Sport-related traumatic brain injury (TBI) elicits a multifaceted inflammatory response leading to brain injury and morbidity. This response could be a predictive tool for the progression of TBI and to stratify the injury of which mild TBI is most prevalent. Therefore, we examined the differential expression of serum inflammatory markers overtime and identified novel markers in repetitively concussed athletes. Neuropsychological assessment by Wechsler Adult Intelligence Scale (WAIS) and Immediate Post Concussion Assessment and Cognitive Test (ImPACT) was performed on rugby players and serum was taken from healthy, concussed and repetitively concussed athletes. Serum was also obtained <1 week and >1 week after trauma and analyzed for 92 inflammatory protein markers. Fibroblast growth factor 21 (FGF21) and interleukin-7 (IL-7) differentiated repetitively concussed athletes. Macrophage chemotactic protein-1 (MCP-1), tumor necrosis factor superfamily member 14 (TNFSF14) were significantly reduced >1 week and chemokine (C-X3-C motif) ligand 1 (CX3CL1) upregulated <1 week after injury. FGF21 and MCP-1 negatively correlated with symptoms and their severity. We have identified dynamic changes in the inflammatory response overtime and in different classes of concussion correlating with disease progression. This data supports the use of inflammatory biomarkers as predictors of symptom development due to secondary complications of sport-related mTBI.  Traumatic brain injury (TBI) causes damaging neurological impairments leading to disability and morbidity. TBI affects young and old, with young victims creating a substantial negative impact to families and wider society with years of lost productivity and an increasing burden on healthcare systems ,2. Such Mild TBI (mTBI) is the most prevalent form of TBI with up to 80% of patients suffering with adverse symptoms such as sleep disturbances, headaches, nausea, impaired memory, or loss of function . mTBI caDifficulties in developing tools for the prediction of progressive pathology in TBI are confounded by the variability in the anatomical location, extent of the mechanical injury to the brain, as well as the multitude of biological response mechanisms involved. TBI is broadly characterized by 2 phases ,20. ImmeIn addition, cytotoxic and ischemic responses enhance axonal death and ultimately injury to neurological structures . This isSimultaneously, microglia potentiate neuronal recovery through the production of pro- and anti-inflammatory mediators . This inIn addition to potential therapies, inflammatory proteins could also be used for the development of biomarkers useful in the clinical assessment of mTBI. Efforts have been focused on quantifying inflammatory markers in easily obtained bodily fluids such as serum, saliva, and urine. It has already been shown that IL-6 was differentially expressed in serum levels of TBI patients within hours after injury  and IL-1However, studies investigating the relationship between inflammatory markers and the ability to track the evolution of both symptoms and physiological responses to sports-related concussion (SRC) over time, are limited. Moreover, little has been done to assess the differential expression of the circulating inflammatory response in athletes who have suffered repetitive concussions. Consequently, in this study we have focused analysis on inflammatory markers in the serum of semi-professional and professional Rugby players who suffered mTBI and repeated mTBI. These samples were obtained less than one week after injury (<1 week) to determine the early inflammatory response and over 1 week (>1 week) following injury to assess longitudinal inflammatory effects. In addition to investigating the inflammatory network in athletes after a single concussion (C), those who suffered repeated concussions (RC) were also investigated. This data has been analyzed in conjunction with and contextualized in relation to cognitive performance and symptom severity, with protein expression as a predictive tool for the evolving inflammatory response seen in mTBI.p = 0.0278). Following the same analysis two clear markers were identified for RC athletes differentiating them from healthy and single concussion groups. In these occurrences FGF21 was significantly downregulated in RC serum (p = 0.037) and IL-7 was significantly increased in RC athletes .The levels of 92 inflammatory markers were measured in the serum from H, C, and RC athletes whose samples were collected within 2–14 days following their most recent concussion. RC athletes were those players that had received 2 concussions within a 3-month period. Following this analysis, the 3 proteins tumor necrosis factor superfamily member 14 (TNFSF14), fibroblast growth factor 21 (FGF21), and interleukin 7 (IL-7) were found to be altered between the groups . TNFSF14p = 0.030) and TNFSF14 (p = 0.038) were significantly reduced in the serum of patients >1 week after the initial injury when compared to controls. The only marker found to be significantly altered in patients that presented <1 week after injury was the chemokine (C-X3-C motif) ligand 1 (CX3CL1) which was increased when compared to H participants (p = 0.027).In order to determine if there are any changes in biomarkers indicative of ongoing mTBI pathology in the extended post-injury time window, we compared the expression of markers in athletes less than a week following concussion and those over a week (average 60 days following injury). Three markers were identified at the different time points. Both MCP-1 (n = 51 patients). This was followed by fatigue (29%), fogginess (29%), drowsiness (27%), and having trouble falling asleep (27%). Patients were clinically assessed by the WAIS symbol search and ImPACT tests. Despite the range of criteria that the WAIS assessment covered only the number of symbols entered was found to be significantly lower in concussed patients (p = 0.0196). The ImPACT test revealed that the amount of impulse control was significantly reduced in concussed patients (p = 0.0110) and their symptom score was higher when compared to controls (p = 0.0336).Concussed patients displayed a range of symptoms with headaches being the most commonly reported at 63%  as well as greater severity of the symptoms . Of these symptoms reported headaches showed a significant negative correlation with protein expression . FGF21 expression was also negatively correlated with a larger impact on impulse control .To determine if the expression of inflammatory proteins correlates with clinical symptoms of secondary TBI all proteins were compared to clinical neuropsychological data using Spearman’s correlation coefficient. Of these proteins, serum expression of FGF21  and MCP-r = 0.455, p = 0.013) and severity of symptoms  reported. Low levels of MCP-1 were correlated with greater numbers of patients feeling foggy  and having poor balance . Additionally, reduced serum MCP-1 levels were found to be consistent with increases in the time to react .Reduced serum levels of MCP-1 were also related to an increase in the number , which is one of the most well correlated TBI proteins ,50. MCP-Differential expression of the chemokine CX3CL1  was also found. CX3CL1 has been shown to have an important role in the CNS where it is present in many areas including the cerebral cortex, amygdala, and the hippocampus . In thesWe have identified changes in the expression of inflammatory proteins that could be further developed as diagnostic tools for the resolution of the evolving injury within sports-related mTBI patients and in those who have suffered repetitive concussion. This included FGF21 and MCP-1 whose expression negatively correlated with symptom severity and cognitive performance. These proteins all have an emerging role in the pathogenesis of mTBI and could also be utilized for therapeutic advancement.Study participants were recruited through the Birmingham concussion clinic at the Queen Elizabeth Hospital Birmingham (QEHB) or at the University of Birmingham (UoB). (UK), as part of the RECOS (The REpetitive COncussion in Sport)  . WrittenMale and female athletes aged 16–34 years, participating in professional and semi-professional Rugby who have been positively diagnosed as having a concussion along with a normal neurological objective examination at assessment, were enrolled in this study. In addition to those that had suffered single concussions, we also recruited athletes that had suffered 2 concussions within a 3-month period. These athletes had an initial concussive event followed by another one within 3 months. Following their last concussion, samples were taken when they first came to the clinic, between 2–14 days. Individuals who require hospital admission after initial assessment for their TBI, presenting intracranial blood, brain tissue injury, or non-TBI related pathologies on initial CT/MR scan, any history of neurodegenerative pathology or history of chronic alcohol or drug abuse were excluded. In addition, age matched controls, who have not received any concussion in the previous 3 months, were recruited as healthy controls (H). Blood samples were taken <1 week after or >1 week after injury. Of these samples, one athlete had taken paracetamol 24 h before entering the clinic. Clinical, demographic , neuropsSamples were collected from non-concussed healthy athletes, athletes who had suffered a concussion with Peripheral venous blood samples were obtained from patients following admission between 2–5 days (<1 week) and 15–75 days (>1 week) after the initial trauma and processed within 2 h after venepuncture. Samples were kept at room temperature for 30 min after which they were centrifuged at 3000 rpm for 10 mins at 4 °C. Serum was separated and stored at −80 °C until further use. Serum expression of 92 inflammatory markers were analyzed using the Proseek Multiplex Inflammation I assay  as described previously ; 29. Brip < 0.05. To identify correlations between protein expression and cognitive performance and symptoms detailed in the WAIS and ImPACT tests, data were analyzed using the Spearman’s correlation coefficient where p < 0.05. In order to generate correlations with symptoms the presentation of a symptom was scored as 1 and where the symptom was not apparent it was scored as 0. All data was analyzed using GraphPad Prism 8 .All data is presented as the mean ± standard deviation. Comparison of protein expression between the control and concussion groups and the time points was first tested for distribution using the Shapiro Wilks test after which a one-way ANOVA with Tukey post hoc test was performed, where


In this study we investigated the degree of genetic differentiation among two types of Polish cold-blooded horses, namely Sokólski and Sztumski. Both these horse types are a subject of conservation breeding as their populations have been dramatically reduced during recent decades. These cold-blooded horses are considered as separate breeds, with separate stud books, but yet still their genetic differentiation has not been precisely determined, especially on the genome–wide level. In this study, we characterized genetic differentiation of these two horse populations as well as their genetic variation, admixture patterns, and level of relatedness within populations with the use of single nucleotide polymorphism (SNP) genotyping arrays. We also attempted to detect genome regions with the largest genetic differences between those horses by the detection of so-called diversifying selection signals. The results of this study provide initial evidence supporting decisions made during conservation breeding program design and answer some of the questions raised by the breeders of Sokólski and Sztumski horses.The current role of the horse as a companion animal resulted in a decrease of interest in breeding and usage of draft horses. This meant that the population of cold-blooded horses in Poland has been dramatically reduced during the last decades. To avoid impoverishment of the gene pool of the local horse population, a conservation program was established which involves draft horses and other local horse breeds. The draft horses bred in Poland can be subdivided in a few horse types of which the most widespread and consolidated are Sztumski and Sokólski horses. These two subpopulations are phenotypically diversified, however, the overall level of their genetic differentiation seems to be relatively low and not precisely determined, especially with the use of molecular markers. In reference to this, in this study we used Illumina genotyping arrays to describe in detail the genetic differentiation of these two cold-blooded horse populations. We describe the genetic distance between them, as well as within-population variation, admixture patterns, and level of relatedness within populations. We also made an attempt to detect genome regions divergently selected between those horses by the detection of diversifying selection signals. The results of this study provide initial evidence supporting breeding decisions that were made during conservation breeding program design and answer questions raised by the breeders of Sokólski and Sztumski horses concerning the level of their genetic variation and differentiation. The current population of draft horses in Poland was dramatically reduced due to the limited interest in usage and breeding of working horses. To maintain biodiversity and protect the existing gene pool, local draft horses were included in a conservation program which follows the guidelines of the FAO (Food and Agriculture Organization of the United Nations) Global Plan of Action for Animal Genetic Resources . Currently, two subpopulations (types) of a draft horse with separate stud books are maintained in Poland within the conservation program, namely Sokólski and Sztumski . These pThe other studied subpopulation of Polish cold-blooded horses is formed by the Sztumski horses which are the largest and the heaviest of the cold-blooded horses maintained in Poland. Sztumski horses were originally created on the basis of a local population crossbred mainly with Ardennes and Belgian sires. They were bred predominantly in the areas of Powiśle, Warmia, and Mazury, where heavy soil that is difficult to cultivate required particularly strong working horses. Thus, the Sztumski horses are characterized by a larger caliber and thicker fat cover in comparison to the Sokólski horses ,2.Despite these clear phenotypical differences between the Sokólski and Sztumski horses, several concerns have arisen regarding their genetic differentiation, mainly because of some stallion exchange events and past breeding history that was not fully registered in the breeding records. The existing pedigree analysis of 2653 Sztumski and Sokólski horses involved in the conservation programs in 2014  showed that the contribution of the Ardennes sires amounted to 61.9% for Sokólski and 64.2% for Sztumski horses, while Belgian sires amounted to 9.3% for Sokólski and 18.3% for Sztumski. The contribution of Breton founders in Sokólski populations was equal to 4.38%. The average inbreeding coefficients were 1.56% and 1.54%, whereas effective population size (Ne) was 493 and 482, in these populations, respectively. The average pedigree completeness for five generations was 98.24 for Sokólski horses and 98.79 for Sztumski [n = 107; SOK) and Sztumski  horses  < 1.0E-06 in each breed separately were excluded. The final SNP panel of 52,023 markers was scattered across the horse genome (EquCab2.0) with an average inter-marker distance of 43.0 kb.SNP filtering was performed as previously described . Only geST distances [ST values obtained using Plink software v1.90b4 (--fst case-control command) [ST values were subsequently ranked and the top 1% of the highest observations pointed to windows corresponding with the most pronounced selection signals. Overlapping windows with the highest FST values were then merged and genomic regions formed by these merged widows were extended on both sides by 25 kb to account for extended linkage and potentially linked genes. The resulting genome regions spanning the strongest diversifying selection signals were analyzed in detail to identify encompassed genes and associated biological processes using the UCSC Genome Browser [ST coefficients into a population-specific component (beta) shared by all loci, and a locus-specific component  shared by all populations using logistic regression. Departure from neutrality at a given locus is assumed when the locus-specific component is necessary to explain the observed pattern of diversity . A positive value of alpha suggests diversifying selection, whereas negative values suggest balancing or purifying selection. This leads to two alternative models for each locus, including or not the alpha component to model selection. For each locus, a reversible-jump Markov chain Monte Carlo MCMC statistic explores models with and without selection  and estimates their relative posterior probabilities [The horse types’ genetic differentiation was evaluated using pairwise Fistances  measurin Browser , ENSEMBL Browser . The gen Browser . In thisST-based approach were analyzed using HaploView 4.2 [The linkage disequilibrium patterns and haplotype block structures at the genome regions with the strongest signs of divergent section identified using initial FView 4.2  softwareView 4.2 .To additionally evaluate the genetic structures of the studied populations, we employed principal components analysis (PCA) based on animal genotypes and inter-individual distance matrix calculated as 1 − IBS (identity-by-state) similarity, with IBS defined as the probability that alleles drawn at random from two individuals at the same locus are the same. The obtained IBS distances were then used to create cladograms based on a neighbor-joining (NJ) method  and visuTo further validate the population structure and the patterns of admixture among populations, Admixture software  was usedGRM), (ii) method-of-moments F coefficient estimate (similar to FIS), (iii) F coefficient based on the correlation between uniting gametes (FU) [ROH, including ROH segments with lengths above 1 Mb) [e) were estimated with the use of NeEstimator v2.1 [A population structure was additionally evaluated by calculating four genomic inbreeding measures: (i) the usual variance-standardized relationship minus 1 (Ftes (FU) , and runve 1 Mb)  using Plve 1 Mb) . The firve 1 Mb) ) and evave 1 Mb) . Effectitor v2.1  softwareAfter initial SNP filtering 52,023 markers were retained for further analysis. SNP panel polymorphism parameters were described in our previous study [ST was low and mean and weighted mean FST values were 0.012 and 0.014, respectively, suggesting that about 98% of genetic variation within the analyzed horse types can be attributed to inter-individual variation. The genomic inbreeding coefficients were comparable for both populations and depending on the applied measure ranged from 0 for FGRM to about 13% for FIS. The average FROH values for ROH segments with length over 1Mb were also similar for both horse types and showed values of about 11%  had size from 278 to 826 kb and covered in total 4.3 Mb of the genomic sequence  had positive Alfa coefficient corresponding to the presence of positive selection   within the region on ECA3. The analysis of F(MIR140)  on the t(MIR140) . The regnd RAB30 . The regnd RAB30 . A commoStandard methods of genetic diversity evaluation and management have proved to be effective in conservation of animals’ genetic diversity during several years of animal breeding. Nevertheless, together with the development of molecular and computational sciences, new methods of genetic variability evaluation have become available which may help in more efficient conservation program managing and a more effective breeding decision-making process. These new methods use tools of advanced molecular and population genetics allowing deep analyses of within- and among-population variation and the analysis of population history as well as providing reliable quantitative measures of genetic differentiation even for poorly-characterized populations . AdditioThe current breeding program of the Sokólski and Sztumski horse assumes separate sections of stud books within cold-blooded horses since 2008) and aims at clear separation of these populations as separate breeds. This is dictated by the fact that these horses possess their historical, genetic, and phenotypic specificity, resulting from the used breeding material on the basis of which they were produced as well as different environmental conditions which shaped their present character 008 and a.ST = 0.03) [NFAT5, NQO1, NOB1, WWP2, and MIR140 genes. While phenotypic differences between Sokólski and Sztumski horses involve mainly body size and constitution, the presumed candidate genes should be connected with e.g., growth, development, and/or metabolism. In this light, the exact role of the genes detected in the region is difficult to explain without building a complex theory unsupported by this study’s results. Similarly, within the another strongly divergently-selected region on ECA7, we detected inter alia CCDC90B, ANKRD42, PCF11, and RAB30 genes, with functions (A previous attempt to characterize genetic differentiation of Sokólski and Sztumski horses with the use of microsatellite markers resulted in similar low estimates of the average genetic distance between those populations (at F = 0.03) . This ge = 0.03) . What is = 0.03) . Moreoveunctions . which cAltogether, the performed analysis shows that despite a generally low level of genetic differentiation, Sokólski and Sztumski horses are characterized by visible differences in their genetic profiles which may result from both discrete fluctuations of allele frequency across the genome as well as from the differentiation of specific genomic loci being probably associated with the presence of gene variants with a large effect on the differing phenotypical traits of these two horse types. Based on these observations, we can conclude that further maintaining of separate stud books is reasonable and will allow preserving and increasing of the observed genetic differentiation among the studied draft horse populations.


The thymus’ key function in the immune system is to provide the necessary environment for the development of diverse and self-tolerant T lymphocytes. While recent evidence suggests that the thymic stroma is comprised of more functionally distinct subpopulations than previously appreciated, the extent of this cellular heterogeneity in the human thymus is not well understood. Here we use single-cell RNA sequencing to comprehensively profile the human thymic stroma across multiple stages of life. Mesenchyme, pericytes and endothelial cells are identified as potential key regulators of thymic epithelial cell differentiation and thymocyte migration. In-depth analyses of epithelial cells reveal the presence of ionocytes as a medullary population, while the expression of tissue-specific antigens is mapped to different subsets of epithelial cells. This work thus provides important insight on how the diversity of thymic cells is established, and how this heterogeneity contributes to the induction of immune tolerance in humans. The thymus supports T cell immunity by providing the environment for thymocyte differentiation. Here the authors profile human thymic stroma at the single cell level, identifying ionocytes as a new medullary population and defining tissue specific antigen expression in multiple stromal cell types. In contrast, generation of TECs in adult murine thymus likely depends on lineage-restricted progenitors as bipotent TEPCs have been shown to become quiescent6. It is however unknown how these observations translate to human thymic development where evidence of bipotent TEPCs is lacking and knowledge of postnatal tissue regulation is minimal.The thymus is a critical primary lymphoid organ that supports the development of a diverse, self-tolerant peripheral T cell pool. The predominant stromal cells found in the postnatal thymus are thymic epithelial cells (TECs), mesenchyme, endothelium, and non-lymphoid hematopoietic cells (dendritic cells and macrophages). Cortical TECs (cTECs) are responsible for T lineage commitment and positive selection of early thymocytes, whereas medullary TECs (mTECs) participate in the deletion of autoreactive cells and the final stages of thymocyte maturation. While the function of TECs has been well established, their developmental origins as well as the mechanisms controlling their maintenance are still not clear. Evidence from mouse models has revealed the existence of common bipotent thymic epithelial progenitor cells (TEPCs) with cTEC-like properties that have the potential to give rise to both cTECs and mTECs in the fetal and early postnatal thymus10. It has been shown that CCL21-producing cells are critical for the recruitment of positively selected CCR7+ thymocytes into the medulla11, whereas thymic tuft cells help shape thymocyte development by promoting an IL-4-enriched environment8. Therefore, while the contributions of such subsets to thymocyte development is an active area of investigation, there is mounting evidence that the murine TEC compartment is highly diverse and functionally compartmentalized. A recent study also noted that the human thymus contains many distinct subpopulations12 but a thorough characterization of the stromal compartment was not the primary focus of this work.Recent studies have shown that the thymic medulla is more heterogeneous than previously appreciated. For example, in addition to the well-characterized population of mature mTECs expressing AIRE and tissue-specific antigens (TSAs), the murine thymic medulla contains functionally diverse populations of CC-chemokine ligand 21 (CCL21)-expressing mTECs, corneocyte-like mTECs , and thymic tuft cellsHere we use single-cell RNA sequencing (scRNA-seq) to investigate cellular heterogeneity in the human thymic microenvironment at different time points during development. Using these data, we identify candidate pathways that regulate TEC fate commitment and uncover previously uncharacterized TEC markers. Importantly, we confirm the presence of distinct mTEC subsets , identify ionocytes as a subset of epithelial cells present in the human thymic medulla, and provide the first transcriptomic characterization of rare populations of thymic ciliated cells and neuronal cells. Finally, we analyze the expression of disease-relevant genes in the epithelial compartment in an effort to better understand how immune tolerance is established in the human thymus. Together, this work advances our understanding of human TEC development and provides an unbiased resource to study human TEC heterogeneity and its relevance to human autoimmune diseases.+ CD45− epithelial cells and EpCAM− CD45− non-epithelial stromal cells , one mesenchymal , one pericyte , one vascular arterial endothelial , two vascular venous endothelial , one lymphatic endothelial , one red blood cell , one immune cell , and one mesothelial  sub-clusters -based purification of CD45 negative cells Fig. . These plls Fig. . Cells ills Fig. . Followiers Fig. . Known m18. The function and cell-type specificity of these soluble factors in human thymic development are, however, not well understood. To define the signals provided by human stromal cells, we analyzed the expression of soluble factors and receptors of the WNT, BMP, transforming growth factor beta (TGF beta), insulin-like growth factor (IGF), and fibroblast growth factor (FGF) signaling pathways, which have been described as critical regulators of the development and function of TECs25  were found in epithelial cells , and the secreted WNT inhibitor Frizzled Related Protein FRZB were expressed more frequently in postnatal and adult mesenchymal cells compared to fetal mesenchyme 26. Endothelial cells also expressed extracellular matrix and adhesion molecules such as fibronectin (FN1) and LGALS3 that have been shown to regulate thymocyte migration28  and BMP4 , which was recently shown to be important for TEC differentiation6, was expressed almost exclusively by pericytes , which promotes TEPC maintenance and inhibits differentiation6, was found mostly in adult mesenchymal cells and a subset of epithelial cells . Cells in the cTEClo cluster expressed lower levels of functional genes  and contained more KI67+-proliferating cells , mTEChi , and corneocyte-like mTECs . Cells in the mTEClo cluster expressed high levels of the chemokine CCL21, reminiscent of the CCL21-expressing mTEClo/jTEC population described in mice32. We also identified one cluster of cells, marked as immature TEC, which express canonical TEC identity genes  but lacked functional genes characteristic of cTECs or mTECs. These immature cells were found in all samples , muscle-like myoid , and myelin+ epithelial cells   was markedly higher in adult cells compared to fetal and postnatal tissues . The atypical cadherin gene CDH13 was also enriched in immature TECs  and CTGF as well as IGF signaling modulators (IGFBP5 and IGFBP6), were also enriched in immature TECs and cTECs. While the expression of many cytokeratins has been extensively studied and used as markers of specific TEC subsets, the expression of KRT15 has only been reported in stem cell-derived TEPCs36. This cytokeratin is particularly interesting since it is found in multipotent progenitor populations in the hair follicle, esophageal epithelium, and small intestine39. In both datasets, KRT15 was highly expressed in mTEClo but was also detected in immature TECs and its expression increased over time , mTECshi , TNFRSF9), or corneocyte-like mTECs   was enriched in multiple epithelial subsets, including cTEChi, mTEClo, a subset of AIRE+ mTEChi, and neuroendocrine cells  Fig. . The pronly Fig. . Co-exprIRE Fig. . NotablycreERT2; Rosa26CAG-stopflox-tdTomato)  to facilitate quantification of Aire expression by flow cytometry. Mice were injected once with tamoxifen and thymi were harvested at 36 h and 5 weeks post-tamoxifen to analyze expression of the fluorescent reporters. As shown in Fig. hi are not expressing Ascl1. These data thus suggest that Ascl1+ mTEClo and Ascl1+ mTEChi are likely replenished from a pool of Ascl1-negative cells.Given the established role of ASCL1 in neural progenitors, we wanted to assess whether this transcription factor also marks a pool of progenitor cells in the thymus. We performed an in vivo genetic lineage-tracing experiment using a fluorescent reporter system (Ascl1to) Fig. . In this44. This method, which considers both spliced and unspliced mRNA counts to estimate how mRNA levels evolve over time, can be used to predict potential directionality of transitions between cell states. Since murine TEC development differs significantly between embryonic and postnatal tissue, we analyzed fetal, postnatal, and adult samples separately. Our analysis shows that cTECslo give rise to cTECshi in both fetal and postnatal tissues  and many of their target genes  were expressed at higher levels in mTECs, indicating that Notch signaling is more active in mTECs than cTECs. Immunofluorescence analysis demonstrated that HES1 protein is detected in KRT15+ TECs  and CD4050, as well as osteoprotegerin (OPG), which acts as a decoy receptor for RANKL51. In contrast, CCL21+ mTEClo depend on LTBR signaling30. Expression of a subset of these receptors was detected in CCL21+ mTEClo and corneocyte-like mTECs (LTBR), mTEChi , OPG , or mTEClo and mTEChi (CD40) , TNFRSF9 ) as well as some TNF ligands in mTEClo (LTB) or mTEChi (LTB and CD70)  Fig. , confirm70) Fig. . Given tPERP, SFN, CTSD, CDKN2A, CDKN2B) as well as many Toll-like receptors  that were upregulated in corneocyte-like mTECs  and revealed that myelin+ cells closely resemble Schwann cells   and cells with a signature reminiscent of the ionocyte population present in lung tissue60   Fig. . CiliateKB) Fig. . Since tKB) Fig. . Ionocytles Fig. . Althougles Fig. .Fig. 6Tu). The resulting clusters were annotated using markers described in Fig. 61 , suggesting that the cells are more similar to their murine counterpart than tuft-like cells found in the human respiratory tract.To validate our findings as well as to increase the number of rare cells in our analysis, we merged our medullary TEC subsets with their equivalent in the Park et al. dataset , and TEC(myo))  or autoimmune polyglandular syndrome type 1 (APS-1)66, direct evidence of TSA expression in human AIRE+ mTECs is lacking. To assess if AIRE+ mTECs expressed higher levels of TSAs than other epithelial subsets, we looked at the number of genes expressed per cell in each cluster  and subtracted the average expression of a reference set of genes. The results were visualized using Uniform Manifold Approximation and Projection (UMAP) . In contrast, other genes were mainly expressed in the neuroendocrine or myelin+ clusters   and GAD2 were found in rare AIRE+/post-AIRE cells while IA-2 (PTPRN) was detected in a few neuroendocrine cells. These data thus confirmed that, similar to the murine thymus, expression of insulin in the human thymus is likely AIRE-dependent. In contrast, genes coding for the acetylcholine receptor (CHRNA1), the muscle antigen titin (TTN), and MUSK, which are associated with the neuromuscular autoimmune disease myasthenia gravis, were predominantly found in the myoid, neuroendocrine, and ciliated subsets  Fig. . ImportaECs Fig. . In addiPZ) Fig. . This anets Fig. . While tets Fig. , it is p71. Our in-depth analysis of epithelial populations identified ionocytes as an additional subset of medullary epithelial cells and provided transcriptome information for rare subsets, including ciliated and Schwann cells that had only been described in histological analyses.In this study, we generated a comprehensive single-cell database of human thymic stromal cells with a particular focus on the epithelial compartment. This work represents a major advance beyond previous efforts, which focused mostly on mouse TECs or immune cell populationsWNT5A as well as molecules that can potentiate the WNT/Ca2+ pathway (RSPO3 and SFRP2). Given that WNT signaling has been implicated as a critical regulator of Foxn1 expression19, thymic cellularity73, and migration of the thymus during development74, our data points to this subset of mesenchymal cells as an important regulator of these processes. Notably, this population also expressed other critical regulators of epithelial proliferation and differentiation 77, further highlighting the importance of mesenchymal cells for TEC homeostasis. In addition, a recent study demonstrated that activin A signaling is critical for TEC maturation while follistatin, an inhibitor of this pathway, contributes to the accumulation of TEPCs by blocking differentiation of TECs6. It is intriguing that our analysis identified pericytes as the main source of activin A. Additional studies will be required to evaluate the role of this understudied population of stromal cells in regulating TEC differentiation. Notably, our analysis also revealed that myoid cells express high levels of follistatin, thus providing insight into why conditions such as myasthenia gravis, which affect myoid cell numbers, perturb human TEC differentiation.While it has been established that non-epithelial stromal cells are critical for TEC development and function, the specific contribution of each stromal cell type is not well understood. Our single-cell dataset provides invaluable information on factors expressed by the diverse thymic stromal populations and will enable a better understanding of the cross-talk between TECs and the rest of the stroma. For example, we identified a subset of postnatal mesenchymal cells that secrete many components of the WNT pathway, including the non-canonical WNT ligand 60, their presence has not been previously reported in the thymus. Intriguingly, pulmonary ionocytes arise from basal cells who also give rise to neuroendocrine and tuft cells59. Given that these cell types are also present in the human thymus and that they were found in close proximity to each other in the medulla, with many subsets associated with Hassall’s corpuscles, it raises the possibility that a similar progenitor exists in the thymus. This hypothesis is compatible with reports of thymomas comprising neuroendocrine differentiation78 and thymic carcinomas containing tumor cells with a neuroendocrine phenotype80. It is also intriguing that myoid cells occur in different thymic tumors, including different types of histologic variants of thymoma and thymic carcinomas81. Tumors showing both rhabdomyoid and epithelial differentiation can also arise in the thymus82, suggesting that there might be a common precursor that can give rise to both epithelial and myoid cells. Our transcriptome data showing a branching point between neuroendocrine and myoid cells as well as co-expression of epithelial and myoid markers by immunofluorescence in fetal tissue support this idea. Our work thus provides additional insight on the origin of epithelial subsets found in the human thymus.A striking outcome of our study is the identification of CFTR+ ionocytes as an additional subset of epithelial cells found in the human thymic medulla. While ionocytes have been described in lung epithelium46. In neuronal and muscle stem cells, high and sustained levels of Hes1 can inhibit cell differentiation by antagonizing master regulators of cell fate like the proneural factor Ascl1 and regulator of myogenesis Myod183. In contrast, when Hes1 expression oscillates, it activates stem cell proliferation by driving oscillations in Ascl1 and Myod1 expression through periodical repression cycles84. Alternatively, terminal differentiation is promoted when expression of Ascl1 or Myod1 is sustained while Hes1 expression and/or activity is inhibited. This mechanism is compatible with our observation that HES6, a HES1 inhibitor, is highly expressed in neuroendocrine and myoid cells. It is thus possible that HES6-mediated inhibition of HES1 allows stable expression of ASCL1 and MYOD1 in progenitor cells that will eventually differentiate into ASCL1+ neuroendocrine or MYOD1+ myoid cells. Although the mechanisms that lead to high Hes1 expression in progenitor cells are not clear, other signaling pathways like BMP have been shown to play an important role in regulating quiescence by upregulating Hes1 in neural stem cells85 or promoting the degradation of ASCL1 in hippocampal stem cells86. Since there is evidence that BMP signaling promotes maintenance of thymic progenitors6, it is possible that this pathway helps establish and maintain quiescence in TEPCs by upregulating HES1 to a level where it constantly suppresses expression of differentiation factors like ASCL1.Our analysis suggested a role for Notch signaling in the development of different thymic epithelial subsets. While Notch signaling has been extensively studied in the context of T cell commitment and epithelial differentiation in other tissues, a role for this pathway in TEC specification has only been recently reportedCYP21A2, CYP17A1, CYP11A1) are often targeted by autoantibodies in thymoma patients but do not correlate with AIRE expression in thymoma samples, implying that they are not AIRE-dependent genes88. In addition to AIRE+ mTECs, other cell types most likely participate in induction of tolerance by providing antigens that can be presented by antigen-presenting cells like dendritic cells. For example, myoid cells likely participate in the induction of immune tolerance to muscle antigens. Indeed, many thymoma patients who typically lack myoid cells89 develop myasthenia gravis (MG), an autoimmune disease of the neuromuscular junction characterized by autoantibodies to the acetylcholine receptor (AChR) or other muscle antigens like titin (TTN)90. APS-I patients also typically don’t have detectable autoantibodies against either AChR or TTN, suggesting that the expression of these antigens is not entirely AIRE-dependent88. Importantly, expression of AChR and TTN in our dataset was much higher in myoid cells compared to AIRE+ mTECs, supporting the idea that myoid cells are the main source of muscle antigens in the human thymic medulla. Our analysis thus provides additional information on the regulation of disease-relevant TSAs in the human thymus.Finally, our study provides a valuable database of genes expressed in TECs that will allow a better understanding of promiscuous gene expression in the human thymus. For example, a significant subset of APS-1 antigens were present in AIRE+ mTECs, supporting the idea that these genes are AIRE-dependent in humans. Interestingly, some of the autoantigens that were not found in AIRE+ cells -approved protocols from the Department of Obstetrics, Gynecology and Reproductive Science, San Francisco General Hospital. Samples were obtained after legal, elective termination of pregnancy with written informed consent for fetal tissue donation to biomedical research. Consent for tissue donation was obtained by clinical staff after the decision to pursue termination was reached by patients. Personal Health Information and Medical Record Identifiers/access is at no point available to researchers, and no such information is associated with tissue samples at any point. Pediatric tissues were obtained from patients undergoing corrective cardiothoracic surgery in accordance with protocols approved by the UCSF Human Research Protection Program Institutional Review Board (IRB #17-22928). Written informed consent was obtained from the patient’s parents, guardians, or Legally Authorized Representatives before sample collection. Human adult thymic tissues were acquired from research-consented deceased organ donors at the time of organ acquisition for clinical transplantation through an IRB-approved research protocol with Donor Network West, the organ procurement organization for Northern California. All donors were free of chronic disease and cancer and were negative for hepatitis B/C and HIV. Tissues were collected after the clinical procurement process was completed, and stored and transported in University of Wisconsin (UW) preservation media on ice, and delivered at the same time as organs for transplantation. The study does not qualify as human subjects research, as defined by the UCSF IRB, as tissue samples were obtained from de-identified deceased individuals without associated personal health information (PHI). Demographic and clinical data were extracted from de-identified materials provided by Donor Network West.CAG-stopflox-tdTomato and Ascl1-creERT2 (ref. 91) mice were obtained from The Jackson Laboratory (RRID:IMSR_JAX:007914 and RRID:IMSR_JAX:012882). ADIG mice have been described previously43. Mice were maintained in the University of California San Francisco (UCSF) specific pathogen-free animal facility in accordance with the guidelines established by the Institutional Animal Care and Use Committee (IACUC) and Laboratory Animal Resource Center. Mice were maintained at a constant humidity between 30 and 70% and temperature 68 and 79 °F, under a 12-h light/dark cycle and had free access to food and water. All experimental procedures were approved by the Laboratory Animal Resource Center at UCSF. Mice aged 12–15 weeks were used for the lineage tracing experiments regardless of their sex. Tamoxifen (Sigma-Aldrich) was dissolved in corn oil (Sigma-Aldrich) and one 4 mg dose was administered by oral gavage with flexible plastic feeding tubes (Instech). All mice were euthanized by CO2 inhalation followed by cervical dislocation according to IACUC guidelines.Rosa26Thymic tissues placed in RPMI (ThermoFisher) containing 100 μg/ml DNase I (Roche) were cut into small pieces using scissors. Tissue pieces were transferred into a gentleMACS C tube (Miltenyi) containing 10 ml of RPMI with DNAse. The gentleMACS Program m_spleen_02 was run three times. Thymic fragments were separated from the thymocytes-rich supernatant by centrifugation. Remaining fragments were transferred back to C tube with fresh RPMI with DNAse before running program m_spleen_01. The supernatant was removed and replaced with 10 ml of digestion medium containing 100 μg/ml DNase I and 100 μg/ml Liberase TM (Sigma-Aldrich) in RPMI. Tubes were moved to a 37 °C water bath and fragments were triturated every 5 min to mechanically aid digestion. At 30 min, tubes were spun briefly to pellet undigested fragments and the supernatant was discarded. Fresh digestion medium or accumax (Stemcell Technologies) was added to remaining fragments and the digestion was repeated for another 15–30 min until most pieces were digested. Supernatant from this second round of digestion was transferred to a tube containing cold MACS buffer  to stop the enzymatic digestion. If necessary, a third round of enzymatic digestion was performed on remaining fragments using accumax for an additional 5–10 min. Cells were pooled with the supernatant from the previous round of digestion and were passed through a 40-μm filter . Some samples were treated with 2 ml of ACK lysing buffer (Lonza) for 5 min prior to stromal enrichment.7 total cells were added instead of 20 μL. LD columns were used for depletion and the CD45-negative fraction was collected in MACS buffer. Stromal cells from adult thymus were enriched using fluorescence-activated cell sorting (FACS). Blocking was done with human Fc receptor binding inhibitor monoclonal antibody (eBioscience) followed by staining for 20 min using human-specific antibodies against EPCAM  and CD45 . After staining, cells were washed and resuspended in FACS buffer containing DAPI. Cells were sorted on BD FACS Aria II. Pre-gating was first done for live cells based the DAPI stain. The percentages of EPCAM+, EPCAM−CD45−, and CD45+ cells in each sample was determined by flow cytometric analysis or by calculating the ratio of cells in epithelial clusters, immune cluster, or other clusters over the total number of cells in the single-cell RNA-seq dataset.For fetal and postnatal tissues, single cells from digested tissue were resuspended in MACS buffer containing 10 μM of the ROCK inhibitor Y-27632 (Tocris). Human CD45 MicroBeads (Miltenyi) were used to deplete immune cells according to the manufacturer’s instructions with the following modification: 5 μL of CD45 MicroBeads per 1092. Cell by gene count matrices of all samples were concatenated to a single matrix. Cells with 200–5000 detected genes and expressing <10% mitochondrial genes as well as genes expressed in ≥3 cells were retained for a total of 68,008 cells  with an average of 4769 counts per cell. Counts were log transformed and total counts per cell were normalized. The dataset was filtered for highly variable genes  and variation caused by mitochondrial gene expression and cell cycle-dependent changes in gene expression were regressed out. BBKNN13 was applied to correct donor-specific effects. K-nearest neighbor graphs were constructed (n_neighbors = 15) and clustering was performed using the Leiden algorithm with a resolution of 0.5. Clustering results were visualized using UMAP. Sub-clustering was performed by isolating clusters of interest with Scanpy and using the Leiden algorithm with a resolution of 0.7 for sub-clustering all TECs and 0.5 for sub-clustering mTECs. Characteristic gene signatures were identified by testing for differential expression of a subgroup against all other cells using a t-test with overestimated variance implemented in the tl.rank_genes_groups function of Scanpy. The TSA and APS-1 scores were calculated using the scanpy.tl.score_genes function. This function calculates the average expression of a set of genes subtracted with the average expression of a randomly selected reference set of genes93. TSA genes were identified using data from the GNF Mouse GeneAtlas as reported by Sansom et al.67. APS-1 genes were selected based on their association with autoantibodies in APS-1 patients68. The average expression level of scored genes was visualized using UMAP previously generated using Scanpy. To quantify the association between specific TSAs and the TSA score for AIRE+ and corneocyte-like mTECs, a Spearman correlation coefficient with associated p value was calculated using the spearmanr function from the SciPy statistical package. The list was filtered to keep genes expressed in at least five cells that had positive correlation coefficients >0.15 and p values < 0.25. Supplementary Data tables were prepared using Microsoft Excel.Single cells were captured using the 10X Chromium microfluidics system (10X Genomics). The cells were encapsulated and barcoded cDNA libraries were prepared using the single-cell 3′ mRNA kit . Single-cell libraries were sequenced using a NovaSeq 6000 (Illumina). The Cell Ranger software pipeline  was used to demultiplex cellular barcodes, map reads to the human genome (GRCh38) and transcriptome using the STAR aligner, and produce a matrix of gene counts versus cells. Single-cell data analysis was performed using Scanpy (version 1.4.4 or 1.6.0)t-test with overestimated variance implemented in the tl.rank_genes_groups function of Scanpy.The raw count matrix and annotated matrix for epithelial cells from the Park et al. dataset were downloaded from the Zenodo repository (10.5281/zenodo.3711134). The dataset was processed through the same pipeline and combined with our epithelial dataset using Scanpy. Batch alignment was performed using the BBKNN algorithm. Clustering was performed using the Leiden algorithm with a resolution of 0.51 and results were visualized using UMAP. Sub-clustering was performed by isolating clusters of interest with Scanpy and using the Leiden algorithm with the resolution set at 0.25 for mTECs and 0.1 for ionocytes/tuft cells. Characteristic gene signatures were identified by testing for differential expression of tuft cells against ionocytes using a 44. Spliced and unspliced expression matrices were generated using the standard velocyto pipeline. The following steps were performed using the scVelo package94. The matrices were size-normalized to the median of total mRNA molecules across all cells. Genes were selected based on a threshold of a minimum of 20 expressed counts for both spliced and unspliced mRNA. The top 2000 highly variable genes were kept for further downstream analysis. Nearest neighbor graphs were calculated with 30 neighbors based on the normalized gene expression matrices from the original analysis. Velocity estimations were calculated using the standard scVelo pipeline and the resulting velocity graphs were projected onto the UMAPs previously generated using Scanpy.Trajectory analysis was performed using RNA VelocityFor immunofluorescence, tissues were fixed in 4% paraformaldehyde, washed with PBS, followed by overnight incubation with 30% (w/v) sucrose (Sigma-Aldrich) in PBS. Tissues were embedded in optimal cutting temperature compound (Tissue-Tek) and stored at −80 °C before sectioning on a cryostat (Leica). Slides were briefly rehydrated in PBS before permeabilization and blocking in CAS block (ThermoFisher) with 0.2% Triton X-100 (Sigma-Aldrich) followed by primary antibody staining at 4 °C overnight. When necessary, secondary antibody staining was performed at room temperature for 1 h. Sections were washed with PBS-Tween 0.1% before mounting with ProLong Diamond Antifade Mountant (ThermoFisher). Images were acquired on an Apotome microscope (Zeiss). Antibody details are provided in Supplementary Table Tissue was fixed in 4% paraformaldehyde (ThermoFisher), washed with PBS, and embedded in paraffin. Antigen retrieval was performed on rehydrated tissue by boiling sections in antigen retrieval Citra Solution (Biogenex). Sections were blocked for 30 min at room temperature using CAS-Block (ThermoFisher) with 0.2% Triton X-100 (Sigma-Aldrich), followed by incubation with primary antibody overnight at 4 °C. Staining with biotinylated secondary antibody was performed for 1 h at room temperature. Slides were developed using an ABC kit (Vector labs) and DAB kit (Vector labs) and counterstained with hematoxylin. Antibody details are provided in Supplementary Table 8. Briefly, mouse thymi were isolated, cleaned of fat and transferred to DMEM (UCSF Cell Culture Facility) containing 2% FBS (Atlanta Biologics) on ice. Thymi were minced with a razor blade and up to four thymi were pooled into a single digestion. Tissue pieces were moved to 15-ml tubes and vortexed briefly in digestion medium . Fragments were allowed to settle before removing the medium and replacing it with fresh digestion medium. Tubes were moved to a 37 °C water bath and fragments were digested for 36 min with trituration with a glass Pasteur pipette every 6 min. At 12 min, tubes were spun briefly to pellet undigested fragments and the supernatant was moved to 20 ml of 0.5% BSA (Sigma-Aldrich), 2 mM EDTA (TekNova) in PBS (MACS buffer) on ice to stop the enzymatic digestion. This was repeated twice for a total of three 12-min digestion cycles, or until there were no remaining tissue fragments. The single-cell suspension was then pelleted and washed once in MACS buffer. Density-gradient centrifugation using a three-layer Percoll gradient  with specific gravities of 1.115, 1.065, and 1.0 was used to enrich for stromal cells. Cells isolated from the Percoll-light fraction, between the 1.065 and 1.0 layers, were resuspended in MACS buffer and counted. Cells were then incubated with Live/Dead Fixable Blue Dead Cell Stain (ThermoFisher) in PBS for 20 min at 4 °C followed by blocking with anti-mouse CD16/CD32 (24G2) (UCSF Hybridoma Core Facility) and 5% normal rat serum for 20 min at 4 °C. Cells were then washed in FACS buffer and stained for surface markers for 30–45 min at 4 °C. Flow cytometry data were collected on a LSRII Flow Cytometer (BD Biosciences) housed within the UCSF Single Cell Analysis Center using the FACSDiva software (BD Biosciences), analyzed using FlowJo software (TreeStar Software), and plotted using GraphPad Prism. Antibody details are provided in Supplementary Table For lineage tracing experiments, single-cell suspensions were prepared as previously describedFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6


In multiple solid tumours, including gliomas, the mechanical properties change as the disease progresses. If and how mechanical cues regulate tumour cell proliferation is currently not fully studied. PIEZO1 has recently been identified as a crucial mechanosensitive cation channel in multiple solid tumours. However, we didn’t find any clinical data describing the association between PIEZO1 expression and glioma. To investigate the role of PIEZO1 in gliomas, we analysed PIEZO1 gene expression at the transcriptome level, genomic profiles and the association of PIEZO1 with clinical practice. In total, 1633 glioma samples with transcriptome data, including data from the Chinese Glioma Genome Atlas RNAseq, the Cancer Genome Atlas RNAseq and GSE16011 databases, were included in this study. Clinical information and genomic profiles including somatic mutations were also obtained. We found that PIEZO1 expression was highly correlated with malignant clinical and molecular subtypes of glioma. Gene ontology analysis showed that expression of PIEZO1 was correlated with tumour microenvironment-related genes that encode proteins involved in extracellular matrix (ECM) organization, angiogenesis and cell migration. Additionally, PIEZO1 was shown to be involved in tumour progression by serving as the central checkpoint of multiple ECM remodelling-related signalling pathways to modulate tumour cell proliferation and the tumour microenvironment in turn. Finally, high PIEZO1 expression was correlated with reduced survival time and acted as a robust biomarker for poor prognosis in gliomas. Taken together, the results indicated that high PIEZO1 expression is closely associated with highly malignant gliomas. Importantly, PIEZO1 serves as a key factor involved in sensing mechanical properties in the tumour and can regulate both tumour cells and their microenvironment to promote glioma progression, and it is also a potential therapeutic target for the treatment of gliomas. The TME comprises all components surrounding a tumour; this includes blood vessels, immune cells, signalling molecules and extracellular matrix proteins. The TME interacts closely with tumour cells by providing nutrients and structural support, by communicating through the exchange of signalling molecules and often by promoting tumour growth and metastasis.The current gold standard treatment for glioma consists of chemotherapy, radiation therapy and surgery. Treatment strategies targeting the tumour microenvironment (TME) are gaining traction in the clinic3. Tumour cells sense mechanical stimuli such as tissue pressure (stiffness), cell membrane tension, shear stress, and tissue pressure from the extracellular matrix (ECM). These mechanical cues are transduced into biochemical signals to modulate both tumour cells and the TME, promoting cancer progression4. These processes strongly depend on mechanosensitive ion channels.In fact, it has yet to be identified that apart from regulation, nutrition and structural support, mechanical stimuli sensed by tumour cells and neighbouring cells are also provided by the TME and affect every step of tumour progression and metastasis6. First described by Coste et al.7, homologues PIEZO1 and PIEZO 2 independently induce cationic non-selective activated currents in response to mechanical stimuli11. In breast cancer cells, PIEZO1 has been shown to increase motility in response to physical cues12, and PIEZO2 has been shown to play a role in the anchoring of brain metastatic cells13. As tumour cells grow, the mechanical forces within the tumour and TME increase, and the Piezo channel is activated by these mechanical signals and interacts with focal adhesions and integrin-FAK signalling to regulate tumour development14.PIEZO transmembrane proteins have been identified as mechanosensitive cation channels15. Despite advances in comprehensive care, the median survival time of patients who suffer from gliomas is only 15 months17. Prolonging the survival time is a critical challenge for clinicians treating glioma patients. In the past decade, there has been a surge in studies investigating molecularly targeted therapies and immunotherapies for the treatment of gliomas. However, interindividual tumour heterogeneity appears to temper the success of these studies18. It has become increasingly apparent that tumours do not result from the unregulated growth of a single cell but that tumour formation involves bidirectional abnormal communication between tumour cells and their microenvironment19. This not only plays an essential role during tumour progression and metastasis but also has a profound impact on therapeutic efficacy20. Additionally, recent studies addressed a feedforward circuit mediated by PIEZO1 and tumour tissue mechanics to promote glioma growth21. However, until now, the relationship between PIEZO1 expression and glioma malignancy in human patients has not been studied.Glioma is the most prevalent primary malignant tumour of the central nervous system (CNS) in adultsTranscriptomes from 325 gliomas from the Chinese Glioma Genome Atlas (CGGA) dataset were analysed to investigate the expression of PIEZO1 in gliomas, and we further verified these results by analysing RNA data from The Cancer Genome Atlas (TCGA) network derived from 1032 gliomas and a GSE16011 microarray of 276 cases. Moreover, genomic profiles containing somatic mutations were also analysed in our study. This is the first integrative study characterizing PIEZO1 expression in whole grade glioma both molecularly and clinically. Our work aims to improve the current knowledge of the role of mechanical forces in tumour aggressiveness mechanisms and provide a potential therapeutic target for gliomas.22. mRNAseq data obtained from 1032 WHO grade II to grade IV gliomas were downloaded from The Cancer Genome Atlas (TCGA) . We included 276 cases from the GSE16011 cohort. The RNAseq data used to study the specific tumour anatomic structure in GBM was collected from the Ivy Glioblastoma Atlas Project .The present study was approved by the Beijing Tiantan Hospital Institutional Review Board (IRB)/Ethics Committee of Beijing Tiantan Hospital. In total, 325 RNAseq samples were included in the CGGA dataset. We collected the molecular and clinical data from the CGGA database. The IDH mutation status detection method was described in Guan et al.23 to evaluate the copy number alternations corresponding to PIEZO1 expression. The thresholded copy number at the alteration peaks was determined by GISTIC analysis 24. The gene ontology (GO) analysis in DAVID Bioinformatics Resources 6.8 was used to explore the biological functions of the related genes25. Gene set enrichment analysis (GSEA) was performed as presented in Li et al.26. GO analysis of the most highly correlated genes was used to generate a heatmap after Spearman correlation analysis. The GO gene set was downloaded from MSigDB (https://software.broadinstitute.org/gsea/msigdb/index.jsp). ECM-related metagenes were described in the work of Rody and Jain28. Metagene expression values were obtained by quantifying the normalized mean expression values of all genes in their respective cluster28.We obtained 741 cases with somatic mutations and associated with RNAseq data from the TCGA database. We used GISTIC 2.0Twenty Paraffin-embedded human glioma tissues for validation , normal human brain and mouse retina tissues were collected for staining. We reviewed the tissue slides through the visual evaluation of haematoxylin and eosin (HE)-stained slides. The results of the morphologic identification were independently confirmed by two neuropathologists. We selected a PIEZO1 antibody  to detect PIEZO1 expression. Immunofluorescent (IF) staining of paraffin sections was performed. Briefly, specimens were blocked in blocking buffer for 60 min, the sections were incubated with primary antibody overnight at 4 °C, and then the sections incubated with the corresponding secondary antibody  at room temperature in the dark for 1 h. After washing with PBS buffer, the slides were coverslipped with Prolong Gold Antifade Reagent. All images were analysed using Image-J based on the area fraction or intensity of positive staining.https://www.r-project.org/) and ImageJ . Spearman correlation analysis was used to evaluate the correlations between continuous variables. The differences in variables between groups were assessed by Student’s t-test, one-way ANOVA, or Pearson's Chi-squared test. We used a Kaplan–Meier survival curve to determine the survival distributions and performed a log-rank test to assess the statistical significance. Univariate and multivariate Cox proportional hazard models were determined to evaluate the prognostic value of PIEZO1. Patients with missing information were excluded from the statistical analysis. All statistical tests were two-sided. A p value < 0.05 was considered significant.All statistical analyses were performed using the R programming language -oligodendroglioma , LGG-astrocytoma , LGG with wild-type IDH status (LGG IDH-wildtype), glioblastoma (GBM) with mutant IDH status (GBM-IDHmut), and GBM with wild-type IDH status (GBM-IDHwt). The expression of PIEZO1 was the highest in GBM-IDHwt and the lowest in LGG-Oligo . From CGGA, 174/188 gene sets were found to be upregulated in samples expressing PIEZO1 at high levels, among which 129 gene sets were significant at an FDR < 25%, and 82 gene sets were significantly enriched at a nominal p value < 5%. In the TCGA, 2522/3453 gene sets were upregulated in high PIEZO1 expression samples, among which 379 gene sets were significantly enriched at a nominal p value < 5%. Figure Next, we used the (c5.bp.v7.0.symbols) gene set from GSEA to identify the activated biological pathways related to ECM regulation. Several signatures were found to strongly correlate with high PIEZO1 expression samples in both CGGA and GCTA. In the CGGA, 3210/3899 gene sets were upregulated in high PIEZO1 expression samples, among which 1999 gene sets were significant at an FDR < 25%, and 1367 gene sets were significantly enriched at a nominal p value in increasing order in the CGGA dataset, cell microenvironment-related genes that were responsible for ECM organization, cell adhesion, angiogenesis, cell migration and proliferation were positively correlated with PIEZO1 expression  in the TCGA and CGGA datasets, respectively, to conduct an analysis. We used GO analysis in the DAVID Bioinformatics Resources 6.8 to investigate the functions of related genes. As the gene functions were sorted by the To investigate the expression of genes associated with PIEZO1 in the microenvironment in gliomas, we included an ECM-related gene set from the GSEA and found 110 genes that were most relevant to PIEZO1 (Pearson |R|> 0.35) in the CGGA database to draw the heatmap. Among the selected genes, 106 genes were positively correlated to PIEZO1 expression, while 4 genes showed negative correlation with PIEZO1 expression  and the integrin-encoded gene family (ITG), are known to regulate ECM remodelling and to generate the cytoskeleton network system, and both are highly expressed along with PIEZO1 Fig. E–G.Among its functions in the tumour microenvironment, PIEZO1 appears to play a particularly strong role in ECM signalling pathways to regulate tumour-associated tissue remodelling. We analysed the matrix metalloproteinase (MMP) family, tissue inhibitors of metalloproteinases (TIMP) family, mitogen-activated protein kinase (MAPK) family and phosphoinositide 3-kinase (PI3Ks) family to clarify the role of PIEZO1 in ECM signal transduction. As shown in Fig. 32. Mechanical cues play a critical role in tumour progression and in shaping the TME by regulating both tumour cells and the TME. PIEZO1 is an intrinsically mechanosensitive ion channel that can interact with many signalling pathways in multiple cancer malignancies33. However, it is unknown whether PIEZO1 regulates glioma development and whether the mechanical environment in tumours interacts with the mechanosensory function of PIEZO1 to promote malignancy in gliomas. Therefore, identification of the molecular and clinical correlations between PIEZO1 expression and glioma malignancy will facilitate the establishment of a potential therapeutic target and shed light on glioma treatment.Gliomas are the most common and lethal primary brain tumours in adults. Limited improvements in outcomes have been made despite extensive study and combination of conventional treatments. New therapeutic targets are urgently needed. Studies on microenvironment-targeted therapeutic strategies have opened a new avenue for tumour treatment4 Pa34 and that tumour stiffness correlates with aggressiveness35. These results suggest that increasing intra-tumoural solid pressure developed by high grade glioma provides mechanical cues that stimulate PIEZO1, which further plays an essential role in tumour cell proliferation and metastasis. Moreover, we also found that a high level of PIEZO1 was predictive of a poor prognosis. This is the first study to present the PIEZO1 expression pattern in gliomas based on different grading systems including 2016 WHO molecular classifications and the WHO Grade system according to the CGGA dataset and be verified by the TCGA dataset.In this study, we analysed PIEZO1 expression in glioma. High levels of PIEZO1 expression showed an association with malignant entities, including a high WHO Grade, IDH wild-type status and mesenchymal subtype gliomas. The most important common characteristic among gliomas with a high WHO grade, IDH wild-type status and mesenchymal subtype is aggressiveness. It has been reported that tissue stiffness in human low to high grade gliomas gradually increased from 100 to 1037. In addition, genomic alternations may also contribute to regulating the TME and promoting tumour cell proliferation38. Therefore, PIEZO1 appears to take part in the glioma aggressive pathophysiological process, and it can act as the essential biomarker for glioma malignancy and provides a potential therapeutic target to overcome glioma.To investigate molecular mechanism of glioma, we analysed the distinct genomic alternations in order of increasing PIEZO1 expression. The data show a positive correlation between somatic mutations and PIEZO1 expression. Tumour suppressor genes, including PTEN and NF1, were detected at high amplification peaks with high PIEZO1 expression. The oncogenic driver EGFR was found to be enriched with high expression of PIEZO1. It has been reported that tumour cell microenvironment stiffness regulates tumour development by interacting with EGFR-dependent signalling in glioma40. These results indicate that PIEZO1 regulates glioma aggressiveness through its biological function in ECM remodelling. ECM components are major molecules in the cell microenvironment36. In solid tumours, the ECM has the capacity to drive tumour aggressiveness3. It has been reported that PIEZO1 can activate integrins intracellularly41. As the key ECM adhesion receptors, integrins interact with the integrin-FAK signalling pathway to regulate cancer cell behaviour, promoting migration, proliferation and survival36. Integrin inhibitors  have been tested in clinical trials45. A randomized phase II trial showed that cilengitide monotherapy was well tolerated and noted a modest anti-tumour activity in recurrent glioblastoma45. However, a phase III trial showed that combination of cilengitide with standard temozolomide chemoradiotherapy had no benefit over conventional therapy in glioblastoma, particularly in tumours with methylated MGMT promoter42. Cilengitide as a subunit antagonist of the integrins αvβ3 and αvβ5 remains controversial42. PIEZO1 is the upstream molecule of integrins and can directly sense the mechanical changes caused by tumour tissue pressure, which makes PIEZO1 a potential biomarker as a therapeutic target.As for the biological function of PIEZO1 expression in gliomas, we found that cell microenvironment-related genes that are related to ECM organization, cell adhesion, angiogenesis, cell migration and proliferation were positively correlated with PIEZO1 expression. Additionally, two gene groups, the collagen-encoded gene family (COL) and the integrin-encoded gene family (ITG) that are known to regulate ECM remodelling and compose the cytoskeleton network system were positively correlated with PIEZO1 expression49. PIEZO1 is also involved in 2D cell migration and 3D invasion in the MCF-7 cell line, a gastric cancer cell line, and small-cell lung carcinoma cell lines49. Taken together, PIEZO1 not only regulates tumour cell proliferation but modulates the TME as well to promote tumour cell metastasis.The molecular mechanisms through which PIEZO1 regulates glioma malignancy involve multiple signalling pathways. As shown in the results, PIEZO1 expression is positively correlated with genes that are responsible for cell proliferation, tumour-associated tissue remodelling, immune response and tumourigenesis. These data indicate that PIEZO1 acts as a central functional point to form an integrated mechanical-regulation molecule network, here giving rise to the hypothesis that a feedback mechanism where the increasing tumour tissue stiffness caused by abnormal regulation of cell growth activates the PIEZO1 ion channel and multiple ECM-related pathways that are downstream, in turn modulating cell proliferation and TME remodelling to increase tissue pressure. This also explains why in high grade gliomas such as GBM, PIEZO1 expression is expressed at a high level. Meanwhile, cytoskeletal dynamics are also associated with PIEZO1 expression. A loose supportive structure due to loss of collagen in the TME provides more physical space for tumour cells. In parallel, tumours with a remodelled TME are able to compress blood supply through exerting physical forces on their microenvironment. Hypoperfusion and hypoxia lower the efficacy of tumour targeted drug delivery, promoting immune evasion and tumour metastasis. Evidence has shown that expression of PIEZO1 increased in multiple malignant diseases including bladder carcinoma, synovial sarcoma, osteosarcoma, breast cancer and gastric cancer+ and Grammostola spatulata spider toxin GsMTx4) are not PIEZO1-specific, it has been reported that the use of GsMTx4 may inhibit PIEZO1 activity in the urothelium, affect PIEZO1-dependent arterial remodelling and reduce cell death due to high strain mechanical injury52. Because PIEZO1 plays a vital role in an abundance of physiological processes, more research needs to be done to investigate tumour-specific PIEZO1 knockdown and provide a potential tumour cell and TME-targeted therapy.As expression of PIEZO1 is positively associated with gliomas, pharmacological inhibition of PIEZO1 might prove an efficacious strategy in treating these malignant gliomas. Although current identified inhibitors (ruthenium red, Gd3Our findings indicate that PIEZO1 might play a role as a biomarker in gliomas. The biological significance of PIEZO1 was explored by analysing two large sample-sized glioma cohorts. PIEZO1 expression is correlated with increasing tumour tissue stiffness as tumourigenesis proceeds, and the function of PIEZO1 aggravates tumour cell proliferation and glioma malignancy. Furthermore, the tumour-specific inhibitor of PIEZO1 needs to be addressed in future studies. However, we still need to clarify the specific molecular mechanism of PIEZO1-related mechanical–biological transduction signalling pathways and how to modulate these molecules to regulate glioma progression.In conclusion, using publicly available transcriptomic and genomic profiling data, we found that PIEZO1 was upregulated in high grade glioma and predictive of a poor prognosis. Additionally, PIEZO1 was positively correlated with microenvironment remodelling-related genes and appears to function as a central checkpoint of ECM remodelling pathways. These findings establish PIEZO1 as a novel focus to study the mechanism of glioma malignancy and as a potential therapeutic target.Supplementary Figure 1.Supplementary Figure 2.Supplementary Legends.Supplementary Table 1.Supplementary Table 2.


To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available. During the next half century, most of the developing countries in the world will continue striving for increased food production by using mineral fertilizers  and will−1, respectively . Th. Th25]. Research focused on improving N use efficiency using chlorophyll meters can be divided into two broad groups: (1) Relationship between chlorophyll meter readings and N content of leaves and (2) Establishment and evaluation of the relationship between chlorophyll meter readings and the fertilizer N doses to be top dressed on field crops.Crop N status can be estimated using a chlorophyll meter but differences in specific leaf weight or leaf thickness can create variations in the relationship between SPAD readings and leaf N content ,72. CropThe relationship between chlorophyll meter readings and leaf N status has generally been found to be linear in maize ,74,75. IMendoza-Tafolla et al.  showed tChlorophyll meters are very sensitive in the deficient to adequate range of N nutrition. Monitoring chlorophyll content in leaves during the crop growing season can indicate slight N deficiencies early enough to correct them before yield potential is affected. Therefore, managing fertilizer N based on crop need as guided by chlorophyll meter readings can maintain optimal crop N status . There a−2 on leaf area basis was found to be the threshold for guiding N top-dressing in rice variety IR72. Fertilizer N applications were necessary below this threshold value to maintain optimal crop N status and avoid yield loss. The underlying principle in real-time site-specific N management using SPAD meter is that the threshold leaf chlorophyll content or N status must be maintained throughout the cropping season so that a dose of fertilizer N must be applied whenever first fully expanded leaf from the top gives a SPAD reading less than the threshold value. Thus, chlorophyll meter guided N fertilizer applications lead to improved congruence of N supply and crop demand thereby resulting in the production of high grain yields and high fertilizer N use efficiency. An account of research on fertilizer N management in rice and wheat in developing countries based on the absolute chlorophyll meter threshold reading approach is given in the following sub-sections.The threshold leaf chlorophyll content expressed in the form of a SPAD threshold reading represents a critical leaf N status below which the crop will suffer from N deficiency and may lead to yield loss . It can On-farm research on using absolute SPAD threshold reference values for fertilizer N management in transplanted wet-seeded rice revealed that for local cultivar groups and climatic conditions in Philippines and Indonesia, the SPAD threshold value of 35 was appropriate in the dry season . But durData compiled in −1 or more was obtained in the no-N control plots. It seems obvious because N uptake by rice within 2 weeks of transplanting is very small [The SPAD meter threshold-based fertilizer N management in rice starts two weeks after its transplanting from the nursery to the main field. Balasubramanian et al.  and Bijary small  as seedlry small . Delayinry small ,90,97.−1 at 14 days after sowing (DAS) and 30 kg N ha−1 at 28 DAS, 30, 40 or 50 kg N ha−1 were applied at 49 and 70 DAS depending upon the SPAD meter readings to be ≥40, 40–35 or <35, respectively. The performance of this fixed-time variable-dose strategy was similar to the real-time fertilizer N management strategy based on applying 30 kg N ha−1 whenever leaf chlorophyll content was below the SPAD threshold value of 37. Optimum rice yield level along with higher N use efficiency were recorded than when fertilizer N was managed following the general recommendation.Ali et al.  attempte−1 following a SPAD threshold value of 44 at maximum tillering stage improved the yield of wheat in the lower Gangetic plains in Bangladesh [Unlike in rice, where fertilizer N can be applied at any time up to flowering stage of the crop, N application in wheat is linked with irrigation events. Therefore decisions regarding chlorophyll meter guided fertilizer N applications become complex. Research on fertilizer N management in wheat using SPAD meter is focused on (a) determining the appropriate crop growth stages for mid-season fertilizer N application on the basis of leaf chlorophyll content and (b) developing the criteria based on chlorophyll meter values to apply fertilizer N doses at different crop growth stages coinciding with irrigation events. In wheat, fertilizer N is typically applied in 2 or 3 split doses at sowing and along with first and second irrigation events around 20–25 days after sowing (crown root initiation stage) and 45–50 days after sowing (maximum tillering stage), respectively. Applying a dose of 20 kg N hangladesh . Hussainngladesh  found thngladesh .−1 if SPAD meter reading of the leaves at full heading stage was between 50 and 52. In case the SPAD reading at heading stage was between 45 and 50, application 60 kg N ha−1 was required to obtain a grain protein content of more than 120 g kg−1.Not only the high yield levels, high protein content in wheat grains also depends upon appropriate fertilizer N management. Takebe et al.  observed−1, whenever leaf chlorophyll content was less than the SPAD threshold of 40 up to heading stage, resulted in higher wheat yield levels than those observed by following the local fertilizer N recommendations. Corresponding increases in the agronomic and the recovery efficiencies were 58% and 15%, respectively. SPAD meter cannot be used to guide N topdressing at crown root initiation stage of wheat (about 3 weeks after sowing) because very little variation in leaf color is observed due to application of a basal dose of N at sowing [−1 at sowing of wheat and 45 kg N ha−1 along with the first irrigation at crown root initiation stage constituted the appropriate prescriptive N management. At the maximum tillering stage, a dose of 30 or 45 kg N ha−1 was applied depending upon SPAD reading of the first fully expanded leaf from the top was more than or equal to 42.5 or less than 42.5, respectively. Following this site-specific fertilizer N management strategy, it was possible to produce wheat grain yields at par with those produced with the general recommendation in the region but with higher fertilizer N use efficiency.In an acid lateritic soil and under mild winters in the eastern India, Ghosh et al.  reportedt sowing . Also, dt sowing  conclude−1 (over no-N application) at the maximum tillering stage exhibited significant negative linear relationship with SPAD values recorded before applying the fertilizer. Thus, with decreasing SPAD meter readings of the first fully expanded wheat leaves from the top at the maximum tillering stage, wheat grain yield response to applied fertilizer N increased linearly. For example, with SPAD readings of 32.5 and 42.5 at the maximum tillering stage, application of 30 kg N ha−1 increased wheat yield by 1.0 or 0.5 t ha−1, respectively [−1 when wheat leaves recorded SPAD meter readings of 44 or more at the maximum tillering stage. However, at SPAD reading of 42, a 20% yield increase was expected by applying a fertilizer N dose of 30 kg ha−1.Maintenance of optimum leaf chlorophyll content and N status between 50 to 75 days after sowing of wheat is crucial for obtaining high yield levels . Bijay-Sectively . Bijay-Sectively  reported−1 was applied. Rice yields recorded for different cultivars using the relative sufficiency values were similar to those obtained in the local recommendation treatment but with application of 30 kg ha−1 less N. Bijay-Singh [−1 less fertilizer than the general recommendation of 120 kg N ha−1 in the study area but with no reduction in the grain yield. A significant increase in fertilizer N use efficiency was recorded by managing fertilizer N following dynamic SPAD threshold as per relative sufficiency values [When absolute threshold leaf chlorophyll content varies among varietal groups, seasons or regions, dynamic threshold thresholds expressed as percentage of sufficient index is used with SPAD meter-based N management. Hussain et al.  monitoreay-Singh  also demay-Singh  followedy values ,110. Suf−1 at sowing and another 45 kg N ha−1 at crown root initiation stage of wheat along with the first irrigation event before applying a fertilizer N dose of 30, 45 or 60 kg ha−1 at maximum tillering stage of the crop on the basis of sufficiency index to be more than 90%, between 85 to less than 90% or less than 85%, respectively. Wheat grain yields produced following this strategy were at par with those observed with the general fertilizer N recommendation but with higher fertilizer N use efficiency. In Egypt, Saudy et al. [−1. Yu et al. [Fertilizer N management following the relative sufficiency approach has also been attempted in wheat. Bijay-Singh  applied y et al.  applied u et al.  used 97%Estimation of in-season N status in crops is critical for precision N management. Crop N demand and soil N supply are reflected in crop growth status . Thus, fFairly accurate prediction of N uptake is essential for setting up non-invasive strategies to optimize N fertilization and to reduce the environmental risks associated with injudicious use of fertilizer N. Zhang et al.  found thNDVI) defined as the NDVI from N-rich strip divided by the NDVI from the test field. As the RINDVI was satisfactorily correlated with post-season harvest response index, it allowed estimation of the yield level that can be expected by applying additional N. Thus yield attainable with fertilizer N application is estimated by multiplying the potential yield without fertilizer N with the RINDVI. The NFOA approach is now the commonly reported method for using crop reflectance sensors for fertilizer N management in cereals in developing countries like Mexico [GreenSeeker, the most commonly used crop reflectance sensor for precision N management in developing countries, works on an N fertilization optimization algorithm (NFOA) as developed by Raun et al. ,120 and e Mexico , China [e Mexico , India [e Mexico ,126,127,e Mexico  and Egype Mexico . A simile Mexico . It subsIn irrigated transplanted rice, fertilizer N is generally applied at three critical growth stages of transplanting, active tillering and panicle initiation. As water on the soil surface interferes in measuring NDVI by canopy reflectance sensor, the sensor-guided fertilizer N dose can be applied only at panicle initiation stage when crop canopy does not allow interference due to water in the field ,131,136.−1 at transplanting and 45 kg ha−1 at active tillering stage was the appropriate prescriptive strategy before applying the GreenSeeker-guided N dose at panicle initiation stage. Data from 19 on-farm locations revealed that farmers applied from 92 to 180 kg N ha−1 but produced significantly less rice grain yield at 6 out of 19 sites than when fertilizer N was managed using GreenSeeker canopy reflectance sensor. Total fertilizer N application was in the range of 75–97 kg N ha−1 under sensor-based N management. Working with dry direct-seeded rice in north-western India, Ali et al. [−1 in 2 or 3 split doses before applying the sensor-guided N dose produced rice grain yield at par with that produced by following general recommendation. However, total fertilizer N application following sensor-based strategy was less than the general recommendation. Yao et al. [−1, of which 45% and 20% N was applied as basal and at tillering stage of the crop. The sensor-guided dose of fertilizer N was applied at stem elongation stage. The partial factor productivity increased by 48% by following the sensor-based N management. Work of Cao et al. [As GreenSeeker sensor-guided fertilizer N dose can be applied to irrigated transplanted rice only at panicle initiation stage, it is important that appropriate fertilizer N doses are applied at transplanting and active tillering stages of the crop. Bijay-Singh et al.  concludei et al.  found tho et al.  conducteo et al.  revealedo et al.  developeGeneral fertilizer recommendation for wheat is to apply N at sowing and at crown root initiation stage but farmers have a tendency to apply additional N at the maximum tillering stage which coincides with second irrigation event. At sowing and at crown root initiation stage when crop canopy is very small, it is not possible to measure NDVI using sensors. Therefore, site-specific fertilizer N management for wheat involving canopy reflectance sensors generally consists of applying moderately reduced doses of fertilizer N at sowing and at crown root initiation stage and applying sensor-guided fertilizer N dose at the second or the third irrigation stages. Bijay-Singh et al. ,127 and −1 was applied in two equal doses at sowing and at crown root initiation stage. Further studies conducted by Bijay-Singh et al. [−1 at sowing and 45 kg N ha−1 at crown root initiation stage of wheat constituted a better N management before applying the sensor-guided dose at maximum tillering stage. Wheat grain yields obtained by following sensor-guided N management were at par with those observed with the standard recommendation but with higher recovery efficiency (by 6.7–16.2%) and agronomic efficiency (by 4.7–9.4 kg grain kg N applied−1). Sulochna et al. [2O emission, greenhouse gas emission and reactive N losses by 54–68% and 20–42%, respectively. Based on 30 on-farm experiments conducted over two years in North China Plain, Li et al. [min approach and farmer practices were 61%, 51% and 13%, respectively.In north-western India, Bijay-Singh et al.  reportedh et al.  conclusia et al.  also fola et al.  conductei et al.  reported−1 in two equal split doses at 14 and 30 days after sowing of maize before applying a GreenSeeker canopy reflectance sensor-guided dose produced maize grain yields similar to that obtained by following general recommendation but with less total fertilizer N application.Working with rain-fed maize in North-east China, Wang et al.  found thMore fertilizer N is being used for crop production in the developing than in the developed countries of the world, where majority of the agricultural land is under smallholder farms (less than 2 ha). Smallholders in developing countries have a tendency to achieve high yield levels by using fertilizer N many times even more than the local recommendations, which cannot take into account the dynamic spatial variability in indigenous N supplying capacity of soils. In recent years, handheld versions of chlorophyll meters and canopy reflectance sensors that allow quantitative estimation of leaf N as chlorophyll content through transmission and reflectance of light by the intact leaves through proximal sensing, have become available for use in smallholder farms in developing countries to optimize crop N status. Like variable rate N-fertilizer applicators in large fields, field-specific fertilizer N application in small fields accounts for the variations in the capacity of the soil to supply N.Measurement of reflectance or transmittance properties of leaves or canopies using proximal sensing can offer great advantages of speed and immediacy in need based fertilizer N application in cereal crops. Whereas chlorophyll meters such as SPAD meter estimate relative chlorophyll content in leaves, the GreenSeeker and the Crop Circle canopy reflectance sensors estimate NDVI from crop canopies. While optical sensors require establishment of N-rich reference strips to relate sensor readings to the fertilizer N requirement, chlorophyll meters can also guide fertilizer N applications without creating N-rich strips.Chlorophyll meters guide both amount and time of fertilizer N application based on the principle that a threshold chlorophyll or N content must be maintained throughout the crop growth season. It is the most studied and popular mode of using SPAD meter for managing fertilizer N in rice and wheat in smallholder farms in developing countries. By establishing an N-rich strip in the field, chlorophyll meters can also be used following sufficiency index approach for fertilizer N management. It has the advantage of being self-calibrating for different soils, seasons and cultivars.In-season fertilizer N rate decisions made by active canopy reflectance sensors are based on expected yield and crop N status. Algorithm are now available which can interpret sensor measurements in terms of fertilizer N requirement of the crop at the sensed growth stage. Managing fertilizer N in terms of dose and time of application prior to application of sensor-guided fertilizer N dose is also vital. Studies carried out with rice, wheat and maize in India and China show promising results, but more studies will be needed before canopy reflectance sensors become useful in smallholder farms in developing countries. For using chlorophyll meters and canopy reflectance sensors for fertilizer N management, adequate supply of water and nutrients other than N needs to be maintained because these influence chlorophyll content in the leaves.−1 in winter wheat in North China, could show huge profits in using optical sensor for managing fertilizer N in wheat. Generally cost of technology is also not taken into account. Thus, simple economic analysis for assessing proximal-sensing based N strategies in developing countries, that is based on calculating partial gross margins but does not include additional cost related to the technology, may show average profit increase. It may not be enough to promote fast adoption by farmers.Adoption of proximal sensing based N-management strategies using chlorophyll meters and optical sensors will greatly depend upon inclusion of dedicated economic analysis in future investigations . Lack of