It has a diverse suite of sources, each presenting its own challenges in quantifying emissions, and while its main sink, atmospheric oxidation initiated by reaction with hydroxyl radical (OH), is well-known, determining the magnitude and trend in this and other smaller sinks remains challenging. Here, we provide an overview of the state of knowledge of the dynamic atmospheric CH4 budget of sources and sinks determined from measurements of CH4 and δ13CCH4 in air samples collected predominantly at background air sampling sites. While nearly four decades of direct measurements provide a strong foundation of understanding, large uncertainties in some aspects of the global CH4 budget still remain. More complete understanding of the global CH4 budget requires significantly more observations, not just of CH4 itself, but other parameters to better constrain key, but still uncertain, processes like wetlands and sinks.Atmospheric CHThis article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'. Based on measurements of air extracted from ice cores and firn, we know that atmospheric CH4 increased from approximately 360 ppb  during glacial periods to approximately 700 ppb during interglacial periods  is the global annual mean CH4 burden and d[CH4]/dt its annual increase, both determined from our surface global means, and τ is the atmospheric CH4 lifetime. After calculating emissions using τ = 9.1 yr ss, ([CH4]ss - [CH4]0), and τ to give a steady-state value of 1794 ppb and a lifetime of 9.2 yr, in agreement with IPCC's Fifth Assessment Report CH4 budget lifetime of 9.1 yr  , but . To date, [OH] and its trend have been assessed indirectly through observations of 1,1,1-trichloroethane , an anthropogenic tracer predominantly removed from the atmosphere by reaction with OH. To be effective, the method required accurate estimates of MC emissions. When MC production was phased out by the Montreal Protocol on Substances that Deplete the Ozone Layer, it allowed Montzka et al. .Oxidation of atmospheric CHa et al.  to explo4 since 2007 may be artefacts of the box-modelling methods used  than reported by Cantrell et al. [δ13CCH4 imposing a 20% uncertainty on emissions from the fossil fuel sector (approx. 30 Tg CH4 yr−1) [Recent reports of a decreasing trend in [OH] driving the renewed growth in atmospheric CHds used  .4 initiated by Cl has little impact on atmospheric CH4 total loss, but strongly fractionates δ13CCH4. Using a CTM (TOMCAT) and sources of Cl from natural and anthropogenic halocarbons and dechlorination of sea-salt aerosol, Hossaini et al. [4 by reaction with Cl of 12–13 Tg CH4 yr−1. Based on analysis of δ13CCO measurements in the extra-tropical Southern Hemisphere, Gromov et al. [4 by reaction with Cl is no greater than 1% of the total atmospheric CH4 sink  budget. Wang et al. [4 from reaction with Cl, 5.3 Tg CH4 yr−1. The impact of Cl on the interpretation of δ13CCH4 observations is still clear; Strode et al. [δ13CCH4. And Lan et al. [4 yr−1) impacts the amount of fossil emissions that can be consistent with observations by approximately 20%.Oxidation of CHi et al.  estimatev et al.  concludei et al. ), and evg et al.  calculate et al.  comparedn et al.  found th4 in soils is intermediate in magnitude, much smaller than loss initiated by reaction with OH and likely much larger than reaction with Cl. Saunois et al. [4 yr−1 based on a literature review. Fractionation of CH4 carbon isotopes by soil loss is reported to be approximately −20‰, with a range of measured values of −16 to 27‰ . Since 7‰ (e.g.  and dist (b)4 emissions from wetlands are the largest natural source of atmospheric CH4. Emissions from wetlands vary greatly over small spatial scales and in time; this variability, combined with relatively few flux measurements, makes up-scaling uncertain. Prior estimates of emissions, therefore, rely on process models, but the range of emissions estimated among different wetland process models is quite large, approximately 40% [4 budget, because estimates of emissions from all other sources are impacted by inaccurate estimates of the magnitude and distribution of CH4 emissions from wetlands.CHtely 40% . One limtely 40% . New mettely 40% . Wetlandet al. [δ13CCH4 source signatures for wetlands, which vary by approximately 10‰ between high-northern latitudes and the tropics. The δ13CCH4 of CH4 emitted from a wetland will depend on factors such as the mechanism of CH4 production (CO2 versus acetate reduction), the source of the carbon (the proportion of plants with C3 versus C4 photosynthetic pathways), and oxidation between production and emission to the atmosphere, which depends on the transport route. Further efforts to identify the underlying processes that control wetland isotopic signature can improve the spatial mapping and thus improve the accuracy of attributing wetland emissions spatially.Using the isotopic constraint effectively requires that the source signatures of different wetland ecosystems are accounted for. Recently, Ganesan et al.  develope. 54 budget are recorded in the atmosphere in CH4 abundance and δ13CCH4 spatial and temporal gradients. High-quality, calibrated observations of atmospheric CH4 and its stable isotopic composition are crucial for decreasing uncertainty in quantifying the evolving global CH4 budget, yet the world is still greatly under-sampled. If we want to track future changes in the global CH4 budget, including potential climate feedbacks, we need to expand these observations to under-sampled regions, such as the tropics, Siberia, Africa and South America. Most studies using CH4 isotope composition as a constraint use stable carbon isotopes, but more extensive observations of stable isotopes of hydrogen would further constrain the budget [δ2HCH4. However, the use of δ2HCH4 would require both an extended time series from an adequate global network of air sampling sites and detailed work to define source signatures by type of source and geography.Changes to the global CHe budget , especia4 burden since 2007 remains challenging, and the exact causes are not yet clear. But, the observations we describe suggest that increased emissions from microbial sources are the strongest driver, with a relatively smaller contribution from other processes, e.g. fossil fuel exploitation. A more difficult question to answer is the one posed by this special issue: is warming feeding the warming? We cannot say for certain, but we cannot rule out the possibility that climate change is increasing CH4 emissions. The strong signals from the tropics combined with the isotopic data are consistent with increased emissions from natural wetlands, but large IAV and inter-decadal variability in wetland drivers like precipitation make it difficult to identify small trends. Observations are needed that will help process models capture this variability. The size of the IAV illustrates the potential scope of uncontrollable near-future change and emphasizes the urgency of reducing the global methane burden by mitigating the methane emissions that we can control, from the fossil fuel and agricultural sectors.Explaining the renewed and accelerating increase in atmospheric CH


Here, we show a fine‐tuning of the reactivity of [(TPA)FeO]2+ by an additional ligand X C6H4I) attached in solution and reveal a thus far unknown role of the ArIO oxidant. The HAT reactivity of [(TPA)FeO(X)]+/2+ decreases in the order of X: ArIO > MeCN > ArI ≈ TfO−. Hence, ArIO is not just a mere oxidant of the iron(II) complex, but it can also increase the reactivity of the iron(IV)‐oxo complex as a labile ligand. The detected HAT reactivities of the [(TPA)FeO(X)]+/2+ complexes correlate with the Fe=O and FeO−H stretching vibrations of the reactants and the respective products as determined by infrared photodissociation spectroscopy. Hence, the most reactive [(TPA)FeO(ArIO)]2+ adduct in the series has the weakest Fe=O bond and forms the strongest FeO−H bond in the HAT reaction.Reactivities of non‐heme iron(IV)‐oxo complexes are mostly controlled by the ligands. Complexes with tetradentate ligands such as [(TPA)FeO] VI=O bond, and forms a stronger FeIIIO−H bond after the HAT reaction.The reaction kinetics of H‐atom transfer mediated by iron(IV)oxo complexes with different cis‐ligands can be studied in a modular flow reactor coupled to MS detection. The HAT reactivity correlates with the intrinsic properties of the isolated complexes in the gas phase. A ligand with a larger binding energy forms a more reactive complex for the HAT reactions, has a weaker Fe The development of non‐heme iron(IV)‐oxo complexes has been inspired by enzymatic oxidations.II(TfO)2] (TfO−=trifluoromethylsulfonate or triflate) can be oxidized by peracids or iodosobenzene (or its derivatives) in acetonitrile to form a fairly reactive [(TPA)FeIVO(MeCN)]2+ complex ‐amine).x Scheme .[5] SuIV(O)]2+ complexes  is that a solvent molecule or another 2 e− donor ligand is often attached as the sixth ligand .The common feature of the [(L)FeIVO(X)]2+ ‐oxo complexes [(TPA)Fetr) in the flow is typically about 10 seconds.Mass spectrometry has been an established method in the field for detection of reactive iron(IV)‐oxo complexes.II(TfO)2]  leads to the detection of the expected iron(II) complexes [(TPA)FeII(MeCN)]2+ and [(TPA)FeII(TfO)]+ . Addition of the oxidant 2‐(tBuSO2)C6H4IO (ArIO) to vial B results in oxidation of the iron(II) complexes and detection of [(TPA)FeIVO]2+, [(TPA)FeIVO(MeCN)]2+, and [(TPA)FeIVO(TfO)]+ ions, in analogy to previously reported results ]2+ and [(TPA)FeIVO(ArI)]2+ ions, corresponding to signals of iron(IV)‐oxo complexes, respectively bound to the oxidant itself as well as the oxidation byproduct ArI.Charging the vial A with an acetonitrile solution of [(TPA)Fes Figure .[5] InThe reaction of the iron(IV)‐oxo complexes that is of most interest is hydrogen atom transfer with hydrocarbons, which we have studied with 1‐methylcyclohexene ]+/2+ complexes, we have systematically varied the concentration of 1‐methylcyclohexene in vial C ]+/2+ complexes as detected by ESI‐MS, we conclude that [(TPA)FeIVO(ArIO)]2+ reacts the fastest, followed by [(TPA)FeIVO(MeCN)]2+ and finally by [(TPA)FeIVO(ArI)]2+ and [(TPA)FeIVO(TfO)]+ with almost identical rates ]+/2+ complexes.In order to compare the kinetics of the reactions of the [(TPA)FeC Figure . With ths Figure . A more IVO(X)]+/2+ complexes can be compared exactly in gas phase experiments. In the gas phase, we had to use the more reactive 1,4‐cyclohexadiene as the substrate ]2+ exhibits the fastest rate, followed by [(TPA)FeIVO(MeCN)]2+, with [(TPA)FeIVO(ArI)]2+ and [(TPA)FeIVO(TfO)]+ having the slowest rates. The gas phase experiments provide quantitatively correct rate constants and the relative HAT rates of [(TPA)FeIVO(X)]+/2+ ions decrease in the order of X as ArIO/MeCN/ArI/TfO−=1:0.45:0.08:0.07.The relative HAT reactivities of the isolated 2+ is more reactive than the usually considered [(TPA)FeIVO(MeCN)]2+. However, the importance of these complexes for the observed oxidation reaction depends on their relative abundance in solution. The equilibrium between the individual [(TPA)FeIVO(X)]2+ complexes depends on the concentration of X and on the relative free energies of the [(TPA)FeIVO(X)]+/2+ complexes in solution.Our results show that the thus far never considered complex [(TPA)FeIVO(X)]2+ obtained from theoretical calculations. Theory predicts that the binding energies of X increase in the order: CH3CN  < ArI  ≈ TfO−  ≪ ArIO . For comparison, we have studied the trend of the binding energies experimentally in the gas phase, which is consistent with these theoretical values . The entropic effects in solution disfavor the stability of [(TPA)FeIVO(ArI)]2+ leading to the predicted trend in the equilibrium reactions shown in Table IVO(ArIO)]2+ should be formed by far preferentially. Note, that we did the experiments with 1.4 equiv of ArIO. Hence only ≈0.4 equiv ArIO is available for the formation of [(TPA)FeIVO(ArIO)]2+. The only other iron(IV)oxo complex will be present as [(TPA)FeIVO(MeCN)]2+ (Table IVO(X)]2+ complexes with X=TfO− or ArI and their role for the reactivity in solution can be neglected.First, we have assessed the relative binding energies of X in [(TPA)FeIVO(ArIO)]2+ is preferentially formed in solution by UV/Vis spectroscopy. The parent S=1 complex [(TPA)FeIVO(MeCN)]2+ exhibits a maximum absorbance (λmax) at 722 nm when generated by oxidation with peracetic acid.λmax is sensitive to the nature of the cis‐ligand.cis to the Fe=O unit to generate [(TPA)FeIVO(ArIO)]2+.We have tested the hypothesis that [(TPA)Feorbance λmax at 72orbance λmax at 72orbance λmax at 72IVO(CH3CN)]2+ and [(TPA)FeIVO(ArIO)]2+ in solution. Hence, with increasing concentration of ArIO, a larger fraction of the iron(IV)‐oxo complexes should be converted to [(TPA)FeIVO(ArIO)]2+, accounting for the observed increase in the blue shift. On the other hand, diluting the solution should contribute to the solvolysis of the complex . Hence, we can conclude that [(TPA)FeIVO(CH3CN)]2+ is the prevailing product under these conditions. However, adding more equivalents of ArIO and working at larger concentrations lead to generation of the more reactive [(TPA)FeIVO(ArIO)]2+ complex.As explained above, we consider only the equilibrium between [(TPA)FeIVO(X)]+/2+ complexes constitutes an ideal system to test to what extent the relative reactivities correlate with properties of the reactive Fe=O bonds and with the newly formed FeO−H bonds. According to the Bell–Evans–Polanyi principle,The family of +/2+ complexes can readily be assigned based on their 16O/18O shifts. We observe that the Fe=O stretching frequencies of these four [(TPA)FeIVO(X)]+/2+ complexes decrease in the following order: 849 cm−1 for X=TfO−, 843 cm−1 for X=ArI, 839 cm−1 for X=ACN, and 834 cm−1 for X=ArIO, ]2+, there is also a feature at about 640 cm−1 that is sensitive to 18O labelling and most likely arises from the I=O stretching vibration of the ArIO ligand.Helium‐tagging infrared photodissociation (IRPD) spectroscopy allows the measurement of IR spectra of mass‐selected ions.III(OH)(X)]+/2+ complexes ]2+ and [(TPA)FeIIIOH(TfO)]+ complexes show several O‐H bands, which is due to the possibility of internal hydrogen bonding between the O‐H group and the oxygen atoms of the SOx moiety at ArI or triflate. This interaction leads to a red shift of the respective O‐H vibrations. We have optimized several conformers that rationalize the presence of several red‐shifted bands . For further discussion, we have focused on the highest lying O‐H band for each complex, because this vibration should correspond to the free O‐H vibration.Similarly, we have determined the stretching frequencies of the O−H bond in the [(TPA)Fes Figure . The [‐oxo complexes with lower FeIVO(X)]+/2+ should follow the order of X: ArIO > MeCN > ArI ≈ TfO−. This trend is the same as the trend observed experimentally. It shows that it is valid to approximately evaluate the reactivities of iron(IV) oxo complexes based on the properties of the FeIV=O and the FeIIIO−H bonds.The measured stretching frequencies of the reacting and the newly formed bonds predict that the HAT reactivities of [(TPA)FeIVO(MeCN)]2+ has the weakest Fe=O bond and [(TPA)FeIVO(TfO)]+ forms the strongest O−H bond. Strikingly, the most reactive [(TPA)FeIVO(ArIO)]2+ complex would be predicted as the least reactive based on the theoretical Fe=O and FeO‐H stretching frequencies. These contradicting trends point to the limits of DFT in describing this type of complexes. There is an ample of examples that DFT describes correctly reactivity and properties of various hypervalent metal complexes. However, there might be a limitation when going towards description of subtle differences such as the effect of labile cis‐ligands as described here. Hence, we believe that our results show the need for experimental data that describe properties and reactivity trends of these complexes to benchmark the theoretical methods.However, the trends of the Fe=O and FeO‐H stretching frequencies are not reproduced by the DFT calculations (Tables IV(O)(X)]+/2+  complexes in a flow setup with electrospray ionization mass spectrometry (ESI‐MS) detection. We show that we can investigate solution reaction kinetics and employ sensitive ESI‐MS detection at the same time. This approach has allowed us to show that the HAT reactivity of [(TPA)FeIV(O)(X)]+/2+ with 1‐methylcyclohexene decreases in the order of X: ArIO > MeCN > ArI ≈ TfO−. The theory predicts that the binding energy of ArIO to the iron core is about 16 kcal mol−1 larger than that of MeCN and that the formation of [(TPA)FeIV(O)(ArIO)]2+ in solution should be largely preferred if an excess of ArIO is used in the experiment. The importance of [(TPA)FeIV(O)(ArIO)]2+, a species which to date has not been considered to be a participant in this chemistry, is further supported by UV/Vis spectroscopy.We have investigated the formation and hydrogen‐atom transfer (HAT) reactivity of [(TPA)FeIV=O and the FeIIIO−H bonds in the [(TPA)FeIV(O)(X)]+/2+ and [(TPA)FeIII(OH)(X)]+/2+ complexes, respectively. The Fe=O stretching frequency increases in the order of X: ArIO < MeCN < ArI < TfO−, while that of the FeO‐H unit decreases in the same order. Hence a weaker Fe=O bond gives rise to a stronger FeO−H bond, which in turn facilitates the HAT reaction. This trend directly correlates with the measured HAT activities of these complexes and demonstrates in particular the role of the O‐H formation in serving as a driving force for HAT reactions, as postulated by ShaikFurthermore, we have evaluated the effect of the ligand X on the FeThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


The integrated A value after heading was positively associated with crop growth rate (CGR) from heading to harvest, but that before heading was not. A curve-smoothing analysis of A after heading showed that accumulated A at > 80% of its maximum (A80) was positively correlated with CGR in analyses of all lines mixed and of lines grouped by genetic background, while the maximum A and accumulated A at ≤ 80% were less strongly correlated with CGR. We also found a genomic region (~ 12.2 Mb) that may enhance both A80 and aboveground biomass at harvest. We propose that maintaining a high A after heading, rather than having high maximum A, is a potential target for enhancing rice biomass accumulation.Leaf photosynthetic rate changes across the growing season as crop plants age. Most studies of leaf photosynthesis focus on a specific growth stage, leaving the question of which pattern of photosynthetic dynamics maximizes crop productivity unanswered. Here we obtained high-frequency data of canopy leaf CO Oryza sativa) is one of the most important cereal crops worldwide. To meet the increasing demand for grain as the world’s population increases, rice productivity must be increased by ~ 50% relative to the current level by 20502. The rice yield increases during the “green revolution” depended largely on the development of semi-dwarf cultivars with greater harvest index and on greatly increased N fertilizer application4. This strategy is reaching its limits, however, because harvest index is reaching its theoretical maximum and excess application of N fertilizer causes environmental pollution7. Further enhancement of grain yield must be achieved through increases of total biomass accumulation via improved radiation use efficiency without increased nutrient inputs8. Single-leaf photosynthesis has long been considered a target trait for increasing radiation use efficiency10. Recent studies have shown the importance of enhancing single-leaf photosynthesis and crop productivity in the field11; for example, the promoted recovery from photoprotection increased biomass production in tobacco (Nicotiana tabacum)12, and overproduction of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) increased grain yield in rice13.Rice (16. Wide intraspecific variation in net CO2 assimilation rate per leaf area (A) has been found in several crop species, including rice19 and wheat (Triticum aestivum)21. The underlying genetic variations can be used in quantitative genetic analyses to identify genomic regions relating to leaf photosynthesis, facilitating DNA marker-assisted selection16. An important question in such an approach is whether the enhanced A effectively increases total biomass production and grain yield22. Positive close correlations of A with plant (or crop) growth rate, biomass production, and final yield through large-scale surveys of diverse sets of accessions have been reported in rice24, wheat26 and soybean (Glycine max)27. Simulation analyses showed that a 25% increase in single-leaf photosynthesis based on rice genetic resources could enhance biomass production by 22–29%28. Furthermore, newer rice cultivars developed in Japan with high yield capacity have higher A than older cultivars, especially after heading30. These studies underpin the potential for enhanced productivity by improved photosynthesis achieved through the use of natural genetic resources.Using natural genetic resources could be a useful approach for improving photosynthesisA and biomass accumulation have been reported in rice31, wheat21 and maize (Zea mays)32. For example, Jahn et al. found a significant negative correlation between A and dry biomass among 20 diverse rice varieties18. Our previous research also showed that a near isogenic rice line with enhanced A significantly reduced the grain yield than its parental cultivars33. Many agronomists have been questioned the effects of the genetic improvement of single-leaf photosynthesis for better crop yields35. In fact, crop breeding has often selected increased leaf area production at the expense of photosynthetic capacity, as occurred in wheat36. The inconsistencies between studies could reduce the potential value of natural genetic resources for improving leaf photosynthesis and delay the enhancement of crop productivity.In contrast, there are many conflicting results on the photosynthesis–productivity relationship. Poor correlations between A changes across the growing season owing to the progression of plant age and leaf senescence39. However, most studies of the photosynthesis–productivity relationship selected only one or two growth stages for evaluation of photosynthesis30. Such a “snapshot” analysis can reveal only limited aspects of crop production and potentially cause inconsistent results. The need for comprehensive evaluation is supported by the fact that the total CO2 uptake per tobacco plant, calculated from multiple measurements of leaves at several positions throughout the day and the growing season, agreed well with actual dry weight increase40. Therefore, multiple photosynthetic measurements are necessary when we examine natural genetic resources across their growing season.The value of 41. To overcome this limitation, we recently created a new closed gas exchange system , which takes 15–20 s per measurement, ~ 90% less than conventional open gas exchange systems. We hypothesize that with the new measurement system, tracing photosynthetic dynamics of multiple rice accessions across their growing season will tell us which photosynthetic dynamics can maximize productivity and which developmental stage should be targeted in breeding for photosynthesis.Conventional open gas exchange systems require several to tens of minutes to acclimatize a leaf to the leaf chamber, limiting the number of samples to be examinedindica cultivar Takanari, which has one of the highest grain yields among Japanese rice cultivars, accumulated more biomass than Nipponbare and Koshihikari, standard japonica cultivars43. Since then, Takanari has been widely used to analyse the physiological and molecular mechanisms of biomass accumulation50 and their effects on grain yield54. Although the higher biomass accumulation in Takanari is characterized by a higher net assimilation rate around the full heading stage, which could be partly explained by the higher leaf photosynthetic capacity, only rough analysis of gas exchange during growth has been conducted43. Here, we aimed at collecting the data on temporal changes in canopy photosynthesis of Koshihikari and Takanari over the entire growing season by using the MIC-100 to analyse its association with crop growth rate (CGR) and total biomass accumulation. We assumed that photosynthesis in the uppermost fully expanded leaf is representative of canopy photosynthesis, since it has the highest photosynthetic capacity and receives the strongest radiation in the canopy55. We also observed ontogenic changes of chlorophyll content  and single leaf area (single LA). To analyse the phenotypic variation caused by introgressions between the cultivars, we used reciprocal sets of chromosome segment substitution lines  derived from a Koshihikari/Takanari cross56. Each CSSL carries a single genomic segment from the donor cultivar (either Koshihikari or Takanari) in the genetic background of the other cultivar, and the full set of substituted segments covers the entire genome57. The variation in flowering date is much smaller in CSSLs than in other populations such as recombinant inbred lines, which is advantageous in examining whether changes in photosynthesis affect biomass accumulation. From this study, we propose that maintaining a high rate of photosynthesis after heading, rather than having a high maximum photosynthetic rate, can increase total biomass accumulation.In previous studies, our research group determined that the A reached the maximum at around 30–35 days after transplanting (DAT) and then gradually decreased over time  were lower by 10% in average than those of Koshihikari-background CSSLs and Koshihikari (Koshihikari lines). During Phase II, A values of Takanari lines remained higher than those of Koshihikari lines —and Phase II—from the first sampling to the second sampling (at harvest) Fig. . See da. As a geime Fig. a. DuringA (Aint), the apparent total CO2 uptake calculated by sum of trapezoidal area under each pair of adjacent measurements, was 10% lower in Takanari lines than in Koshihikari lines during Phase I, but was 23% higher during Phase II  or in CGR during Phase I (CGR Phase I) between Koshihikari lines and Takanari lines, while AGB at the second sampling (AGB II) and CGR during Phase II (CGR Phase II) were significantly higher in Takanari lines than in Koshihikari lines, by 25% and 40%, respectively , not with CGR Phase I  (r = − 0.10) and was only slightly correlated with mean single LA during Phase I (LAmean Phase I) (r = 0.28), while CGR Phase II was strongly correlated with these values  and the maximum A (Amax) were higher in Takanari than in Koshihikari, by around 26% each  and accumulated A at ≤ 80% of Amax (Adec) at Donset (1 day before A declined below 80% of Amax), Takanari had a higher A80 than Koshihikari but a similar Adec  than Koshihikari , but it was significantly correlated with A80  and with Donset  during Phase I than during Phase II , differences in leaf photosynthesis had little effect on biomass production. Takanari lines had lower es Figs. , 2. AGB  II Fig. , which sA value of Takanari lines increased and remained higher than that of Koshihikari lines until the final examination  and biomass accumulation parameters (CGR Phase II and AGB II) in the analysis of all datasets combined indicate that the consistently higher photosynthetic capacity in Takanari lines contributes to the enhanced biomass accumulation compared to Koshihikari lines  A80 and CGR Phase II without increasing Amax , photosynthetic parameters were closely associated with biomass accumulation. The ion Fig. a. The pones Fig. . The larnes Fig. . In addimax Fig. . We propmean Phase II was also associated with the variation in CGR Phase II in both the combined analysis of all lines and the separate analyses of Koshihikari and Takanari lines  of Tokyo University of Agriculture and Technology  on 22 May with a basal dressing of inorganic fertilizer supplying 30 kg N, 60 kg P, and 60 kg K ha−1. One-third of the total N was applied as ammonium sulphate, and the other two-thirds as slow-release urea . No topdressing was applied. The plant density was 22.2 m−2 (at a spacing of 30 cm × 15 cm) with one plant per hill, and plants were grown under submerged conditions. Each line was grown in three replicate plots in 2 rows of 20 plants (60 cm × 300 cm). Plots were randomized, but lines of each background group were planted adjacent 2 assimilation rate was measured with a closed-type portable photosynthesis system , which consists of a console and a chamber head with an aperture area of 2 cm × 3 cm . The SPAD value as a proxy for leaf chlorophyll content was measured with a chlorophyll meter . Measurements were conducted once a week from 3 weeks after transplanting  to harvest , and additional measurements were also conducted around heading and mid-ripening stage . In total, 246 leaves were measured per day, 4632 leaves during the experiment. Aint was calculated by summing the trapezoidal area under each pair of adjacent measurement cycles. Mean single LA was calculated as the average of linear interpolated values.The uppermost newly expanded leaf on the main tiller of one plant per plot was used for phenotypic analyses. The net CO I) and harvest . Eight plants in each plot were sampled and air-dried in a greenhouse until weighing. Air-dried samples of parental plants were dried in a ventilated oven to calculate the water content ratio of the air-dried samples. The biomass accumulation was expressed as dry weight (g) per m2. CGR from transplanting (considered as 0) to heading (Phase I) and from heading to harvest (Phase II) were calculated.The aboveground biomass was examined at heading  algorithm with the smoothing parameter fixed at 1.072. We defined Amax (μmol m−2 s−1) as the maximum fitted value of A, and Donset (day) as 1 day before A declined below 80% of Amax  as accumulated A by curve-smoothing during Phase II (from 72 to 128 DAT), A80 (μmol m−2 s−1 phase−1) as accumulated A from 72 DAT to Donset, and Adec (μmol m−2 s−1 phase−1) as accumulated A from Donset to 128 DAT (Fig. Phase II and A10 to A90 (accumulated A at > 10% to > 90% of Amax, Supplementary Fig. 80 is the mean SPAD value from 72 DAT to Donset, and SPADdec is mean SPAD value from Donset to 128 DAT. Statistical differences were tested by Welch’s two-sided t-test. Pearson’s correlation coefficient was calculated, and the significance of relationships was tested by two-sided t-tests.All statistical analyses were performed in R v. 4.0.2 softwaremax Fig. a. We alsDAT Fig. a. We shoSupplementary Dataset S1.Supplementary Dataset S2.Supplementary Information.


However, αGPC did not show any effects on anxiety. These data suggest that αGPC can be used to increase motivation in healthy individuals.Alpha-glycerylphosphorylcholine (αGPC) is a precursor of acetylcholine and can increase acetylcholine concentration in the brain. In addition, αGPC has a role in cholinergic function as well as monoaminergic transmission, including dopaminergic and serotonergic systems. These monoaminergic systems are related to feelings and emotions, including motivation, reward processing, anxiety, and depression. However, the precise effects of αGPC on human feelings and emotions remain to be elucidated. In this study, we investigated changes in the subjective feelings of healthy volunteers using the KOKORO scale before and after administering αGPC. Thirty-nine volunteers participated in a single-blind, placebo-controlled design. Participants completed a KOKORO scale test to quantify self-reported emotional states, three times each day for two weeks preceding treatment and then for a further two weeks while self-administering treatment. αGPC treatment show a tendency to increase motivation during the intervention period. Furthermore, motivation at night was significantly higher in the αGPC group than in the placebo group ( Alpha-glycerylphosphorylcholine (αGPC) is a deacylated phosphatidylcholine derivative and is widely used as a dietary supplement. In addition, αGPC is a precursor of acetylcholine and can enhance cholinergic transmission in the brain ,2. Pre-cThe study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the RIKEN Center for Biosystems Dynamics Research (Kobe2 2018-02) (19 June 2018). Forty volunteers  aged 22–59 years were recruited between July and October of 2018 for this study. The inclusion criterion was healthy individuals aged between 20 and 60 years, and exclusion criteria included: (1) women who are planning pregnancy, currently pregnant, or lactating, (2) individuals visiting the hospital and/or were on medications for diseases, and (3) smokers. All participants provided written informed consent before beginning the present study. The study was registered with the University hospital Medical Information Network (UMIN) Clinical Registry (UMIN000044563).Participants were randomly divided into placebo and αGPC treatment groups by age and sex. One participant in the placebo group was excluded from data analysis for lack of compliance with data entry in the KOKORO scale system (≤75%). Thus, KOKORO data from 19 participants in the placebo group  and 20 in the αGPC group  were used for analysis .® GPC85R, powder containing 85% αGPC) , or cellulose (placebo). The participants self-administered two capsules, once daily at bedtime for 2 weeks, for a total daily dose of 400 mg αGPC in the treatment group. Previous clinical studies have shown that there are no serious side effects or toxicities when human subjects were orally administered αGPC (1200 mg/day) for 6 months [50 values of αGPC in rodent and dogs were estimated at ≥10 and ≥3 g/kg respectively, by oral administration [Participants were administered capsules containing either 200 mg of αGPC, which was purchased from NOF Corporation . Statistically significant differences between the two groups were determined using the Mann–Whitney In this study, subject recruitment started in July 2018 and was completed in October 2018. No serious side effects were observed during the clinical trials. Changes in subjective feelings in both placebo and αGPC groups were calculated as the difference between values before and after treatment. As shown in αGPC is a water-soluble deacylated metabolite of phosphatidylcholine and a precursor of acetylcholine. In clinical trials, αGPC improved cognitive impairment in patients with Alzheimer’s disease ,6 and vaIn this clinical trial, healthy subjects self-administered a total αGPC dosage of 400 mg per day for 2 weeks. This dose of αGPC was determined based on previous studies in animals and humans. In dogs, chronic administration of αGPC (≥300 mg/kg/day) did not show serious adverse effects for 26 weeks . In addiHowever, the present study has some limitations. In this study, we could not address whether αGPC acted directly or indirectly on human motivation and neural circuits, especially the dopamine system in the human brain. Further research with experimental animals may resolve this limitation. Next, this study was conducted on a relatively small scale, using a short-term (2 weeks) intervention and a single-blind test as a pilot clinical trial. Thus, our results can be validated by performing further studies on a large scale and with long-term intervention. In this study, the investigation and the data analysis were performed by different researchers to minimize experimental biases. Finally, the changes of human feelings and emotions might be affected by several factors and events in daily life. Thus, the participants were instructed to input the matters with their feelings if they experienced extraordinary events and sensations. These data were excluded from the data analysis.We showed that use of αGPC increased motivation in human subjects without serious side effects in this study. These findings suggest that αGPC supplementation has an increased effect on motivation of healthy subjects.


These findings show that multispectral assessment of the cell autofluorescence in urine exfoliated proximal tubule kidney cells has the potential to be developed as a sensitive, non-invasive diagnostic method for CKD.Optimally preserved urinary exfoliated renal proximal tubule cells were assessed by multispectral imaging of cell autofluorescence. We demonstrated different multispectral autofluorescence signals in such cells extracted from the urine of patients with healthy or diseased kidneys. Using up to 10 features, we were able to differentiate cells from individuals with heathy kidneys and impaired renal function (indicated by estimated glomerular filtration rate (eGFR) values) with the receiver operating characteristic area under the curve (AUC) of 0.99. Using the same method, we were also able to discriminate such urine cells from patients with and without renal fibrosis on biopsy, where significant differences in multispectral autofluorescence signals (AUC = 0.90) were demonstrated between healthy and diseased patients ( Standard magnetic resonance imaging (MRI), computed tomography (CT) scans and positron emission tomography (PET) and single photon positron emission tomography (SPECT) are principally limited to the detection of masses, density changes or morphology/texture with findings being impacted by poor image quality, motion artifacts or reliance on contrast agents4. These include podocytes, progenitor cells and proximal tubule epithelial cells (PTCs)5. The proximal tubules make up the majority of the kidney mass. Proximal tubule-related pathology is a hallmark of multiple kidney diseases and it correlates with future decline in kidney function7. This suggests that the biology of these non- invasively obtained cells could give a direct window into kidney pathology. Indeed, it has been established that a small number of exfoliated PTCs can be isolated and cultured on collagen plates for signal amplification9. The disadvantage of this approach is the loss of cellular properties during culture that could be pivotal for diagnosis. The currently available clinical methods to identify CKD are insufficient because the physiological markers used currently only become abnormal once significant pathology is evident. Hence non-invasive methods of diagnosing CKD are required and examination of cells exfoliated from the kidney using novel isolation and assessment techniques presents an attractive opportunity.Multiple cells are exfoliated from the kidney into the urine daily11, which play a pivotal role in cell and tissue metabolism. Multispectral microscopy can be used to collect native emission data across a broad range of excitation wavelengths. This data yields the spectral profile of autofluorescent features for each cell, including parameters such as average channel intensities, channel intensity ratio12, pixel standard deviations and skewness13. This gives each cell a unique signature, arising directly from its intracellular biochemistry and organisation, which has been used in the literature for the sensitive discrimination of specific cellular characteristics, which have included cell cycle stage14, the presence of inflammatory disease15, levels of reactive oxygen species16, neoplasia17 and age18. We have previously demonstrated increased oxidative stress markers in the proximal tubules of animals with early diabetic or obesity related kidney disease20. Oxidative stress is a key factor affecting kidney function22. Multispectral assessment of cell autofluorescence has been specifically shown to be sensitive to metabolic changes and oxidative stress16. As such, it could be expected that the application of multispectral autofluorescence microscopy to exfoliated renal tubule cells will enable the sensitive, non-invasive characterisation of kidney dysfunction, providing a new window into physiology of this deep organ.Cell autofluorescence originates from native fluorophores such as collagen, elastin, tryptophan and reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), and flavins23. We also present an optimised protocol which enables the preservation and characterisation of exfoliated proximal tubule cells without the requirement of cell culture.In this work, we demonstrate that multispectral autofluorescence colour and colour-dependent morphology measured in a small number of exfoliated kidney cells (PTCs) reflects established markers of kidney disease determined by assessing estimated glomerular filtration rate (eGFR) and renal pathology (current gold standard measure obtained via invasive kidney biopsy)2), Group 2— for at least 12 months prior to the procedure.Participants of either sex aged between 18 and 75 years of age were included and informed consent was obtained from all participants involved in this study. The study was carried out in accordance with relevant guidelines and regulations was approved by the human Ethic committee at Royal North Shore Hospital (Ref: HREC/17/HAWKE/471) and University of New South Wells, Sydney (Ref: HC180710). Two cohorts of patients were assessed in this study. The first cohort included 22 patients with Type 2 diabetes with varying levels of eGFR. Patients were grouped as follows: Group 1  and Group 2—with detectable IFTA in the kidneys. Kidney pathology was assessed in a blinded manner by 2 different pathologists as we have previously described25. These cells were resuspended in 50 μl of PBS on a 35 mm glass bottom petri dish  then assessed using a multispectral and brightfield microscopy. Due to field restriction, only a fraction of the total number of cells per sample can be imaged.Urine samples (100 ml) were collected from patients fulfilling the above criteria. The urine was placed on ice immediately after collection for transportation, then spun at 4℃ before freezing it down and stored frozen prior to assessment. Renal proximal tubular cells were extracted from the thawed urine using immune- magnetic separation, anti-CD13 and anti-Sodium-glucose linked transporter-2 (SGLT2) antibodies. Briefly, urine cells were spun at 3000/4000 rpm for 20 min (at 4 °C) and then washed twice with phosphate buffered-saline (PBS). Cells were stored at 4 °C overnight or − 80 °C in Dulbecco's Modified Eagle Medium (DMEM), Hanks' Balanced Salt solution (HBSS) or Phosphate buffer saline (PBS) with 10% Dimethyl Sulfoxide (DMSO) to assess the optimal storage condition and maximal cell viability. On the day of the experiment, frozen cells were quickly thawed at 37 °C and then washed in PBS. Renal proximal tubule cells were then extracted following incubation with 5 µg of mouse anti-human CD13 antibody (Invitrogen) and 2.5 µg of SGLT2 antibody (Abcam) for 1 h (at 4 °C) followed by 5 µl of CELLection Pan anti-mouse dynabeads (Invitrogen) for 30 min (at 4 °C). Specific proximal tubule cells bound to a magnet were then washed in PBS before elution from the beads according to the manufacturer’s instructions. The number of exfoliated proximal tubule cells in 100 ml urine is around 20–50 cells as previously demonstrated26. Briefly, urine cells were collected as described above and washed twice with PBS before incubation with either 5 μl of 7-AAD  or 5–10 μl of PI  for 10 min at room temperature in the dark. Human kidney (HK2) cells were used as a positive control. In a separate experiment, urinary exfoliated cells were collected, and cells were incubated with 5 μl of PE-conjugated anti-CD13 antibody , an epithelial marker, and 2 μl of FITC conjugated anti-SGLT2 antibody , a proximal tubule marker, for 1 h at room temperature in the dark. Cells were then washed and resuspended in PBS and subjected to the flow cytometer for fluorescence analysis. The population has been gated to exclude the debris and dead cells. We also added EDTA to the washing buffer to dissolve precipitates.Cell viability was assessed by flow cytometry  using either 7- Aminoactinomycin D (7-AAD) or propidium iodide (PI) staining as previously describedSimilar to flow cytometry, urinary exfoliated cells were incubated with CD13, SGLT2 and angiotensinogen (AGT) at 4 °C overnight, washed with PBS and stained with fluorophore-conjugated secondary antibodies when necessary. The cells were washed twice with ice cold PBS, stained with 1 drop of nuclear dye  and loaded onto 35 mm coverslip-bottom dishes for imaging. Fluorescent signals before and after proximal tubular cell enrichment was assessed using a confocal microscope .27 was built by adapting a standard fluorescence microscope (Olympus iX83™). Multispectral excitation lamp  has been applied to excite the single photon-excited autofluorescence signal of cell samples in a number of defined narrowband (± 5 nm) excitation wavelength ranges provided by low power LEDs and several epifluorescence filter cubes to produce defined spectral channels (34 channels). The channels span wide excitation (340–510 nm) and emission (420–650 nm) wavelength ranges  as described in29. The uneven illumination of the field of view was then flattened, separately in each channel by using the image of the calibration fluid whose spectrum spans across all 34 spectral channels. Further image pre-processing steps comprised image equalization, primary denoising by removing undetectable pixels and outliers (spikes or dips)28. Furthermore, cells were manually segmented from the channel images to produce single-cell images30. During this segmentation process data register  were separately preserved for indexing the cell samples. Following image preparation and segmentation segmented single-cell images were used to extract the channel image features. These features include average channel intensity per cell, and associated measures such as channel intensity ratio, as defined in Reference12.After obtaining the spectral images, they were prepared for subsequent quantitative analysis using a multistep procedure to minimize sources of errors such as Poisson’s noise, dead or saturated pixels, background fluorescence and uneven illumination of the field of view, as reported in detail in31 in Matlab 2019b. The best n cellular features were chosen for further analysis. This number of features, n, was kept lower than the number of cells in each group (N) to reduce the possibility of overfitting. To visualise the separation of different patient groups, the data points corresponding to n-dimensional feature vectors for each cell were first projected onto an optimal two-dimensional (2-D) space created by discriminative analysis33. This space was spanned by two canonical variables given by a specific linear combination of cellular features34. This space maximizes between-group distance while minimizing within-group variance32. We then developed a classifier to predict the pre-defined cell labels corresponding to the patients groups35, and the classifier performance was evaluated by using a ROC curve35. Nested cross validation was applied to validate our classifier for separating the groups which provides an unbiased performance assessment, by using external validation test36 We have demonstrated that urinary exfoliated cells contain sufficient living cells Fig. . OptimalWe have additionally demonstrated that HK2 cells cryopreserved in the standard condition (DMEM + 10%DMSO) contained 22.6% dead cells, 11.5% apoptotic cells and nearly 50% live cells  and SGLT2 are uniquely expressed on PTCs (as expected) and not in other epithelial cell types such as squamous and transitional epithelial cells that are present in the urine  were detectable in normal individuals and diabetic patients with and without CKD Fig. . This de2 (referred to as “high”) compared to those from patients with eGFR < 60 ml/min/1.73 m2 (referred to as “low”) as shown in Figs. 2 from eGFR > 60 ml/min/1.73 m2 with the area under the curve, AUC = 0.75  and high eGFR (> 60 ml/min/1.73 m2), a second analysis was performed where we used up to ten features and we included features which do not currently have a biologically interpretable meaning. This resulted in excellent separation of these two classes of cells and an AUC of 0.99  is related to the cellular redox ratio41. Thus, by using the conservative model we increased the likelihood of the observed changes to be underpinned by defined and known biological changes in cells as kidney dysfunction develops.We then isolated cells from the urine of patients with diabetes mellitus with different levels of eGFR indicating varying degrees of kidney dysfunction and from patients from whom kidney biopsies had been taken and assessed for fibrosis (the gold standard for diagnosis of kidney dysfunction). We were successful in categorising these cells using their autofluorescent characteristics assessed through multispectral microscopy and related these characteristics to a binary kidney health status based on eGFR values and, separately, on kidney pathology determined from biopsies. This was explored in two models: a more conservative model with a smaller number of cell features drawn from channel intensities and their ratios, and an extended model where more features and also additional types of mathematically abstract features were allowed (e.g. kurtosis). Limiting the features used to channel intensities and ratios allows the features to be more closely related to biological properties such as the content of individual fluorophores, some of whom tend to dominate specific spectral channels (for example intensity in channel 2 is predominately related to NAD(P)H content and intensity in channels 15 and 32 is dominated by flavinsLow sample size limited the complexity of the models we were able to apply due to the risks of overfitting, however in secondary analyses, when the restrictions on the models were loosened, very high accuracy was achieved. Greater accuracy was achieved in a cohort where cells were assessed relative to the status of kidney biopsies. This is not surprising as the proximal tubule cells which we examined by multispectral assessment originated directly from the kidney cells and their characteristics should directly reflect the biological status of the kidneys, more so than a secondary measure like eGFR. A further limitation of this study is that we are unable to relate any changes in autofluorescence to other potential factors of influence, limiting any causal relationships which could be determined based on our data. Putative parameters potentially affecting cell autofluorescence may include age (through cell senescence) and sex, while the potential clinical factors may include eGFR, urinary protein, HbA1c etc. Among these factors, age and sex were matched between first and second cohort, while the levels of eGFR were independently analysed. We are mindful that the difference in eGFR in the second cohort could have an impact on autofluorescence instead of fibrosis. A larger cohort is required in order to match eGFR levels in for cohort 2 to confirm whether the levels of renal fibrosis are responsible for changes in hyperspectral autofluorescence independent of eGFR levels. Taken together, our findings indicate that multispectral measurement of proximal tubule cell autofluorescence—autofluorescence colour—is different in healthy and diseased kidneys.The data that support the findings of this study are available from the corresponding author on request.Supplementary Information.


The last decade has been somewhat of a renaissance period for the field of macrophage biology. This renewed interest, combined with the advent of new technologies and development of novel model systems to assess different facets of macrophage biology, has led to major advances in our understanding of the diverse roles macrophages play in health, inflammation, infection and repair, and the dominance of tissue environments in influencing all of these areas. Here, we discuss recent developments in our understanding of lung macrophage heterogeneity, ontogeny, metabolism and function in the context of health and disease, and highlight core conceptual advances and key unanswered questions that we believe should be focus of work in the coming years. Technological advances have revealed tremendous diversity and heterogeneity between macrophages from different tissues and even within different niches of the same tissue in terms of phenotype, transcriptome and metabolome4. While macrophages generally excel at the ‘silent’ clearance of debris, apoptotic host cells and the capture and destruction of microbial intruders, it is now clear they also play far broader roles and are exquisitely tailored to meet the demands of the local tissue microenvironment in which they reside. Indeed, the in vivo tissue environment may be the major determinant governing macrophage development, recruitment, activation and function, highlighting the importance of careful consideration of the distinctive properties that different tissues possess, in steady state and during inflammation, to fully understand the role of macrophages in different locations throughout the body.Macrophages are largely sessile, tissue dwelling phagocytes that are present in every organ of the body. The biology of macrophages has been studied for well over one hundred years since being described by Elie Metchnikoff at the end of the nineteenth century. However, arguably, it is the last 10–15 years that has seen major revisions of key concepts in macrophage biology, from nomenclature of subsets and activation states to the ontogeny of these cells. For instance, while macrophages have historically been described as M1  or M2  largely based on in vitro culture systemsIn this article, we review recent developments in the understanding of macrophage heterogeneity, ontogeny and function in lung health and during inflammation, immunity and tissue repair. Using pulmonary fibrosis as an example, we discuss how dysregulated macrophage behaviour can contribute to lung pathology and how mouse models have revealed the complexity of the macrophage response to lung injury. Along the way we identify key areas that we believe warrant further investigation.11.Macrophages are distributed throughout the lung and can broadly be divided into those present in the airways/alveoli and those in the tissue interstitium/parenchyma. While it has been clear for decades that pulmonary macrophages exist in both airways and tissues, it is only recently that we have come to appreciate the degree of heterogeneity and diversity between different macrophage subsets in each location. In particular, the advent of single cell technologies, such as single cell RNA sequencing (scRNA-seq), has allowed tissue macrophage heterogeneity to be assessed in a completely unbiased manner across several species14. Their residence in the airways can be confirmed by performing bronchoalveolar lavage (BAL) where they are the sole macrophage population in health15, although it is important to note that this method only retrieves a fraction of the macrophages resident in the airways. Murine interstitial macrophages (IntMϕs) express high levels of CD11b, but lack expression of SiglecF14. Compared with AlvMϕs, considerably less is known about the IntMϕ compartment, likely reflecting the fact that these macrophages are difficult to isolate from lung tissue using standard enzymatic protocols. Indeed, flow cytometric analysis of whole mouse lung digests suggests that IntMϕs form a small fraction of the overall macrophage compartment in health, with AlvMϕs outnumbering them by 5–10-fold16. Whether this is also the case in humans is currently hard to determine with certainty, as no comparable set of surface markers has yet been identified to unequivocally distinguish human AlvMϕs from IntMϕs in BAL, sputum or in lung tissue digests. Nevertheless, limited fluorescence microscopy of mouse and human lung, which circumvents the need to dissociate solid tissue, suggests that tissue (IntMϕs) are more abundant than suggested by flow cytometry19. Thus, the notion that these cells represent a minor macrophage population should be reconsidered  and SiglecF+CD11b+ eosinophils, expression of these surface markers alone is not sufficient for the characterisation of murine lung macrophages. Instead, a more rigorous approach is needed to define bona fide murine pulmonary macrophage subsets, with a growing consensus that the optimal strategy is by their co-expression of the high affinity FcγR1 (CD64) and Mer-tyrosine kinase (MerTK), a key efferocytic receptor20. In health, AlvMϕs and IntMϕs can then be identified amongst the CD64+MerTK+ fraction by their distinct CD11c/CD11b profiles and other phenotypic traits  which have been used by some as defining features of so-called “M1” and “M2” macrophages23. However, Ym-1 expression by homeostatic AlvMϕs is independent of IL-4–IL-4R signalling15, the axis controlling “alternative” activation of macrophages, and CD11c expression is independent of exposure to microbial products or inflammatory cytokines thought to drive so-called “M1” polarisation25. This highlights the inadequate nature of the M1/M2 nomenclature in defining macrophages in vivo and that as a field we need to abandon using it26.It is also important to note that due to the overlapping expression of CD11c, CD11b and SiglecF by other myeloid cells, in particular CD11c6, the IntMϕ compartment harbours at least two distinct subsets defined by their expression of CD206 (mannose receptor)27. The anatomical locale in which each of these subsets are found remains contentious. CD206− IntMϕs that mostly express MHCII+ have been suggested to be enriched in the interstitial tissue adjacent to the alveoli, whereas their CD206+ MHCII− counterparts appear more numerous in the interstitium surrounding the bronchi18. Multi-parameter fluorescence microscopy has also started to elucidate the nature of the niche in which these subsets may exist. For instance, CD206–MHCII+ IntMϕs can be found to interact with nerves, whereas CD206+ MHCII– cells may occupy a perivascular niche27. Transcriptional profiling has revealed other useful markers for the identification of these subsets, including Lyve-1, folate receptor beta (FRβ) and CD36, which appear to be more highly expressed by perivascular CD206+ MHCII– macrophages. Work by the Khanna group identified a population of CD169-expressing IntMϕs28, although it seems these largely overlap transcriptionally with the CD206–MHCII+ IntMϕs identified by others. CX3CR1 has also been suggested as a defining marker of these subsets27, although other studies have shown that CX3CR1 is expressed equally across these subsets29. Thus, while it is clear that the murine IntMϕ compartment is heterogeneous, consensus is yet to be reached on the best strategy to define these cells.Recent studies employing scRNA-seq have begun to reveal additional heterogeneity in the murine pulmonary macrophage compartment. Whereas AlvMϕs appear to be relatively homogeneous30. CD163 appears to define two subsets of AMϕ in lung tissue from humans and non-human primates. However, whereas CD163hi AMϕs are abundant in BALF, CD163lo AMϕs are relatively rare13, questioning whether they truly represent AlvMϕs. Importantly, while SiglecF has become somewhat of a de facto marker of murine AlvMϕs, its human paralog, Siglec8, is absent from AMϕs in man13. Despite only partial conservation in terms of phenotype between mice and humans, recent transcriptional profiling suggests that these cells share a core gene signature, including expression of PPARG, FABP4, FFAR4, FN134 , and ‘interstitial’ to refer to CD11b+SiglecF– lung tissue macrophages (IntMϕs). We will use the term monocyte-derived AMϕs to refer to macrophages in the BALF that have been shown to arise in the context of inflammation and exhibit some, but not all, features of bona fide AlvMϕs. We will use the term ‘ex-interstitial’ (ex-IntMϕ) to try to make clear when we are referring to cases where macrophages with this phenotype are found in the airways. Since it is currently impossible to distinguish tissue vs airway origins of human pulmonary macrophages, we will simply refer to cells obtained by BAL as airway macrophages (AMϕs) and those obtained from human lung tissue as tissue macrophages (tissue Mϕs).In order to discuss pulmonary macrophage subsets, some clarity on nomenclature is needed. Historically, macrophages found in the airways/bronchoalveolar lavage fluid (BALF) of naïve mice have been termed ‘alveolar macrophages’, which express high levels of CD11c, SiglecF and CD169 while lacking CD11b expression. These airway/alveolar macrophages are phenotypically and transcriptionally distinct from those found in the lung tissue, which express CD11b and variable levels of CD11c, but lack SiglecF expression and are often termed as ‘lung tissue’, ‘interstitial’ or ‘parenchymal’ macrophages. It is now clear that, during inflammation, macrophages with a tissue/interstitial phenotype can be readily found in the airways/BALF. Thus, for clarity, in this review we will use ‘alveolar’ only to refer to airway macrophages (AMϕs) that are CD11cBox 2 Key ChallengesEstablish “signatures” to faithfully distinguish human AlvMϕs from IntMϕs and ex-IntMϕsDevelop methods of lineage tracing in human lung macrophage subsetsReduce reliance on in vitro functional assays of questionable relevance and develop models/systems/approaches that better represent the lung environmentReach consensus on the heterogeneity, function and spatial distribution of IntMϕsDevelop novel transgenic murine models to selectively target AlvMϕs vs IntMϕs vs ex-IntMϕs and understand the molecular levers that control their differentiationTransition to using mouse models with microbial exposure/status more aligned with that encountered by humans.41. However, as described below, to avoid excessive and potentially harmful pro-inflammatory responses being mounted against environmental antigens encountered by airway macrophages, these cells are held in a state of relative hyporesponsiveness via a range of powerful inhibitory mechanisms42. In the absence of infection, the principal role of airway macrophages is the regulation of pulmonary surfactant, the lipid-protein complex produced by the respiratory epithelium to lubricate the lungs and allow frictionless expansion/contraction. Consistently, the AlvMϕ transcriptional signature is dominated by genes associated with lipid metabolism in mouse and man44. Macrophages are indispensable for this function, as spontaneous pulmonary alveolar proteinosis (PAP) develops in mice and humans with absent or dysfunctional AlvMϕs50. Likewise, dead, dying and senescent cells accumulate in the absence of functional airway macrophages, demonstrating their key efferocytic role43. Airway macrophages may also maintain the integrity and responsiveness of the respiratory epithelium. For instance, production of immunoregulatory cytokines, such as TGFβ and IL-10, may modify epithelial cell function through regulation of ion and fluid transport51 and AlvMϕ-derived fibronectin may act as a proliferative factor for airway epithelial cells52. Suppressor of cytokine signalling (SOCS)-containing vesicles released from AlvMϕs may regulate the responsiveness of the epithelium to e.g., TLR ligands54.As immunologists, we tend to consider the primary function of macrophages is to act as the first line of defence against pathogens. Indeed, the positioning of macrophages in the airways means that they will be one of the first cells to encounter air-borne pathogens and altering the ability of airway macrophages to detect, engulf and kill respiratory pathogens leads to increased susceptibility to a variety of bacteria, viruses and fungi55, although it is clear that all murine IntMϕ subsets are avidly phagocytic and can capture E.coli bioparticles in vivo18, suggesting they may act as a second line of defence should the epithelial barrier be breached. Their constitutive production of IL-10 under normal physiological conditions, in both mouse and man58, suggests an immunoregulatory role. It is likely this involves supporting regulatory T cells locally in the lung parenchyma, given that IntMϕs are thought to be non-migratory59. However, IntMϕs may also alter T cell responses indirectly through IL-10-dependent modulation of DC migration and priming activity56.Compared with AlvMϕs, the mechanisms of regulation and homeostatic functions of IntMϕs are poorly understood29, suggesting these cells may be inherently anti-inflammatory. Indeed, Cx3cr1-mediated deletion of Il10 leads to increases susceptibility to allergic asthma in mice60. Production of platelet-derived growth factor (PDGF) by IntMϕs suggests they may also support fibroblast and epithelial homeostasis61. Finally, given their occupation of distinct anatomical niches, it is intriguing to speculate they may differentially contribute to nerve and vascular endothelial cell homeostasis, similar to their counterparts in the gut wall62.Exposure to bacterial CpG DNA leads to IntMϕ expansion and augmentation of IL-10 production63. However, over the last 10 years there has been a conceptual revolution in our understanding of macrophage ontogeny with the discovery that many tissue macrophages derive from embryonic progenitors and maintain themselves autonomously through in situ self-renewal74. While some older studies had demonstrated the ability of macrophages to self-renew75, it is the development of elegant lineage tracing models that has led to major advances in our understanding of macrophage origins. For instance, genetic fate mapping using mice with tamoxifen-inducible Cre recombinase under the control of the Csf1r, Runx1, Cx3cr1 or Tie2 promoters has shown that brain microglia derive from yolk sac progenitors and require little, if any, contribution from blood monocytes across the life of an animal76. Using similar systems, it was shown that yolk sac progenitors contribute minimally to lung AlvMϕs77. Instead, tracing of foetal and adult haematopoiesis using Flt3Cre mice shows haematopoietic stem cell (HSC)-derived cells make a major contribution to AlvMϕs74. This, combined with the fact that AlvMϕs develop within the first few days of life in mice and humans in parallel with alveolisation of the lung79, led to the idea that they derive predominantly from foetal monocytes. That AlvMϕs are unaffected in adult monocytopenic Ccr2–/– mice and show little exchange in the context of parabiosis or tissue protected bone marrow chimeric mice supported the notion that these cells self-maintain throughout adult life in the absence of inflammation or infection78. Moreover, analysis of AMϕ longevity in the human context supported these observations in mice. By analysing macrophages obtained by transbronchial biopsies of recipients of sex mismatched lung transplants in a longitudinal manner, Eguíluz-Garcia et al. showed that the majority of AMϕs remain of donor origin in this setting, suggesting human AMϕs maintain themselves autonomously in situ80, a finding supported by an independent study analysing AMϕs obtained by bronchoalveolar lavage81.Historically, macrophages were thought to be part of a linear mononuclear phagocyte system where tissue macrophages were continually replaced by blood monocytes, which themselves are replaced by dedicated bone marrow progenitorsFlt3Cre-Rosa26LSL-YFP reporter mice showed increases in labelling of AlvMϕs over time, indicative of age-dependent contribution of HSC-derived cells to the AlvMϕpool, a phenomenon not seen in brain microglia65. This highlights the need for longitudinal analysis when considering macrophage dynamics, something that was not always performed in early lineage tracing studies74. Indeed, longitudinal analysis of Ms4a3Cre reporter mice, which allow tracing of all cells deriving from bone marrow granulocyte-monocyte progenitors (GMPs), supports the idea that AlvMϕs require replenishment from bone marrow-derived monocytes over the life course71  for X/Y chromosomes, or the degree of injury caused by transplantation-related ischaemia and reperfusion. Clearly further work is warranted to clarify the dominant replenishment mechanisms underlying the homeostatic maintenance of AlvMϕs.However, several recent studies have started to challenge this model. First, longitudinal analysis of 77, but these appear to be largely replaced, first by foetal liver-derived macrophages and then by HSC-derived macrophages during the early post-natal period65 , commensals  and nutrient levels , all of which can change markedly during inflammation.hi phenotype and constitutively produce TNFα92. While phenotypic alterations are not evident in AlvMϕs from naïve SP-A deficient mice93, SP-A may amplify IL-4Rα-mediated AlvMϕ activation while regulating their responsiveness to exogenous stimulation through direct interactions with Toll-like receptor 4 (TLR4) and MD-295. As collectins, surfactant conformation can dictate their function in a context-dependent manner, with structural changes altering their binding to targets and in doing so conferring pro- vs anti-inflammatory ability96.As well as acting as a lubricant, pulmonary surfactant constituents can also influence AlvMϕ behaviour. For instance, in the absence of surfactant protein D (SP-D) AlvMϕs display an unusual CD11b97. Additionally, mucus can be important for control of bacterial infection, regulation of hydration, resolution of inflammation, and modulation of immune and epithelial cell function99. The main mucins expressed in the lung are Muc5b and Muc5ac, with Muc5b being essential for maintenance of healthy airways99, while Muc5ac is up-regulated during inflammation102. Each of these mucins has different properties that determine their dominant function. Mucus also provides a home for commensal lung microbes97, which can exert a dramatic effect over airway function, for example through invasion/colonisation, consumption of nutrients, and production of metabolites – all of which can influence pulmonary inflammation and macrophage activation and function. Although most is currently known about bacteria and their products in this context, more widespread metagenomic approaches will increase our understanding of the abundance and diversity of bacterial, viral and fungal commensals in the lung, and how this changes during inflammation and disease.The other major constituent of pulmonary fluids is mucus, as vital for lubricating the airways as surfactant, and also for entrapment and cilliary clearance of inhaled particles and microbes104. Indeed, epithelial cells lining the lung express high levels of glucose transporters apically105, with which they can rapidly and efficiently reduce glucose levels in the airways, with the blood and tissues underlying the epithelium possessing over twelve times the levels of glucose than are found in the airway fluids105. This low-glucose environment, coupled with abundant lipid-rich surfactant, likely plays a central role in governing AlvMϕ metabolism and function. Indeed, it is now clear that metabolism is a central factor in governing macrophage activation and function, with the general principle being that glycolysis may be more associated with ‘type 1’ macrophages, while lipid metabolism tends to be more typical of ‘type 2’ macrophages (reviewed by107). However, the majority of the work that has established this paradigm has relied on in vitro assessment of bone marrow or monocyte-derived macrophages, with much less understanding of how tissue environments influence macrophage metabolism in vivo. In the context of the lung, we and others have shown that AlvMϕs display a distinctive metabolic profile, expressing elevated levels of genes associated with lipid metabolism and peroxisome proliferator-activated receptor gamma (PPARγ), and reduced expression of genes associated with glycolysis20. Further, we have shown that glycolysis is a key determinant of AlvMϕ activation and function in type 2 inflammation15, suggesting that glycolytic ability, and availability of glucose, may be centrally involved in enabling AlvMϕ activation and function in the airways.In terms of nutrients, the airways present AlvMϕs with one of the lowest glucose environments in the body, a tissue adaptation that has likely evolved to prevent outgrowth of glucose-hungry opportunistic bacteria111. This may be particularly relevant in chronic conditions where metabolite balance in the airways can be modified long-term, with consequent long-term alteration of AlvMϕ metabolism and function. In more acute settings, such mechanisms may enable a window of opportunity for glycolytic ‘boosting’ of AlvMϕ function that will reduce as the epithelium heals and metabolite levels recalibrate, in essence providing a metabolic rheostat for fine-tuning of AlvMϕ activation and function directly linked to how local substrate levels change in line with levels of tissue damage vs resolution.During inflammation, with compromised epithelial integrity, the balance of airway nutrients can change markedly, with reports of elevated airway glucose evident in a range of disease settings, including COPD, cystic fibrosis and asthmaMycobacterium tuberculosis112. In this way, IntMϕs may be less susceptible to substrate-related functional control than AlvMϕs, though this likely changes if IntMϕs migrate into the airways in the context of inflammation as they adapt to this new environment. Indeed, a key outstanding question is to what extent does the metabolic status of monocyte-derived macrophages play in their differentiation to AlvMϕs during and following an inflammatory insult. It is important, however, to remember that the methods to extract tissue macrophages can have profound effects on their biology, including their transcriptional and metabolic status. Given that IntMϕs are embedded in the tissue, it is plausible that some of the differences seen in their metabolic profile may reflect their response to extraction.In contrast to AlvMϕs, less is currently known about metabolic control of IntMϕ activation and function. IntMϕs residing in the more nutrient-rich environment of the lung tissue appear much more glycolytically active than their AlvMϕ counterparts, and are consequently more effective at producing the reactive oxygen species necessary for killing intracellular bacteria such as 3. In the airways, AlvMϕs are highly dependent on GM-CSF for their development and maintenance117, consistent with their high expression of GM-CSF receptor. Consequently, disruption to the GM-CSF-GM-CSFR axis leads to defective AlvMϕ differentiation and the development of PAP in both mice and humans50. Recent work using GM-CSF reporter mice and cell-specific deletion of GM-CSF has identified alveolar type 2 epithelial cells as the indispensable source of GM-CSF during the pre- and post-natal stages of AlvMϕ development from foetal progenitors117. Interestingly, although innate lymphoid cells (ILCs) are major sources of GM-CSF in the steady state lung, haematopoietic deletion of GM-CSF does not affect AlvMϕ development117. Moreover, although it has been proposed that GM-CSF may control AlvMϕs indirectly through induction of Il6, Il13 and Csf1 expression in lung basophils118, genetic depletion of basophils has little, if any, effect on AlvMϕ number or phenotype117. Indeed, that CD11c-mediated deletion of Csf2rb, which encodes one of the GM-CSFR subunits, or Stat5, which lies downstream of GM-CSFR, leads to aborted differentiation of AlvMϕs120 supports the notion that GM-CSF acts directly on developing AlvMϕs.As described above, there is continual bi-directional crosstalk between macrophages and the structural/stromal cells that comprise their niche, and this crosstalk leads to niche-specific outcomes for macrophage recruitment, differentiation and function. Removing macrophages from their niche leads to phenotypic and transcriptional changes, directly demonstrating the need for continual crosstalk with structural cells121. Consequently, genetic disruption of Itgb6, which encodes integrin β6, leads to development of emphysema due to excessive production of MMP12 by dysfunctional AlvMϕs; a phenotype that can be rescued by constitutive expression of TGFβ1122. Human AMϕs are known to have a gene signature consistent with TGFβR signalling123 and myeloid-specific deletion of TGFβR in mice leads to aborted AlvMϕ development, demonstrating a need for cell intrinsic TGFβR for this process125. Interestingly, although many cells can produce TGFβ, macrophages themselves are thought to be an important source124. Moreover, AlvMϕs can facilitate integrin-mediated release of active TGFβ through production of amphiregulin, at least in the context of helminth infection126. Loss of TGFβR signalling leads to reduced expression of GM-CSFR, suggesting cooperation between these factors124. However, the indispensable nature of TGFβ in their development makes assessing its role in regulation of AlvMϕ behaviour during homeostasis difficult. Nevertheless, TGFβ is thought to upregulate the inhibitory receptor CD200R1, which is known to be key for maintaining the activation threshold of AlvMϕs127. Consistent with this, loss of autocrine TGFβ leads to spontaneous production of pro-inflammatory cytokines and chemokines by AlvMϕs123.As mentioned above, crosstalk between AlvMϕs and alveolar epithelial cells also involves the TGFβ-TGFβR axis. TGFβ is a potent immunomodulatory cytokine which is abundant in the mouse and human lung in health. It is produced in a latent form and must be converted to active TGFβ to have biological effects. In the airways, integrin-mediated activation is thought to be the principal mechanism of TGFβ activation. Specifically, the αvβ6 integrin, which is expressed by alveolar epithelial cells, is crucial for generating active TGFβ124. Mice with myeloid-specific deletion of Pparg also develop PAP, consistent with regulation of molecules involved in lipid catabolism by PPARγ129. Given that PPARγ is expressed by macrophages in other tissues, including splenic red pulp macrophages and macrophages of the erythroblastic islands in the bone marrow130, until recently, it remained unclear how specificity was conferred to AlvMϕs. We recently uncovered the transcription factor early growth response 2 (EGR2) as a key evolutionarily conserved regulator of AlvMϕ differentiation downstream of PPARγ in the lung but not spleen125. Interestingly, mice with Egr2 deficient AlvMϕs and individuals with mutations in EGR2 do not appear to develop spontaneous PAP131, demonstrating that PPARγ must cooperate with other transcriptional regulators to regulate distinct aspects of AlvMϕ biology. For instance, EGR2 appears particularly important for regulating expression of adhesion molecules, chemotactic machinery and apparatus for the detection and elimination of respiratory pathogens125. EGR2 appears to maintain expression of CCAAT/enhancer-binding protein beta (C/EBPβ), which has been implicated in AlvMϕ differentiation132. The transcription factors Bhlhe40 and Bhlhe41 have also been shown to control the phenotypic identity and proliferative capacity of AlvMϕs, and seem to rely on TGFβR signalling in a PPARγ-independent manner133. The histone deacetylase (HDAC) sirtuin 1 (SIRT1) also plays a key role in regulating the proliferative activity of AlvMϕs134. Finally, Bach2  has been shown to be essential for surfactant regulation by AlvMϕs135. Thus, while much progress has been made in understanding the transcriptional control of lung macrophages, if and how these transcriptional regulators interact or cooperate to control the discrete molecular programmes required for homeostatic function of AlvMϕs is only starting to be understood and warrants further study using state-of-the-art technologies.Both GM-CSF and TGFβ induce expression of the transcription factor PPARγ, which is considered the master transcription factor for AlvMϕsCx3cr1 deficiency14. Unlike their alveolar counterparts, IntMϕs rely on signalling through CSF1R for their development and maintenance as evidenced by their depletion with anti-CSF1R antibody treatment28 and failure to develop from Csf1r–/– precursors in a competitive bone marrow chimera setting136. The relative role of the ligands for the CSF1R, M-CSF and IL-34, has not been examined exhaustively. For instance, although cDC2s are reported to be affected in Il34LacZ/LacZ mice137, these cells were simply defined as CD11c+CD11b+ non-AlvMϕs and it is highly likely this compartment contains both IntMϕs and cDC2s. Similarly, analysis of Csf1op/op mice, which have a naturally occurring inactivating mutation in the Csf1 gene, has shown an effect on the abundance of CD169+ but not CD169– IntMϕs28, suggesting differential reliance on M-CSF by discrete IntMϕ subsets. Application of novel reporter and conditional “KO” mice, such as those used to identify the cellular sources of M-CSF in the lymph node and spleen139, should help discern the relative roles and cellular origin of M-CSF and IL-34 in regulating survival and differentiation of IntMϕ subsets.The environmental control of IntMϕs is much less well understood. Despite high expression of CX3CR1 by at least some IntMϕs, their survival, phenotype and proliferative capacity is unaffected by Maf, Mafb, Irf5, Jun and Atf3 have been identified through scRNA-seq studies as highly expressed by murine IntMϕs125, if and how these transcription factors control their differentiation remains unexplored. Furthermore, given that some of these  have been implicated in AlvMϕ homeostasis140, high expression does not always equate to specificity. Importantly, although dispensable for the phenotypic identity and survival of IntMϕs, β-catenin signalling has recently been implicated in the control of the metabolic profile of IntMϕs, in response to the Wnt family molecule Rspondin3 derived from pulmonary endothelial cells141. IntMϕs are intimately associated with extracellular matrix and interaction with collagen via the collagen receptor, LAIR1, appears to alter the composition of the IntMϕ pool. Notably, LAIR1 appears to regulate CSF1R expression142 and therefore interactions with the ECM may regulate macrophage longevity, although this remains to be tested experimentally.The downstream molecular pathways that govern IntMϕ differentiation remain largely elusive. Although expression of 146, organoids148, and maintenance of whole lung tissue ex vivo.Thus, it is clear that the lung environment exerts multiple layers of control over macrophage development, activation and function in health and during inflammation. This highlights the current over-reliance on in vitro methods to research lung macrophages, particularly for human research, which are likely of questionable relevance. Innovative new approaches are needed that better reflect the lung environment, such as ‘lung on a chip’150). In many cases the cause of pulmonary fibrosis is not identified (idiopathic pulmonary fibrosis (IPF)), whereas in others it can be attributed to exposure certain occupational substances  or drugs . Moreover, there are indications that following severe coronavirus disease 19 (COVID-19), certain individuals develop pulmonary fibrosis151, although whether this results in permanent, irreversible scarring is still being understood.Despite their key roles in lung homeostasis, macrophages are implicated in the pathogenesis of many chronic lung pathologies, including pulmonary fibrosis (PF). PF is a common feature of a group of conditions known as interstitial lung diseases (ILDs), where excessive ECM deposition leads to irreversible scarring of the lung 158, which has long been established as a pro-fibrotic mediator, in part through activation of TGFβ1158. Osteopontin-producing macrophages are found in the airways and parenchyma of IPF individuals159, although highest expression is attributed to MAFB+PPARG– macrophages, which most likely represent IntMϕs32. Moreover, high expression of inhibitors of collagenolytic enzymes, including tissue inhibitor of metalloproteinases 1 (TIMP1) and TIMP2 is a feature of fibrosis-associated macrophages154. Studies in mice have shown macrophages to be able to produce certain collagens , and collagen VI deficiency limited to the haematopoietic compartment ameliorates experimental bleomycin-induced fibrosis160. Interestingly, however, elevated expression of matrix metalloproteinases, such as MMP-9, MMP-12 and MMP-14, also defines fibrosis-associated macrophages across species. Thus, the relative contribution of collagen production versus collagen clearance remains poorly understood.While the prevailing school of thought is that PF arises from ineffective repair of airway epithelium following repetitive injury, there is now compelling evidence that macrophages contribute to PF pathology+ monocytes in both experimental models and human PF161. The presence of CCR2+ monocytes and their macrophage progeny correlates with the presence of fibrotic tissue in mouse and man161, and bleomycin-induced experimental fibrosis can be exacerbated by adoptive transfer of classical monocytes162. Consistent with this, experimental fibrosis is blunted in monocytopenic Ccr2 deficient mice164, by neutralisation of the CSF1-CSF1R axis154 or by rendering monocyte-derived macrophages susceptible to apoptosis165. Moreover, one of the few treatments for PF, pirfenidone, has recently been shown to reduce accumulation of CCR2+ monocytes in bleomycin-induced experimental fibrosis161. However, the Akira group has suggested that developmentally distinct, pro-fibrotic monocytes termed “segregated-nucleus-containing atypical monocytes” (SatM) arise in the context of experimental lung fibrosis and are responsible for driving disease166. So-called ‘SatM’ appear to depend on the transcription factor C/EBPβ, but derive from FcεR1+ granulocyte/macrophage progenitors (GMPs) and not macrophage/DC progenitors (MDPs)166. Indeed, work since has described distinct pathways to generate monocytes from GMPs and MDPs in health and following infection167. How these SatM relate to the CCR2-dependent monocytes described in other studies remains unclear.Macrophage accumulation results, at least in part, from de novo recruitment of CCR2156. In support of the latter, sustained epithelial injury is a feature of human PF168 and reducing epithelial damage through administration of a specific inhibitor of sphingosine kinase 1, which is elevated in IPF lungs, reduces experimental fibrosis, at least in part by reducing recruitment of fibrogenic monocytes169. Attributing key pathogenic roles to macrophage subsets is made difficult by the breakdown in clear phenotypic boundaries between parenchymal and AlvMϕs in the context of inflammation and fibrosis, and by the fact that, at least some, IntMϕs may differentiate into AlvMϕs during lung repair156. PF incidence and severity positively correlates with age172 and given monocytes may progressive replace embryonically-derived AlvMϕs with age, it is intriguing to speculate that these two phenomena could be related. In addition to blood-derived monocytes, it is plausible that macrophages in the pleural cavity may contribute to the pro-fibrotic pool of macrophages in the lung. Consistent with this notion, fibrosis in IPF patients is often concentrated in the subpleural region150 and there is transcriptional similarity between pleural and MHCII– IntMϕs in mice173. Moreover, it’s been suggested serous cavity macrophages may contribute to tissue repair in neighbouring solid organs following injury in mice175. However, elegant intersectional genetics and a combination of injury models have shown that, while pleural GATA6+ macrophages may accumulate on the pleural membrane, they do not migrate deep into the lung parenchyma nor are they essential for fibrogenesis or resolution176.Whether pro-fibrotic macrophages are limited to the lung parenchyma or if monocyte-derived AMϕs also contribute to fibrosis is still under debate152), but the relative and combinatorial roles of these factors is poorly understood. Recently, overactive Notch signalling has been implicated in the pro-fibrotic behaviour of macrophages, as deletion of RBPJ reduces fibrosis in mice through abrogating TGFβ production by Ly6ChiMHCII+ monocyte-derived macrophages177. Whether these effects are attributable to airway or IntMϕs is difficult to discern in this study177. TGFβ is of particular interest given its long-standing role in tissue fibrosis178. Recent work has uncovered discrete functions of TGFβ isoforms in the fibrotic process179. However, if and how excessive TGFβ isoforms influence macrophage function in the fibrotic niche in vivo has not been tested directly.Why macrophages become excessively pro-fibrotic is only starting to be understood, although, again, this probably reflects their plasticity. IL-4, IL-13, IL-33 and TGFβ have all been implicated in altering macrophage behaviour in PF  AMϕs. For instance, genetic ablation of milk fate globule epidermal growth factor 8 (Mfge8), a receptor typically associated with efferocytosis, leads to failed collagen clearance following bleomycin-induced injury180. Moreover, macrophage-derived ApoE may facilitate binding and targeting of type I collagen for phagocytosis via the low-density lipoprotein receptor-related protein 1 (LRP1)181. Pro-resolution roles of monocyte-derived AMϕs are also seen following influenza infection and their absence can result in the development of fibrosis, at least in mice182. Such functions may include metabolic rewiring of monocyte-derived macrophages as deletion of Acod1, the enzyme required for generation of the metabolite itaconate, leads to persistent fibrosis183. Thus, while generally considered as key pro-fibrotic cellular players, monocyte-derived AMϕs appear to have a crucial role in re-establishing lung homeostasis and may hold great promise for therapeutic targeting to promote fibrosis regression and lung repair.Like in many pathologies, the role of macrophages in lung fibrosis is very much context dependent. The self-resolving nature of some experimental models allows macrophage dynamics and behaviour to be assessed during fibrosis regression and resolution, something that cannot be gleaned from human disease. This has revealed that severe lung injury leads to almost complete replacement of embryonically derived AlvMϕs with monocyte-derived cellsThe past few years have seen a leap forwards in our understanding of pulmonary macrophage development, heterogeneity and function, and how environmental features of the lung can exert a dramatic influence over these processes in both disease and in health. A major challenge for the coming years is to develop much greater clarity on how different inflammatory conditions alter pulmonary macrophage subset diversity and function, in particular in the context of human disease, to identify core mechanisms that might enable development of the targeted therapeutics of the future.


Banjac et al. (J Optim Theory Appl 183(2):490–519,  Due to its very good practical performance and ability to handle nonsmooth functions, the Douglas–Rachford algorithm has attracted a lot of interest for solving convex optimization problems. Provided that a problem is solvable and satisfies certain constraint qualification, the algorithm converges to an optimal solution .Results on the asymptotic behavior of the Douglas–Rachford algorithm for infeasible problems are very scarce, and most of them study some specific cases such as feasibility problems involving two convex sets that do not intersect –5. AlthoA is a real matrix and The constraint set of the problem studied in  is repreesult of  to real The paper is organized as follows. We introduce some definitions and notation in the remainder of Sect. s and define Let closure. Then nonexpansive if-averaged with T by graphmonotone ifinverse of B, denoted by Let Consider the following convex optimization problem:When dered in , where tdered in –12. To pWe can rewrite problem 1) in the form in the fThe authors in  derived (i)If there exists a (ii)If there exists an ([(i)(ii)Suppose that The first result is , Cor. 3 The following proposition provides essential ingredients for generalizing the results in , §5.(i)(ii)(iii)(iv)Let (i): Follows from [(ii) and (iii): A related result was shown in [mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimmentclass2pt{minim(iv): Taking the limit of the inequalitymentclass2pt{minimmentclass2pt{minimThe results of Prop. mentclass2pt{minimA result that motivated our generalization of these limits to an arbitrary nonempty closed convex set f and g are proper lower semicontinuous convex functions. An iteration of the algorithm in application to problem  asing form , 10: 7a\lgorithm :Iteration  amounts The solution to the subproblem in  satisfieearrange  to isola it into  and 8),,8\documeteration : 10a\docondition  for all tisfying  and 3b)8\documenThe following corollary follows directly from Fact (i)(ii)(iii)(iv)(v)(vi)(vii)(viii)(ix)Let the sequences given by  and 10)\documentThe following two propositions generalize [(i)(ii)(iii)(iv)(v)The following relations hold between (i)n, take the limit, and use Cor. Divide  by n, ta(ii)n, take the inner product of both sides with Divide  by n, ta we used  and Cor.mentclass2pt{minim(iii)n, take the limit, and use n, and taking the limit yield Subtracting  at iteraows from .(v)From  we have(i)(ii)The following identities hold for n, taking the limit, and using Prop. Q. Comparing inequalities (ii)(iii)(iv)(v)(vi)Let the sequences given by  and 21)\documentThe proofs of the following two propositions follow similar arguments as those in Sect. (i)(ii)(iii)The following relations hold between (i)(ii)The following identities hold for The authors in  use simiities in  show thanditions  are satiWeak infeasibility of problem (1) means that the sets