Rapid assay development for low input targeted proteomics using a versatile linear ion trap

Rapid assay development for low input targeted proteomics using a versatile linear ion trap | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Rapid assay development for low input targeted proteomics using a versatile linear ion trap Brian Searle, Ariana Shannon, Rachael Teodorescu, No-Joon Song, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4702746/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Advances in proteomics and mass spectrometry enable the study of limited cell populations, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive low-mass accuracy measurements, these instruments are effectively restricted to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. Here, we describe a workflow using a new hybrid quadrupole-LIT instrument that rapidly develops targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Using an automated software approach for scheduling parallel reaction monitoring assays (PRM), we show consistent quantification across three orders of magnitude in a matched-matrix background. We demonstrate measuring low-level proteins such as transcription factors and cytokines with quantitative linearity below two orders of magnitude in a 1 ng background proteome without requiring stable isotope-labeled standards. From a 1 ng sample, we found clear consistency between proteins in subsets of CD4 + and CD8 + T cells measured using high dimensional flow cytometry and LIT-based proteomics. Based on these results, we believe hybrid quadrupole-LIT instruments represent an economical solution to democratizing mass spectrometry in a wide variety of laboratory settings. Physical sciences/Chemistry/Analytical chemistry/Mass spectrometry Biological sciences/Biotechnology/Proteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Systems biology is the study of interactions within and between cells, where the goal is to learn how those interactions give rise to the complex behavior seen in an entire system. 1 One challenge is that many complex biological processes, such as adaptive immunity, are built from small populations of distinct cell types acting in concert. 2,3 Improvements in proteomics methods and mass spectrometry (MS) instrumentation have paved the way for low-input and single-cell proteomics, which make it possible to study how limited cell populations contribute to the whole. While the majority of single-cell methods use tandem mass tags (TMT) 4 to increase signal (and thus consistency) with data-dependent acquisition (DDA), 5,6 several groups have demonstrated that data-independent acquisition (DIA) is an effective solution to measuring low-input samples. 7–9 However, high-mass accuracy instruments are required in nearly all cases. While single-cell and low-input global proteomics is typically acquired using high-mass accuracy instruments, nominal-mass instruments, such as triple quadrupoles, lead in quantitative sensitivity using targeted selected reaction monitoring (SRM). 10 With SRM, peptides are detected based on monitoring multiple fragment ion signals produced by each selected precursor ion. Transitions (diagnostic precursor/fragment ion pairs) in a pre-specified schedule must be provided to the instrument for monitoring at specific times within the chromatographic gradient. 11 While triple quadrupoles are extremely quick instruments capable of rapidly switching between ion pairs, they can only monitor a single m/z at a time. As such, triple quadrupoles are limited to targeted experiments, which require a high-mass resolution instrument to select and schedule targeted peptides and transitions before migrating to a nominal-mass instrument for high-throughput monitoring. An alternative targeted method to SRM is parallel reaction monitoring (PRM), which uses a quadrupole-equipped high-resolution mass spectrometer where the third quadrupole is replaced with an Orbitrap™ (Q-Orbitrap, also known as a Q-Exactive™) or a time-of-flight analyzer (Q-ToF). Rather than measure precursor/fragment transitions, all precursor-specific fragment ions are collected in a full tandem mass spectrum with PRM. 12 A major advantage of PRM is that diagnostic fragment ions are selected after the experiment is performed, which can vastly simplify the assay development process. PRM has provided meaningful biological insight into several diseases, including systemic autoimmune diseases, 13 multiple sclerosis, 14 and colorectal cancer. 15 When coupled with global proteomics, PRM is a powerful tool for interrogating system-wide interactions between cells. Linear ion traps (LITs) are another versatile, fast, nominal-mass analyzer comparable in resolution and complexity to triple quadrupoles. Modern Thermo Scientific™ Tribrid™ instruments have incorporated LITs as a tertiary analyzer, coupled with an Orbitrap. 16 Using a Tribrid instrument, Heil et al 17 showed that the benefit of PRM lies within its ability to monitor multiple product ions produced within a selected precursor m/z range and that the LIT in Tribrids was an effective readout for targeted proteomics. A LIT measures ions trapped in an electric field by adjusting RF and DC voltages to selectively eject ions based on their m/z to collect MS/MS spectra. Unlike triple quadrupoles, which have to “dwell” at each increment of m/z to form a spectrum, LITs acquire full scan MSn data quickly and sensitively, 18 making them also viable for global proteomics using DDA or DIA. 19 As a result, a hybrid quadrupole-LIT (Q-LIT) could act as an “all-in-one” nominal-mass instrument capable of both targeted and global proteomics. As with triple quadrupoles, LITs are extremely sensitive, ion-efficient mass analyzers apt for low-input proteomics. 20 In some circumstances, LITs can be more effective than high-resolution mass analyzers for low-input samples (≤ 10 ng) 21 and can measure single cells without multiplexing reagents. 22 At higher sample input (≥ 100 ng), the lack of high mass accuracy overshadows the increased sensitivity of LITs. There exist other compelling reasons to consider LIT-based instruments in high-throughput applications. In particular, LITs operate at high pressure (10 − 3 mTorr) in comparison to ToF analyzers (10 − 6 mTorr), where ions have to travel uninterrupted for meters, or Orbitrap analyzers (10 − 10 mTorr), where ions can travel for more than a kilometer. Lower vacuum pump requirements allow LITs to be built more affordably, robustly, and housed in smaller instrument footprints. Here, we present a workflow using a hybrid quadrupole-LIT (Q-LIT) instrument from Thermo Scientific as a single instrument for rapidly generating targeted assays for low-input experiments. With the Q-LIT, we demonstrate how to build nominal-mass targeted transition libraries using both DDA and gas-phase fractionated (GPF) DIA libraries. We then show the quantitative accuracy of targeted PRMs with a Q-LIT using matched-matrix calibration curves collected with 1, 10, and 100 ng total protein to model low abundant immune cell populations. To facilitate this, we developed an open-source software tool that directly schedule-optimized PRM assays from DDA and DIA libraries. Finally, we show quantitative consistency measuring low-level biological targets in cytokine-stimulated CD4 + and CD8 + T cells with as little as 1 ng on column. These results suggest that Q-LITs can perform as inexpensive stand-alone instruments for quantitative proteomics, capable of a wide range of measurements without needing high-resolution mass spectrometry. Results and Discussion Linear ion traps (LITs) are robust, sensitive, and fast mass analyzers, yet these instruments have limited mass resolution. Previously, our lab demonstrated that LITs could be used effectively as stand-alone mass analyzers to measure low-input samples using an Orbitrap Eclipse™ Tribrid mass spectrometer. 22 In that work, we detected approximately 400 proteins from single cells using data-independent acquisition coupled with chromatogram libraries to help make detections. 23 While our Eclipse instrument configuration ignored the high-resolution Orbitrap mass analyzer, we performed those experiments in the context of a high-end Tribrid instrument. Furthermore, the 400 proteins we measured were the easiest to observe but not necessarily the most biologically useful to monitor. While reduced representation approaches 24,25 that quantify a limited panel of easily observed proteins can help infer biological states, significant hurdles must be overcome to predict the expression patterns of unmeasured proteins. As such, directly measuring panels of proteins of interest in low-input samples using targeted proteomics may be preferable to global proteomics. In this work, we sought to answer three remaining questions. First, by eliminating the Orbitrap, could an affordable quadrupole-LIT (Q-LIT) mass spectrometer perform at a high level as a stand-alone instrument for both library generation and targeted proteomics measurement? Second, can a Q-LIT mass spectrometer quantify peptides at and below the level of single cells? Third, can quantitative experiments measure low-level biologically relevant proteins like cytokines and transcription factors at or below 1 ng? To this end, we assessed several parameters of the Stellar™ mass spectrometer, a new hybrid Q-LIT design produced by Thermo Scientific. First, we tested proteome-wide library generation; then, we assessed quantitative linearity using targeted PRM experiments with 100, 10, and 1 ng sample inputs. Finally, we tested sensitivity and measurement consistency in a biological context. A Q-LIT workflow for generating PRM assays using DDA and DIA data The Stellar MS is a hybrid Q-LIT mass spectrometer with improved ion transmission features capable of performing rapid scans up to 200 kDa/s ( Figure 1A ). The instrument shares many of the same design components as existing Orbitrap-based instruments. 26–28 Tribrid instruments accumulate fragment ions in the collision cell (also known as the ion routing multipole, IRM) before transfer to the mass analyzer. Analogously, the Stellar accumulates fragment ions in the collision cell (Q2, also known as the ion concentrating routing multipole, ICRM) before transfer to the LIT for mass analysis. These arrangements produce high scanning speeds by performing fragment accumulation in parallel with mass analysis in the low-pressure LIT. 29,30 An advantage of the Q-LIT geometry is that it is suitable for both global discovery proteomics as well as targeted proteomics. To leverage this, we implemented a workflow to generate high-quality peptide libraries using off-line fractionated DDA or GPF-DIA, and software to build on-the-fly PRM assays for the same instrument ( Figure 1B ). Briefly, the software takes a DDA-based spectrum library or a DIA-based chromatogram library as input and compares it with potential targeted proteins. The target list contains a list of critical accession numbers along with other optionally desired entries in a selected FASTA database. The assay can be modified using both a peptide inclusion and exclusion list. Assays can be adjusted depending on instrument settings, where the maximum assay density and a retention time scheduling window width must be selected. A recent single-injection DIA run also ensures that the retention time schedule matches the current LC column conditions. Peptides are selected using this software tool based on the highest signals recorded in the library. For DDA, this signal is based on precursor intensity if available. For GPF-DIA, this signal is based on the intensity of the third largest fragment ion per peptide following common SRM/PRM conventions of requiring at least three transitions. 29 The algorithm chooses peptides using a greedy approach, where the most abundant peptides are scheduled first. After the algorithm chooses a specified number of peptides for a given protein (typically 3-5), no additional peptides from that protein are considered. Additionally, peptides cannot be added to a retention time region if any time point in that region has already reached the maximum assay density. Once the algorithm iterates through all possible peptides, the software tool produces a scheduling report and a target inclusion list for the Thermo method editor. While the tool focuses on simplifying scheduling for Thermo instruments, it is analyzer and vendor agnostic, supporting scheduling for both Orbitrap and ToF instruments. This software workflow has an accessible graphical user interface built into the EncyclopeDIA code base (see Supplemental Note for more details). Developing a comprehensive target library PRM assays are commonly generated from various sources, including public repositories that store targeted proteomics data such as the PeptideAtlas, 31 CPTAC, 32 or Panorama. 33 Additionally, assays can be built from global data, with targets selected from empirical measurements on the biological matrix of interest. For this work, we wanted to use methods that could be fully acquired on the Q-LIT but still be capable of detecting low-abundant peptides. One advantage of this approach is that targets are tuned for the instrument from the context of retention time scheduling and optimal transition selection. To generate a low-input PRM assay on the Q-LIT, we tested two standard methods of building libraries: a chromatogram library using GPF-DIA and a spectral library using fractionated DDA. We collected libraries from a pool of IL-2 and IL-15-stimulated T cell proteomes. To build the chromatogram library, 6x gas-phase fractions were used with 2 m/z wide isolation windows across mass ranges of 100 m/z per injection. Since the background proteome matrix is not chemically altered or diluted, this approach produces retention times that closely match the quantitative PRM experiments. In contrast, we offline fractionated the DDA library samples using high-pH reverse phase separations to yield a total of 10 fractions, which were analyzed in separate injections. Consequently, each fraction has a simplified matrix background, which may not reflect retention times as consistently in unfractionated quantitative samples. The DDA and DIA methods produced libraries that were similar in size but of surprisingly distinct populations of peptides ( Figure 2A ), presumably due to the different fractionation methods and matrix backgrounds used to generate each library. In addition to producing slightly more peptide detections, peptide-centric extraction 34 of DIA datasets is more akin to fragment-level quantification using targeted methods than DDA measurements. 35 As such, peptides detected using GPF-DIA are more likely to produce robust, targeted assays since the mode of discovery uses similar methodologies to the final quantitative measurements. However, some sample types, such as enriched phosphopeptides, may be better suited to library generation with DDA since they can take advantage of stochastic sampling to detect more peptides and proteins over technical replicates. 36 For this work, we chose to proceed with the GPF-DIA library for assay development, but the scheduling software produced for this work functions with either library source. For DIA injections, search results from EncyclopeDIA and CHIMERYS were combined for downstream work ( Figure 2B ). CHIMERYS is a spectrum-centric search engine that builds on INFERYS to provide spectra and retention-time predictions for peptides in a given FASTA database. 37 In comparison, we searched a Prosit-predicted spectral library 38,39 with the peptide-centric search engine, EncyclopeDIA, which was adapted for analyzing ion trap data. Consequently, EncyclopeDIA was limited to searching +2 and +3 peptides to maintain a reasonable search space, while CHIMERYS was configured to consider modifications and higher charge states as well. More peptides were detected from CHIMERYS compared to EncyclopeDIA in each gas-phase fraction ( Supplemental Figure S1A ), but considering the superset of detections increased the total number of potential targets ( Supplemental Figure S1B ) and both search engines produced an equal number of viable peptide targets that could be used in downstream PRM experiments ( Supplemental Figure S1C ). In all cases, the retention times from CHIMERYS-detected peptides were re-peak picked using EncyclopeDIA to identify candidate target transitions for PRM measurement in a combined DIA library. Assessing Q-LIT PRM quantitative accuracy at low input With low-input global proteomics, we preferentially measure only the most abundant proteins. We stress-tested the quantitative accuracy of the Q-LIT system using PRMs by measuring biologically relevant proteins that tend to occur at a range of levels in the proteome. To accomplish this, we first functionally annotated candidate peptides in the combined DIA library using the PANTHER database. 40 We selected target proteins based on GO-terms and Reactome pathways for T cell differentiation, immune biology, T cell activation, cytokines, and transcription factors with a focus on choosing proteins associated with the dynamics of memory T cells. Using the PRM scheduling algorithm, we constructed three assays using the same bank of proteins, where each assay was suited to a different input level: up to 50 peptides/cycle for 100 ng of material, 20 peptides/cycle for 10 ng of material, and 10 peptides/cycle for 1 ng of material. Ultimately, the 100 ng assay monitored 481 peptides, the 10 ng assay monitored 151, and the 1 ng assay monitored 61. To maintain a 2-second cycle time using 1 ng of material, the maximum ion injection time (maxIIT) was set to 200 ms. Similarly, at 10 ng of material, the maxIIT was set to 95 ms (slightly below 100 ms to accommodate the additional time required to route ions in the mass spectrometer). At 100 ng of material, the ion injection time was set to 50 ms; however, each scan rarely met that length of time. We performed matrix-matched calibration curves 41 at 100 ng, 10 ng, and 1 ng levels to assess the quantitative accuracy of the Q-LIT over several orders of magnitude. Dilutions in a buffer background are useful to assess instrument sensitivity, but because background noise decreases at the same rate as target peptides, quantitative linearity will always appear to be more accurate than in a real background matrix. Matrix-matched calibration curves are more effective at assessing linearity in real-world scenarios since the background signal does not change with dilution. To accomplish this, we had to build a suitable background matrix of similar composition to our target T cell proteome. Our approach used dimethyl labeling to modify the foreground T cell proteome, which kept the same composition while also producing different precursor and fragment masses. Dimethyl labeling was first introduced as a multiplexing method where multiple samples would be labeled and mixed prior to mass spectrometry. 42 In our approach, only the background is modified, where free amines are mass-shifted by two methyl groups (28 Da). This shifts any labeled precursors (even incomplete reactions with a single methyl group) outside of the precursor isolation window used by PRM measurements, ensuring that any foreground signals will not be confused with background signals. Additionally, dimethyl labeling is affordable, easy, and quick, as peptides are labeled to 99.9% completion within a 1-hour reaction. While ion traps generally have a more limited dynamic range compared to Orbitrap-based mass spectrometers, the sensitivity of ion traps allows for superior detection and quantification of low-input samples. 17 Additionally, Orbitraps have slower scanning speeds compared to the Q-LIT, limiting the number of peptides that can be targeted within a cycle. Here, we found that reasonable quantitative accuracy can be achieved with the Q-LIT at low input while targeting a similar number of peptides as with higher-input Orbitrap-based PRM assays. At 100 ng, the quantitative accuracy of most peptides acquired with PRM remains consistent for nearly two orders of magnitude ( Figure 3A ), where the median lower limit of detection (LoD) was 0.83:100 and the median lower limit of quantification (LoQ) was 2.8:100 ( Figure 3B ), where only 0.6% of peptides could not be assigned a LoQ. Quantification was slightly worse at the 10 and 1 ng levels, where 4.6% and 20% of peptides could not be assigned a LoQ. Unsurprisingly, at the 100 ng level signal is more easily distinguishable from noise and the LoD distribution is generally higher than at 10 ng or 1 ng ( Supplementary Data ). Single cells typically produce between 0.1 and 0.3 ng of peptides, depending on the cell type. Considering the 1 ng sample, the median measured peptide produced a linear signal in this range (0.198:1). Several peptides showed a linear response below 0.1 ng. For example, the peptide ECESYFK from Granzyme B was found to have a LoQ of 0.043:1, equating to a proteome fraction consisting of 43 pg in a background of 1 ng, and was still measurable above background at the 18 pg level ( Figure 4A and 4B ). Two other Granzyme B peptides, VAAGIVSYGYK and TQQVIPMVK, produced even lower LoDs (below 10 pg equivalents). Granzyme B is a serine protease implicated in multiple autoimmune diseases. 43 All told, 61 peptides with estimated LoQs in the 1 ng assay corresponded to 30 quantified proteins. At least 6 points across the peak were sampled for all peptides in the assay achieving 8-10 points across the peak on average. Validated cell populations for quantitative testing In addition to showing quantitative accuracy in a controlled matched matrix, we wanted to validate measurement precision in low-input biological experiments. The interleukins (IL) family of proteins is a class of cytokines expressed by many cells, including immune cells, which bind to specific receptors that elicit pro- and anti-inflammatory roles. 44 Certain cytokines, such as IL-2 and IL-15, bind to receptors on the surface of T cells in specific biological events, such as activation and differentiation. Both of these molecules have been successfully used as part of immunotherapies to combat cancer. 45–48 Interestingly, IL-2 and IL-15 are structurally similar in homology and activate T cells through the same receptor subunits (IL-2/IL-15Rβγ), 49,50 mediating largely similar biological effects on T cells. 51,52 However, possibly related to expression of the private IL-2Rα and IL-15Rα chains, IL-2 induces an effector-like phenotype (with low CD62L expression) while IL-15 induces a memory-like phenotype (with higher CD62L). 53 We generated activated T cells cultured in IL-2 or IL-15 to replicate an effector-like and memory-like phenotype for CD4 + and CD8 + cells ( Supplemental Figure S2A ). We selected this model system to showcase the ability to generate LIT-PRM assays using well-studied biology at inputs below 1 ng. Additionally, flow cytometry was used as an orthogonal technique to validate the cell populations present in IL-2 and IL-15 treated T cells on days 5, 6, and 10 exhibited an effector-like and memory-like phenotype ( Supplemental Figure S2B and S2C ). At day 10, flow cytometry identified that each culture was predominantly composed of T cells, with CD8 + T cells being the majority subset in both IL-2 (83.8%) and IL-15 (92.2%) cultures ( Figure 5 ). Correspondingly, we found that CD4 + T cells composed 14.6% of the cells stimulated with IL-2 and 6.9% of cells stimulated by IL-15. This was reflected in our targeted proteomics data, as the CD4 protein was the second most downregulated protein in IL-15-stimulated T cells compared to IL-2-stimulated cells ( Figure 6A ). We note that this protein was not technically quantified at 1 ng, as the one peptide for CD4 (VVQVVAPETGLWQCLLSEGDKVK) lacked linearity in signal as estimated by the calibration curve. We measured the same peptide at the 10 ng level, where we calculated the LoD to be 0.96:10 (ratio of foreground to background) with an LoQ of 8.3:10 ( Supplemental Figure S3 ). This indicated that at the 1 ng level, CD4 should be above the LoD but below the LoQ, and our results match these calculations. IL-2 stimulation is known to push activated T cells into an effector-like population, reflected by the paired flow cytometry data on day 10. Granzyme B, from which we estimated the most responsive peptide (TQQVIPMVK) was quantitative to 0.025:1 (ratio of foreground to background), is an effector molecule secreted by cytotoxic CD8 + T cells. We found peptides associated with this protein were 1.55x lower in IL-15 than IL-2 stimulated cells using targeted proteomics ( Figure 6A ), which matches flow cytometry data indicating that the number of effector CD8 + T cells (T EFF ) are lower when stimulated with IL-15 than IL-2. While both IL-2 and IL-15 resulted in the activation of T cells, IL-15 stimulation led to the differentiation of memory-like T cells, as demonstrated in the flow cytometry data. The CD44 receptor antigen is a cell surface receptor that helps cells facilitate cell-cell interaction and response to the tissue microenvironment. Interestingly, we found that the expression of CD44 is slightly higher in IL-2 compared to IL-15, indicating that IL-2 stimulated cells had a higher population of activated cells, with a 1.6x median fold change in abundance. T cells that express CD62L have an increased population of memory T cells after IL-15 stimulation. 50 Flow cytometry data indicated that we had a higher population of CD62L + cells in the IL-15 stimulated condition compared to T cells stimulated with IL-2 ( Supplemental Figure S2 ), indicating a higher population of memory-like T cells. Compared to the IL-15 stimulated cells, IL-2 stimulated T cells expressed IL-2Rβ/IL-15Rβ at a higher ratio ( Figure 6A ), which is associated with a memory phenotype. In general, we observe high analytical precision using PRM with a Q-LIT platform, even in 1 ng assays. Most peptides were measured with less than a 20% coefficient of variation between 3 technical replicates ( Figure 6B ). Ultimately, we detected 100% of the proteins monitored with flow cytometry using global proteomics during library generation. While some of these proteins were hard to observe at low input (1 ng), we were able to quantify 75% above an estimated LoQ with targeted proteomics. This overlap indiates complementary benefits of using flow cytometry in tandem with targeted proteomics to fully capture immune cell state. While single-cell proteomics using mass spectrometry continues to develop, flow cytometry is the best method for measuring a small number of proteins (6-12) on thousands of individual cells within a single day. On the other hand, targeted mass spectrometry on 1-10 T cells (equivalent to around 0.1 and 1 ng) can monitor tens to hundreds of proteins, including cytokines and transcription factors, which cannot be easily monitored using flow cytometry. Conclusion Here, we demonstrate a complete workflow for acquiring global libraries and rapidly building de novo PRM assays using only a Q-LIT mass spectrometer. While the Q-LIT is capable of DDA, we found that equivalently large libraries could be quickly generated using GPF-DIA. We also found that PRM assays using the Q-LIT were linearly quantitative even at low input, enabling us to accurately measure difficult to measure cytokines, transcription factors, and immune proteins. From a broader perspective, high-resolution mass spectrometry is expensive in terms of instrument costs and requiring greater technical experience to operate successfully. In contrast, Q-LIT mass analyzers are easier to maintain and more cost-effective to operate in part because they have less stringent vacuum requirements compared to Orbitrap-based mass spectrometers, making them an appealing option for low-input proteomics. These factors are especially important in single-cell proteomics, where each biological sample needs thousands of injections. Our results suggest that Q-LITs provide laboratories with competent low-input proteomics analysis in situations where high resolution is impractical. We believe these instruments offer a high value-to-expense ratio, potentially democratizing mass spectrometry on a broader array of laboratory settings. This democratization is particularly impactful in immuno-oncology, where proteome-based analysis of immune cell populations can uncover crucial biomarkers to guide clinical decisions. Declarations Acknowledgments This research was supported by NIH R35GM150723 and R21CA267394 to BCS. This research was also supported by The Ohio State University Comprehensive Cancer Center (OSUCCC) and the National Institutes of Health (NIH) under grant P30CA016058. This research was made possible through resources, expertise, and support provided by the Pelotonia Institute for Immuno-Oncology (PIIO), which is funded by the Pelotonia community and the OSUCCC. We thank the PIIO and the Immune Monitoring and Discovery Platform for flow cytometry access. Conflict of Interest Statement BCS is a founder and shareholder in Proteome Software, which operates in the field of proteomics. The Searle Lab at the Ohio State University has a sponsored research agreement with Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research. However, analytical methods were designed and performed independently of Thermo Fisher Scientific. LRH, CCJ, and PMR are employees of Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research. Data/code availability Proteomics data and Skyline documents are available on Panorama at https://panoramaweb.org/StellarIonTrapForLowInput.url. All proteomics raw data is also publicly available on the MASSIVE repository under the accession number MSV000094904 (ftp://massive.ucsd.edu/v08/MSV000094904/). Open-source software developed for this project is publicly available as part of the EncyclopeDIA project at https://bitbucket.org/searleb/encyclopedia. Experimental Methods T cell cultures Splenocytes from C57BL/6 mice were stimulated with plate-bound anti-CD3 mAb (145-2C11 clone) on day 0 in complete media, as described previously. 54 On day 2, cells were washed and re-plated with human (h) IL-2 or hIL-15 at 200 ng/mL. Cells were washed and split on days 4 and 6. Flow cytometry was performed on days 5, 6, and 10, and cells were washed three times with DPBS, centrifuged at 500 RCF for 5 minutes, pelleted, and stored at -80°C on days 6 and 10 for mass spectrometry. A third condition was maintained without stimulation as a control for flow cytometry as well. Flow cytometry Flow cytometry was performed as previously described. 54 Briefly, cells collected on days 5, 6, and 10 were stained with live/dead fixable blue dead-cell stain (Invitrogen #L23105), and antibodies for B220, CD4, CD8, CD25, CD44, CD62L, CD69, and TCRb (see antibody details in Supplemental Table 1 ). Stained cells were acquired with a Cytek Biosciences Aurora™ 5-laser flow cytometer and analyzed using BD Biosciences FlowJo™ software. Proteomics sample preparation Frozen cell pellets were lysed in a 5% SDS buffer with 50 mM TEAB, 1x HALT, and 2 mM MgCl 2 . DNA was sheared with a Bioruptor® Pico by sonicating at 14℃ for 30 seconds, followed by 30 seconds of rest, a total of 10 times. Sheared cells were then spun down at 13,000 RCF for 10 minutes, and the protein supernatant was retained. Protein quantities were estimated using a Pierce™ bicinchoninic acid (BCA) Protein Assay Kit. Proteins were reduced with 40 mM dithiothreitol (DTT), alkylated with 40 mM iodoacetamide, and quenched with 20 mM DTT. Acidification was done with 2.5% phosphoric acid, and protein was loaded onto suspension trap (s-trap) micros (Protifi LLC). Digestion was performed with trypsin at a 1:20 ratio of enzyme to protein at 47℃ for 2 hours, then eluted. Peptides were dried down and stored at -80℃. According to the kit protocol, an aliquot of dried peptides was separated according to basicity using a Pierce High pH Reverse-Phase Fractionation Kit. Briefly, 50 μg of peptides were resuspended in 0.1% trifluoroacetic acid in HPLC-grade water. The separation mini-columns from the kit were centrifuged at 5000 RCF for 2 minutes to remove any liquid and pack the resin. The mini-columns were then equilibrated with 100% acetonitrile and washed 3 times with water. Resuspended peptides were loaded, and the flow through was collected as the first fraction. The mini-columns were washed with water, and the eluent was collected as the second fraction. The elution buffers specified from the kit were then used to produce the following 8 fractions. Fractionated peptides were then dried down and stored at -80℃ until mass spectrometry-based analysis for DDA-based library generation. A separate aliquot of the eluted peptides was dimethyl labeled using an in-solution amine-labeling reaction published by Boresema et al. 42 Digested peptides were resuspended in 100 mM TEAB (pH = 8.5). Formaldehyde (4%) was added to the resuspended peptides and mixed. Sodium cyanoborohydride (0.6 M) was then added to catalyze the dimethyl labeling reaction for 90 minutes at 22℃ while mixing vigorously. The reaction was quenched with 1% ammonia and 5% formic acid. All peptides were resuspended in 2% acetonitrile with 0.1% formic acid. Calibration curves were generated by mixing labeled and unlabeled peptides at different concentrations. In these mixtures, unlabeled peptides were diluted in a dimethyl-labeled background over 4 orders of magnitude ( Supplemental Table 2 ) and aliquoted at different concentrations prior to mass spectrometry analysis. LC-MS settings Data was acquired on a Thermo Scientific™ Stellar™ MS coupled to a Vanquish™ Neo UHPLC system. Solvent A consisted of 100% water with 0.1% formic acid, and solvent B contained 80% acetonitrile with 0.1% formic acid. An Easy-Spray™ source was used for ionization at 2000 V, and the ion transfer tube was set to 275℃. Peptides were separated on a 25 cm C18 analytical Easy-Spray column, packed with 2 μm beads along a 50-minute linear gradient as follows: from 0-4 minutes, 2% B, 4-8 minutes increased to 8% B, 8 to 58 minutes increased with 28% B, 58 to 65 minutes increased to 44% B, followed by a 10-minute wash at 100% B. The flow for the entire gradient was set to 250 nL/min. The instrument was configured to expect chromatography of approximately 15 seconds and fragment peptides with a default charge state of 2 and a collision cell gas pressure of 8 mTorr. DDA on an ion trap instrument For DDA experiments, the RF lens was set to 30%. Precursor spectra were collected ranging from 350-1250 m/z at a scan rate of 67 kDA/s. The automatic gain control (AGC) target was set to “Standard” with an absolute AGC target of 3e4. The maximum ion injection time (maxIIT) was set to 100 ms and spectra were collected using centroiding in positive mode. MS2 scans were collected only on peptides with a charge state greater than 1, excluding undetermined charge states. An intensity threshold of 5E2 was used to trigger an MS2 scanand an HCD NCE of 30%. Following the MS2 measurement, the peptide m/zs were placed on a dynamic exclusion list for fragmentation for 2 seconds using a precursor mass tolerance of +/- 0.5 m/z . Twenty DDA scans were taken in each cycle with a 1.6 m/z isolation window around the precursor of interest. Fragment ions were scanned at 125 kDa/second scan rate from 200-1500 m/z using an AGC target of 1E4 and a maxIIT of 50 ms. DIA and PRM on an ion trap instrument For both DIA and PRM experiments, the precursor range was set to 350-1250 m/z and measured at a rate of 67 kDa/second. The AGC target was set to “Standard,” which is equivalent to 1e4, and the maxIIT was set to 100 ms. The loop control was set to “all.” Peptides were fragmented with HCD with NCE set to 30%, and fragments were scanned at 67 kDa/second over a range of 200-1500 m/z . Precursor isolation windows for DIA were consistently 8 m/z wide, where margins were set to forbidden zone locations. Six gas phase fractions were used to collect chromatogram libraries over 400-500, 500-600, 600-700, 700-800, 800-900 and 900-1000 m/z . The majority of settings were the same as a wide-window DIA scan, with the exception of 2 m/z wide isolation windows over the adjusted precursor m/z range for each method. All settings for PRM scans were the same except for the isolation window width and maxIIT. For PRM assays at 10, 20, and 50 peptides per cycle, the maxIIT was set to 200, 95, and 50 ms, respectively. Precursor isolation windows were set to 2 m/z ,where MS/MS were collected over 200-1500 m/z . Data analysis Global data was first converted to the universal mzML format using peak picking. DIA data was analyzed with EncyclopeDIA v.3.0.0-SNAPSHOT using the Ion Trap/Ion Trap mode. Mass tolerance was set to 0.4 Da where a minimum of 3, but a maximum of 5 ions were used for quantification. The chromatogram library was generated by searching 6 gas phase fractions against a Prosit 38 predicted library. The Prosit library contained spectrum predictions of all +2 and +3 ions from a mouse FASTA from UniProt, which was accessed on October 22, 2019. The predicted library allowed for up to 1 missed cleavage, with a default charge state of 3, and default NCE of 33 over 396.4-1002.7 m/z . Wide-window (8 m/z) injections were searched against the chromatogram library using the same search settings. Global DDA data was searched in Scribe using the Ion Trap/Ion Trap instrument mode for b and y tryptic peptides, with a library mass tolerance of 0.4 Da. The 10 high-pH fractionated injections were searched against the same Prosit predicted library used to generate the DIA library. Global DIA data was searched using CHIMERYS 55 intelligent search algorithm (MSAID GmbH) in Thermo Scientific™ Proteome Discoverer™ 3.1, using analogous settings for the EncyclopeDIA search. A predicted spectrum library was generated from the mouse fasta database by INFERYS™ deep learning framework (MSAID GmbH) for all tryptic +2, +3, and +4 peptides between 7-30 amino acids in length. For processing, spectrum files were selected using the ion trap MS setting, with a signal-to-noise peak threshold of 1.5. The top 24 peaks were selected in each window with a fragment mass tolerance set to 0.4 Da. Fixed carbamidomethyl modifications and a maximum of 2 oxidized methionines were allowed per peptide. The retention times from CHIMERYS were extracted for all detections and combined with EncyclopeDIA’s detections. For peptides detected by both software tools, the EncyclopeDIA retention times were preferred. The combined detections and retention times were used to select peaks within EncyclopeDIA and run against a 1% FDR to obtain a combined search engine library. The fractionated injections were also searched in Proteome Discoverer, using a mass tolerance of 0.4 Da for all +2, +3, and +4 peptides, and the same settings used for the CHIMERYS search, Skyline 56,57 version 23.1.0.455 was used for targeted analysis. For analyzing the calibration curves and other PRM injections of IL-2 and IL-15 replicates, the chromatogram library was first imported to serve as a reference point for integrating low-input PRMs. With the imported DIA results, transition settings were altered, and the PRM samples were imported. For both imports, the settings peptide settings were set to Trypsin [KR\P], with a maximum of 2 missed cleavages, and the mouse fasta used to generate the Prosit library was used to generate a background proteome. Retention time window predictions were set to 5 minutes; however, measured retention times were used when present. Peptides between 7 and 40 amino acids in length were used, and “auto-select all matching peptides” was left checked. Only carbamidomethylation modifications were considered for cysteine. For transition settings, peptides of +2, +3, and +4 precursor charges, along with +1 and +2 fragment ion charges were considered for b and y ion types. For product ion selection, we considered the third ion to the second to last ion. DIA precursor windows were used for exclusion when importing the chromatogram library. The ion match tolerance for the library was set to 0.4 Da, and 6-9 product ions were used from filtered product ions. For the instrument parameters, a 200-1500 m/z range was considered, with a method match tolerance of 0.4 Da, and “dynamic min product m/z ” and “triggered chromatogram acquisition” were checked. For the full-scan parameters, DIA was used when importing the chromatogram library file as a reference point for integrating calibration curves. The gas-phase fractionated isolation windowing scheme was imported from the files for a QIT mass analyzer, with a resolution of 0.4 Da and retention time filtering within 5 minutes of MS/MS IDs. For importing PRM injections, the “PRM” acquisition method was used rather than the DIA method. Finally, lower limits of detection (LoD) and quantification (LoQ) were estimated using EncyclopeDIA, and calculated quantities of IL-2 and IL-15 replicates were determined using calibration curves. References Kitano, H. Systems biology: a brief overview. Science 295 , 1662–1664 (2002). Arsenio, J. et al. 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IL-12 conditioning improves retrovirally mediated transduction efficiency of CD8+ T cells. Cancer Gene Ther. 22 , 360–367 (2015). Frejno, M. et al. Unifying the analysis of bottom-up proteomics data with CHIMERYS. bioRxiv 2024.05.27.596040 (2024) doi:10.1101/2024.05.27.596040. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26 , 966–968 (2010). Pino, L. K. et al. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom. Rev. 39 , 229–244 (2020). Additional Declarations Yes there is potential Competing Interest. BCS is a founder and shareholder in Proteome Software, which operates in the field of proteomics. The Searle Lab at the Ohio State University has a sponsored research agreement with Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research. However, analytical methods were designed and performed independently of Thermo Fisher Scientific. LRH, CCJ, and PMR are employees of Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research. Supplementary Files SupplementalDataLowInputAES.xlsx Dataset 1 Supplemental Data: Figures of Merit reports and calibration curves made from EncyclopeDIA. nrreportingsummaryAES.pdf Reporting Summary LowInputStellarProteomicsSupplemental.pdf Supplemental Table S1: Flow cytometry panel Supplemental Table S2: Dilutions used for calibration curves at 100 ng, 10 ng, and 1 ng of total material Supplemental Figure S1: Comparison of Chimerys and EncyclopeDIA detections for chromatogram libraries Supplemental Figure S2: Additional flow cytometry validation data which contributed to Figure 5. Supplemental Figure S3: The calibration curve for a peptide from the CD4 antigen protein. Supplemental Note: Tutorial for using PRM Scheduler embedded in EncyclopedDIA Cite Share Download PDF Status: Published Journal Publication published 23 Apr, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4702746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328793865,"identity":"e42572d2-afe0-4781-8119-734f53f62677","order_by":0,"name":"Brian Searle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACAwbGBiiTuYHhA0yYhzgtjA2MM4jTAgeMDcxwlfi0mEsfbvxcwFAnxz/7YONjm4q6xLXtBxgfvG3DrcWyL7FZegbDYWOJc4nNxjlnDiduO5PAbDgXjxaDM4wN0jwMBxIbzjC2See2HUjcdoOBTZoXv5bm3zwMdfXzzzC2/7ZsqwNpYf9NQEsb0BbmBBCDGYjAtjDj02LZw9hmzWNw2HAj0DrJnjOHjbedSWyWnHMOtxZzHvbHt3kq6uTlzjAf/PCjok522/HDBz+8KcOtBeo8FB48ckfBKBgFo2AUkAsAVn5Pu/ZMoqYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8760-6731","institution":"Ohio State University","correspondingAuthor":true,"prefix":"","firstName":"Brian","middleName":"","lastName":"Searle","suffix":""},{"id":328793866,"identity":"4e8941c6-80c5-4da1-8465-c805f3f36703","order_by":1,"name":"Ariana Shannon","email":"","orcid":"https://orcid.org/0000-0002-5563-8824","institution":"Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"Ariana","middleName":"","lastName":"Shannon","suffix":""},{"id":328793867,"identity":"81cf0e77-d607-418f-8f11-8be770c05c9c","order_by":2,"name":"Rachael Teodorescu","email":"","orcid":"","institution":"Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"Rachael","middleName":"","lastName":"Teodorescu","suffix":""},{"id":328793868,"identity":"485191ed-7326-49c3-b160-1de6feb9dcf7","order_by":3,"name":"No-Joon Song","email":"","orcid":"","institution":"The Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"No-Joon","middleName":"","lastName":"Song","suffix":""},{"id":328793869,"identity":"be539d92-13e2-4cb4-806b-e81711cd6b81","order_by":4,"name":"Lilian Heil","email":"","orcid":"","institution":"Thermo Fisher Scientific","correspondingAuthor":false,"prefix":"","firstName":"Lilian","middleName":"","lastName":"Heil","suffix":""},{"id":328793870,"identity":"aa6aae7e-98c4-45ad-819b-7eed99283371","order_by":5,"name":"Cristina Jacob","email":"","orcid":"","institution":"Thermo Fisher Scientific","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Jacob","suffix":""},{"id":328793871,"identity":"f107fba3-87b4-4a64-a05f-c8b529f752d6","order_by":6,"name":"Philip Remes","email":"","orcid":"","institution":"Thermo Fisher Scientific","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Remes","suffix":""},{"id":328793872,"identity":"277feccf-80ba-41dd-baf8-8a907e75920a","order_by":7,"name":"Zihai Li","email":"","orcid":"https://orcid.org/0000-0003-4603-927X","institution":"The Ohio State University Comprehensive Cancer Center – James Cancer Hospital and Solove Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Zihai","middleName":"","lastName":"Li","suffix":""},{"id":328793873,"identity":"ef9e8d91-60ab-4d31-820e-1f89af55e285","order_by":8,"name":"Mark Rubinstein","email":"","orcid":"","institution":"The Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Rubinstein","suffix":""}],"badges":[],"createdAt":"2024-07-08 05:20:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4702746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4702746/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-58757-8","type":"published","date":"2025-04-23T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60673838,"identity":"38f9ee3d-6578-4c1d-b71d-231469e50286","added_by":"auto","created_at":"2024-07-19 10:44:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113253,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the instrument and workflow for developing targeted assays using a LIT. \u003cstrong\u003eA\u003c/strong\u003e) The instrument schematic of the Stellar MS. Ions enter the first QR5 Plus Segmented Quadrupole Mass Filter with Hyperbolic surface before entering into the Ion Concentrating Routing Multipole. The Ion Concentrating Routing Multipole behaves as the collision and storage cell. Ions are then moved to the high-pressure cell of the dual-pressure LIT, and eventually to the low-pressure cell for mass analysis. \u003cstrong\u003eB\u003c/strong\u003e) A schematic of the methodology taken. Chromatogram libraries were generated using a GPF-DIA approach, and DDA libraries were generated from offline high-pH reverse-phase fractionated proteomes. We searched samples using both EncyclopeDIA and CHIMERYS, where the combined results were used to schedule PRM assays at the 100, 10, and 1 ng levels using 50, 20, and 10 peptides per cycle, respectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/a4bf6f7969b4ce683a997813.png"},{"id":60673840,"identity":"9191cef5-93c5-4bc2-b8c3-ed4c42965780","added_by":"auto","created_at":"2024-07-19 10:44:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102860,"visible":true,"origin":"","legend":"\u003cp\u003eA spectral library using offline high-pH reverse phase fractionated data-dependent acquisition (HPRP-DDA) and the chromatogram library using gas-phase fractionated data-independent acquisition (GPF-DIA). \u003cstrong\u003eA\u003c/strong\u003e) A Venn diagram of the peptides detected from each library. \u003cstrong\u003eB\u003c/strong\u003e) Detections from CHIMERYS were combined with EncyclopeDIA to generate a combined search engine library to mine PRM targets.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/fa52d2342f253d1201cc957b.png"},{"id":60674770,"identity":"e41b878d-8a29-46fa-a6f9-213c7e35153a","added_by":"auto","created_at":"2024-07-19 10:52:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) The quantitative accuracy of matrix-matched curves on an ion trap of pooled IL-2 and IL-15 peptides in a background of dimethyl-labeled pooled peptides. We generated three curves loading 100 ng, 10 ng, and 1 ng of material on-column. Each dilution is a different color where colored dashed lines indicate the expected fold change. Box plots show the spread of measured values where the whiskers indicate 5% and 95% points, and the bold line indicates the median measurement. \u003cstrong\u003eB\u003c/strong\u003e) For each curve, there is a histogram of the number of peptides with assigned lower limits of detection (LoD) and quantification (LoQ).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/9da024c596f9767820cae014.png"},{"id":60674772,"identity":"5dd723ed-0903-4ede-9762-8b63a5f090e2","added_by":"auto","created_at":"2024-07-19 10:52:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":222362,"visible":true,"origin":"","legend":"\u003cp\u003eThree representative peptides that were quantifiable below 1 ng. \u003cstrong\u003eA\u003c/strong\u003e) Each row displays a peptide chromatogram at each dilution within the 1 ng curve. Each peptide contains three representative transitions. The first peptide from Granzyme B had the best estimated LoQ at 0.043:1, while the third peptide from IL-2 receptor subunit alpha had an estimated LoQ at 0.132:1 at 1 ng. \u003cstrong\u003eB\u003c/strong\u003e) LoQ and LoD were estimated on a peptide-by-peptide basis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/a7b1a16102dfc0f34e945d1d.png"},{"id":60673841,"identity":"788d61ce-4d88-497c-9ad4-4fac02cf1538","added_by":"auto","created_at":"2024-07-19 10:44:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":159862,"visible":true,"origin":"","legend":"\u003cp\u003eA summary of the cell populations in IL-2 and IL-15 stimulated T cells determined by the flow cytometry panel described in \u003cstrong\u003eSupplemental Table S1\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e) The gating procedure used for determining the relative percentage of each cell type in the IL-2 and IL-15 samples (more details in \u003cstrong\u003eSupplemental Figure S2\u003c/strong\u003e). \u003cstrong\u003eB\u003c/strong\u003e) The estimated cell populations based on back calculations of the gating results.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/8eb733a32738b1d870de4eea.png"},{"id":60673846,"identity":"8a771b6f-50e4-42a4-a535-17bc46faf320","added_by":"auto","created_at":"2024-07-19 10:44:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131128,"visible":true,"origin":"","legend":"\u003cp\u003eQuantifying immune cell biological replicates at 1 ng. \u003cstrong\u003eA\u003c/strong\u003e) Quantitative ratios for the panel proteins assayed in the 10 peptide/cycle PRM. The assay was collected in technical triplicate injections of Day 10 IL-2 and IL-15-stimulated T cell proteomes. The selected panel of proteins is associated with T cell activation, differentiation, or cytokine signaling. No LoQ was determined for CD4 with the 1 ng calibration curve, indicated by a red 5-point star. In the IL-2 stimulated sample, IL2RB was measured below the LoQ determined by the 1 ng calibration curve, indicated by a 6-point star. \u003cstrong\u003eB\u003c/strong\u003e) Coefficient of technical variation (% CV) plots for all peptides quantified in the 1 ng assay.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/2423f144655dc79d176e2a1f.png"},{"id":81179377,"identity":"5e5f1c58-411a-4e3e-943a-4f2085c15b16","added_by":"auto","created_at":"2025-04-23 07:08:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1687486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/d4c1d7be-e486-4609-8069-79a13e9a4c13.pdf"},{"id":60675501,"identity":"7e634d38-f962-4ddc-a8a7-a33c8e6b1ff2","added_by":"auto","created_at":"2024-07-19 11:00:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1949938,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Data: \u003c/strong\u003eFigures of Merit reports and calibration curves made from EncyclopeDIA.\u003c/p\u003e","description":"","filename":"SupplementalDataLowInputAES.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/60e9732382a087eb57a87c71.xlsx"},{"id":60673844,"identity":"b291b919-b16f-4265-a390-653031214689","added_by":"auto","created_at":"2024-07-19 10:44:53","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":310198,"visible":true,"origin":"","legend":"\u003cp\u003eReporting Summary\u003c/p\u003e","description":"","filename":"nrreportingsummaryAES.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/bc80023f442e5b490bde7630.pdf"},{"id":60673847,"identity":"922792cd-15e1-4d3f-8a51-635f31307fd0","added_by":"auto","created_at":"2024-07-19 10:44:53","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2916098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table S1\u003c/strong\u003e: Flow cytometry panel\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Table S2\u003c/strong\u003e: Dilutions used for calibration curves at 100 ng, 10 ng, and 1 ng of total material\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Figure S1\u003c/strong\u003e: Comparison of Chimerys and EncyclopeDIA detections for chromatogram libraries\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Figure S2:\u003c/strong\u003e Additional flow cytometry validation data which contributed to Figure 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Figure S3\u003c/strong\u003e: The calibration curve for a peptide from the CD4 antigen protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Note:\u003c/strong\u003e Tutorial for using PRM Scheduler embedded in EncyclopedDIA\u003c/p\u003e","description":"","filename":"LowInputStellarProteomicsSupplemental.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4702746/v1/cc5b5d570aabdce4beb1d078.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nBCS is a founder and shareholder in Proteome Software, which operates in the field of proteomics. The Searle Lab at the Ohio State University has a sponsored research agreement with Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research. However, analytical methods were designed and performed independently of Thermo Fisher Scientific. LRH, CCJ, and PMR are employees of Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research.","formattedTitle":"Rapid assay development for low input targeted proteomics using a versatile linear ion trap","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSystems biology is the study of interactions within and between cells, where the goal is to learn how those interactions give rise to the complex behavior seen in an entire system.\u003csup\u003e1\u003c/sup\u003e One challenge is that many complex biological processes, such as adaptive immunity, are built from small populations of distinct cell types acting in concert.\u003csup\u003e2,3\u003c/sup\u003e Improvements in proteomics methods and mass spectrometry (MS) instrumentation have paved the way for low-input and single-cell proteomics, which make it possible to study how limited cell populations contribute to the whole. While the majority of single-cell methods use tandem mass tags (TMT)\u003csup\u003e4\u003c/sup\u003e to increase signal (and thus consistency) with data-dependent acquisition (DDA),\u003csup\u003e5,6\u003c/sup\u003e several groups have demonstrated that data-independent acquisition (DIA) is an effective solution to measuring low-input samples.\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e However, high-mass accuracy instruments are required in nearly all cases.\u003c/p\u003e \u003cp\u003eWhile single-cell and low-input global proteomics is typically acquired using high-mass accuracy instruments, nominal-mass instruments, such as triple quadrupoles, lead in quantitative sensitivity using targeted selected reaction monitoring (SRM).\u003csup\u003e10\u003c/sup\u003e With SRM, peptides are detected based on monitoring multiple fragment ion signals produced by each selected precursor ion. Transitions (diagnostic precursor/fragment ion pairs) in a pre-specified schedule must be provided to the instrument for monitoring at specific times within the chromatographic gradient.\u003csup\u003e11\u003c/sup\u003e While triple quadrupoles are extremely quick instruments capable of rapidly switching between ion pairs, they can only monitor a single \u003cem\u003em/z\u003c/em\u003e at a time. As such, triple quadrupoles are limited to targeted experiments, which require a high-mass resolution instrument to select and schedule targeted peptides and transitions before migrating to a nominal-mass instrument for high-throughput monitoring.\u003c/p\u003e \u003cp\u003eAn alternative targeted method to SRM is parallel reaction monitoring (PRM), which uses a quadrupole-equipped high-resolution mass spectrometer where the third quadrupole is replaced with an Orbitrap\u0026trade; (Q-Orbitrap, also known as a Q-Exactive\u0026trade;) or a time-of-flight analyzer (Q-ToF). Rather than measure precursor/fragment transitions, all precursor-specific fragment ions are collected in a full tandem mass spectrum with PRM.\u003csup\u003e12\u003c/sup\u003e A major advantage of PRM is that diagnostic fragment ions are selected after the experiment is performed, which can vastly simplify the assay development process. PRM has provided meaningful biological insight into several diseases, including systemic autoimmune diseases,\u003csup\u003e13\u003c/sup\u003e multiple sclerosis,\u003csup\u003e14\u003c/sup\u003e and colorectal cancer.\u003csup\u003e15\u003c/sup\u003e When coupled with global proteomics, PRM is a powerful tool for interrogating system-wide interactions between cells.\u003c/p\u003e \u003cp\u003eLinear ion traps (LITs) are another versatile, fast, nominal-mass analyzer comparable in resolution and complexity to triple quadrupoles. Modern Thermo Scientific\u0026trade; Tribrid\u0026trade; instruments have incorporated LITs as a tertiary analyzer, coupled with an Orbitrap.\u003csup\u003e16\u003c/sup\u003e Using a Tribrid instrument, Heil et al\u003csup\u003e17\u003c/sup\u003e showed that the benefit of PRM lies within its ability to monitor multiple product ions produced within a selected precursor \u003cem\u003em/z\u003c/em\u003e range and that the LIT in Tribrids was an effective readout for targeted proteomics. A LIT measures ions trapped in an electric field by adjusting RF and DC voltages to selectively eject ions based on their \u003cem\u003em/z\u003c/em\u003e to collect MS/MS spectra. Unlike triple quadrupoles, which have to \u0026ldquo;dwell\u0026rdquo; at each increment of m/z to form a spectrum, LITs acquire full scan MSn data quickly and sensitively,\u003csup\u003e18\u003c/sup\u003e making them also viable for global proteomics using DDA or DIA.\u003csup\u003e19\u003c/sup\u003e As a result, a hybrid quadrupole-LIT (Q-LIT) could act as an \u0026ldquo;all-in-one\u0026rdquo; nominal-mass instrument capable of both targeted and global proteomics.\u003c/p\u003e \u003cp\u003eAs with triple quadrupoles, LITs are extremely sensitive, ion-efficient mass analyzers apt for low-input proteomics.\u003csup\u003e20\u003c/sup\u003e In some circumstances, LITs can be more effective than high-resolution mass analyzers for low-input samples (\u0026le;\u0026thinsp;10 ng)\u003csup\u003e21\u003c/sup\u003e and can measure single cells without multiplexing reagents.\u003csup\u003e22\u003c/sup\u003e At higher sample input (\u0026ge;\u0026thinsp;100 ng), the lack of high mass accuracy overshadows the increased sensitivity of LITs. There exist other compelling reasons to consider LIT-based instruments in high-throughput applications. In particular, LITs operate at high pressure (10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mTorr) in comparison to ToF analyzers (10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mTorr), where ions have to travel uninterrupted for meters, or Orbitrap analyzers (10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e mTorr), where ions can travel for more than a kilometer. Lower vacuum pump requirements allow LITs to be built more affordably, robustly, and housed in smaller instrument footprints.\u003c/p\u003e \u003cp\u003eHere, we present a workflow using a hybrid quadrupole-LIT (Q-LIT) instrument from Thermo Scientific as a single instrument for rapidly generating targeted assays for low-input experiments. With the Q-LIT, we demonstrate how to build nominal-mass targeted transition libraries using both DDA and gas-phase fractionated (GPF) DIA libraries. We then show the quantitative accuracy of targeted PRMs with a Q-LIT using matched-matrix calibration curves collected with 1, 10, and 100 ng total protein to model low abundant immune cell populations. To facilitate this, we developed an open-source software tool that directly schedule-optimized PRM assays from DDA and DIA libraries. Finally, we show quantitative consistency measuring low-level biological targets in cytokine-stimulated CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells with as little as 1 ng on column. These results suggest that Q-LITs can perform as inexpensive stand-alone instruments for quantitative proteomics, capable of a wide range of measurements without needing high-resolution mass spectrometry.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eLinear ion traps (LITs) are robust, sensitive, and fast mass analyzers, yet these instruments have limited mass resolution. Previously, our lab demonstrated that LITs could be used effectively as stand-alone mass analyzers to measure low-input samples using an Orbitrap Eclipse™ Tribrid mass spectrometer.\u003csup\u003e22\u003c/sup\u003e In that work, we detected approximately 400 proteins from single cells using data-independent acquisition coupled with chromatogram libraries to help make detections.\u003csup\u003e23\u003c/sup\u003e While our Eclipse instrument configuration ignored the high-resolution Orbitrap mass analyzer, we performed those experiments in the context of a high-end Tribrid instrument. Furthermore, the 400 proteins we measured were the easiest to observe but not necessarily the most biologically useful to monitor. While reduced representation approaches\u003csup\u003e24,25\u003c/sup\u003e that quantify a limited panel of easily observed proteins can help infer biological states, significant hurdles must be overcome to predict the expression patterns of unmeasured proteins. As such, directly measuring panels of proteins of interest in low-input samples using targeted proteomics may be preferable to global proteomics.\u003c/p\u003e\n\u003cp\u003eIn this work, we sought to answer three remaining questions. First, by eliminating the Orbitrap, could an affordable quadrupole-LIT (Q-LIT) mass spectrometer perform at a high level as a stand-alone instrument for both library generation and targeted proteomics measurement? Second, can a Q-LIT mass spectrometer quantify peptides at and below the level of single cells? Third, can quantitative experiments measure low-level biologically relevant proteins like cytokines and transcription factors at or below 1 ng? To this end, we assessed several parameters of the Stellar™ mass spectrometer, a new hybrid Q-LIT design produced by Thermo Scientific. First, we tested proteome-wide library generation; then, we assessed quantitative linearity using targeted PRM experiments with 100, 10, and 1 ng sample inputs. Finally, we tested sensitivity and measurement consistency in a biological context.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA Q-LIT workflow for generating PRM assays using DDA and DIA data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Stellar MS is a hybrid Q-LIT mass spectrometer with improved ion transmission features capable of performing rapid scans up to 200 kDa/s (\u003cstrong\u003eFigure 1A\u003c/strong\u003e). The instrument shares many of the same design components as existing Orbitrap-based instruments.\u003csup\u003e26–28\u003c/sup\u003e Tribrid instruments accumulate fragment ions in the collision cell (also known as the ion routing multipole, IRM) before transfer to the mass analyzer. Analogously, the Stellar accumulates fragment ions in the collision cell (Q2, also known as the ion concentrating routing multipole, ICRM) before transfer to the LIT for mass analysis. These arrangements produce high scanning speeds by performing fragment accumulation in parallel with mass analysis in the low-pressure LIT.\u003csup\u003e29,30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAn advantage of the Q-LIT geometry is that it is suitable for both global discovery proteomics as well as targeted proteomics. To leverage this, we implemented a workflow to generate high-quality peptide libraries using off-line fractionated DDA or GPF-DIA, and software to build on-the-fly PRM assays for the same instrument (\u003cstrong\u003eFigure 1B\u003c/strong\u003e). Briefly, the software takes a DDA-based spectrum library or a DIA-based chromatogram library as input and compares it with potential targeted proteins. The target list contains a list of critical accession numbers along with other optionally desired entries in a selected FASTA database. The assay can be modified using both a peptide inclusion and exclusion list. Assays can be adjusted depending on instrument settings, where the maximum assay density and a retention time scheduling window width must be selected. A recent single-injection DIA run also ensures that the retention time schedule matches the current LC column conditions.\u003c/p\u003e\n\u003cp\u003ePeptides are selected using this software tool based on the highest signals recorded in the library. For DDA, this signal is based on precursor intensity if available. For GPF-DIA, this signal is based on the intensity of the third largest fragment ion per peptide following common SRM/PRM conventions of requiring at least three transitions.\u003csup\u003e29\u003c/sup\u003e The algorithm chooses peptides using a greedy approach, where the most abundant peptides are scheduled first. After the algorithm chooses a specified number of peptides for a given protein (typically 3-5), no additional peptides from that protein are considered. Additionally, peptides cannot be added to a retention time region if any time point in that region has already reached the maximum assay density. Once the algorithm iterates through all possible peptides, the software tool produces a scheduling report and a target inclusion list for the Thermo method editor. While the tool focuses on simplifying scheduling for Thermo instruments, it is analyzer and vendor agnostic, supporting scheduling for both Orbitrap and ToF instruments. This software workflow has an accessible graphical user interface built into the EncyclopeDIA code base (see \u003cstrong\u003eSupplemental Note\u003c/strong\u003e for more details).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDeveloping a comprehensive target library\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePRM assays are commonly generated from various sources, including public repositories that store targeted proteomics data such as the PeptideAtlas,\u003csup\u003e31\u003c/sup\u003e CPTAC,\u003csup\u003e32\u003c/sup\u003e or Panorama.\u003csup\u003e33\u003c/sup\u003e Additionally, assays can be built from global data, with targets selected from empirical measurements on the biological matrix of interest. For this work, we wanted to use methods that could be fully acquired on the Q-LIT but still be capable of detecting low-abundant peptides. One advantage of this approach is that targets are tuned for the instrument from the context of retention time scheduling and optimal transition selection.\u003c/p\u003e\n\u003cp\u003eTo generate a low-input PRM assay on the Q-LIT, we tested two standard methods of building libraries: a chromatogram library using GPF-DIA and a spectral library using fractionated DDA. We collected libraries from a pool of IL-2 and IL-15-stimulated T cell proteomes. To build the chromatogram library, 6x gas-phase fractions were used with 2\u003cem\u003e m/z \u003c/em\u003ewide isolation windows across mass ranges of 100 \u003cem\u003em/z\u003c/em\u003e per injection. Since the background proteome matrix is not chemically altered or diluted, this approach produces retention times that closely match the quantitative PRM experiments. In contrast, we offline fractionated the DDA library samples using high-pH reverse phase separations to yield a total of 10 fractions, which were analyzed in separate injections. Consequently, each fraction has a simplified matrix background, which may not reflect retention times as consistently in unfractionated quantitative samples. The DDA and DIA methods produced libraries that were similar in size but of surprisingly distinct populations of peptides (\u003cstrong\u003eFigure 2A\u003c/strong\u003e), presumably due to the different fractionation methods and matrix backgrounds used to generate each library.\u003c/p\u003e\n\u003cp\u003eIn addition to producing slightly more peptide detections, peptide-centric extraction\u003csup\u003e34\u003c/sup\u003e of DIA datasets is more akin to fragment-level quantification using targeted methods than DDA measurements.\u003csup\u003e35\u003c/sup\u003e As such, peptides detected using GPF-DIA are more likely to produce robust, targeted assays since the mode of discovery uses similar methodologies to the final quantitative measurements. However, some sample types, such as enriched phosphopeptides, may be better suited to library generation with DDA since they can take advantage of stochastic sampling to detect more peptides and proteins over technical replicates.\u003csup\u003e36\u003c/sup\u003e For this work, we chose to proceed with the GPF-DIA library for assay development, but the scheduling software produced for this work functions with either library source.\u003c/p\u003e\n\u003cp\u003eFor DIA injections, search results from EncyclopeDIA and CHIMERYS were combined for downstream work (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). CHIMERYS is a spectrum-centric search engine that builds on INFERYS to provide spectra and retention-time predictions for peptides in a given FASTA database.\u003csup\u003e37\u003c/sup\u003e In comparison, we searched a Prosit-predicted spectral library\u003csup\u003e38,39\u003c/sup\u003e with the peptide-centric search engine, EncyclopeDIA, which was adapted for analyzing ion trap data. Consequently, EncyclopeDIA was limited to searching +2 and +3 peptides to maintain a reasonable search space, while CHIMERYS was configured to consider modifications and higher charge states as well. More peptides were detected from CHIMERYS compared to EncyclopeDIA in each gas-phase fraction (\u003cstrong\u003eSupplemental Figure S1A\u003c/strong\u003e), but considering the superset of detections increased the total number of potential targets (\u003cstrong\u003eSupplemental Figure S1B\u003c/strong\u003e) and both search engines produced an equal number of viable peptide targets that could be used in downstream PRM experiments (\u003cstrong\u003eSupplemental Figure S1C\u003c/strong\u003e). In all cases, the retention times from CHIMERYS-detected peptides were re-peak picked using EncyclopeDIA to identify candidate target transitions for PRM measurement in a combined DIA library.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAssessing Q-LIT PRM quantitative accuracy at low input\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWith low-input global proteomics, we preferentially measure only the most abundant proteins. We stress-tested the quantitative accuracy of the Q-LIT system using PRMs by measuring biologically relevant proteins that tend to occur at a range of levels in the proteome. To accomplish this, we first functionally annotated candidate peptides in the combined DIA library using the PANTHER database.\u003csup\u003e40\u003c/sup\u003e We selected target proteins based on GO-terms and Reactome pathways for T cell differentiation, immune biology, T cell activation, cytokines, and transcription factors with a focus on choosing proteins associated with the dynamics of memory T cells. Using the PRM scheduling algorithm, we constructed three assays using the same bank of proteins, where each assay was suited to a different input level: up to 50 peptides/cycle for 100 ng of material, 20 peptides/cycle for 10 ng of material, and 10 peptides/cycle for 1 ng of material. Ultimately, the 100 ng assay monitored 481 peptides, the 10 ng assay monitored 151, and the 1 ng assay monitored 61. To maintain a 2-second cycle time using 1 ng of material, the maximum ion injection time (maxIIT) was set to 200 ms. Similarly, at 10 ng of material, the maxIIT was set to 95 ms (slightly below 100 ms to accommodate the additional time required to route ions in the mass spectrometer). At 100 ng of material, the ion injection time was set to 50 ms; however, each scan rarely met that length of time.\u003c/p\u003e\n\u003cp\u003eWe performed matrix-matched calibration curves\u003csup\u003e41\u003c/sup\u003e at 100 ng, 10 ng, and 1 ng levels to assess the quantitative accuracy of the Q-LIT over several orders of magnitude. Dilutions in a buffer background are useful to assess instrument sensitivity, but because background noise decreases at the same rate as target peptides, quantitative linearity will always appear to be more accurate than in a real background matrix. Matrix-matched calibration curves are more effective at assessing linearity in real-world scenarios since the background signal does not change with dilution. To accomplish this, we had to build a suitable background matrix of similar composition to our target T cell proteome. Our approach used dimethyl labeling to modify the foreground T cell proteome, which kept the same composition while also producing different precursor and fragment masses. Dimethyl labeling was first introduced as a multiplexing method where multiple samples would be labeled and mixed prior to mass spectrometry.\u003csup\u003e42\u003c/sup\u003e In our approach, only the background is modified, where free amines are mass-shifted by two methyl groups (28 Da). This shifts any labeled precursors (even incomplete reactions with a single methyl group) outside of the precursor isolation window used by PRM measurements, ensuring that any foreground signals will not be confused with background signals. Additionally, dimethyl labeling is affordable, easy, and quick, as peptides are labeled to 99.9% completion within a 1-hour reaction.\u003c/p\u003e\n\u003cp\u003eWhile ion traps generally have a more limited dynamic range compared to Orbitrap-based mass spectrometers, the sensitivity of ion traps allows for superior detection and quantification of low-input samples.\u003csup\u003e17\u003c/sup\u003e Additionally, Orbitraps have slower scanning speeds compared to the Q-LIT, limiting the number of peptides that can be targeted within a cycle. Here, we found that reasonable quantitative accuracy can be achieved with the Q-LIT at low input while targeting a similar number of peptides as with higher-input Orbitrap-based PRM assays. At 100 ng, the quantitative accuracy of most peptides acquired with PRM remains consistent for nearly two orders of magnitude (\u003cstrong\u003eFigure 3A\u003c/strong\u003e), where the median lower limit of detection (LoD) was 0.83:100 and the median lower limit of quantification (LoQ) was 2.8:100 (\u003cstrong\u003eFigure 3B\u003c/strong\u003e), where only 0.6% of peptides could not be assigned a LoQ. Quantification was slightly worse at the 10 and 1 ng levels, where 4.6% and 20% of peptides could not be assigned a LoQ. Unsurprisingly, at the 100 ng level signal is more easily distinguishable from noise and the LoD distribution is generally higher than at 10 ng or 1 ng (\u003cstrong\u003eSupplementary Data\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSingle cells typically produce between 0.1 and 0.3 ng of peptides, depending on the cell type. Considering the 1 ng sample, the median measured peptide produced a linear signal in this range (0.198:1). Several peptides showed a linear response below 0.1 ng. For example, the peptide ECESYFK from Granzyme B was found to have a LoQ of 0.043:1, equating to a proteome fraction consisting of 43 pg in a background of 1 ng, and was still measurable above background at the 18 pg level (\u003cstrong\u003eFigure 4A and 4B\u003c/strong\u003e). Two other Granzyme B peptides, VAAGIVSYGYK and TQQVIPMVK, produced even lower LoDs (below 10 pg equivalents). Granzyme B is a serine protease implicated in multiple autoimmune diseases.\u003csup\u003e43\u003c/sup\u003e All told, 61 peptides with estimated LoQs in the 1 ng assay corresponded to 30 quantified proteins. At least 6 points across the peak were sampled for all peptides in the assay achieving 8-10 points across the peak on average.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eValidated cell populations for quantitative testing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to showing quantitative accuracy in a controlled matched matrix, we wanted to validate measurement precision in low-input biological experiments. The interleukins (IL) family of proteins is a class of cytokines expressed by many cells, including immune cells, which bind to specific receptors that elicit pro- and anti-inflammatory roles.\u003csup\u003e44\u003c/sup\u003e Certain cytokines, such as IL-2 and IL-15, bind to receptors on the surface of T cells in specific biological events, such as activation and differentiation. Both of these molecules have been successfully used as part of immunotherapies to combat cancer.\u003csup\u003e45–48\u003c/sup\u003e Interestingly, IL-2 and IL-15 are structurally similar in homology and activate T cells through the same receptor subunits (IL-2/IL-15Rβγ),\u003csup\u003e49,50\u003c/sup\u003e mediating largely similar biological effects on T cells.\u003csup\u003e51,52\u003c/sup\u003e However, possibly related to expression of the private IL-2Rα and IL-15Rα chains, IL-2 induces an effector-like phenotype (with low CD62L expression) while IL-15 induces a memory-like phenotype (with higher CD62L).\u003csup\u003e53\u003c/sup\u003e We generated activated T cells cultured in IL-2 or IL-15 to replicate an effector-like and memory-like phenotype for CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e cells (\u003cstrong\u003eSupplemental Figure S2A\u003c/strong\u003e). We selected this model system to showcase the ability to generate LIT-PRM assays using well-studied biology at inputs below 1 ng. Additionally, flow cytometry was used as an orthogonal technique to validate the cell populations present in IL-2 and IL-15 treated T cells on days 5, 6, and 10 exhibited an effector-like and memory-like phenotype (\u003cstrong\u003eSupplemental Figure S2B and S2C\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAt day 10, flow cytometry identified that each culture was predominantly composed of T cells, with CD8\u003csup\u003e+\u003c/sup\u003e T cells being the majority subset in both IL-2 (83.8%) and IL-15 (92.2%) cultures (\u003cstrong\u003eFigure 5\u003c/strong\u003e). Correspondingly, we found that CD4\u003csup\u003e+\u003c/sup\u003e T cells composed 14.6% of the cells stimulated with IL-2 and 6.9% of cells stimulated by IL-15. This was reflected in our targeted proteomics data, as the CD4 protein was the second most downregulated protein in IL-15-stimulated T cells compared to IL-2-stimulated cells (\u003cstrong\u003eFigure 6A\u003c/strong\u003e). We note that this protein was not technically quantified at 1 ng, as the one peptide for CD4 (VVQVVAPETGLWQCLLSEGDKVK) lacked linearity in signal as estimated by the calibration curve. We measured the same peptide at the 10 ng level, where we calculated the LoD to be 0.96:10 (ratio of foreground to background) with an LoQ of 8.3:10 (\u003cstrong\u003eSupplemental Figure S3\u003c/strong\u003e). This indicated that at the 1 ng level, CD4 should be above the LoD but below the LoQ, and our results match these calculations.\u003c/p\u003e\n\u003cp\u003eIL-2 stimulation is known to push activated T cells into an effector-like population, reflected by the paired flow cytometry data on day 10. Granzyme B, from which we estimated the most responsive peptide (TQQVIPMVK) was quantitative to 0.025:1 (ratio of foreground to background), is an effector molecule secreted by cytotoxic CD8\u003csup\u003e+\u003c/sup\u003e T cells. We found peptides associated with this protein were 1.55x lower in IL-15 than IL-2 stimulated cells using targeted proteomics (\u003cstrong\u003eFigure 6A\u003c/strong\u003e), which matches flow cytometry data indicating that the number of effector CD8\u003csup\u003e+\u003c/sup\u003e T cells (T\u003csub\u003eEFF\u003c/sub\u003e) are lower when stimulated with IL-15 than IL-2. While both IL-2 and IL-15 resulted in the activation of T cells, IL-15 stimulation led to the differentiation of memory-like T cells, as demonstrated in the flow cytometry data. The CD44 receptor antigen is a cell surface receptor that helps cells facilitate cell-cell interaction and response to the tissue microenvironment. Interestingly, we found that the expression of CD44 is slightly higher in IL-2 compared to IL-15, indicating that IL-2 stimulated cells had a higher population of activated cells, with a 1.6x median fold change in abundance. T cells that express CD62L have an increased population of memory T cells after IL-15 stimulation.\u003csup\u003e50\u003c/sup\u003e Flow cytometry data indicated that we had a higher population of CD62L\u003csup\u003e+\u003c/sup\u003e cells in the IL-15 stimulated condition compared to T cells stimulated with IL-2 (\u003cstrong\u003eSupplemental Figure S2\u003c/strong\u003e), indicating a higher population of memory-like T cells. Compared to the IL-15 stimulated cells, IL-2 stimulated T cells expressed IL-2Rβ/IL-15Rβ at a higher ratio (\u003cstrong\u003eFigure 6A\u003c/strong\u003e), which is associated with a memory phenotype. In general, we observe high analytical precision using PRM with a Q-LIT platform, even in 1 ng assays. Most peptides were measured with less than a 20% coefficient of variation between 3 technical replicates (\u003cstrong\u003eFigure 6B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eUltimately, we detected 100% of the proteins monitored with flow cytometry using global proteomics during library generation. While some of these proteins were hard to observe at low input (1 ng), we were able to quantify 75% above an estimated LoQ with targeted proteomics. This overlap indiates complementary benefits of using flow cytometry in tandem with targeted proteomics to fully capture immune cell state. While single-cell proteomics using mass spectrometry continues to develop, flow cytometry is the best method for measuring a small number of proteins (6-12) on thousands of individual cells within a single day. On the other hand, targeted mass spectrometry on 1-10 T cells (equivalent to around 0.1 and 1 ng) can monitor tens to hundreds of proteins, including cytokines and transcription factors, which cannot be easily monitored using flow cytometry.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHere, we demonstrate a complete workflow for acquiring global libraries and rapidly building \u003cem\u003ede novo\u003c/em\u003e PRM assays using only a Q-LIT mass spectrometer. While the Q-LIT is capable of DDA, we found that equivalently large libraries could be quickly generated using GPF-DIA. We also found that PRM assays using the Q-LIT were linearly quantitative even at low input, enabling us to accurately measure difficult to measure cytokines, transcription factors, and immune proteins. From a broader perspective, high-resolution mass spectrometry is expensive in terms of instrument costs and requiring greater technical experience to operate successfully. In contrast, Q-LIT mass analyzers are easier to maintain and more cost-effective to operate in part because they have less stringent vacuum requirements compared to Orbitrap-based mass spectrometers, making them an appealing option for low-input proteomics. These factors are especially important in single-cell proteomics, where each biological sample needs thousands of injections. Our results suggest that Q-LITs provide laboratories with competent low-input proteomics analysis in situations where high resolution is impractical. We believe these instruments offer a high value-to-expense ratio, potentially democratizing mass spectrometry on a broader array of laboratory settings. This democratization is particularly impactful in immuno-oncology, where proteome-based analysis of immune cell populations can uncover crucial biomarkers to guide clinical decisions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by NIH R35GM150723 and R21CA267394 to BCS. This research was also supported by The Ohio State University Comprehensive Cancer Center (OSUCCC) and the National Institutes of Health (NIH) under grant P30CA016058. This research was made possible through resources, expertise, and support provided by the Pelotonia Institute for Immuno-Oncology (PIIO), which is funded by the Pelotonia community and the OSUCCC. We thank the PIIO and the Immune Monitoring and Discovery Platform for flow cytometry access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBCS is a founder and shareholder in Proteome Software, which operates in the field of proteomics. The Searle Lab at the Ohio State University has a sponsored research agreement with Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research. However, analytical methods were designed and performed independently of Thermo Fisher Scientific. LRH, CCJ, and PMR are employees of Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData/code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteomics data and Skyline documents are available on Panorama at https://panoramaweb.org/StellarIonTrapForLowInput.url. All proteomics raw data is also publicly available on the MASSIVE repository under the accession number MSV000094904 (ftp://massive.ucsd.edu/v08/MSV000094904/). Open-source software developed for this project is publicly available as part of the EncyclopeDIA project at https://bitbucket.org/searleb/encyclopedia.\u003c/p\u003e"},{"header":"Experimental Methods","content":"\u003cp\u003e\u003cem\u003eT cell cultures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSplenocytes from C57BL/6 mice were stimulated with plate-bound anti-CD3 mAb (145-2C11 clone) on day 0 in complete media, as described previously.\u003csup\u003e54\u003c/sup\u003e On day 2, cells were washed and re-plated with human (h) IL-2 or hIL-15 at 200 ng/mL. Cells were washed and split on days 4 and 6. Flow cytometry was performed on days 5, 6, and 10, and cells were washed three times with DPBS, centrifuged at 500 RCF for 5 minutes, pelleted, and stored at -80°C on days 6 and 10 for mass spectrometry. A third condition was maintained without stimulation as a control for flow cytometry as well.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFlow cytometry \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFlow cytometry was performed as previously described.\u003csup\u003e54\u003c/sup\u003e Briefly, cells collected on days 5, 6, and 10 were stained with live/dead fixable blue dead-cell stain (Invitrogen #L23105), and antibodies for B220, CD4, CD8, CD25, CD44, CD62L, CD69, and TCRb (see antibody details in \u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e). Stained cells were acquired with a Cytek Biosciences Aurora™ 5-laser flow cytometer and analyzed using BD Biosciences FlowJo™ software.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProteomics sample preparation \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrozen cell pellets were lysed in a 5% SDS buffer with 50 mM TEAB, 1x HALT, and 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e. DNA was sheared with a Bioruptor® Pico by sonicating at 14℃ for 30 seconds, followed by 30 seconds of rest, a total of 10 times. Sheared cells were then spun down at 13,000 RCF for 10 minutes, and the protein supernatant was retained. Protein quantities were estimated using a Pierce™ bicinchoninic acid (BCA) Protein Assay Kit. Proteins were reduced with 40 mM dithiothreitol (DTT), alkylated with 40 mM iodoacetamide, and quenched with 20 mM DTT. Acidification was done with 2.5% phosphoric acid, and protein was loaded onto suspension trap (s-trap) micros (Protifi LLC). Digestion was performed with trypsin at a 1:20 ratio of enzyme to protein at 47℃ for 2 hours, then eluted. Peptides were dried down and stored at -80℃.\u003c/p\u003e\n\u003cp\u003eAccording to the kit protocol, an aliquot of dried peptides was separated according to basicity using a Pierce High pH Reverse-Phase Fractionation Kit. Briefly, 50 μg of peptides were resuspended in 0.1% trifluoroacetic acid in HPLC-grade water. The separation mini-columns from the kit were centrifuged at 5000 RCF for 2 minutes to remove any liquid and pack the resin. The mini-columns were then equilibrated with 100% acetonitrile and washed 3 times with water. Resuspended peptides were loaded, and the flow through was collected as the first fraction. The mini-columns were washed with water, and the eluent was collected as the second fraction. The elution buffers specified from the kit were then used to produce the following 8 fractions. Fractionated peptides were then dried down and stored at -80℃ until mass spectrometry-based analysis for DDA-based library generation.\u003c/p\u003e\n\u003cp\u003eA separate aliquot of the eluted peptides was dimethyl labeled using an in-solution amine-labeling reaction published by Boresema et al.\u003csup\u003e42\u003c/sup\u003e Digested peptides were resuspended in 100 mM TEAB (pH = 8.5). Formaldehyde (4%) was added to the resuspended peptides and mixed. Sodium cyanoborohydride (0.6 M) was then added to catalyze the dimethyl labeling reaction for 90 minutes at 22℃ while mixing vigorously. The reaction was quenched with 1% ammonia and 5% formic acid. All peptides were resuspended in 2% acetonitrile with 0.1% formic acid. Calibration curves were generated by mixing labeled and unlabeled peptides at different concentrations. In these mixtures, unlabeled peptides were diluted in a dimethyl-labeled background over 4 orders of magnitude (\u003cstrong\u003eSupplemental Table 2\u003c/strong\u003e) and aliquoted at different concentrations prior to mass spectrometry analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cbr\u003e LC-MS settings\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData was acquired on a Thermo Scientific™ Stellar™ MS coupled to a Vanquish™ Neo UHPLC system. Solvent A consisted of 100% water with 0.1% formic acid, and solvent B contained 80% acetonitrile with 0.1% formic acid. An Easy-Spray™ source was used for ionization at 2000 V, and the ion transfer tube was set to 275℃. Peptides were separated on a 25 cm C18 analytical Easy-Spray column, packed with 2 μm beads along a 50-minute linear gradient as follows: from 0-4 minutes, 2% B, 4-8 minutes increased to 8% B, 8 to 58 minutes increased with 28% B, 58 to 65 minutes increased to 44% B, followed by a 10-minute wash at 100% B. The flow for the entire gradient was set to 250 nL/min. The instrument was configured to expect chromatography of approximately 15 seconds and fragment peptides with a default charge state of 2 and a collision cell gas pressure of 8 mTorr.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDDA on an ion trap instrument\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor DDA experiments, the RF lens was set to 30%. Precursor spectra were collected ranging from 350-1250 \u003cem\u003em/z \u003c/em\u003eat a scan rate of 67 kDA/s. The automatic gain control (AGC) target was set to “Standard” with an absolute AGC target of 3e4. The maximum ion injection time (maxIIT) was set to 100 ms and spectra were collected using centroiding in positive mode. MS2 scans were collected only on peptides with a charge state greater than 1, excluding undetermined charge states. An intensity threshold of 5E2 was used to trigger an MS2 scanand an HCD NCE of 30%. Following the MS2 measurement, the peptide m/zs were placed on a dynamic exclusion list for fragmentation for 2 seconds using a precursor mass tolerance of +/- 0.5\u003cem\u003e m/z\u003c/em\u003e. Twenty DDA scans were taken in each cycle with a 1.6 m/z isolation window around the precursor of interest. Fragment ions were scanned at 125 kDa/second scan rate from 200-1500 \u003cem\u003em/z\u003c/em\u003e using an AGC target of 1E4 and a maxIIT of 50 ms.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cem\u003eDIA and PRM on an ion trap instrument\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor both DIA and PRM experiments, the precursor range was set to 350-1250 \u003cem\u003em/z\u003c/em\u003e and measured at a rate of 67 kDa/second. The AGC target was set to “Standard,” which is equivalent to 1e4, and the maxIIT was set to 100 ms. The loop control was set to “all.” Peptides were fragmented with HCD with NCE set to 30%, and fragments were scanned at 67 kDa/second over a range of 200-1500 \u003cem\u003em/z\u003c/em\u003e. Precursor isolation windows for DIA were consistently 8 \u003cem\u003em/z\u003c/em\u003e wide, where margins were set to forbidden zone locations. Six gas phase fractions were used to collect chromatogram libraries over 400-500, 500-600, 600-700, 700-800, 800-900 and 900-1000 \u003cem\u003em/z\u003c/em\u003e. The majority of settings were the same as a wide-window DIA scan, with the exception of 2 \u003cem\u003em/z\u003c/em\u003e wide isolation windows over the adjusted precursor \u003cem\u003em/z\u003c/em\u003e range for each method.\u003c/p\u003e\n\u003cp\u003eAll settings for PRM scans were the same except for the isolation window width and maxIIT. For PRM assays at 10, 20, and 50 peptides per cycle, the maxIIT was set to 200, 95, and 50 ms, respectively. Precursor isolation windows were set to 2 \u003cem\u003em/z\u003c/em\u003e,where MS/MS were collected over 200-1500 \u003cem\u003em/z\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGlobal data was first converted to the universal mzML format using peak picking. DIA data was analyzed with EncyclopeDIA v.3.0.0-SNAPSHOT using the Ion Trap/Ion Trap mode. Mass tolerance was set to 0.4 Da where a minimum of 3, but a maximum of 5 ions were used for quantification. The chromatogram library was generated by searching 6 gas phase fractions against a Prosit\u003csup\u003e38\u003c/sup\u003e predicted library. The Prosit library contained spectrum predictions of all +2 and +3 ions from a mouse FASTA from UniProt, which was accessed on October 22, 2019. The predicted library allowed for up to 1 missed cleavage, with a default charge state of 3, and default NCE of 33 over 396.4-1002.7 \u003cem\u003em/z\u003c/em\u003e. Wide-window (8 \u003cem\u003em/z) \u003c/em\u003einjections were searched against the chromatogram library using the same search settings. Global DDA data was searched in Scribe using the Ion Trap/Ion Trap instrument mode for b and y tryptic peptides, with a library mass tolerance of 0.4 Da. The 10 high-pH fractionated injections were searched against the same Prosit predicted library used to generate the DIA library.\u003c/p\u003e\n\u003cp\u003eGlobal DIA data was searched using CHIMERYS\u003csup\u003e55\u003c/sup\u003e intelligent search algorithm (MSAID GmbH) in Thermo Scientific™ Proteome Discoverer™ 3.1, using analogous settings for the EncyclopeDIA search. A predicted spectrum library was generated from the mouse fasta database by INFERYS™ deep learning framework (MSAID GmbH) for all tryptic +2, +3, and +4 peptides between 7-30 amino acids in length. For processing, spectrum files were selected using the ion trap MS setting, with a signal-to-noise peak threshold of 1.5. The top 24 peaks were selected in each window with a fragment mass tolerance set to 0.4 Da. Fixed carbamidomethyl modifications and a maximum of 2 oxidized methionines were allowed per peptide. The retention times from CHIMERYS were extracted for all detections and combined with EncyclopeDIA’s detections. For peptides detected by both software tools, the EncyclopeDIA retention times were preferred. The combined detections and retention times were used to select peaks within EncyclopeDIA and run against a 1% FDR to obtain a combined search engine library. The fractionated injections were also searched in Proteome Discoverer, using a mass tolerance of 0.4 Da for all +2, +3, and +4 peptides, and the same settings used for the CHIMERYS search,\u003c/p\u003e\n\u003cp\u003eSkyline\u003csup\u003e56,57\u003c/sup\u003e version 23.1.0.455 was used for targeted analysis. For analyzing the calibration curves and other PRM injections of IL-2 and IL-15 replicates, the chromatogram library was first imported to serve as a reference point for integrating low-input PRMs. With the imported DIA results, transition settings were altered, and the PRM samples were imported. For both imports, the settings peptide settings were set to Trypsin [KR\\P], with a maximum of 2 missed cleavages, and the mouse fasta used to generate the Prosit library was used to generate a background proteome. Retention time window predictions were set to 5 minutes; however, measured retention times were used when present. Peptides between 7 and 40 amino acids in length were used, and “auto-select all matching peptides” was left checked. Only carbamidomethylation modifications were considered for cysteine. For transition settings, peptides of +2, +3, and +4 precursor charges, along with +1 and +2 fragment ion charges were considered for b and y ion types. For product ion selection, we considered the third ion to the second to last ion. DIA precursor windows were used for exclusion when importing the chromatogram library. The ion match tolerance for the library was set to 0.4 Da, and 6-9 product ions were used from filtered product ions. For the instrument parameters, a 200-1500 \u003cem\u003em/z\u003c/em\u003e range was considered, with a method match tolerance of 0.4 Da, and “dynamic min product \u003cem\u003em/z\u003c/em\u003e” and “triggered chromatogram acquisition” were checked. For the full-scan parameters, DIA was used when importing the chromatogram library file as a reference point for integrating calibration curves. The gas-phase fractionated isolation windowing scheme was imported from the files for a QIT mass analyzer, with a resolution of 0.4 Da and retention time filtering within 5 minutes of MS/MS IDs. For importing PRM injections, the “PRM” acquisition method was used rather than the DIA method. Finally, lower limits of detection (LoD) and quantification (LoQ) were estimated using EncyclopeDIA, and calculated quantities of IL-2 and IL-15 replicates were determined using calibration curves.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKitano, H. 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Rev.\u003c/em\u003e\u003cstrong\u003e39\u003c/strong\u003e, 229\u0026ndash;244 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4702746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4702746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdvances in proteomics and mass spectrometry enable the study of limited cell populations, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive low-mass accuracy measurements, these instruments are effectively restricted to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. Here, we describe a workflow using a new hybrid quadrupole-LIT instrument that rapidly develops targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Using an automated software approach for scheduling parallel reaction monitoring assays (PRM), we show consistent quantification across three orders of magnitude in a matched-matrix background. We demonstrate measuring low-level proteins such as transcription factors and cytokines with quantitative linearity below two orders of magnitude in a 1 ng background proteome without requiring stable isotope-labeled standards. From a 1 ng sample, we found clear consistency between proteins in subsets of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells measured using high dimensional flow cytometry and LIT-based proteomics. Based on these results, we believe hybrid quadrupole-LIT instruments represent an economical solution to democratizing mass spectrometry in a wide variety of laboratory settings.\u003c/p\u003e","manuscriptTitle":"Rapid assay development for low input targeted proteomics using a versatile linear ion trap","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 10:44:48","doi":"10.21203/rs.3.rs-4702746/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"23e1d869-96c6-4472-be8b-a2b2783cdbbf","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34804835,"name":"Physical sciences/Chemistry/Analytical chemistry/Mass spectrometry"},{"id":34804836,"name":"Biological sciences/Biotechnology/Proteomics"}],"tags":[],"updatedAt":"2025-04-23T07:08:04+00:00","versionOfRecord":{"articleIdentity":"rs-4702746","link":"https://doi.org/10.1038/s41467-025-58757-8","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-04-23 04:00:00","publishedOnDateReadable":"April 23rd, 2025"},"versionCreatedAt":"2024-07-19 10:44:48","video":"","vorDoi":"10.1038/s41467-025-58757-8","vorDoiUrl":"https://doi.org/10.1038/s41467-025-58757-8","workflowStages":[]},"version":"v1","identity":"rs-4702746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4702746","identity":"rs-4702746","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}