Matrix (EDM) output head and is trained with conditional flow matching: the network learns to denoise corrupted cell positions, conditioned on the slice’s gene expression, by predicting per-cell embeddings whose pairwise squared distances match the ground-truth distance matrix. At inference, a fast locally-optimal-block (LOBPCG) multidimensional scaling step turns the predicted distance matrix into 2-D coordinates. On a published MERFISH mouse primary motor cortex benchmark, G2T improves over the previous state-of-the-art method, LUNA, across all three standard metrics— Spearman correlation of pairwise-distance ranks, Contact F1, and per-cell-class Sum RSSD — and even larger relative gains on the mouse central-nervous-system scRNA-seq atlas, evaluated against an imputed spatial reference (STARmap PLUS-integrated locations, not measured coordinates). By predicting this geometry in a higher-dimensional embedding space rather than regressing 2-D coordinates, G2T relaxes the 2-D output parameterisation of prior diffusion-based methods and yields a compact, scalable building block for reconstructing tissue from dissociated cells, enabling downstream spatial niche and cell–cell communication analysis.

Learning Discrete Cell and Niche Codes from Spatial Transcriptomics Using Dual Residual Vector Quantization