Single-cell and spatial transcriptomics

Deconvolving organogenesis in space and time via spatial transcriptomics in thick tissues

The paper develops a spatial transcriptomic method to analyze hair follicle development in 3D, revealing molecular stages and structural changes over time.

PertAdapt: Unlocking Single-Cell Foundation Models for Genetic Perturbation Prediction via Condition-Sensitive Adaptation

PertAdapt improves genetic perturbation prediction by adapting single-cell foundation models with a perturbation adapter and adaptive loss, outperforming non-pretrained baselines across multiple datasets.

Connecting single neuron transcriptomes to the projectome in mouse visual cortex

The paper integrates transcriptomic, morphological, and electrophysiological data to define neuron types in mouse visual cortex and predict their projection targets.

BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection

BatchSVG identifies batch-biased genes in spatially variable gene detection to improve downstream analyses by removing technical artifacts.

scEPS integrates genetic and single-cell disease atlas data to provide granular mechanistic insights into complex human diseases

scEPS integrates GWAS and single-cell data to identify disease-associated cell neighborhoods by testing gene expression variance explained in disease contexts.

Characterization of spatial homogeneous regions in tissues with concordex

This paper introduces concordex, a method to identify spatially homogeneous regions in tissues by decomposing gene expression patterns into cell-type and spatial variation, demonstrating its effectiveness across datasets.

Genetically informed single-cell and spatial mapping of metabolic programs in human health and disease

The paper introduces gmMAP, a framework that integrates metabolite GWAS with single-cell and spatial transcriptomes to map metabolic programs at cellular and spatial resolution, revealing associations between exogenous metabolites and cellular states.

Spike-in-normalised single-cell RNA-seq reveals cell-type-specific transcriptional repression during ageing

This study uses spike-in normalization to analyze age-related changes in gene expression across mouse cell types, finding decreased mRNA content in non-immune cells and increased in immune cells, along with reduced gene expression and shared regulatory processes between ageing and proliferation arrest.

A Simple, Cost-Effective, High-Throughput Method for Measuring Chromatin Accessibility and Gene Expression in Single Nuclei

The paper introduces and benchmarks two droplet-based single-nucleus assays for chromatin accessibility and gene expression, showing they are cost-effective and produce high-quality data.

CyStainer: A transformer-based variational autoencoder for robust marker imputation in high-parameter cytometry

CyStainer imputes markers in high-parameter cytometry data without shared markers, outperforming existing methods in panel merging and dataset integration.

Accurate trajectory inference in time-series spatial transcriptomics with structurally-constrained optimal transport

This paper introduces SOCS, a trajectory inference method for time-series spatial transcriptomics that preserves structural coherence of biological units over time, improving accuracy compared to existing approaches.

G-LATO: Inference of Spatial Latent Ordering via Deep Gaussian Processes

G-LATO infers spatial gradients using deep Gaussian processes, revealing known and novel spatial organization in liver and glioblastoma data.

Comparative evaluation of reference genomes and cell-type annotation frameworks for single-nucleus transcriptomic analysis in apple

The paper evaluates reference genomes and cell-type annotation frameworks for snRNA-seq in apple, finding that GDDH13 provides the best mapping and annotation performance, and that combining marker genes with cross-species frameworks improves cell-type identification.

SC-Framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets

SC-Framework provides a unified, containerized environment for single-cell data analysis to improve reproducibility and scalability.

Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE

CONCISE improves spatial ligand-receptor interaction inference by accounting for spatial autocorrelation and measurement errors, reducing false positives and enhancing detection power in spatial transcriptomics data.

Fault-tolerant 3D reconstruction from 2D spatial proteomics sections

This paper introduces 3D-Omics-Flow, a method that reconstructs 3D molecular volumes from 2D spatial proteomics sections by repairing damaged sections and interpolating between them.

IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches

IntegrateRigor optimizes batch integration for cell identity recovery in single-cell and spatial transcriptomics data, revealing cancer-immune interface niches previously obscured by integration biases.

A unified spatial transcriptome profiling of ten mouse organs

The paper presents a unified spatial transcriptomic dataset of 10 mouse organs, providing high-resolution expression matrices paired with histological images for method development and benchmarking.

A systematic assessment of single-cell language model configurations

This study evaluates single-cell language model configurations by systematically testing pre-training strategies and identifies best practices for improving performance.

scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes

scVIP models single-cell transcriptomes to predict phenotypes and infer trajectories in development and disease.

Compound models and Pearson residuals for single-cell RNA-seq data without UMIs

This paper introduces a compound model for single-cell RNA-seq data without UMIs, using Pearson residuals to improve gene selection and embeddings in Smart-seq2 datasets.

The challenges of single cell transcriptomics on difficult human tissue: the placenta

The paper evaluates single cell transcriptomic methods on challenging placental tissue, finding that sample processing and storage have greater impact on results than technical variations.

A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq Nanopore long-read data

This study benchmarks bioinformatics methods for single-cell and spatial RNA-seq Nanopore long-read data, evaluating their accuracy, robustness, and scalability across different protocols and sequencing conditions.

A single-cell transcriptomic atlas of the pigtail macaque placenta in late gestation

This study creates a single-cell RNA-Seq atlas of the pigtail macaque placenta in late gestation, revealing similarities to human placental cell populations.

SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics

SPEAK identifies spatial domains in spatial transcriptomics data using a large language model and expert-aligned prompts, showing improved accuracy and interpretability.

Spatiotemporal cellular landscape of the human utricle sensory epithelium

This study maps the spatiotemporal cellular landscape of the human fetal utricle using single-nucleus transcriptomics and spatial transcriptomics, revealing cell states and regulatory pathways involved in vestibular development.

SMURF: soft-segmentation for single-cell reconstruction and topological analysis of spatial transcriptomic data

SMURF improves spatial transcriptomic analysis by accurately assigning mRNAs to cells and revealing gene-expression zonation in tissues.

Learning Perturbation Effects Through Contrastive Alignment of Multimodal Biological Embeddings

This paper introduces PertOmni, a multimodal framework that aligns transcriptomic data with text and image embeddings to improve perturbation effect prediction.

Consistent consensus-based annotation of spatial adaptive immune receptor repertoires from long-read sequencing using LongAIRR

The paper develops LongAIRR, a computational framework for spatially resolving adaptive immune receptor repertoires from long-read sequencing, improving consensus accuracy and enabling platform-agnostic analysis.

CoLa-VAE: A Cell-Cell Communication-Aware Variational Autoencoder for Representation Learning and Expression Denoising

CoLa-VAE improves denoising and representation learning in single-cell data by integrating cell-cell communication information, enhancing downstream analyses like differential communication detection and spatial deconvolution.