Single-cell and spatial transcriptomics

The paper compares gene co-expression networks between mouse and human frontal cortex at single-cell resolution, finding modest gene partner similarity but strong module conservation and no species-specific disease gene partner differences.

This study shows that spatial RNA and protein measurements are largely overlapping for lineage markers and abundance but discordant for specific molecules and spatial features, indicating they are complementary rather than interchangeable.

The paper introduces an imaging-based pooled screening method to identify genetic perturbations and their effects on cell states and neighboring cells in intact tissues.

The paper derives a closed-form cell quota to determine the number of cells needed to accurately estimate perturbation direction in single-cell screens, validated across multiple datasets.

The paper introduces c-UMAP, a cell-driven information fusion framework for single-cell RNA-seq data that improves clustering accuracy and interpretability over traditional methods.

This paper investigates whether histology can improve transcriptomic foundation models by providing cross-modal supervision, but finds limited benefits due to slide-specific structure in gene-expression embeddings.

scDEcrypter improves differential expression analysis in viral infection studies by accounting for uncertainty in cell-state and infection labels, identifying more biologically relevant genes and pathways.

Crecerelle integrates gene expression and alternative splicing to improve cell-type annotation and uncover regulatory pathways in single-cell transcriptomics.

ST-DISTAL improves cell-type deconvolution in spatial transcriptomics by integrating spatial and molecular information through a dual-branch graph convolutional framework.

In a 63-tool benchmark, dataset identity explained ≈84% of Cohen's κ variance on synthetic data versus ≈4% for tool identity, and k-nearest-neighbor (kNN) purity predicted accuracy near-linearly (R² = 0.85–0.99).

The paper compares scRNA-seq and snRNA-seq in Praesagittifera naikaiensis, finding snRNA-seq better recovers neural transcripts and reduces symbiont reads.

The paper benchmarks domain segmentation algorithms for spatial transcriptomics data, showing they fail to identify biologically defined niches due to noise and highlights the need for specialized methods.

The paper shows that differential expression methods like Wilcoxon rank-sum test produce unreliable cell-type markers due to cluster size dependence, and proposes a classification-based alternative with better specificity.

The paper introduces OCTAVE, a scale-resolved evaluation method for spatial gene expression prediction from histology, showing that fine-scale differences are missed by standard metrics.

The paper shows that applying a metacell model to scRNA-seq data results in PCA embeddings that follow a Gaussian mixture model, but underestimates variance in real datasets due to unaccounted gene correlations.

SFUMATO provides a Bayesian probabilistic framework for spatial transcriptomics that captures uncertainty and enables continuous visualization of transcriptional domains.

RAMP improves cross-platform proteomics analysis by preserving protein expression order, enhancing cell-type annotation and prediction across multiple cancer types.

SCITRAM predicts master transcription factors from single-cell transcriptomics data by reconstructing gene regulatory networks and modeling transcriptional cascades.

The paper introduces scPFA, a lineage-informed factor analysis method, to uncover heritable gene expression programs from single-cell lineage-tracing data.

The paper quantifies maternal microchimerism in the human brain using snRNA-seq and identifies diverse cell types across age groups.

This study evaluates how pretrained gene representations contribute to virtual spatial transcriptomics by separating their effects on mean expression and spatial patterns across multiple datasets.

COSMIC, a generative model, quantifies the relationship between nuclear morphology and gene expression using paired single-cell data, revealing morphology-associated genes in prostate cancer and spatial transcriptomics of zebrafish embryos.

The paper uses single-nuclei RNA sequencing to analyze intracranial thrombi, identifying distinct cell populations and transcriptional changes, and inferring ligand-receptor interactions relevant to stroke.

This paper introduces scReMeMber-Seq, a method to reconstruct single-cell trajectories by linking past and present transcriptomes using methylation-based memory recording.

A free Meta Quest virtual reality app harmonizes CODEX, Visium, Xenium, volumetric immunofluorescence and electron microscopy data from 5 organs into 4 scale levels using the Human Reference Atlas.

ProxiNet transfers spatially learned cellular proximity from spatial transcriptomics to dissociated single-cell transcriptomes, revealing tissue organization and age-related changes in Alzheimer's.

This paper uses a multi-model consensus framework to identify a dual-interferon gene signature in ischemic stroke using single-cell RNA sequencing data from a mouse model.

MechanoMaST integrates mechanical and transcriptomic data from tissue samples to enable spatial correlation with high accuracy, revealing a stiffness-related gene signature in colorectal cancer.

BEACON improves demultiplexing accuracy in single-cell RNA sequencing by learning background counts, outperforming existing methods and enabling new biological insights.

Translating cells between mouse, macaque and human scRNA-seq with a CycleGAN, without ortholog mapping, preserved developmental time structure, imputed missing states and produced more realistic synthetic cells than scGAN.