Single-cell and spatial transcriptomics

SpatialCCCbench: Standardized Metrics for the Systematic Evaluation of Spatial Cell-Cell Communication Methods

SpatialCCCbench evaluates spatial cell-cell communication methods using standardized metrics to guide tool selection and benchmark future developments.

An integrated protocol for multiplexed DNA FISH and protein detection in large tissue sections

The paper presents a protocol combining DNA FISH and multiplexed immunofluorescence to analyze gene and protein expression in large tissue sections at the single-cell level.

Transcriptomic profiling of the embryonic C. elegans intestine with single-cell resolution

This study uses single-cell RNA sequencing to analyze the embryonic C. elegans intestine, identifying 13 cell clusters and their developmental and spatial characteristics.

Optimisation of Xenium automated in situ sequencing for PAXgene-fixed tissue samples

This study optimizes Xenium in situ sequencing for PAXgene-fixed tissues, showing comparable or better spatial RNA detection than FFPE samples.

A community machine learning challenge to predict the effects of gene perturbations on T cell differentiation for cancer immunotherapy

The paper presents a machine learning challenge to predict gene perturbation effects on T cell differentiation, finding that feature representation from perturbational data improves performance and validating top genes experimentally.

scDIG: An R Shiny Application for Interactive Density-Based Gating of Single-Cell Proteomic and Transcriptomic Data

scDIG is an R Shiny tool for interactive density-based gating of single-cell proteomic and transcriptomic data, resolving CD4+ T cell subpopulations in CITE-seq data.

N-Orbit: towards a universal model and metric for comparing tissue microenvironments

The paper introduces N-Orbit, a mathematical model for comparing tissue microenvironments by encoding cell type composition and spatial relationships, showing improved performance over existing metrics in neighborhood discrimination and clinical prediction.

Echo characterizes the desynchronization of gene expression and chromatin accessibility during cell-state transitions

Echo identifies desynchronized cell states and their drivers from paired single-cell RNA and ATAC data, revealing structured desynchronization in human fetal retina and hematopoiesis.

Heterogeneity-driven adaptive scale graph learning for subcellular spatial transcriptomics

This paper introduces HAST, a framework for spatial transcriptomics that adapts graph learning scales to tissue heterogeneity, improving spatial structure identification and tumor region characterization.

Cell Trajectory Inference based on Optimal Transport and a mechanistic stochastic gene expression model

This paper introduces a method for cell trajectory inference using optimal transport with a mechanistic stochastic gene expression model, showing the Bursty model better captures gene dynamics than the Diffusive model.

Multimodal single-cell analysis uncovers transcription factor networks underlying T-cell aging

This study uses multimodal single-cell analysis to identify transcription factor networks involved in T-cell aging.

Spectral Prompting: Unsupervised Recovery of Human Hair Follicle Cell-Type and Multiscale Systems Architecture from Bulk and Single-Cell RNA-Seq Datasets via Single-Gene Seeded Spectral Unfolding

This paper introduces a method to recover cell-type and systems architecture from bulk and single-cell RNA-seq data using spectral unfolding, revealing new gene expression signatures in human hair follicles.

DGAT: A Dual-Graph Attention Network for Inferring Spatial Protein Landscapes from Transcriptomics

DGAT imputes spatial protein expression from transcriptomics-only data, revealing cell states and tissue organization not visible from RNA alone.

Multi-layer transcriptomic characterization of age-related immune dynamics

This study characterizes age-related immune dynamics through multi-layer transcriptomic analysis, revealing non-linear transcriptional reprogramming and novel regulatory elements associated with immune aging.

scDeepVariant: A population-informed deep learning framework for germline variant calling in scRNA-seq

scDeepVariant improves germline variant calling in scRNA-seq by integrating population allele frequency data, outperforming existing methods in precision, recall, and rare variant detection.

Evolutionary diversity of cell-type-specific expression and stress response in Brassicaceae roots

This study profiles root cells from five Brassicaceae species under stress treatments, revealing cell-type-specific gene expression divergence and stress response variation across species.

Deciphering interaction syntax via decoupling intrinsic lineages and niche pressure

TRINUS decouples cell lineage identity from niche pressure to uncover interaction syntax in spatial transcriptomics data, showing improved clustering and domain detection across biological systems.

A harmonized single-cell RNA-seq atlas of human localized and metastatic prostate cancers and benign tissues

This paper harmonizes single-cell RNA-seq data from 17 studies to create a comprehensive atlas of human prostate cancer and benign tissues, identifying 17 cell types and providing standardized resources for analysis.

Intervention-consistent causal-source recovery from covariance-response geometry reveals upstream organisation in sporadic ALS

This paper introduces a framework to identify upstream organizational changes in sporadic ALS using single-nucleus RNA-seq data, revealing oligodendrocytes as structurally upstream and NEMF as a key gene with disease-specific co-expression loss.

The ATLAS Penalty: Auxiliary-Transformed Location-Aware Smoothing with Applications to Spatial Transcriptomics

The paper introduces the ATLAS penalty for spatially smooth deconvolution in spatial transcriptomics, showing improved spatial domain detection using auxiliary covariates.

Spatiotemporal Transcriptomic Dissection Uncovers Age-Dependent Deceleration of Esophageal Cell Differentiation

This study uses single-cell transcriptomics to analyze age-related changes in esophageal stem cell dynamics and differentiation, finding that impaired differentiation, not stem cell self-renewal, drives aging-related homeostasis decline.

Histopathology-inferred spatial transcriptomics characterizes the tumor microenvironment in 1,500 head and neck tumors and predicts clinical outcomes

This paper develops HEiST, a deep learning method to infer spatial transcriptomes from H&E slides, identifies two prognostic Spatiotypes in HNSC, and shows they predict treatment response better than existing biomarkers.

SpatialArtifacts: a computational framework for tissue artifact detection in spatial transcriptomics data

SpatialArtifacts identifies and classifies tissue artifacts in spatial transcriptomics data using MAD-based outlier detection and mathematical morphology operations.

Global cell-state and gene-program representations reveal conserved and context-specific perturbation responses of cells

The paper develops a contrastive learning model to integrate scRNA-seq data and perturbation responses, revealing conserved and cell-type-specific cellular responses to genetic perturbations.

Aging and reproductive "rejuvenation" at a single nuclei resolution

The paper analyzes single-nuclei transcriptomes of Daphnia magna to study aging and reproductive rejuvenation, identifying age-related and reproductive changes in cell type abundance and transcriptional patterns.

BARseq3: a modular system for integrating spatial multi-omics and cellular barcoding in single cells

BARseq3 integrates spatial multi-omics and cellular barcoding in single cells to enable multimodal understanding of cellular identity.

Spatially resolved transcriptomic identification of thousands of neurons recorded in vivo.

This paper introduces coppaFISH 3D and CASTalign to enable transcriptomic identification of thousands of in vivo recorded neurons at scale.

Identifying Treatment Related Signatures In Glioblastoma Using KaleidoCell

The paper introduces kaleidoCell, a framework for identifying transcriptional meta-programs in single-cell datasets, and applies it to glioblastoma data to uncover treatment-related signatures.

Dynamic uterine microenvironment drives endometrial adenocarcinoma carcinogenesis and progression

This study uses scRNA-seq on uterine blood samples to analyze immune and stromal changes in endometrial cancer progression, identifying neutrophil signatures that stratify disease states.

Data-modeling the interplay between single cell shape, single cell protein expression, and tissue state

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.