News & Comment

High-resolution sequencing methods that capture the epigenetic landscape within the T cell receptor (TCR) gene loci are pivotal for a fundamental understanding of the epigenetic regulatory mechanisms of the TCR repertoire. In our opinion, filling the gaps in our understanding of the epigenetic mechanisms regulating the TCR repertoire will benefit the development of strategies that can modulate the TCR repertoire composition by leveraging the dynamic nature of epigenetic modifications.

Nanopore direct RNA sequencing (DRS) reads continuous native RNA strands. Early adopters have used this technology to document nucleotide modifications and 3′ polyadenosine tails on RNA strands without added chemistry steps. Individual strands ranging in length from 70 to 26,000 nucleotides have been sequenced. In our opinion, broader acceptance of nanopore DRS by molecular biologists and cell biologists will be accelerated by higher basecall accuracy and lower RNA input requirements.

Recent studies have revealed multifaceted roles of long noncoding RNAs (lncRNAs) in gene regulation, accompanying an increased understanding of lncRNA processing, localization, interacting macromolecules and structural modules. Here, progress and recently developed technological advances for understanding lncRNA biogenesis, modes of action and cellular phenotypes are highlighted, and challenges and opportunities towards higher-resolution and in vivo studies in this field are discussed.

In recent years, the number of annotated noncoding RNAs (ncRNAs) and RNA-binding proteins (RBPs) has increased dramatically. The wide range of RBPs identified highlights the enormous potential for RNA in virtually all aspects of cell biology, from transcriptional regulation to metabolic control. Yet, there is a growing gap between what is possible and what has been demonstrated to be functionally important. Here we highlight recent methodological developments in the study of RNA–protein interactions, discuss the challenges and opportunities for exploring their functional roles, and provide our perspectives on what is needed to bridge the gap in this rapidly expanding field.

The EQIPD framework for rigor in animal experiments aims to unify current recommendations based on evidence behind their rationale and was prospectively tested for feasibility in multicenter animal experiments.

Light microscopy enables researchers to observe cellular mechanisms with high spatial and temporal resolution. However, the increasing complexity of current imaging technologies, coupled with financial constraints of potential users, hampers the general accessibility and potential reach of cutting-edge microscopy. Open microscopy can address this issue by making well-designed and well-documented hardware and software solutions openly available to a broad audience. In this Comment, we provide a definition of open microscopy and present recent projects in the field. We discuss current and future challenges of open microscopy and their implications for funders, policymakers, researchers and scientists. We believe that open microscopy requires a holistic approach. Sample preparation, designing and building of hardware components, writing software, data acquisition and data interpretation must go hand in hand to enable interdisciplinary and reproducible science to the benefit of society.

The study of human–animal chimeras is fraught with technical and ethical challenges. In this Comment, we discuss the importance and future of human–monkey chimera research within the context of current scientific and regulatory obstacles.

The release of the first telomere-to-telomere (T2T) human genome sequence marks a milestone for human genomics research and holds promise of complete genomes for evolutionary genomic studies. Here we describe the advances that this new human genome assembly represents and explore the potential insights that the complete genome sequence could bring to evolutionary genomics. We also discuss the potential challenges to be faced in applying this new sequencing strategy to a broad spectrum of extant species.

Most research aiming at understanding the molecular foundations of life and disease has focused on a limited set of increasingly well-known proteins while the biological functions of many others remain poorly understood. We propose to form the Understudied Protein Initiative with the objective of reducing the annotation gap by systematically associating uncharacterized proteins with proteins of known function, thereby laying the groundwork for future detailed mechanistic studies.

Here we discuss barriers to reproducibility in regard to microscopes and related hardware, along with best practices for sharing novel designs created using computer-aided design (CAD). We hope to start a fruitful community discussion on how instrument development, especially in microscopy, can become more open and reproducible, ultimately leading to better, more trustworthy science.

Interactions between carbohydrates and the proteins that bind them (lectins) are often some of the first between a host cell and a viral invader. With its highly glycosylated spike protein, SARS-CoV-2 is no exception. Interrogating glycosylation is vital to understand viral infection, yet it has been a challenge. Improvement in methods ranging from mass spectrometry to glycan arrays and modeling simulations are yielding atomic-level information about the glycans that decorate viruses and host cells alike.

This Comment discusses the main animal models that have had a key role in our understanding of the immune and viral dynamics of SARS-CoV-2.

Critical technological advances have enabled the rapid investigations into the immune responses elicited by SARS-CoV-2, the pathogen responsible for the COVID-19 pandemic. We discuss the cutting-edge methods used to deconvolve the B-cell responses against this virus and the impact they have had in the ongoing public health crisis.

High-resolution structural information is critical for rapid development of vaccines and therapeutics against emerging human pathogens. Structural biology methods have been at the forefront of research on SARS-CoV-2 since the beginning of the COVID-19 pandemic. These technologies will continue to be powerful tools to fend off future public health threats.

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated the development of testing methods and allowed the timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific and organizational challenges. Here, we discuss the application of genomic and computational methods for efficient data-driven COVID-19 response, the advantages of the democratization of viral sequencing around the world and the challenges associated with viral genome data collection and processing.

The imminent release of tissue atlases combining multichannel microscopy with single-cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards to guide data deposition, curation and release. We describe a Minimum Information about Highly Multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and for other microscopy data to highly multiplexed tissue images and traditional histology.

The splendid computational success of AlphaFold and RoseTTAFold in solving the 60-year-old problem of protein folding raises an obvious question: what new avenues should structural biology explore? We propose a strong pivot toward the goal of reading mechanism and function directly from the amino acid sequence. This ambitious goal will require new data analytical tools and an extensive database of the atomic-level structural trajectories traced out on energy landscapes as proteins perform their function.

AlphaFold is a neural-network-based approach to predicting protein structures with high accuracy. We describe how it works in general terms and discuss some anticipated impacts on the field of structural biology.

Deep learning has transformed protein structure modeling. Here we relate AlphaFold and RoseTTAFold to classical physically based approaches to protein structure prediction, and discuss the many areas of structural biology that are likely to be affected by further advances in deep learning.

The release of protein structure predictions from AlphaFold will increase the number of protein structural models by almost three orders of magnitude. Structural biology and bioinformatics will never be the same, and the need for incisive experimental approaches will be greater than ever. Combining these advances in structure prediction with recent advances in cryo-electron microscopy suggests a new paradigm for structural biology.