Volume 19 Issue 12, December 2022

Scientists with visible and invisible disabilities take on adversity, helping themselves and others.

If you sit on the sofa for your entire life, you’re running a higher risk of getting heart disease and cancer. —Alex Honnold, American rock climber

An approach combining in situ tagmentation and transcription with MERFISH enables spatial profiling of the epigenome in tissues with single-cell resolution.

A rapidly evolving toolbox is helping researchers to get a handle on the biological and functional diversity of these ubiquitous — but still somewhat enigmatic — cell-secreted nanoparticles

Multiplexing real-time single virus tracking with imaging paves the way for detailed information on virus–host interactions, offering a potential paradigm shift.

We developed a FAIR (findable, accessible, interoperable, reusable) framework for researchers to share spatially standardized brain models. TemplateFlow enables the implementation of more reliable data processing pipelines by maximizing the accessibility of such models. It equips neuroimaging researchers with a foundational tool to bridge gaps between populations and species in neuroscience research.

The ability to measure protein complexes in single cells is currently limited to a very small number of targets. Combining a proximity ligation assay with single-cell sequencing creates the ability to measure hundreds of extracellular protein complexes and thousands of mRNAs in individual cells.

By modeling the probability of N⁶-methyladenosine (m6A) RNA modifications for individual reads from direct RNA sequencing, m6Anet achieves high classification accuracy and takes a step towards transcriptome-wide maps of m6A modifications at single-base, single-molecule resolution.

Detecting rare-variant associations in the noncoding genome is challenging. We present a scalable, flexible and streamlined rare-variant association analysis framework for biobank-scale whole-genome sequencing data, including gene-centric and non-gene-centric analyses by incorporating multiple variant functional annotations using various coding and noncoding units, conditional analysis, result summary and visualization.

Hyperfolder yellow fluorescent protein (hfYFP) and its variants are fluorescent proteins with high chemical and thermal stability. They resist aggregation, withstand diverse chemical challenges and show promise in expansion and electron microscopies. The chloride resistance and uncanny stability in guanidinium of hfYFP enable fluorescence-guided protein purification under denaturing conditions.

Common cellular segmentation models based on machine learning perform suboptimally for test images that differ greatly from training images. Cellpose 2.0 allows biologists to quickly train state-of-the-art segmentation models on their own imaging data. This was previously only possible using large, annotated datasets and required expert machine learning knowledge.

This Perspective describes common pitfalls that can occur when using light sheet microscopy and offers guidance for improved quantitative imaging with these instruments.

This Resource presents and analyzes four datasets containing both gene expression and morphological profile data for cells subjected to hundreds to thousands of chemical or genetic perturbations and highlights their complementary nature.

An improved version of the MS2-MCP system for imaging RNA dynamics involves tethering translation termination factors to tagged mRNAs to bypass destabilization caused by NMD machinery.

RS-FISH is a user-friendly software for accurate spot detection that is applicable to smFISH experiments, spatial transcriptomics, and spatial genomics. The approach enables fast spot detection in even very large volumetric datasets.

TemplateFlow is a repository for human and other brain templates and atlases, which operates under the FAIR principles.

AutoLFADS models neural population activity via a deep learning-based approach with automated hyperparameter optimization.

This work presents Prox-seq that couples sequencing and proximity ligation assay to simultaneously measure extracellular proteins, protein–protein interactions and mRNA in single cells.

This work presents m6Anet, which implements a neural-network-based multiple instance learning model to detect m6A modifications from direct RNA sequencing data.

STAARpipeline is a comprehensive framework for flexible and scalable rare-variant association analysis using whole-genome sequencing data and annotation information.

The engineered hyperfolder YFP (hfYFP) and variants offer unprecedented chemical and thermal stability, making them versatile probes for microscopy as well as for challenging applications like correlative light and electron microscopy and expansion microscopy.

Image-seq isolates cells from specific tissue locations under image guidance for analysis by single-cell RNA sequencing. The technique can be combined with in vivo imaging to document the temporal and dynamic history of the cells prior to sequencing.

Cellpose 2.0 improves cell segmentation by offering pretrained models that can be fine-tuned using a human-in-the-loop training pipeline and fewer than 1,000 user-annotated regions of interest.

3D-TrIm combines real-time, high-speed, single-virion tracking with volumetric imaging of cellular microenvironments.

Unsupervised discovery of tissue architecture with graphs (UTAG) combines information on cellular morphology and protein expression with the physical proximity of cells to identify architectural domains from highly multiplexed imaging data.