Bioscaffolds are widely used for tissue engineering, but failed and inconsistent preclinical results have hampered the clinical use of bioscaffolds for tissue engineering. We aimed to construct a cellular remodelling landscape and to identify the key cell subpopulations and important genes driving bladder remodelling.Twenty‐four reconstructed mouse bladders using porcine small intestinal submucosa (PSIS) were harvested at 1, 3, and 6 weeks to perform single‐cell RNA sequencing. Cell types were identified and their differentially expressed genes (DEGs) at each stage were used for functional analysis. Immunofluorescence was used to validate the specific cell type.+ fibroblasts might mediate tissue remodelling. The DEGs of SMCs at 1, 3, and 6 weeks were mainly involved in the inflammatory response, muscle cell proliferation, and mesenchyme development, respectively. Moreover, we found that Notch3+ SMCs potentially modulated contractility. From 1 to 6 weeks, synchronous development of endothelial cells was observed by trajectory analysis.The remodelling landscape included 13 cell types. Among them, fibroblasts, smooth muscle cells (SMCs), endothelial cells, and macrophages had the most communications with other cells. In the process of regeneration, DEGs of fibroblasts at 1, 3, and 6 weeks were mainly involved in wound healing, extracellular matrix organization, and regulation of development growth, respectively. Among these cells, Saa3A remoulding landscape was successfully constructed and findings might help surficial modifications of PSIS and find a better alternative. However, more in vivo and in vitro studies are needed to further validate these results. + fibroblasts mainly mediated tissue remodelling, Notch3+ SMCs might modulate contractility, endothelial cells developed synchronously, and M2‐like macrophages balanced inflammation responses. We constructed bladder regeneration landscape using PSIS, investigated the cell–cell interactions, and highlighted the key genes and signalling pathways involving in this process. These findings may help surficial modifications of PSIS and find a better alternative of PSIS.Bioscaffolds are widely used for tissue engineering, but failed and inconsistent preclinical results have hampered the clinical use of bioscaffolds for tissue engineering. We performed single‐cell RNA sequencing to decipher a cellular remodelling landscape and to identify the key cell subpopulations and important genes driving bladder remodelling. A total of 13 cell types were identified during the bladder regeneration process. Among them, Saa3 Then, augmentation cystoplasty was performed in the surgery group.The animal experiments were approved by the Animal Research Ethics Committee of West China Hospital, Sichuan University (No. 2019270A). Eight‐week‐old male C57BL/6 mice were purchased from Dossy Experimental Animals Co., Ltd.  and maintained in the specific pathogen‐free animal research centre. Twenty‐four mice were randomly divided into 4 groups: week 1 postoperatively  in a watertight fashion. Subsequently, permanent sutures were placed to identify PSIS scaffolds from host bladder tissues at harvest. Then, bladders were infused with 10 ml saline to test a watertight closure. The abdominal wall and skin were closed sequentially, and animals were then quarantined. In sham‐operated controls, mice were subjected to midline abdominal incision followed by abdominal wall and skin closure without performing incision or bladder augmentation. After surgery, the mice were individually housed in conventional cages and monitored daily.All procedures were performed by one researcher Figure . AC usinTo perform histological staining and gene sequencing, the mice were sacrificed, and their bladders were harvested post AC at weeks 1, 3, and 6 Figure .2.3Mouse bladder were harvested and fixed in fresh 4% paraformaldehyde for 24 h, embedded in paraffin, cut into 4‐μm‐thick sections, and mounted on slides. The slides were stained with haematoxylin and eosin (H&E) to present cell distribution or stained with Masson's trichrome to show collagen deposition in each group. Images from each section were captured by a digital camera .2.4g for 5 min at 4°C. After discarding the supernatant, the cells were resuspended in DPBS at 4°C and sieved through a 40‐μm cell strainer. Finally, viability assessment and cell counting were performed, and a viability over 90% was considered optimal for downstream analysis.In each group, 6 mouse bladders were harvested after the mice were euthanized. The partial bladder tissues wrapping PSIS were then cut into pieces and enzymatically digested with collagenase type 1  and DNase I  for 20 min at 37°C with agitation. Then, we applied StemPro Accutase Cell Dissociation Reagent  to dissect the sticky clumps of cells for 10 min and then terminated digestion with DPBS. Subsequently, we removed red blood cells using RBC lysis buffer  for 5 min and then centrifuged them at 3002.5Single‐cell RNA‐seq libraries were constructed with Chromium Single‐cell 3' Reagent Kits (v3) based on the manufacturer's protocol. A single‐cell suspension was loaded on the Chromium Single Cell Controller Instrument (10X Genomics) to generate single‐cell gel beads in emulsions (GEMs). After GEMs were normally formed, reverse transcription reactions engaged barcoded full‐length cDNA followed by the disruption of emulsions using the recovery agent and cDNA clean up with DynaBeads Myone Silane Beads (Thermo Fisher Scientific). cDNA amplification was performed by PCR. Subsequently, the amplified cDNA was fragmented, end‐repaired, A‐tailed, index adaptor ligated and library amplified. Finally, these libraries were sequenced on the Illumina sequencing platform (HiSeq X Ten), and 150 bp paired‐end reads were generated .2.6Preliminary sequencing results were converted to FASTQ files by CellRanger (v3.1.0). The sequenced data were then aligned to the mouse genome reference sequence (mm10). Cell Ranger was also applied for preliminary data analysis and generated a web‐based file containing basic sequencing data Table . Then, t2.7Batch differences were considered and removed given that the sequenced data were obtained at different stages per time setting. Then, the top 2000 variable genes were selected by FindVariableFeautres for principal component analysis (PCA). Cells were separated into 13 clusters by the FindClusters function using the top 20 principal components. The uniform manifold approximation and projection (UMAP) algorithm was applied to visualize cells in a two‐dimensional space. Conventional markers described in a previous study were used to categorize each cell into a known biological cell type,2.8p value  using the Benjamini–Hochberg method. The “adj. p value <0.05” and “|logFC| > 0.5” were set as the cut‐off criteria. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on DEGs with a Bonferroni‐corrected p value of <0.05.Genes expressed in more than 10% of the cells in a cluster and with an average log (fold change) of greater than 0.5 were selected as DEGs by the Seurat FindMarkers function. Furthermore, the DEGs between the two selected groups were identified. The false‐positive result was corrected by the adjusted 2.9p value of <0.05 were returned and used to evaluate links between cell types. The mouse genes were transformed to human genes using Biomart.Cell–cell interactions between the selected cell type and residual cell types were predicted based on known ligand–receptor pairs by Cellphone DB.2.10q‐value <0.01.The developmental trajectory of subclustered cell types was predicted by monocle2.2.11IF was performed to validate the specific cell types. The sections originating from mouse bladders of each group were labelled with primary antibodies, including anti‐Cd163  and anti‐Cd31 , both followed by 488 goat anti‐rabbit FITC‐conjugated IgG secondary antibodies . Then, both anti‐Fbln1  and anti‐Saa3  antibodies and anti‐Acta2  and anti‐Notch3  antibodies were added, followed by 488 goat anti‐rabbit FITC‐conjugated IgG secondary antibodies  and 568 goat anti‐mouse Cy3‐conjugated IgG secondary antibodies . In addition, nuclei were labelled with 4′6‐diamidino‐2‐phenylindole . All IF images were captured by a digital camera .2.12p value <0.05 was considered statistically significant.The statistical methods were detailly described in each step. Statistical analysis was performed with R software (v3.6.1). A 33.1To establish a cell map of tissue regeneration after AC using PSIS, scRNA‐seq was applied to investigate the cell subsets and their molecular functions in dynamically growing mouse bladders. After bladder reconstruction with PSIS was performed Figure , mouse b3.2Cell–cell communication mediated by ligand–receptor complexes is critical to coordinating diverse biological processes, such as development, differentiation, and inflammation.3.3+ fibroblasts  was enriched in angiogenesis. Then, angiogenesis drove the migration of immune and nonimmune cells. At 6 weeks, vascular production guided tissue development Figure . Focused3.6Macrophages are key components of tissue repair and remodelling that occur during wound healing and allergies.3.7Epithelial cells (Eps) promoted wound healing at an early stage, but they were involved in tissue development and growth via the Wnt signalling pathway at 6 weeks4Patients with low‐compliance bladders resulting from various etiologies might require surgical reconstruction to increase bladder compliance and improve kidney function.+ fibroblasts might mediate tissue remodelling and Notch3+ SMCs might modulate contractility in the current study, which are the key components for bladder morphology and functional restoration. The functions of Notch3+ SMCs or Saa3+ fibroblasts could be modified and controlled in basic studies. We believe that a better outcome could be achieved if a modified PSIS seeded Notch3+ SMCs or Saa3+ fibroblasts was applied to perform AC.Superficial or structural modification of the scaffold material has a considerable influence on the biocompatibility and functionality of materials.Importantly, synchronous development of all Ens was observed, suggesting that vascularization at an early stage directly affected cell migration and tissue regeneration, and vascularization with tissue orientation would play an important role in bladder remodelling.The normal process of wound healing involves infiltration of inflammatory cells. Nine clusters of immune cells were identified in our study. They promoted cell regeneration, coordinated cell migration, and adhesion, and balanced the inflammatory response via cytokine secretion at each stage. Implantation of PSIS may initiate injuries and early pro‐inflammation reactions characterized by infiltration of neutrophils and M1 macrophages. Pro‐inflammation reactions may lead to degradation of the PSIS and drive the matrix into a fetal‐like state.One of the most challenging issues of functional bladder regeneration is innervation.Several limitations should be noted. First, the current study was performed with a short‐term follow‐up, and the functionality of the bladder was not tested. Second, C57BL/6 mice were included for landscape visualization due to genetic stability, but it is difficult to perform urodynamic studies in the mouse bladder. Long‐term studies in patients who needed AC but failed should be considered. Third, the function of specific cells was mostly assessed based on the expression of some functional genes and GO/KEGG analyses, and more in vivo and in vitro studies are needed to validate these results.5The single‐cell remodelling landscape of tissue‐engineered bladder with PSIS included 13 cell types. Each cell type had a unique function at each stage. Major four types of cells with unique functions might underlie an unsatisfied remodelling process of bladder after PSIS. These findings may help surficial modifications of PSIS and finding a better alternative of PSIS, but more in vivo and in vitro studies are needed to validate these results.De‐yi Luo, Hong Shen, Liao Peng, Qing He, and Xi Jin designed the project; Liao Peng, Xiao‐shuai Gao, Wei Wang, and Qing He performed sample processing; Liao Peng and Qing He analysed experiments; Liao Peng performed wet laboratory experiments; Liao Peng performed computational analysis; Xi Jin and Xiao Zeng. assisted with computational analysis; Writing and editing were carried out by Liao Peng; Supervision was performed by De‐yi Luo and Hong Shen; De‐yi Luo, Xi Jin and Hong Shen obtained funding.The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.Table S1. The basic information of single‐cell RNA sequencing of 24 mice.Table S2. Composition ratio of cell frequency in the four stages .Table S3. Top 30 interaction pairs at each stage.Table S4. The proportion of specific cell subsets at each stage.Click here for additional data file.Figure S1. Function analysis of specific cell types. (A) UMAP plot annotated by sample. (B) Violin plots showing the representative markers of each cluster. (C) The distribution of cells at each stage. (D) Enrichment analysis  of differentially expressed genes (DEG) from each cluster. B, B cell; DC, dendritic cell; En, endothelial cell; Ep, epithelial cell; Fib, fibroblast; MΦ, macrophage; Mast, mast cell; Mon, monocyte; Neu, neutrophil; NK, natural killer cell; Plasma, plasma cell; SMC, smooth muscle cell; T cells.Click here for additional data file.Figure S2. The interaction between SMCs, Fibs, Ens, and MΦs and the representative ligands and receptors of these four cell types at each stage. B, B cell; DC, dendritic cell; En, endothelial cell; Ep, epithelial cell; Fib, fibroblast; MΦ, macrophage; Mast, mast cell; Mon, monocyte; Neu, neutrophil; NK, natural killer cell; Plasma, plasma cell; SMC, smooth muscle cell; T cells.Click here for additional data file.Figure S3. Enrichment analysis  of the top 30 ligands and receptors at each stage.Click here for additional data file.Figure S4. Focused analysis of Eps and Neus. (A) Enrichment analysis  of DEGs of Eps at each stage obtained by comparison with the sham group. (B) Three clusters of Eps were identified by reclustering. Pseudotime trajectory analysis of sub‐Eps by distribution (C), pseudotime (D), and cluster (E),(F) The deceased expression of inflammatory genes over time. (G),(H) Eps‐3 response to stimulation via the HIF‐1 signalling pathway. (I) Three clusters of Neus were identified by reclustering. Pseudotime trajectory analysis of sub‐Neus by distribution (J), pseudotime (K), group (L), and cluster (M),(N) Enrichment analysis  of DEGs of Neus‐2. DEG, differently expressed gene; Neu, neutrophil.Click here for additional data file.Figure S5. T cell diversity. (A) Enrichment analysis  of DEGs of T cells at each stage obtained by comparison with the sham group. (B) Eight clusters of T cells were identified by reclustering. (C) The top 10 DEGs of each T‐cell subset. Pseudotime trajectory analysis of T‐cell subsets by pseudotime (D), cluster (E), group (F), and distribution (G),(H) Enrichment analysis  of DEGs of proliferating T cells. DEG, differently expressed gene.Click here for additional data file.


These data are reported here according to FAIR data principles to enable users to further explore agriculture-nutrition linkages.The dataset comprises primary data for the concentration of 29 mineral micronutrients in cereal grains and up to 84 soil chemistry properties from GeoNutrition project surveys in Ethiopia and Malawi. The work provided insights on geospatial variation in the micronutrient concentration in staple crops, and the potential influencing soil factors. In Ethiopia, sampling was conducted in Amhara, Oromia, and Tigray regions, during the late-2017 and late-2018 harvest seasons. In Malawi, national-scale sampling was conducted during the April–June 2018 harvest season. The concentrations of micronutrients in grain were measured using inductively coupled plasma mass spectrometry (ICP-MS). Soil chemistry properties reported include soil pH; total soil nitrogen; total soil carbon (C); soil organic C; effective cation exchange capacity and exchangeable cations; a three-step sequential extraction scheme for the fractionation of sulfur and selenium; available phosphate; diethylenetriaminepentaacetic acid (DTPA)-extractable trace elements; extractable trace elements using 0.01 M Ca(NO  The risk of MNDs in populations can be informed by understanding the supply of micronutrients within food systems. This approach has typically been conducted at a national level, based on secondary interpretation of food consumption, expenditure or supply data from household surveys and food balance sheets5.Micronutrients are the vitamins and minerals that our bodies require in small amounts, and which are obtained from the food we eat. Micronutrient deficiencies (MNDs) among people remain a major global concern; more than 2 billion people are likely to be affected worldwide, with greater deficiency risks in sub-Saharan Africa than in most other regions8. Some of this variation is spatially correlated at distances of up to several hundred kilometres. What this means is that for people consuming food sourced locally, as is the case for many smallholder farming communities, the location of residence will be a major (sometimes the largest) influencing factor in determining the dietary intake of micronutrients from cereals7. Furthermore, for the micronutrient Se, there is strong evidence of linkages between soil and landscape features, cereal grain concentrations, and biomarkers of Se status in people10.Recent studies from Ethiopia and Malawi have reported substantial variation in the micronutrient concentration of the grains of staple cereal crops at subnational levels, including for calcium (Ca), iron (Fe), selenium (Se) and zinc (Zn)et al.7, Mossa et al.11, and Botoman et al.12. These data were obtained as part of ongoing work within two ‘GeoNutrition’ projects, funded primarily by the Bill & Melinda Gates Foundation (BMGF) and the UK Government’s Global Challenges Research Fund (GCRF). Soil and grain samples were collected in both countries using spatially balanced sampling designs, along with meta-data, with the informed consent of farmers.Here we report the wider set of primary data for cereal grains and soils from these studies in Ethiopia and Malawi Tables  and 2, wThe research work that generated these data involved metadata collection using semi-structured questionnaires, and sampling of cereal grain from farmers’ fields or grain stores and soils from the corresponding crop fields with the prior informed consent of the farmers. Farmers who participated in the survey received an information sheet 14. These constraints may introduce possible biases into predictions made at locations outside the designed sample frame, however, it would not otherwise have been possible to visit all the sample locations in the time available. Of a total land area of around 558,500 km2 in the three regions of Ethiopia, the total cropland mask represented 354,325 km2, of which 220,467 km2 was within 2.5 km of a OSM mapped road13. Because the sample frame was somewhat fragmented spatially the sample points were selected to achieve spatial balance and spread, the latter denoting spatial coverage15. A total of 1,825 sample sites were selected this way, and 175 of these were selected at random to be supplemented with a close pair site.Ethiopia’s Amhara, Oromia and Tigray regions were sampled Fig. . Target 16 land-use maps. The agricultural area used was defined as all raster cells that included the category of ‘cropland’ in their description  and its companion mobile app KoBoCollect via questionnaires  circular plot. This was centred as close as practical to the middle of the field unless this area was unrepresentative due to disease or crop damage. Five subsample points were located . The first point was at the centre of the plot. Two subsample points were then selected at locations on a line through the plot centre along the crop rows, and two more points on a line orthogonal to the first through the plot centre. Where possible, the central sampling location was fixed between crop rows, and the long axis of the sample array (with sample locations at 5.64 m and 4.89 m) was oriented in the direction of crop rows with the short axis perpendicular to the crop rows. A single soil subsample was collected at each of the five subsample points with a Dutch auger with a flight length of 0.15 m and diameter of 0.05 m. The auger was inserted vertically to the depth of one flight and the five subsamples were combined in a single Kraft© paper bag. Where a mature or ripe crop was still standing in the field, grain samples were taken close to each augering position by a different operator, to minimise further contamination by dust and soil. For maize, a single cob was taken at each of the five points. Maize kernels were stripped from around 50% of each cob lengthways and composited into a single sample envelope for each location. For small-grained crops, sufficient stalks were taken so that approximately 20–50% of the sample envelope was filled (dimensions 0.15 m × 0.22 m), with samples placed grain‐first into the sample bag and the stalks were twisted off the grain heads and discarded. If a crop was in field stacks, then a subsample, comprising five cobs for maize, or a representative sample for other crops was taken from each available stack, taking material from inside the stack to minimize contamination by dust and soil . If a crop was in a farmer’s store, it was considered an already-composited sample and a sample was taken while avoiding grain from the store floor if grain was loosely stored and avoiding grain with visible soil or dust contamination.Within a selected field, samples were taken from a 100 mWhole-grain samples were air-dried in their sample bags. Each sample was then ground in a domestic stainless-steel coffee grinder, which was wiped clean before use and after each sample with a non-abrasive cloth. All preparation was done away from sources of contamination by soil or by dust. A 20-g subsample of the ground material was then shipped to the University of Nottingham. Soil samples were oven-dried at 40 °C for 24–48 h depending on the moisture content of the soil. Preparation took place in a soil laboratory to avoid cross-contamination with grain samples. Plant material was removed from each soil sample, which was then disaggregated and sieved to pass 2 mm. This material was then coned and quartered to produce subsample splits. A 150-g subsample of soil was poured into a self-seal bag, labelled and shipped to the UK for analysis in the laboratories at Rothamsted Research and the University of Nottingham.All grain and soil analyses were conducted in numerical sequence of the sample ID at Rothamsted Research and the University of Nottingham, UK.3, trace analysis grade). Samples collected from the Amhara Region of Ethiopia in 2017 were microwave digested using a Multiwave 3000 48-vessel MF50 rotor  in 2 mL HNO3, 1 mL Milli-Q water  and 1 mL H2O2 at a power of 1400 W, temperature 140 °C, pressure of 2 MPa for 45 min. Samples collected in Malawi and Ethiopia in 2018–2019 were microwave digested in a Multiwave Pro with a 41HVT56 rotor and pressure-activated venting vessels made of modified polytetrafluoroethylene . The digestion was achieved using 6 mL of HNO3. at a power of 1,500 W, with 10 min heating to 140 °C, 20 min holding at 140 °C, and 15 min cooling to 55 °C. After digestion, the samples were made to 15 mL using Milli-Q water, then stored in capped tube at room temperature for ≈ 1 week until the chemical analysis. Prior to analysis by inductively coupled plasma mass spectrometry , samples were further diluted 1:5 with Milli-Q water.Elemental concentrations  when measured at a mass to charge ratio of 78 (m/z = 78) on ICP-MS. Consequently, samples collected in Malawi and Ethiopia were re-analysed for Se using QQQ-ICP-MS  with oxygen mass shifting of the Se peak at m/z = 80 to m/z 96.3)2 (pH_CaNO) suspension using a Mettler-Toledo AG pH meter  also with a temperature compensating electrode. Total C and N were determined by dry combustion19 using a Leco TruMac CN Combustion analyzer . Inorganic C was determined by Skalar Primacs Inorganic Carbon Analyser . Estimates of amorphous oxides and poorly crystalline oxides (EOxa) were determined following ammonium oxalate extraction20. Soil effective cation exchange capacity (eCEC) and exchangeable cations  were determined using one-step extraction21 with cobalt(III) hexamine chloride solution and analysis by inductively coupled plasma optical emission spectrometry . Available phosphorus (POlsen) was determined after extraction with sodium bicarbonate as described by Olsen22. Phosphate buffering index (PBI) was also determined, as an indicator of the soil’s ability to control changes in P concentration in the soil solution23.Unless otherwise specified, all analysis were performed on samples sieved to < 2 mm. Soil pH was measured in de-ionised water (pH_w) suspension (1:2.5 solid to solution ratio) using a Jenway 3540 meter , with a temperature-compensated combination pH electrode. Soil pH was also measured in the 0.01 M Ca(NOTot) in the soils were determined after aqua regia extraction24 of finely ground samples using ICP-OES  and ICP-MS.Quasi-total concentrations of major and trace elements  were determined by shaking c. 5 g soil with 10 mL of 0.005 M DTPA, 0.1 M triethanolamine (TEA) and 0.01 M CaCl2 at pH = 7.3 for 2 h on an end-over-end shaker25, followed by centrifugation (3500 rpm), filtration (0.22 µm) and analysis by ICP-MS . Soluble major and trace elements  were determined in the solution phase of soil suspensions in 0.01 M Ca(NO3)2 (1:10 soil: solution ratio) following equilibration for 4 days on an end-over-end shaker. The solutions were isolated by centrifugation and filtration (0.22 μm) prior to elemental analysis by ICP-MS (iCAP Q). For soil samples collected from the Amhara Region of Ethiopia in 2017, soluble major and trace elements (E_CaCl2) were also determined in the solution phase of soil suspension in 0.01 M CaCl2 (1:10 soil:solution ratio) and equilibration for 2 hours on end-over-end shaker followed by centrifuge and filtration prior to analysis by . Non-purgeable organic carbon (NPOC) was also determined in 0.01 M CaCl2 extraction using Chemical UV Oxidation Total Organic Carbon Analyser . Concentrations of the free ion activity of Zn were predicted for these samples (Amhara Region of Ethiopia) using the Windermere Humic Aqueous (WHAM) geochemical model as described in detail in Mossa et al.11.Elements extractable with DTPA  a ‘soluble’ fraction in 0.01 M KNO3, (ii) a ‘specifically adsorbed’ fraction in 0.016 M KH2PO4, and (iii) an organically bound fraction in 10% tetra methyl ammonium hydroxide (TMAH). It is important to note that none of the three fractions are likely to contain the analyte as a single species. For example, the ‘soluble’ Se fraction will typically include selenate, selenite and dissolved organic forms of Se. A mass of dried soil equivalent to ≈4.0 g, sieved to <2 mm, was weighed into a polyethylene centrifuge tube. After adding 20 mL of 0.01 M KNO3, the tubes were shaken for 2 h on an end-over-end shaker, then centrifuged at 3500 rpm for 30 min. A volume of 9 mL of supernatant was filtered (0.22 µm) using PTFE syringe filters, into tubes containing 1 mL of a mixture of 0.1 M KH2PO4 and 10% TMAH to preserve the samples before analysis. After removing the excess supernatant, the centrifuge tubes with wet soil pellets were weighed to account for carry over of 0.01 M KNO3 extract then 20 mL of 0.016 M KH2PO4 was added. The tubes were vortexed to disaggregate the soil pellet and then shaken for 1 h before centrifugation at 3500 rpm for 30 min. A volume of 9 mL of the supernatant was filtered to (0.22 µm), into a tube containing 1 mL of 10% TMAH. After removing excess supernatant, the tubes were weighed again before 10 mL of 10% TMAH was added. The tubes were vortexed to disaggregate the pellet, loosely capped and incubated at 70 °C for ∼16 h before centrifugation (3500 rpm for 30 min). Extracts (1 mL) were then diluted with 9 mL of ultrapure MQ water to give a final solution of 1% TMAH. Samples were analysed for S and Se using a QQQ-ICP-MS operated in oxygen cell mode with rhenium  and indium  as internal standards to correct for instrumental drift. Sulfur and Se were measured in mass-shift mode following reaction with oxygen to form the analyte ions SO+ (m/z 32 → 48), and SeO+ (m/z 80 → 96).This fractionation scheme was adapted from the one used by Mathers et al.11. Briefly, a mass of 2 g sieved, air–dried soil was equilibrated with 20 mL of 0.01 M Ca(NO3)2 for 24 h. The soil suspension was then spiked with a 70Zn isotopic tracer and further equilibrated for 72 h. To avoid acidification, the pH of the spike solution was adjusted to pH 4.0–4.5 using an ammonium acetate buffer immediately before use. Samples were centrifuged (3500 rpm for 15 minutes), filtered (0.22 µm) and the supernatant acidified to 2% HNO3. Isotopic analysis was carried out using ICP-MS (iCAP Q) operating in collision cell mode using He for kinetic energy discrimination (KED). Significant and variable interferences (soil derived 70Ge+ and (plasma generated) doubly charged 140Ce++) on 70Zn required correction and was achieved by analysing Ge and Ce standards alongside the samples and inferring the intensity  from the measured CPS ratio 72/70 for Ge standards and 70/140 for Ce standards. The interference from Ge produced a correction for 70Zn CPS ranging from 0.01–25% , while the correction resulted from Ce interference ranged from 0.03–88%   files of the worksheets for Ethiopia and for Malawi. The Ethiopian workbook contain six worksheets: ETH_CropSoilData_Raw, ETH_CropSoilData_NA, ETH_Crop_LOD_ByICPRun, CropElements, SoilProperties and Notes. The worksheets in the Malawian workbook are MWI_CropSoilData_Raw, MWI_CropSoilData_NA, Crop_LOD_ByICPRun, CropElements, SoilProperties, and Notes. The individual CSV filename is the same as the worksheet name in the workbooks. The ‘Notes’ worksheets in both Ethiopian and Malawian workbooks and CSV zipped folders provide greater detail about the data structure and contents of each worksheet and fields. It also provides details of how data from different worksheets may be linked using the various identifier fields . The dataset is accessible currently from the figshare data repositoryETH_CropSoilChemData and for Malawi MWI_CropSoilChemData. Due to the complexity in data recording and reporting, we have reported the data in two ways28. In each workbook, the first worksheet (Country_CropSoilData_Raw) contains raw data recorded from the analytical equipment and software used to process the data. To avoid blank cells, “NM” (not measured) is used here to indicate where data were not measured. In the second worksheet (Country_CropSoilData_NA) data below the LOD, including negative data values, and missing data are replaced by NA (not available).Workbook and spreadsheet names containing these data begin with the three-letter ISO country code for Ethiopia (ETH) and Malawi (MWI), as well as details about what is being stored. The workbook and CSV folders containing the cereal grain and soil properties data for Ethiopia is named FundingSource: The source of funding to carry out the research that generated these data. BMGF = Bill & Melinda Gates Foundation (INV-009129); GCRF = Global Challenges Research Fund (BB/P023126/1).ID: Data record (row) identification number (ID) for the dataset. These are unique IDs that can be used as a primary key to conduct separate analyses on cereal grain and soil chemistry properties data.Crop_ICP_Run: The ICP-MS (Inductively Coupled Plasma Mass Spectrometer) run number to determine the crop elemental LOD for the raw cereal grain dataset using the LOD data in the ETH_Crop_LOD_ByICPRun worksheet.Latitude: In decimal degrees, WGS84 datum, coordinate reference system EPSG:4326.Longitude: In decimal degrees, WGS84 datum, coordinate reference system EPSG:4326.Altitude: The altitude in meters above sea level.LocationPrecision: The horizontal and vertical positional accuracy, in meters.SamplingStart: Start of sampling date and time stamp, UTC + 3. This is the date and time automatically recorded by the KoBoCollect app when the enumerators start recording cereal grain and soil sample information in the field. The start sampling date for the ID ETH1219 was incorrect and removed.SamplingEnd: End of sampling date and time stamp, UTC + 3. This is the date and time automatically recorded by the KoBoCollect app when the enumerators either save or submit the sample metadata recording questionnaire.Crop: Type of crop from which cereal grains were sampled.GrainSource: Source of cereal grain .Site: Whether the site is “main” or “close pair”. Please see Gashu et al.7 for further details on this.The first 12 fields in the raw and cleaned Ethiopian cereal grain and soil chemistry data worksheets are auxiliary and field data for the cereal grain-soil sample pairs (records). These are described as follows:Crop_ICP_Run: The ICP-MS run number to determine the crop elemental LOD for the raw cereal grain dataset except Se using the data in the Crop_LOD_ByICPRun worksheet.Crop_ICP_Run_Se: The ICP-MS run number to determine the cereal grain Se concentration LOD for the raw cereal grain dataset using the LOD data in the Crop_LOD_ByICPRun worksheet.Identical to the Ethiopian dataset, the first 12 fields in the raw and cleaned Malawian cereal grain and soil chemistry data worksheets are auxiliary and field data for the cereal grain-soil sample pairs (records). The data details which vary from above are as follows:_grain suffix after the elemental symbol.In both Ethiopia and Malawi datasets, the cereal grain elemental concentration for 29 elements (columns or data fields) was reported   in approximately every fourth digestion batch. An LOD was calculated as 3 times the standard deviation of 10–14 blanks assuming a hypothetical mass of 0.2 g of sample. The data for percentage recoveries of CRM is shown in Table Oxa, and in-house standards Leuven and Woburn (Rothamsted Research) were used. No CRM was available for eCEC, and in-house standard Leuven soil (Rothamsted Research) was used. No CRM was available for POlsen, and in-house Hoosfield Plot 714 and Plot 444 (Rothamsted Research) were used, as well as WEPAL proficiency testing. No CRM was available for PBI, and in-house standards Leuven and Woburn were used.Quality control protocols included two operational blanks in each digestion batch and two samples of an internal or external reference material. Ten percent of samples were repeated through both the extraction/digestion and analysis stages, and the results were expected to fall within ± 5%, if not, the sample batch was repeated apart from results near to the LOD (which occurs with some samples that have low trace element concentrations). All instruments used were drift-corrected during each run as part of the calibration procedure. In each batch of soil pH_w determination, 10% unknowns were repeated and duplicate samples of in-house standard Broadbalk 082 (Rothamsted Research) were included for quality control, and we participated in the Wageningen Evaluating Programmes for Analytical Laboratories (WEPAL) inter-laboratory proficiency testing for soil pH. Total C and N analysis was set up with LECO Soil standard LCRM 502–697, lot 1000, and validated over time using proficiency WEPAL testing. No CRM was available for inorganic C, so in-house standards Summerdells, Broadbalk, Sacrewell (Rothamsted Research) were used in each batch, as well as WEPAL proficiency testing. No CRM was available for EFor quasi-total concentrations, 10% blanks (extraction solution) were included to check for potential contamination of analytes of interests. Batches were rejected if the blanks showed apparent signals greater than 3x SD of the background signal of the instrument (limit of detection). WEPAL soil reference material ISE 962 were used to check analytical precision of every batch  were preserved in a mixture of 0.01 M KH2PO4 and 1% TMAH.For the three-step sequential extraction scheme of S and Se in soil, a preliminary experiment was conducted to test the temporal stability of the analytes in a 1% TMAH matrix; analytes were measured after the extraction was completed and then again after storage for 4 days. The results showed that the agreement between the two measurements was quite poor for the KNO Se Fig. , whereasThe reproducibility of the isotopic dilution analysis was tested by repeating c. 10% of the samples with precision determined by calculating the relative standard deviation  of the duplicates. The average (error) RSD was 6.34% (sd = 7.68%), and 80% of the repeated samples had values of RSD < 10% . When data values ≤ LOD are excluded, the median Se concentration in maize is 0.02448 mg kg−1 (n = 1199). Users wanting to use descriptive data from summary tables should be aware of this, and select data in formats which are appropriate to their purpose. This note also applies to some of the soil chemistry summary table where there are many negative concentrations in the data.Cereal grain elemental concentration data presented in Supplementary File Users should be aware that some soil properties were not measured in both the GCRF-funded and BMGF-funded rows in the Ethiopian dataset. The following soil chemistry data were not measured in the GCRF-funded records: Cd_DTPA, Co_DTPA, Cu_DTPA, Fe_DTPA, Mn_DTPA, Ni_DTPA and Pb_DTPA. Similarly, the following soil chemistry properties were not measured in BMGF- funded records: Co_CaCl2, Cu_CaCl, Fe_CaCl, K_CaCl, Mg_CaCl, Mn_CaCl, Mo_CaCl, Na_CaCl, Ni_CaCl, NOPC, P_CaCl, Se_CaCl, Zn_CaCl and Zn_FIA.Users wanting to carry out any geospatial analyses using the latitude and longitude fields in these data should ensure that the spatial  accuracy reported meets the requirements of their analyses.Supplementary file 3Supplementary file 4Supplementary file 5Supplementary file 6ETH_CropSoilData_RawETH_CropSoilData_NAETH_Crop_LOD_ByICPRunETH_CropElementsETH_SoilPropertiesETH_NotesMWI_CropSoilData_RawMWI_CropSoilData_NAMWI_Crop_LOD_ByICPRunSupplementary file 1Supplementary file 2MWI_CropElementsMWI_Notes


Staphylococcus aureus antibody responses, patient characteristics including demographics and standard clinical tests. This nomogram was formally tested in a prospective cohort study comparing 303 hospitalized patients with culture‐confirmed S. aureus infection compared with a cohort of 223 healthy screened preoperative patients. Serum anti‐S. aureus antibody responses, standard of care clinical tests, and patient demographic data were utilized to perform multivariate logistic regression analysis to quantify the presence of infection and adverse outcome using odds ratios (OR) and to assess predictive ability via area under the ROC curve (AUC). At enrollment, high anti‐S. aureus IgG titers were predictive of infection. Remarkably, low serum albumin was found to be significantly associated with infection  and this finding was surprisingly higher than BMI or HbA1c‐associations. Combining all risk factors in the nomogram yielded a diagnostic AUC of 0.949 for predicting S. aureus infection. Our results indicate that a serum‐based multi‐parametric nomogram can be useful in diagnosing S. aureus infections, and importantly, malnourishment is significantly associated with these infections.Postsurgical deep musculoskeletal infections are a major clinical problem in Orthopaedic Surgery. A serum‐based nomogram, which can objectively risk‐stratify patients, and aid surgeons in delineating infection risk associated with orthopedic surgical interventions, would be immensely helpful. Here, we constructed a multi‐parametric nomogram based on serum anti‐ Antibody titers against these S. aureus antigens can be measured by immunoassay of a patient's sera.S. aureus is to obtain a fluid/tissue sample followed by microbial culture, which can be a lengthy process sometimes limited by culture‐negative results. There is no currently accepted diagnostic blood test to confirm infection with S. aureus. Such a diagnostic test would be invaluable to surgeons, as it would eliminate the need to perform invasive procedures for diagnosing deep infections. Additionally, the ability to risk‐stratify patients based upon laboratory values and patient characteristics could help surgeons identify patients at increased infection risk associated with orthopedic surgery.We have previously identified multiple immunodominant Staphylococcus aureus infection. By measuring antibody levels in individuals infected with S. aureus, we can quantify the humoral immune response to S. aureus in individuals. We hope to create a combined nomogram to predict infection risk patients undergoing orthopedic procedures. The aims of our study are (1) to analyze and compare anti‐S. aureus humoral immune responses in patients with S. aureus infections to individuals without infection, by measuring their antibodies. (2) To produce a multiparametric nomogram based upon host factors and anti‐S. aureus antibodies that would give a risk‐adjustment for S. aureus infection in patients undergoing orthopedic procedures.We hypothesize that host factors, notably high BMI, and hemoglobin A1c, contribute to the risk of developing a 22.1S. aureus . For the control group, inclusion criteria included healthy, infection‐free patients ≥18 years old undergoing elective orthopedic surgery. We excluded prisoners, patients undergoing active cancer treatment, and patients with a history of solid organ transplant on immunosuppressive medications. Patient demographic data  and laboratory data  were collected. For the patients with infection, infection location , and resistance or sensitivity of the cultured S. aureus to methicillin was collected. All samples were collected before December 2019, before the onset of the COVID‐19 pandemic to avoid possible confounding effects of COVID‐19 on the immune system.This Level 2 prospective cohort study was approved by the University Institutional Review Board. Inclusion criteria included hospitalized patients ≥18 years old with culture‐confirmed 3Outcomes were categorized as good, adverse, or unknown. A good outcome was defined as “infection control.” An adverse outcome was defined as death, persistent infection despite treatment, amputation, or joint fusion. Outcomes were unknown if there was no documented follow‐up at one year.4S. aureus IgG antibodies were measured using a custom multiplex Luminex assay following the methodology previously reported.S. aureus antigens were measured; three iron‐regulated surface determinant (Isd) proteins: , three secretory proteins: chemotaxis inhibitory protein from S. aureus (CHIPS), α‐hemolysin (Hla), and staphylococcal complement inhibitor (SCIN), and two cell wall autolysin (Atl) domains: amidase (Amd) and glucosaminidase (Gmd). Serum IgG titers are reported as median fluorescent intensity (MFI) units for each antigen at a constant serum dilution level.Serum anti‐As in previous studies, each biotinylated antigen was coupled at a density of 50 pmol/million beads to distinct bead regions of avidin‐coated magnetic beads . Fifty microliters containing 1000 coupled beads were then mixed with 50 µl of 1:5000 diluted sera per well yielding 100 µl of 1:10,000 diluted sera with 1000 beads per well and were incubated for two hours while shaking on a microplate shaker at room temperature. Afterward, a wash step removed unbound sera, 100 µl of 1:500 diluted phycoerythrin‐conjugated goat anti‐human IgG reagent  was added as the detection antibody and the plate was incubated for 1 h while shaking at room temperature. The plate was then washed to remove unbound detection antibodies and 130 µl of PBST‐BSA  was added to each well. Finally, the beads were resuspended by 2 min of shaking and mixing by pipette before measurement of IgG bound to each antigen via Luminex .5S. aureus IgG titers were categorized based on data quartiles as Low (below the first quartile), Intermediate (between the first and third quartiles), and High (above the third quartile). Infection rates and adverse outcome rates were compared using Fisher's exact test for High versus Low and using Cochran–Armitage exact trend tests across Low, Intermediate, and High. The odds of infection and adverse outcome were modeled using univariate and multivariate logistic regression models with risk characterized using odds ratios (OR) with 95% confidence intervals and p‐values. Models treating IgG titers as continuous predictors utilized base‐10 log‐transformations to reduce skewness and improve model fit, resulting in ORs per 10‐fold increase in antibody titers values. For simplicity and clinical relevance, multivariate models used predictors that were dichotomized based on the category of greatest risk . Clinical and serologic risk factors were assessed for their predictive ability in discriminating infection and adverse outcome status using receiver operating characteristic (ROC) curve analysis, with overall prediction accuracy summarized by the area under the ROC curve (AUC). All analyses were performed using SAS version 9.4 and R version 3.5.1. A p‐value less than 0.05 was considered significant.Patient characteristics were compared between the infected and uninfected patients, with further categorization based on body mass index, age, sex, smoking status, hemoglobin A1c, and albumin. Anti‐66.1S. aureus‐infected patients were collected. Demographic and laboratory data, infection type, sensitivity to methicillin, and clinical outcomes for infection patients are shown in Table In total, 223 control specimens from healthy individuals and 303 specimens from 6.2S. aureus antigens in our custom Luminex immunoassay. Interestingly, we observed that high levels of serum IgG titers were associated with the presence of infection (Table p < 0.0001) . When combining clinical and serologic patient factors, the area under the curve was 0.949, which suggests a high predictive value for S. aureus infection.The sera for all 526 patients were tested for antibody responses against these aforementioned on Table . When co1) Table . A multi1) Table . Remarka6.3p < 0.01) and 2.936  respectively. With antigens and risk factors combined, Hla still predicted adverse outcomes with an odds ratio of 5.946  and low albumin indicated adverse outcomes with an odds ratio of 2.678 .High levels of all antigens were also predictive of adverse outcome Table . Low alb6.4With an increasing number of risk factors, the odds ratio of infection increased Table . The sam7S. aureus and those individuals not infected, by measuring their anti‐staphylococcal antibodies. We hypothesized that host factors, notably high BMI and Hemoglobin A1c, contribute to S. aureus infections. We were able to quantify the immune response to S. aureus infections, demonstrating that high levels of all antigens tested increased odds of infection. Additionally, these data show the tremendous increase in infection risk with low albumin. This suggests the significant role that malnourishment plays in susceptibility to S. aureus infection. High hemoglobin A1c did contribute, but high BMI did not. By combing all clinical and serologic host factors, we were able to achieve an AUC of 0.949 for predicting S. aureus infection.The primary aim of this study was to analyze and compare the immune response of patients infected with S. aureus infection, but high BMI was not associated with increased infection risk in this sample of patients. Surprisingly, low albumin had a more significant impact on infection risk with an OR of 200 when comparing low versus high albumin. Thus, we disproved our hypothesis.High hemoglobin A1c does increase the risk of S. aureus infection for patients undergoing orthopedic procedures. By combining all host factors, we were able to achieve a high AUC of 0.949 for identifying S. aureus infections.The secondary aim of this study was to produce a nomogram based upon host factors that would give a risk adjustment for S. aureus infection has been previously studied. Our previous studies on serum samples obtained from patients with confirmed S. aureus bone infections  or outcome data were available for every patient.There are several strengths of our study. It is prospective with large sample size and uses both clinical demographic data with quantifiable humoral immune response measurements to analyze 8S. aureus infection and adverse outcome—more so than high BMI or hemoglobin A1c. By combining all host risk factors, we were able to achieve an AUC of ∼0.95 for predicting S. aureus infection.Of the host risk factors evaluated, low albumin was most strongly associated with Michael P. Campbell: project design, sample collection, data analysis, interpretation, drafting manuscript. Makinzie D. Mott; project design, sample collection, sample analysis. John R. Owen: project design, sample collection, sample analysis, statistical analysis. Julie E. Reznicek: project design, sample identification, and collection. Christopher A. Beck: complex data analysis and interpretation, drafting manuscript. Gowrishankar Muthukrishnan: data analysis and interpretation, drafting manuscript. Gregory G. Golladay: project design, sample collection. Stephen L. Kates: project design, funding, data analysis and interpretation, drafting manuscript.


Interstitial cystitis/bladder pain syndrome (IC/BPS) has a significant impact on quality of life, but the etiopathogenesis remains largely unknown. The bladder microenvironment of patients with IC/BPS to obtain biological evidence supporting diagnosis and novel therapy is systematically characterized. Single‐cell RNA sequencing (scRNA‐seq) and image mass cytometry (IMC) are applied to bladder biopsies of the IC/BPS cohort. A total of 42 distinct cell clusters are identified from different groups. The increased hyperactivated Th1‐biased response, but not Th2‐biased response, and decreased immunosuppressive Treg are elucidated in the bladder microenvironment of non‐Hunner‐type IC (NHIC)/Hunner‐type IC (HIC). M2/M2‐like macrophage extends in the HIC and M1‐like macrophage extends in NHIC, all of which secrete a range of chemokines with different pattern. The pro‐inflammatory mediators, TNF‐α, produced by tissue‐resident macrophages and IL6, by the inflammatory fibroblasts are identified as key mediators of IC/BPS pathogenesis. Additionally, a regulatory network between different cell types is observed as a shift from structural cell communication in unaffected normal bladder to a Macrophage‐Endothelial‐dominated interactome in NHIC/HIC. The results demonstrate the high heterogeneity in NHIC/HIC, and provide an essential resource for diagnosis, and treatment of IC/BPS in the future by highlighting the importance of the microenvironment of bladder mucosa. Single‐cell RNA sequencing analyses and imaging mass cytometry reveal increased hyperactivated Th1‐biased response and decreased immunosuppressive Treg in the IC/BPS. Pro‐inflammatory cytokines and TNF‐α from macrophages and IL6 from fibroblasts are identified as key mediators of IC/BPS pathogenesis. This study provides an essential resource for diagnosis and treatment of IC/BPS by highlighting the importance of the microenvironment of bladder mucosa. The etiology of IC/BPS is still unknown. The human bladder mucosa is composed of a single layer of urothelial cells and multiple types of immune cells. The urothelial barrier prevents solutes and bacteria from entering the urothelium to activate immune cells. Changes in a variety of immune cells including plasma cells and mast cells have been observed and are believed to be associated with IC/BPS development. Pro‐inflammatory cytokines were also identified in the urine of IC/BPS patients. Thus, immunologic dysfunction may be one of the possible causes of IC/BPS. Compositional and functional alterations of the structural cells  can be seen in the bladder tissue of patients with IC/BPS. On the other side, IC/BPS is a highly heterogeneous disease. The two pathological subtypes, NHIC/HIC, show different clinical features in many aspects. It is unclear whether HIC represents a separate disease process compared to NHIC. Determining the abnormal pathways in NHIC/HIC could help to identify whether HIC is part of the NHIC or a unique disease.Interstitial cystitis/bladder pain syndrome (IC/BPS), a chronic bladder disease with an increasing incidence, affects millions of individuals across the world.1 Accoα agents are under development. However, the currently available treatments often fail or become less effective over time. Defining key cellular subsets and their activation states in IC/BPS is also a critical step for developing novel diagnosis methods and defining new therapeutic targets.Several treatments are available for IC/BPS, including steroid injections, immunosuppressive drugs, and novel medications such as anti‐TNFelopment.9 Howe In our current study, we performed scRNA and IMC to profile cells from five patients with NHIC/HIC and two from unaffected control (UC) bladder tissues to explore the molecular mechanisms of IC/BPS. It was found that the common pathogenesis features of IC/BPS included inflammatory fibroblasts, activated CD4+ T cells, neutrophils migration and activation of autoimmune‐associated B cells. Also, we conducted a comprehensive transcriptomic analysis for novel candidate disease biomarkers and dissected the intercellular crosstalk between structural and inflammatory cells, which might shed a light on the treatment strategies for IC/BPS.Single‐cell RNA sequencing (scRNA) and image mass cytometry (IMC) techniques offer an opportunity to identify disease‐associated cell subsets in human tissues at high resolution in an unbiased fashion.10 In 22.1Figure  was applied to detect stressed cells. About 10% of the total cells were removed due to a high stress signature  plot , CD8+ T cells , myeloid cells , B cells , endothelial cells , fibroblasts , and epithelial cells . Two cell clusters expressed canonical markers from different cell types, and they were considered doublets , regulatory T cells , two NK cells (NKG7+) and three double negative T (DNT) cells  remarkably decreased in patients with NHIC (0.417)/HIC (0.814) than in UC , GZMK+GZMB+ (C17 and C27), and GZMB+ cytotoxic T lymphocytes (C21). In addition, we also identified a CD8+ T population (C16) which expressed IL1B suggestive of an effector phenotype . IMC confirmed the presence of activated CD4+ cells close to urothelium, consistent with the results of scRNA‐seq in the HIC group  for gene expression from UC and NHIC/HIC patients was used to calculate the signature scores from bulk samples  and their putative target genes across conditions revealed that genes of IRF family , a group of transcriptional regulators of IFNs and IFN‐inducible genes involved in the control of T cell differentiation during inflammation, were the most specific regulons associated with the NHIC group , naïve neutrophils , mast cells (KIT+/TPSAB1+), 3 tissue‐resident macrophages , and DCs (HLA‐DRA+/CCR7+). The pro‐inflammatory neutrophils (C40 and C30) were mainly present in the HIC (14.8%) and NHIC (72.9%). Aged neutrophils (C40) highly expressed pro‐inflammatory cytokine oncostatin M  expressed markers of DCs (CD1C and CLEC10A); C1QA+ TRMs (C24) highly expressed complement pathway activators (C1QA/C1QB) and markers of M2 macrophage (SELENOP); and IL1B+ TRMs (C29) expressed IL1B, CCL3, and CXCL3. TRMs from C15 and C29 were highly expressed the pro‐inflammatory cytokine, TNF‐α, that mediate tissue pathology in rheumatoid arthritis and IBD. We indeed observed significant enrichment of the TNF‐alpha signaling via nuclear factor‐κB (NF‐κB) pathway in these two groups. Moreover, IMC analysis also identified that there were more TRMs with high levels of TNF in the NHIC/HIC than UC  and NRP2; State 3, which includes IFITM2, TXNIP, PTPRC, and JUNB, was enriched in the infection response  RGS5+ fibroblasts, which have characteristics similar to vascular smooth muscle cells with expressions of ACTA2, TAGLN, and MTY11; and c) inflammatory fibroblasts, which strongly express chemokines including CCL2, CXCL2, CXCL8, and IL6, suggesting these cells were in an active cytokine‐producing state and similar to cancer‐associated fibroblasts described in many studies  PDy studies22  cells. C37, which identified naive B cells enriched in the NHIC with strongly expressed IGHD, which was absent from HIC and represented a major B‐cell subset in NHIC; C39, which identifies germinal center B (GCB) cells with expressed BCL‐6 and MKI67 and C42, which was an IgM+IgD− B‐cell subset without CD27 expression and assigned as double negative B cell. Plasma significantly expanded in the NHIC/HIC groups compared to UC  cells.23 C37C Figure . In contC Figure . Heavy c2.6Figure p < 0.05). The shift in cellular phenotypes in tissue may be due to the local production of pro‐inflammatory cytokines in our NHIC/HIC cohorts. CellChat detected 9503 ligand–receptor pairs among all cell clusters from each group, which were further categorized into 102 signaling pathways  damage. Tregs are central to immune homeostasis, and their phenotypic heterogeneity reflects the diverse environments and target cells that they regulate. According to a previous study, in the presence of IL6, Tregs lost their suppressive capacity and was converted to unstable Tregs to fighting pathogenic disease. Our data demonstrated that IL6‐stimulated Tregs from NHIC/HIC lost the characteristics of immunosuppression. Failure of such regulatory mechanisms can also lead to a state of chronic inflammation.Herein, we elucidated gene transcription profiles for populations of T cells, including cytotoxic  The immune response during bladder cystitis is characterized by granulocyte infiltration in the bladder mucosa. In our study, the majority of M1 macrophages were presented in the NHIC samples, while M2 were expanded in the HIC group, suggesting macrophages are pro‐inflammatory in NHIC but anti‐inflammatory in HIC. Functional analysis also showed macrophages were another major player in antigen presentation. As there is a strong link between TRMs and neutrophils migration, chemokines released by macrophages increases the production and recruitment of neutrophils and inhibits the apoptosis of neutrophils, thereby extending their life span in bladder. In particular, fibroblasts formed a positive feedback to maintain an inflammatory environment through the expression of chemokine ligand. As in other autoimmune diseases, inflammatory TRMs that dominate the bladder mucosal milieu have high expressions of the pro‐inflammatory cytokines including IL‐1β and TNF‐α.Myeloid cells are major innate immune phagocytes that constitute the first line of defense against infections in the bladder.18a Th IL6 has been repeatedly linked to IC/BPS, and blockade of IL6 has been shown to alleviate inflammation in preclinical studies of other autoimmune diseases. However, the source of IL6 was typically thought to derive from innate lymphocytes such as macrophages. In our study, IL6 is secreted by activated inflammatory fibroblasts, which was validated by scRNA‐seq and IMC. IL6 is also a key cytokine involved in B cell activation, which explains why plasma cells dramatically increase in IC/BPS patients. Also in our current study, we observed a significantly expanding plasma cells and detected both IgD+ and switched B cell populations, which may contribute to the pathogenesis of IC/BPS. The gene expression of B cell markers suggested a balance between germinal center and plasma cell differentiation within the bladder. This observation may serve as a starting point for identifying candidate mechanisms, diagnostic markers, or therapeutics for IC/BPS.Our study also highlighted inflammatory fibroblasts as a potential therapeutic target in NHIC/HIC samples. Inflammatory fibroblasts are a major source of pro‐inflammatory cytokines such as IL6.22b IL In our current study, endothelial cells had significant interactions with TRMs. By analyzing the dominant ligand–receptor pairs that mediate the organizations of bladder microenvironment in NHIC/HIC samples, we found that CXCL8‐ACKR1/CXCL2/3‐ACKR1 were ranked top in mediating interactions among endothelial cells and macrophages. Modulating macrophage and/or endothelial functions by interfering with signaling pathways offers new perspectives for therapeutic strategies. This also strongly supports the possibility that TNF‐α signaling alone cannot fully explain the pathogenic cellular organization we identified in the bladder in terms of TNF‐α blockade, additional inflammatory macrophage‐derived stimulatory mediators such as IL‐1b and OSM are sufficient to trigger pathogenic stromal activation. More interestingly, the treatment of IC/BPS with phosphodiesterase 4 (PDE4) which can inhibit TNF‐α and IL‐6 simultaneously will be an attractive option in our future study.Finally, our ligand–receptor analysis revealed that interactions between members of the bladder microenvironment were complex and involved many signaling pathways. The comprehensive analysis of the cell–cell interactions in NHIC/HIC identifies dominance of TRMs interacting with structural cells. Several studies have highlighted the interconnection between endothelial cells and macrophages in these groups at the level of growth factor and cytokine signaling.24 In In summary, we identified the expression profiles of different cells in bladder tissue from NHIC/HIC patients and confirmed the characteristics of different cell types. This cell atlas provides deep insight into the molecular mechanisms of IC/BPS, signaling receptors, and gene data set and will aid in future studies on the relationships among cell types. It also facilitated discovery of novel biomarker for IC/BPS diagnosis and new treatment.4n = 2) and NHIC (n = 3). In addition, two female patients with non‐muscle invasive bladder cancer (NMIBC) were enrolled as UC. In order to validate the result of scRNA‐seq, additional three patients were enrolled for the IMC analysis. IC/BPS diagnosis and subtyping were performed in accordance with the East Asian clinical guidelines and the ESSIC criteria. The clinical characteristics of IC/BPS were further assessed, including the IPSS , OSS (O'Leary‐Saint Score), OABSS (Overactive Bladder Symptoms Score), MBC , ICSI , and ICPI  in IC/BPS cases. Additional information about these patients are summarized in Table IC/BPS patients were subclassified into two subtypes, HIC , aiming for an estimated 10 000 cells per library. Libraries were sequenced on an Illumina HiSeq X‐Ten, and mapped to the human genome (build Hg38) using CellRanger (version: 3.1.0). which assesses whether the RNA content associated with a cell barcode is significantly distinct from the ambient background RNA present within each sample. Cells with p  < 0.05 (Benjamini‐Hochberg‐corrected) were considered for further analysis. Filtered cells were converted to a Seurat object using the Seurat (version 3.1.1). Thus, all cells that had either fewer than 200 UMIs, over 6000, or over 15% UMIs derived from mitochondrial genome were removed. From the remaining 44 490 cells, gene expression matrices were normalized to total cellular read count and mitochondrial read count. To reduce dimensionality of this dataset, the variably expressed genes were normalized by SCTransform, and then summarized using t‐SNE dimensionality reduction using the default settings. The Clustree package was used to visualize how clusters changed with increasing resolution; also an appropriate resolution (resolution 1.3) was selected. To assess the quality of cell cluster identified in this study ROGUE scoring, an entropy‐based metric for assessing the purity of single cell populations, was used. To assign clusters and individual cells to cell types, the SingleR and clustifyr and default parameters with Immgen as the reference dataset were used. With the integrated data, NHIC/HIC data as a benign reference with inferCNV (https://bioconductor.org/packages/infercnv/) were taken. CNVs were estimated based on two major steps: initial CNV (CNVi) calculation and final CNV (CNVf) estimation.To call real cells from empty droplets, the emptyDrops function from R package DropletUtils was used,27 whip < 0.05 were used as the cut‐off criteria. Gene Set Variation Analysis (GSVA) was performed to determine whether an a priori defined set of genes showed statistically significant, concordant differences on DEGs with MSigDB collections, including hallmark, KEGG (C2), GO (C5) and immunologic signature gene sets (C7). Gene Ontology (GO) analysis for bulk sequence was performed by using the R package clusterProfiler.Based on the normalized data, the FindMarkers function was used to identify differentially expressed genes (DEGs). |FoldChange| > 1.5 and adjusted s on DEGs32 wit Briefly, first Monocle2 was used to estimate size factors, dispersion, and differential gene expression of the target cells, and then the top 1000 most differentially expressed genes were used to order cells in pseudotime. Then the branch with the cells from control group as the root state of the tree were defined. The BEAM feature of Monocle2 was used to define genes that showed significant divergent expression across each branch point in the pseudotime analysis, using default parameters.The Monocle2 was used to reconstruct the divergence of cell lineages/trajectories in the analysis.35 BriIndividual cells were scored for their expressions of gene signatures representing certain biological functions. The AddModuleScore function in Seurat was used to implement the scoring with default settings. The functional signatures were derived from the GO database. Cytotoxicity‐associated genes , co‐stimulation , and six well‐defined exhaustion markers  were used to define the cytotoxicity, stimulation, and exhaustion score, respectively. Apoptosis score was measured by the upregulation of the integrated proapoptotic pathways and downregulation of pro‐survival gene expression. It estimates the Macrophage Polarization Index (MPI) and Activation induced Macrophage Differentiation Index (AMDI) based on input RNA‐seq count data; both scores have a range from −50 to 50. A higher MPI value indicates greater pro‐inflammatory features and a higher AMDI value indicates greater maturity. In the current analysis, zero was used as a threshold to define “pre‐activation” or “M0” cells , “M1‐transitional” or “M1 pre‐activation” cells , “M2‐like” cells , and “M1‐like” cells .For macrophage polarization analysis, MacSpectrum was used, a single‐cell RNA‐sequencing based gene enrichment tool to infer the macrophage activity of the immune population.36 It  In the current study, the motif databases that allow the use of RcisTarget (version: 0.99) were used. The 20 003 motifs (Position Weight Matrices) in the cisTarget database (v8) on the regulatory regions up to 5 KB upstream of each gene and within its introns were scored. The gene‐motif rankings were then built taking the best‐scoring region for each motif and gene.SCENIC was run as described in the literature.37 In  CellChat is a manual curated repository of ligands, receptors, and their interactions, integrated with a new statistical framework for inferring cell–cell communication networks from single cell transcriptome data.To enable a systematic analysis of cell–cell communication molecules, CellChat was used.38 Celhttp://velocyto.org, version 0.6). Briefly, spliced/unspliced reads were annotated by velocyto.py with CellRanger, which generated BAM files and an accompanying GTF; then they were saved in .loom files. After the cells in the bottom 0.5% of the total unspliced transcript count were filtered out, genes were removed according to an average spliced variant expression of 0.2 or an average unspliced variant expression of 0.05 in at least one cluster. Lastly, the velocity vector arrows were projected onto the t‐SNE plot which was obtained in Seurat.RNA velocity analysis was performed using the velocyto.R program (ion 0.6).39 Brihttps://github.com/BodenmillerGroup/ImcSegmentationPipeline). In brief, individual cells and regions were segmented using a combination of Ilastik v.1.3 and CellProfiler v.4.6. Ilastik was used to generate a probability map by classifying pixels on the basis of a combination of antibody stains to identify membranes and nuclei. Probability maps were then segmented into single‐cell object masks using CellProfiler. Single‐cell segmentation masks and TIFF images of the 32 channels were overlaid and the mean expression levels of markers and spatial features of single cells were extracted using the histoCAT.The antibody panel includes lymphocyte types, cytokine expression, lymphocyte activation, vascular and spatial structure of tissue cells. Descriptions of antibodies, isotope tags, clones and concentrations used for staining can be found in Table tik v.1.340 and DEGs analysis was performed with Limma to evaluate the relative abundance of each cell type identified in the present study. Gene signatures from scRNA‐seq were evaluated with GSVA.IC/BPS samples (GSE11783) in the GEO databases were downloaded and normalized.16 DEGp value after false discovery correction procedures < 0.05 were considered as statistically significant. For the experimental data, graphics were produced using ggplot2. Detailed descriptions of statistical tests are specified in Section For the scRNA‐seq data, statistical analyses and graphics production were performed using R (v3.6.3) and python. In general, non‐parametric tests were used and The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Over 80% of wastewater worldwide is released into the environment without proper treatment. Whilst environmental pollution continues to intensify due to the increase in the number of polluting industries, conventional techniques employed to clean the environment are poorly effective and are expensive. MXenes are a new class of 2D materials that have received a lot of attention for an extensive range of applications due to their tuneable interlayer spacing and tailorable surface chemistry. Several MXene‐based nanomaterials with remarkable properties have been proposed, synthesized, and used in environmental remediation applications. In this work, a comprehensive review of the state‐of‐the‐art research progress on the promising potential of surface functionalized MXenes as photocatalysts, adsorbents, and membranes for wastewater treatment is presented. The sources, composition, and effects of wastewater on human health and the environment are displayed. Furthermore, the synthesis, surface functionalization, and characterization techniques of merit used in the study of MXenes are discussed, detailing the effects of a range of factors  on the synthesis, surface functionalization, and performance of the resulting MXenes. Finally, the limits of MXenes and MXene‐based materials as well as their potential future research directions, especially for wastewater treatment applications are highlighted. 2D nanomaterials, especially MXenes have recently seen exciting developments in research and development, and their application in wastewater treatment has drawn wider attention. This review examines state‐of‐the‐art strategies employed in synthesis, functionalization, and characterization of surface functionalized MXenes. Furthermore, engineering strategies like composition design, and how surface protection influence their properties as adsorbents, photocatalysts, and membranes in wastewater treatment are discussed. The UN estimate of wastewater produced annually is approaching 1500 km3, almost six times more water than that exists collectively in all the rivers of the world. The consequences are alarming, with 4 billion cases of annually, resulting in more than 2 million deaths of children under age five. There has also been a significant decline in biological species in inland waters where 24% of mammals and 12% of birds connected to non‐coastal waters are considered endangered. The mean nitrate contaminant levels have risen ≈36% in global waterways since 1990 with the most drastic growth observed in the Eastern Mediterranean and Africa. Furthermore, some newer wastewater treatments have been identified to contribute remarkably to Greenhouse Gas (GHG) emissions in the form of nitrous oxide and methane—contributing threefold to the emissions of conventional wastewater treatment. In addition to these challenges, over 80% of wastewater worldwide, especially in developing countries, is released untreated into the environment.Water is the lifeline of ecosystems, vital to human health and well‐being, and a requisite for economic prosperity.1 Howe The challenges associated with these methods are incomplete removal of impurities, high energy inefficiency, production of toxic sludge, inefficient and sensitive operating methods. These bottlenecks have, however, paved the way for the heightened interest in the unique properties of 2D nanomaterials within the scientific community, of which MXenes have been identified as a promising aspirant for wastewater treatment.To address these issues, conventional techniques like chemical precipitation, membrane filtration, solvent extraction, ion exchange, electrochemical removal, and coagulation have been utilized and continue to be used as water treatment techniques.9 The n+1XnTx and are derived from the precursor MAX phase (Mn+1AXn), where the integer n is 1, 2, or 3, M is an early transition metal, X is either carbon, nitrogen or both, A represents a group IIIA or IVA element, and T represents surface terminal groups like fluorine, oxygen, chlorine, or hydroxyl, and x denotes the number of the surface functional group. The incredible intrinsic features of MXenes such as high surface area, high metallic conductivity, ease of functionalization, environmentally friendly nature, antibacterial properties, chemical stability, activated metallic hydroxide sites, and hydrophilicity, make them the preferred candidates for applications in environmental remediation, energy storage, catalysis, sensors, and electronics  chemistry, synthesis and surface functionalization routes, characterization techniques, and their application  in the removal of toxic pollutants  from wastewater. We also discuss how a range of factors  affect the synthesis, surface functionalization, and performance of the resultant MXene‐based material. Finally, we highlight the limits of MXenes and MXene‐based materials as well as their potential future research directions, especially for wastewater treatment applications. We believe this review will attract the attention of scientists in material, chemistry, and related fields and promote the development of MXene and MXene‐based materials to address the global challenge of wastewater treatment and water scarcity.2 Depending on the source of the solid loads, the resulting wastewater is classified into industrial, domestic, and storm sewage  wastewater as depicted in Figure  Water is indispensable and serves various purposes in many industries including, petroleum, mining, pharmaceutical, textile, fertilizer pulp and paper and pesticides, and a substantial proportion of this water ends up as industrial wastewater. For example, manufacturing or chemical processes and their resultant discharge with the associated pollutants  released into the environment creates significant and detrimental footprints and related health hazards. Domestic wastewater from homes is a primary source of microorganisms , metals , cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), and mercury (Hg), and plant nutrients . These pollutants present a direct threat to public health and water quality. Furthermore, pharmaceuticals such as ibuprofen, metoprolol, paracetamol, and sulfamethoxazole, when released into the environment can be highly toxic to humans, animals, and aquatic life. Storm sewage includes sewage overflow and road runoff from precipitation that is collected in a system of pipes or open channels and may contain nitrogen and phosphorus pollutants from fertilizers, yard, and petrochemical wastes that pollute water bodies during heavy rainfall or snowmelt.Wastewater can be defined as polluted water generated from homes, communities, farms, or industries that contain dissolved or suspended matter. Wastewater is made up of ≈0.06% solids dissolved or suspended in 99.94% water flow.49 Dep The contamination of wastewater is generally given in terms of chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), suspended solids, and total dissolved solids (TDS). To achieve the above objectives, wastewater treatment systems combine physical, chemical, and biological approaches to get rid of pollutants in wastewater.The main objectives for wastewater treatment are to reduce the levels of solids, biodegradable organic matter, pathogens, and other toxic compounds in wastewater, to meet regulatory limits that are protective of both public health and the environment.61 TheFigure  A variety of chemical techniques are also harnessed to precipitate, convert, or destroy contaminants, for example, solids are removed by coagulation and flocculation, pathogens are destroyed by disinfection, and nutrients like phosphorus are eliminated by chemical precipitation. Finally, biological techniques help to remove biodegradable organic matter and non‐settleable colloidal solids using microorganisms. Secondary treatment using physical and biological methods remove ≈90% of the BOD and 90% of the suspended solids. Pollutants that escape removal via the secondary treatment process undergo tertiary treatment typically involving the removal of nitrogen, phosphorus, soluble COD, and heavy metals.Wastewater treatment plants combine physical, chemical, and/or biological methods to provide three levels of treatment: primary, secondary, and tertiary treatments Figure 3. Prima In particular, 2D nanomaterials are suitable alternatives for existing water and wastewater purification methods.In more recent years, nanomaterials have come under intense research and development, and their application in water and wastewater treatment has drawn wider attention.68 In 2.1 a diverse range of other 2D nanomaterials have been explored and subjected to intensive and extensive studies in the fields of condensed matter physics, chemistry, material science, and nanotechnology. 2D materials, which are sheet‐like structures with single‐ or few‐layer thickness (<5 nm), and lateral sizes that range beyond 100 nm, are promising candidates for wastewater treatment and other environmental remediation applications.Since the discovery of the single‐atomic thick graphene in 2004, and the revelation of its excellent electron transport capacity, electrical conductivity, thermal conductivity, mechanical characteristics, and chemical properties,73 a d The top‐down approach includes mechanical cleavage and solution‐based exfoliation, which works to overcome the Van der Waals energy stored in the bulk crystals or parent layered material by exerting an external force such as peeling or ultrasonication. Conversely, the bottom‐up approach includes chemical vapor deposition (CVD) and wet chemical synthesis, where precursors or smaller molecules are chemically reacted to synthesize layered materials on substrates or in solution. MXenes as a class of 2D materials are prepared via the top‐down approach and the process involves chemical etching and subsequent rupturing of Van der Waals forces. In the next section, attention is focused on the chemistry, synthesis, characterization, and applications of MXenes and their surface‐modified form.The properties of 2D materials are dictated to a greater extent by how they are prepared, and the techniques employed in their preparation can be grouped into either a top‐down or bottom‐up approach.80 The3n+1AXn , where M is an early transition metal, A = group IIIA or IVA elements, X is carbon/nitrogen or both. By etching out the A element as shown in Figure n+1 Xn Tx  are obtained, where T stands for the surface terminal groups  that render MXenes hydrophilic (x denotes their number). The first example of MXene material, Ti3C2, was synthesized by selectively etching the Al atoms in the layered hexagonal ternary carbide, Ti3AlC2, using hydrofluoric acid (HF) at room temperature. Figure 2, M3X2, and M4X3.MXenes represent a family of 2D early transition‐metal (M) carbides and nitrides, which were first synthesized from bulk layered crystalline M3.1P63/mmc) with metallic, covalent, and ionic bonds. The A‐group layer is relatively weak and more reactive and is between closely packed M layers, and X atoms . The bond between M and X is exceptionally strong exhibiting a mixed covalent/metallic/ionic characteristic such that they are inseparable by mechanical exfoliation, ultrasonication, or dispersion. The bonding mode of MXene is different from those of other layered materials like graphite in which weak van der Waals interactions hold similar structures together. When the A element is etched out, the exposed “M” becomes very reactive and easily bonds with electronegative atoms  employed during synthesis. It is these surface functional groups that dictate the properties of the resulting MXenes. For example, studies by Agresti predict that bandgap energies of MXenes could range from 1.67 to 6.25 eV when OH and O surface terminal groups, respectively. This suggests that by introducing and optimizing surface terminal groups, the bandgap energy, and work function properties can be tuned. The easy functionalization of MXenes makes them very versatile in various applications, as new properties can be easily conferred on them.Typically, MXene is prepared from the parent MAX phase. The MAX phase has three types of unit cells with hexagonal crystal structure  and sum3C2 layers is depicted by Reactions (The formation of surface groups (OH and F) on the exfoliated 2D Tiactions (983.2n+1 Xn (OH)x Oy Fz). These electronegative terminal groups allow for easy surface modification with organic groups and have been reported to significantly regulate the physical and chemical properties of MXenes. It has been shown that 2D titanium carbide possesses unique properties from a decrease in F surface groups and an increase in OH groups.As mentioned previously, etching of the A layer from the MAX phase often leaves —O, —OH, and/or —F functional groups on the surface of MXenes (M3.3 The techniques employed for this purpose include X‐Ray diffractometer (XRD), X‐Ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy/high‐resolution transmission electron microscopy (TEM/HRTEM), energy dispersive X‐ray analysis (EDAX), Fourier transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller analysis (BET), Raman spectroscopy, and thermogravimetric analysis (TGA). MXenes have been functionalized with several materials ranging from metal nanoparticles to cellulose aerogel, amino groups, etc. to enhance their properties and performance toward wastewater treatment.Evidence of successful surface functionalization of MXenes has been studied using various material characterization techniques. These techniques reveal information about the chemical composition and crystal structures before and after surface modification MXene. Knowledge about the composition and distribution of surface functional groups —O, —OH, and —F has been explored in different applications. Furthermore, the morphology and interlayer distance of the MXene flakes before and after surface functionalization have also been studied.17 The3.3.1 Pristine MXene usually shows a characteristic peak of the (002) plane at ≈7.51° with 1.18 nm spacing between the layers. Feng et al. fabricated MXene/PEI/SA (MPA) composite aerogel by first functionalizing pristine MXene with (3‐aminopropyl)trimethoxysilane (APTES) to obtain an amino‐functionalized MXene (MXene‐NH2). The amino‐functionalized product was then transformed into MPA using polyethylenimine (PEI) , sodium alginate (SA) (as an aerogel carrier), and epichlorohydrin (as a crosslinking agent). When the XRD data of MXene‐NH2 was compared to that of pristine MXene, they observed reduced peak intensities at 2θ = 18.2 at 2θ = 27.84° for the (006) and (008) peaks, respectively, and disappearance of thee (002) peak at 2θ = 8.86°  and (008) peaks further diminished when MPA was formed  adsorption and observed as is shown in Figure 3C2Tx to 9.20° (NH2‐Ti3C2Tx‐0.2), 9.32° (NH2‐Ti3C2Tx‐0.5) and 9.48° (NH2‐Ti3C2Tx‐1.0). This depicts tightly packed NH2‐Ti3C2Tx nanosheets via favorable electrostatic or hydrogen interactions between the separated ionic layers. Lei et al., after modifying Ti3C2 MXene with sulfonic acid to form Ti3C2‐SO3H, observed a downward shift in the peak position of the (002) phase of pristine Ti3C2 from a 2θ value of 9.52° to 6.16°. Besides, the (002) peak became sharper for the Ti3C2‐SO3H because of the thickening of the MXene layer. The XRD technique was employed by Jiang et al., to show that the 002 peak of MXene, which they observed at 7°, was well preserved even after functionalizing with a diazonium salt. This indicates that the covalent grafting of the salt does not result in the collapse of the layered structures of the MXene nanosheets  characterization.122 Prd Figure . They ats Figure .dimethyl‐(3‐sulfopropyl)ammonium hydroxide (DMAPS) and the acrylic acid, poly(DAMPS‐co‐AA). The TGA data showed PE contents of ≈14.15 wt% for MXene‐g‐poly(AMPS‐co‐AA), and ≈21.58 wt% for MXene‐g‐poly(DMAPS‐co‐AA).In a study, by Sun and his colleagues, where MXene‐PDA and MXene‐PDA‐IL were prepared by functionalizing Tiy Figure . These hybrid materials also favored the removal of europium, Eu (III), with an adsorption capacity of 97.1 mg g−1. It is interesting to note that the surface modification with the phenyl carboxylic moiety increased the stability of the composite material in water. Ling Wang et al. presented a first case study of V2CTx MXene nanosheets with a maximum adsorption capacity uptake of 174 mg g−1  sorption which showed that more than 95% of uranyl is removed after 5 min at pH 5.0 with the maximum adsorption capacity was 294 mg g−1. These same composite materials were loaded with a carbon cloth, where the application of an electric field increased the uranium adsorption capacity from 294 to 626 mg g−1. The nuclear fission of uranium (235U) produces a radioactive element, technetium‐99 (99Tc), which has significant demand as a medical radioisotope, is usually present as pertechnetate anion Tc(VII) in an aqueous solution. Wang et al. introduced poly diallyldimethylammonium chloride (PDDA) on to the surface of Ti3C2Tx MXene nanosheets, which exhibited a removal rate of 363 mg g−1 for ReO−4; the ReO−4 being used as a model chemical surrogate for TcO−4 (Tc VII) also shows the effect of competitive anion species on Re(VIII) removal by Ti2CTx MXene nanocomposite/PDDA composite  composite was used. This composite relied on the self‐polymeriyation of dopamine for polydopamine (PDA) films. The maximum adsorption capacity for iodide ions was 64.65 mg g−1. It was clear that adsorption was more favorable in acidic solutions than in alkaline solutions. The composite was recycled five times, and the adsorption decreased after the first cycle, because the iodide ions adsorbed were retained by the adsorbent.Table Radioisotopes of iodine have been noted to have a range of half‐lives from ≈8.04 days up to 1.57 × 10dsorbent.218Ta5 In addition to these fascinating properties, the simplistic method of large quantity production of MXene and the possibility of their production in powders to suspensions to thin films make them very attractive to the scientific and industrial community for practical applications. The unique and tunable 2D in‐layer nanostructures and chemical compositions are controllable by functionalizing their surfaces and hence modifying their surface energy. For water treatment, surface functionalized MXenes have shown better results for the adsorption of ionic and polar pollutants and enhanced selectivity in removing heavy metal ions, antimicrobial pollutants, radionuclides, and organic dyes as compared to pristine MXenes. Notably, MXene based materials on different support materials also show significant potential for adsorption of contaminants with water permeability much higher than that of many commercially produced materials. Pristine MXenes have some disadvantages toward photocatalytic applications such as poor photon absorption and a high rate of charge carrier recombination. However, there are plenty of possibilities for MXenes to be used as efficient photocatalysts by changing their surface chemistry. Surface functionalized MXene shows photocatalytic substrates, adsorbents, and antimicrobial agents. Particularly creating an integrated system of MXenes with other support materials can provide cost‐effective nanocomposites with exceptional properties for wastewater treatment, which strongly suggests their potential to be applied in real industrial cases. With improvements on their long‐term stabilities, reductive sequestration and/or adsorption of heavy metals from water and degradation of dyes will be achievable. Although water treatment applications have improved, further development of high‐performing MXene based nanomaterials is very important. MXene‐based nanoarchitectures with higher light‐harvesting ability, satisfying quantum efficiency, and enhanced carrier separation can be used for efficient solar to chemical applications. Plasma processes have been used in modifying other 2D materials to provide improved properties, however, the plasma process has not been fully explored in modifying the surface functionalities of MXenes. Exploring surface functionalized MXenes adsorption interface for other harmful and toxic heavy metal compounds such as Ag, Au, etc, will be an interesting task. Furthermore, increasing the optical absorption range of MXene will make them highly efficient in photocatalytic wastewater treatment, although this remains a challenge.Rapid progress has been made recently in the realm of wastewater treatment and other applications of MXenes due to their excellent mechanical, thermal, tunable optical, and electronic properties, high ion adsorption abilities, and impressive electric conductivities.84 In Overall, MXenes and their composites have rapidly emerged and positioned themself as promising 2D nanomaterial for various applications. Expansion of the family of MXene with the choice of surface functionalization offers new structures, chemistries, and enhanced properties, which still needs to be further explored for sequestration of emerging pollutants from water and wastewater streams. Finally, more research is needed to optimize, enhance, and safely design, use, and dispose of MXene containing products to avoid any risks to the environment.The authors declare no conflict of interest.