Single-cell and spatial transcriptomics

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

On 495 human Patch-seq neurons, cell type means predicted electrophysiology better than neural networks on scGPT embeddings or gene panels; fusing scGPT embeddings with cell type labels predicted best.

scACORN: Context-engineered agent orchestration of specialized small language models for single-cell transcriptomic interpretation

scACORN improves single-cell transcriptomic interpretation by combining specialized small language models with context-engineered agent orchestration, achieving higher accuracy and reducing unsupported gene citations.

Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis

FORGE is a configurable Nextflow pipeline for snMultiome analysis that integrates snRNA-seq and snATAC-seq data through latent-variable models and enables regulatory network inference and differential testing.

Accurate and scalable decontamination of imaging-based spatial transcriptomics via optimal transport

CellDot improves spatial transcriptomics by accurately decontaminating transcript assignments using optimal transport, enhancing biological interpretation and spatial pattern recovery.

Towards foundation models that learn across biological scales

The paper introduces Xpressor, a framework for cross-scale learning in biology, improving cell-type prediction and embedding quality.

Cell-level random splits leak group-owned answers in single-cell benchmarks

The paper identifies and quantifies leakage in single-cell benchmarks caused by group-owned answers and introduces a tool to assess this leakage before model training.

DiffGSP: reversing mRNA diffusion to unlock high-fidelity spatial transcriptomics

DiffGSP reverses mRNA diffusion to improve spatial transcriptomics fidelity, revealing fine anatomical structures and spatial gene modules in various tissues.

LIGER2: Scalable Single-Cell Integration with On-Disk Datasets

LIGER2 improves single-cell data integration by enhancing scalability and efficiency while preserving biological variation.

Differential co-localisation analysis of multi-sample and multi-condition experiments with spatialFDA

The paper introduces spatialFDA, a framework for analyzing differential co-localisation in spatial omics data, showing its effectiveness in controlled simulations and biological applications.

ImmuneLens: linking transcriptional states and TCR clonotypes through disentangled multimodal learning

ImmuneLens links transcriptional states and TCR clonotypes using disentangled multimodal learning to improve antigen-specificity prediction and reveal T cell heterogeneity in immunotherapy.

Mapping Uterine Remodeling from Pregnancy to a New Homeostatic State

This study maps uterine remodeling postpartum using single-cell transcriptomics, revealing a novel homeostatic state that does not revert to pre-pregnancy cellular architecture.

LucaCell: a sequence-centric foundation model for cross-species single-cell analysis

LucaCell is a sequence-centric foundation model that improves cross-species single-cell analysis by using mRNA sequence embeddings instead of fixed gene identifiers.

Single-cell transcriptomic and epigenomic analysis reveals X-linked sex differences in aging mouse hypothalamus

The paper analyzes single-nucleus multi-omics data from mouse hypothalamus to reveal sex-specific aging differences, particularly on the X chromosome.

SpaCoEx: Sparse Gene Selection for Spatially Varying Co-expression in Spatial Transcriptomics

SpaCoEx selects sparse genes for spatially varying co-expression in spatial transcriptomics, improving spatial coherence and annotation agreement in cancer datasets.

Dynamics of bleomycin-induced lung injury at single-nucleus resolution

This study uses snRNA-seq to analyze bleomycin-induced lung injury in mice, identifying dynamic changes and rare cell types not well captured by scRNA-seq.

A single-cell view of human tissue aging reveals architectural decline beyond cellular composition

The paper analyzes single-cell data from human tissues to show that aging involves both cell loss and architectural remodeling, with structural decline independent of cell composition.

spAlignDE unifies cross-sample and cross-modal spatial alignment with mismatch-aware differential expression

spAlignDE aligns spatial omics data across samples and modalities, improving differential expression analysis by accounting for alignment errors and cell-type composition.

Determining the age of single cells using scMLEAge

The paper develops a Bayesian statistical framework to predict single-cell age from transcriptomic profiles and demonstrates its accuracy on Tabula Muris Senis data.

An immunophenotype-coupled transcriptomic atlas of mouse hematopoietic progenitors

This paper constructs a CITE-seq-based atlas of mouse hematopoietic progenitors, identifying 28 progenitor stages across eight lineages and novel surface markers.

CellMAGE: cell-type deconvolution for multi-parent population analysis of gene expression

CellMAGE deconvolves gene expression in multi-parent populations by leveraging known parental genetic contributions, outperforming existing methods in accuracy and efficiency.

A Single-Cell Atlas of the Mouse Dural Meninges Reveals Pervasive Sex Differences Across Cellular Compartments

This study creates a single-cell transcriptomic atlas of the adult mouse dural meninges, revealing widespread sex differences across cell types, communication, and disease-related features.

Kintsugi decides, gene by gene, where spatial transcriptomics borrows information

Kintsugi improves spatial transcriptomics by letting data decide how much to borrow from neighbors and genes, showing it can identify epithelial programs and distinguish fibroblastic foci by composition rather than RNA amount.

scOLAR: Ontology-Anchored Open-Set Annotation of Single-Cell RNA-seq Data

scOLAR annotates single-cell RNA-seq data using an ontology-guided framework to detect both known and novel cell types with high accuracy.

scPyviewer: a Python-native interactive viewer from AnnData single-cell data

scPyviewer provides a Python-native interactive viewer for AnnData single-cell data, matching R Shiny tools' features and outperforming them in speed and memory efficiency.

FlashDeconv reveals resolution horizons in atlas-scale spatial transcriptomics

Leverage-weighted sketching plus sparse spatial regularization deconvolves 1.6 million Visium HD bins in 153 seconds on commodity hardware, revealing tissue-specific resolution limits where cell-type co-localization can reverse sign.

RNA-aware tissue preservation workflows for high-quality spatial transcriptomics

This paper introduces an RNA-aware preservation method, RIPIN, that improves spatial transcriptomics data quality by inhibiting RNase activity across human and mouse tissues.

Reference-guided pseudotime inference across species and biological contexts

Cavebear improves pseudotime inference by using reference species time-series data to guide ordering of cells in query species or conditions without time labels.

CoTRA: a comprehensive R/Shiny framework for transparent bulk and single-cell RNA-seq analysis

CoTRA provides an R/Shiny framework for bulk and single-cell RNA-seq analysis, offering integrated workflows and reproducibility across 49 criteria.

A Single-cell Spatiotemporal Atlas Encoding Age-dependent Evolution of the Ischemic Penumbra

This study creates a spatiotemporal transcriptomic atlas of the ischemic penumbra in young and aged mice, revealing age-dependent molecular changes and potential therapeutic targets.

Highly resolved tumor architecture via matched spatial and nucleus transcriptomics from a single tissue section

SIMPlex generates matched spatial and single-nucleus transcriptomic profiles from the same tissue section to resolve cell states with spatial signatures in mouse and human cancer tissues.