The critical fluxes were well-described using a modified concentration polarization model that captures the observed dependence on feed flow rate and feed concentration. Long-duration filtration experiments were conducted under stable SPTFF conditions, with the results suggesting that sustainable performance could potentially be achieved for as much as 6 weeks of continuous operation. These results provide important insights into the potential application of SPTFF for the concentration of viral vectors in the downstream processing of gene therapy agents.Recent approval of several viral-vector-based therapeutics has led to renewed interest in the development of more efficient bioprocessing strategies for gene therapy products. Single-Pass Tangential Flow Filtration (SPTFF) can potentially provide inline concentration and final formulation of viral vectors with enhanced product quality. In this study, SPTFF performance was evaluated using a suspension of 100 nm nanoparticles that mimics a typical lentivirus system. Data were obtained with flat-sheet cassettes having 300 kDa nominal molecular weight cutoff, either in full recirculation or single-pass mode. Flux-stepping experiments identified two critical fluxes, one based on boundary-layer particle accumulation (J The recent success of nucleic-acid-based biotherapeutics has led to a resurgence of interest in gene therapy to provide treatments for many debilitating diseases . Currentem cells ,6.8 infectious particles/mL using small-scale Amicon Ultra centrifugal ultrafilters. Several studies have demonstrated the use of tangential flow filtration (TFF) to concentrate lentivirus in larger-scale flat-sheet cassettes [One of the major challenges in the commercialization of these innovative therapies is the need for significant concentration of the dilute lentivirus as part of the downstream process . Zimmermassettes ,10,11,12assettes  through assettes ,15.An attractive alternative to conventional TFF is to use Single-Pass Tangential Flow Filtration (SPTFF), in which the product is concentrated along the length of the module in a single pass without any recycling . In addiThis study explores the use of SPTFF for the filtration of lentiviral vectors using spherical 100 nm nanoparticles as a model system based on the reported size and spherical shape of lentivirus . SeveralFlux-stepping experiments were used to identify two distinct values for the critical flux: one associated with particle loss due to accumulation in the concentration polarization boundary layer, and one associated with a pressure increase due to membrane fouling. The critical flux data were effectively described using a concentration polarization model, with the calculated value of the critical wall concentration corresponding to a particle volume fraction of ≈5%. Operating the SPTFF module below the critical flux enabled continuous operation for 24 h with less than 0.1 psi increase in TMP, suggesting that SPTFF could be successfully employed for continuous concentration of lentiviral vectors for extended periods of time without any need for membrane regeneration or replacement.® 100 nm fluorescent carboxylate-modified polystyrene latex microspheres  as a model lentivirus system. The stock nanoparticles were suspended in distilled water and provided at a concentration of 2% solids. Nanoparticle feed suspensions were prepared by diluting the stock suspension in 10 mM phosphate-buffered saline (PBS) at pH 7.4 . Prior to use, feed suspensions were sonicated in an ultrasonic cleaner bath  at an operating frequency of 35 kHz for 15 min to break apart any aggregates that might have formed during storage/preparation.Experimental studies were performed using FluoSpheresNanoparticle concentrations were determined from their fluorescence intensity, evaluated using a Tecan Infinite m200 Pro microplate reader  at excitation and emission wavelengths of 580 and 605 nm, respectively. The size distribution and zeta potential were evaluated by both dynamic light scattering (DLS) using a Zetasizer Nano ZS , and nanoparticle tracking analysis (NTA) using a ZetaView Particle Tracking Analyzer . Zeta potential measurements were performed using both PBS at pH 7.4 and a 50 mM tris, 5% sucrose, and 50 mM L-arginine buffer at pH 7.5, since those conditions have been used previously for lentivirus characterization .® 3 cassettes with Ultracel® regenerated cellulose membranes having a nominal molecular weight cutoff of 300 kDa and total membrane area of 88 cm2 . The module contained two membranes, separated by a C-screen.All experiments were performed using Pellicon® Mini Cassette Holder  and torqued to 190 inch-pounds to prevent leakage. The membrane module was connected to two Masterflex® L/S® peristaltic pumps  to control the feed and permeate flow rates. The transmembrane pressure (TMP) was measured using Ashcroft® digital pressure gauges connected to the feed inlet and permeate outlet ports (the retentate outlet was open to the atmosphere):f, Pr, and Pp corresponding to the pressures at the feed inlet, retentate outlet, and permeate outlet, respectively.Cassettes were mounted in a PelliconSPTFF experiments were conducted in both full recirculation and single-pass mode. In full recirculation mode, both the retentate and permeate outlet streams were recycled back to the feed reservoir, whereas in single-pass mode, the outlet streams were sent to their own separate collection vessels (as illustrated in The average hydrodynamic diameter of the polystyrene latex carboxylate-modified nanoparticles was 122 nm measured by Dynamic Light Scattering (DLS) and 104 nm measured by Nanoparticle Tracking Analysis (NTA). The larger size in DLS is due to weighting by the intensity, which scales strongly with particle size. The zeta potential in 50 mM tris, 5% sucrose, and 50 mM L-arginine at pH 7.5 was –25 mV, consistent with the carboxylation of the nanoparticle surface. These values are similar to those reported in the literature for a lentiviral vector under comparable conditions as shown in 10 particles/mL performed at a feed flux of 33.1 L/m2/h (LMH) and a permeate flux of 29.5 LMH, corresponding to a conversion of 89%, are shown in Typical data for an SPTFF experiment (run in single-pass mode) for a feed suspension containing 3 × 10To determine the nature of the particle accumulation, a series of experiments were performed in which the permeate line was periodically clamped to eliminate the permeate flux (which drives particles towards the membrane surface). In each case, clamping the permeate caused a significant release of particles from the module (detected by a spike in fluorescence intensity in samples obtained from the retentate exit), suggesting that particle accumulation occurs primarily on the membrane surface (as opposed to within the spacer), with the deposited particles only loosely adherent to the membrane.10 particles/mL with the modules operated in full recirculation mode, i.e., with both the retentate and the permeate exit lines recycled to the feed reservoir. The feed flow rate was maintained at a constant value, while the permeate flux was increased stepwise every 40–60 min by adjusting the pump on the permeate exit line.Additional insights into the origin of the particle loss seen in 10 particles/mL feed corresponds to a particle volume fraction of <0.00003. Our hypothesis is that the initial reduction in particle concentration is due to the accumulation of particles in a concentration polarization boundary layer that forms adjacent to the membrane, but this boundary layer only causes a significant increase in TMP when there is sufficient buildup of particles to provide a substantial additional resistance to transmembrane flow.Typical results for the TMP and particle concentration in the feed reservoir during two separate flux-stepping experiments are shown in bl) was defined as the flux at which greater than 25% particle loss was observed, while the critical flux for fouling (Jfoul) was defined as the flux at which the TMP gradient exceeds 0.005 psi/min (corresponding to a 7 psi increase over 24 h of operation). In each case, the critical fluxes were evaluated as the average of the flux values during operation immediately above and below the transition. Note that a similar approach to determining the critical flux was used by Kwon et al. [bl described as the “mass balance” critical flux based on a change in particle concentration in the feed reservoir.Based on these observations, we defined two different values of the critical flux during SPTFF of these dilute nanoparticle suspensions: the boundary-layer critical flux (Jfeed), which is likely due to the reduction in the extent of particle polarization associated with the higher shear rates within the module. Both Jbl and Jfoul show a power-law dependence on the feed flux (linear relationship on the log-log plot), with exponents of 0.50 ± 0.01 and 0.67 ± 0.02, respectively. The stronger dependence of the fouling critical flux on the feed flux means that the difference between Jbl and Jfoul increases with increasing Jfeed. At very low feed flow rates, it is likely that the boundary-layer accumulation occurs rapidly when the permeate flux exceeds Jbl, leading to an almost immediate increase in TMP. This is discussed in more detail below.w >> Cb, with the length-averaged flux given as:o is the overall mass transfer coefficient, and Cw and Cb are the nanoparticle concentrations at the membrane surface and in the bulk suspension, respectively. The mass transfer coefficient in the Pellicon® 3 module was evaluated using a correlation presented by Da Costa et al. accounting for mixing generated by the spacer in the feed channel [® 3 cassette with C-screen, as reported by Jabra et al. [bl and only slightly below that for Jfoul in F with position in the Pellicon® 3 module.The data in the previous section suggest that the critical flux is determined by nanoparticle concentration polarization. However, classical polarization models, which predict a logarithmic dependence on the bulk particle concentration, neglect the effect of the filtrate flux (transmembrane velocity) on mass transport, an effect that is likely to be particularly important during SPTFF due to the requirement of high single-pass conversion. Trettin and Doshi  develope channel :(3)Sh=koa et al. . Equatiow at which particle loss and fouling first become significant. For example, the data in bl = 85 LMH and Jfoul = 119 LMH at a feed flow rate of 30 mL/min (feed flux of 204 LMH), yielding Cw = 4.9 × 1013 and 1.1 × 1014 particles/mL, respectively. These correspond to particle volume fractions of 5 and 10%, calculated based on the volume of an individual spherical nanoparticle with a 122 nm diameter, both of which are almost 4 orders of magnitude greater than the nanoparticle concentration in the bulk suspension. Although the calculated value of the wall concentration for Jfoul is somewhat lower than one might expect for the boundary layer to provide a significant resistance to flow, this discrepancy is likely a result of the approximate nature of Equation (2) in describing the appropriate concentration dependence at the very large concentration driving forces (Cw/Cb) seen in these experiments.The critical flux data can be used to estimate the value of Cbl) or fouling (Jfoul). The conversion decreases with increasing feed flow rate, since the critical flux varies with feed flow rate to a power-law exponent less than one. The solid curves in w = 4.5 × 1013 (4.3% by volume) and Cb = 2.7 × 1010 particles/mL for the boundary-layer critical flux and Cw = 8.7 × 1013 (8.3%) and Cb = 2.4 × 1010 particles/mL for the fouling critical flux. The bulk concentrations were taken as the average feed concentration prior to the critical flux across all experiments, and the wall concentrations were calculated using Equations (2) and (3). The model is in good qualitative agreement with the data, properly capturing the observed dependence of the conversion on the feed flow rate. The predicted conversion does exceed 100% at very low critical flux, since the model ignores the reduction in ko (due to the reduction in retentate flow rate) and increase in Cb with length within the module.The effect of feed flow rate on the conversion, defined as the ratio of the permeate flow rate to the inlet feed flow rate, is examined in foul on Cb compared to that observed experimentally. This likely reflects the approximate nature of the concentration driving force in the concentration polarization model (Equation (2)), as well as the assumption of constant nanoparticle diffusivity and suspension viscosity. The very high predicted conversions at nanoparticle concentrations below ~1.2 × 1010 particles/mL is again due to the large variation in QF and Cb within the module under these conditions, neither of which are accounted for in Equation (2), which simply uses the inlet values of Re and Cb and the length-average value of the filtrate flux.The effect of the bulk nanoparticle concentration on the conversion corresponding to the fouling critical flux is examined in ® 3 cassette at a feed flux of 102 LMH and a nanoparticle concentration of ≈3 × 1010 particles/mL, which gives a critical flux of Jbl = 65 LMH and Jfoul = 72 LMH based on the results in Although SPTFF can be employed in batch operations, one of the major advantages of SPTFF technology is in the development of continuous bioprocesses that can be operated for long periods of time in a nearly steady state ,46. The v = 82 LMH displayed a significantly larger increase in TMP (>1 psi), and there was a nearly 40% loss in particles; the feed reservoir needed to be periodically spiked with small volumes of a concentrated nanoparticle suspension to maintain the nanoparticle concentration at the target of 3 × 1010 particles/mL throughout the run. The nanoparticle loss was considerably higher for the run at 99 LMH, forcing this experiment to be conducted in single-pass mode, with 3 L of the nanoparticle feed suspension fed into the system over the 3 h filtration time. The TMP increased to more than 7 psi in this experiment, reflecting the high degree of fouling when the module is operated at Jv > Jfoul.The TMP for the run at 55 LMH increased by < 0.4 psi (from 1.6 to 2.0 psi) after 1360 min (~22 h) of continuous operation, and aside from an initial drop due to hold-up volume dilution effects, there was less than a 5% change in the nanoparticle concentration in the feed reservoir over the final 13 h of the experiment. The filtration at Jv = 55 LMH, which is below the critical flux of Jfoul = 72 LMH, was more than 6 weeks, but this was reduced to 11 days at Jv = 82 LMH and only 7 h at 99 LMH. However, it is important to note that these changes in operating flux also correspond to significant changes in the nanoparticle concentration factor achieved during SPTFF, with the concentration factor decreasing from 34 at 99 LMH to only 2.2 at 55 LMH. High conversion can only be achieved in the Pellicon® 3 module (with Jv < Jfoul) by operating at a much lower feed flow rate or by using multiple Pellicon® 3 modules in series to increase the overall amount of permeate removal.Although considerable care must be taken in extrapolating the results from these 24 h experiments to even longer operating times, the data in ® 3 modules in series, with Pellicon® Single-Pass TFF diverter plates used to separate the feed and permeate channels. The retentate exit from the first channel was directly connected to the inlet feed of the second channel. Both permeate outlets were left open to the atmosphere, allowing independent measurements of the permeate fluxes from both cassettes. Pumps were placed on the inlet feed for the first cassette and on the retentate exit for the second cassette to control the overall flux. The two-cassette system provided stable operation at a feed flow rate of 30 mL/min corresponding to an overall feed flux of 102 LMH , with a total permeate flow rate of 21 mL/min , giving a conversion of 71%. In this case, 88% of the permeate flow was obtained in the first cassette, with the remaining 12% obtained from the second cassette, giving an overall concentration factor of 3.3. The critical flux for the second cassette was estimated as Jfoul = 53 LMH based on Equations (2) and (3), using the calculated inlet flow rate and the bulk nanoparticle concentration after accounting for the permeate removal in the first cassette. This is well above the measured permeate flux in the second cassette, which was 19 LMH (based on the area of only the second cassette). These results demonstrate that it is possible to design SPTFF processes to operate at higher conversion simply by increasing the total membrane length, e.g., by using two cassettes in series, with the filtrate fluxes reduced to minimize fouling and particle loss.The effect of module length on SPTFF performance was examined by using two Pellicon4 due to the very dilute suspensions common in viral vector processing.The data obtained in this study provide the first published investigation of the use of SPTFF for suspensions of ≈100 nm model nanoparticles with size and charge similar to that of commercially relevant lentivirus. Flux-stepping experiments identified the presence of two distinct values of the critical flux: one associated with particle accumulation within the module and one associated with membrane fouling. Both critical fluxes increased with increasing feed flow rate and decreasing nanoparticle concentration. This behavior was effectively described using a modified concentration polarization model, with the critical particle concentration at the membrane surface corresponding to a particle volume fraction of approximately 7.5%. Note that the degree of concentration polarization in this system, defined as the ratio of the wall to bulk nanoparticle concentrations, was ≈10The model was used to identify conditions that should provide sustainable operation during long-duration SPTFF experiments. Data obtained during a continuous SPTFF experiment showed minimal increase in TMP or particle loss for operation below the boundary-layer and fouling critical flux values. Extrapolation of the TMP data suggests that it may be possible to operate under these conditions for upwards of 6 weeks, although this process only provided a concentration factor of 2.2. Higher concentration factors can be achieved by increasing the membrane path length, e.g., using a holder that allows operation with two cassettes in series.The simple concentration polarization model developed in this study was in good agreement with experimental data at low to moderate conversions, but it was unable to describe the behavior at high conversions, since it does not account for the large variations in feed flow rate, bulk particle concentration, and local critical flux in the SPTFF module under these conditions. Future studies will be required to extend this framework to the design and operation of SPTFF systems that can provide high single-pass concentration factors, and to determine the generality of this approach for the concentration of actual lentivirus suspensions of interest in gene therapy manufacturing. Additionally, these studies will need to consider the effects of the shear flow, both in the module and the inlet/outlet ports, on the infectivity of the lentivirus.


Type 2 Diabetes (T2DM) prevalence is increasing in low- and middle-income countries along with high levels of obesity which vary according to socioeconomic and contextual characteristics. We aim to estimate the prevalence of T2DM and obesity in men and women in a hard-to-reach rural area in northern Ecuador considering socio-demographic characteristics.Cross-sectional descriptive study based on a population-based survey in the Eloy Alfaro health district of Esmeraldas between October 2020 and January 2022. We collected sociodemographic information and risk factors for non-communicable diseases with an adapted version of the STEPS survey, performed oral glucose tolerance tests, biochemistry and took physical measurements. We estimated the prevalence of T2DM, obesity, and calculated Odds Ratios (OR) with confidence intervals by logistic regression in Stata v.15.The overall prevalence of T2DM was 6.8% (CI95%: 4.9–8.7), markedly higher among women compared to men . The risk of having T2DM in women was 5 times higher than in men adjusting for age, ethnicity, employment, household earnings and obesity . Regarding age, the risk of T2DM increased by 6% per year . Obesity prevalence was 30.8% (CI95%: 27.3–34.3), in women was nearly three times higher than in men . Indigenous women had a lower prevalence of obesity compared with the Afro-Ecuadorian women  after adjusting for age, employment status, household earnings and setting.We found alarming differences between the prevalence of T2DM and obesity in women and men that may be explained by gender roles, exacerbated in the rural context. Health promotion measures with a gender perspective should be adapted according to the characteristics of isolated rural contexts. Type 2 Diabetes mellitus (T2DM) is a major public health problem due to its increasing prevalence, high morbidity and mortality, and high healthcare costs. Although the disease now affects more than 500 million people, low- and middle-income countries are the most affected and already have four-fifths of the population with diabetes . MoreoveAccording to the CARMELA study, individuals with obesity are two to three times more likely to have non-communicable diseases (NCDs) such as type 2 diabetes (T2DM), hypertension, and dyslipidaemia . With 63Although diabetes and obesity affect all age groups, there are socioeconomic and contextual differences that influence disease risk, such as ethnicity, gender, region or income , 8. SevePopulation-based cross-sectional study conducted in the Eloy Alfaro health district of Esmeraldas Province, Ecuador.The survey was conducted between October 2020 and January 2022 in the health district 08D02 (Eloy Alfaro) in Esmeraldas Province, a densely forested rural area in the northwest coastal region of the country with a population of 45,629 (est. 2020) . Differehttp://www.qgis.org) from the residential portions of the census sections in the urban centres (207 in Borbón and 121 in Limones). Community health promotors engaged by the Centre of Community Epidemiology and Tropical Medicine (CECOMET) visited the closest house to each GPS point and listed all individuals that resided within the house. We randomly selected one individual from the list and if they were not present when the survey team visited, the survey team tried to reschedule their visit up to 3 times, after which the GPS point was replaced. At times there was a significant time lag from listing the residents of the house to inviting the selected individual to participate in the study. The survey team visited new GPS points until the proposed sample size was achieved. We intended to recruit 480 individuals from the 150 rural communities by multistage stratified cluster sampling. Firstly, each community was classified according to the majority ethnicity  and isolation (three categories according to the distance and time from the main urban area following the riverbeds) to define a total of 12 strata. We then selected 60 community clusters, whereby a number of communities were selected in each stratum with disproportionate allocation . Finally, from each cluster, we selected 8 participants using simple random sampling which was possible thanks to an existing census developed and updated between November 2018 and January 2020 by community health promotors in collaboration with CECOMET.As described in the study protocol, we proposed a sample size of 720 individuals  assumingThe survey team was made up of 4 women and 4 men aged between 24 and 57 from different cultural and ethnic groups, all with healthcare training . At least one member of the team was able to translate questions verbally to Cha’palaa (the language spoken by the indigenous population in the area) when required. We collected information on sociodemographic characteristics and health-related behaviours through face-to-face questionnaires in the participants’ homes. We used the WHO STEPS NCD risk factor survey forms , composeThe main outcome variables of this study are type 2 diabetes and obesity. The diagnosis of T2DM was primarily made using the OGTT  as it is considered the gold standard for diagnosing T2DM due to its higher sensitivity according to the IDF guidelines . For indWe categorised the following sociodemographic and behavioural variables to facilitate the analysis and interpretation. Age was considered as a continuous variable and when required categorised into 3 groups . Self-reported ethnicity was simplified to Afro-Ecuadorian, Mestizo and Indigenous. We classified education into three levels . Marital status was classified into two categories: partnered (married or free union) or unpartnered . Employment status was dichotomised into formal employment  or not in formal employment  due to the low frequencies in some categories. The item for estimated household earnings was divided according to the median ($0 to $100 and over $100). Tobacco consumption was classified into three categories . Alcohol consumption was calculated based on the reported frequency and quantity of alcohol intake and classified as low-risk consumption when individuals consumed less than 60 g/day for men and 40 g/day for women. Physical activity included both recreational activities and work and is reported according to the WHO recommendation of at least 150 min of moderate to high-intensity activity per week. We considered diets to be unhealthy considering consumption of fruit and vegetables (< 2 portions/day), salt  and sugar . Although the recommended fruit and vegetable consumption is 5 pieces per day, we used 2 pieces as a reference due to the low fruit and vegetable consumption in this population.We conducted all statistical analyses using Stata version 15.0 . We described the prevalence of our main outcome variables with 95% confidence intervals, disaggregated by sex. We compared the sociodemographic and behavioural characteristics using proportions and Fisher’s exact test for categorical variables, and the mean and Student’s t-test for continuous variables. We used logistic regression to estimate Odds Ratios (ORs) with 95% confidence intervals and created a multivariable model to assess the role of sex in diabetes and obesity (separately) using backward elimination considering all variables that are known risk factors for diabetes or obesity, were associated with the outcome variable (diabetes or obesity) in the univariate analysis with at least a p-value ≤ 0.1 or showed potentially meaningful differences between men and women. Missing data were excluded from the logistic regression analysis. We considered confounding to be present when the OR for sex varied by at least 10% after removing the variable from the model. The factors associated with obesity in men and women are reported separately due to the differences observed (effect modification). We considered the pertinence of performing the analysis segregated by ethnicity due to marked cultural differences relating to living and working conditions among the indigenous population. We performed a sensitivity analysis excluding the indigenous population and found no impact in the findings presented here. Thus, a global analysis is presented where ethnicity is included as a potential explanatory variable. All analyses considered the sampling strategy used in the survey and were performed by sex to facilitate the gender analysis in the interpretation of the results. Missing data were excluded from the models.We randomly selected 791 individuals from the total of 13,687 residents living in 150 rural communities in Eloy Alfaro from which 490 completed the survey (78% of those who met the inclusion criteria) and 469 also completed physical and biological measurements. In the urban communities of Borbón and Limones, 241 (81%) completed the survey and 210 (71%) provided physical and biological measurements  reported unhealthy diets, where low consumption of fruit and vegetables stands out, with more than three in four individuals having less than two portions ). Two out of five people reported consumption of sweets or sweetened drinks 5 or more days per week (40.5%), contrasting with the low consumption of processed foods high in salt (3%). Men answered always adding salt to foods (19.7%) more frequently than women (12.8%), while there was no difference in the addition of sugar to beverages (37%) (data not shown in table).Forty-six participants had T2DM (47.8% were unaware of their condition at study recruitment), giving an overall prevalence of 6.8% (CI95%: 4.9–8.7). The prevalence was markedly higher among women compared to men . The risk of having T2DM in women was 5 times higher than in men adjusting for age, ethnicity, employment, household earnings and BMI . The prevalence of obesity in women was three times higher than in men . The prevalence of obesity among women under the age of 65 was particularly high, with over half of the women aged between 40 and 64 years old being obese and 42.4% of younger women aged 18–39  with barely any difference between men (2.6%) and women (2.8%) –22. AnotHowever, while both men and women are exposed to the same context characteristics, the social and cultural norms differ. As shown in other studies, the employment characteristics of the sample shows how gender roles are strongly entrenched in rural areas, permeating the daily activities of men, who have the role of breadwinners, and women, who care for the family and household –28. The Another interesting finding is the high proportion of people who eat an unhealthy diet. The low diversity of food groups, the low consumption of fruits and vegetables, the high consumption of processed foods, and industrially produced beverages high in refined sugar show a clear pattern of consumption associated with overweight, obesity and diabetes. The reasons for this type of diet are not only the lack of availability in the area and the high cost of buying fruits and vegetables but also resulting from an important cultural aspect. Ancestors subsisted on green plantain, yucca and proteins from hunting and fishing, and today a satiating diet high in carbohydrates and low in fruit and vegetable consumption remains predominant. The fact that men consume more vegetables could be explained by their improved access to them while working in the fields, while women tend to eat available foods in the household.Our randomly selected population-based methodology provides the study with a representative sample of the Eloy Alfaro district as a major strength, along with around an 80% response rate thanks to the fieldwork experience of CECOMET, an organization of lay health promoters established in the region for more than 40 years. The COVID-19 pandemic influenced the study in several ways that could have affected the results: Firstly, the duration of results collection was longer than planned due to the movement restriction measures and protocol adjustment we carried out to prevent COVID infections. This increased the time lag between population census and data collection, which may have caused higher rates of displaced or deceased persons in our sample, along with migration to other regions to cope with the economic difficulties of the pandemic. Secondly, due to the impact of the pandemic on the economy of the population, information about household income could be affected, as well as physical activity and diet among other health behaviours –36. WhilIn contrast with other studies, our sample did not detect differences in T2DM and obesity by socioeconomic characteristics  –39. One These results may not be generalisable to a broader range of populations living in different rural areas, but they further support the idea of gender roles having a major impact on health inequities , 41. FurGender roles play an important role in the higher prevalence of obesity and diabetes in women compared to men, especially in hard-to-reach rural areas with a similar low socioeconomic status. Further research on the differential impact of gender roles on NCDs between men and women is needed. Gender-sensitive prevention and health promotion programmes should be implemented in order to reduce obesity and the burden of T2DM.


Extracellular vesicles (EVs) are released by all cells into biofluids such as plasma. The separation of EVs from highly abundant free proteins and similarly sized lipoproteins remains technically challenging. We developed a digital ELISA assay based on Single Molecule Array (Simoa) technology for ApoB-100, the protein component of several lipoproteins. Combining this ApoB-100 assay with previously developed Simoa assays for albumin and three tetraspanin proteins found on EVs , we were able to measure the separation of EVs from both lipoproteins and free proteins. We used these five assays to compare EV separation from lipoproteins using size exclusion chromatography with resins containing different pore sizes. We also developed improved methods for EV isolation based on combining several types of chromatography resins in the same column. We present a simple approach to quantitatively measure the main impurities of EV isolation in plasma and apply this approach to develop novel methods for enriching EVs from human plasma. These methods will enable applications where high-purity EVs are required to both understand EV biology and profile EVs for biomarker discovery. Extracellular vesicles (EVs) are membrane vesicles released by all cells. EVs contain RNA and protein cargo from their cell of origin and are a promising class of biomarkers in biofluids such as plasma . Since EWe have previously developed a framework for comparing EV isolation methods by measuring three tetraspanin transmembrane proteins present on EVs  and albumin . We usedIn this work, we developed a Simoa assay for ApoB-100, the major protein component of several lipoproteins. ApoB-100 is present on low-density lipoproteins (LDL), intermediate-density lipoproteins (IDL), and very low-density lipoproteins (VLDL) . By combTo measure lipoproteins, we developed a Simoa assay against ApoB-100, the protein component of lipoproteins LDL, IDL, and VLDL. Simoa is a digital ELISA method where individual immuno-complexes are trapped on magnetic beads that are loaded into microwells that fit, at most, one bead per well. Counting the ‘on wells’ thus translates to counting individual protein molecules, providing much higher sensitivity than traditional ELISA . First, We first investigated whether EVs can be separated from ApoB-100-containing lipoproteins using existing techniques. We tested EV separation based on size using size exclusion chromatography (SEC) columns with three different resins . We collected 0.5 ml fractions after performing SEC and used Simoa to measure CD9, CD63, CD81, albumin, and ApoB-100 in each fraction . As in oBecause separating EVs from lipoproteins based on size alone was not fruitful, we attempted to separate EVs from lipoproteins based on other properties. We first investigated the separation of EVs from lipoproteins and albumin using density gradient ultracentrifugation (DGC). We loaded 1 ml of plasma on an iodixanol gradient and performed ultracentrifugation for 16 hr. We then collected 1 ml fractions and used Simoa to measure tetraspanins, albumin, and ApoB-100 in each fraction. We found that we could readily separate EVs from ApoB-100-containing lipoproteins  using DGWe next considered whether SEC could be modified to maximize EV yield while removing both free proteins and lipoproteins. First, we wanted to assess the absolute recovery of EVs by SEC using Sepharose CL-6B, the resin with the highest EV yield . We tookWe then decided to take advantage of the property that ApoB-100 is positively charged , while EWe compared EV purification from plasma using SEC, DMC, and TMC columns. We first used electron microscopy to image EVs from each column. We found that, although lipoproteins were still present, DMC and TMC led to a higher purity of EVs relative to lipoproteins . We thenTo assess the utility of highly pure EVs isolated with TMC, we performed mass spectrometry-based proteomic analysis. Performing mass spectrometry on EVs from plasma is challenging because levels of both free proteins and lipoproteins are several orders of magnitude higher than those of EV proteins . Using TSingle-use chromatography columns, whether SEC to maximize EV yield or TMC to maximize EV purity, represent an attractive platform for isolating EVs from clinical samples as they are inexpensive and take a short time to run . The thrIn this work, we developed methods to enrich EVs from both lipoproteins and free proteins in plasma based on our ability to measure proteins associated with these different components using ultrasensitive assays. First, we developed and validated a Simoa assay for ApoB-100. We then combined this assay with previously developed Simoa assays for the tetraspanins CD9, CD63, and CD81, as well as albumin . With thPlasma contains several types of lipoproteins with varying protein and lipid compositions. Although there is not a single present on all lipoproteins, we chose to measure ApoB-100, as it is a protein component of several lipoproteins  that overlap in size with EVs . We evalWe used our assays to develop novel methods for separating EVs from lipoproteins. A previous study described DMC columns that deplete lipoproteins by combining SEC using Sepharose CL-4B with a second bottom layer of cation exchange resin . We modiThis work presents a framework for quantitatively comparing EV isolation methods. There is not a single optimal way to isolate EVs because the purification method must be matched to the application; therefore, it is crucial to have effective ways of comparing both the yield and purity of different isolation methods. We developed TMC columns for applications where EVs of very high purity are needed and optimized these columns for EV purification from plasma using our Simoa assays. We envision these columns will be particularly useful for EV biomarker discovery using proteomics, where EV contamination with lipoproteins and free proteins prevents deep coverage . A receng for 10 min. The supernatant was filtered through a 0.45-μm Corning Costar Spin-X centrifuge tube (MilliporeSigma) at 2000 × g for 10 min. For all direct comparison experiments, plasma was first pooled and 1 ml used per EV isolation.Pooled human plasma (collected in K2 EDTA tubes) was ordered from BioIVT. Plasma was thawed at room temperature and centrifuged at 2000 × Simoa assays for CD63, CD81, and albumin were performed as previously described . Due to EV yield was calculated for EV isolation from plasma for SEC (fractions 7–10), DMC (fractions 9–12), or TMC (fractions 9–12). Levels of the three tetraspanins CD9, CD63, and CD81 in the designated EV fractions and their levels in total plasma were measured using Simoa. The yield of each tetraspanin was calculated by dividing its level in the EV fraction by its level in total plasma. The total EV yield was then calculated as the average of the three ratios. Relative EV yield for comparing multiple conditions was calculated by dividing the EV yield of each condition by the highest EV yield of all the conditions. The purity of EVs with respect to free proteins or lipoproteins was determined by dividing the relative EV yield by relative levels of albumin or ApoB-100 in the EV fractions.Antibodies were first cross-tested using serial dilutions of purified protein standard. The antibody pair with the highest signal-to-background ratio was chosen. The assay was validated using dilution linearity and spike and recovery experiments. Plasma samples were diluted serially in the assay-specific buffer, a dilution factor in the middle of the linear range was chosen to be the dilution factor for the spike and recovery test. Three protein concentrations of purified ApoB-100 were spiked into the diluted plasma from the top calibrator used in the calibration curve. All recoveries fell in the range of 85–100% . The assSepharose CL-2B, Sepharose CL-4B, and Sepharose CL-6B resins (Cytiva) were washed with PBS in a glass bottle. The volume of resin was washed three times with an equal volume of PBS before use. Econo-Pac Chromatography columns (Bio-Rad) were packed with resin and a frit was inserted into the column above the resin. For all columns in Sample (1 ml plasma) was loaded once PBS from wash had finished going through the column. Once the sample fully entered the column, 0.5 ml fractions were collected. PBS was then added in volumes equal to those being collected for one fraction (0.5 ml) or a set of four fractions (2 ml). In experiments where just the EV fractions were collected, fractions 7–10 were collected for SEC and fractions 9–12 were collected for DMC and TMC.DGC was performed as previously described . Four laCarbon-coated grids  were glow discharged, and 5 μl of the sample was absorbed to the grid for 1 min. Excess sample was blotted with a Whatman paper. The grid was then stained with 5 μl 1% uranyl acetate for 15 s and excess stain was blotted. Samples were imaged on a JEOL 1200EX – 80 kV transmission electron microscope with an AMT 2k CCD camera.g for 15 min at 4°C. Supernatant was removed and pellet was left to air dry for 10 min. Sample was then sent to Bruker for proteomics analysis. Sample was resuspended in 50 mM triethylammonium bicarbonate (Thermo Fisher Scientific) and digested for 2 hr at 50°C using Trypsin Platinum (Promega) using 1:50 Trypsin to sample ratio by mass. After evaporating solution in a Vacufuge (Eppendorf) ar 45°C, sample was resolubilized in 10 μl 0.1% formic acid (Thermo Fisher Scientific). Next, 1.5 μl of sample was injected into C18 tips (Evosep) and eluted into a 25-cm length 150 μm internal diameter PepSep analytical column packed with 1.5 μm C18 beads (Dr. Maisch). Sample was eluted into a Bruker timsTOF HT. A gradient from 3% of to 28% of 0.1% formic acid in acetonitrile at 63 min was then increased to 85% until 80 min. Data were analyzed using Spectronaut 17 (Biognosys) software for data-independent acquisition (DIA). A false discovery rate of 1% was used at both the peptide and protein levels. Keratin proteins (likely contaminants) were manually removed from the list.EVs were isolated from 1 ml plasma using TMC columns with a 2-ml bed volume bottom layer of 2:1 of Fractogel EMD SO3- (M) (MilliporeSigma) to Capto Core 700 (Cytiva) and 10-ml bed volume top layer of Sepharose CL-6B (Cytiva). EVs were concentrated using Amicon Ultra-2 centrifugal 10 kD filter (MilliporeSigma). After concentration, EV protein was precipitated by adding 9 volumes of 100% ethanol to 1 volume of EVs, vortexing and leaving at −20°C for 30 min. Sample was then centrifuged at 16,000 × https://github.com/Wyss/automated-chromatography/,  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Suzanne Pfeffer as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: James Byrd (Reviewer #2); Qing Zhou (Reviewer #3).Thank you for submitting your article "Improved Isolation of Extracellular Vesicles by Removal of Both Free Proteins and Lipoproteins" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Please observe that, due to the methodological nature of your study, it is extremely important to make sure that you consider the experimental suggestions provided by reviewers 1-3;2) We expect this manuscript to be of use to a wide audience of biologists since the EV community is growing rapidly. Please also consider the suggestions for improving the manuscript's presentation and discussion depth.Reviewer #1 (Recommendations for the authors):The paper can be very useful, but can be improved by addressing a few comments:1. 'We then decided to take advantage of the property that ApoB-100 is positively charged (12), while EVs are generally negatively charged (13) to separate EVs from lipoproteins.'LDL particles, as particles, are negatively charged. See, for example:• La Belle M, Blanche PJ, Krauss RM. Charge properties of low-density lipoprotein subclasses. J Lipid Res. 1997 Apr;38(4):690-700. PMID: 9144084.• Reynolds L, Mulik RS, Wen X, Dilip A, Corbin IR. Low-density lipoprotein-mediated delivery of docosahexaenoic acid selectively kills murine liver cancer cells. Nanomedicine (Lond). 2014 Jul;9(14):2123-41. doi: 10.2217/nnm.13.187. Epub 2014 Jan 7. PMID: 24397600; PMCID: PMC4156561.The paper cited shows the charge of certain ApoB100 peptides!LDL, VLDL, etc., contain many other components, not just ApoB100.2. 'We compared EV isolation from plasma using SEC, DMC, and TMC columns. We first used electron microscopy to image EVs from each column and found that TMC led to EVs of the highest purity .'The majority of the particles present on TEM images are lipoproteins, more likely VLDL or Chylomicrons. One can notice more dense round shaped particles, different from 'doughnut-shaped' EVs. For example, Figure 4B TMC column TEM image has a single EV, while eleven lipoprotein particles are visible.3. 'Using TMC, we were able to detect 780 proteins from EVs isolated from only 1 ml of plasma (Supplementary table 1). These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume.'According to the dataset provided in the supplementary materials, ApoB100 remains the most abundant protein, as well as other lipoproteins, including the ones specific for High-density lipoproteins. Also, a lot of free proteins were identified. In my experience, 780 proteins after albumin depletion are not as high for raw plasma proteomics analysis.4. 'In this work, we developed methods to separate EVs from both lipoproteins and free proteins in plasma based on our ability to measure proteins associated with these different components using ultrasensitive assays.'This statement is not confirmed by proteomics analysis and TEM images. Authors can use the more accurate term 'enriched EVs' compared to lipoproteins. Also, depletion of chylomicrons and HDL were not shown.Reviewer #2 (Recommendations for the authors):I have a few suggestions for the authors' consideration:It may be more accurate to describe the free proteins and lipoproteins as impurities , rather than contaminants The introduction could be more laser-focused on plasma since that's the focus of the work, and the lipoprotein issue may be greatly magnified compared to in e..g, urineA few more words about the principle of the Simoa assay could be useful to the reader"These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume." Some comparison to the prior state of the art would bolster this conclusion.Beyond the relative yields, can synthetic EV spike-ins be used to calculate the absolute yields?Figure legend for Figure 2: The EV yield is calculated "by averaging the ratios of CD9, CD63, and CD81." I think this be explained in clearer terms. This is the average of what is taken in ratio to what? This comment applies to a variety of other places in the paper where ratios are mentioned but without precise definitions of the numerator and denominator."Simoa measurements in the designated fractions for CD9, CD63, and CD81 are taken as a ratio relative to measurements of these proteins from diluted plasma and these three rations are then averaged to calculate recovery." Here again, I think this can be explained with a clearer description of what is in the numerator and denominator of these ratios It would be helpful if the authors wrote down the calculation they're referring to here: "by averaging the ratios of each of the tetraspanin levels between conditions" The numerator and denominator in the ratios are not as clear as they ideally could be.A challenge is that after the removal of the main known impurities, whether additional proteins identified in proteomics experiments are in the vesicles or free proteins is not simple to distinguish. How does the relative quantitation of proteins after the application of the new column compare with plasma proteomics performed without enrichment for extracellular vesicles? Is the rank order of abundance similar?The authors have shared all the relevant data, as well as additional resources that will be useful to the scientific community.Reviewer #3 (Recommendations for the authors):1) The "relative EV yields" was used to quantify the amount of EVs across different methods. The authors described it as "the average of the ratios of CD9, CD63, and CD8". Please describe how this is calculated exactly. The same for relative EV/ Albumin and relative EV/ApoB-100.2) Please also calculate the relative EV yields, relative EV/ Albumin, and relative EV/ApoB-100 for the density gradient centrifugation experiment. Also, Figure 3 is a bit hard to follow due to the distance between the bars.3) For Figure 5, it will be great to draw lines between 2 replicas to provide a sample-to-sample comparison.4) Different fractions were selected for SEC (fractions 7-10), DMC (fractions 9-12), and TMC (fractions 9-12). Please provide the reason and the potential influence of the fairness of the comparison.5) Figure 5—figure supplement 2, the decrease of EV yield cross SEC, DMC, and TMC is different for CD9, CD63, and CD8 (increasing in DMC). What is the reason?6) The mechanism for the better performance of TMC needs to be discussed. Essential revisions:1) Please observe that, due to the methodological nature of your study, it is extremely important to make sure that you consider the experimental suggestions provided by reviewers 1-3;2) We expect this manuscript to be of use to a wide audience of biologists since the EV community is growing rapidly. Please also consider the suggestions for improving the manuscript's presentation and discussion depth.Thank you to the reviewers for their suggestions. We address the specific comments below.Reviewer #1 (Recommendations for the authors):The paper can be very useful, but can be improved by addressing a few comments:1. 'We then decided to take advantage of the property that ApoB-100 is positively charged (12), while EVs are generally negatively charged (13) to separate EVs from lipoproteins.'LDL particles, as particles, are negatively charged. See, for example:• La Belle M, Blanche PJ, Krauss RM. Charge properties of low-density lipoprotein subclasses. J Lipid Res. 1997 Apr;38(4):690-700. PMID: 9144084.• Reynolds L, Mulik RS, Wen X, Dilip A, Corbin IR. Low-density lipoprotein-mediated delivery of docosahexaenoic acid selectively kills murine liver cancer cells. Nanomedicine (Lond). 2014 Jul;9(14):2123-41. doi: 10.2217/nnm.13.187. Epub 2014 Jan 7. PMID: 24397600; PMCID: PMC4156561.The paper cited shows the charge of certain ApoB100 peptides!LDL, VLDL, etc., contain many other components, not just ApoB100.It is certainly true that ApoB-100 is not the only protein component of lipoproteins. We initially chose ApoB-100 as it was the main protein we detected by mass spectrometry after isolating EVs from plasma using SEC. This makes sense, as ApoB-100 is present in several lipoproteins that overlap in size with EVs (1). Regarding the question of whether ApoB-100 can be depleted based on cation exchange, it was not clear that this would work just based on the knowledge that there are positively charged peptides in ApoB-100, but we were inspired by previous work reporting the development of the dual mode chromatography columns (2). We were able to confirm the ability of cation exchange resin to deplete lipoproteins containing ApoB-100 with our ApoB-100 Simoa assay.2. 'We compared EV isolation from plasma using SEC, DMC, and TMC columns. We first used electron microscopy to image EVs from each column and found that TMC led to EVs of the highest purity .'The majority of the particles present on TEM images are lipoproteins, more likely VLDL or Chylomicrons. One can notice more dense round shaped particles, different from 'doughnut-shaped' EVs. For example, Figure 4B TMC column TEM image has a single EV, while eleven lipoprotein particles are visible.We agree that the majority of particles in by TEM are lipoproteins and not EVs. However, the TEM demonstrates that there is an increase in purity of EVs relative to lipoproteins for TMC/DMC compared to SEC using a technique other than Simoa. Despite the significant reduction of ApoB-100 in TMC, there is still ApoB-100 present. The ApoB-100 can be further reduced by increasing the amount of cation-exchange resin in the TMC column. We provide additional data in Figure 4-supplemental figure 1 showing how varying the volumes of the Fractogel cation exchange resin and the Capto Core 700 resin changes the levels of tetraspanins, albumin, and ApoB-100. Increasing the amount of cation-exchange resin reduces ApoB-100, but also leads to a decrease in tetraspanins.3. 'Using TMC, we were able to detect 780 proteins from EVs isolated from only 1 ml of plasma (Supplementary table 1). These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume.'According to the dataset provided in the supplementary materials, ApoB100 remains the most abundant protein, as well as other lipoproteins, including the ones specific for High-density lipoproteins. Also, a lot of free proteins were identified. In my experience, 780 proteins after albumin depletion are not as high for raw plasma proteomics analysis.Both the electron microscopy (discussed above) and mass spectrometry analysis show that ApoB-100 is still present after TMC. We think it is likely impossible to fully separate EVs from lipoproteins and free proteins, but by increasing the ratio of EVs to lipoproteins and free proteins, as we have done with the TMC column, the depth of coverage for proteomics can be greatly increased. Our focus here was to provide a framework for quantitatively comparing EV isolation method yield and purity (with respect to both lipoproteins and free proteins); mass spectrometry is one application where having high purity EVs is important. The optimal EV isolation method will vary for different applications and will be highly dependent on the amount of starting material necessary for the technique being used. In mass spectrometry, different instruments have different requirements for the amount of input protein material necessary, so the optimal isolation technique will be instrument-dependent. We provide additional data in Figure 4-supplemental figure 1 (discussed above) showing how increasing the amount of cation-exchange resin allows for further depletion of ApoB-100. We did not, however, test all the different TMC variations using mass spectrometry. We are currently working on optimizing mass spectrometry for EVs from plasma, but this work is beyond the scope of this paper, as it also depends on many parameters downstream of EV isolation. Nonetheless, 780 proteins is high for a single-step technique from a starting volume of only 1 mL. For comparison, one study used a three step protocol (PEG precipitation followed by iohexol gradient fractionation and SEC) to enrich EVs from 1 mL plasma and reported detecting 250 proteins (3). Another group reported detecting 1187 proteins from plasma using ultracentrifugation, density gradient, and then SEC, but started with 40-80 mL plasma (4). We’ve added this information to the Discussion section.4. 'In this work, we developed methods to separate EVs from both lipoproteins and free proteins in plasma based on our ability to measure proteins associated with these different components using ultrasensitive assays.'This statement is not confirmed by proteomics analysis and TEM images. Authors can use the more accurate term 'enriched EVs' compared to lipoproteins. Also, depletion of chylomicrons and HDL were not shown.Thank you for the suggestion. We modified the wording of this sentence and replaced “separate” with “enrich.” We did not specifically analyze cholymicrons or HDL in this study. As mentioned in the Discussion section, Simoa assays for protein components of lipoproteins other than ApoB-100 (such as Apo-A1 for HDL) could be developed and an identical analysis framework could be employed. In this study, we decided to focus on the most abundant protein component of lipoproteins found in our mass spectrometry analysis, ApoB-100.Reviewer #2 (Recommendations for the authors):I have a few suggestions for the authors' consideration:It may be more accurate to describe the free proteins and lipoproteins as impurities , rather than contaminants We’ve changed “contaminants” to “impurities.”The introduction could be more laser-focused on plasma since that's the focus of the work, and the lipoprotein issue may be greatly magnified compared to in e..g, urineWe’ve added this clarification to the last paragraph of the introduction.A few more words about the principle of the Simoa assay could be useful to the readerWe’ve added two sentences about Simoa to the Results section."These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume." Some comparison to the prior state of the art would bolster this conclusion.We have added these comparisons (as mentioned in response to one of the comments from Reviewer #1) to the Discussion section.Beyond the relative yields, can synthetic EV spike-ins be used to calculate the absolute yields?EV spike-ins from cell lines can be used, but we don’t think this is a way to provide absolute quantification since existing EV analysis techniques are unable to provide absolute quantification of these cell-culture derived EVs (as there are non-EV particles in cell culture media as well). Additionally, using EV spike-ins assumes that these EVs have very similar properties to plasma EVs, which may not necessarily be the case.Figure legend for Figure 2: The EV yield is calculated "by averaging the ratios of CD9, CD63, and CD81." I think this be explained in clearer terms. This is the average of what is taken in ratio to what? This comment applies to a variety of other places in the paper where ratios are mentioned but without precise definitions of the numerator and denominator."Simoa measurements in the designated fractions for CD9, CD63, and CD81 are taken as a ratio relative to measurements of these proteins from diluted plasma and these three rations are then averaged to calculate recovery." Here again, I think this can be explained with a clearer description of what is in the numerator and denominator of these ratios It would be helpful if the authors wrote down the calculation they're referring to here: "by averaging the ratios of each of the tetraspanin levels between conditions" The numerator and denominator in the ratios are not as clear as they ideally could be.We have added a paragraph about the EV yield and purity calculations to the Methods section and also expanded the explanation in the Results section.A challenge is that after the removal of the main known impurities, whether additional proteins identified in proteomics experiments are in the vesicles or free proteins is not simple to distinguish. How does the relative quantitation of proteins after the application of the new column compare with plasma proteomics performed without enrichment for extracellular vesicles? Is the rank order of abundance similar?We agree that distinguishing whether a given protein in an EV preparation is truly in EVs or stuck to the outside remains a major challenge in the field. One way to approach this challenge is with proteinase protection assays, but significant optimization is required to ensure both sufficient proteinase activity and full inactivation of proteinase before analyzing the protected EV contents. Although comparing the proteomes of total plasma and plasma EVs was beyond the scope of this study, such a comparison  has been performed before (5). In that comparison, fewer than half of the proteins detected between total plasma and plasma EVs were shared, and for those proteins that were shared, the correlation of protein abundances was relatively poor.The authors have shared all the relevant data, as well as additional resources that will be useful to the scientific community.Reviewer #3 (Recommendations for the authors):1) The "relative EV yields" was used to quantify the amount of EVs across different methods. The authors described it as "the average of the ratios of CD9, CD63, and CD8". Please describe how this is calculated exactly. The same for relative EV/ Albumin and relative EV/ApoB-100.We have expanded the explanation of how we calculate EV yield in the Results section and added a paragraph on this to the Methods section.2) Please also calculate the relative EV yields, relative EV/ Albumin, and relative EV/ApoB-100 for the density gradient centrifugation experiment. Also, Figure 3 is a bit hard to follow due to the distance between the bars.We have replotted Figure 3 to display each marker separately. We have also redone the density gradient centrifugation experiment to include SEC from the same sample of plasma, as well as a condition combining density gradient centrifugation and SEC. We have added Figure 3—figure supplement 1 to compare relative EV yields, EV/albumin and EV/ApoB-100 for these conditions.3) For Figure 5, it will be great to draw lines between 2 replicas to provide a sample-to-sample comparison.For the experiment in Figure 5, we started with 16 samples (1 mL each) of the same pooled plasma. We ran eight of these samples using the automated SEC device and the other eight samples performing SEC manually. Each plotted point for a given marker is an average of two Simoa measurements . We reworded description of this experiment in the Results section and the figure caption to clarify this.4) Different fractions were selected for SEC (fractions 7-10), DMC (fractions 9-12), and TMC (fractions 9-12). Please provide the reason and the potential influence of the fairness of the comparison.-6B resin, and the DMC and TMC columns have 2 mL additional resin below the 10 mL Sepharose CL-6B resin, the difference in column volume leads to a change in where the EVs elute. We decided that taking four 0.5 mL fractions for each column would be best for directly comparing them. To decide which fractions to take, we analyzed the tetraspanins, albumin, and ApoB-100 in each individual fraction .As the SEC column has 10 mL Sepharose CL5) Figure 5—figure supplement 2, the decrease of EV yield cross SEC, DMC, and TMC is different for CD9, CD63, and CD8 (increasing in DMC). What is the reason?We find a difference in yield for the different tetraspanins between SEC and DMC/TMC. One possibility could be a difference in charge between different tetraspanins or subpopulations of EVs containing different tetraspanins.6) The mechanism for the better performance of TMC needs to be discussed.The TMC has improved performance in terms of free protein depletion relative to the SEC because the Capto Core 700 multimodal resin irreversibly binds free proteins that co-elute in the EV fractions. The TMC column has improved performance relative to SEC in terms of lipoprotein depletion for the same reason as the DMC column. The cation exchange resin binds and retains positively charged ApoB-100-containing lipoproteins. We have expanded the explanation of why TMC provides EVs of higher purity than SEC in the Discussion section.


Several studies have shown that dual-energy CT (DECT) can lead to improved accuracy for proton range estimation. This study investigated the clinical benefit of reduced range uncertainty, enabled by DECT, in robust optimisation for neuro-oncological patients.DECT scans for 27 neuro-oncological patients were included. Commercial software was applied to create stopping-power ratio (SPR) maps based on the DECT scan. Two plans were robustly optimised on the SPR map, keeping the beam and plan settings identical to the clinical plan. One plan was robustly optimised and evaluated with a range uncertainty of 3% ; the second plan applied a range uncertainty of 2% (2%-plan). Both plans were clinical acceptable and optimal. The dose–volume histogram parameters were compared between the two plans. Two experienced neuro-radiation oncologists determined the relevant dose difference for each organ-at-risk (OAR). Moreover, the OAR toxicity levels were assessed.e.g. for brainstem D0.03cc in 10 patients, and hippocampus D40% in 6 patients. Furthermore, 12 patients had a reduction in toxicity level for one or two OARs, showing a clear benefit for the patient.For 24 patients, a dose reduction >0.5/1 Gy (relevant dose difference depending on the OAR) was seen in one or more OARs for the 2%-plan; Robust optimisation with reduced range uncertainty allows for reduction of OAR toxicity, providing a rationale for clinical implementation. Based on these results, we have clinically introduced DECT-based proton treatment planning for neuro-oncological patients, accompanied with a reduced range uncertainty of 2%.This study shows the clinical benefit of range uncertainty reduction from 3% to 2% in robustly optimised proton plans. A dose reduction to one or more OARs was seen for 89% of the patients, and 44% of the patients had an expected toxicity level decrease. However, estimating the proton range based on conventional X-ray CT images, so-called single-energy CT (SECT), by the use of an empirical piecewise linear conversion curve leads to an uncertainty in the proton range estimation. This range uncertainty is typically accounted for by adding an extra margin or by applying robust optimisation, with a range uncertainty of 3–3.5% of the proton range. Several factors contribute to the range uncertainty, amongst others CT imaging artefacts, CT-based stopping-power ratio (SPR) estimation, and mean excitation energy.One of the main advantages of proton therapy is the finite range which ensures healthy tissue sparing distally to the tumour. A thorough investigation of the different contributions to DECT-based range uncertainties, including, e.g. CT scanner calibration and I-value uncertainty, was performed by Peters et al, and they found that the range uncertainty was 1.7% for neuro-oncological and 2% for pelvic patients. They implemented DECT-based SPR estimation in clinical routine and showed the benefit of this range reduction on a single patient. Li et al also looked into the size of the range uncertainty when applying DECT, and they as well found the range uncertainty to be around 2%. Furthermore, Kassaee et al have also clinically introduced DECT imaging for neuro-oncological and head-and-neck cancer patients along with a range uncertainty of 2% in their robust optimisation. The clinical benefit of range uncertainty reduction in the robust optimisation has also been investigated by re-optimising proton plans with different levels of range uncertainty for neuro-oncological patients and for head-and-neck cancer patients. It was further shown that the reduced range uncertainty may allow for the use of beam directions which would typically be avoided not to risk overdosage and the placement of a high linear energy transfer (LET) in organs-at-risk (OARs) located distally to the target. However, a comprehensive analysis of the clinical impact on a large cohort of patients is still missing. Furthermore, the effect of reduced range uncertainty needs to be put in a clinical context by estimating the reduction in expected treatment-related toxicity.Dual-energy CT (DECT) has been shown to provide more accurate proton SPR estimation. The aim of this study was therefore to evaluate if the effort of changing clinical procedures to start using DECT for treatment planning would be justified by a clinical benefit to the patients achieved by the reduced range uncertainty.In this study, we investigated the clinical relevance of reducing the range uncertainty from 3%  to 2% (as reported in the literature to be meaningful for DECT-based SPR estimation), based on a sizable cohort of neuro-oncological patients clinically treated with proton therapy. To assess the clinical relevance, we evaluated if the potential dose reduction to the OARs led to a reduction in the expected toxicity level, based on the recently published Radiation Oncology Collaborative Comparison Group (ROCOCO) Performance Scoring System.In this study, 27 neuro-oncological patients were included. The patient characteristics are listed in The DECT scans were acquired in either Dual Source mode with a Siemens SOMATOM Drive scanner  for 15 patients, or in Dual Spiral mode on a Siemens SOMATOM Confidence scanner  for 12 patients. The DECT images were reconstructed applying a Qr40 kernel with beam hardening correction for bone and iterative reconstruction—ADMIRE (Drive) or SAFIRE (Confidence)—at strength level 3. The SECT scans were acquired at 120 kVp and were reconstructed with same reconstruction algorithm, in general the settings for the DECT scans were based on the clinical settings for the SECT scans.syngo.via). The DirectSPR images were stored in DICOM format and had a header information similar to that of CT images, whereby the treatment planning system (TPS) accepted them as CT images. These SPR maps were imported into the TPS RayStation 11A . The voxel-values in the DirectSPR were scaled SPR values, similar to how CT numbers in the Hounsfield unit scale represent scaled X-ray attenuation coefficients, i.e. SPRscaled = (SPR-1)/1000. A regular CT-to-SPR conversion curve could therefore be specified in RayStation, where the scaled SPR values were seen as CT numbers. This conversion curve was linear over the full range, and simply used to rescale the SPR values and since a conversion curve needs to be connected to a CT image to allow for dose computation in RayStation. The SPR values in the DirectSPR images are given at an initial proton energy of 100 MeV, the conversion curve in RayStation was therefore specified at this energy. The SPR estimation in the DirectSPR software is based on the method proposed by Hünemohr et al, and the mean excitation energies used in this software is calculated based on the Bragg additivity rule for both tissues and water.The DECT scans were converted to SPR maps using commercially available software , with either three beams (N = 17) or four beams (N = 10). The number of beams and the beam angles were individually chosen for each patient based on the location and the size of the tumour. For the plans made in this study, all these settings were kept the same as for the original clinical plan, and the settings were also not changed between the two plans created in this study.Each patient had a clinical proton treatment plan created for our Mevion S250i Hyperscan system .The pSECT and the reSECT were rigidly registered as part of clinical routine workflow, and the reSECT and DECT were automatically registered as these had the same frame-of-reference. The structures were rigidly copied from the reSECT to the DECT image as patient movement between the SECT and DECT acquisition was very limited because of proper patient fixation. A python script was used to copy all the plan settings  from the clinical plan created on the pSECT to create a new plan on the DECT scan. For the optimisation, the same objectives were, where possible, used as for the clinical plan (which was optimised on the pSECT), but the objective weights were allowed to be changed and the objectives could be adjusted if needed to obtain a clinical acceptable plan. The clinical acceptability was judged following the clinical procedure, which is based on robust evaluation based on voxel-wise minimum and maximum dose distribution (VWmin/VWmax). For the robust evaluation, the same setup uncertainty (1 mm) and range uncertainty (3%) was used as for the robust optimisation.For each patient, two plans were created on the DECT image. The planning strategy used in this study followed the clinical treatment planning procedures. The first plan denoted “3%-plan” was taken as the reference plan. This plan was robustly optimised with a setup uncertainty of 1 mm, and a range uncertainty of 3%, as also used in clinical practice.i.e. to individually optimise the plan for the patient by reducing the dose to the OARs as much as possible. Therefore, no restrictions were imposed on how much the planners were allowed to change the objective functions or objective function weights between the 3%-plan and 2%-plan. For each patient, the same planner created the 3%-plan and 2%-plan.When a clinically acceptable 3%-plan was obtained, this plan was copied to create a “2%-plan”. This plan had the same beam arrangement and beam settings as the 3%-plan, but the range uncertainty for the robust optimisation and robust evaluation was reduced to 2% (the setup uncertainty was kept at 1 mm). The optimisation objectives were mainly kept the same as for the 3%-plan, but again the treatment planners were allowed to change the optimisation objectives to achieve a clinical acceptable plan, and they were also allowed to change the optimisation objective weights if it was possible to reduce the OAR doses further, in accordance with the as-low-as-reasonably-achievable (ALARA) principle. In general, they were asked to plan the patient following clinical procedures, To evaluate if the 2%-plan provided clinically relevant benefits over the 3%-plan, two separate evaluations were performed. First, the dose–volume histogram (DVH) parameters for the 3%-plan and the 2%-plan were compared. Here, all DVH parameters used in clinical routine to judge if a plan is acceptable were extracted for the two plans. For each OAR DVH parameter, two experienced neuro-radiation oncologists determined which dose threshold they considered as a clinically relevant dose difference . The dos That is, it was assessed if a dose reduction seen for the 2%-plan would also result in a clinical benefit for the patient by leading to a reduction of the estimated risk of toxicity. This scoring system goes from 1 (lowest risk) to 5 (highest risk).Second, to take into account that a dose difference is of less  relevance if the two doses are already far below the dose tolerance for the given OAR, the expected risk of toxicity was also evaluated by applying the ROCOCO Performance Scoring System.e.g. the hippocampus, the DVH parameter was only reported for either the left or the right OAR, depending on which of the two got the highest dose in the 3%-plan. As done in our clinical practice, DVH parameters for mean doses (Dmean) were extracted from the nominal dose distribution, while the maximum doses (quantified by D0.03cc or D3cc) were extracted from the VWmax dose distribution. The target coverage was extracted from the VWmin dose distribution. All plans (both 3%-plans and 2%-plans) met the clinical acceptance criterion for the clinical target volume (CTV) V94% > 98% in the VWmin dose distribution; moreover, all OAR dose constraints were also met for all plans. The dose constraints followed the guideline published by Lambrecht et al. In addition, clinical acceptability also entails that the dose to the OARs is reduced as much as possible, in accordance with the ALARA principle, this was therefore also imposed for both the 3%- and 2%-plans.For bilateral OARs, N = 69; marked in green) were above the clinically defined threshold in favour of the 2%-plan, while only 5%  showed a clinically relevant worsening. The 18 dose differences in favour of the 3%-plan were distributed among 10 patients, but 9 of these patients had other dose differences which were in favour of the 2%-plan. For the one patient who had two DVH parameters in favour of the 3%-plan and no DVH parameters in favour of the 2%-plan (patient 22), still one dose difference in favour of the 2%-plan resulted in a toxicity level decrease, whereby the 2%-plan was preferable. 14 of the patients (52%) had one or more DVH parameters with a clinically relevant dose difference in favour of the 2%-plan, and no DVH parameters in favour of the 3%-plan, showing a clear benefit from the 2%-plan.In The data presented in 98%), the target coverage (quantified by V94%) and the maximum dose (quantified by D0.03cc) for the CTV  had a decrease in the expected toxicity level by one level for one or two OARs, with three patients going from toxicity level 5 to 4, which was considered to be a clear clinical benefit for the patient . Only foIn The aim of this study was to quantify the clinical benefit proton treatment planning with a reduced range uncertainty for neuro-oncological patients. Introducing DECT in the clinical workflow allows for a reduction in range uncertainty which is not possible using SECT-based SPR estimation methods. Although, it might be expected that a range uncertainty reduction would lead to a dose reduction, it is not clear if such a dose reduction will also lead to a clinical benefit by a reduced toxicity probability. A reduction in the expected toxicity while keeping the target coverage similar is one of the main goals of treatment planning. such as CT acquisition settings and artefacts, mean excitation energy and SPR estimation. For an overview of the different contributions to the SPR uncertainty, see amongst others the following references. Utilising DECT to reduce the range uncertainty is one of the ways to improve the treatment planning strategy. Based on the results presented in this study, as well as the evidence in the literature, it is seen that a DECT-based treatment planning workflow is clinically feasible in clinics nowadays. Based on this, we have introduced DECT clinically for our neuro-oncological patients using the DirectSPR algorithm along with a reduced range uncertainty of 2%.There are several contributing factors to the proton range uncertainty,0.03cc) which could be caused by only a small volume having a higher dose in the 2%-plan. For example, for all three patients shown in We observed a reduction in the expected toxicity level for 44% of the patients, which was deemed clinically relevant. However, since the 2%-plan was a new treatment plan optimisation compared to the 3%-plan, there might be the possibility for trade-offs between different OARs which is inherently always the case for reoptimisation, as optimisation is not a deterministic problem. This explains why for some patients, a dose increase was seen for some OARs in the 2%-plan, while a dose reduction was seen for other OARs. However, these dose differences were often small, and mainly seen for the maximum doses  for an OAR. New help structures and objective functions for these could also be introduced in the optimisation of the 2%-plan, while some objective functions applied for the 3%-plan could also be removed in the optimisation of the 2%-plan, especially for maximum dose help structures used for the 3%-plan. In general, the optimisation of both the 3%-plan and the 2%-plan followed the clinical trial-and-error optimisation procedure used to obtain a plan fulfilling all dose constraints, and with the best possible target coverage and lowest possible OAR dose. For some patients, the only changes were to push harder on the dose to the OARs, while for other patients even objective functions for the target had been changed in order to achieve a clinically acceptable plan for the 2%-plan. This mainly happened for challenging plans, were many OAR objective functions were included and where several optimisation help structures had to be introduced. For such plans, it would not have led to a fair comparison if the optimisation objectives had not been allowed to be changed between the two plans, since this would have led to suboptimal plan for the 2%-plan. For the simple plans, where there was room to spare the OARs additionally with the 2%-plan, this was done even though this makes the comparison less straightforward. We admit that allowing for changing the objection functions/objection function weights between the two plans, this study is not only a comparison of using a range uncertainty of 3% or 2% in the robust optimisation. But as the aim was to investigate the clinical benefits of a range uncertainty reduction, this was allowed for, since in a clinical routine the treatment planners would also always try to obtain the best possible plan even for difficult geometries.To have a fair comparison between the two plans, However, such optimisation strategies are currently not available for proton treatment planning for the Mevion proton machine.Automated treatment planning strategies, including multi-criteria optimization, which ensures Pareto optimality can be used to ensure that treatment plans are achieved with the lowest dose to the OARs.The patient cohort in this study was very diverse with a variety of tumour types and locations . The ben Peters et al found the range uncertainty to be 1.7–2% when using a well-calibrated DECT-based SPR estimation method. Therefore, based on the literature, the benefit of a proper validated DECT-based SPR estimation algorithm allows for a range uncertainty reduction from 3 to 2%. Tattenberg et al assessed the influence of different levels of range uncertainty (0–5%). We only compared two range uncertainties: the clinically used (3%) and the level suggested to be feasible when using a DECT-based treatment planning workflow (2%), as our main aim was to assess the clinical benefit of a range uncertainty of 2% (instead of 3%) made possibly by the introduction of DECT (replacing SECT).Several studies have shown the improved SPR estimation accuracy of DECT in comparison to SECT to be of an order which would justify a range uncertainty reduction from 3 to 2%. The LET has a peak slightly beyond the Bragg peak. Reducing the dose beyond the tumour, by decreasing the range uncertainty applied in the robust optimisation, therefore could also have the opportunity to reduce the RBE to the healthy tissue beyond the tumour. However, clear relationships between RBE and LET can be hard to establish. Furthermore, LET calculation and reporting is not yet standardised, and in our clinical practice, the dose distributions are still exclusively used to judge the quality of the treatment plan. We calculated the LET distribution for a few of the patients (data not shown) but did not observe any large changes in LET distribution between the 3%-plan and 2%-plan.The relative biologic effect (RBE) has often been correlated with the dose-averaged LET. However, for neuro-oncological patients, most DECT modes can be used. In this study, we used two different DECT scanners, one with sequential acquisition  and one with simultaneous acquisition (but with a limited field-of-view of 33 cm). Both of these two limitations are of minor importance for neuro-oncological patients, which fit within the limited field-of-view and where no large movement is expected between the two scans, since the patients are fixated in a mask and no internal movement is expected in the brain.Today, several different DECT scanning modes are offered on different commercial scanners, but most DECT modes have some limitations for different patient groups. By applying so-called pseudo-monoenergetic images (PMIs) at different energies, several advantages can be obtained from a single DECT acquisition, since PMIs at low energies will improve the image contrast while high energies can reduce streak and beam hardening artefacts caused by, e.g. metals.In this study, we showed a benefit of a reduced range uncertainty resulting in a dose decrease to the OARs. This range uncertainty reduction was feasible by applying appropriate CT reconstruction settings and a well-calibrated DECT-based SPR estimation method. For proton as well as photon therapy, DECT can also be beneficial for delineation.We have shown that a clinical introduction of DECT-based treatment planning using a reduced range uncertainty of 2% will have a positive impact on neuro-oncological patients treated with proton therapy. In this study, 12/27 (44%) had a toxicity level decrease using the 2%-plan compared to the 3%-plan, which justified the implementation in our clinical routine. It is therefore expected that a large number of patients will benefit from the clinical implementation of DECT and such strategies need to be broadly implemented in clinical routine.