Single-cell and spatial transcriptomics

Neueste Paper

• CellMSA: Context Modeling for Single-Cell Representation Learning

  Suyuan Zhao, Minghao Liu, Yizhen Luo, Zaiqing Nie · 1. Oktober 2026

  Single-cell transcriptomics enables profiling of cellular states at unprecedented resolution, but its high dimensionality, sparsity, and technical batch effects pose significant challenges for representation learning. Existing single-cell foundation models typically encode each cell independently or…

• scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning

  Yunhak Oh, Yoonho Lee, Junseok Lee, Namkyeong Lee, Sang-Yeon Hwang, Yinhua Piao, Hyomin Kim, Seonghwan Kim, Jaechang Lim, Woo Youn Kim, Sungsoo Ahn, Chanyoung Park · 1. Oktober 2026

  Single-cell RNA-seq representation learning is fundamentally label-free: cell identities, states, and contexts are not fixed training targets, so what constitutes signal or nuisance is analysis-dependent. A single representation must therefore preserve biological identity and state, remain robust to…

• GATE-ST: Gene-Aware Text-image Encoder for Spatial Transcriptomics

  Lucas Ni, Jian Luo, Wentao Huang, Chao Chen · 1. Oktober 2026

  Spatial transcriptomics enables spatially resolved gene expression analysis from slide-level images while preserving morphological features, providing valuable information for studying disease mechanisms and developing treatments. However, spatial gene expression profiling typically requires expensi…

• KoopCell: Koopman-Based Generative Model for Learning Single-Cell Dynamics from Distribution Snapshots

  Wanfeng Lu, Yutong Zhang, Keyi Zhou, Chenxin Ge, Wei Lin, Qunxi Zhu · 29. September 2026

  Learning population dynamics from temporally sparse, unpaired distribution snapshots is a fundamental challenge in developmental biology. Recent approaches based on neural differential equations and flow matching can interpolate between observed population snapshots, but may struggle to extrapolate …

• AmbiModBench: Benchmarking Gene Perturbation Prediction Beyond Shared Responses

  Sikai Huang, Zhiwen Yang, Kai Yu, Jiayuan Chen, Stan Z. Li · 29. September 2026

  Predicting cellular responses to genetic perturbations helps prioritize experiments in single-cell genomics, where exhaustive measurement is infeasible. While computational models increasingly predict these responses, three evaluation deficiencies obscure what their scores demonstrate. First, absolu…

• CRNDiff: Count-Native Diffusion Framework via Chemical Reaction Networks

  Yuxuan Qiu, Praful Gagrani, Tetsuya J Kobayashi · 28. September 2026

  Scientific measurements such as single-cell RNA (scRNA) sequencing often take the form of nonnegative integer counts, whereas continuous-state diffusion models approximate this discrete structure using continuous coordinates. Building on stochastic chemical reaction networks (CRNs), a class of count…

• SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference

  Shiting Ruan, Xitong Ling, Qiming He, Ziyou Yan, Huaitian Yuan, Tian Guan, Ying Xiao, Xu Guan, Yonghong He · 25. September 2026

  Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting spatial expression from routinely available hematoxylin and eosin (HE) images therefore offers a scalable alternative. However, conventional method…

• Pheno-GS: Phenoscape-scale Geodesic Sinkhorn

  Alistair Wilkinson, Christopher J. Tape, Smita Krishnaswamy · 24. September 2026

  High-throughput single-cell data is now collected across large patient cohorts. Understanding patient-level heterogeneity from cellular-level data motivates phenoscaping: embedding each single-cell distribution as a "datapoint," with distances given by optimal transport (OT). Computing geometry-awar…

• Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics

  Zheyu Zhu, Junchao Zhu, Fengbei Liu, Tianyuan Yao, Gelei Xu, John Cannon, Haichun Yang, Yuankai Huo, Mert R. Sabuncu, Ruining Deng · 24. September 2026

  Spatial transcriptomics (ST) measures gene expression in tissue context, but dense capture grids can be costly and may repeatedly sample morphologically similar regions. Most active learning strategies were developed for categorical labels and independent samples. We conduct a retrospective pool-bas…

• Correlation-Guided Flow Matching with Annealed Masking for Spatial Transcriptomics Generation

  Yupei Zhang, Hao Chen, Li Pan, Chao Li, Xiaohan Xing · 22. September 2026

  Spatial transcriptomics (ST) provides spatially resolved gene expression profiling but remains expensive, motivating the prediction of ST from histology images. Generative models have emerged as a mainstream paradigm for ST prediction due to their ability to model the conditional distribution of gen…

• CellRFT: Reinforcement Fine-Tuning for Single-Cell Perturbation Modeling

  Jie Yan, Li Liu, Hanze Guo, Jiaxin Hu, Houxin He, Xiaoning Qi, Haoran Wang, Cong Li, Zhong-Yuan Zhang, Yong Wang · 18. September 2026

  Predicting cellular responses to perturbations supports the study of gene function, disease mechanisms, and therapeutic strategies. Despite advances in single-cell perturbation modeling, existing models typically optimize surrogate losses that do not directly reflect the biological criteria used for…

• Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics

  Daniela Vega, Paula C\'ardenas, Hannah Ceballos, Leonardo Manrique, Pablo Arbela\'ez · 16. September 2026

  Spatial Transcriptomics (ST) has transformed biomedical research by enabling the spatial mapping of gene expression across tissue sections. However, high operational costs, specialized equipment requirements, and sensitivity to experimental noise limit the accessibility and scalability of ST. Recent…

• Towards a knowledge-enhanced single-cell foundation model

  Hanqing Zhang, Jie Bao, Mei Ma, Shuai Liu, Jiaying Ma, Jiaguan Liu, Jiaxiao Li, Zhenbo Li, Wenwen Gong, Zhijun Ca · 15. September 2026

  Single-cell foundation models (scFMs) increasingly rely on large-scale transcriptomic pretraining, yet expanding pretraining data can yield diminishing gains while substantially increasing computational cost. Our data scaling analyses showed that incorporating biological knowledge, including cell-le…

• scDEFT: A deep learning framework for drug-effect prediction and counterfactual reasoning

  Murthy Devarakonda · 11. September 2026

  Longitudinal single cell atlases now capture matched pre treatment and post treatment states from responders and non responders, presenting an opportunity to mechanistically explain why two patients on the same drug diverge. We introduce scDEFT (single cell Drug EFfect Transducer), which treats a dr…

• Subcellularly Resolved Single-Cell Embedding Learning with Transcriptomic data, Protein Structure and Localization Information

  Zhen Zhou, Jiachen Li, Yuan Liu, Xiaoyong Pan, Hong-Bin Shen · 3. September 2026

  Existing cell embedding methods predominantly rely on transcriptomic or proteomic measurements and represent each cell as a holistic entity, thereby overlooking the subcellular localization of individual molecules. Moreover, they rarely incorporate protein structural information, despite its fundame…

• PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction

  Handong Wang, Jiaxin Qi, Haochen Feng, Baisheng Lai · 2. September 2026

  Predicting transcriptional responses to specific perturbations is critical for understanding cellular regulatory mechanisms and accelerating drug discovery. Single-cell RNA sequencing destroys each measured cell, yielding only unpaired populations of control and perturbed cells. However, existing me…

• BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval

  Seungik Cho, Betul Orcan-Ekmekci · 26. August 2026

  Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences. Existing histology--transcriptomics objectives can emphasize instance-level matching even when non-paired spots share molecular or spatial context. …

• OmicSync: Reliability-Aware Spatial Multi-Omics Clustering with Evidence-Constrained LLM Reasoning

  Rabeya Tus Sadia, Qiang Ye, Qiang Cheng · 25. August 2026

  Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discovery methods provide only cluster assignments, without indicating assignment reliability, modality contributions, or why a domain decision should be tru…

• DeMixPert: Decomposed Response Modeling with Gaussian Mixtures for OOD Single-Cell Perturbation Prediction

  Jiawen Liu, Xuechenxiao Cao, Yutong Li, Bing Liu, Jiaming Liang, Tinghe Zhang, Xiaoqi Sheng, Hongmin Cai · 25. August 2026

  Predicting transcriptome-wide responses to unseen genetic perturbations remains a major computational challenge because accurate prediction requires recovering both perturbation-specific transcriptional shifts and heterogeneous cellular responses. Existing methods often entangle deterministic respon…

• DMT-Dens: Density-preserving manifold visualization for biological data

  Ruizhe Wang, Yixuan Dong, Bolin Yang, Bingo Wing-Kuen Ling, Fuji Yang, Zelin Zang · 19. August 2026

  Motivation: Low-dimensional embeddings are widely used to explore cell-state heterogeneity in single-cell and other high-dimensional biological data. Although many methods preserve local neighborhoods, they may distort the apparent sampling density of processed observations, altering the visual cont…

• PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data

  Zhenchao Tang, Xiaogang Xu, Tianxu Lv, Jiahui Guan, Jiale Zhou, Haohuai He, Zhi Song, Hanbo Huang, Jiehui Huang, Jiafei Wu, Zhe Liu · 18. August 2026

  Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here we show that cellular perturbation atlases can instead become reinforcement-learning environments, where measured gene responses provide computable re…

• A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data

  Mary Chriselda Antony Oliver, Kaitlyn Hohmeier, Tuyen Tran, Alejandra Castillo, Caroline Moosmüller, Shiying Li · 18. August 2026

  High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeated measurements of the same cells over time. Consequently, temporal and spatial dynamics must be inferred from independe…

• PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining

  Azim Dehghani Amirabad, Junchao Zhu, Pushpak Pati, Walid Abdelmoula, Tommaso Mansi, Rui Liao · 18. August 2026

  Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression. However, existing approaches suffer from two key limitations: spatially informative gene selection is often dominated by ubiquitou…

• Path2ST: Hierarchical Cell-Tissue Grounded Cross-Modal Translation for Spatial Transcriptomics

  Ruochen Liu, Wei Lou · 18. August 2026

  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 cel…

• $D^{2}R^{2}$: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction

  Ninghan Fan, Qi Liu, Xunuo Zhu, Yukai Sun, Luyuan Chen, Xuheng Zhou, Yuetian Du, Ming Kong, Xiaojun Zhu, Jie Liu, Zhan Zhou, Qiang Zhu · 18. August 2026

  Predicting single-cell transcriptomic responses to genetic perturbations is central to functional genomics and virtual-cell modeling. Existing approaches, however, typically predict an entire expression profile as a whole, leaving the order in which individual gene responses are generated unmodeled.…