Volume 20 Issue 8, August 2023

Some researchers have the good fortune of two academic affiliations. Sometimes these affiliations are not exactly within easy commuting distance.

Originally from central Mexico and now found worldwide, the axolotl is a prominent model organism in numerous fields of research.

Human neuroscience is enjoying burgeoning population data resources: large-scale cohorts with thousands of participant profiles of gene expression, brain scanning and sociodemographic measures. The depth of phenotyping puts us in a better position than ever to fully embrace major sources of population diversity as effects of interest to illuminate mechanisms underlying brain health.

Ribosome-bound mRNA mapping (RIBOmap) enables three-dimensional in situ detection of spatially resolved protein synthesis in single cells.

To study soil with a view to ecosystems, researchers iterate between experiments in the lab, the field and controlled environments.

A decade ago, the first bioinformatics pipelines to detect circular RNA molecules based on short-read sequencing data were published. Here, we show that dozens of such circular RNA detection tools differ vastly in their sensitivity but not in their specificity.

CheckM2 is a tool that applies machine learning to evaluate the quality of genomes from metagenomic data. CheckM2 is faster and more accurate than existing methods, and it outperforms them when applied to novel lineages and lineages with reduced genome sizes, such as Patescibacteria and the DPANN superphylum.

We developed CREST (CRISPR editing-based lineage-specific tracing) to enable high-throughput mapping of single-cell lineages in any Cre lineage of interest in mice. In addition, we delineated a comprehensive lineage landscape of the developing mouse ventral midbrain, revealing novel differentiation trajectories and molecular programs underlying neural specification.

We developed LIONESS, a technology that leverages improvements to optical super-resolution microscopy and prior information on sample structure via machine learning to overcome the limitations (in 3D-resolution, signal-to-noise ratio and light exposure) of optical microscopy of living biological specimens. LIONESS enables dense reconstruction of living brain tissue and morphodynamics visualization at the nanoscale.

This Review provides an overview of computational methods recently developed for detecting and analyzing structural variants using long-read sequencing data.

This study describes benchmarking and validation of computational tools for detecting circRNAs, finding most to be highly precise with variations in sensitivity and total detection. The study also finds over 315,000 putative human circRNAs.

This study shows, when analyzing multi-sample metagenomic datasets, the multi-coverage binning approach outperforms the single-coverage binning alternative in generating bins with higher quality and less contamination.

Organ Mapping Antibody Panels are a community-led initiative to create standardized antibody panels for multiplexed spatial imaging.

Expansion spatial transcriptomics (Ex-ST) combines the power of tissue expansion with an improved RNA capture protocol for carrying out capture array-based spatial transcriptomics at high spatial resolution.

Open-3DSIM is a versatile open-source software for high-fidelity reconstruction of three-dimensional structured illumination microscopy data (with polarization). It is available in three convenient forms for user-friendly and customizable applications.

Leveraging long-read RNA-seq data and machine learning, Bambu facilitates accurate transcript discovery and quantification.

This study presents a significance analysis framework for evaluating single-cell clusters. Application of the method detects cases of over-clustering in reported single-cell RNA-sequencing analysis results.

This work presents CheckM2, which is a machine learning-based tool to predict genome quality of isolate, single-cell and metagenome-assembled genomes.

The PanGenome Research Tool Kit (PGR-TK) achieves flexible and scalable representation, visualization and analysis of genomic variation using pangenome graphs.

By learning a joint representation using deep generative modeling, MultiVI integrates multimodal and single-modality single-cell datasets, which enhances multiple functionalities.

CherryML is a method to scale up maximum likelihood estimation for general phylogenetic models of molecular evolution, providing several orders of magnitude speedup over traditional methods.

Subcellular spatial transcriptomics cell segmentation (SCS) combines information from stained images and sequencing data to improve cell segmentation in high-resolution spatial transcriptomics data.

CREST is a dual-recorder single-cell lineage tracing method for spatiotemporal mapping of the mouse brain.

A combination of gentle stimulated emission depletion microscopy imaging and deep-learning-based improvements in signal-to-noise ratio enables high-resolution reconstruction of neuronal architecture in living tissue.