An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A protein co-regulation database facilitates proteome-wide functional annotations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A method using CRISPR–Cas technology, nanopore sequencing and bioinformatics enables quantification of short tandem repeats with high accuracy.

Multimodal omics measurement offers opportunities for gaining holistic views of cells one by one.

Direct library preparation (DLP) for amplification-free single-cell whole-genome sequencing has been upgraded to a high-throughput, scalable platform for identification of clonal populations and their genomic features, integrating them with single-cell morphology.

The field of single-cell RNA sequencing (scRNA-seq) has been paired with genomics, epigenomics, spatial omics, proteomics and imaging to achieve multimodal measurements of individual cellular phenotypes and genotypes. In its purest form, single-cell multimodal omics involves the simultaneous detection of multiple traits in the same cell. More broadly, multimodal omics also encompasses comparative pairing and computational integration of measurements made across multiple distinct cells to reconstruct phenotypes. Here I highlight some of the biological insights gained from multimodal studies and discuss the challenges and opportunities in this emerging field.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mice implanted with human lung tissue model pathogen infection and immune responses.

Stereochemically defined chemical probes aid discovery of ligandable proteins.

Deep learning has made a resounding impact on microscopy; we highlight the state-of-the-art and potential future directions in this focus issue.

Researchers show that mouse stem and adult cells can develop into blastocyst-like structures.

The resolution of subtomogram averages calculated from cryo-electron tomograms (cryo-ET) of crowded cellular environments is often limited owing to signal loss in, and misalignment of, the subtomograms. By contrast, single-particle cryo-electron microscopy (SP-cryo-EM) routinely reaches near-atomic resolution of isolated complexes. We report a method called ‘tomography-guided 3D reconstruction of subcellular structures’ (TYGRESS) that is a hybrid of cryo-ET and SP-cryo-EM, and is able to achieve close-to-nanometer resolution of complexes inside crowded cellular environments. TYGRESS combines the advantages of SP-cryo-EM (images with good signal-to-noise ratio and contrast, as well as minimal radiation damage) and subtomogram averaging (three-dimensional alignment of macromolecules in a complex sample). Using TYGRESS, we determined the structure of the intact ciliary axoneme with up to resolution of 12 Å. These results reveal many structural details that were not visible by cryo-ET alone. TYGRESS is generally applicable to cellular complexes that are amenable to subtomogram averaging.

X-ray crystallography often requires non-native constructs involving mutations or truncations, and is challenged by membrane proteins and large multicomponent complexes. We present here a bottom-up endogenous structural proteomics approach whereby near-atomic-resolution cryo electron microscopy (cryoEM) maps are reconstructed ab initio from unidentified protein complexes enriched directly from the endogenous cellular milieu, followed by identification and atomic modeling of the proteins. The proteins in each complex are identified using cryoID, a program we developed to identify proteins in ab initio cryoEM maps. As a proof of principle, we applied this approach to the malaria-causing parasite Plasmodium falciparum, an organism that has resisted conventional structural-biology approaches, to obtain atomic models of multiple protein complexes implicated in intraerythrocytic survival of the parasite. Our approach is broadly applicable for determining structures of undiscovered protein complexes enriched directly from endogenous sources.

Single-cell genetic screens can be incredibly powerful, but current high-throughput platforms do not track dynamic processes, and even for non-dynamic properties they struggle to separate mutants of interest from phenotypic outliers of the wild-type population. Here we introduce SIFT, single-cell isolation following time-lapse imaging, to address these limitations. After imaging and tracking individual bacteria for tens of consecutive generations under tightly controlled growth conditions, cells of interest are isolated and propagated for downstream analysis, free of contamination and without genetic or physiological perturbations. This platform can characterize tens of thousands of cell lineages per day, making it possible to accurately screen complex phenotypes without the need for barcoding or genetic modifications. We applied SIFT to identify a set of ultraprecise synthetic gene oscillators, with circuit variants spanning a 30-fold range of average periods. This revealed novel design principles in synthetic biology and demonstrated the power of SIFT to reliably screen diverse dynamic phenotypes.

Autophagy is a degradative program that maintains cellular homeostasis. Autophagy defects have been described in numerous diseases. However, analysis of autophagy rates can be challenging, particularly in rare cell populations or in vivo, due to limitations in currently available tools for measuring autophagy induction. Here, we describe a method to monitor autophagy by measuring phosphorylation of the protein ATG16L1. We developed and characterized a monoclonal antibody that can detect phospho-ATG16L1 endogenously in mammalian cells. Importantly, phospho-ATG16L1 is only present on newly forming autophagosomes. Therefore, its levels are not affected by prolonged stress or late-stage autophagy blocks, which can confound autophagy analysis. Moreover, we show that ATG16L1 phosphorylation is a conserved signaling pathway activated by numerous autophagy-inducing stressors. The described antibody is suitable for western blot, immunofluorescence and immunohistochemistry, and measured phospho-ATG16L1 levels directly correspond to autophagy rates. Taken together, this phospho-antibody represents an exciting tool to study autophagy induction.

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.

Magnetic resonance imaging and spectroscopy are versatile methods for probing brain physiology, but their intrinsically low sensitivity limits the achievable spatial and temporal resolution. Here, we introduce a monolithically integrated NMR-on-a-chip needle that combines an ultra-sensitive 300 µm NMR coil with a complete NMR transceiver, enabling in vivo measurements of blood oxygenation and flow in nanoliter volumes at a sampling rate of 200 Hz.