Single-cell and spatial transcriptomics

This paper optimizes full spectrum flow cytometry for analyzing non-immune cells in solid tissues by addressing autofluorescence and improving subpopulation resolution.

EnsAgent improves spatial transcriptomics annotation by integrating multiple agents for robust, evidence-based labeling across datasets.

This study creates a single-cell transcriptomic atlas of mouse oocyte development from growth to ovulation using a new platform and integrates transcriptomic and morphological data to identify temporal shifts in gene expression.

HitAnno annotates cell types in atlas-level scATAC-seq data using a hierarchical language model, accurately handling major and rare cell types across multiple settings.

KaroSpace provides a framework for interactive exploration of multi-sample spatial omics data, overcoming computational bottlenecks and proprietary software dependencies.

This study benchmarks zero-shot single-cell foundation model embeddings for cellular dynamics reconstruction and finds they underperform a traditional HVG baseline in capturing complex temporal patterns.

BenchDrop-seq provides a microfluidics-free, benchtop platform for single-cell long-read RNA sequencing, enabling isoform-resolved transcript analysis from thousands of cells using standard equipment.

RETrace2 enables high-resolution retrospective lineage tracing and cell-type identification using mutable homopolymers, achieving resolution below five cell divisions in microsatellite-unstable organisms.

This paper performs a cross-organ meta-analysis of single-cell transcriptomic data to identify shared and organ-specific gene expression programs in fibrotic diseases.

DeepTracing disentangles lineage and intrinsic cellular variation in single-cell lineage tracing data, outperforming existing methods in clustering, phylogenetic recovery, and tumor-type classification.

This study uses single-cell and spatial transcriptomics to analyze stomach evolution across 23 vertebrate species, identifying cell-type adaptations and genes linked to dietary changes and rumination.

CellDEEP improves differential gene expression analysis in scRNA-seq by aggregating cells into metacells, reducing false positives and increasing true positives compared to single-cell and pseudobulk methods.

SpatioCAD detects spatially variable genes by accounting for cell density variations, improving accuracy in heterogeneous tissues like tumors.

MapMyCells maps unlabeled single-cell datasets to reference brain taxonomies, enabling cross-study integration and reproducible cell-type annotation.

This study benchmarks deconvolution methods for inferring tissue and cell type-of-origin from plasma cfRNA, showing method and reference variability impact performance.

The paper compares scRNA-seq protocols for clinical trial samples, finding that GEM-X Flex with chop-fix outperforms standard protocols and snRNA-seq in fixed tissue and FFPE blocks.

SR2P predicts protein abundance from RNA expression in spatial transcriptomics data, improving immune cell identification in tumors.

FX-seq improves snRNA-seq from FFPE and PFA-fixed tissues by removing crosslinks and preventing RNA leakage, enabling analysis of 321,710 nuclei from mouse and human samples.

MERFISH 2.0 improves transcript detection sensitivity in degraded and archival tissues, enhancing spatial transcriptomic analysis of human and mouse samples.

The paper uses single-cell foundation models to identify genetic targets that reverse inflammation in endothelial cells by analyzing Perturb-seq data with and without proinflammatory stimulation.

CellSweep removes technical contamination from single-cell genomics data, improving accuracy in downstream analysis.

QuantCell improves cell annotation in spatial imaging by integrating quantitative and qualitative data, achieving high accuracy and detecting rare cell types.

The paper maps a mesoscale molecular architecture of the human striatum across cell types and age, revealing zonal differences in gene expression and aging effects.

This paper introduces scLSD, a thermodynamics-inspired model for cell differentiation that reconstructs differentiation trajectories and predicts fate commitment, outperforming existing methods.

ESGI provides a flexible framework for demultiplexing and processing single-cell sequencing data with arbitrary barcode architectures, supporting complex and emerging experimental designs.

GrooD improves bulk transcriptome deconvolution using gradient boosting and convolution, showing better performance and interpretability on human blood samples from sepsis patients.

AlphaCell simulates perturbation-induced cellular dynamics by addressing limitations in current models through three innovations: latent manifold rectification, biological reality reconstruction, and universal state transition.

PerturbGen predicts how genetic perturbations affect cellular trajectories across time in human datasets, showing effects on immune responses, hematopoiesis, and skin development.

COSIE is a computational framework that integrates multiple spatial omics modalities to generate high-resolution molecular landscapes, outperforming existing methods across diverse datasets.

CellTypeAI improves scRNA-seq cell type annotation by using local generative-AI models, outperforming conventional and cloud-based methods in accuracy and sensitivity.