An optimized SP3 sample processing workflow for in-depth and reproducible phosphoproteomics

An optimized SP3 sample processing workflow for in-depth and reproducible phosphoproteomics | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results An optimized SP3 sample processing workflow for in-depth and reproducible phosphoproteomics View ORCID Profile Leonard A. Daly , View ORCID Profile Christopher J. Clarke , View ORCID Profile Sally O. Oswald , View ORCID Profile Andris Jankevics , View ORCID Profile Philip J. Brownridge , View ORCID Profile Richard A. Scheltema , View ORCID Profile Claire E. Eyers doi: https://doi.org/10.1101/2025.03.13.643088 Leonard A. Daly 1 Centre for Proteome Research, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool, L69 7ZB, U.K 2 Department of Biochemistry & Systems Biology, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool L69 7ZB, U.K Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leonard A. Daly For correspondence: ceyers{at}liverpool.ac.uk leonard.daly{at}liverpool.ac.uk Christopher J. Clarke 1 Centre for Proteome Research, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool, L69 7ZB, U.K 2 Department of Biochemistry & Systems Biology, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool L69 7ZB, U.K Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher J. Clarke Sally O. Oswald 1 Centre for Proteome Research, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool, L69 7ZB, U.K 2 Department of Biochemistry & Systems Biology, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool L69 7ZB, U.K Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sally O. Oswald Andris Jankevics 1 Centre for Proteome Research, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool, L69 7ZB, U.K Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andris Jankevics Philip J. Brownridge 1 Centre for Proteome Research, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool, L69 7ZB, U.K Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philip J. Brownridge Richard A. Scheltema 1 Centre for Proteome Research, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool, L69 7ZB, U.K 2 Department of Biochemistry & Systems Biology, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool L69 7ZB, U.K Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard A. Scheltema Claire E. Eyers 1 Centre for Proteome Research, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool, L69 7ZB, U.K 2 Department of Biochemistry & Systems Biology, Institute of Systems, Molecular & Integrative Biology, University of Liverpool , Crown Street, Liverpool L69 7ZB, U.K Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Claire E. Eyers For correspondence: ceyers{at}liverpool.ac.uk leonard.daly{at}liverpool.ac.uk Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Protein phosphorylation is a ubiquitous post-translational modification (PTM) found across the kingdoms of life, and is critical for the regulation of protein function in health & disease. Advances in high-throughput mass spectrometry have transformed our ability to interrogate the phosphoproteome. However, sample preparation methodologies optimized for phosphoproteomics have not kept pace, compromising the ability to fully exploit these technological advances. In this study, we present an optimized phosphoproteomics workflow using carboxylated SP3 magnetic beads which have simplified proteomics sample preparation. By employing a washing step with 8 M urea and omitting the conventional C 18 SPE clean-up, we demonstrate a significant improvement in phosphopeptide identifications, with application of this refined protocol to HEK-293T cell extracts increasing the number nearly 2-fold compared to standard SP3 techniques (7908 cf . 4129). We also observed a substantial improvement in the detection of multiply phosphorylated peptides. Our findings suggest that the complexity of PTM crosstalk using current peptide-based proteomics workflows is currently under-represented and underscores the necessity of methodological innovations to better capture the intricacies of the phosphoproteome landscape. Introduction Protein phosphorylation is one of the most abundant post-translational modifications (PTMs) with estimates suggesting that ∼90% of human proteins can be phosphorylated in vivo at some point in their life cycle 1 . The diverse and critical physiological roles regulated by protein phosphorylation in both a protein-dependent and site-dependent manner means that the phosphoproteome (the phosphorylated protein landscape) has been extensively investigated 2 - 4 . Continuing advancements in mass spectrometry (MS) and associated (phospho)proteomics analysis techniques have been a cornerstone for interrogation of phos-phorylation-driven signaling networks, with workflows now capable of identifying >10,000 sites in a single experiment 5 - 9 . As newer technologies (instruments and/or software) become available, a wave of studies follow, each claiming to have greater (phospho)peptide identification rates. However, the advancements afforded by new technologies remain fundamentally dependent on the quality of the samples that are analyzed. Over the past decade, the development of magnetic carboxylated sera-mag speedbeads (SP3 beads) has been a game changer for proteomics pipelines 10 , 11 . Their application has facilitated rapid preparation of even the most difficult of samples that have been historically problematic to couple with MS-based workflows in the absence of extensive sample clean-up, which can be both time- and sample-consuming 12 - 16 . Following commercialization of SP3 beads, a multitude of reports claiming to improve proteome coverage by altering all steps in the sample processing workflow have been published 10 , 11 , 14 , 17 - 22 . However, conflicting ‘optimal’ conditions have been reported leading to disparity in the field. As a specialized branch of proteomics, phosphoproteomics typically requires adapted protocols for optimal sample preparation, consequently few phosphoproteome-specific SP3 studies have been reported 8 , 9 , 20 , 23 - 26 . Here, we benchmark SP3 bead technology for phosphoproteomics sample preparation against well-established in-solution processing pipelines, methodically optimizing for improved phosphopeptide coverage. Our final workflow negates the need for the inclusion of protease and phosphatase inhibitors, precipitates in 80% ethanol, includes an additional 8 M urea ‘eluting’ step post-digest and removes the generally accepted C 18 stagetip clean-up step. This optimized phosphoproteomics pipeline improves both the reproducibility and total phosphopeptide numbers, with nearly a 2-fold improvement in phosphopeptides identified in 2 out of 3 replicates. Moreover, we observe a substantial increase in multiply phosphorylated peptides, with ∼6.7- and ∼42.5-fold more doubly and triply phosphopeptides identified. From a biological standpoint, our findings suggest that current understanding of the phosphoproteome is under-represented with regard to multiply phosphorylated peptides and clustering of phosphorylation sites. Implementation of our new workflow could thus have substantial benefit in advancing understanding of phosphorylation-driven signaling networks, phosphorylation-based cross-talk and total phosphorylation site clustering, particularly when combined with automation and new MS instrumentation and workflows with ever increasing sensitivity. Experimental section Reagents Powdered chemical reagents were purchased from Merck and high-performance liquid chromatography (HPLC) grade solvents from ThermoFisher Scientific. All Eppendorf tubes used were ultra-high recovery Eppendorf tubes (STARLAB). Sera-Mag Carboxyl SpeedBeads (SP3) were purchased from Cytivia. Rapigest was purchased from WATERS. Cell culture and sample preparation Adherent HEK-293T cells were seeded at a density of ∼1.75×10 4 cells/cm 2 in DMEM media supplemented with 10% (v/v) fetal calf serum, 1% (v/v) non-essential amino acids and 1% (v/v) penicillin/streptomycin and maintained at 37 ° C, 5% CO 2 until ∼80% confluent (∼5×10 6 cells/10 cm plate). Cells were washed twice in phosphate buffered saline (PBS) before lysis in 500 μL of stated buffer. All in-solution digest lysis buffers (1% (v/v) Rapigest; 8 M urea; 6 M Guanidine hydrochloride (GuHCl)) were constituted in 100 mM ammonium bicarbonate pH 8 (AmBic). Samples for SP3 processing were lysed using a detergent-based lysis buffer (1% Nonidet P-40, 1% sodium deoxycholate, 1% SDS, 500 mM NaCl, 5 mM EDTA, 100 mM Tris pH 8) ± 1x cOmplete protease inhibitor -EDTA complex (Roche) and phosSTOP (Roche) as stated. Protein content was determined by BCA assay and lysates diluted to 1 μg/μL in relevant lysis buffer prior to reduction and alkylation with dithiothreitol and iodoacetamide 5 . For in-solution digests, samples (250 μg) were diluted 10-fold in 100 mM AmBic pH 8, and Trypsin Gold (Promega) added at a 50:1 (w/w) protein:trypsin ratio and incubated overnight (37 ° C with 600 rpm shaking). For SP3 bead-based digests, a 1:1 mixture of magnetic hydrophobic and hydrophilic Seramag-speedbeads (Cytivia) were added at a 1.2:1 (w/w) ratio of beads:protein prior to adding organic solvent (ethanol (EtOH) or acetonitrile (ACN), as stated) to a final concentration of 80% (v/v). Samples were then incubated at 25 ° C with 1500 rpm shaking for 30 min. Using a MagRack, the supernatant was discarded and beads washed 3x in 200 μl of 100% of relevant organic solvent. Beads were dried by vacuum centrifugation for 10 min and resuspended in 200 μL of 100 mM AmBic by water bath sonication for 2 min. Trypsin Gold (Promega) was added at a 50:1 (w/w) protein:trypsin ratio, samples were topped up to 220 μL with AmBic and incubated overnight at 37 °C with 1500 rpm shaking. The peptide-containing supernatant was transferred into a fresh tube and, where stated, the remaining beads were washed with a 10:1 (w/v) ratio of beads:stated ‘elution’ solution (300 μg:30 μl, 1% Rapigest, 8 M Urea or 6 M GuHCl, in 100 mM AmBic pH 8) for 30 min with 1500 rpm shaking at 25 °C, before the supernatants were pooled with the corresponding digested material. Trifluoroacetic acid (TFA) was added to a final concentration of 0.5% (v/v) and incubated for 30 min with 1500 rpm shaking at 37 °C followed by 30 min on ice prior to centrifugation (13,000 g , 10 min, 4 °C). Clarified supernatants were collected into fresh low-bind tubes, with some being subjected C 18 stage-tip clean-up. All samples were split 1:99 for analysis of all peptides or titanium dioxide (TiO 2 ) enriched phosphopeptide, respectively, prior to vacuum centrifugation or lyophilization to completion, as stated. C18 stage-tip clean-up Samples were subjected to in-house C 18 stage-tip (Empore Supelco 47 mm C 18 extraction discs) clean-up prior to LC-MS/MS analysis 27 . Briefly, 3 C 18 discs were used per 200 μL tip and centrifuged (5000 g , 5 min). Stage-tips were equilibrated by sequential washing with 200 μL of methanol, elution solution (75% ACN, 0.1% TFA in water) and wash solution (0.1% TFA in water) prior to loading peptide samples, centrifuging at 4000 g for 2 min (or until all liquid had passed through the tip) at room temperature. Flow through was re-applied before washing in 200 μL of wash solution and eluting in 200 μL elution solution. Eluted peptides were dried to completion by vacuum centrifugation. Titanium dioxide enrichment TiO 2 enrichment was performed as previously described 5 . Briefly, dried peptides were resuspended in TiO 2 loading solution (80% ACN, 5% TFA, 1M glycolic acid) to a concentration of 1 μg/μl by water bath sonication for 10 min. For post-digest elution optimizations, resuspended peptides were incubated on ice for 30 min prior to centrifugation at 13000 g for 10 min at 4 °C and cleared supernatant collected. TiO 2 resin (GL-Sciences) was added at 5:1 (w/w) TiO 2 resin:peptide and incubated at 25 ° C with 1500 rpm shaking for 30 min before centrifugation at 2000 g for 1 min (room temperature) and supernatant removed. TiO 2 resin-peptide complexes were sequentially washed in 200 μl of TiO 2 loading solution, solution 1 (80% ACN, 1% TFA in water) and solution 2 (20% ACN, 0.1% TFA in water) prior to vacuum centrifugation for 15 min. Phosphopeptides were eluted in 200 μl of 5% (v/v) ammonium hydroxide in water and shaking at 1500 rpm for 10 min. Samples were centrifuged as before, supernatants collected and dried to completion by vacuum centrifugation. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis All dried peptides were resuspended in MS loading solution (3% ACN, 0.1% TFA in water) by water bath sonication for 10 min, centrifuged (13000 g , 4 ° C, 10 min) and the cleared supernatant collected. Samples were separated by reversed-phase HPLC over 90 min gradients using an Ultimate 3000 nano system (Dionex) 5 . Data acquisition was performed using a Thermo Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientific) using 32% NCE HCD for 2+ to 5+ charge states over a m/z range of 400-1500. MS1 spectra were acquired in the Orbitrap [60K resolution at 200 m/z ], normalized automatic gain control (AGC) = 50%, maximum injection time = 50 ms and an intensity threshold for fragmentation = 2.5e 4 . For total peptide data, MS2 spectra were acquired in the ion trap set to rapid mode, AGC target = standard, maximum injection time = 35 ms. For phosphopeptide data, MS2 spectra were acquired in the Orbitrap [30K resolution at 200 m/z], AGC target = standard, maximum injection time = dynamic. A dynamic exclusion window was applied to both MS2 approaches for 30 s with a 10 ppm window. Equal volume injections were performed for every experiment. Mass spectrometry data analysis Data was processed with Proteome Discoverer 2.4 (Thermo Scientific) using the MASCOT search engine, searching against the UniProt Human Reviewed database [accessed December 2023]. All data was searched using trypsin (K/R, unless followed by P) with 2 missed cleaves permitted with constant modifications = carbamidomethylation (C) and variable modification = oxidation (M). For phosphopeptide data, the variable modification = phosphorylation (STY) was also included. Label-free quantification was performed using the Minora feature detector node, calculating the area under the curve for m/z values. All MS1 mass tolerances = 10 ppm, total peptide MS2 mass tolerance = 0.5 Da, phosphopeptide MS2 mass tolerance = 0.01 Da. All data were filtered to 1% FDR with the percolator node with standard settings applied. Peptides without a label free quantification measurement in at least 1 replicate of a study were removed from calculations. Non-phosphopeptides were removed from the calculations for phosphopeptide data, unless stated otherwise. Results SP3 bead-based protocols identify more phosphopeptides with greater consistency than traditional in-solution digest protocols Multiple publications have reported the benefits of SP3 bead technology to enhance proteomics analysis, primarily due to its versatility and adaptability to different sample lysis conditions, ease of use, and compatibility with automated (robotic) sample processing 10 , 11 , 14 , 17 - 22 . While a number of these studies have been optimized with respect to total peptide identification numbers, these improvements have typically been modest, and few have considered the effect on phosphopeptide recovery. Primarily these focus on protein:bead ratio 18 , lysis buffer and precipitation conditions 20 or Eppendorf tube pre-labelling 9 . In agreement with previous studies, our comparison of well-established in-solution sample preparation workflows with the original SP3 protocol employing a detergent-based lysis buffer 10 , demonstrated the benefits of detergent lysis and SP3 processing ( Figure 1 ). This benefit was particularly relevant with respect to Rapigest lysis, given our observations of a >40% increase in identified peptides (≥2 replicates). Interestingly, we also observed notable improvement in the numbers and reproducibility of identification of phosphopeptides using this SP3 workflow, with an increase in phosphopeptide identification of ∼3-fold compared with Rapigest lysis, and over 2-fold compared with either urea or GuHCl. Given these obvious benefits, and the fact that this SP3 workflow was not originally designed with phosphoproteomics in mind, we set out to see if we could further improve this SP3 workflow for phosphopeptide recovery. Download figure Open in new tab Figure 1: Percentage of total- and phospho-peptides identified using different cell lysis methods. Cells were lysed using the stated condition. Peptides and phospho peptides (after TiO 2 enrichment) were analyzed by LC-MS/MS. Reported are the numbers of (phospho)peptides identified for each condition (in 1, 2 or 3 replicates (N=3)) as a proportion of the numbers of (phospho)peptides identified across all conditions. Ethanol-based precipitation is better for phosphoproteomics The most controversial step in the SP3 protocol is the precipitation stage: early publications used 50% ACN + 0.1% formic acid 10 , while successive optimizations have suggested that precipitation at neutral pH 14 , 17 , 20 , 22 , ethanol (EtOH) 11 , 17 , 20 , 22 , different concentrations of organic solvent 18 , 20 , 22 , phosphatase/protease inhibitors 10 , 20 , SP3 bead:protein (w/w) ratios 18 , 22 or even bead type 21 can improve the numbers of peptides identified. However, several groups report conflicting data on their effectiveness. To simplify workflow optimization, we initially evaluated the ratio of SP3 beads to protein (w/w), and the final concentration of ACN. In agreement with Dagley et al 18 , we observed that too high (10:1) a bead:protein (w/w) ratio, or a low final concentration (50%) of ACN decreased the numbers of peptides identified (data not shown). As such, we used a 1.2:1 bead:protein ratio (as previously optimized Dagley et al 18 ), and 80% ACN as a basis for the next steps. We then evaluated the effect of organic solvent, using EtOH as a replacement for ACN, and a reduction in pH by inclusion of TFA ( Figure 2 ). Download figure Open in new tab Figure 2: Optimization of SP3 lysis and precipitation conditions. Different precipitation solutions were used as stated for lysates ± complete protease inhibitors and phosSTOP. A total of 17,336 total- and 5313 phospho-peptides were identified and used for normalization respectively. A) Percentage of the total number of total- and phospho-peptides identified across precipitation conditions per replicate. B) Percentage of the total number of peptides identified which contain tryptic miscleaves for total peptide data across precipitation conditions per replicate (0x = 14,668. 1x = 2,398. 2x = 270). C) Percentage of the number of phosphorylation events identified on a peptide across precipitation conditions per replicate (1x = 3,891. 2x = 1,094. 3x = 251). The number of peptides identified was generally consistent across all conditions, with 52-55% of the 17,336 peptides identified across all conditions being observed in at least 2 replicates of the same condition ( Fig 2A ). The exception, 80% EtOH + 0.1% TFA, only contained 44% of the peptide complement, supporting observations reported by Moggridge et al 17 & Leutert et al 20 . Replacing ACN with EtOH had little discernible effect on the numbers of peptides or proteins identified ( Fig 2 , Supp. Fig 1). For phosphopeptides, greater variance was observed across the different conditions, with 47-59% (of the 5,313 phos-phopeptides identified in total for this experiment, consistent with the expectations of this LC-MS/MS workflow employing a Thermo Fusion Tribrid) being observed (≥2 replicates) dependent on the condition. Notably, a lower pH was generally detrimental, irrespective of whether ACN or EtOH was used ( Fig 2A ), with exclusion of TFA increasing phosphopeptide numbers by 41%. Opinions also differ on the benefits of including protease and phosphatase inhibitors during lysis for phosphopeptide work-flows. While most pipelines use these as standard 2 , 5 , 6 , 9 , 28 - 33 , Leutert et al . reported a notable reduction in the efficiency of digestion, and a >5-fold decrease in peptide identifications in an SP3-enabled pipeline where phosphatase inhibitors (50 mM sodium fluoride, 10 mM sodium pyrophosphate, 50 mM beta-glycerophosphate, 1 mM sodium orthovanadate) and protease inhibitors (protease inhibitor mix, Pierce) were included during lysis 20 . In our hands, inclusion of phosSTOP (Roche) and complete protease inhibitors (Roche) had a limited effect on the number of peptides with a missed cleavage identified, with <5% difference being observed upon inclusion ( Fig 2B ). Interestingly, although the presence of inhibitors increased or maintained consistency of identification at the peptide level (irrespective of condition) their inclusion was differentially effective for phosphopeptides, dependent on the pH of the solution ( Fig 2A ). SP3 sample processing conditions also had a noticeable effect on the number of multiply phosphorylated peptides identified ( Fig 2C ). For example, there was a ∼20% increase in the number of doubly (2x) and triply (3x) phosphorylated peptides when inhibitors were excluded from the 80% EtOH workflow, with SDS-based lysis conditions that anyway typically render protein kinases inactive 34 . Taken in combination, our data demonstrates that using an SDS-lysis buffer lacking protease/phosphatase inhibitors and precipitating using neutral pH 80% EtOH is optimal for both the number and reproducibility of phosphopeptide identifications, particularly with regard to multiply phosphorylated peptides ( Fig 2 ). Removal of SPE clean-up and inclusion of a SP3 ‘elution’ markedly improves phosphopeptide identifications We have previously observed that standard SP3-based processing of purified α/β-casein tryptic peptides yields fewer phosphopeptides compared to in-solution digests of the same material (data not shown). This supports observations by others that some peptides are not completely eluted from the SP3 bead surface post-digestion in aqueous solutions. Reports suggest that washing in SDS, followed by C 18 SPE clean-up, can recover an additional 16% median signal intensity 20 , 25 . However, the more hydrophilic nature of phosphopeptides 27 , 35 , 36 means that they will likely be subject to greater losses during this reversed-phase clean-up step. We therefore wanted to examine the effects (and necessity) of a C 18 SPE step post SP3 digest, and whether post-digest elution influences the identifiable phosphoproteome. To identify buffers capable of eluting proteins (and presumably peptides) from the surface of SP3 beads, HEK-293T cell lysate precipitated onto SP3 beads was resuspended in a 10:1 (w/v) bead : eluent ratio of either 50 mM AmBic, 1% Rapigest, 8 M urea or 6 M GuHCl, and elutions analyzed by SDS-PAGE alongside an equal protein quantity of non-precipitated lysate (Supp. Fig 2). In-line with Batth et al 25 , we observed that aqueous solution (50 mM AmBic) was an inefficient protein eluent. However, all other elution conditions performed equally well and were on par with the non-precipitated lysate, confirming efficient elution. Unlike samples in 8 M urea or 6 M GuHCl (which require C 18 SPE clean-up prior to LC-MS/MS), AmBic and Rapigest samples are LC-MS/MS compatible (post acidification and centrifugal clarification) and can forego this additional sample preparation step. To evaluate the effect and necessity of C 18 SPE clean-up across elution conditions, we thus analyzed the pooled digest (combined supernatant and elution) by LC-MS/MS with or without C 18 processing ( Fig 3 ). Download figure Open in new tab Figure 3: SP3 beads preferentially retain phosphopeptides post digest. Different solutions were used to elute bound peptides from SP3 beads post digest and collection of peptides. A total of 23,817 total- and 5,124 phospho-peptides were identified and used for normalization respectively. A) Percentage of the total number of total- and phospho-peptides identified across different elution solutions per replicate. B) Percentage of the multiply phosphorylated peptides identified across different elution solutions per replicate (1x = 3,844. 2x = 1,051. 3x = 201). C) UpSet plot of phosphopeptides identified across elution solutions and replicates. D) Normalized average abundance of total- and phospho-peptides observed in all replicates of each condition versus 50 mM AmBic +C18 (4,206 and 869 respectively). At the peptide level, there was little observable difference across any of the elution conditions, with or without C 18 SPE processing, with ∼50% of all observable peptides being identified in ≥2 replicates (of the ∼23,820 peptides identified across all samples in this experiment, Fig. 3A ). Elution conditions and sample clean-up had a much greater effect on phosphopeptide numbers. Considering C 18 -processed samples in the first instance, 8 M urea elution exhibited the best recovery with ∼31% more phos-phopeptides identified (≥2 replicates) versus AmBic processing ( Fig 3A ). Even greater improvements were observed when C 18 SPE clean-up was omitted, with >20% more phosphopeptides being identified for AmBic -C 18 (≥2 replicates) compared to 8 M urea +C 18 ( Fig 3A ). These results were more pronounced for multiply phosphorylated peptides, with ∼1.5, ∼1.8 and ∼2.2-fold increases in singly, doubly and triply phosphorylated peptides, respectively, identified with 8 M urea +C 18 compared with AmBic +C 18 (≥2 replicates, Fig 3B ). Similarly, omission of C 18 clean-up from AmBic samples increased observations of singly, doubly and triply phosphorylated peptides by ∼1.3, ∼3.7 and ∼7.3-fold respectively (≥2 replicates). Delving further into the overlap between conditions demonstrates that the biggest cohort of condition-specific phosphopeptide identifications are due to omission of this C 18 desalting clean-up ( Fig 3C ), accounting for 368 (∼17%) of observed phosphopeptides across all replicates, a combined improvement of ∼42%. Finally, label-free quantification (LFQ) confirmed that the increase in phosphopeptide numbers is due to a substantial increase in phosphopeptide recovery ( Fig 3D ); compared with AmBic +C 18 , there was a ∼2-fold increase in phosphopeptide abundance for all elution +C 18 SPE treated samples whereas exclusion of the C 18 SPE step increased the average phosphopeptide abundance by ∼12-fold (equivalent to a 92% loss upon its inclusion, Fig 3D ). A similar, but less substantive effect was observed with the non-enriched peptide samples, with omission of the C 18 clean-up step yielding up to ∼4-fold increase in average abundance of all peptides ( Fig 3D ). Given this data, and considering the inexpensive nature of urea, we posited that a phosphoproteomics pipeline that used 8 M urea elution but circumvented C 18 clean-up could be beneficial, particularly for multiply phosphorylated peptides. SP3 digest with urea elution improves identification rates, consistency and enrichment efficiency, particularly for multiply phosphorylated peptides Although urea-based cell lysis buffers are often used for (phos-pho)proteomics, they incorporate some form of sample cleanup for salt removal, typically either protein precipitation prior to digestion, or C 18 SPE (or equivalent) prior to downstream processing or LC-MS analysis 28 , 37 - 39 . Building on our previous findings, we sought to develop a phosphoproteomics workflow that negated the need for such lossy clean-up steps but permitted urea removal prior to phosphopeptide enrichment. Chemically, urea is highly insoluble in ACN 40 , the major constituent of phosphopeptide enrichment buffers (80% ACN in our case), and is volatile. Therefore, we postulated that an evaporative technique (vacuum centrifugation or lyophilization) post digest, followed by ACN-based TiO 2 phosphopeptide enrichment may overcome any detrimental effects resulting from urea. We therefore evaluated the utility of either vacuum centrifugation or lyophilization in our SP3 urea elution-based workflow prior to phosphopeptide enrichment. Pooled samples (SP3 digest and urea bead elution) were diluted 4-fold to a final concentration of ∼0.25 M urea, prior to either: i) C 18 SPE, ii) vacuum centrifugation (as performed for AmBic and Rapigest eluted samples), iii) lyophilization, or iv) 3 rounds of lyophilization (resuspending in 1 ml water between rounds). These four conditions were compared to our previously optimized 50 mM AmBic and 1% Rapigest pipelines ( Fig 4 ). Download figure Open in new tab Figure 4: 8 M Urea elution post SP3 digest improves phosphopeptide identification rates and TiO 2 enrichment factors. Different elution solutions and clean-up strategies were used to elute bound peptides from SP3 beads post digest, TiO 2 data only. A total of 10,508 phospho- and 17,501 non-phospho-peptides were identified and used for normalization respectively. A) Percentage of the total number of phospho- and non-phospho peptides identified post TiO 2 enrichment across different elution and clean-up strategies per replicate. B) Normalized abundance-based enrichment factor. Average abundance of phosphopeptides observed in all replicates by average abundance of non-phosphopeptides observed in all replicates (1,804 and 682 respectively) normalized against 50 mM AmBic +C 18 . C) Percentage of the multiply phosphorylated peptides identified across different elution solutions per replicate (1x = 8,192. 2x = 1,992. 3x = 299). Similar to our previous observations, upon removal of the C 18 SPE clean-up step for the AmBic and Rapigest samples ( Fig 3A ), all non-SPE-based methods improved phosphopeptide identifications ( Fig 4A ). Promisingly, all the urea desalting strategies that circumvented C 18 SPE outperformed our previous ‘best’ methods (AmBic, -C 18 ) yielding a 6-8% increase (∼430-490 peptides) in the numbers of phosphopeptides identified (≥2 replicates, Fig 4A ), with no substantial difference being observed dependent on the evaporative process used (Supp Fig 3A). While there were slight gains in the numbers of multiply phosphorylated peptides with a single lyophilization step ( Fig 4C ), it is worth noting that vacuum centrifugation benefits from speed. An additional benefit of evaporative urea removal was the improved TiO 2 -based phosphopeptide enrichment. Phosphopeptides accounted for only ∼30% of the total peptides identified using the ‘standard’ AmBic +C 18 TiO 2 workflow (10,069 non-phosphorylated peptides cf 4,129 phosphopeptides, observed in ≥2 replicates), increasing to >50% upon removal of C 18 cleanup (6,319 non-phosphorylated peptides cf 7,418 phosphopeptides, observed in ≥2 replicates). Using urea + lyophilization improved phosphopeptide enrichment over 3-fold compared with the standard workflow (2,345 non-phosphorylated peptides cf 7,850 phosphopeptides, observed in ≥2 replicates i . e . 77% enrichment; Fig 4A/B ). UpSet plots and LFQ analysis (Supp. Fig 3) further highlight the efficiency of our urea elution strategy, revealing a bias of C 18 SPE for preferential selection of non-phosphopeptides (Supp. Fig 3A/B). Indeed, the relative phosphopeptide abundance increases by >18-fold for those workflows that do not include a C 18 SPE step (equivalent to a ∼94% loss). Finally, and in-line with our observations with both AmBic and Rapigest in the absence of C 18 SPE ( Fig 3D ), we observed substantial improvement in the identification of multiply phosphorylated peptides, in particular for the three urea-based evaporative workflows. All three were similarly beneficial, resulting in ∼1.6-, ∼6- and ∼36-fold increases in singly, doubly and triply phosphorylated peptides respectively (≥2 replicates), compared with the standard AmBic +C 18 method ( Fig 4C ). Overall, our updated sample preparation workflow demonstrates substantive benefits for phosphopeptide analysis, improving sensitivity, reproducibility, throughput and sustainability given the faster workflow and reduced consumables requirements. Conclusions In considering the disparate published pipelines for (phos-pho)proteomics sample processing, we evaluated and optimized in-solution and SP3-based digestion workflows, focusing on the number of (phospho)peptides identified, and the reproducibility of identification. As previously reported, we observed (marginally) better data firstly when cells were lysed in an SDS-based lysis buffer lacking protease and phosphatase inhibitors, and secondly when ethanol rather than ACN was used for SP3 precipitation. However, unlike previous studies, we did not observe any notable increase in the rate of trypsin missed cleavages when inhibitors were omitted, and we optimally used a higher concentration of ethanol 11 , 17 , 20 . We therefore recommend that for these types of cell-based (phospho)proteomics investigations that inhibitors be excluded for the sake of data quality and cost. However, it is worth noting that the levels of cellular proteases are much higher in some other biological/tissue samples (e.g. pancreas, liver) and as such their omission should be re-evaluated in context. The single biggest improvement that we identified was removal of the routine C 18 SPE stage-tip sample clean up step 17 , 18 , 20 , 23 - 25 , which resulted in a dramatic ∼12-fold increase in phospho-peptide abundance and ∼1.6-fold more phosphopeptides being identified, with relatively small differences in the numbers of non-phosphorylated peptides. We hypothesize that this is due to the increased hydrophilicity of phosphopeptides cf non-phos-phorylated peptides 27 , 35 , 36 , hence loss of these are preferentially exacerbated during this SPE step. The second biggest improvement was post-digest washing of SP3 beads in 8 M urea. Interestingly, our data shows minimal benefit for total peptide identifications but increased the identified phosphoproteome >1.3-fold, suggesting preferential binding of these typically more hydrophilic and acidic peptides to these carboxylated SP3 beads. The final protocol, which included evaporative removal (lyophilization or vacuum centrifugation) and precipitation of the urea eluent post-digest, prior to TiO 2 phosphopeptide enrichment increased phosphopeptide identification rates by nearly 2-fold (≥2 replicates) compared with current ‘standard’ SP3 protocols. This substantial improvement in phosphopeptide identifications is likely in part due to the greater phosphopeptide enrichment afforded with this urea-based workflow. Overall, our data shows that SP3 bead elution with 8 M urea followed by non-C 18 -based salt removal substantially improves phosphopeptide recovery and thus the reproducibility and numbers identified. The amendments described here are readily adaptable to robotics coupling, which is a major advantage of SP3 technology. Biologically, our data undoubtably raises questions regarding our current understanding of proximal phosphorylation crosstalk, given the marked increase in the identification of doubly and triply phosphorylated peptides, equivalent to >6- and >36-fold more versus typical SP3 protocols. Additionally, while not considered here, it is important to state that our optimized pipeline (up to TiO 2 enrichment) is pH neutral and performed at room temperature, and is therefore compatible with the preparation of samples for the analysis of atypical sites of phosphorylation (His, Lys, Arg, Cys, Asp, Glu 28 , 41 , 42 ) and may be relevant for the analysis of other acidic PTMs including sulfation 27 . ASSOCIATED CONTENT AUTHOR INFORMATION Author Contributions L.A.D. performed all sample preparation, enrichment and mass spectrometry analysis, contributed to design of experiments and manuscript writing. C.J.C. and S.O.O. contributed to sample preparation. A.J. and R.A.S. supported data analysis pipelines. P.J.B. supported mass spectrometry methods development. C.E.E. contributed to design of experiments, analysis of data, and manuscript writing. The manuscript was written by L.A.D. and C.E.E. All authors have given approval to the final version of the manuscript. Notes The authors declare no competing financial interest. All MS data has been deposited at the ProteomeXchange Consortium ( http://proteomecentral.proteomexchange.org ) via the PRIDE partner repository with the data set identifier PXD061013. Username: reviewer_pxd061013{at}ebi.ac.uk. Password: j44E8kW6v9iU. Download figure Open in new tab Table of Contents ACKNOWLEDGMENTS This work is supported by funding from the Biotechnology and Biosciences Research Council (BBSRC; BB/S018514/1, BB/M012557/1, BB/S017054/1, BB/R000182/1, BB/X002780/1 and BB/W00349X/1) and the Royal Society Wolfson Foundation (RSWF\R2\232006). REFERENCES 1. ↵ Ardito , F. ; Giuliani , M. ; Perrone , D. ; Troiano , G. ; Lo Muzio , L., The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy (Review) . Int J Mol Med 2017 , 40 ( 2 ), 271 – 280 . OpenUrl CrossRef PubMed 2. ↵ Daly , L. A. ; Brownridge , P. J. ; Batie , M. ; Rocha , S. ; Sée , V. ; Eyers , C. E. , Oxygen-dependent changes in binding partners and post-translational modifications regulate the abundance and activity of HIF-1α/2α . Sci Signal 2021 , 14 ( 692 ). 3. Vonderach , M. ; Byrne , D. P. ; Barran , P. E. ; Eyers , P. A. ; Eyers , C. E. , DNA Binding and Phosphorylation Regulate the Core Structure of the NF-κB p50 Transcription Factor . J Am Soc Mass Spectrom 2019 , 30 ( 1 ), 128 – 138 . OpenUrl CrossRef PubMed 4. ↵ Lanucara , F. ; Lam , C. ; Mann , J. ; Monie , T. P. ; Colombo , S. A. ; Holman , S. W. ; Boyd , J. ; Dange , M. C. ; Mann , D. A. ; White , M. R. ; Eyers , C. E. , Dynamic phosphorylation of RelA on Ser42 and Ser45 in response to TNFα stimulation regulates DNA binding and transcription . Open Biol 2016 , 6 ( 7 ). 5. ↵ Ferries , S. ; Perkins , S. ; Brownridge , P. J. ; Campbell , A. ; Eyers , P. A. ; Jones , A. R. ; Eyers , C. E. , Evaluation of Parameters for Confident Phosphorylation Site Localization Using an Orbitrap Fusion Tribrid Mass Spectrometer . J Proteome Res 2017 , 16 ( 9 ), 3448 – 3459 . OpenUrl CrossRef PubMed 6. ↵ Frese , C. K. ; Zhou , H. ; Taus , T. ; Altelaar , A. F. ; Mechtler , K. ; Heck , A. J. ; Mohammed , S. , Unambiguous phosphosite localization using electron-transfer/higher-energy collision dissociation (EThcD) . J Proteome Res 2013 , 12 ( 3 ), 1520 – 5 . OpenUrl CrossRef PubMed Web of Science 7. Urban , J. , A review on recent trends in the phosphoproteomics workflow. From sample preparation to data analysis . Analytica Chimica Acta 2022 , 1199 , 338857 . OpenUrl CrossRef PubMed 8. ↵ Kverneland , A. H. ; Harking , F. ; Vej-Nielsen , J. M. ; Huusfeldt , M. ; Bekker-Jensen , D. B. ; Svane , I. M. ; Bache , N. ; Olsen , J. V. , Fully Automated Workflow for Integrated Sample Digestion and Evotip Loading Enabling High-Throughput Clinical Proteomics . Mol Cell Proteomics 2024 , 23 ( 7 ), 100790 . OpenUrl CrossRef PubMed 9. ↵ Muneer , G. ; Chen , C.-S. ; Lee , T.-T. ; Chen , B.-Y. ; Chen , Y.-J. , A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics . Journal of Proteome Research 2024 , 23 ( 8 ), 3294 – 3309 . OpenUrl CrossRef PubMed 10. ↵ Hughes , C. S. ; Foehr , S. ; Garfield , D. A. ; Furlong , E. E. ; Steinmetz , L. M. ; Krijgsveld , J. , Ultrasensitive proteome analysis using paramagnetic bead technology . Molecular Systems Biology 2014 , 10 ( 10 ), 757 . OpenUrl Abstract / FREE Full Text 11. ↵ Hughes , C. S. ; Moggridge , S. ; Müller , T. ; Sorensen , P. H. ; Morin , G. B. ; Krijgsveld , J. , Single-pot, solid-phase-enhanced sample preparation for proteomics experiments . Nat Protoc 2019 , 14 ( 1 ), 68 – 85 . OpenUrl CrossRef PubMed 12. ↵ Mikulášek , K. ; Konečná , H. ; Potěšil , D. ; Holánková , R. ; Havliš , J. ; Zdráhal , Z. , SP3 Protocol for Proteomic Plant Sample Preparation Prior LC-MS/MS . Frontiers in Plant Science 2021 , 12 . 13. Brajkovic , S. ; Rugen , N. ; Agius , C. ; Berner , N. ; Eckert , S. ; Sakhteman , A. ; Schwechheimer , C. ; Kuster , B. , Getting Ready for Large-Scale Proteomics in Crop Plants . Nutrients 2023 , 15 ( 3 ), 783 . OpenUrl CrossRef PubMed 14. ↵ Hughes , C. S. ; McConechy , M. K. ; Cochrane , D. R. ; Nazeran , T. ; Karnezis , A. N. ; Huntsman , D. G. ; Morin , G. B. , Quantitative Profiling of Single Formalin Fixed Tumour Sections: proteomics for translational research . Scientific Reports 2016 , 6 ( 1 ), 34949 . OpenUrl CrossRef PubMed 15. Palmer , K. S. ; Makarewicz , C. A. ; Tishkin , A. A. ; Tur , S. S. ; Chunag , A. ; Diimajav , E. ; Jamsranjav , B. ; Buckley , M. , Comparing the Use of Magnetic Beads with Ultrafiltration for Ancient Dental Calculus Proteomics . Journal of Proteome Research 2021 , 20 ( 3 ), 1689 – 1704 . OpenUrl CrossRef PubMed 16. ↵ Cleland , T. P. , Human Bone Paleoproteomics Utilizing the Single-Pot, Solid-Phase-Enhanced Sample Preparation Method to Maximize Detected Proteins and Reduce Humics . Journal of Proteome Research 2018 , 17 ( 11 ), 3976 – 3983 . OpenUrl CrossRef PubMed 17. ↵ Moggridge , S. ; Sorensen , P. H. ; Morin , G. B. ; Hughes , C. S. , Extending the Compatibility of the SP3 Paramagnetic Bead Processing Approach for Proteomics . Journal of Proteome Research 2018 , 17 ( 4 ), 1730 – 1740 . OpenUrl CrossRef PubMed 18. ↵ Dagley , L. F. ; Infusini , G. ; Larsen , R. H. ; Sandow , J. J. ; Webb , A. I. , Universal Solid-Phase Protein Preparation (USP3) for Bottom-up and Top-down Proteomics . Journal of Proteome Research 2019 , 18 ( 7 ), 2915 – 2924 . OpenUrl CrossRef PubMed 19. Müller , T. ; Kalxdorf , M. ; Longuespée , R. ; Kazdal , D. N. ; Stenzinger , A. ; Krijgsveld , J. , Automated sample preparation with SP3 for low‐input clinical proteomics . Molecular Systems Biology 2020 , 16 ( 1 ), e9111 . OpenUrl CrossRef PubMed 20. ↵ Leutert , M. ; Rodríguez-Mias , R. A. ; Fukuda , N. K. ; Villén , J. , R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies . Mol Syst Biol 2019 , 15 ( 12 ), e9021 . OpenUrl CrossRef PubMed 21. ↵ Johnston , H. E. ; Yadav , K. ; Kirkpatrick , J. M. ; Biggs , G. S. ; Oxley , D. ; Kramer , H. B. ; Samant , R. S. , Solvent Precipitation SP3 (SP4) Enhances Recovery for Proteomics Sample Preparation without Magnetic Beads . Analytical Chemistry 2022 , 94 ( 29 ), 10320 – 10328 . OpenUrl CrossRef 22. ↵ Sielaff , M. ; Kuharev , J. ; Bohn , T. ; Hahlbrock , J. ; Bopp , T. ; Tenzer , S. ; Distler , U. , Evaluation of FASP, SP3, and iST Protocols for Proteomic Sample Preparation in the Low Microgram Range . J Proteome Res 2017 , 16 ( 11 ), 4060 – 4072 . OpenUrl CrossRef PubMed 23. ↵ Friedrich , C. ; Schallenberg , S. ; Kirchner , M. ; Ziehm , M. ; Niquet , S. ; Haji , M. ; Beier , C. ; Neudecker , J. ; Klauschen , F. ; Mertins , P. , Comprehensive micro-scaled proteome and phosphoproteome characterization of archived retrospective cancer repositories . Nature Communications 2021 , 12 ( 1 ), 3576 . OpenUrl CrossRef PubMed 24. He , M. ; Wang , J. ; Herold , S. ; Xi , L. ; Schulze , W. X. , A Rapid and Universal Workflow for Label-Free-Quantitation-Based Proteomic and Phosphoproteomic Studies in Cereals . Current Protocols 2022 , 2 ( 6 ), e425 . OpenUrl CrossRef 25. ↵ Batth , T. S. ; Tollenaere , M. X. ; Rüther , P. ; Gonzalez-Franquesa , A. ; Prabhakar , B. S. ; Bekker-Jensen , S. ; Deshmukh , A. S. ; Olsen , J. V. , Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation . Mol Cell Proteomics 2019 , 18 ( 5 ), 1027 – 1035 . OpenUrl Abstract / FREE Full Text 26. ↵ Kohale , I. N. ; Burgenske , D. M. ; Mladek , A. C. ; Bakken , K. K. ; Kuang , J. ; Boughey , J. C. ; Wang , L. ; Carter , J. M. ; Haura , E. B. ; Goetz , M. P. ; Sarkaria , J. N. ; White , F. M. , Quantitative Analysis of Tyrosine Phosphorylation from FFPE Tissues Reveals Patient-Specific Signaling Networks . Cancer Research 2021 , 81 ( 14 ), 3930 – 3941 . OpenUrl Abstract / FREE Full Text 27. ↵ Daly , L. A. ; Byrne , D. P. ; Perkins , S. ; Brownridge , P. J. ; McDonnell , E. ; Jones , A. R. ; Eyers , P. A. ; Eyers , C. E. , Custom Workflow for the Confident Identification of Sulfotyrosine-Containing Peptides and Their Discrimination from Phosphopeptides . Journal of Proteome Research 2023 , 22 ( 12 ), 3754 – 3772 . OpenUrl CrossRef PubMed 28. ↵ Hardman , G. ; Perkins , S. ; Brownridge , P. J. ; Clarke , C. J. ; Byrne , D. P. ; Campbell , A. E. ; Kalyuzhnyy , A. ; Myall , A. ; Eyers , P. A. ; Jones , A. R. ; Eyers , C. E. , Strong anion exchange-mediated phosphoproteomics reveals extensive human non-canonical phosphorylation . The EMBO Journal 2019 , 38 ( 21 ), e100847 . OpenUrl CrossRef PubMed 29. Olsen , J. V. ; Vermeulen , M. ; Santamaria , A. ; Kumar , C. ; Miller , M. L. ; Jensen , L. J. ; Gnad , F. ; Cox , J. ; Jensen , T. S. ; Nigg , E. A. ; Brunak , S. ; Mann , M. , Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis . Science Signaling 2010 , 3 ( 104 ), ra3 – ra3 . OpenUrl Abstract / FREE Full Text 30. Bekker-Jensen , D. B. ; Bernhardt , O. M. ; Hogrebe , A. ; Martinez-Val , A. ; Verbeke , L. ; Gandhi , T. ; Kelstrup , C. D. ; Reiter , L. ; Olsen , J. V. , Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries . Nature Communications 2020 , 11 ( 1 ), 787 . OpenUrl CrossRef PubMed 31. Kemper , E. K. ; Zhang , Y. ; Dix , M. M. ; Cravatt , B. F. , Global profiling of phosphorylation-dependent changes in cysteine reactivity . Nature Methods 2022 , 19 ( 3 ), 341 – 352 . OpenUrl CrossRef PubMed 32. Bouhaddou , M. ; Memon , D. ; Meyer , B. ; White , K. M. ; Rezelj , V. V. ; Correa Marrero , M. ; Polacco , B. J. ; Melnyk , J. E. ; Ulferts , S. ; Kaake , R. M. ; Batra , J. ; Richards , A. L. ; Stevenson , E. ; Gordon , D. E. ; Rojc , A. ; Obernier , K. ; Fabius , J. M. ; Soucheray , M. ; Miorin , L. ; Moreno , E. ; Koh , C. ; Tran , Q. D. ; Hardy , A. ; Robinot , R. ; Vallet , T. ; Nilsson-Payant , B. E. ; Hernandez-Armenta , C. ; Dunham , A. ; Weigang , S. ; Knerr , J. ; Modak , M. ; Quintero , D. ; Zhou , Y. ; Dugourd , A. ; Valdeolivas , A. ; Patil , T. ; Li , Q. ; Hüttenhain , R. ; Cakir , M. ; Muralidharan , M. ; Kim , M. ; Jang , G. ; Tutuncuoglu , B. ; Hiatt , J. ; Guo , J. Z. ; Xu , J. ; Bouhaddou , S. ; Mathy , C. J. P. ; Gaulton , A. ; Manners , E. J. ; Félix , E. ; Shi , Y. ; Goff , M. ; Lim , J. K. ; McBride , T. ; O’Neal , M. C. ; Cai , Y. ; Chang , J. C. J. ; Broadhurst , D. J. ; Klippsten , S. ; De Wit , E. ; Leach , A. R. ; Kortemme , T. ; Shoichet , B. ; Ott , M. ; Saez-Rodriguez , J. ; tenOever , B. R. ; Mullins , R. D. ; Fischer , E. R. ; Kochs , G. ; Grosse , R. ; García-Sastre , A. ; Vignuzzi , M. ; Johnson , J. R. ; Shokat , K. M. ; Swaney , D. L. ; Beltrao , P. ; Krogan , N. J. , The Global Phosphorylation Landscape of SARS-CoV-2 Infection . Cell 2020 , 182 ( 3 ), 685 - 712 .e19. OpenUrl CrossRef PubMed 33. ↵ Altelaar , A. F. M. ; Frese , C. K. ; Preisinger , C. ; Hennrich , M. L. ; Schram , A. W. ; Timmers , H. T. M. ; Heck , A. J. R. ; Mohammed , S. , Benchmarking stable isotope labeling based quantitative proteomics . Journal of Proteomics 2013 , 88 , 14 – 26 . OpenUrl CrossRef PubMed 34. ↵ Brabek , J. ; Hanks , S. K. , Assaying protein kinase activity . Methods Mol Biol 2004 , 284 , 79 – 90 . OpenUrl PubMed 35. ↵ Steen , H. ; Jebanathirajah , J. A. ; Rush , J. ; Morrice , N. ; Kirschner , M. W. , Phosphorylation analysis by mass spectrometry: myths, facts, and the consequences for qualitative and quantitative measurements . Mol Cell Proteomics 2006 , 5 ( 1 ), 172 – 81 . OpenUrl Abstract / FREE Full Text 36. ↵ Ohguro , H. ; Palczewski , K. , Separation of phospho- and non-phosphopeptides using reverse phase column chromatography . FEBS Lett 1995 , 368 ( 3 ), 452 – 4 . OpenUrl CrossRef PubMed Web of Science 37. ↵ Zhou , H. ; Di Palma , S. ; Preisinger , C. ; Peng , M. ; Polat , A. N. ; Heck , A. J. R. ; Mohammed , S. , Toward a Comprehensive Characterization of a Human Cancer Cell Phosphoproteome . Journal of Proteome Research 2013 , 12 ( 1 ), 260 – 271 . OpenUrl CrossRef PubMed Web of Science 38. Bian , Y. ; Ye , M. ; Song , C. ; Cheng , K. ; Wang , C. ; Wei , X. ; Zhu , J. ; Chen , R. ; Wang , F. ; Zou , H. , Improve the Coverage for the Analysis of Phosphoproteome of HeLa Cells by a Tandem Digestion Approach . Journal of Proteome Research 2012 , 11 ( 5 ), 2828 – 2837 . OpenUrl CrossRef PubMed 39. ↵ Casado , P. ; Rodriguez-Prados , J.-C. ; Cosulich , S. C. ; Guichard , S. ; Vanhaesebroeck , B. ; Joel , S. ; Cutillas , P. R. , Kinase-Substrate Enrichment Analysis Provides Insights into the Heterogeneity of Signaling Pathway Activation in Leukemia Cells . Science Signaling 2013 , 6 ( 268 ), rs6 – rs6 . OpenUrl Abstract / FREE Full Text 40. ↵ Loeser , E. ; DelaCruz , M. ; Madappalli , V. , Solubility of Urea in Acetonitrile–Water Mixtures and Liquid–Liquid Phase Separation of Urea-Saturated Acetonitrile–Water Mixtures . Journal of Chemical & Engineering Data 2011 , 56 ( 6 ), 2909 – 2913 . OpenUrl CrossRef 41. ↵ Attwood , P. V. ; Piggott , M. J. ; Zu , X. L. ; Besant , P. G. , Focus on phosphohistidine . Amino Acids 2007 , 32 ( 1 ), 145 – 56 . OpenUrl CrossRef PubMed Web of Science 42. ↵ Besant , P. G. ; Attwood , P. V. ; Piggott , M. J. , Focus on phosphoarginine and phospholysine . Curr Protein Pept Sci 2009 , 10 ( 6 ), 536 – 50 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted March 13, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following An optimized SP3 sample processing workflow for in-depth and reproducible phosphoproteomics Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share An optimized SP3 sample processing workflow for in-depth and reproducible phosphoproteomics Leonard A. Daly , Christopher J. Clarke , Sally O. Oswald , Andris Jankevics , Philip J. Brownridge , Richard A. Scheltema , Claire E. Eyers bioRxiv 2025.03.13.643088; doi: https://doi.org/10.1101/2025.03.13.643088 Share This Article: Copy Citation Tools An optimized SP3 sample processing workflow for in-depth and reproducible phosphoproteomics Leonard A. Daly , Christopher J. Clarke , Sally O. Oswald , Andris Jankevics , Philip J. Brownridge , Richard A. Scheltema , Claire E. Eyers bioRxiv 2025.03.13.643088; doi: https://doi.org/10.1101/2025.03.13.643088 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (7622) Biochemistry (17648) Bioengineering (13871) Bioinformatics (41880) Biophysics (21423) Cancer Biology (18561) Cell Biology (25461) Clinical Trials (138) Developmental Biology (13364) Ecology (19866) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15590) Genomics (22475) Immunology (17713) Microbiology (40328) Molecular Biology (17148) Neuroscience (88473) Paleontology (666) Pathology (2827) Pharmacology and Toxicology (4816) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)