Single-cell and spatial transcriptomics

Single-cell foundation modeling with species-nativeprotein tokens links regenerative competence across frog and mouse

This paper develops a species-native representation for Xenopus laevis to compare regenerative competence across species and identifies a vector competence ruler that distinguishes regenerative from fibrotic repair in mice.

simPIC: flexible simulation of single-cell ATAC-seq paired-insertion counts from individuals to populations

simPIC simulates single-cell ATAC-seq count data across individuals and populations to enable controlled evaluation of chromatin accessibility QTL mapping methods.

A human cytokine response atlas to reconstruct underlying gene regulatory networks

This paper constructs a cytokine response atlas using single-cell data to model gene regulatory networks and identifies SOD2 as a key hub in innate immunity.

A single-nucleus atlas of the adult laying hen liver reveals metabolic specialization and improved cellular resolution through enhanced genome annotation

The paper creates a single-nucleus RNA sequencing atlas of the adult chicken liver, revealing cellular composition and showing improved resolution through enhanced genome annotation.

Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE

This paper introduces SPAE, a model for analyzing cell cycle dynamics and states in scRNA-seq data, showing improved accuracy and robustness in cell cycle characterization and cancer cell cycle transition prediction.

Scalable single-cell isoform profiling with sequencing-by-expansion

This paper introduces a high-throughput single-cell RNA isoform sequencing method using sequencing-by-expansion (SBX) and the CUDLL algorithm, achieving high accuracy and enabling isoform-level analysis across cell types.

Analog intrinsic recoding measures RNA dynamics without chemical conversion

The paper introduces AIR-seq, a method that measures RNA dynamics without chemical conversion, improving RNA velocity inference and revealing hidden regulation.

Cell identities along the proximal-distal and micropylar-chalazal axes in the Arabidopsis heart-stage seed

The paper uses single-cell RNA-sequencing to analyze cell diversity in Arabidopsis seeds, identifying distinct cell clusters and spatial domains along developmental axes.

Ribosome-tethered in situ sequencing for single-cell translatome analysis

The paper introduces Ribo-ISS, a spatial translatomics method for single-cell translatome profiling in mouse brain, revealing transcription-translation uncoupling and cell-type-specific responses to sleep deprivation.

Dataset-Dependent Utility of Discrete Ricci Curvature for Transition-Region Ranking in Single-Cell Lineage Graphs

The paper evaluates the utility of discrete Ricci curvature in single-cell lineage graphs, finding it adds value in specific datasets but is not sufficient on its own.

Accurate and memory-efficient cell type annotation from multimodal single-cell RNA and protein data

pyODIN improves cell type annotation by integrating RNA and protein data, achieving higher accuracy and resolving finer cell subtypes than existing methods.

A user-friendly single-nucleus RNA sequencing pipeline to identify alterations in gene expression in small brain circuits

The paper modifies single-nucleus RNA sequencing protocols to study gene expression in small brain circuits, focusing on the dentate gyrus in a mouse Alzheimer's disease model.

scINTILLA: Single-Cell Integrated Inference, Labelling, and Landscape Analysis for Cell-Type Annotation Quality Assessment

scINTILLA assesses cell-type annotation quality in single-cell datasets by combining supervised and unsupervised learning to identify label ambiguity and hidden heterogeneity.

Routine FFPE sections support clinically compatible single-nucleus transcriptomics across six human cancer types

This paper develops a workflow for single-nucleus transcriptomics from FFPE tumor samples, showing reproducible cellular composition analysis across six cancer types.

The Proposed Bone Post-Arterial Type R Capillaries Resolve into Venous and Fatty Acid-Handling Endothelial Cells

The paper redefines skeletal endothelial heterogeneity by showing the proposed type R population is part of a venous continuum and includes fatty acid-handling endothelial cells.

CellCover Defines Marker Gene Panels Capturing Developmental Progression in Neocortical Neural Stem Cell Identity

CellCover identifies marker gene panels for cell types by solving a set-covering problem, outperforming differential expression methods in capturing cell-specific signals and developmental progression.

Interpretable gene networks from single-cell foundation models reveal conserved neurogenic dysfunction in Parkinson's disease

The paper introduces scGENet, a framework to generate interpretable gene networks from single-cell foundation models, revealing conserved neurogenic dysfunction in Parkinson's disease.

Essential Resources for Curated Immune Markers and Antibody Panels in Cytometry Analysis

The paper introduces ImapDB, a database for immune markers and antibody panels, to streamline immune profiling assays and enhance research efficiency.

RIPPLE: replicate-aware detection of cell-type-anchored proximity gradients in spatial transcriptomics

RIPPLE detects cell-type-anchored proximity gradients in spatial transcriptomics data by analyzing gene expression variation with distance to a query cell type.

Spaceland: Histology-Guided Reconstruction of High-Resolution Whole-Organ 3D Molecular Atlases from Sparse Spatial Transcriptomics

Spaceland reconstructs high-resolution 3D molecular atlases from sparse spatial transcriptomics and histology, improving resolution and accuracy across multiple biological contexts.

Single-cell foundation models identify shared and divergent transcriptomic signatures of aging across invertebrates and mammals

GPSNorm: Gaussian Process Spatial Normalization for Spatial Transcriptomics

GPSNorm fits slide, spatial technical and biological effects in one Bayesian negative binomial model, carrying normalization uncertainty into differential expression and lowering log-fold change error in simulations.

Pseudotime graph diffusion for post hoc visualization of inferred single-cell trajectories

PGD improves visualization of single-cell trajectories by smoothing cell-level features along pseudotime, enhancing continuity and structure in complex biological processes.

Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI

The paper introduces SEEK-VFI, a method for selecting trajectory-relevant genes in single-cell analysis, which improves trajectory inference and downstream analyses.

Towards Principled Evaluation of Single-Cell Perturbation Prediction Models

The paper evaluates single-cell perturbation prediction models by developing a taxonomy and tool to standardize benchmarking protocols across datasets.

IDEAL-Age: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

IDEAL-Age is a deep learning framework that profiles immunological aging at single-cell resolution, showing improved predictive performance and revealing cellular contributions to aging processes.

Spatium: A Protein Language Foundation Model for Spatial Proteomics

Spatium is a foundation model for spatial proteomics that learns stable cell representations from constrained protein measurements, enabling accurate cell identity recovery and spatial microenvironment analysis.

Deep learning enables cross-species annotation and attribution of ageing states in haematopoietic stem and immune cells

The paper develops a cross-species deep learning framework to annotate and attribute ageing states in haematopoietic stem and immune cells, achieving high AUROC scores in human data.

PROTOTYPE-BASED CONTINUAL CELL-TYPE ANNOTATION REVEALS CELLULAR STATE TRANSITIONS IN EXPANDING SINGLE-CELL ATLASES

scEvolver enables continual cell-type annotation across expanding single-cell atlases, revealing cellular state transitions in disease contexts.

Integrative AI-Enabled Virtual Cell Modeling Reveals a Clinically Relevant Latent Effector State of Human CD8 T Cells Undetectable by Conventional Analyses

The paper uses AI to model CD8 T-cell responses during ICI therapy, identifying a latent effector state that conventional methods miss.