Volume 21 Issue 5, May 2024

For a postdoctoral fellowship, it’s advisable to be selective about lab choice and to be clear about expectations.

Spectral flow cytometry enables the simultaneous analysis of a large number of cell surface markers at the single-cell level

Custom-built mathematical models make the immune response more predictable and offer mechanistic insights into fundamental immunology.

To those who seek transcriptomic information at high resolution, scale and throughput, single-cell RNA sequencing brings the data. Scientists share tips and future plans as they reflect on the method’s rise to stardom.

Several research groups are making it easier for other neuroscientists to analyze large datasets by providing tools that can be accessed and used from anywhere in the world.

Single-cell inference of class-switch recombination (sciCSR) is a computational method that analyzes single-cell RNA sequencing data to deduce the temporal trajectory of how B cells develop antibody response.

We developed Significant Latent Factor Interaction Discovery and Exploration (SLIDE), an interpretable machine learning approach that can infer hidden states (latent factors) underlying biological outcomes. These states capture the complex interplay between factors derived from multiscale, multiomic datasets across biological contexts and scales of resolution.

We introduce a biomimetic antigen-presenting system that uses hexapod heterostructures for specific T cell recognition at the single-molecule and single-cell levels. The system enables high-resolution T cell activation, uses magnetic forces to increase immune responses, and offers flexible and precise identification of antigen-specific T cell receptors, aiding the study of T cell recognition and immune cell mechanics.

We established a method to generate complex self-organizing bone marrow-like organoids (BMOs) via concomitant differentiation of human induced pluripotent stem cells. These BMOs consist of hematopoietic cells, stromal niche cells and de novo vascular networks. In addition, they contain multipotent hematopoietic stem and progenitor cells, as well as mesenchymal stem and progenitor cells; they model aspects of the three-dimensional bone marrow architecture and can be used to study developmental and aberrant hematopoiesis.

RoboEM, an artificial intelligence (AI)-based flight agent, automatically steers through three-dimensional electron microscopy (3D-EM) images of brain tissue to follow neurites. RoboEM substantially improves state-of-the-art automated reconstructions, eliminating manual proofreading needs in complex connectomic analysis problems and paving the way for high-throughput, cost-effective, large-scale mapping of neuronal networks — connectomes.

This Perspective discusses the potential of protein structure-prediction models for exploring the structural landscape and specificity of TCR–pMHC interactions.

This Review provides an overview of bioinformatic approaches that enable immune repertoire analyses at the single-cell level.

SQANTI3 offers a flexible tool for quality control, curation and annotation of long-read RNA sequencing data.

This work presents a highly versatile open-top, dual-view and dual-illumination light-sheet microscope for live imaging of large specimens.

Neurodesk is a platform for analyzing human neuroimaging data, which provides numerous tools in a containerized form, thereby ensuring reproducibility and portability.

brainlife.io is a one-stop cloud platform for data management, visualization and analysis in human neuroscience. It is web-based and provides access to a variety of tools in a reproducible and reliable manner.

scGHOST offers a computational tool to annotate single-cell subcompartments from scHi-C or imaging data through graph representation learning with constrained random walk sampling.

sciCSR is a computational workflow that leverages single-cell RNA sequencing data to predict B cell dynamics and class-switch recombination events.

SLIDE is a machine learning-based method for identification of significant latent factors across multiomics datasets.

SABR-IIs are engineered bifunctional receptors that enable CD4⁺ T cell antigen discovery.

A biomimetic antigen-presenting system uses hexapod heterostructures to probe T-cell recognition at single-cell resolution.

The authors describe stem cell-derived bone marrow organoids that accurately model the structural and functional properties of the human bone marrow niche.

Multi-sheet RESOLFT combines the speed and optical sectioning of light-sheet fluorescence microscopy with reversibly photoswitchable fluorescent proteins to enable fast, volumetric super-resolution imaging in live cells.

The combination of light sheet illumination and reversibly switchable fluorophores enables improved structured illumination microscopy for fast, low-background super-resolution imaging in cells and spheroids.

Improved green cAMP and red calcium sensors were developed to facilitate dual-color imaging in vivo. These sensors will allow studying the relationship between calcium and cAMP signaling.

RoboEM enables automated proofreading of electron microscopy datasets using a strategy akin to that of self-steering cars. This decreases the need for manual proofreading of segmented datasets and facilitates connectomic analyses.

Kilosort4 is a spike-sorting algorithm with improved performance compared to previous versions, owing to the use of a graph-based clustering approach. The tool extracts the activity of individual neurons from electrophysiological recordings acquired with, for example, Neuropixels electrodes.