SEM images and the determination of viscoelasticity showed that MAS-1 has a dense and robust spatial network structure in the fracturing fluid. The sedimentation velocity of the proppant was 0.0528 cm min−1 at 90 °C. When enough ammonium persulfate was added to yield approximately 0.10 wt%, the viscosity of the broken fluid was 4.5 mPa s, and the gel broken fluid was transparent without visible residue. In addition, the fracturing fluid did little damage to the reservoir. The drag reduction rate of MAS-1 was always higher than KYPAM-6A and HPAM with the shear rate ranging from 1000 s−1 to 7000 s−1. Therefore, this fracturing fluid could be an alternative for low permeability reservoir stimulation.A new terpolymer (MAS-1) was created by copolymerizing acrylamide (AM), acrylic acid (AA), and 4-isopropenylcarbamoyl-benzene sulfonic acid (AMBS). Thermogravimetric analysis (TGA) suggested that MAS-1 has better heat resistance and meets the requirements for a fracturing fluid at 200 °C. X-ray diffraction (XRD), and conductivity tests showed that MAS-1 has good water solubility. The viscosity of the fracturing fluids containing MAS-1 was found to be about 135 mPa s after 120 min at 170 s A new terpolymer (MAS-1) was created by copolymerizing acrylamide (AM), acrylic acid (AA), and 4-isopropenylcarbamoyl-benzene sulfonic acid (AMBS). Hydraulic fracturing is a technique widely adopted to stimulate the production of low-permeability oil and gas wells.4–7 It has been used in well stimulation for more than 40 years.8 As an indispensable component of fracking, fracturing fluid ultimately affects the success or failure of fracking.9,10 In the last decade, domestic and foreign scholars have conducted extensive research on fracturing fluids. Various fracturing fluid systems have been investigated, such as guar gum fracturing fluid, polymer fracturing fluid, foam fracturing fluid, acid-based fracturing fluid, emulsified fracturing fluid, oil-based fracturing fluid and VES fracturing fluid.11 Meanwhile, because of their low frictional resistance, good sand-suspension, and lower filter loss, guar gum and polymer-based fracturing fluids are the main fracturing fluids used in oilfields.12–14With the development of the oil and gas industry, low-permeability reservoirs account for an increasing proportion of the total oil and gas resources.15 Not only that, because guar gum is mainly imported from places such as Pakistan, India, and Brazil, the availability and price of guar gum is subject to fluctuations. Moreover, synthetic polymers have the advantages of strong thickening ability, good gel stability, strong ability to carry sand, insensitivity to bacteria, less residue formation, and low damage to reservoirs.15–18 Nevertheless, polymer-based fracturing fluids are unstable at high temperatures and high shear rates. In addition, the water solubility of synthetic polymers is poor compared to viscoelastic surfactants. Based on the early work with fracturing fluids, it was believed that the heat-resistance and shear-resistance of polymers could be improved by introducing a benzene ring, and the water solubility of polymers could be improved by introducing strongly hydrophilic groups, such as sulfonate and carboxyl groups. Naturally, the terpolymer MAS-1 was synthesized by copolymerizing acrylamide (AM), acrylic acid (AA), and 4-isopropenylcarbamoyl-benzene sulfonic acid (AMBS). The benzene ring improves the rigidity of the polymer, which will keep the main chain of the polymer stretched in solution, giving the polymer solution an excellent resistance to temperature and shear.However, from a field operational point of view, guar gum-based fracturing fluids usually have the disadvantages of high insoluble residue, poor shear resistance, and large damage to reservoirs due to the plugging of pore throats. Hence, it difficult to use guar gum fracturing fluids in high-temperature and low-permeability oil and gas wells.In addition, organic zirconium is used to crosslink with the polymer. This further improves the temperature and shear resistance of the fracturing fluid, allowing the fracturing fluid to transport the proppant deeply into the cracks. Using MAS-1 in fracturing fluids could improve the stimulation efficiency of fracking, which could greatly increase the production of oil and gas wells. Therefore, it is important to investigate this fracturing fluid as an alternative for low-permeability reservoir stimulation.Mw = 16–18 × 106) were purchased from the chemical market. KYPAM-6A having 26.4% degree of hydrolysis, Mw = 25.14 × 106, was purchased from the Hengju Oil Field Chemical Reagents Co., Ltd. . 4-Isopropenylcarbamoyl-benzene sulfonic acid (AMBS) is a functional monomer prepared in our lab. Zirconium crosslinking agent (Zr-CL) was prepared in our lab by chelating zirconium oxychloride octahydrate with triethanolamine and glycerol at a certain temperature. All chemicals were of analytical reagent grade and were utilized without further purification.Acrylamide (AM), acrylic acid (AA), sodium hydroxide, 2,2′-azobis (2-methylpropionamide) dihydrochloride (V50), ammonium persulfate and HPAM ((i)First, 997 g of deionized water in the beaker was stirred on the agitator. When a vortex is formed in the water, 3 g of terpolymer MAS-1 was added into the water slowly to prevent the formation of insoluble particles. After the terpolymer was completely dissolved in water, it was kept at 25 °C for 24 h before being used. In other words, the 0.3 wt% polymer aqueous solution was obtained.(ii)−1 to 1000 s−1 under 25 °C for 30 min using a HAAKE RS600 rheometer. The viscosity retention rate was calculated by the viscosity at 1000 s−1 divided by the viscosity at 10 s−1.50 mL of the above 0.3 wt% polymer aqueous solution was taken in a measuring cylinder and was placed into the RS600 rheometer. Then, the polymer aqueous solution was subjected to continuous shear testing with a shear rate ranging from 7.34 s(iii)−1 in the HAAKE RS600 rheometer. Then, the rheology of fracturing fluid was measured at the temperature of 150 °C and the shear rate of 170 s−1 for 110 min using a HAAKE RS600 rheometer.The fracturing fluid was prepared by mixing 0.3 wt% MAS-1 aqueous solution and 0.8 wt% Zr-CL. First, the fracturing fluid was heated from 25 °C to 150 °C with a step length of 10 °C under the shear rate of 170 sThe detailed experimental procedures can be found in the ESI.†Certain proportions of acrylamide (AM), acrylic acid (AA), and 4-isopropenylcarbamoyl-benzene sulfonic acid (AMBS) were added into a 100 mL beaker in deionized water under an inert nitrogen atmosphere. The pH value was adjusted to 7.0 by sodium hydroxide. Copolymerization was allowed to be initiated by a certain amount of V50 at 50 °C for 4 h. Then, the polymer was obtained and cut into small pieces, which were dried at 50 °C under a vacuum desiccator for approximately 10 h after being purified repeatedly with ethanol, and named it as MAS-1. The synthesis process is shown in The variation in the apparent viscosity of 0.3 wt% copolymer solutions was determined by a single variable method with different synthetic conditions. All these experiments are based on a neutral pH solution, a polymerization time of 4 h, and an acrylamide and acrylic acid molar ratio of 80 : 20. The viscosities were measured with a ZNN-D6 viscometer at room temperature (25 °C). The experimental results were shown in Therefore, we concluded that the optimum synthesis conditions for MAS-1 were (1) a total monomer concentration of 22 wt%, (2) a pH of 7.0 for the solution, (3) an initiator concentration of 0.05% of the mass of the total monomers, (4) a reaction temperature of 50 °C, (5) an AM to AA mole ratio of 80 : 20, and (6) an AMBS concentration of 0.8% of the moles of the total monomers.(i)−1 is due to –N–H. The peak at 2913 cm−1 is generated by the antisymmetric vibration of C–H. The strong absorption peak at 1644 cm−1 is created by the stretching vibration of –C PBM data was replaced with SVG by xgml2pxml:<glyph-data id="z.dbd" format="PBM" resolution="300" x-size="8" y-size="10" xml:space="preserve">00000000000000000000000000000000111111110000000011111111000000000000000000000000</glyph-data><svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="13.200000pt" height="16.000000pt" viewBox="0 0 13.200000 16.000000" preserveAspectRatio="xMidYMid meet"><metadata>Created by potrace 1.16, written by Peter Selinger 2001-2019</metadata><g transform="translate scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg>O in the amide group. The peak at 1110 cm−1 is due to the stretching vibration of –C–N–. The peak at 1110 cm−1 comes from the stretching vibration of –C–C–. In addition, the absorption peaks at around 625 cm−1 and 525 cm−1 in the IR spectrum were due to the stretching vibrations of the –SO32− groups. The results verified that the synthesized polymer was consistent with the designed MAS-1.The structure of MAS-1 was confirmed by FT-IR. (ii)2O) for the solvent peak, 1.57–1.68 ppm (a) and 2.02–2.25 ppm (b) for (–CH2–CH–), 0.93–0.96 ppm (c) for the methyl proton peak of AMBS, 7.47–7.52 ppm (e) and 7.70–7.75 ppm (d) for the benzene ring proton of AMBS, 1.09–1.12 ppm and 3.55–3.63 ppm for the ethanol solvent peaks. It was verified that the synthesized polymer was consistent with the designed MAS-1  was fitted by three physical models , which are shown in 21–23In hydraulic fracturing, fracturing fluids are subjected to greatly varying shear rates as they move from the ground pump sets to the target reservoir through pipelines. Excellent shear-resistance ensures that the fracturing fluid will maintain good viscosity and proppant suspension. Viscosity is usually used to evaluate the shear resistance of the fracturing fluids under variable shear conditions. Carreau:Carreau-Yasuda:η0 represents the zero shear viscosity (Pa s), η∞ represents the ultimate shear viscosity (Pa s), b represents the shear rate parameters, n represents the flow behavior index, λ represents the relaxation time, K represents the consistency coefficient (mPa sn), and α represents the Carreau constant.Ostwald de Waele:r = 0.9931). Therefore, the fitting parameters can reflect the actual variable shear property of the solution. In addition, wider cracks in the fracturing process can be formed when the non-Newtonian fluid (0.3 wt% MAS-1) is used to suspend the proppant because the flow behavior index is smaller (n < 1) and the consistency coefficient is larger (K = 2049).−1. 24The fracturing fluid was prepared by mixing 0.3 wt% MAS-1 and 0.8 wt% Zr-CL. Then, fracturing fluid was placed into the RS600 rheometer at 150 °C and 170 sTo a certain extent, the elasticity of the fracturing fluid reflects the density of the network structure in the solution. A stronger spatial network structure will result in a larger storage modulus, indicating more extensive network structures in fracturing fluids.G′) and loss modulus (G′′) of the fracturing fluid was small and that the ratio of storage modulus and loss modulus is 2.842 when the shear stress was less than 3.0 Pa, which indicates that the solution is in the linear viscoelastic zone. Therefore, the shear stress should be less than 3.0 Pa during the frequency scanning experiment for the fracturing fluids. For experimental convenience, frequency scanning was usually performed when the shear stress was set to 1.0 Pa.Shear stress must be within the linear viscoelastic area during material property constant measurements, so linear viscoelastic zone measurements of the fracturing fluids should be conducted prior to the viscoelasticity measurements. It is well accepted that the macroscopic properties of the copolymers are determined by their microstructures. The change of the storage modulus and loss modulus of the MAS-1 fracturing fluids implies that their spatial network structure (SNS) has experienced microstructural changes. −1 at 25 °C, 0.0372 cm min−1 at 60 °C, and 0.0528 cm min−1 at 90 °C. It is clear that the sedimentation velocities of the proppant at three different temperatures were all less than 1.08 cm min−1, indicating that the fracturing fluid can meet the requirement of proppant suspension and transportation in fracking.25To determine the proppant carrying capacity, fracturing fluid was poured into a 50 mL cylinder with proppant (20/40 mesh), and the settlement height and time were measured at 25 °C, 60 °C, and 90 °C. Then, the settlement height and time were used to calculate the sedimentation velocities. The viscosities of the fracturing fluid with different concentrations of breaker were investigated at 90 °C. The core damage tests were measured by injecting the broken fluid into the core at 25 °C, 60 °C, and 90 °C to further detect the damage of the fracturing fluid to the formation. 26 Meanwhile, the drag reduction rate reaches a maximum value when the thickness of the elastic bottom layer reaches its limit at the center of the pipe. Moreover, it should be emphasized that the drag reduction rate of MAS-1 fluids was always higher than for KYPAM-6A or HPAM fluids, which were up to 70.5%, indicating that the MAS-1 solution could reduce friction and improve the pump efficiency in fracking. Therefore, this fluid is an alternative to presently used fracturing fluids for low permeability reservoir stimulation.In order to obtain a high-performance fracturing fluid, a new terpolymer (MAS-1) was synthesized by free-radical copolymerization under optimized conditions using the single variable method. MAS-1 was characterized by FT-IR. Investigations of the use of this polymer in the fracturing fluid were carried out, and the following conclusions were made:−1 and 150 °C. (2) Viscoelasticity determination and SEM images showed that there was a dense and robust spatial network structure in the MAS-1 fracturing fluid. The sedimentation velocity of proppant was 0.0528 cm min−1 at 90 °C. (3) When approximately 0.10 wt% ammonium persulfate was added, the viscosity of the broken fluid was 4.5 mPa s at 90 °C after 3 h. The permeability damage rate of the gel broken fluid to cores was 12.64% to 17.62%, indicating that the fracturing fluid causes little damage to the reservoir. (4) The drag reduction rate of MAS-1 solutions was always higher than for KYPAM-6A or HPAM solutions, with a shear rate ranging from 1000 s−1 to 7000 s−1, which would reduce the pump pressure and increase the efficiency of fracking.(1) TGA suggested that MAS-1 has better heat resistance and meets the requirement for a fracturing fluid at 200 °C. XRD and conductivity tests showed that MAS-1 has good water solubility. In addition, it was investigated that MAS-1 fracturing fluid maintained a viscosity of about 135 mPa s after 120 min at 170 sThere are no conflicts to declare.RA-009-C8RA09483G-s001


Here we demonstrate the improved precisions of retention time estimateswithin plexDIA and how this enables more accurate protein quantification.plexDIA has demonstrated multiplicative gains in throughput, and thesegains may be substantially amplified by improving the multiplexingreagents, data acquisition, and interpretation. We discuss futuredirections for advancing plexDIA, which include engineering optimizedmass-tags for high-plexDIA, introducing isotopologous carriers, anddeveloping algorithms that utilize the regular structures of plexDIAdata to improve sensitivity, proteome coverage, and quantitative accuracy.These advances in plexDIA will increase the throughput of functionalproteomic assays, including quantifying protein conformations, turnoverdynamics, modifications states and activities. The sensitivity ofthese assays will extend to single-cell analysis, thus enabling functionalsingle-cell protein analysis.Accurate protein quantification is key to identifyingprotein markers,regulatory relationships between proteins, and pathophysiologicalmechanisms. Realizing this potential requires sensitive and deep proteinanalysis of a large number of samples. Toward this goal, proteomicsthroughput can be increased by parallelizing the analysis of bothprecursors and samples using multiplexed data independent acquisition(DIA) implemented by the plexDIA framework:  These limitationsare increasingly mitigated by new approaches that increase the sensitivity6 and throughput8 of MS-based proteomics. Many ofthese advances take advantage of data independent acquisition (DIA),which was introduced decades ago9 and hasdeveloped into powerful methodologies.15Tandem mass-spectrometry (MS) has longbeen established as themost specific, comprehensive, and versatile method for protein analysis.16 The wide isolation windows used by DIA allowfor parallel accumulation of ions for fragmentations and MS2 analysis,which may enable analyzing many peptides using the long ion accumulationtimes required for single-cell proteomics.17 Indeed, DIA allows obtaining MS2 fragmentation spectra from alldetectable peptide features even when using long ion accumulationtimes, as shown in 18 However, long accumulationtimes reduce the number of times elution peaks are sampled .Subsequently, it was extended to pulsed SILAC,23 which allowed measuring protein turnover rates. These studies convincinglydemonstrated that the complex spectra of multiplexed DIA can be interpretedand used to quantify proteins. Yet, this came at a price: The numberof proteins quantified by label-free DIA (LF-DIA) was about 2-foldlarger than the number of proteins quantified by pulsed SILAC DIA.23 Furthermore, the effect of metabolic multiplexingon quantitative accuracy was not directly benchmarked, and subsequentstudies highlighted the challenges for quantification.25Sample multiplexing by data independent acquisition (DIA) was demonstratedby Minogue et al.26 rigorouslybenchmarked quantitative accuracy by SILAC-DIA and found that it exceedsthe accuracy achieved by SILAC-DDA. Yet, SILAC-DIA quantified about2-fold fewer peptide sequences in the mixed heavy and light samplescompared to only heavy or only light samples. This result is similarto the 2-fold reduced proteome coverage reported by Liu et al.23 and reinforced the notion that multiplexingwith DIA may not increase throughput (defined as the number of quantitativedata points per unit time) over label-free DIA. Furthermore, the throughputof SILAC-DIA did not exceed the throughput of SILAC-DDA. Indeed, thenumbers of precursors quantified in the mixed heavy and light sampleswere very similar for SILAC-DDA and SILAC-DIA, suggesting that SILAC-DIAdid not increase the throughput and proteome coverage compared toSILAC-DDA.26 These results highlight boththe potential and the challenges of multiplexed DIA. Indeed, multipleimplementations of multiplexed DIA have reported proteome coverageof about 2,000 proteins or fewer, significantly below the proteomecoverage that may be achieved by the corresponding LF-DIA analysis.29 This reduction in proteome coverage by multiplexedDIA reduced its appeal despite its demonstrated ability to multiplexsamples.More recently, Pino et al.Multiplexed DIA can beimplemented with different types of mass tags, each type having theirown distinct characteristics. One important property that distinguishesmass tags is whether labeled peptides produce sample-specific precursorsand fragments, as listed in 16 and providesa reliability estimate based on the consistency of MS1 and MS2 levelquantification.4 These benefits come atthe expense of more complex MS2 spectra. These mass tags can includeneutron-encoded (NeuCode) chemical labels that introduce small mass-offsetsdue to the mass defect of neutron binding energy.31 Dependingon the resolution of MS analysis, such NeuCode labels may appear asisobaric (at low resolution) or as nonisobaric (at high resolution).Thus, if fragments can be analyzed with low resolving power as isobaricand precursors analyzed with high resolving power as nonisobaric,these tags will function as Type II mass tags , avoidingoverlap between the isotopic envelopes of complement reporter ionsrequires using only tags having reporter ions that are separated byat least 4 Da or by detectable mass defect. This requirement meansthat only a small subset of the TMT tags can be used together formultiplexing. These limitations of Type III mass tags informed ourchoice to use nonisobaric mass tags for plexDIA.36Type III mass tags are isobaric and result in precursors that areshared across samples and some fragments that may be sample and peptidespecific. Only complement reporter ions attached to peptide specificfragments may be sample and peptide specific.29 Toward overcoming these challenges and increasing the proteome coverage,we introduced plexDIA.37 It uses Type I mass tags anda computational framework that allows increased throughput withoutsacrificing proteome coverage. Furthermore, plexDIA increased datacompleteness and the accuracy of relative protein quantification asbenchmarked by mixed species proteomes.As discussed above, the complex spectra of multiplexed DIA have posedformidable challenges, especially to matching the proteome coverageof LF-DIA.16 These gains stem from computationalapproaches leveraging the fact that isotopically labeled samples coelute.Thus, peptide sequences which are confidently identified in one isotopicchannel may be confidently propagated to other coeluting channelsbecause of the ability to accurately and precisely predict the m/z and retention time of each precursorand its corresponding fragments.plexDIA improved data-completenessby allowing consistent quantification of more proteins across diversesamples than what can be achieved with LF-DIA using 3-times less instrumenttime.Achieving high quantitativeaccuracy when using Type I mass tagsmay be challenging since they increase the spectral complexity linearlywith plex. Therefore, multiplexed DIA has the potential to be affectedby increased interference, which may result in reduced quantitativeaccuracy. Despite this potential, the accuracy of 3-plexDIA was madecomparable to LF-DIA by limiting the impact of interferences through1) quantifying peptides based on MS scans nearer the elution peakapex, and 2) developing an algorithm that quantifies precursors withina set relative to the most confidently assigned channel, as illustratedin 16 already provides substantial advantages forpractical applications,it has much potential for further improvement.36 plexDIA opens new avenues for methodological advances,both for developing new mass-tags for multiplexing and for advancingthe computational frameworks for data interpretation. These developmentsare discussed in the subsections below, along with their interdependenceand requirements toward MS instrumentation.While the demonstrated performance of plexDIAMass tagsoptimized for plexDIA can increase both the proteome coverage (byenhancing amino acid sequence identification) and the number of samplesanalyzed simultaneously (by increasing the plex), as discussed below.Thefragmentation properties of mass tags are crucial for their utilityfor plexDIA. The desired fragmentation properties for optimal performancediffer depending on the type of mass tags listed in 35 Another potential benefitfor sensitive proteomics could be the increased signal from poolingpeptide fragments originating from different samples. Such poolinghappens when the same peptide sequence labeled with different masstags from Type II generates the same fragments. This can also happenwith the y-ions of of C-terminal arginine tryptic peptides labaledwith Type I mass tags. Such pooling can enhance peptide sequence identificationanalogously to the pooling that happens with isobaric carriers,38 but it also may limit the specificity of sample-specific sequenceidentification. Thus, rigorous models of amino acid sequence identificationshould ensure robust FDR estimations and benchmark them with mixedspecies experiments as described in the community white paper on single-cellproteomics.4Mass tags may be engineered to contribute additional benefits tosequence identifications and sensitive quantification. For example,mass tags may stabilize charge on fragment ions and thus increasethe detectable fragments. plexDIA already benefited from the propensityof mTRAQ to stabilize b-ions, but this propensity can be further enhancedin the next generations of mass tags. As another benefit, mass tagscan be engineered to contribute additional charge (such as by addingamine groups), which will increase the sensitivity of detection byMS. Such high-charge designs will be particularly useful for single-cellproteomics.16 and we expect this framework to extend to suitablyengineered higher plex mass tags.39 The mass tagsmay be designed with both large (4 Da or more) and small  mass shifts, similar to the design by the Coon laboratory.30 The sub-Dalton differences should be large enoughto be resolved without requiring MS scan times that would extend thetime of duty cycles beyond the times that are optimal for maximizingsequence coverage. Designing such tags within the constraints requiredfor optimal performance of plexDIA is challenging, but it has thepotential to extend the multiplicative scaling of throughput high-plexDIA.The multiplicativescaling of throughput by plexDIA has been demonstrated with a 3-plexDIA,16 suggesting that the capacityof ion traps and detectors was not saturated by 200,000 precursors.Thus, we expect the possibility to further increase the number ofaccurately quantified precursors, especially when using optimizedexperimental designs with smaller isolation windows and newer instruments,such as high-field orbitraps or fast TOFs combined with narrow MS2isolation windows. This potential for scaling is particularly greatfor single-cell proteomics because current methods quantify fewerprecursors per single-cell sample. The current coverage of our 10,000precursors quantified per single cell and 200,000 precursors quantifiedby Derks et al.16 in a single run correspondto a 20-plex single-cell set. The analysis of such a set will be furtherfacilitated by its smaller dynamic range, which reduces the potentialfor interference. Using ion mobility and/or higher resolution MS detectorscan further increase the number of samples that can be multiplexedwithout undermining quantification accuracy. Therefore, we expecthigh-plexDIA to be particularly powerful in scaling up single-cellproteomics.39Realizing this potential also requires MS instrumentation and experimentaldesigns that can keep up with the increasing complexity of MS spectra.The proof of principle plexDIA demonstration used Type I mass tagswith 4 Da mass-shifts, which increase the ion complexity at both theMS1 and MS2 levels. Despite the added complexity, the 3-plexDIA quantifiedover 200,000 precursors while using a 1 h active gradient and a first-generation Q-Exactive.This analysis resulted in comparable quantitative accuracy to matchedLF-DIA,As discussed above, the plexDIA framework can allow propagating aminoacid sequences within a run with much higher sensitivity and rigorthan propagating sequences between runs. These capabilities stem fromthe coelution of peptides labeled with nonisobaric isotopologous masstags. Algorithms that effectively leverage the time information inherentin coeluting peptides can further amplify the power of sensitive andrigorous sequence propagation within plexDIA sets.41 which providessupport for plexDIA.Optimizationfor accuracy of quantification and depth of proteome coverage startfrom data acquisition. For example, duty cycles can incorporate multipleMS1 survey scans to increase the temporal frequency of sampling precursorsand thus the probability of sampling close to the apex with reducedinterference. Furthermore, duty cycle parameters can be algorithmicallyoptimized to distribute ions evenly across isolation windows and thusreduce overcrowding of ions in some windows and the associated potentialfor increased interference. Such optimization can be performed withmultiple tools, including with DO-MS,46 All these methods for matching between runs exploitretention time (RT) alignment between runs. However, even the bestRT alignment between runs is likely to result in larger RT deviationthan the one measured within a run. To precisely measure RT deviationswithin and between runs, we acquired plexDIA data with a duty cyclethat included an MS1 survey scan before and after each MS2 scan. Thisresulted in frequent sampling of precursors, which supports good estimatesof elution peak apexes, Sequence propagation has long been fruitfullyapplied across differentruns using a variety of software tools.Even after RT alignment, the median RT differencefor precursorsacross different runs, replicate injections of the same sample, isabout 1.2 s both for all precursors and for the most abundant precursors, 47 precise RT estimatesincrease the sensitivity and specificity of sequence propagation.Thus, the increased RT precision within plexDIA sets , andthen peptide sequences identified in the carrier may be confidentlypropagated to the single cells. This strategy extends the ideas ofisobaric carriers48 and motivated us to develop plexDIA.36 It appears promising, though it will encounter similar challengesas the isobaric carrier approach, such as the limited capacity ofion traps and detectors.20 This capacitylimitation is likely to be more pronounced for DIA analysis usingisotopologous carriers because of the wider isolation windows usedby DIA. Thus, the development of isotopologous carriers can be informedby established principles for the use of carriers,20 andisotopologous carriers compatible with accurate and precise quantificationare likely to be smaller than isobaric carriers. Thus, the optimalcarrier size and data acquisition methods should be rigorously benchmarkedusing mixed species experiments as with the development of plexDIA.16One such strategysuggested by Derks et al.8 and protein covariation.50 Furthermore, plexDIA can confer additional advantages. Below wehighlight some examples, though we expect that the community willfind many more.Increasing the throughput and depth ofproteome coverage will empowermany applications, especially those requiring many samples for estimatingrobust associationsProteomics is increasingly applied to clinical samples, and inmany cases the samples may be collected and analyzed over a long periodof time. If analyzed by LF-DIA, changes in peptide separation andMS acquisition  may be challengingto control and compensate for. Such undesired technical variabilitymay be mitigated by including a common reference to all plexDIA setsanalyzed. For example, a 6-plexDIA experiment may run five biologicalsamples and a reference standard; the biological samples can be normalizedto the unchanging reference to control for technical variability duringdata acquisition.The abilityto confidently propagate peptide sequences within plexDIA sets allowsdetecting peptides present at levels that may not support confidentsequence assignment with LF-DIA, even with match between runs. Thisincreased sensitivity of propagating sequences within a run effectivelylowers the limit of detection and increases data completeness. Asa direct consequence, it increases the confidence in interpretingmissing values as corresponding to very low levels of peptide abundance,below the limit of detection, which can be useful for downstream datainterpretation. For example, if an N-terminal peptide from a proteinbecomes undetectable in a plexDIA sample while all other peptidesfrom the protein are detectable, we may infer that the abundance ofthe proteoform containing the N-terminal peptide has fallen belowthe limit of detection. This may be reflected in an alternative openreading frame or a post-translational modification, thus suggestinghypotheses for further investigation.51 using chemical labeling, such as dimethyl labelingof surface exposed lysines that allows quantifying protein conformationsin live cells.53 With this approach, differentsamples can be labeled with isotopically coded dimethyl tags and combinedinto a plexDIA set, whose analysis will take less time than analyzingeach sample individually. Furthermore, it will benefit from increaseddata completeness and sensitivity arising from sequence propagationwithin a plexDIA set. Other example for functional assays that benefitfrom plexDIA include (i) quantifying regulatory proteolysis basedon labeling free amine groups prior to protein digestion,54 (ii) activity-based protein profiling (ABPP)employing isotopically coded molecular probes,56 and (iii) plexDIA pulsed SILAC for measuring protein synthesis anddegradation rates. Since these applications can involve efficientand specific binding of chemical probes or mass tags, the resultingmodified peptides may be produced in stoichiometric amounts and thusquantifiable in very small samples, even in single cells.53 Thus, such extensions of plexDIA hold the potentialof extending the toolset of single-cell analysis to functional assaysquantifying protein shapes and activities.57In additionto mass tags for increasing sample throughput, the plexDIA frameworkcan be extended to a wide array of functional assays using covalentprotein modifications. One example includes footprinting methods16 U-937 monocytes were cultured in RPMI 1640 Medium  and supplemented with 10% FBS  and 1% penicillin–streptomycin. Pancreatic ductal adenocarcinoma (PDAC) cells  were cultured in EMEM , and likewisesupplemented with 10% FBS and 1% penicillin–streptomycin. Melanomacells (WM989-A6-G3) (a kind gift from Arjun Raj at University of Pennsylvania)were cultured in TU2% media. All cells were grown at 37 °C, harvestedat a density of 106 cells/mL, washed with sterile PBS,then resuspended to a concentration of 3 × 106 cells/mLin pure LC-MS grade water, then stored at −80 °C. Cellnumbers were estimated and diluted to 100-cell samples as describedin the SCoPE2 protocol.6Cells were culturedand prepared as previously described.59 In short, the frozen samples were thawed, aliquoted to PCR tubes,heated at 90 °C for 10 min in a thermal cycler, tthen digestedwith Trypsin Gold  at a 1:25 ratio of protease:substratein the presence of 100 mM Triethylammonium bicarbonate (TEAB) and0.2 units/μL benzonase nuclease  for 18 hat 37 °C. Melanoma, PDAC, and U-937 digests were labeled withmTRAQ-Δ0, mTRAQ-Δ4, and mTRAQ-Δ8 mass tags , respectively, then pooled to forma plexDIA set.Cell suspensionswere prepared for proteomic analysis by mPOP.For the purpose of assessing RT-deviationswithin a run and between runs 2, we neem/z. The duty cyclewas: 1 survey scan, 1 MS2 (380–460 m/z), 1 survey scan, 1 MS2 (460–540 m/z), 1 survey scan, 1 MS2 (540–620 m/z), 1 survey scan, 1 MS2 (620–740 m/z), 1 survey scan, 1 MS2 (740–980 m/z), 1 survey scan, 1 MS2 (980–1400 m/z). Each MS1 was performed at a 70k resolvingpower, 240 ms max fill time, and 3 × 106 AGC max.Each MS2 was performed at a 35k resolving power, 110 ms fill time,and 3 × 106 AGC max, and 27 NCE with a default chargeof 2. This method enables high temporal resolution of MS1 features.Mass spectrometry data acquired on a first-generation Q-ExactiveHybrid Quadrupole-Orbitrap with the following DIA duty cycle in positiveion mode using frequent survey scans, MS1 scans spanning the range379–1401 13 version 1.8.1 with thefollowing commands: {fixed-mod mTRAQ, 140.0949630177, nK}, {channelsmTRAQ,0,nK,0:0; mTRAQ,4,nK,4.0070994:4.0070994; mTRAQ,8,nK,8.0141988132:8.0141988132},{peak-translation}, {original-mods}, {report-lib-info}, {ms1-isotope-quant}.This search used the spectral library that was previously generatedfrom 100-cell plexDIA runs of Melanoma, PDAC, and U-937 cells.16Raw filesfrom triplicate plexDIA sets of 100-cell Melanoma, PDAC, and U-937cells were searched by DIA-NN60 Precursors which were quantified as reported by DIA-NN in all threechannels were mapped to the corresponding features ±5 ppm andwith the apex RT falling within the elution start and stop RTs asreported by DIA-NN. The “within run” condition comparedapex RTs of PDAC cells to U-937 cells as reported by Dinosaur as theycoeluted within a run. The “between run” condition subtractedthe “Predicted.RT” column output by DIA-NN to the apexRT reported by Dinosaur. This was performed for all precursors andfor the top 10% most abundant precursors averaged between U-937 andPDAC channels across triplicates.Peptide-like features were extracted fromthe raw files by processing with Dinosaur.16 and are availableat MassIVE: MSV000089093. The errors were estimated as the differencebetween the measured and mixing proteome ratios.16 DIA-NN reports which have columns “MS1.Area”,“MS1.Translated”, “Precursor.Quantity”,and “Precursor.Translated” correspond to the MS1 orMS2-level quant that is either translated or not-translated. Thesevalues were used to compute the empirically observed precursor ratiosfrom the DIA-NN report of a single raw file, “wJD804”.Empirically observed ratios and expected ratios were log transformed,then subtracted from each other. Then, the absolute value was plottedas boxplots to display the errors of precursor quantitation.Themixed-species plexDIA data used in


The European Confederation of Medical Mycology (ECMM) collected data on epidemiology, risk factors, treatment, and outcomes of culture proven candidemia across Europe in order to assess how adherence to guideline recommendations correlate with outcomes.Each participating hospital  included the first ∼10 culture proven IC cases after 01-Jul-18 and entered data into the ECMM Candida III database on the FungiScope™ platform. EQUAL Candida Scores (10.1111/myc.12746) reflecting adherence to recommendations of IDSA and ESCMID Guidelines were assessed.A total of 632 Candidemia cases were included from 64 institutions in 20 European countries. Patients characteristics are displayed in Table 1. Overall mortality was 45% (286/632), and hospital stay was prolonged (median 2 days) for completion of parenteral therapy only in 16% (100/621) of patients. EQUAL Candida Score was evaluable for 589 cases with candidemia . Candida scores correlated significantly with duration of hospitalization  and - after exclusion of patients hospitalized < 7 days (n=119) - were significantly higher in patients who survived versus those who died (p< 0.001). Duration of hospitalization was in median 16 days after diagnosis of candidemia. Initial echinocandin treatment was associated a.) with lower overall mortality  versus those without initial echinocandin therapy , and b.) with longer duration of hospitalization among survivors . In those where candidemia was treated for at least 14 days, 78% (239/306) survived, compared to 66% (67/102) in those treated for less than 14 days, but who survived beyond day 14 after diagnosis.Initial echinocandin treatment was associated with increased overall survival, but also longer duration of hospitalization . Overall mortality of IC was 45%. EQUAL Candida scores were significantly higher on those who survived, indicating that adherence to clinical guidelines may increase survival.Martin Hoenigl, n/a, Astellas: Grant/Research Support|Gilead: Grant/Research Support|NIH: Grant/Research Support|Pfizer: Grant/Research Support|Scynexis: Grant/Research Support Maiken C. Arendrup, DMSci, PhD, MD, Chiesi, Gilead: Honoraria|F2G, Cidara, Scynexis: Grant/Research Support Oliver A. Cornely, Prof. Dr., Abbott: Honoraria|Abbvie: Advisor/Consultant|Actelion: Board Member|Al-Jazeera Pharmaceuticals: Honoraria|Allecra Therapeutics: Board Member|Amplyx: Advisor/Consultant|Amplyx: Grant/Research Support|Astellas: Honoraria|Basilea: Advisor/Consultant|Basilea: Grant/Research Support|Biocon: Advisor/Consultant|Biosys: Advisor/Consultant|BMBF: Grant/Research Support|Cidara: Advisor/Consultant|Cidara: Board Member|Cidara: Expert Testimony|Cidara: Grant/Research Support|CoRe Consulting: Stocks/Bonds|Da Volterra: Advisor/Consultant|DLR: Grant/Research Support|DZIF: Grant/Research Support|Entasis: Board Member|EU Directorate-General for Resarch and Innovation: Grant/Research Support|F2G: Grant/Research Support|German Patent and Trade Mark Office: German patent (DE 10 2021 113 007.7)|Gilead: Advisor/Consultant|Gilead: Grant/Research Support|Grupo Biotoscana/United Medical/Knight: Honoraria|Hikma: Honoraria|IQVIA: Board Member|Janssen: Board Member|Matinas: Advisor/Consultant|Matinas: Grant/Research Support|MedPace: Advisor/Consultant|MedPace: Grant/Research Support|MedScape: Honoraria|MedUpdate: Honoraria|Menarini: Advisor/Consultant|Merck/MSD: Grant/Research Support|Merck/MSD: Honoraria|Molecular Partners: Advisor/Consultant|MSG-ERC: Advisor/Consultant|Mundipharma: Grant/Research Support|Mylan: Honoraria|Noxxon: Advisor/Consultant|Octapharma: Advisor/Consultant|Octapharma: Grant/Research Support|Paratek: Board Member|Pardes: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Projektträger Jülich: Grant/Research Support|PSI: Advisor/Consultant|PSI: Board Member|Pulmocide: Board Member|Scynexis: Advisor/Consultant|Scynexis: Grant/Research Support|Seres: Advisor/Consultant|Shionogi: Board Member|Wiley (Blackwell): Editor-in-Chief, Mycoses.


Throughout human history, infectious diseases have been one of the leading causes of death. However, due to advances in modern medicine, life expectancy and the total population increased considerably. Among the key drivers of this major change, vaccines have led to the elimination or significant reduction in the burden of several viral and bacterial diseases. Unfortunately, none of the clinically available vaccines target fungal diseases, which have been estimated to cause some 1.5 million deaths each year . Kischkel et al. used two different but complementary strategies to seek novel Histoplasma capsulatum protein epitopes that elicit robust immune responses in mice. One strategy revealed proteins that were recognized by antibodies, whereas the other showed peptides that were presented by dendritic cells to activate T cells. After identifying such immunogenic epitopes, they focused on those present in proteins that are highly conserved among different pathogenic fungal species, which could lead to cross-species immunogenicity. When these peptides were tested, they led to T cell activation and secretion of Th1- and Th17-type cytokines, a promising indication that they might enhance the kind of immune response that is protective in most systemic and mucosal mycoses.These pan-fungal vaccines are a possibility that stems from another study in this Research Topic . Studies with P. brasiliensis have previously shown that peptides derived from the immunodominant gp43 protein can be protective in animal models  awards 0193.001048/2015 and 0193.001561/2017 and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) grant 437484/2018-1. JVD was supported by the Division of Intramural Research (DIR) of the National Institute of Allergy and Infectious Diseases (NIAID). MaS was supported by NIH grant R00DE026856, an American Association of Immunologists Careers in Immunology Fellowship, and a TLI seed grant. MuS was supported by the Wellcome Trust (grant#: 211360/Z/18/Z) and the National Research Foundation of South Africa . IMD was supported by the Wellcome Trust Strategic Award in Medical Mycology and Fungal Immunology (grant# 097377).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Over the last decade, there has been an irreversible shift from hydrocarbon exploration towards carbon storage, low-carbon energy generation and hydrogen exploration. Whilst basin modelling techniques may be used to predict the migration of hydrocarbons through sedimentary basins on geological timescales, there remains little understanding of how fluids behave at the basin scale on present-day timescales. We apply the Darcy flow equation to present an algorithm to determine the basin-scale mobilities and maximum vertical velocity,  Shifting geopolitical climates around the world have also seen several countries  commit to achieving net-zero economies and power generation by 20504. Many of the world’s largest energy producing companies have responded by committing to achieve carbon net-zero by 20506. Hence, there is a growing interest across industry and the scientific community at applying well known techniques for hydrocarbon exploration to low-carbon technologies, such as carbon capture and storage (CCS) and green hydrogen exploration. Development of these techniques is dependent on understanding how CO7. The ability to simulate the passage of fluids through rocks is of significant importance to both hydrocarbon exploration and low-carbon activities, such as CCS. A large portion of processing time in basin modelling is used to solve Darcy flow equations in each grid cell of the model. The Darcy flow equation defines fluid velocity as7. The mobility of a fluid is therefore a function of both fluid properties and rock type of the carrier, whereas buoyancy is independent of rock type. The terms of Eq.  with depth and surface temperature. Depositional parameter values for two generalised rock types, sandstone and carbonate, are applied in this study. For carbonate, parameter values for micrite are used. Depositional porosity  to improve overall model robustness in future studies.Whilst the Darcy flow equation Eq.  is widel22. With low levels of radiogenic heat production, shallow sandstone and carbonate layers within onshore sedimentary basins will typically exhibit lower pressures and temperatures compared to offshore depleted hydrocarbon reservoirs. However, the nature and the effects of surface and near-surface temperature variations on the physical properties of subsurface pore fluid–COThe geological storage of CO24. However, fluids transported via pathways created by heterogeneities intrinsic to rock volumes and fractures (e.g. faults), may play an important role in transporting heat from the surface to rocks at depth25. Low-frequency temperature signals  may be retained at depth . Using values for the normal pressure gradient, it is reasonable to assume that the EoS applied in this study are accurate for modelling insoluble hydrogen properties to a depth of Hydrogen is generally considered insoluble in water below pressures of 40 MPa, with the Peng-Robinson and SRK EoS decreasing in accuracy as pressure increasesClearly, the application of well known basin modelling techniques presents an attractive opportunity for hydrogen exploration. Application of the algorithm proposed in this study to model hydrogen migration at greater depths or in regions of high pressure may be possible with future research that focuses on amending widely used EoS to account for hydrogen solubility at pressures We present an algorithm to calculate vertical fluid mobility and buoyancy for CO7, the driving forces for fluid flow are pressure potential differences, where the fluid pressure potential is the pressure reduced by the pressure of a static fluid column with a corresponding vertical pressure gradient. The potential, p is thus defined as:p, p and p. This provides a basic formulation of Darcy’s law, with a comprehensive formulation given by:p and water that is given by:As described in Hantschel and Kauerauf9. The subject of fluid analysis can be subdivided into three parts, the determination of the coexisting phases, their compositions and their properties. In this study, we consider both pure fluids , which depends on several factors including fluid and rock properties. Fluid properties are described by Equations of State (EoS), which define the relationship between pressure, volume and temperature. The most well known EoS is the Ideal Gas Equation, however such a simple relationship does not accurately model properties of non-ideal fluids and cannot be used in complex scenarios such as geological basin modelling. There are numerous widely used EoS that describe the relationship between fluid pressure, temperature and volume for non-ideal molecules. The Peng–Robinson 1978 (PR78) and Soave–Redlich–Kwong (SRK) EoS are considered one of the more common models in reservoir engineering to predict the phase-change behaviour of hydrocarbon mixtures in reservoirs. Both EoS are known for their accuracy for multi-phase and multi-component fluids at pressures 43. We extend application of these EoS to CO2, methane and hydrogen as the PR78 and SRK EoS only become unstable when the reduced temperature, 44. As 19. Other EoS have been developed specifically for use with hydrocarbons, e.g. volatile, light and black oils18. Component properties for COThe primary driving force responsible for fluid flow in the geological media is buoyancy  make these unsuitable for basin modelling7.Whilst EoS may be applied to calculate fluid pressure, temperature and volume, viscosity is another important indicator for phase property characterisations and fluid velocities within rocks and geological basins. Viscosity is often modelled as a quantity dependent only on pressure, temperature, density and the amount of dissolved gas45. Viscosities can be evaluated very fast due to the simple nature of the LBC-formulas. However, models with lower performance are often not usable in fluid flow simulators. It must also be noted that the LBC-model is based on a polynomial of degree 16. Polynomials of such a high degree are known to easily become numerically unstable and therefore LBC-based models must be evaluated with care7. Furthermore, the pressure correction for gases in the LBC method had a tabular formula and was not presented entirely45. Using this distinction introduces a discontinuity between the liquid and gas viscosity. A modification to the LBC method may be made by applying the Herning mixing rule46. This was validated to successfully model multicomponent and multiphase fluid viscosity using the modified LBC approach for an example using the independent Stiel–Thodos method47. Hence, this modified LBC method is preferred over the original LBC method45.Compared to methods that require laboratory or field data, direct approaches such as the empirical Lohrenz–Bray–Clark (LBC) model are simpler to apply in basin modelling scenarios where data is often scarce13. The modified-LBC method was used to calculate fluid viscosity .The range of pressure and temperature (PT) paths in in Fig. itions 0. MPa/K waThe transition between phases  is controlled by a fluid’s pressure and temperature relative to its critical pressure, k is the Athy compaction parameter (Table 48).The velocity of fluids in rocks (e.g. sandstone and carbonate) depend on a number of factors, including properties of fluids Table  and rockentclass1pt{minimaThe flow of fluids through rocks generally involves more than a single phase. The ability of one fluid to flow through a rock or reservoir is affected by the presence of other fluids. Relative permeability, 7 or revised Kozeny-Carman relation49 for practical use in basin modelling. The multipoint method describes the relationship between 7. The revised Kozeny–Carman relation49 describes 7), however as the latter is not suitable for carbonate rocks we restrict our analyses to the multipoint method. Combination with Eq.  and 50 . However, greater upscaling factors  to basin scale elements with lengths greater 50.Vertical and horizontal rock permeabilities for use in basin modelling may be calculated from hand-specimen measurements by using appropriate anisotropy and upscaling factors. Anisotropy is the ratio of horizontal and vertical permeability and is dependent on rock type. Basin scale values for horizontal and vertical permeabilities are calculated from hand specimen values multiplied a horizontal and vertical upscaling factor, respectively. The resulting higher values for larger scales are caused by macro-fractures, inhomogeneities and permeable inclusions51. The relative permeability of any fluid is zero below its critical saturation, where it becomes immobile. Saturations are for often rescaled into normalised or effective saturations (51. In this case, the relative permeability of oil, 7 may therefore be applied to determine relative permeabilities from normalised effective saturation values. We use this relationship to approximate the flow of the fluids (Table Numerous models for estimating relative permeability have been proposed over the last half century. Most relative permeability models assume relative permeability to be a function of connate water saturation (mentclass2pt{minimCombinations of these methodsThe amount of data that is available often dictates which method to use to determine Determining water saturation, 52. The magnitude of this constant was shown to be a related to rock type and indirectly to permeability, k. Better quality rocks were found to correspond with low constant values. Extensive analysis of core data and petrophysical estimates of porosity and irreducible water saturation, from all types of reservoirs worldwide, suggests that this relation is a unique solution to a more general equation:Q ranges from 0.8 to 1.3, with many reservoirs close to 1.0. For sandstones, Q and constant C can be easily derived by plotting logC53. Figure 53. Uncertainty increases significantly as porosity is reduced through compaction (The results of early experiments indicated that the product of mentclasspt{minima