Single-cell foundation models are increasingly adopted for downstream applications such as cell-type prediction. However, these predictions are often utilized without assessing their reliability, or b...

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Current approaches to inferring cell-cell interactions (CCIs) are largely constrained by predefined ligand-receptor databases, particularly for low-resolution spatial transcriptomics (ST) platforms such as Visium. Due to the difficulties in accurately resolving interacting cells at coarse spatial resolution, other modes of interaction are often overlooked. Low-resolution ST data, however, can serve as an alternative to high-resolution ST, which suffers from low sensitivity, and to image-based ST, which is limited by restricted gene panels. Here, we present CellNeighborEX v2, a database-free framework that directly infers CCI-associated genes from ST data by detecting deviations between observed and expected gene expression at the spot-population level. These deviations are rigorously evaluated through a hybrid statistical framework involving permutation testing and are further refined by considering the abundance of interacting cell-type pairs. Compared with other conventional approaches relying on ligand-receptor databases, CellNeighborEX v2 can capture CCI genes from a broad spectrum of interactions, including both paracrine signaling and contact-dependent communication. Across datasets from hippocampus, liver cancer, colorectal cancer, ovarian cancer, and lymph node infection, CellNeighborEX v2 accurately recapitulated previously identified CCIs. Notably, it uniquely detected interactions absent from existing ligand-receptor databases, enabling detection of context-specific CCIs from Visium data. CellNeighborEX v2 is a tool that expands the analytical spectrum of Visium data and deepens our understanding of the molecular language of intercellular communication. ### Competing Interest Statement The authors have filed a patent application related to the algorithms described in this work.

This Perspective offers a roadmap for tackling the challenges of the facsimile problem, moral multidimensionality and moral pluralism in large language models.

An optical archival storage technology based on femtosecond laser direct writing in glass addresses the practical demands of archival storage.

AI is a powerful tool for facilitating data science programming in biomedicine. This study benchmarks the performance of existing tools and introduces a reliable AI agent copilot.

Nuclear fusion. People on Mars. Artificial general intelligence. These are just some of the advances that could come by the mid-century mark.

Single-cell RNA-seq data from clinical samples often suffer from batch effects, but data sharing is limited due to genomic privacy concerns. We present FedscGen, a privacy-preserving communication-eff...

Spatial transcriptomics has transformed our ability to explore gene expression within its tissue context, enabling us to dissect subtle yet biologically significant variations in situ . While numerous...

The alignment of heterogeneous spatial samples has become a growing challenge. Here, authors present a multi-scale, multi-context, and interpretable mapping strategy to map cells across space, time, a...

The alignment of heterogeneous spatial samples has become a growing challenge. Here, authors present a multi-scale, multi-context, and interpretable mapping strategy to map cells across space, time, a...

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Just breathe.

Just breathe.

Taking lessons from fiction to elevate scientific presentations and story telling.

Arc Institute is a independent nonprofit research organization headquartered in Palo Alto, California.

Taking lessons from fiction to elevate scientific presentations and story telling.

Following a brief description of his academic and professional background before joining ARPA, Taylor describes his impression of the IPT Office when he arrived in 1965. Most of the interview is conc...

We introduce the first method for translating text embeddings from one vector space to another without any paired data, encoders, or predefined sets of matches. Our unsupervised approach translates an...