Where do proteins go in cells? Next-generation methods map the molecules\u2019 hidden lives<\/p>\n <\/a><\/p>\n Other questions are also yielding to the technology. Researchers now recognize, for instance, that many of the most clinically significant events in the formation of tumours originate from individual cells camouflaged in the chaotic tumour environment. \u201cSingle-cell proteomics may help us understand why patients have different outcomes due to tumour evolution, immune responses and cell differentiation \u2014 all driven by cellular heterogeneity,\u201d says Yu-Ju Chen, a mass spectrometrist at Academia Sinica in Taipei.<\/p>\n Just a few years ago, protein biologists largely dismissed the possibility of single-cell proteomics \u2014 profiling thousands of proteins in individual cells \u2014 as the stuff of science fiction. \u201cI\u2019m almost on the record as saying, \u2018not in my lifetime\u2019, because it seemed to be so far off,\u201d says Matthias Mann, a proteomics researcher at the Max Planck Institute of Biochemistry in Martinsried, Germany. But over the past decade, SCP has become not only possible but practical \u2014 and scientifically informative. Mann himself is now an avid practitioner, using SCP to study illnesses ranging from liver conditions to Alzheimer\u2019s disease. The bar to entry remains high, however, and the need for expensive equipment, deep expertise and methodological precision to process cell-scale volumes of protein still limits the potentially transformative impact of this technology.<\/p>\n Precious cargo<\/b><\/p>\n Biology is already deep in the single-cell era, but most such analyses focus on the transcriptome. Profiling gene expression at the level of the individual cell can provide valuable information about a cell\u2019s identity or physiological state. But researchers cannot necessarily predict the protein contents of a cell from its RNA alone \u2014 not every RNA gets translated, and numerous factors shape the timing and extent of protein production from a given transcript. Many disease mechanisms can best be understood by surveying the protein contents \u2014 for example, the pathology of Alzheimer\u2019s and Parkinson\u2019s disease is strongly linked to aberrant aggregates of protein. \u201cThe proteome reflects the current state of the cell,\u201d Mann says.<\/p>\n Focus on single-cell proteomics<\/p>\n <\/a><\/p>\n To survey that state, proteomics researchers typically work in bulk, pooling and analysing thousands or millions of cells using mass spectrometry (MS). This technique uses specialized instruments that break samples down into ionized pieces, which are then analysed in terms of their mass and charge. The resulting \u2018fingerprint\u2019 enables precise identification of the molecule from which the fragment originated, and researchers can routinely detect 10,000 or more distinct proteins per sample. But this is an averaged perspective that can obscure the underlying biology. \u201cAre they really reflecting what\u2019s going on in all cells or is it just a small population of cells where you see dramatic changes?\u201d asks Jesper Olsen, a mass spectrometrist at the University of Copenhagen. \u201cThese are things we have not really been able to address before.\u201d<\/p>\n Enter single-cell proteomics<\/b><\/p>\n In some ways, the technology should be simpler than its transcriptomic analogue. Protein is more stable than RNA is, and more abundant. \u201cWith RNA, we count zero, one, two, three [copies] most of the time; with proteins, we count 100, 500, 10,000,\u201d says Slavov. But transcriptomics researchers can boost the cellular RNA signal by using the PCR amplification technique. No such tool exists for the proteome, and researchers must learn all they can from a few hundred picograms of material.<\/p>\n Getting a robust and informative signal from such tiny quantities requires a highly sensitive mass spectrometer \u2014 but sample preparation poses a more formidable challenge. \u201cThe raw sensitivity was there \u2026 but the sample prep was optimized for bulk level,\u201d says Ryan Kelly, a biochemist at Brigham Young University in Provo, Utah.<\/p>\n Matthias Mann\u2019s Deep Visual Proteomics strategy adds spatial data to single-cell proteomics.Credit: Axel Griesch\/Max Planck Institute of Biochemistry<\/p>\n In 2018, a team led by Slavov demonstrated an important first step towards broad proteomic coverage at the single-cell level2<\/a>. The researchers developed a sample preparation and analysis workflow called SCoPE-MS (single-cell proteomics by mass spectrometry), in which they chemically labelled proteins in individual cells of interest and then mixed these with an excess of unlabelled \u2018carrier\u2019 cells. This limited protein loss from the target cells and made protein identification easier, enabling the detection of more than 1,500 proteins across eight individual cells. \u201cWe essentially gained about four orders of magnitude in sensitivity,\u201d he says.<\/p>\n Slavov recalls that the publication of his results was met with scepticism, but other groups soon replicated them. And importantly, a second group led by Kelly published an independent approach to SCP that same year, using a sample preparation device called the nanoPOTS chip (nanodroplet processing in one pot for trace samples) to identify 670 proteins in small numbers of individual cells3<\/a>. \u201cThe whole point of those early papers was, \u2018this is real signal from single cells\u2019,\u201d says Kelly. \u201cIt\u2019s not a question any more.\u201d<\/p>\n A growing crowd<\/b><\/p>\n Since then, the field has advanced at a remarkable pace. Jennifer Van Eyk, a clinical proteomics researcher at Cedars-Sinai Medical Center in Los Angeles, California, says that her team has developed a workflow that can consistently identify 1,500\u20132,000 proteins per cell. \u201cThe biology we\u2019re seeing is just unbelievable,\u201d she says.<\/p>\n Proteomics sets up single-cell and single-molecule solutions<\/p>\n <\/a><\/p>\n And deeper coverage is possible. In 2025, two independent studies inventoried more than 5,000 proteins per cell \u2014 a figure that is already being surpassed. Olsen, who led one study4<\/a>, says his group has work under review in which they identify up to 7,000 proteins in individual cells. And Karl Mechtler, a proteomics researcher at the Research Institute of Molecular Pathology in Vienna, who co-directed the other study5<\/a>, notes that with some larger cells, \u201cyou can get up to 8,000 or 9,000 proteins\u201d.<\/p>\n Many SCP laboratories now rely on the cellenONE, a device commercialized by Cellenion in Lyon, France, that makes it possible to isolate individual cells from a culture or tissue sample while also imaging them. That allows users to record each cell\u2019s shape and size, which matter because cells can vary considerably in volume and protein content.<\/p>\n Downstream processing steps have also been optimized, minimizing sample loss and preventing cross-contamination that could confound interpretation of the data. Although chemical labelling of proteins was a key step in Slavov\u2019s 2018 publication, many groups have adopted unlabelled approaches that are simpler and faster. For example, Kelly\u2019s team developed a single-step process in 2023 that remains its go-to protocol6<\/a>. And Chen and her collaborator at Academia Sinica, Hsiung-Lin Tu, have integrated the full sample preparation process into a compact microfluidic device. Known as the SciProChip, it performs all the necessary steps in nanoscale volumes of liquid7<\/a>. \u201cThe chip substantially improved our sensitivity, reproducibility and quantitation accuracy,\u201d says Chen.<\/p>\n The SciProChip uses microfluidics to capture, count and process individual cells for analysis.Credit: Hsiung-Lin Tu and Yu-Ju Chen<\/p>\n Advances in MS instrumentation have also propelled the field. Popular options include the Orbitrap Astral machines from Thermo Fisher Scientific in Waltham, Massachusetts, and the timsTOF Ultra line from Bruker in Billerica, Massachusetts. These systems employ different strategies to detect and quantify proteins that would be invisible with older technology, but can cost around US$1 million. \u201cWe can quantify much more sensitively, much more deeply, and at high throughput even more proteins in single cells,\u201d says Slavov. However, he notes that the more affordable, older-generation systems remain effective for profiling high-abundance proteins.<\/p>\n The biggest hurdle is speed. Most SCP pipelines top out at a few hundred cells per day \u2014 orders of magnitude lower than the throughput of transcriptomics studies. This is mainly because of the need to separate cellular peptides by size before MS analysis. Going faster can mean sacrificing precious data, says Kelly: \u201cWhen we switch from 288 samples per day to 500 samples per day, we do see a big hit in our proteome coverage.\u201d<\/p>\n![\"\"](\"https:\/\/www.europesays.com\/uk\/wp-content\/uploads\/2026\/09\/d41586-026-02805-w_53443738.jpg\"\/)
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