Trajectory-conditioned reconstruction of single-cell expression suggests regulatory programs

Foundation models for single-cell transcriptomics learn cell representations from millions of profiles, but are commonly pretrained on unordered cells and therefore do not explicitly condition on cell history. We introduce single-cell Transformer-iN-Transformer (scTNT), which conditions gene-expression reconstruction on inferred trajectories, represented here as ordered cell sequences. scTNT combines a frozen reduced-layer scGPT autoencoder with a trainable decoder-only transformer over sequences of latent cell embeddings and is trained by masked gene-expression reconstruction. On a CD8 T-cell exhaustion dataset with optimal transport-derived cell sequences, scTNT improves masked reconstruction relative to the scGPT baseline and outperforms alternative sequence backbones under controlled evaluations. We further propose a gradient-based gene-history attribution pipeline and apply TRRUST regulon enrichment to generate hypotheses about context-associated regulatory programs.