Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here, we quantified the misclassification that occurred when using different exposure approaches to predict the mortality burden of a population using London as a case study. We developed a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM2.5) in four major microenvironments (MEs)  and outdoors) in the Greater London Area (GLA), in 2017. We demonstrated that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, was used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimating the change in mortality on a fine resolution. Indoor exposure to PM2.5 is the largest contributor to total population exposure concentration, accounting for 83% of total predicted population exposure, followed by the London Underground, which contributes approximately 15%, despite the average time spent there by Londoners being only 0.4%. After incorporating housing stock and time-activity data, moving from static to most dynamic metric, Inner London showed the highest reduction in exposure concentration  and as a result the largest change in mortality  was observed in central GLA. Overall, our findings showed that using outdoor concentration as a surrogate for total population exposure but ignoring different exposure concentration that occur indoors and time spent in transit, led to a misclassification of 1174–1541 mean predicted mortalities in GLA. We generally confirm that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification of health burden assessments.Exposure to PM The spatiotemporal variability of people’s activity (population time–activity patterns) in various MEs .Around 75% of European populations live in cities, with a highly variable range of activities carried out at different times and in different places [2.5 concentrations in the U.S. by utilizing the Community Multiscale Air Quality (CMAQ) model. They found that population exposures, maximum concentrations and standard deviations all reduced at coarser resolutions. At 408 km resolution, exposure and maximum concentration were 27% and 71% lower, respectively than those at 12km resolution. Attributable mortality also reduced as the resolution became coarser. Several studies have shown that coarse resolutions might result in lower mortality attributed to PM2.5 [2.5, the mortality reduced by 70% in the whole country (Finland) and 74% in urban areas when the resolution changed from 250 m to 50 km.Traditionally, epidemiological studies relied on centralized ambient concentration measurements of limited monitoring sites ,7,8,9,10to PM2.5 . Fenech to PM2.5  concludeto PM2.5  found th2.5 actually occurred in the indoor microenvironments [2.5 that infiltrates indoor microenvironments can vary due to particle size, building characteristics, meteorological conditions and human activities [2.5 due to the spatial variability of both outdoor concentrations and the indoor transport of ambient PM2.5. Baxter et al. [2.5 exposure prediction, where each model was of a different complexity. In their study they focused on the heterogeneity in exposures but did not investigate the influence on health effect predictions. They suggested that geographic heterogeneity in both housing stock (and thus a relatively consistent Air Change Rate) and human activity patterns contribute to significant heterogeneity in ambient PM2.5 exposure both within and between cities that is not demonstrated by stationary monitors. Ma et al. [2.5 exposure estimates to illustrate the differences in exposure levels between estimates obtained from different approaches. They found that the daily average PM2.5 exposures for residents with different activity patterns may vary significantly even when they were living in the same neighborhood. Several studies have also investigated the correlation between outdoor PM2.5 and mortality, although their results are skewed by the fact that people spend the majority of the time indoors. Ji and Zhao [2.5-related mortality to estimate mortality associated with indoor exposure to outdoor-generated PM. This was the first attempt to quantify that relationship and their results indicated that outdoor PM had substantial effects on health caused by exposure within indoor MEs. Recently, Fenech and Aquilina [2.5 concentrations derived from local fixed monitoring stations to estimate the PM2.5-related mortality in the Maltese Islands. They found that the attributable fraction of all-cause mortality associated with long-term PM2.5 exposure ranged from 5.9% to 11.8%, indicating that PM2.5 concentration is a major component of attributable deaths. Azimi and Stephens [2.5 of both indoor and outdoor origins. They found that residential exposure to outdoor-generated PM2.5 accounted for 36% to 48% of total exposure, indicating that efforts to mitigate mortality associated with exposure to PM2.5 should consider indoor pollution control as well.Apart from the exposure misclassification due to the different levels of spatiotemporal resolution of outdoor concentration, there are other significant contributors, in particular the infiltration of outdoor pollutants to indoor MEs and different time-activity patterns in MEs. As particles infiltrate and persist indoors, where people living in urban areas spent over 80% of their daily time , most ofronments ,24,25. Ttivities . Consequr et al.  compareda et al.  comparedand Zhao  used exiAquilina  used theStephens  used a m2.5 exposure surrogates (that account for important factors such as the infiltration and time-activity) and the fixed-site monitor PM2.5 concentrations to indicate the importance of including more dynamic data to epidemiological studies and to demonstrate how more complex modelling approaches modify mortality predictions. By using BenMap-CE we were able to provide the spatial distribution of health outcomes influenced by the exposure misclassification. While a number of studies have already investigated exposure misclassification when using different approaches and others have estimated health effects based on specific exposure metrics, the aim of this work is to move one step further and answer the question: how much is the misclassification that occurs when using different exposure approaches to predict health burden?That of particular importance is how different exposure approaches impact long-term health burden/mortality predictions and the magnitude of the resultant impact. We made multiple comparisons between refined ambient PM2.5 exposure metrics are utilized. An ecologic design was used to generate associations between air pollution exposure and health outcomes. We investigated the Greater London Area (GLA), building on recent exposure studies that have explicitly estimated London population exposure using hybrid dynamic models [2.5 concentrations were based on average hourly data measured by 23 monitoring stations located in the GLA [This work aims to quantify the long-term health burden misclassification that occurs when different PMc models . Here, w the GLA  and the 2.5 exposures, which were then used as an input for BenMap-CE [To capture different exposure assessment methods that have been used in epidemiology, we developed five different Tier models of increased complexity, moving from static to more dynamic approaches . This meenMap-CE .The Tier-model stages and the respective approaches are briefly described below.out is mean outdoor concentration of PM2.5.Tier model 1: OutdoorHourly readings were extracted from the London Air Quality Network (LAQN) . LAQN coin is the mean indoor  concentration.Tier model 2: IndoorThis Tier model utilized the information of the spatially distributed concentration and the total average Indoor/Outdoor (I/O) ratio in GLA to estimate the exposure inside the residence .i is the infiltration rate of each dwelling type (i) and xi is the frequency (%) of this type in London.Tier model 3: Indoor (dwellings)2.5 concentration in the GLA. The average ratios, including all dwelling types and their frequency, ranged from less than 0.54 to 0.59. The highest ratios were observed in Outer London, whereas the lowest ratios were observed in Inner and South West London, probably due to the newer building stock and the large number of flats in large buildings (London Datastore), where the available surface for infiltration was considerably smaller.In this study, all the indoor environments were combined into one single ME  without considering other indoor environments, such as office or commercial buildings, due to the lack of infiltration data. Subsequently, the I/O ratios that we used also represented offices and other indoor places, assuming that the I/O ratios for other indoor MEs had the same values as domestic home buildings . The I/Oout, tind, tabg and tundg is the fractional time spent (%) annually outdoors, indoors, aboveground tME and London Underground (LU) tME, respectively.Tier model 4: Outdoor + Indoor + Transportation (aboveground and underground)This Tier-model includes transportation as an additional microenvironment, where an urban population spends time during the day. This ME was categorized into aboveground and underground transportation. Aboveground transportation refers to car, bus and train, whereas underground to London subway. By separating transportation into 2 groups we were able to evaluate the influence of a highly polluted ME, like the London Underground (described in the next section), on the total population exposure concentration.The space–time–activity data for our study were based on the London Travel Transport Agency (LTDS) of Transport for London (TfL)  for the According to Smith et al. , the ave2.5 concentration by solving the mass balance equation [out is the outdoor concentration around the vehicle, Cin the concentration inside the vehicle, λwin and λHVAC are the hourly air exchange rates from the windows and mechanical ventilation system, respectively, n is the filter removal efficiency taking values between 0–1, Vg is the deposition velocity in (m/h), A’ is the internal surface area, V is the volume of the vehicle and Q is the in-vehicle particle emission rate in μg/h. To solve this equation, the same values with Smith et al. [For the in-vehicle exposure of the aboveground sub-microenvironment, we calculated the PMequation : dCin / h et al.  were useh et al. , in ordeTier model 5: Outdoor + Indoor + Transportation (aboveground and underground→ deep lines + subsurface lines).The time-weighted exposure equation associated with this Tier model stage is:2.5 concentrations than the deep lines due to air filtration (explicitly described in the next section). Hence, by dividing the underground into two subgroups we were able to improve the exposure estimates and to examine the contribution of a very highly polluted microenvironment to the total exposure. The proportion of time spent in each of those two subcategories was assumed according to the number of annual journeys completed in each line during 2017, where 77% were made by the deep-line underground and 33% by the subsurface.In the 5th and most complex Tier model, the same procedure as in Tier 4 was followed but the London underground microenvironment was further divided into subsurface and deep lines to reflect the significant difference in concentration on two types of lines. The use of mechanical ventilation in the subsurface lines results in much lower PM2.5 and its limited connection to the outside world, a series of air pollution measurements were conducted inside the London Underground. The PM2.5 measurements took place on five major London Underground platforms and trains  by using the portable DustTrak II Aerosol Monitor 8534, a light scatter laser photometer, which could provide a large number of real-time readings. The current selection of the lines was decided in order for both the deep without mechanical ventilation lines and the subsurface with HVAC lines to be represented by our measurements. As the London Underground microenvironment was unable to be accurately represented by the outdoor measurements, due to its high concentration of PM3, albeit when we grouped the lines into deep without HVAC lines  and subsurface lines with HVAC  we noticed a remarkable difference between the two concentrations (70.2 μg/m3 for the subsurface lines and 365.6 μg/m3 for the deep lines). The PM2.5 concentration levels in the unmeasured lines were assumed to be similar to these measured. The classification of the unmeasured lines was made according to their depth and ventilation system. Our original intention was that the measurements would reflect the cold and the warm period of 2017. Hence, the experiment was conducted during the morning and the afternoon for one week in February and one week in July. The average concentration in the London Underground for the whole year was very high, approximately 218 μg/m3. Recently, Smith et al. [2.5 concentration levels  and the deep unventilated Victoria line the highest , while the mean concentration in the LU, according to their measurements, was 302 μg/m3. Although their monitoring method and equipment were different from those used in this study and the sampling period was longer, their findings do not differ significantly from ours. Even though the station measurements in the UK are limited, most of the studies made so far have measured approximately two times higher concentrations in the London Underground than in other undergrounds worldwide [In the London Underground, Seaton et al.  reportedh et al.  assessedorldwide ,44, probThe environmental Benefits Mapping and Analysis Program—Community Edition (BenMap-CE) is a powerful Geographical Information system (GIS)-based program that estimates the health effects associated with the change in air quality ,45. Thes0 is the baseline mortality rate , β is the unitless beta coefficient, ΔPM is the change in the exposure rates between Tier 1 and the other Tier models (Tier 1 is the base case) and Pop is the exposed population. After uploading the essential data and determining the appropriate Health Impact Function (HIF) for our analysis, we were able to quantify the health impact misclassification  resulting from the exposure metric differences. In this study, the following long-term health impact function was used to estimate the change in all-cause mortality :ΔY = Y0*2.5 introduced a method for modifying the mortality effect estimate  based on the average infiltration factor combined with the mean fraction of time spent in indoor MEs [2.5 of outdoor origin and not of indoor PM2.5 in total. The way indoor particle sources are treated has a larger impact than the adjustment of the coefficient for the outdoor-generated fine particles and remains an evidence gap of considerable public health importance. In another study, Logue et al. [2.5, which was directly derived from the epidemiology literature. In our case, due to the mobile monitoring conducted in the LU and the distinct function of BenMap-CE, a central mortality effect coefficient from Pope et al. [One limitation of the aforementioned effort to estimate the health impact of indoor air pollution is the use of the mortality effect estimate  that is usually taken directly from the epidemiology literature on the studies conducted for outdoor air pollution. Therefore, to account for that fact, some studies on the health effects of outdoor-generated PMdoor MEs ,47,48. He et al.  used a ce et al.  was used3 for Tier model 1. Tier model 2 and Tier model 3 indicated that the exposure that occurred indoors was much lower than outdoors due to the infiltration rates of the buildings, resulting in annual average exposure concentrations of 7.18 μg/m3 and 7.26 μg/m3, respectively. There was an approximately 45% reduction between Tier 1 and Tier 3. This result clearly suggests that spending long periods of time indoors, reduces the exposure to outdoor-generated air pollution. The incorporation of transportation and predominately the highly polluted London Underground in Tier model 4 resulted in an elevated exposure concentration (8.28 μg/m3), pinpointing that even though the time spent in transit is only 2.9%, this microenvironment has a significant contribution to the total exposure. By dividing the London Underground into subsurface with HVAC and deep line without HVAC, we were able to quantify the impact of the most highly polluted ME on the total exposures (the deep-line underground). Tier 5 showed an approximately 0.30 μg/m3 higher exposure concentration (8.60 μg/m3) than Tier 4, where an average concentration for the whole underground was used , whereas the lowest exposures were observed in Western GLA . The incorporation of indoor infiltration along with time-activity data led to an overall mitigation of the exposure concentrations in GLA when Tiers 2, 3, 4 and 5 were used. After the utilization of our most complex model, Tier 5 had the highest difference observed at the centre with approximately 37% , was estimated to be 1257 (95% CI: (347–2151)) cases. Due to the significance of the deep-line underground, the most complex Tier model 5 presented the lowest number of cases compared with the other 3 metrics . Namely, once Tier 5 was applied the prediction for the estimated avoided mortalities were 1174 (95% CI: (324 – 2010)). We can assume that the calculated change in mortality represents the potential health burden misclassification that might occur when changing the exposure metrics to assess the population exposure. Subsequently, we were able to estimate the percentage decrease in predicted mortalities when altering the exposure metric’s complexity. The substantial changes in avoided mortality predictions indicate that using a static exposure approach in a study might lead to significant uncertainty in a health burden assessment. As anticipated, the predicted mortality was significantly reduced when increasing the model complexity. The highest changes were observed in Tier-model 2 and 3, due to the time that people spent indoors in urban areas, the big difference between outdoor and indoor exposure and the absence of highly polluted transportation MEs, pinpointing the importance of taking into serious consideration the exposure that occurs inside buildings when estimating health effects. The model predicted most avoided cases when Tier 2 was applied and while increasing complexity the cases showed a decrease of 1.95%, 18.4% and 23.8% for Tier 3, 4 and 5, respectively. As explained above, the London Underground contributes significantly to the total average exposure concentration of the study population by increasing the estimates. Therefore, we can securely presume that this is the main reason for the high decrease in avoided mortalities when Tier 4 and, predominantly, Tier 5 were used in BenMap-CE. Because in epidemiology the concentration from central-site monitors is used as a proxy for the exposure to air pollution, we selected Tier 1 as our reference and compared it with the estimates of Tiers 2, 3, 4 and 5. The mean change in the estimates of all-cause mortality when applying Tier model 2 was predicted to be 1541 (95% CI: (427–2633)) deaths, while when using Tier 3 exposure concentration estimates the death cases were reduced to 1521 (95% CI: (421– 2598)). The impact on mortality when applying the 4All results are summarized in 2.5 concentration, the housing stock (I/O ratios) and the population. As described above, after the inclusion of the time-activity data there was an overall reduction in exposure concentration because people usually spend most of their time (>95%) in indoor MEs (excluding transportation), where the concentration of outdoor PM2.5 is lower than the measured ambient levels. Because the health impact function used by BenMap-CE is a concentration response function, the amount of the reduced exposure concentration determines the fraction of the mortality reduction. In our case, knowing that moving from Tier 1 to Tier 5 would result in a greater reduction of exposure concentration that appeared in central GLA  with more dynamic air pollution methods and proved that there is a reduction in average total exposure levels in urban areas with related characteristics as GLA [2.5 than the static approach . Similarly, by adopting a staged modelling approach to evaluate the effect of including dynamic components to our exposure models we found that the absence of mobility and infiltration factors in the static Tier-model 1 led to an overestimation of annual PM2.5 population exposure. Overall, the exposure estimates of our most complex model (Tier 5) were around 34% lower than those of the static baseline model (Tier 1). These findings were different from Tang et al.’s [2.5 concentrations in London and assessed the importance of including movement and indoor infiltration to total population exposure. They found that their refined exposure assessment predicted 28% lower total population exposure than the traditional static exposure method. As in this study, the time-activity data were derived from the LTDS [2.5. Ebelt et al. [Due to the limited time most people spend outside, the amount of ambient concentration of PMccupying . ConsequStephens  highlighs et al.  determins as GLA ,57. Tangs as GLA  combinedet al.’s  quantifithe LTDS  and the t et al.  found foThis work provides further understanding as to the impact of an exposure assessment on the mortality predictions and helps to mitigate the uncertainty in health risk assessments of air pollution. As a result, it would be possible to increase the efficiency of regional or local air quality management strategies.2.5 and their locations were not uniformly spread across the study area. Consequently, this may have affected the simulation accuracy and the interpolated ambient concentration estimates in the unmonitored areas that were far from the stations that might have contained higher uncertainty. Furthermore, another limitation was the assumption that the Indoor microenvironment and the average dwelling I/O ratios also represented the office and commercial buildings. The toxicity of PM2.5 was not included, but mainly because it was out of the scope of the study to investigate the toxicity of the particles.The current study contains several limitations. Only some of the deep and subsurface underground lines were monitored and only for a small sampling period. In this study, we assumed that these measurements also represented the corresponding lines that were not measured. Moreover, only 23 monitoring stations were available for PMThe space–time–activity data is based upon the London Travel Demand Survey for the period 2005–2010 and may not be fully accurate locally, spatially and temporally, for the year 2017. Moreover, the annual average of the time-activity data that we used, assuming that people followed the typical daily mobility patterns for the whole year, may have increased the uncertainty in our models, because those data might not have accurately represented a part of the population. Since the main body of our study was based on averages and the population was not divided into different age groups, our health burden predictions may be less accurate for special groups of people that have different behaviours .Parameters that could affect particle infiltration, such as differences in indoor-outdoor air pressure due to the impact of the surrounding micro-environment, and the existence and efficiency of mechanical filtration, were not the focus of the current study and were therefore not investigated.2.5 concentrations and different indoor characteristics . Taking into consideration that each urban area may have different characteristics, it is important to examine how the incorporation of the local urban or building features could make an impact on exposure concentration estimates and health burden predictions.In the future, this study could be improved by conducting further measurements in the London Underground and for larger periods of time. Simple sensitivity tests could be made in order to check each model’s response and how the misclassification affects our estimates. As the next stage of this work we could investigate how this framework applies to other cities with higher ambient PM2.5 and health effects due to inadequate spatial variability of the concentration and the absence of people’s mobility. The magnitude of the misclassification related to the inclusion of indoor MEs and the metric’s complexity was large relative to the dynamic nature of human exposure to air pollution.The use of ambient centralized monitoring concentrations as a surrogate for people’s exposure may not provide an accurate representation in a population study. In this study we developed a static exposure approach, commonly used in epidemiology, as our baseline metric and by incrementally enhancing the metric we were able to report the potential impact that the application of different metrics would have on a health outcomes assessment. We demonstrated that studies focusing on centralized monitoring ambient concentrations may show reduced ability to detect the true associations between exposure to PM2.5 concentrations outside the residence are representative of the total population exposure. Particularly, our most complex and accurate Tier-model estimated an approximately 34% lower mean exposure concentration compared with using simply an outdoor concentration.This analysis illustrates the significance of allowing for population activity and indoor infiltration. The indoor ME showed the highest contribution to the total population exposure , while the LU contributed approximately 15%, although people spend only 0.4% of their time there. Consequently, all our models showed lower total exposures than the traditional exposure approach that assumes that the PMThe exposure misclassification due to home infiltration and underground ME is likely important in assessing the health burden in an urban area because people in cities spend the majority of their time inside the residence or workplace and the pollution concentrations that occur underground are remarkably high. The misclassification between the traditional exposure approach to estimate health outcomes and our most dynamic metric was found to be 1174 mean predicted mortalities in GLA, with the highest numbers observed in Inner London. Overall, by quantifying the health burden misclassification we managed to pinpoint the importance of developing a metric that can adequately represent the study population concerned and showed that the use of more dynamic data in epidemiology could significantly increase the accuracy of health impact assessments.


Yersinia pseudotuberculosis (Yptb) growth in microfluidics-driven microdroplets that regenerates microbial social behavior in tissues. Chemical generation of nitric oxide (NO) in the absence of immune cells was sufficient to reconstruct microbial social behavior, as witnessed by expression of the NO-inactivating protein Hmp on the extreme periphery of microcolonies, mimicking spatial regulation in tissues. Similarly, activated macrophages that expressed inducible NO synthase (iNOS) drove peripheral expression of Hmp, allowing regeneration of social behavior observed in tissues. These results argue that topologically correct microbial tissue growth and associated social behavior can be reconstructed in culture.Within deep tissue sites, extracellular bacterial pathogens often replicate in clusters that are surrounded by immune cells. Disease is modulated by interbacterial interactions as well as bacterial-host cell interactions resulting in microbial growth, phagocytic attack and secretion of host antimicrobial factors. To overcome the limited ability to manipulate these infection sites, we established a system for  A variety of bacterial pathogens colonize and replicate within tissues despite the presence of the host immune system . Growth Yersinia pseudotuberculosis (Yptb) is an enteric pathogen that replicates in the intestinal lumen and regional lymph nodes, with the potential for disseminating via a poorly characterized route into deep tissue sites such as the liver or spleen  topology. To encase single bacterial cells in matrix, we utilized droplet-based microfluidics, which is commonly used for isolating single mammalian cells  was encapsulated in droplets, oil was removed, and the droplets were incubated at 26°C in 2xYT broth with rotation. Microcolony growth was determined by identifying a threshold that defines edges of microcolonies in the GFP channel and determining the number of pixels in the region of interest (ROI). The data were then converted to metric scale using a stage micrometer and displayed as μm2. The area of the microcolony increased over time with roughly logarithmic kinetics over a 10 hr period, indicating that the agarose/HyStem-C Hydrogel droplets support efficient Yptb microcolony formation  which is innocuous and can be used as an electron acceptor by the pathogen  is synthesized by immune cells to produce gaseous NO as an important line of host defense . NO has pathogen . During  disease . To eval-NONOate . The mic-NONOate . In contnditions . Althougcolonies . Similarcolonies . The Δhmer doses . At the er doses . NeverthYptb, subpopulations of bacteria within microcolonies respond to distinct microenvironments  after exposure to DETA-NONOate using flow cytometry. Phmp::mcherry expression should be uniform in broth cultures of WT Yptb, while hmp should exhibit nonuniform expression in droplets if microcolony structure drives spatial regulation of hmp expression  cells and are physically separated from the bacterial microcolony . To recapitulate tissue dynamics, therefore, BMDMs were activated to produce iNOS+ and added to droplets containing pregrown microcolonies . After construction, each microfluidics chip was stored at 37°C in a box with the chip covered in tape. Prior to droplet generation, the device was primed with fluorinated oil  and connected to two programmable syringe pumps (Harvard Apparatus 11 Elite Series) located in a temperature-controlled room at 37°C. One pump was fitted with a 1 ml syringe  having a 27-gauge needle (BD #305109), which was filled with 1.5% Pico-Surf 1  in Novec 7500 oil. In parallel, bacteria were diluted 1:2000 in 1% ultra-low-melt agarose containing Hystem-C hydrogel, yielding approximately 5 × 106 cells/ml, and the mix was loaded into a 1 ml syringe that was fitted onto a second pump. Polyethylene (PE/2) tubing  was attached to each syringe and then inserted into the appropriate ports on the microfluidics device  (Sigma #370533–25G) in Novec 7500 was added to the droplets. After mixing vigorously, the tube was placed in a centrifuge for 30 s at 250 RCF and 400 μl of 25% Extralink (ESI BIO #GS3006) in PBS was added to crosslink the hydrogel within the agarose. After crosslinking, the droplets were flicked into suspension and centrifuged for 30 s at 250 RCF. The PBS/droplet layer was transferred to a new 1.5 ml Eppendorf tube, subjected to centrifugation for 30 s at 250 RCF, and washed with PBS. The remaining droplets were resuspended in 2xYT broth.Droplets were washed once in PBS at the indicated timepoints, incubated for 3 min at 50°C, and a mixture of agarase (Sigma #A6306), collagenase and hyaluronidase  was added to the samples for 45 min at 37°C. Samples were then incubated for 3 min at 60°C, subjected to centrifugation in a microfuge at 14 K RPM for 1 min. The supernatant was then aspirated, and the bacterial pellets were washed in PBS and prepared for flow analysis.Y. pseudotuberculosis were generated, oil was removed, and the droplets were rotated for 7 hr at 26°C in 2xYT broth to allow colony formation. After the 7 hr growth period, droplets were subjected to centrifugation for 30 s at 250 RCF and washed twice in PBS. 50 μl of droplets were transferred to a 1.5 ml Eppendorf tube and resuspended in 1 ml 2xYT broth and exposed to DETA-NONOate (Sigma #AC32865) during growth at 26°C with aeration for the indicated timepoints. For experiments performed at 37°C with 5% CO2 , approximately 2 x 106 BMDMs in RPMI 1640 supplemented with 10% FBS and 2 mM glutamine were added to the well, and the incubation proceeded at 37°C in the presence of 5% CO2 for 4 hr. Samples were fixed in 2% paraformaldehyde (PFA) in PBS, washed in PBS, and fluorescent reporters were visualized by microscopic observation.Droplets containing 3 bacteria. Three days post inoculation, spleens were removed and processed as described previously  were injected intravenously with 10eviously . All aniAfter microcolony dispersal, samples were fixed in 2% PFA in PBS, washed in PBS, and filtered through 40 μm filters. Fluorescent reporters were detected using a Bio-Rad S3e cell sorter. 100,000 events were collected for each sample.gfp+ Phmp::mcherry strain. Three days post inoculation, spleens were harvested and fixed in 4% PFA in PBS for 3 hr, tissue was frozen embedded in Sub Xero freezing medium  and cut into 10 μm sections using a cryostat microtome (Microm HM505E). To visualize reporters, sections were thawed in PBS, stained with Hoechst at a 1:10,000 dilution, washed in PBS, and coverslips were mounted using ProLong Gold (Life Technologies). Tissue was imaged with a 63x objective on a Zeiss Axio Observer.Z1 (Zeiss) microscope with Colibri.2 LED light source, an Apotome.2 (Zeiss) for optical sectioning, and an ORCA-R2 digital CCD camera (Hamamatsu) . Droplets that were incubated with BMDMs were visualized in 12-well coverslip-bottom plates (Cellvis). For visualization of BMDMs, cells were fixed in 2% PFA in PBS, permeabilized for 30 s in ice cold methanol and non-specific binding was blocked using 2% BSA in PBS. Cells were stained with rabbit anti-mouse iNOS  at a dilution of 1:100 and goat anti-rabbit-Alexa 488  at a dilution of 1:500. Nuclei were stained with Hoechst at a dilution of 1:10,000. To analyze tissue samples, C57BL/6 mice were inoculated intravenously with the WT mamatsu) .2.For determination of droplet size, images were captured by phase contrast microscopy using a 20x lens, and images were analyzed to identify 300 droplets, with analysis in Volocity. Droplet size was determined by identifying a threshold that defines edges of droplets and determining the diameter in pixels. The data were then converted to metric scale using a stage micrometer and displayed as a histogram. To determine bacterial counts, images were captured by phase contrast and fluorescence microscopy using a 20x lens and the number of bacteria/droplet was manually counted in 500 droplets. To determine microcolony area, images were captured by phase contrast and fluorescence microscopy using a 20x lens with analysis in Volocity. Microcolony area was determined by identifying a threshold that defines edges of microcolonies in the GFP channel and determining the number of pixels in the region of interest (ROI). The data were then converted to metric scale using a stage micrometer and displayed as μmhttps://github.com/isberg-lab/droplet; https://github.com/elifesciences-publications/droplet). Periphery to point of lowest intensity (PLI) ratios were calculated by generating a mask representing the periphery of a defined width of the microcolony, and a mask representing the PLI of a defined radius. The periphery mask was generated by first thresholding the green channel using Otsu’s method and converting the resulting matrix into a binary matrix. The binary matrix was morphologically opened with a disk-shaped structuring element of radius two and then morphologically closed with a disk-shaped structuring element of radius four. Morphologic opening and closing eliminate dim artifacts and fuse the microcolony mask into a single region of interest (ROI). Any remaining holes within contiguous ROIs are filled. ROIs touching the edge of the field are removed, as are all but the largest ROI in the field. This ROI, covering the entire microcolony, is eroded with a disk-shaped structuring element of radius 12 and complemented. The initial mask and complemented eroded mask are combined to achieve a binary mask of the periphery of the object. The number of pixels and the total intensity in the resulting mask was recorded. The mask was applied to the raw image data in the red channel, and the total intensity was recorded. Total intensity of the red was normalized to the number of pixels to achieve an average intensity for the entire periphery. The average intensity of the red channel was normalized to the average intensity of the green channel. To calculate the PLI, the determined eroded mask was applied to the red channel to isolate the pixels belonging to the microcolony while excluding those that are part of the periphery. A Gaussian blur of sd = 2.0 was applied to reduce the effects of low-intensity noise introduced by the imaging process. The coordinates of the minimum value of this image were calculated, generating a binary matrix with value = 1 at the coordinates of the PLI and zero at all other points. This matrix was morphologically dilated with a structuring element of radius five to sample a region of lowest intensity rather than a single pixel. The number of pixels in this resulting mask are then recorded and the total intensity of the green channel was recorded. The mask was applied to the raw image data in the red channel, and the total intensity recorded. Total intensity was normalized to number of pixels to achieve an average intensity for the entire PLI region in the red and this value was normalized to the average intensity of the green channel.MATLAB scripts were written to calculate the average intensity of fluorescence about the periphery of microcolonies and point of lowest fluorescence .Thank you for submitting your article "Topologically correct synthetic reconstruction of pathogen social behavior found duringThe reviewers only have a few minor suggestions.Reviewer #1:In this report, Clark et al. present the creation of a microdroplet-based microfluidics model recapitulating the bacteria-bacteria as well as host-bacteria interactions occurring usually in deep tissues of the infected host. In the host, bacteria often form clonal microcolonies that display heterogeneous behaviour in response to spatial organisation with respect to host immune attacks. Authors show that in such a model, Yersinia pseudotuberculosis can grow in microcolonies, which, similarly to what the group had observed in other experimental set ups, rely on expression of Hmp mostly by peripheral bacterial cells for resilience of the entire population to added NO. Importantly, such model is also shown to be amenable to addition of bone marrow derived macrophages to the droplets, mimicking thus the situation in tissues.This is a great report, easy to read and well presented. The model has been extensively tested by the authors who provide the community here with an interesting alternative in between pure tissue culture models and animal models which are in themselves often limiting in being either too simple for the former and too complex for the latter. Importantly, many features of the bacterial cells are accessible in such model and it is going to be very useful to many research groups.I have no essential revisions to request, the manuscript is an excellent piece of work.Reviewer #2:hmp) in the bacterial microcolonies with the bacteria in the periphery of the microcolony showing the highest induction. To better study these processes and with the aim of developing a system amenable for development of high-throughput screens, the authors employ droplet-based microfluidics to facilitate the growth of bacteria in gel-based matrix. Initially they characterize these droplets and demonstrate exponential growth of the bacteria in these droplets, where they form microcolonies. These microcolonies were then exposed to different concentrations of NO and the important role of hmp in resisting this stress was confirmed. Using a fluorescent reporter for hmp expression, the heterogeneous and spatially organized expression is demonstrated in wild-type strains using FACS and microscopy, which is lost in strains deleted for hmp. Similar pattern of expression is seen when the droplets containing bacteria are coated with macrophages activated to express NOS and is similar to the expression seen in sections of spleens from infected mice. This peripheral induction of hmp is lost when the NOS response is inhibited using chemical inhibitors of NO.In this study, the authors have attempted to build a system that allows them to capture microbial behavior and its interactions with host immune cells. Yersinia bacteria typically replicates in intestinal lumen and lymph nodes forming microcolonies surrounded by neutrophils, macrophages and other immune cells. Previous in vivo studies have shown that there is a topological induction of detoxifying proteins (While there are no new observations on the pathogenesis or host-microbe interactions aspect, this study presents the development of a novel in vitro system that facilitates future studies on spatial relationships between immune cells and bacteria in a more amenable manner. It might be a good fit for the Tools section. Overall this is a nice and elegant study and the manuscript and data has been presented well.I do not have any major issues with the manuscript. I do have a few suggestions that might improve the presentation.– If I understood correctly, the reporter carries the fluorescent marker on a episomal plasmid. Variation in copy number potentially could influence their readouts for heterogeneity in gene induction. The authors could comment on this briefly in the Discussion.– Figure 5 could be moved to the supplementary data.Reviewer #3:In this study by Isberg and colleagues, the authors provide to the community a very interesting experimental platform. The study appears motivated by the years-long interest of the Isberg lab in the pathogenesis of Yersinia. These bacteria create microcolonies in several tissues of an infected animal, the significance of which has only been explored in theory. The major question related to these microcolonies is that of the social behavior of individual bacteria.Herein, the authors present a microfluidics system to create droplet-encased bacteria, which form clusters that resemble those identified in vivo. The major advance provided here is the demonstration that these synthetic clusters recapitulate behaviors observed in vivo, as they relate to NO responsiveness. The source of the NO could be chemical or macrophage derived, and the NO induces the expression of NO-responsive genes in bacteria that are present in the periphery of the cluster.The potential for using this platform to ask "sufficiency questions" related to immune signals that influence intra-cluster bacterial behavior is powerful and should be embraced by the community.While there are several experimental questions that I would like answered, they all would require an extension of this work that is already of sufficient quality to attract attention from the community. Reviewer #2:[…] While there are no new observations on the pathogenesis or host-microbe interactions aspect, this study presents the development of a novel in vitro system that facilitates future studies on spatial relationships between immune cells and bacteria in a more amenable manner. It might be a good fit for the Tools section. Overall this is a nice and elegant study and the manuscript and data has been presented well.I do not have any major issues with the manuscript. I do have a few suggestions that might improve the presentation.– If I understood correctly, the reporter carries the fluorescent marker on a episomal plasmid. Variation in copy number potentially could influence their readouts for heterogeneity in gene induction. The authors could comment on this briefly in the Discussion.We have added text to the fourth paragraph of the Discussion in this regard. Noted in this section is the fact that we used a chromosomally-integrated reporter in a previous publication and it behaved identically to the plasmid-borne reporter.– Figure 5 could be moved to the supplementary data.We decided to keep Figure 5 in place, since it is an important strategy that should be used in future image analysis and is central to understanding how we quantitate our data.


Background: Pre-symptomatic transmission can be a key determinant of the effectiveness of containment and mitigation strategies for infectious diseases, particularly if interventions rely on syndromic case finding. For COVID-19, infections in the absence of apparent symptoms have been reported frequently alongside circumstantial evidence for asymptomatic or pre-symptomatic transmission. We estimated the potential contribution of pre-symptomatic cases to COVID-19 transmission.Methods: Using the probability for symptom onset on a given day inferred from the incubation period, we attributed the serial interval reported from Shenzen, China, into likely pre-symptomatic and symptomatic transmission. We used the serial interval derived for cases isolated more than 6 days after symptom onset as the no active case finding scenario and the unrestricted serial interval as the active case finding scenario. We reported the estimate assuming no correlation between the incubation period and the serial interval alongside a range indicating alternative assumptions of positive and negative correlation.Results: We estimated that 23% (range accounting for correlation: 12 – 28%) of transmissions in Shenzen may have originated from pre-symptomatic infections. Through accelerated case isolation following symptom onset, this percentage increased to 46% (21 – 46%), implying that about 35% of secondary infections among symptomatic cases have been prevented. These results were robust to using reported incubation periods and serial intervals from other settings.Conclusions: Pre-symptomatic transmission may be essential to consider for containment and mitigation strategies for COVID-19. In less than two months the COVID-19 outbreak progressed from fewer than 300 cases across four countries to more than 100,000 cases in over 100 countries3. COVID-19 infection in the absence of clinical symptoms has been frequently described7 alongside anecdotal evidence for potential asymptomatic or pre-symptomatic transmission or transmission while symptoms were suppressed by fever-reducing and prescription-free medication11. In combination with highly non-specific and generally mild symptoms in most cases12, pre-symptomatic transmission of COVID-19 would substantially hinder control and mitigation efforts, which typically focus on rapid syndromic identification and isolation of cases14. We thus estimated the possible extent of pre-symptomatic transmission to help inform infection control and mitigation plans.Since its emergence in early December 2019, SARS-CoV-2 has spread rapidly despite multiple layers of interventions designed to prevent a pandemicet al. derived from 391 cases and their 1286 contacts as identified by the Shenzen Centre for Disease Control15. This study currently includes the most extensive investigation of the infector-infectee transmission dynamics in a single setting.Multiple estimates for the COVID-19 incubation period and serial interval have been reported to date. While estimates for the incubation period are relatively similar, the serial interval is dependent on the local context, in particular local contact patterns and the presence of interventions, active case finding with subsequent isolation, in particular, that may right-censor the serial interval. As our base case, we used the estimates provided by BiThe probability of pre-symptomatic transmission can be expressed as the probability of onward transmission during one’s incubation period. Ideally, this probability could be estimated from individual-level observations on exposure, onset of symptoms and the transmission to others. However, such data remains largely unavailable and is inherently difficult to generate. Instead, we approximated this quantity from population estimates of the density distribution of the incubation period and the serial interval. We did so by first using the incubation period density distribution to calculate the probability of having developed symptoms on each day since infection and then using this to stratify the serial interval distribution, assumed to be a proxy for the generation time, into likely pre-symptomatic and symptomatic onward transmissions.The observations of the incubation period and the serial interval may be correlated. In other words, individuals with a long incubation period may not have a serial interval of typical length but may be exactly those who have a long serial interval, or alternatively, exactly those who have a short serial interval. To account for this, we also calculated the proportion of pre-symptomatic transmission by randomly drawing from the incubation period and serial interval distributions and pairing samples not randomly, but aligning them either both in ascending or one in ascending and the other in descending order (rank ordering), before calculating the proportion of draws with a shorter serial interval than the incubation period. We report the estimates assuming no correlation accompanied by a range indicating estimates that assume positive and negative correlations.We consider two scenarios: transmission with and transmission (largely) without active case finding to accelerate case isolation. For the no active case finding scenario we use the serial interval reported based on cases that were only isolated at least 6 days after their symptom onset; i.e. 8.0 days : 4.7 – 11.3). For the active case finding scenario we used the overall serial interval; i.e. 6.3 days (95% eCI: 0.9 – 16.9) . The inc16. Further, Nishiuraet al. provide estimates of the serial interval from published reports across all affected countries, mostly including attempted rapid case finding and isolation17. We use their mean serial interval estimates based on high certainty transmission pairs (4.8 days (95% eCI: 1.1 – 9.6)) in combination with either the incubation time reported by Biet al.15 or Liet al.16. We implemented all density distributions to match reported estimates and their distributions18.We also included sensitivity analyses. We used the reported mean incubation period (5.2 days (95% eCI: 1.1 – 14.3)) and serial interval (7.5 days (95% eCI: 2.5 – 15.3)) from the first 425 cases in Wuhan province as an alternative scenario for a setting with no or limited active case findingϕ) Letx be the proportion of pre-symptomatic transmission in the absence of active case finding, andy the same proportion in the presence of active case finding. Then,y can be expressed as a function ofx while accounting for a reductionϕ in the symptomatic cases:We then calculated the effectiveness of case isolation in reducing the number of secondary cases during the symptomatic period  of onward transmissions in Shenzen have occurred during the pre-symptomatic period .Active case finding and subsequent accelerated isolation right censors the serial interval and thereby reduces the number of symptomatic onward transmissions. In Shenzen, the presence of active case finding increased the percentage pre-symptomatic among all transmissions to 46% (21 – 46%). Therefore, case isolation in Shenzen has on average reduced the number of secondary cases during the symptomatic period by 35% (42 – 59%) and hence the total number of secondary cases by 27% (32 – 45%).Both the sensitivity analyses for the scenario of limited to no active case finding and for the scenario of real-world effects of active case finding and isolation during the containment phase of the outbreak response are similar to the estimates in our main analysis .We estimated that without active case finding to accelerate isolation in Shenzen 12 to 28% of transmissions occurred during the pre-symptomatic period. We further estimated that active case finding reduced the number of secondary infections after symptom onset and thereby increased the percentage of pre-symptomatic transmissions to 21 to 46%. This implies that in Shenzen accelerated case isolation has prevented 35 – 60% of secondary cases among symptomatic infectees. These results were similar when alternative settings with and without accelerated case isolation were considered.13. It implies that control and mitigation of COVID-19 may be substantially improved if also targeting pre-symptomatic infections. This may be achieved via social distancing interventions that do not rely on the identification of symptoms. Having 10 – 30% pre-symptomatic transmission also suggests that immediate isolation at symptom onset can at best prevent 70–90% of cases. However, immediate case isolation on the day of symptoms onset is unlikely. We also find that, based on parameters estimated under no active case finding scenarios, case isolation on days 1, 3 or 7 after symptom onset reduces the proportion of or preventable onward transmissions by syndromic case finding and isolation to approximately 60%, 50% and less than 20%, respectively Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of theReplication code is available from:https://github.com/yangclaraliu/2019nCoV_proportion_asymhttps://doi.org/10.5281/zenodo.370994218.Archived source code at the time of publication:CC0 1.0 UniversalLicense: et al. is a clean and straightforward analysis that brings together early data on the incubation period and serial interval of COVID-19 to provide important insight into the contributions of pre-symptomatic individuals to onward disease transmission. The authors' analysis is strengthened by the inclusion of sensitivity analysis on assumptions about the incubation period and serial interval as well as scenarios that account for the impact of active case finding on the relative contribution of pre-symptomatic infections to onward transmission.This report by LiuI would like a more explicit description of how the pre-symptomatic transmission percentages were estimated. As is, I'm not sure why there are only point estimates presented in Table 1, unless only a single set of draws were performed for each category.Can the authors comment on the sufficiency of follow up time for the studies that informed the serial interval estimates? Are the authors reasonably confident that there is limited right censoring for individuals that were non-symptomatic and positive at identification . This seems an important consideration that would bolster the validity of the authors' estimates.Since this paper was originally posted, I am wondering whether there have been additional estimates of the serial interval under conditions of active case finding. Presumably locations outside of China may have had time to prepare their surveillance systems so that serial interval estimates would directly reflect scenarios with active case finding (as opposed to making a cutoff assumption). Comments:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:infectious disease dynamicsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. et al. to be rather interesting and enjoyed reading it. The analysis is well-designed and the main formula in the Results section is quite elegant. I also think that the results lead to important conclusions. I have found the paper of Liu I have only few remarks listed below.Table 1: It would interesting if the authors could give some insight on “fully anti-correlated” SI and incubation period. Under which circumstances, may this happen? I could imagine uncorrelated and fully correlated cases, probably, partially anti-correlated, but it was difficult for me to imagine the setting of fully anti-correlated cases. Typos, etc:Page 6, sixth line from above: probably, the authors could specify that it was the Diamond Princess cruise ship docked in Japan.Figure 3: I would recommend to make the lines grey and a bit transparent, so the reader would be able to distinguish between individual lines.Github repo : the author could improve their README.md file, so to make it as a graphical abstract of their paper. This may help to ease the access to the repo for the readers. Remarks:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Infectious disease modelingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is a nice simple analysis of the incubation period distribution and the serial interval distribution, showing that substantial pre-symptomatic transmission has likely been occurring. There is fairly good consistency in published estimates of the incubation period distribution, but relatively less consistency in published estimates of the serial interval distribution. Authors deal with this variability/uncertainty through sensitivity analyses.Minor comments:Figure 3 is difficult to read, can you use semitransparency to distinguish the density of simulated lines?Do you have any estimate of how early people might become contagious before symptom onset? In Figure 3 you show infectivity is persisting for up to a week after onset, but how many days before onset did infectiousness start?Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Infectious disease epidemiologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.