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Multiple established and emerging technologies are being applied for the analysis of the single-cell proteome, as covered in this month’s Focus on single-cell proteomics.
Single-cell proteomics is a challenging goal and an area of rapid methods development. This Focus issue highlights the many paths toward high-throughput, high-sensitivity measurements.
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.
The development of mass spectrometry-based single-cell proteomics technologies opens unique opportunities to understand the functional crosstalk between cells that drive tumor development.
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.
The nanopore community is stepping toward a new frontier of single-molecule protein sequencing. Here, we offer our opinions on the unique potential for this emerging technology, with a focus on single-cell proteomics, and some challenges that must be overcome to realize it.
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.
Optimal design of spatial transcriptomic experiments allows statistical evaluation of the impact of various biological and technological features on the discovery of cell phenotypes.
Stimulated Raman scattering (SRS) microscopy has the capability to simultaneously visualize the spatial distribution of different biomolecules, but it remains challenging to reach super-resolution. To achieve this goal, a deconvolution algorithm, A-PoD, was developed and combined with SRS microscopy, enabling examination of nanoscopic biomolecular distribution and subcellular metabolic activity in cells and tissues.
This Review covers the state of the art in applying mass spectrometry- or next-generation sequencing-based techniques for single-cell proteomics analysis, offering suggestions for maximizing the advantages of both approaches.
A community of researchers working in the emerging field of single-cell proteomics propose best-practice experimental and computational recommendations and reporting guidelines for studies analyzing proteins from single cells by mass spectrometry.
Spatial Omics DataBase (SODB) is a web-based platform for sharing and analyzing >25 types of spatial omics data. SODB promotes data reuse, offers novel visualization tools and can streamline development of omics analysis tools.
EnzymeML is an XML-based markup language that enables FAIR (findable, accessible, interoperable and reusable) storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model.
A modular architecture for managing and sharing electrophysiology, behavior, colony management and other data has been built to support individual laboratories or large consortia.
An optimized pipeline for improved inference and analysis of structural variants (SVs) has been developed, which uses Iris for refining breakpoints and sequences, and Jasmine for comparing SV calls at population scale.
A statistical approach for optimal design of multiplexed imaging studies has been developed. It determines experimental parameters that facilitate cell phenotype identification.
Bidirectional, cyanobacteriochrome-based light-inducible dimers (BICYCL)s enable optogenetic control of protein–protein interactions with green and red light, allowing multiplexing with existing blue light-controlled tools.
An unsupervised machine learning approach for anomaly detection, implemented as both a user-defined feature matrix and a self-supervised deep neural network, improves the mass sensitivity of iSCAT by a factor of 4 to below 10 kDa.
Adam optimization-based pointillism deconvolution (A-PoD) is a broadly applicable super-resolution deconvolution algorithm. A-PoD-coupled SRS microscopy reveals heterogeneous metabolic activity in subcellular structures like lipid droplets.
FD-DeepLoc uses field-dependent deep learning for precise localization of spatially variant point emitters over the full chip of a modern sCMOS camera, enabling fast and high-throughput volumetric localization microscopy.