Volume 22 Issue 9, September 2025

As a postdoctoral fellowship ends, for some, next is a trek to their new lab.

Cichlid fishes are a family of thousands of recently evolved species. As charismatic laboratory models, they are useful for studying anatomical, physiological and behavioral traits that vary across these closely related species.

Leveraging data generated by UUATAC-seq, a method for cross-species chromatin accessibility profiling, the deep learning model NvwaCE deciphers cis-regulatory grammar from DNA sequence.

With a focus on the evolutionary forces that shape cancer cell behavior, some researchers work out ways to outmaneuver cancer.

Two deep-learning frameworks — GHIST and iSCALE — turn routine histology images into a rich molecular resource, and predict spatial gene expression at single-cell resolution (GHIST) and at super-resolution across large tissue sections (iSCALE), for scalable, data-driven tissue biology.

We present MAPIT-seq, a method that uses antibody-directed RNA editing to concurrently profile in situ RNA-binding protein (RBP)–RNA interactions and transcriptome-wide gene expression in limited input material, including single cells and frozen tissues. This dual-omic strategy streamlines mechanistic analyses of post-transcriptional regulation in dynamic biological processes and clinically relevant samples.

We present a cost-effective ultra-high-throughput cytometry-based framework for the detection of physical interactions between cells, along with the characterization of complex cellular landscapes. Application of our approach can offer a systems-level understanding of immunity and facilitate study of the kinetics, mode of action and personalized response prediction of immunotherapies.

A lightweight miniature two-photon microscope features multi-wavelength excitation, correction of aberrations, and interchangeable objectives for scalable fields of view. It enables multicolor, deep-brain and scalable neural imaging in freely moving mice.

We created T-CellAnnoTator (TCAT), a computational method that helps to identify T cell subsets, activation states and functions. It does this using reproducible gene expression programs found across many disease contexts and tissues. TCAT outperforms conventional approaches for T cell subset prediction, is easy to use programmatically or through a website, and can be adapted for other cell types.

This Perspective discusses recent progresses in development of in vitro tumor models and the current challenges in this field.

This Perspective explores the challenges and future directions in the world of human tumor microbiome research.

This Review discusses the current challenges and the available methods to study the four physical hallmarks of cancer.

A standardized, realistic phantom dataset consisting of ground-truth annotations for six diverse molecular species is provided as a community resource for cryo-electron-tomography algorithm benchmarking.

This work presents MAPIT-seq, an antibody-guided RNA editing method that enables co-profiling of RBP-RNA interactions and gene expression at single-cell resolution and in tissue contexts.

Scvi-hub is a versatile and efficient platform for model-based analysis of single-cell sequencing studies with access to a diverse array of datasets and downstream analysis.

Clonalscope is a method for cancer subclone detection leveraging copy number profiles estimated using spatial and single-cell sequencing data.

DeepMVP is a deep learning framework for predicting PTM sites and variant-induced alterations across six modification types, including phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation.

Mutational effect transfer learning (METL) is a protein language model framework that unites machine learning and biophysical modeling. Transformer-based neural networks are pretrained on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics.

Individual proteins tend to adopt preferred orientations when subjected to vitrification for cryo-electron microscopy analysis. A laser flash melting procedure followed by rapid revitrification provides a simple approach to mitigate this issue, reducing the number of micrographs required for successful structure determination at high-resolution.

Interact-omics, a high-throughput cytometry-based framework, resolves the cellular interaction landscape.

GHIST is a deep learning-based method that can predict spatial gene expression at high resolution using histology data.

iSCALE leverages histology and spatial transcriptomics to infer gene expression at super resolution in large tissues.

CLEM-Reg automates the three-dimensional alignment of volume correlative light and electron microscopy datasets by leveraging probabilistic point cloud registration techniques for fast and accurate results across diverse datasets.

FHIRM-TPM 3.0 is a miniature microscope for multicolor two-photon imaging in freely moving mice. In addition to the multicolor imaging abilities achieved with the help of a specially designed optical fiber, the microscope is also compatible with multiple lenses for a choice of field of view and resolution.

WISDEM is a hybrid detector for simultaneous EEG and fMRI recordings without artifacts or crosstalk, which allows access to neural activity at high temporal and spatial resolution, respectively, as demonstrated in rats.

CIGS is a high-resolution dataset comprising chemical perturbation-induced 319,045,108 gene expression events.

TCAT is a pipeline that can simultaneously capture gene expression programs related to T cell subsets and activation states for accurate T cell characterization.

uMAIA is an analytical framework designed to enable the construction of metabolic atlases at high resolution using mass spectrometry imaging data.