Volume 18 Issue 1, January 2021

Creativity, passion, inclusion and a targeted way to study circular RNAs.

“We demand rigidly defined areas of doubt and uncertainty!” —D. Adams

Nature Methods has crowned spatially resolved transcriptomics Method of the Year 2020.

As single-cell omics continue to advance, the field of spatially resolved transcriptomics has emerged with a set of experimental and computational methods to map out the positions of cells and their gene expression profiles in space. Here we summarize current transcriptome-wide and sequencing-based methodologies and their applications in genomics research.

The recent advent of genome-scale imaging has enabled single-cell omics analysis in a spatially resolved manner in intact cells and tissues. These advances allow gene expression profiling of individual cells, and hence in situ identification and spatial mapping of cell types, in complex tissues. The high spatial resolution of these approaches further allows determination of the spatial organizations of the genome and transcriptome inside cells, both of which are key regulatory mechanisms for gene expression.

One major challenge in neuroscience is to gain a systematic understanding of the extraordinary diversity of brain cell types and how they contribute to brain function. Spatially resolved transcriptomics holds unmatched promise in unraveling the organization of brain cell types and their relationship with connectivity, circuit dynamics, behavior and disease. Here we discuss neuroscience applications of various spatially resolved transcriptomics methods, as well as technical challenges that need to be overcome to realize their full potentials.

Prime editors enable different types of small mutations to be introduced into genomes.

Organoids generated by spatially organizing multiple cell types, called assembloids, will enable deeper insights into tissue function.

Single-objective light sheet fluorescence microscopes are driving innovation in volumetric imaging.

Glycoproteomics is coming of age, thanks to advances in instrumentation, experimental methodologies and computational search algorithms.

Method development pushes the limit of completeness of genome sequences.

Behavioral analysis has come a long way from tedious manual annotation, and further strides in automation are expected.

Computational approaches help us explore complexities of the T cell receptor repertoire.

Researchers are putting new spins on familiar dyes and showing their versatility for labeling living systems.

DeepCell Kiosk is a user-friendly tool that enables fast and affordable deep learning–based analysis of large imaging datasets on Google cloud.

A two-photon miniature microscope with enlarged field of view and axial scanning capabilities has been developed and applied in freely moving mice.

This paper describes a CRISPR–Cas13 system to effectively target circRNAs and screen their functions in vitro and in vivo, which enables the study of relevant circRNA phenotypes in human cell proliferation and in mouse embryogenesis.

Megabodies, built by grafting nanobodies onto larger protein scaffolds, help alleviate problems of particle size and preferential orientation at the water–air interfaces during cryo-EM based structure determination experiments and are shown to be generalizable to soluble and membrane-bound proteins.

The iterative Build and Retrieve (BaR) methodology facilitates the solving of cryo-EM structures of multiple membrane (and soluble) proteins simultaneously, including small and low-abundance membrane proteins.

A careful analysis of how carrier proteome levels used in the SCoPE-MS method affect the quantitative accuracy of single-cell proteomics results, yields guidelines for method users.

Cell surface thermal proteome profiling allows characterization of ligand-induced changes in protein abundances and thermal stabilities at the plasma membrane.

Tessa, a Bayesian model, integrates TCR sequencing data with gene expression data to analyze the effect of the TCR repertoire on T cell functionality.

Cellpose is a generalist, deep learning-based approach for segmenting structures in a wide range of image types. Cellpose does not require parameter adjustment or model retraining and outperforms established methods on 2D and 3D datasets.

Click-ExM uses click-chemistry-based labeling to increase the versatility of expansion microscopy. Click-ExM enables imaging of numerous classes of biomolecules including lipids, glycans, proteins, DNA, RNA and small molecules.