NEW YORK – A team led by researchers at Pacific Northwest National Laboratory has developed a high-throughput workflow for single-cell proteomics.
Detailed in a study published last week in Analytical Chemistry, the approach is able to identify up to 2,300 proteins in single cells and analyze around 100 cells per day. The researchers are in discussions with vendors including Thermo Fisher Scientific and Shimadzu about commercializing portions of the workflow, said Ying Zhu, a senior research scientist at PNNL and senior author on the study.
As interest in cellular heterogeneity has grown in fields like cancer research and life science research more generally, demand for single-cell analysis techniques has grown with it. Proteomics has lagged behind nucleic acid-based approaches, with mass spec-based proteomics proving particularly challenging due to the extremely low sample volumes involved.
The difficulty has been threefold. In the first place, mass spec sample preparation at the single-cell level faces the issue of sample loss, which can reduce the amount of sample that ultimately makes it into the instrument. Secondly, mass specs haven't traditionally had the sensitivity required to measure proteins in single cells at the proteome-wide level. And finally, mass spec's relatively low throughput had made it a challenge to analyze enough single cells (given that each cell requires its own mass spec experiment) to collect the data needed from a statistical perspective.
The PNNL approach combines several existing single-cell approaches to tackle these challenges. To address the sample preparation step, the researchers used the NanoPOTS platform previously developed by study co-author and former PNNL researcher Ryan Kelly (now at Brigham Young University). Designed for use with extremely small proteomic samples, the NanoPOTS (nanodroplet processing in one pot for trace samples) platform shrinks sample processing volumes down to less than 200 nanoliters to limit sample loss and speed trypsin digestion, which has slow kinetics at small sample volumes.
They combined this upfront sample prep approach with an isobaric tagging-based signal boosting strategy that has previously been used by researchers including the Northeastern and Harvard University team that developed one of the first single-cell proteomic methods, Single Cell Proteomics by Mass Spectrometry (SCOPE-MS).
Based on the TMT Calibrator approach devised by Proteome Sciences, the SCOPE-MS approach combines TMT-labeled peptides from single cells of interest with another sample source, such as tissue, where the target proteins are produced in higher abundance. By including the second supplementary sample source at a higher concentration than the sample of interest, researchers are able to ensure that even analytes present in low abundance in the sample of interest are present at high abundance in the overall sample, making them more likely to be fragmented and detected by the mass spec.
Use of the TMT approach also helps address the throughput challenge inherent in single-cell proteomics, as the multiplexing offered by TMT labeling allows researchers to run as many as 11 samples in a single mass spec experiment.
"We think it’s the perfect marriage of the two technologies," Zhu said, noting that of the 2,300 proteins he and his colleagues were able to identify using the technique, they were able to quantify around 1,400 of them in at least 50 percent of the cells they analyzed.
This marks an advance from work published by the group last year, in which they were able to identify an average of 669 proteins and quantify 332 proteins in single HeLa cells.
Charles Ansong, a senior scientist at PNNL and co-author on the Analytical Chemistry study, said he and his colleagues were currently using the method for a variety of research purposes, including exploring the heterogeneity of lung tissue both during normal development and under disease conditions like infections or allergic responses.
The researchers are also working to improve the method by moving it onto Thermo Fisher's newest flagship instrument, the Orbitrap Eclipse Tribrid, which is capable of performing a new isobaric tagging workflow developed by Harvard University professor Steven Gygi called RTS-MS3 that boosts the quantitative performance of the approach.
When done using MS2-level mass spec analysis, as the PNNL team used, isobaric tagging suffers from precursor interference issues that reduced the method's quantitative accuracy. Researchers have addressed this by adding an extra level of fragmentation, doing quantitation at the MS3 level, but this adds time to the instrument's duty cycle, which cuts into depth of coverage. The RTS-MS3 method allows for MS3 analysis without losing coverage depth and so should boost the number of proteins the PNNL team's single-cell approach is able to identify and accurately quantify.
"You can imagine applying that approach and significantly improving on the results that we have without changing anything on the front end," Ansong said, adding that he and his colleagues have seen improved performance running the RTS-MS3 method on the Eclipse. The PNNL team along with BYU's Kelly and Thermo Fisher presented preliminary data using the approach in a poster at the American Society for Mass Spectrometry annual meeting in June.
Another adjustment the researchers are exploring is the use of Thermo Fisher's High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) device to improve their analysis, Zhu said. Gygi's lab has found that using FAIMS for upfront separation significantly reduces isobaric tagging's precursor interference challenge.
Additionally, Zhu said that in the case of single-cell mass spec work FAIMS could prove useful for reducing sample contamination, which he said has been a challenge for his team.
TMT reagents are very reactive, he noted, which is important in that it allows them to effectively label proteins of interest. However, in the case of single-cell proteomics, the quantity of protein available for labeling is so small that many of the TMT reagents end up reacting with contaminants, which reduces the sensitivity of the mass spec analysis.
"I think maybe the value of FAIMS [for single-cell mass spec] will be to decrease that contamination," Zhu said.