Comment in 2023

The development of mass spectrometry-based single-cell proteomics technologies opens unique opportunities to understand the functional crosstalk between cells that drive tumor development.

Recent technological advances in mass spectrometry promise to add single-cell proteomics to the biologist’s toolbox. Here we discuss the current status and what is needed for this exciting technology to lead to biological insight — alone or as a complement to other omics technologies.

Increasing evidence suggests that the spatial distribution of biomolecules within cells is a critical component in deciphering single-cell molecular heterogeneity. State-of-the-art single-cell MS imaging is uniquely capable of localizing biomolecules within cells, providing a dimension of information beyond what is currently available through in-depth omics investigations.

We argue that the study of single-cell subcellular organelle omics is needed to understand and regulate cell function. This requires and is being enabled by new technology development.

Mammalian cells have about 30,000 times as many protein molecules as mRNA molecules, which has major implications in the development of proteomics technologies. We discuss strategies that have been helpful for counting billions of protein molecules by liquid chromatography–tandem mass spectrometry and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges posed by the wide dynamic range of the proteome.

Human neuroscience is enjoying burgeoning population data resources: large-scale cohorts with thousands of participant profiles of gene expression, brain scanning and sociodemographic measures. The depth of phenotyping puts us in a better position than ever to fully embrace major sources of population diversity as effects of interest to illuminate mechanisms underlying brain health.

Dramatic advances in protein structure prediction have sparked debate as to whether the problem of predicting structure from sequence is solved or not. Here, I argue that AlphaFold2 and its peers are currently limited by the fact that they predict only a single structure, instead of a structural distribution, and that this realization is crucial for the next generation of structure prediction algorithms.

Long-read sequencing has made closed microbial genomes a routine task, and the dramatic increase in quality and quantity now paves the way to a complete microbial tree of life through genome-centric metagenomics.

Advances in long-read sequencing technologies have broadened our understanding of genetic variation in the human population, uncovered new complex structural variants and offered an opportunity to elucidate new variant associations with disease.

Long-read sequencing has become a widely employed technology that enables a comprehensive view of RNA transcripts. Here, we discuss the importance of long-read sequencing in interpreting the variables along RNA molecules, such as polyadenylation sites, transcription start sites, splice sites and other RNA modifications. In addition, we highlight the history of short-read and long-read technologies and their advantages and disadvantages, as well as future directions in the field.

The year 2022 will be remembered as the turning point for accurate long-read sequencing, which now establishes the gold standard for speed and accuracy at competitive costs. We discuss the key bioinformatics techniques needed to power long reads across application areas and close with our vision for long-read sequencing over the coming years.

As long-read sequencing technologies continue to advance, the possibility of obtaining maps of DNA and RNA modifications at single-molecule resolution has become a reality. Here we highlight the opportunities and challenges posed by the use of long-read sequencing technologies to study epigenetic and epitranscriptomic marks and how this will affect the way in which we approach the study of health and disease states.