Single-cell and spatial transcriptomics

STiLE automates TMA dearraying using cell centroid coordinates, achieving high accuracy and robustness across platforms and artifacts.

This paper creates a cross-species single-cell atlas of Macaca fascicularis using human reference labels to improve target qualification and toxicity interpretation in preclinical research.

This paper introduces scAPEX-seq, a method to map subcellular transcriptomes, and identifies CTSW as a regulator of CAR T cell function in tumor co-cultures.

The paper presents a low-cost lab-made spatial transcriptomics chip that enables isoform-resolved analysis using a dual-platform sequencing workflow, identifying novel splicing events in tomato-pepper grafts and mouse embryos.

InSTaPath integrates spatial transcriptomics and histopathology images using multimodal topic learning to improve spatial domain identification and uncover gene-morphology relationships.

The paper develops a method to co-profile transcriptomes and proteomes of rare CSF-CTCs, revealing malignant dormancy and post-transcriptional buffering as key mechanisms of treatment resistance.

The paper maps gene expression responses to loss of 1,947 disease-associated genes across the mouse brain using Perturb-seq, revealing cell-type-specific essentiality and transcriptional programs.

scRGCL improves single-cell clustering by integrating contrastive learning with cluster-level guidance, achieving better performance on multiple datasets.

This paper introduces a unified dynamical-systems and control-theoretic model to predict single-cell fate dynamics by integrating pseudotime, RNA velocity, optimal transport, and stochastic trajectories under partial observability.

The paper introduces CytoType and ESM-CE, species-specific small models for cell type classification that achieve performance comparable to large foundation models with fewer parameters.

The paper introduces mCanonicalTockySeq, a framework that integrates temporal and developmental progression using fluorescent timer data, showing it can map human thymic cells to a mouse reference and correlate temporal coordinates with age.

DeSCENT improves cancer survival analysis by integrating bulk RNA-seq deconvolution with single-cell RNA-seq data across multiple cancer cohorts.

SpatialFusion identifies spatial niches through pathway activation patterns, revealing pre-malignant and malignant microenvironments in cancer tissues.

GOTFlow infers directed population transitions from cross-sectional biomedical data using optimal transport, revealing biologically meaningful dynamics in endometrial remodelling, breast cancer risk, and prion disease.

PalmaClust detects ultra-rare cell types in scRNA-seq by fusing graphs based on the Palma ratio, improving rare-class F1 scores by 20% over existing methods.

This paper develops an in vivo multiomic Perturb-seq platform to improve gRNA recovery and applies it to study neurodevelopmental disorder risk genes in the cortex.

CROCHET is a pipeline for automated analysis of single-cell spatialomic data and creation of visual atlases, supporting diverse data modalities and downstream analyses.

The paper uses spatial transcriptomics to identify immune hubs in TNBC lymph node metastases driven by TMSB4X and CD74, revealing spatially expanded inflammatory networks and myeloid-endothelial signaling.

The paper uses spatial transcriptomics to analyze thyroid heterogeneity, identifying a damage-response thyrocyte state that balances with hormone-producing states across scales.

SpotGraphs provides a flexible R package for directly interacting with spatially resolved transcriptomic data through graph analysis, offering tools for quality filtering and adjacency editing.

ITEC reconstructs high-fidelity cell lineages and fate maps of embryos with over 99.7% accuracy using iterative tracking and error correction.

This paper benchmarks 26 spatial domain detection methods across real and semi-synthetic spatial transcriptomics data to identify performance drivers and introduce a modular benchmarking framework.

The paper introduces pertTF, a transformer-based model for predicting genetic perturbation responses, showing improved performance in expression prediction and cell identity shifts across cellular contexts.

SC-BIG improves single-cell SNV detection by integrating bulk sequencing data, outperforming existing methods in simulated scenarios.

UniST reconstructs dense 3D spatial transcriptomics data from sparse serial sections, restoring structural continuity and biologically meaningful expression patterns in multiple tissue contexts.

The paper introduces gateway analysis to identify rare transient cell states at fate transitions in single-cell atlases and finds it detects previously missed molecular programs.

STEVE evaluates cell-type annotation robustness in scRNA-seq datasets through subsampling, novel cell detection, and benchmarking against ground-truth labels.

The paper improves the FLASH-seq protocol for long-read scRNA-seq on ONT, develops barcoding strategies, and presents a bioinformatics pipeline, while highlighting technical challenges.

scprocess is a Snakemake pipeline that automates and integrates single cell RNA sequencing data processing, enabling reproducible and scalable analysis of large datasets.

MOSAICField aligns spatial slices across modalities to reconstruct 3D tissue models and map morphology, outperforming existing methods on simulated and prostate cancer data.