Single-cell and spatial transcriptomics

A Multidimensional Arithmetical Ontology for High-Parameter Cytometry: Gaussian Primes and Cryptographic Hashing for Cell Population Standardization

This paper introduces a multidimensional arithmetical ontology for cell population standardization using Gaussian primes and cryptographic hashing to resolve nomenclature ambiguity and enable reproducibility.

Optimal transport reveals unresolved cells in single cell multi-omics

This paper introduces optimal transport to explicitly reveal cell correspondences in single-cell multi-omics, improving integration and label transfer by identifying and removing ambiguous cells.

X-SPEAR: An Extensible Framework for Spatial Panel Evaluation and Refinement

X-SPEAR improves spatial transcriptomics panel design by integrating detection-aware marker selection and probe-level off-target assessment, achieving higher accuracy with fewer genes.

Gestational reorganization of cellular niches and communication revealed by a spatially resolved transcriptional atlas of the macaque maternal-fetal interface

This paper constructs a spatial transcriptomic atlas of the macaque maternal-fetal interface across gestation, revealing shifts in cellular composition and signaling pathways.

Survival-associated cellular communication networks in human solid cancers

The paper develops SKyNet to identify survival-associated cell-cell communication networks in solid cancers using single-cell and bulk RNA-seq data, finding specific interactions linked to patient outcomes.

Spatially resolved host-fungal transcriptomics profiling by fungal-DBiT

This study introduces fungal-DBiT, a spatial transcriptomic platform that maps host and fungal RNAs in tissue sections, revealing spatial heterogeneity in fungal localization and host responses during infection and under physiological conditions.

scOmnom: scalable end-to-end single-cell RNA-seq analysis with auditable resolution selection

scOmnom provides a scalable, semi-automated workflow for single-cell RNA-seq analysis, enabling auditable clustering-resolution selection and annotation refinement across large datasets.

Auditing Confidence-Calibrated Benchmarking of Single-Cell Perturbation Models

The paper audits a benchmarking framework for single-cell perturbation models, revealing flaws in metric interpretation and calibration, and identifies a specific calibration failure in split-conformal prediction.

CytoRipple: Mapping Tissue Polarization through Subcellular Cytoskeleton Dynamics

CytoRipple identifies tissue-level polarization vector fields from spatial multi-omic data to reveal multi-cellular structures and their functional associations.

LineageFlow: Growth-Corrected State Dynamics for Probabilistic Cell-Fate Prediction

LineageFlow predicts clonal fate and transcriptional profiles by integrating growth dynamics and state transitions in single-cell lineage data.

Determining cellular phylogeny and migration at high resolution in spatial genomics

StarTrak infers cellular migration and phylogeny by integrating somatic variants, spatial data, and transcriptomics at single-pixel resolution in spatial genomics.

Hierarchical distribution matching enables comprehensive characterization of common and condition-specific cell niches in spatial omics data

This paper introduces Harmonics, a framework for identifying common and condition-specific cell niches in spatial omics data, demonstrating its effectiveness across diverse datasets and diseases.

Order Matters: Ambient RNA correction confounds doublet detection in single-cell RNA sequencing

This study shows that ambient RNA correction can reduce doublet detection accuracy and recommends detecting doublets before correction, except when using CellBender.

SubcellPert: Benchmarking Subcellular Perturbation Prediction

SubcellPert benchmarks subcellular perturbation prediction using imaging data, showing Gaussian transport achieves low spatial error in out-of-distribution datasets.

Scale-resolved correlation functions unify spatial omics across platforms and resolutions

SCOTE quantifies spatial relationships across all length scales in spatial omics data, revealing biological insights while distinguishing platform artifacts.

On technical biases in imaging spatial transcriptomics

This paper identifies and categorizes technical biases in imaging spatial transcriptomics and proposes a unified framework to model these biases.

Single-cell profiling of physically interacting immune cells using image-guided sorting

The paper uses image-guided sorting to distinguish physically interacting T cell-monocyte pairs from coincidental doublets in tuberculosis patients, revealing distinct transcriptional signatures of contact.

Cross-Platform Evaluation of a Tumor-Budding-Associated Gene Signature in Colorectal Cancer.

The paper evaluates a tumor-budding-associated gene signature across bulk and single-cell RNA-seq data, finding stronger co-expression in single-cell data.

Navigating the rapidly evolving landscape of sequencing-based spatial transcriptomics technologies: a comprehensive multi-platform benchmark

This study benchmarks multiple sequencing-based spatial transcriptomics platforms across tissue types and preservation methods to evaluate their performance and establish standardized metrics for future research.

MOFI reveals dynamic multi-omics coordination during cell fate progression

MOFI reconstructs continuous cell fate transitions from paired multi-omics snapshots across time, revealing coordinated changes in transcriptomic, chromatin, and protein layers.

scRIPT: a rapid, high-coverage single-cell lineage-tracing method applied to the hematoendothelial differentiation of embryonic stem cells

scRIPT is a single-cell lineage-tracing method that improves coverage and reliability in differentiation protocols by integrating rapid barcoding, sorting, and sequencing with quality control.

Same-cell mapping of epigenetic regulatory state to functional proteomic output

Proteo-Seq links chromatin accessibility and proteomic output in the same cell, revealing new protein-defined states in breast cancer cells.

Beyond the gene: contextualizing novel isoforms through pathway analysis in long-read single-cell RNA sequencing

The paper develops a computational framework to contextualize novel isoforms identified through long-read single-cell RNA sequencing by integrating pathway analysis with expression-based neighborhoods.

Deciphering Spatial Cellular Organization through RNA-Anchored Multi-Omics Deconvolution

SMODER deconvolves spatial multi-omics data by prioritizing RNA for cell-type identification and integrating other modalities, improving accuracy and spatial coherence.

Alpha Spatial: Language-guided prediction of single-cell spatial gene expression from histology

Alpha Spatial predicts single-cell gene expression from histology using language-guided models, improving accuracy with matched biological context and outperforming existing methods in breast cancer analysis.

Biological and technical spatial variation in spatial transcriptomics

The paper develops a model to distinguish biological from technical spatial variability in spatial transcriptomics data and introduces the stipple software for analysis.

scMTG reconstructs single-cell temporal dynamics with Markov transition generators

scMTG reconstructs single-cell temporal dynamics from unpaired time-series data by learning cell representations and transitions, outperforming existing methods in interpolation and transition matrix inference.

From Closed-Set Annotation to In-Context Discovery: Biologically Grounded LLMs for Cellular Profiling

CellScribe is a generative framework that unifies cell identity and state by using a language model to generate cell type annotations and meta-program profiles, outperforming baselines and identifying new cell types through in-context learning.

Bayesian phylogenetics with incomplete lineage tracing data

This paper introduces a Bayesian framework to improve phylogenetic inference from sparse lineage tracing data by modeling barcode loss, showing it enhances accuracy and recovers higher growth rates than standard filtering methods.

SVC: A Vision Transformer-based Spatially and Virtually Embedded Cell Model for Deciphering Subcellular Spatial Transcriptomic Heterogeneity

SVC predicts subcellular transcript localization and co-organization using a Vision Transformer model, revealing disease patterns and spatial biology not accessible at the cell level.