Simple Method to Quantify Protein Abundances from 1000 Cells

Droplet-based proteomics reveals CD36 as a marker for progenitors in mammary basal epithelium

Deep proteomic profiling of rare cell populations has been constrained by sample input requirements. Here, we present DROPPS (droplet-based one-pot preparation for proteomic samples), an accessible low-input platform that generates high-fidelity proteomic profiles of 100-2,500 cells. By applying DROPPS within the mammary epithelium, we elucidated the connection between mitochondrial activity and clonogenicity, identifying CD36 as a marker of progenitor capacity in the basal cell compartment. We anticipate that DROPPS will accelerate biology-driven proteomic research for a multitude of rare cell populations.

SpinTip: A Simple, Robust, and Versatile Preanalytical Method for Microscale Suspension Cell Proteomics

Sample loss and contamination are critical preanalytical pitfalls in microscale proteomic applications of nonadhering cells. Common assays and workflows are not easily adoptable to microscale sample sizes of suspension cells due to inadvertent sample loss. This impedes preanalytical experimental manipulation of limited suspension cell samples for microscale proteomics applications, such as encountered for primary human materials. Here, we describe and test a simple manual batch technique for single-step 100-fold concentration of scarce numbers of diluted suspension cells (down to 5000 cells) by volume reduction, facilitating microscale experiments with suspension cells. Pipette tips with heat-sealed orifices (SpinTips) are manufactured within 1 min and serve as versatile microcentrifugation vessels from which supernatant can be aspirated with minimal cell loss. A residual volume of approximately 3 μL can be achieved without visualization of the cell pellet. The results show that SpinTips enable the concentration, medium exchange, washing, and culture of highly limited amounts of suspension cells for functional manipulation and microscale proteomics and are readily incorporated into standard workflows. The application is illustrated by profiling ex vivo responses of primary acute myeloid leukemia (AML) cells from one AML patient to daunorubicin (DNR) to a depth of 3462 quantified proteins with excellent repeatability.

To the proteome and beyond: advances in single-cell omics profiling for plant systems

Recent advances in single-cell proteomics for animal systems could be adapted for plants to increase our understanding of plant development, response to stimuli, and cell-to-cell signaling.

New horizons in the stormy sea of multimodal single-cell data integration

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.

Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.

Tsai, Zhang, Scholten et al. develop a surfactant- assisted one-pot sample preparation coupled with mass spectrometry method (SOP-MS) for label-free global single-cell proteomics. This method allows researchers to measure hundreds of proteins from single human cells, suggesting its utility for quantitative single-cell proteomics.