
==== Front
J Proteome Res
J Proteome Res
pr
jprobs
Journal of Proteome Research
1535-3893
1535-3907
American Chemical Society

39163279
10.1021/acs.jproteome.4c00384
Technical Note
An Inflection Point in High-Throughput Proteomics with Orbitrap Astral: Analysis of Biofluids, Cells, and Tissues
Hendricks Nathan G. †
Bhosale Santosh D. †
Keoseyan Angel J. †
Ortiz Josselin †
https://orcid.org/0000-0003-4794-1097
Stotland Aleksandr ‡
Seyedmohammad Saeed ‡
Nguyen Chi D. L. †
Bui Jonathan T. †
https://orcid.org/0000-0002-0407-2031
Moradian Annie *†
Mockus Susan M. †§̂
https://orcid.org/0000-0001-9050-148X
Van Eyk Jennifer E. †‡§̂
† Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Beverly Hills, California 90211, United States
‡ Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
* email: Annie.Moradian@cshs.org.
20 08 2024
06 09 2024
23 9 41634169
03 05 2024
29 07 2024
24 07 2024
© 2024 The Authors. Published by American Chemical Society
2024
The Authors
https://creativecommons.org/licenses/by-nc-nd/4.0/ Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).

This Technical Note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cell and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates the reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24 min active gradient. In 200 ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45 min run covers ∼90% of the expressed proteome. This complete workflow allows for large swaths of the proteome to be identified and is compatible with diverse sample types.

Orbitrap Astral
high-throughput
plasma proteomics
tissue proteomics
automation
biomarker
mass spectrometry
PBMCs
missing proteins
National Heart, Lung, and Blood Institute 10.13039/100000050 R01 HL155346-01A1 Cedar Sinai Medical Center - Proteomics and Metabolomic core and Precision Health NA NA Erika Glazer Family Foundation 10.13039/100018847 NA National Institute of Diabetes and Digestive and Kidney Diseases 10.13039/100000062 U01 DK124019-01 National Heart, Lung, and Blood Institute 10.13039/100000050 U01 DK124019-01 National Heart, Lung, and Blood Institute 10.13039/100000050 R01HL111362 document-id-old-9pr4c00384
document-id-new-14pr4c00384
ccc-price
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pmcIntroduction

The introduction of the Orbitrap Astral mass spectrometer is a major technological feat in mass spectrometry (MS) due to a significant boost in the depth and throughput capabilities of LC-MS-based proteomic workflows. Because of the high sensitivity, scan speeds up to 200 Hz, and highly parallelizable acquisition, various groups have described impressive gains in protein identifications and throughput with good reproducibility.1−11 Users have reported remarkable levels of depth achievable through this new instrumentation in cellular, plasma, and metaproteomic studies. In the context of clinical proteomics, the large-scale analysis of patient cohorts must combine precision, reproducibility, and robustness. Accordingly, we focused on leveraging the power of this new technology, together with automated methods for sample lysis and digestion. Specifically, we describe methods and identify benchmarks for biospecimens in three categories, 1) Biofluids, plasma (naïve, depleted, perchloric acid-precipitated, and Seer nanoparticle-fractionated), dried blood spots, 2) Cells, HeLa, PBMCs, HEK293, and 3) mouse tissues, heart, intestine, liver, and lung.

To facilitate high-throughput analysis of these biospecimens, we have optimized the analysis conditions for a 15 cm by 150 μm ID Evosep (PepSep) column with 1.9 μm particle size. To our knowledge, the use of this column has not been described with Vanquish LC and Astral MS. Current publications on the Orbitrap Astral MS primarily describe workflows with EasySpray (Pepmap) columns and standard methods for the NeoVanquish system.11 We have found that an Evosep column-oriented frontend to the Orbitrap Astral MS also offers reliable chromatography, run time utilization, and robustness.

Experimental Section

Biospecimens Collections

Animal procedures were approved by the Cedars-Sinai Medical Center Institution Animal Care and Use protocol. Heart, intestine, liver, and lung samples were dissected from 129.S1 mice (Jackson Laboratory, Stock 002448). Frozen tissue was cut on dry ice using a blade or a disposable biopsy puncher (Integra Miltex Disposable Biopsy Puncher with plunger 1.5 mm), and ∼3–6 mg was used for lysis. Healthy human pooled plasma (BioIVT) was used with and without the enrichment of low-abundance proteins for proteomics analysis. HEK293 cells were procured from ATCC and maintained in DMEM supplanted with 10% FBS and antibiotic/antimycotic, in an incubator with 5% CO2 at 37 °C. Peripheral Blood Mononuclear Cells (PBMCs) were acquired from Stem Cell Technologies and Pierce Hela protein digest standard from Thermo Fisher Scientific.

Homogenization and Lysis

Various cell and tissue types were lysed using AFA technology on a Covaris LE220-plus sonicator (Covaris) with either 8 AFA-tube TPX strips or m-130 glass tubes with beads depending on the sample type. When working with less than one million cells, an 8 AFA-tube TPX strip (Covaris) was used. Cells were resuspended in 40 μL of 100 mM Tris-HCl pH 8 and 2% SDS in the TPX strip. After 1 min of centrifugation, the samples were sonicated using parameters set A values on the Covaris instrument (Table S1). The total procedure time was 5 min per column. Afterward, sample strips were centrifuged for 1 min, and then the supernatant was collected in a new tube and centrifuged for 20 min at 2721 RCF. For tissues and cell samples over one million cells, m-130 glass tubes with beads (Covaris) were used in place of the above procedure, but the total volume was 120 μL and parameter set B values were used (Table S1).

Digestion and Depletion

Cell and tissue lysates were digested by an automated SP3 protocol adapted to a Beckman i7 workstation. Bead aliquoting, reduction, alkylation, digestion, and elution were all performed on-deck with a 96-well plate format. Briefly, 20 μg of protein in 40 μL of the previously mentioned lysis buffer was reduced with the addition of 10 μL of 200 mM dithiothreitol and incubated 30 min at 60 °C with shaking at 300 rpm, then alkylated with 10 μL of 400 mM IAA at room temperature for 15 min in the dark. The volume was brought to 70 μL with Tris-HCl pH 8, then 5 μL of bead suspension (10:1 mass ratio of beads to protein, 1:1 mixture of hydrophilic/hydrophobic beads (Cytiva)) was aliquoted into the samples using the span-8 pipetting head with constant agitation of the bead reservoir between transfers. Samples were brought to 50% ACN and incubated for 18 min, and then the solvent was removed on-magnet, and samples were rinsed with 2× 80% EtOH then 2× ACN with 200 μL volumes each. After the solvent was completely removed, the samples were resuspended in 50 mM Tris-HCl pH 8 and 10 mM CaCl2 with trypsin at a 1:20 ratio. Samples were sonicated for 5 min then incubated 18 h at 37 °C and 1200 rpm overnight. After digestion, the samples were then removed from beads and brought to 0.1% FA and 2% DMSO for injection into the instrument.

Healthy pooled plasma samples were depleted by using a 96-well filter plate (prototype plate, Thermo Fisher). A 10 μL amount of pooled plasma was pipetted directly into the plate, and 300 μL of Pierce Top-14 Depletion resin (ThermoFisher) was added to the wells. Samples were incubated at room temperature with gentle mixing for 1 h at 350 rpm. Samples were then eluted by positive pressure and dried before digestion. Alternatively, samples were depleted by perchloric acid precipitation according to the protocol in Viode et al.12

Healthy pooled plasma samples were also prepared by treatment with proprietary nanoparticle from Seer Proteograph, as described in Ferdosi et al.8,13 This automated platform offers a complete workflow from enrichment of low abundant proteins to peptide solution ready to be analyzed by LC-MS/MS using proprietary reagents.

Here 5 μL of naïve plasma or dried depleted plasma samples was digested on the i7 automation workstation (Beckman Coulter). Samples were digested using the procedure described in Ardle et al.,14 using 1% SDC in place of TFE as a denaturing agent.

LC-MS/MS Analysis

Approximately 500 ng of peptides from digested samples were analyzed on a Thermo Orbitrap Astral coupled to a NeoVanquish LC. To assess carryover, a blank injection was included after every 3 injections. Samples were separated using 8-, 11-, 24-, 45-, or 60 min gradients, with details given in Supporting Information Table S2. The compositions for solvent A and B were 0.1% FA in water and 80% ACN with 0.1% FA, respectively. With all gradients the NeoVanquish LC was operated in direct injection mode, using a 150 μm ID × 15 cm 1.9 μm column (Evosep). The Evosep EasySpray adapter with a 30 μm ID metal emitter (Evosep) was coupled to a nano source (Thermo EasySpray) on the Orbitrap Astral MS platform (ThermoFisher).

All sample runs were acquired in data independent acquisition (DIA) mode from 380 to 980 Da with 240k Orbitrap resolution and 5 ms maximum injection time for MS1. All DIA scans were set to a 7 ms maximum injection time with varying window schemes between 2 and 5 Th depending on gradient length.

Data Analysis

The MS raw data files were searched against UniProt human reviewed protein sequence entries (accessed April 2023) using DIA-NN (v 1.8.1)15 in library free mode with default parameters. Based on recent comparisons with library-based approaches, DIA-NN in library-free mode has been found to produce results that are comparable or better than those of experimental library-based searches while being freely available and was hence chosen for the analysis of all data.16 The analysis of DIA-NN was performed on ProEpic, an in-house platform that hosts a collection of proteomics workflows. The ProEpic platform facilitates high-throughput and bulk analysis of large data sets. Seer Proteograph (nanoparticle) samples were analyzed in the Proteograph Analysis Suite, using a DIA-NN (v 1.8.1) environment in library-free mode. The output protein group matrix from DIA-NN was used to perform downstream analysis using Excel and R (see the Supporting Information for additional details).

Results and Discussion

For this study we assessed the depth and technical reproducibility of the NeoVanquish-Astral workflow with a 3 × 3 study design (three replicates on three separate days) of assorted tissue and biofluid sample types. We achieved the optimal results using a 150 μm × 15 cm Evosep (Pepsep) column with a 1.9 μm particle size in a direct-injection configuration. The moderate backpressure of this column enables fast loading even in direct injection, and higher flow rates to elute peaks early and maximize the utilization of the gradient time (graphical abstract).

Table 1 illustrates the total identifications (as retrieved from the DIA-NN PG matrix) for all 9 replicates per sample type, gradient, and MS method. HeLa samples were injected at 200 ng—a typical benchmarking sample load used across many instruments for all gradients and methods. We focused on HeLa and plasma (depleted and naïve) for different run times and DIA method parameters to explore trade-offs in coverage, reproducibility, and throughput. The high scan speed of the Astral mass analyzer translates to lower cycle times, enabling small DIA windows with short gradients while still sampling many points across the analyte elution peak. We explored the trade-offs in pushing these limits while comparing two DIA method parameters in most gradients: one method with a quantitation-optimized window size (e.g., 24 min 3Th) giving ≥6 points across the peak on average, and another with a smaller window size (e.g., 24 min 2Th) and longer cycle time. As reported in DIA-NN, the average full width at half-maximum (fwhm) scans per peak for each method with 200 ng of HeLa injections are shown in Table S3. The values range from 2.3 to 3.4 and window sizes were chosen to keep fwhm scan numbers consistent, where the smaller window size for each method has ∼2.5 points vs ≥3 points for the larger window size. In all cases, we see more identifications with a smaller window size, but we also examined the impact on quantitative accuracy calculated as % coefficient of variance (%CV). As seen in Figure 1A, the impact of window size for the 24 min gradient noticeably skews the % CV distribution when comparing 2 versus 3 Th windows. Still, with the less quantitatively robust 2 Th window size the average %CV is below 13% for HeLa and 20% for Plasma. Interestingly, in shorter gradient times the impact of window size on %CV is less pronounced, particularly with the 11 min gradient where there is little difference in the two window sizes, and in general shorter gradients have tighter CV distributions. Considering the fwhm scans are consistent across the different run lengths, this tightening of CV distributions could be due to 1) peak sharpening from the shortening of the gradients and 2) noisier low-abundance proteins being more exclusive to the 24 min methods. For HeLa, a 45 min gradient covers approximately 90% of the expressed proteome,17 while an 8 min gradient can deliver 78% as many hits in less than 1/5 of the time. In terms of copy numbers per cell, this level of coverage can observe proteins at ∼100 copies, with the lowest observed protein at an estimated 13 copies per cell (Figure S1). Furthermore, this deep coverage of the proteome uncovers some of the “missing proteins” in the human proteome—proteins that are expected to be present based on the transcriptome or genome but lack direct protein-level evidence.18,19 Five of these proteins were detected with two or more peptides >9 amino acids in length, meeting the stringent criteria to be moved to Protein Existence level 1 (Table S4).20

Figure 1 A) Comparison of the different gradients and DIA window sizes by %CV. Box plots are given for naive plasma (left) and HeLa (right) according to three different gradients, with two window sizes per gradient. Median %CVs are overlaid. B) CV plots of all identification in naïve (right) and depleted plasma (left), in both 24 (top) and 45 min (bottom) runs. Published FDA biomarkers are listed highlighted in red.

Table 1 Total Protein Group (PG) Identifications in Each Sample Type with Respect to the Different Gradients and MS Methods Testeda

Gradient (min)	Window (Th)	HeLa (200 ng)	Naïve Plasma	Depleted Plasma	PerCA Plasma	Seer nanoparticle	PBMCs	Dried Blood Spots	HEK293 Cells	Mouse Heart	Mouse Lung	Mouse Liver	Mouse Intestine	
8	5	8126	475	834	 	 	 	 	 	 	 	 	 	
7906	474	823	
(97%)	(>99%)	(99%)	
8	4	8265	565	837	 	 	 	 	 	 	 	 	 	
7988	555	819	
(97%)	(98%)	(98%)	
11	4	8598	572	974	 	 	 	 	 	 	 	 	 	
8365	561	889	
(97%)	(98%)	(91%)	
11	3	8700	636	1051	 	 	 	 	 	 	 	 	 	
8484	621	1018	
(98%)	(97%)	(97%)	
24	3	9353	695	1198	1567	3359*	8157	2693	10140	7056	10808	8649	10842	
9153	684	1166	1502	7959	2578	9854	6840	10460	8347	10462	
(98%)	(98%)	(97%)	(96%)	(98%)	(96%)	(97%)	(97%)	(97%)	(96%)	(96%)	
24	2	9817	896	1307	 	 	 	 	 	 	 	 	 	
9533	834	1258	
(97%)	(93%)	(96%)	
45	2	10619	1177	1541	 	 	 	 	 	 	 	 	 	
10422	1042	1472	
(98%)	(88%)	(95%)	
a The top bold number in each cell is the total unique protein groups, the number under is the average identifications per individual run, and in parentheses is the average protein completeness for the identifications. Numbers are given for 9 total injections (3 replicates by 3 days), except for the Seer nanoparticle runs, where 3 replicates of five-nanoparticle fractions were analyzed on the given gradient for a total of 15 injections on 1 day.

The high dynamic range of plasma still poses difficulty for deep proteomic analysis, but protein identifications in naïve plasma still represent an impressive technical advancement, with over 1100 proteins seen in a 45 min gradient. Depletion offers considerable gains in depth particularly in shorter runs, giving more than 350 additional identifications in an 8 min gradient. Impressively, the perchloric acid (PerCA) precipitation approach to depletion delivers over 1500 identifications. Additionally, Seer nanoparticles were run on pooled plasma and analyzed with the 24 min gradient. Three replicates were analyzed, but each replicate represents 5 injections (for each of the nanoparticles). These different approaches to increasing the depth of coverage in plasma are compared alongside Naïve with 24 min gradient (Figure 2A). The preparations we explored are evidently complementary, with no single method having total overlap with the others (Figure 2B). Considering that the depletion methods work by different mechanisms, this is not unexpected. An enrichment analysis of functional pathways for the different approaches is shown in Figure S2, but also worth consideration is the significant cost difference between these methods (PerCA < antibody ≪ nanoparticle).

Figure 2 A) Protein abundance rank plots of the of naïve and depleted plasma samples in the 24 min 3 Th MS method. B) Proteins seen in antibody-depleted of top 14 abundant plasma proteins, PerCA precipitated, and Seer Proteograph plasma samples and their overlap with Naïve are shown in a Venn diagram.

We also investigated the coverage of known biomarkers in plasma that are currently used in clinical chemistry laboratories for clinical decision making. Figure 1B shows FDA-approved circulating biomarkers21 as seen in naïve and depleted plasma in 24- and 45 min gradients arranged by %CV and protein rank according to the list of 216 described in Bhowmick et al. Between 91 and 122 biomarkers are seen per sample set, the majority of which lie below 20% CV. Even in healthy pooled plasma, low-abundance markers such as ovarian carcinoma antigen CA125 (Mucin-16, uniprot: Q8WXI7), Pancreatic triacylglycerol lipase (PNLIP, uniprot: P16233), and Platelet glycoprotein 4 (CD36, uniprot: P16671) are observed. In addition to biofluids, tissue and cell samples hold significant importance in the development and discovery of biomarkers in biopsy samples and in fundamental research of diagnostics and therapeutics. We analyzed an assortment of different tissue and cell types with the 24 min 3 Th gradient. Heart, liver, lung, and intestine samples from mice were processed in an automated, high-throughput sample preparation workflow. AFA sonication technology, capable of bulk sonication of samples in plate format, was used for efficient sample lysis of tissues in 2% SDS. Sample lysates were then digested by automated SP312,22,23 on the Beckman i7 automated workstation. The Beckman i7 has 96 and 8-channel pipettor heads, along with on-deck shaking, incubation, and magnetic pulldown of SP3 beads. The protocol we developed for this study includes use of the span-8 pipettor and orbital shaker to perform bead aliquoting into sample wells on-deck (Figure S3), which has not been previously described, removing all of the laborious bead-handling steps associated with SP3.

PBMCs consist of two main types of white blood cells lymphocytes (including B, T, and NK cells) and monocytes. Studying PBMCs provides a detailed understanding of how specific cells respond to various stimuli such as infections, cancers, or therapeutic drugs.24,25 To the best of our knowledge, close to 9000 protein identification from PBMCs represents the most extensive proteome coverage achieved in a 24 min analysis. Figure 3 shows highly significant enrichment of Reactome pathways seen in PBMCs covering metabolism, disease, cell cycle, and immune response, reflecting the variety of responses to stimuli that could be interrogated in a 24 min analysis.

Figure 3 Reactome pathways based on the molecular function of proteins identified in PBMC samples on the Astral 24 min runs. Node size represents the number of proteins and is filtered to display only pathways with 150 proteins or more. All nodes have significant adjusted p-value enrichment, with a range of 3.6e-3 to 9.9e-141.

Also of note is the number of identifications in heart tissue, which is a typically challenging for analysis due to the high-abundant sarcomeric myofilament proteins responsible for heart contractile motion that obscure low-abundant species, acting much like albumin in plasma.26,27 When searched together with the other murine tissues, we identify over 10k protein groups corresponding to 8928 unique proteins. The ontology of these identifications is displayed in Figure 4, with coverage of the Golgi apparatus, nucleus, and mitochondria inset. More than 800 of the estimated 1100–1400 mitochondrial proteins are identified in heart tissue.28

Figure 4 Gene ontology of the identifications in murine heart tissue samples when coanalyzed with other murine tissues in DIA-NN. Coverage of Golgi, mitochondrial, and nuclear proteins within each section is further expanded.

Conclusions

Our study outlines efficient, high-throughput workflows for automated sample preparation and rapid analysis of diverse biospecimens using the Orbitrap Astral MS platform. A significant breakthrough lies in our ability to characterize approximately 90% of a sample proteome of cells and tissue within a mere 24 -minute gradient. Our fast proteomics analysis of biofluids such as whole blood, naive, and depleted plasma demonstrates high-quality quantitative measurements across a wide dynamic range. Notably, our results demonstrate that CVs perform exceptionally well at short gradients, enabling high-throughput analyses with an impressive precision. These capabilities open new opportunities in biomarker discovery and drug development. In summary, the study’s findings pave the way for more efficient and comprehensive proteomic analyses across diverse sample types, with potential implications for advancing medical research and therapeutic innovations.

Data Availability Statement

Data is made available on ProteomeXchange data set PXD054015.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00384.Table S1: Covaris instrument parameter sets of A and B. Table S2: MS and LC gradient parameters. Figure S1: HeLa proteome coverage by copies per cell. Table S3: Data points per peak across different methods. Figure S2: Pathway enrichment seen in proteins exclusive to different depletion methods. Table S4: “Missing” proteins with protein existence level >1 as Identified in Astral data. Figure S3: Automation deck layout. Data analysis of PBMCs for pathway enrichment. Downstream Analysis with Excel and R (PDF)

Supplementary Material

pr4c00384_si_001.pdf

Author Contributions

§̂ SMM and JVE are shared senior author.

The authors declare no competing financial interest.

Acknowledgments

We acknowledge Lior Zilberberg for providing mouse tissues, Christopher Hughes, Sandi Spencer Miko, and Gregg Morin for insightful discussions on troubleshooting the SP3 protocol, Tony Herren for providing Thermo’s prototype filter plates for plasma depletion, and Sameer Vasantgadkar for all the help with Covaris instrumentation setup, discussions, and suggestions for protocol improvements of cell lysis procedure on Covaris. NIH R01HL111362 (JVE), U01 DK124019-01 (JVE) and R01 HL155346-01A1 (JVE), Erika Glazer Endowed Chair (JVE), and Cedars-Sinai Academic Affairs, Proteomics and Metabolomic core and Precision Health.
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