Abstract
Background:
The epithelium in the colonic mucosa is implicated in the pathophysiology of various diseases, including inflammatory bowel diseases and colorectal cancer. Intestinal epithelial organoids from the colon (colonoids) can be used for disease modeling and personalized drug screening. Colonoids are usually cultured at 18-21% oxygen without accounting for the physiological hypoxia in the colonic epithelium (3% to <1% oxygen). We hypothesize that recapitulating the in vivo physiological oxygen environment (i.e., physioxia) will enhance the translational value of colonoids as pre-clinical models. Here we evaluate whether human colonoids can be established and cultured in physioxia and compare growth, differentiation, and immunological responses at 2% and 20% oxygen.
Methods:
Growth from single cells to differentiated colonoids was monitored by brightfield images and evaluated with a linear mixed model. Cell composition was identified by immunofluorescence staining of cell markers and single-cell RNA-sequencing (scRNA-seq). Enrichment analysis was used to identify transcriptomic differences within cell populations. Pro-inflammatory stimuli induced chemokines and Neutrophil gelatinase-associated lipocalin (NGAL) release were analyzed by Multiplex profiling and ELISA. Direct response to a lower oxygen level was analyzed by enrichment analysis of bulk RNA sequencing data.
Results:
Colonoids established in a 2% oxygen environment acquired a significantly larger cell mass compared to a 20% oxygen environment. No differences in expression of cell markers for cells with proliferation potential (KI67 positive), goblet cells (MUC2 positive), absorptive cells (MUC2 negative, CK20 positive) and enteroendocrine cells (CGA positive) were found between colonoids cultured in 2% and 20% oxygen. However, the scRNA-seq analysis identified differences in the transcriptome within stem-, progenitor- and differentiated cell clusters. Both colonoids grown at 2% and 20% oxygen secreted CXCL2, CXCL5, CXCL10, CXCL12, CX3CL1 and CCL25, and NGAL upon TNF + poly(I:C) treatment, but there appeared to be a tendency towards lower pro-inflammatory response in 2% oxygen. Reducing the oxygen environment from 20% to 2% in differentiated colonoids altered the expression of genes related to differentiation, metabolism, mucus lining, and immune networks.
Conclusions:
Our results suggest that colonoids studies can and should be performed in physioxia when the resemblance to in vivo conditions is important.
Introduction
The colon harbors an abundant microbial community adjacent to numerous immune cells, only separated by a mucous layer with a single-layered epithelium (). The colonic epithelial cells are polarized, with an apical side facing the lumen and a basolateral side facing the lamina propria and orchestrate crosstalk between the microbial and immune cells communities (). Continuously proliferating stem cells reside at the bottom of the crypts of Lieberkühn, giving rise to progenitor cells that migrate towards the lumen as they differentiate into postmitotic specialized epithelial cells (, ), including absorptive-, goblet-, enteroendocrine- (EEC) and tuft cells (). Terminally differentiated epithelial cells undergo apoptosis and exfoliate into the lumen after two to five days, thus demanding a high turnover rate from the intestinal stem cells (). Stem cell renewal and proliferation are stimulated by WNT signaling (). Progenitor cells can commit to an absorptive or secretory lineage depending on the signaling molecules present. Central to the process is Notch signaling. Briefly, when the Notch molecule is present, the absorptive lineage dominates, and when Notch is suppressed, the secretory line is favored (). Absorptive colonocytes are the fate of the absorptive lineage, while the secretory lineage differentiates into goblet-, EEC, or tuft cells. The differentiation of the secretory lineage also depends on other signaling molecules (, ).
The colonic epithelium is involved in the pathophysiology of inflammatory bowel disease (IBD), microscopic colitis, and colorectal cancer (–). Colorectal cancer is the third most prevalent cancer type globally, constituting 10.7% of all cancers diagnosed in 2020 (). Thus, the colonic epithelium is central to cancer and inflammation research. The last decade has seen significant progress in gastrointestinal pathophysiology research with the introduction of 3D structured human intestinal organoid systems (). Intestinal epithelial organoids (IEOs) mimic the epithelium’s architecture, cell composition, and signaling (). IEOs from tissue-derived stem cells can recapitulate interindividual differences. Hence, they represent a tool in precision medicine for drug screening and can be used to discover how genetic and epigenetic variations influence pro-inflammatory responses (–). Culturing human IEOs from the colon (colonoids) in vitro has enabled researchers to study human colonic epithelial cell mechanisms directly, thus gaining new knowledge about colorectal cancer (), IBD (), and other diseases involving the intestinal epithelium (). The advantages of human colonoids include ethical, economic, and applicability aspects (). However, the strength of a model system lies in its ability to mimic in vivo conditions.
In the colonic epithelium, an oxygen gradient exists from the blood vessels in the submucosa (~3% oxygen), decreasing towards the luminal epithelial cells (< 1% oxygen) that are in juxtaposition to anaerobic bacteria (). Thus, the colonic epithelium is adapted to thrive in a hypoxic environment (). A central transcription factor for the epithelial adaptation to physiological hypoxia is Hypoxia-inducible factor-1 (HIF-1) (). HIF-1 is a heterodimer formed by binding the oxygen-regulated HIF-1α and the continuously expressed HIF-1β. During low oxygen conditions, HIF-1α is stabilized, thus entering the nucleus, forming a heterodimer with HIF-1β. In the presence of high oxygen tensions, HIF-1α is continuously degraded (). The interplay between the microbiome and colonic epithelium is partly responsible for stabilizing HIF-1 (). The intestinal epithelium utilizes short-chained fatty acids (SCFA) produced by the microbiome for energy (). Through β-oxidation of butyrate, local oxygen is depleted, and HIF-1α is stabilized (, ). HIF-1 is crucial for cell survival, metabolism, and other functions in low oxygen environments, including maintaining epithelial barrier integrity and antimicrobial functions (). Stabilization of HIF-1α upregulates the expression of, e.g., thigh junction proteins (), mucus-related genes (), and anti-microbial proteins like defensins ().
Preclinical studies have shown that stabilization of HIF-1α leads to improved intestinal barrier functions (), and gut-targeted HIF-1α stabilizers like GB004 may be a promising therapeutic approach for ulcerative colitis patients (). Recently, Kumar et al. () demonstrated that tumor cells from e.g. mouse colon and mammary tissue collected, processed, and propagated at physioxia (3% oxygen) displayed distinct differences in crucial signaling networks, including LGR5/WNT, YAP, and NRF2/KEAP1 and sensitivity to targeted therapies compared to tumors in ambient air (21% oxygen). The authors concluded that evaluating cancer cells under physioxia could more closely recapitulate their physiopathologic status in the in vivo microenvironment. Although IEO studies are generally performed at 18-21% oxygen, several have examined the benefits of growing other ex vivo models in physioxia (–). For example, kidney organoids cultured in physiological hypoxia (7% oxygen) instead of 21% oxygen showed enhanced sprouting and interconnectivity while maintaining renal cell types and their spatial organization (). Primary human corneal endothelial cells can successfully be cultured at 2.5% oxygen resembling in vivo corneal aqueous environment containing 2.8% oxygen (). Compared to room air (∼21% oxygen), corneal endothelial cells cultured at physiological hypoxia showed considerable differences in cell metabolism, viability, oxygen-consuming reactions, and glycolytic metabolism. Thus, the authors suggested that the culture of cells under conditions that most closely resemble their physiological environment would maintain the native phenotype and function and reduce external stressors.
Recently, we showed that short-time exposure (i.e. 40 hours) to physiological hypoxia (2% oxygen) did not alter viability and cell type expression in differentiated colonoids, but increased HIF-1α expression (). Furthermore, differentiated colonoids cultured in physiological hypoxia for 40 hours expressed anti-inflammatory gene regulation traits upon TNF/IL17 stimulation. The present study aimed to evaluate whether physiological hypoxia should be a culture standard for human colonoids. First, we investigated if it was possible to culture human colonoids in a 2% oxygen environment throughout the culturing process. We assessed growth, colonoid cell composition, transcriptomic alterations, and response to pro-inflammatory stimulation. We then examined the direct effect of reducing the oxygen level on gene regulation in differentiated colonoids. Our findings support incorporating physioxia for human colonoid cultures.
Materials and methods
Materials are listed in Supplementary File 1 (SF1)
Human colonoid cultures and experimental design
Colonoids were generated from human colonic biopsies as previously described (, ). For experiments, colonoids were dissociated into single cells, resuspended in ice-cold basement membrane matrix Matrigel GFR (Corning®, New York City, NY), plated on pre-warmed 24-well plates (8000-10000 cells in 50μl Matrigel per well), and 500μl of complete growth medium (CGM) was added to each well. The CGM is extensively described in previous publications (, ) and listed in SF1, sheet 2
The general experimental design is illustrated in Figure 1A. For each independent experimental replicate, the colonoids were cultured in parallel in two separate incubators (New Brunswick Galaxy 170R CO2, Eppendorf, Hamburg, Germany) at 37°C with 5% CO2 and 2% or 20% oxygen. The oxygen concentration was lowered to 2% by calibrating the nitrogen input. The colonoids were cultured in CGM for the first 9 days (unless otherwise specified). Thereafter, half of the colonoids were differentiated, while the other half continued with growth medium and thus remained undifferentiated. Differentiation was induced by lowering Wnt-3A concentration to 5%, withdrawing Nicotinamide and SB202190 factor from the CGM, and adding the pan-Notch inhibitor DAPT (4.324 μg/mL, #2634, Bio-Techne). On day 14, A-83-01 was removed from the differentiation media before the TNF + Poly(I:C) stimulation assays (described below). Medium change was performed every two to three days until the end of the experiment. An overview of each donor and in which analyses they were included is found in Table 1.
Figure 1
Table 1
| Donor (D) | D1 HC | D2 HC | D3 HC | D4 UC | D5 HC | D6 HC | D7 UC | D8 HC | D9 UC |
|---|---|---|---|---|---|---|---|---|---|
| Age | 21 | 73 | 55 | 18 | 28 | 65 | 20 | 43 | 29 |
| Sex | F | M | F | M | F | F | M | F | F |
| Growth analysis | x | x | x | x | x | x | x | ||
| Cell markers | x | x | x | x | x | x | x | ||
| Bulk RNA seq.1 | x | x | x | ||||||
| ScRNA-seq. | x | ||||||||
| NGAL ELISA | x | x | x | x | x | x | |||
| Multiplex | x | x | x | x | x | x |
Colonoid donor characteristics and experimental analysis.
IBD, Inflammatory bowel disease; M, Male; F, Female; UC, Ulcerative colitis; HC, healthy control (non-IBD); RNA-seq, RNA sequencing; ScRNA-seq, Single-cell RNA-sequencing; NGAL, Neutrophile gelatinase-associated lipocalin.
The number of independent experimental replicates is listed in the figure legend.
1: A previously performed bulk RNA-sequencing (GSE172404) was also analyzed where a group of colonoids (n = 6) was cultured in 20% oxygen for 15 days, while another group (n = 6) was cultured in 20% oxygen except for the last 40 hours where they were cultured in 2% oxygen ().
Brightfield image analysis and linear mixed model of colonoid growth
During colonoid culture, brightfield images were acquired from each well with an EVOS microscope (Thermo Fischer Scientific) every two to three days, using 4X and 10X objectives to assess the total colonoid area. Images containing air bubbles were excluded (SF1, sheet 3). The images were analyzed in silico through Fiji using batch processing (). The brightfield images (6-18 per condition) were converted into 8-bit images, and the auto local threshold function “Phansalkar” (radius = 10) was used to threshold the images. A region of interest was selected around the Matrigel-covered region. The “Particle analysis” function measured the area within the region of interest covered by colonoids as a proxy for cell mass. The settings for the particle analysis function included measuring particles from “10μm to infinity”, “excluding particles at the edges,” and “filling in holes.”
The experimental design had a hierarchical structure where every independent experimental replicate had several conditions with multiple technical replicates acquired at different time points. Thus, a linear mixed model (LMM) was utilized to evaluate colonoid growth statistically:
Formula = log(cell mass) ~ Day*factor(Oxygen concentration) + Condition + (1|Donor) + (1|Day)
The analysis was performed in R using the Lme4 () and LmerTest () packages. Day of image acquirement (1|Day) and independent experimental replicates (1|Donor) were introduced as random effects, (i.e., variability that might be present but not of interest). The fixed effects in the model were time (Day), the oxygen environment (Oxygen concentration), whether the colonoids were undifferentiated or differentiated (Condition), and the interaction between day and oxygen concentration (Day*Oxygen concentration), thus investigating if the Day variable changed the Oxygen concentration variable’s influence on colonoid growth. The model’s residuals followed a normal distribution.
Immunofluorescence staining and confocal imaging of colonoids
Colonoids were collected for immunostaining at the end of the experiments (Figure 1A). In brief, colonoids from each experimental condition were pooled (n=3-6 wells) and resuspended in 50 μL Richard-Allan Scientific™ HistoGel™ Specimen Processing Gel (#HG-4000-012, Thermo Fisher Scientific, Waltham, MA), then fixed in 10% buffered formalin for 24-48 hours before they were embedded in paraffin as described elsewhere (). Formalin-fixed paraffin-embedded sections were deparaffinized, and antigen retrieval was accomplished by boiling the sections in citrate buffer (pH 6.0) or Tris-EDTA buffer (pH 9.0) for 15 minutes in a commercial microwave oven. The sections were blocked with tris-buffered saline (TBS) + 5% bovine serum albumin (BSA) for five minutes. Primary antibodies against KI67 (Dako Agilent, Santa Clara, CA), Cytokeratin 20 (CK20) (Dako Agilent), Mucin-2 (MUC2) (Abcam, Cambridge, Great Britain), and Chromogranin A (CGA) (Abcam) were diluted in TBS + Tween® 20 + 1% BSA and incubated overnight at 4°C (SF 1, sheet 2). The secondary staining was performed with the MaxFluor immunofluorescence detection system (MaxVision Bioscience, Ontario, Canada). Lastly, the sections were counterstained with DAPI (Thermo Scientific) and mounted with Glycergel (Dako Agilent).
Confocal imaging of colonoids
Immunofluorescent images of the colonoids were captured with a confocal microscope (LSM 880 Airyscan, ZEISS, Oberkochen, Germany). Every image was captured at 20X magnification with standardized settings normalized to the section with the highest immunofluorescent expression (SF1, sheet 4). Five to ten images per section from eight independent experimental replicates were acquired.
Immunofluorescent image analysis and quantification of cell markers in colonoid sections
Quantification of the cell markers was performed with three different methods depending on the distribution and expression of the proteins: 1) For MUC2 and CGA, the positive cells were manually counted; 2) KI67 was quantified by thresholding the images with “Otsu” in Fiji, and the number of KI67 positive cells was calculated with the “Particle analysis” function; 3) CK20 was processed through Fiji using the “HiLo” lookup table to reduce background noise while preserving the integrity of the signals from the colonoids. Then the “Measurement” function was used to measure each image’s signal intensity, resulting in an Integrated Density Score. The background of each image was measured independently three times, and the mean background was subtracted from the Integrated Density score. Thus, the corrected total cell fluorescence (CTCF) was calculated. The result from each quantification method was divided by the number of DAPI-positive cells present in each image to normalize the fluorescent expression to the number of cells. DAPI positive cells were quantified by thresholding the images with “Otsu” applying the “Watershed” and “Particle analysis” functions. The quantification scores are found in SF1, sheets 5-8.
Bulk RNA-sequencing, enrichment analysis, and supervised datasets
RNA was extracted from the colonoids with the RNeasy mini kit (Qiagen, Hilden, Germany) per the manufacturer’s protocol, as previously described (). Sequencing libraries were generated with SENSE total RNASeq library prep kit with RiboCop rRNA depletion (Lexogen GmbH, Vienna, Austria). The sequencing included 75 cycles of single-end reads conducted with the Illumina HiSeq4000 (Illumina, Inc., San Diego, CA, USA). FASTQ files were produced with bcl2fastq 2.18 (Illumina). LIMMA linear models identified differential gene expression between conditions with least square regression and empirical Bayes moderated t statistics. Correction-adjusted P-values ≤ 0.05 with Benjamin-Hochberg’s false discovery rate were statistically significant.
For the dataset GSE217663, RNA was extracted from untreated undifferentiated and differentiated colonoids cultivated at 20% oxygen (n= 3 donors) (SF1 sheet 9). A supervised dataset of cell marker genes was created by searching the PangloaDB database () and used to identify differentially expressed cell markers in undifferentiated and differentiated colonoids. The cell marker gene list includes gen-sets for stem-, goblet-, absorptive-, enteroendocrine-, and tuft cells (SF1, sheet 10). It was used to identify cell clusters in the single-cell RNA-sequencing (scRNA-seq) dataset described below.
We also re-analyzed the in-house bulk RNA-seq dataset GSE172404, comparing fully differentiated colonoids cultivated continuously at 20% oxygen with colonoids cultivated at 2% oxygen for the last 40 hours, as previously described (). Hypoxia-related genes were selected based on 1) Gene ontology (GO) term “cellular response to hypoxia” (GO:0071456, with applied filters: protein, homo sapiens. n=196 unique protein-coding genes) using the Amigo2 tool (version 2.5.12) gene ontology platform (–), downloaded 24.09.2019 and 27.03.2020 (DOI 10.5281/3727280); 2) HIF-1 downstream targets (n = 98 genes) as reviewed by Slemc and Kunej (); 3) process network in MetaCore™ version 19.4 build 69900 “Transcription_HIF-1α targets” downloaded 01.04.2020 (n = 95 genes); and 4) individually investigated genes with experimental data indicating a connection to the cell’s response to hypoxia (n = 44 genes), resulting in a list of 369 unique genes (SF1, sheet 11).
Single-cell RNA-sequencing, bioinformatics, and enrichment analysis
Single cells were processed through the 10x Genomics Chromium Single Cell Platform (Single Cell 3’ v3) (pipeline version 3.1.0) (10x Genomics, Pleasanton, CA). In brief, differentiated and undifferentiated colonoids grown in 2% and 20% oxygen were enzymatically (Trypsin + Y-27632 for 10 minutes at 37°C) and mechanically (18G needle) dissociated into a single cell suspension consisting of Phosphate-Buffered Saline (PBS) + 0.05% BSA. Ten thousand cells were loaded onto the platform for each sample. Single-cell sequencing data was analyzed using the 10x Genomics Cellranger software (version 3.1.0). In total, 13904 cells were successfully sequenced (see Table 2). The BCL files were converted to FASTQ format and mapped to the GRCh38 reference genome. Cellranger was further used to generate UMI counts from those droplets likely to contain at least one cell and aggregated into a read depth normalized feature count matrix. The 10x Genomics cellbrowser (Cloupe version 5) was used for visualization purposes and cell type assignment of the aggregated data. Downstream analysis was conducted primarily using the Seurat R package (). Cells with low counts (<200) or classified as low quality by the miQC R package were excluded (). Features with counts lower than 500 or higher than 40000 were also excluded. The count data was further normalized with the transform varians accounting for the mitochondrial fraction, and the top 3000 highly variable genes were used for principal component analysis (PCA) dimension reduction. The top 40 principal components were used as input to the uniform manifold approximation (UMAP) method and clustered using the “FindClusters” function. The cell-type classifications were refined to match the clustering output, and differential expression between groups was identified with the non-parametric Wilcoxon rank-sum test using the Seurat function “FindMarkers.”
Table 2
| Sample | indentsecretEstimated number of cells | Mean reads per cell | Median genes per cell |
|---|---|---|---|
| 2% undifferentiated | 5219 | 21539 | 2356 |
| 20% undifferentiated | 4476 | 22946 | 2648 |
| 2% differentiated | 2111 | 50602 | 3092 |
| 20% differentiated | 2098 | 63352 | 3560 |
Single-Cell RNA-sequencing.
Enrichment analyses were performed with Metacore (Clerviate, London, Great Britain) to compare cell clusters cultured in 2% vs. 20% oxygen. Gene lists were created in R with the Seurat package for the stem-like, progenitor, and differentiated cell clusters, including a prefiltering process (logfc.threshold = log (), min.pct = 0.25 and min.diff.pct = 0.1).
Multiplex chemokine profiling and ELISA
Colonoids were treated with TNF (100 ng/mL, PeproTech) + Poly(I:C) (20 μg/mL, In vivoGen, San Diego, CA) on day 14 for 24 hours to investigate chemokine and Neutrophil Gelatinase-Associated Lipocalin (NGAL) secretion (Figure 1A). The analyzed samples included four conditions (2% oxygen untreated, 2% oxygen treated, 20% oxygen untreated, and 20% oxygen treated) from six independent experimental replicates (Table 1). The conditioned medium was collected, frozen at -80°C, and later thawed for analysis. The Bio-Plex Pro Human Chemokine Panel, 40-plex (Bio-Rad Laboratories, Hercules, CA, United States), was used to analyze undiluted samples according to the manufacturer’s instructions using the Bio-Plex 200 Systems.
Per the manufacturer’s protocol, a sandwich ELISA kit (R&D Systems, Minneapolis, MN) was used to measure the NGAL concentration within the conditioned media. The samples were analyzed in duplicates with a working dilution of 1:50 and 1:100. Shortly, the capture antibody was diluted in PBS and incubated overnight at room temperature (RT) in 96 well plates. The plates were washed three times with PBS + 0.05% Tween® 20 before being blocked with Reagent diluent (1% BSA in PBS) for one hour at RT. The samples were distributed and incubated for two hours at RT before detection antibody was added and incubated for two hours in RT. The plates were removed from light sources, incubated with Streptavidin-HRP (1:40), and then substrate solutions for 20 minutes. Lastly, a stop solution (2N H2SO4) was added, and the plates were immediately analyzed with a microplate reader (iMark, Bio-Rad, Hercules, CA).
Figures
Graphs were created using the R packages ggplot2 () and Seurat (), and GraphPad Prism 9.0 (GraphPad Softwear Inc., San Diego, CA).
Statistical analysis
Statistical analyses, excluding bioinformatical analysis of sequencing data and the linear mixed model (LMM) of colonoid growth described above, were conducted in GraphPad Prism 9.0. The Shapiro-Wilk test was used to determine normal distribution. Cell marker data were analyzed with Wilcoxon’s test and one-way ANOVA followed by the Šídák test for multiple comparisons. The chemokine and ELISA data were analyzed with repeated measures (RM) one-way ANOVA followed by Šídák’s multiple comparisons test. Normally distributed paired groups were analyzed with Paired t-test. Non-parametric analyses with two paired groups were performed with the Wilcoxon test. P-values < 0.05 were considered statistically significant.
Ethical considerations
The current study was carried out under relevant approvals by the Central Norway Regional Committee for Medical and Health Research Ethics (reference numbers 5.2007.910, 134436 and 2013/212/REKmidt). All patients included in the study provided informed written consent.
Results
A low oxygen environment is beneficial for colonoid growth
To investigate whether colon-derived stem cells could establish and grow in a low physiologic oxygen environment ex vivo, we cultured colonoids at 2% and 20% oxygen in parallel (Figure 1A). Brightfield images were captured every two to three days following colonoid development (Figure 1B). Single stem cells established into colonoids in both 2% and 20% oxygen environments. As outlined by the white arrows in Figure 1B, colonoid growth was initially slow, followed by a rapid expansion around day six at both oxygen conditions. We then assessed the growth process in silico. The computational analysis included converting brightfield images to binary images and measuring the total cell mass present in the picture as a proxy for cell quantity and size. In total, 1875 images were acquired from 11 independent experiments (Table 1 and Supplementary Figure 2B) with six to twelve technical replicates (i.e., plate wells) per condition (i.e., 2% undifferentiated, 2% differentiated, 20% undifferentiated, and 20% differentiated) (Figure 2A). The image analysis showed that the growth curve of the colonoids was equal to classical cell growth curves with an initial lag phase followed by an exponential phase where the cell mass increased rapidly (Figure 2B and Supplementary Figure 2). Since the assays ended on day 14, the death phase did not become apparent. Cultivation at 2% oxygen did not impede colonoid growth (Figure 2B). For differentiated colonoids, the growth appeared enhanced at 2% oxygen. Since we observed experiment-to-experiment variations, we investigated if the donor’s IBD status or age (Table 1) affected the colonoids growth patterns. Interindividual differences were more prominent than disease status (Figure 2C) and age (Figure 2D), as shown by overlapping CI between the groups. However, it may be of interest for further studies that colonoids from younger donors (<40 years) appeared to be larger in 20% oxygen than colonoids from older donors(<40 years).
Figure 2
An LMM was performed for statistical information about variables that affected cell growth (Figure 2E). The variables included time (Day), the oxygen environment with 20% as a baseline (20% oxygen), whether the colonoids were undifferentiated or differentiated (Differentiated colonoids), and the interaction between time and oxygen concentration (Day x 20% oxygen). The most important variable for colonoid mass was the time (P-value < 0.0001, confidence interval (CI) 0.36 to 0.42). The total cell mass increased every day. Culturing colonoids in 20% oxygen had a statistically significant negative impact on colonoid growth (P-value < 0.0001, (CI) -0.1 to -0.29). The result applied to the group of independent experimental replicates together, although there were interindividual variations (Supplementary Figure 2B). As previously noted, the effect of the oxygen environment was more pronounced from day six. The LMM supports this because the interaction variable between time and oxygen concentration had less impact on growth than oxygen alone. How much the oxygen environment affected total cell mass depended on when the comparison was performed. Between oxygen concentrations, the disparity in cell mass was less pronounced on day two than on day twelve (e.g., Donor 8_A, Supplementary figure 2B). The interaction effect had a significant P-value, although with a CI overlapping zero (P-value 0.001, CI -0.03 to 0.01). Whether the colonoids remained undifferentiated or differentiated did not impact total cell mass. Overall, we found that human colonoids grew similarly or even better in a 2% oxygen environment as in a 20% oxygen environment (Figure 2E).
Colonoids proliferate and differentiate into specialized cell types in both 2% and 20% oxygen
A hallmark of human-derived colonoids is their ability to mimic the cellular composition of the human colon epithelium (). We investigated whether the same cell types were present in colonoids cultured in 2% and 20% oxygen. In eight independent experiments (Table 1), colonoids were cultured at 2% and 20% oxygen in parallel and divided into undifferentiated and differentiated colonoids (Figure 1A). Sections of paraffin-embedded colonoids were stained for KI67, MUC2, CK20, and CGA, to detect cells with proliferation potential, goblet cells, differentiated epithelial surface cells, and enteroendocrine cells, respectively. Images of the sections were captured with a confocal microscope (Figure 3), as described in the method section.
Figure 3
First, we investigated differences in cell marker expression between undifferentiated and differentiated colonoids. The expression of KI67 was significantly higher in undifferentiated compared to differentiated colonoids (Figures 3A, B, row 1) (P-value = 0.0008 for 2% and 0.003 for 20%). The undifferentiated colonoids expressed almost no MUC2 (Figures 3A, B, row 2). Differentiated colonoids expressed MUC2 in addition to the semilunar cell nuclei characteristic for goblet cells. The expression of MUC2 was significantly higher in differentiated vs. undifferentiated colonoids (Figure 3B, row 2) (P-value = 0.03 for 2% and 0.02 for 20%). CK20 expression was significantly increased in differentiated compared to undifferentiated colonoids (Figures 3A, B, row 3), (P-value = 0.009 for 2% and 0.004 for 20%). Co-staining of CK20 and MUC2 showed numerous cells positive for CK20 but negative for MUC2 (Supplementary Figure 1). Since absorptive cells are the most numerous in the colonic epithelium and simultaneously do not express MUC2, these CK20 positive, MUC2 negative cells probably represent absorptive cells. CGA was present in some cells within the differentiated colonoids but not in undifferentiated colonoids (Figure 3A, row 4), except for some expression in two independent replicates from D2 cultured in 20% oxygen (Figure 3B, row 4). A unique feature of colonic enteroendocrine cells is their cellular processes extending into the lumen (). This quality is also present in colonoid enteroendocrine cells, as depicted in Figure 3A, row 4. Due to the low number of cells expressing CgA, the increase in differentiated compared to undifferentiated colonoids was marginally non-significant (P-values for 2% and 20% = 0.06). In brief, cell markers for post-mitotic intestinal epithelial cells (IECs) had an increased expression in differentiated colonoids. IECs with a potential for proliferation were more abundant in undifferentiated colonoids. Whether the oxygen environment affects stem cell differentiation in colonoids is not previously described. Therefore, we compared the expression of cell markers across oxygen conditions. There were no significant differences in the expression of KI67, MUC2, CK20, or CGA between colonoids cultured in 2% and 20% oxygen environments (Figure 3C). Overall, this suggests that the oxygen environment does not influence the colonoid’s IECs composition.
Single-cell RNA-sequencing shows similar cell clusters in 2% and 20% oxygen
Our data showed that culturing colonoids in a low oxygen environment is possible, without differences in KI67, MUC2, CK20, or CGA protein expression between physiological hypoxia (2% oxygen) and supraphysiological 20% oxygen level. However, whether there are any alterations in the transcriptome of the different cell types when culturing colonoids in a low oxygen environment remains unknown. Thus, we performed scRNA-seq to investigate cell-dependent transcriptomic alterations between colonoids cultured in 2% vs. 20% oxygen (Figure 4). To annotate the cell clusters present within the scRNA-seq, we first created a supervised gene list by searching for gene markers in the PangloaDB (). The regulation of the cell marker genes was confirmed in an in-house bulk RNA-seq dataset of undifferentiated vs. differentiated colonoids cultured in 20% oxygen (SF1, sheet 9). Lastly, the gene list was utilized to annotate the cell clusters of the scRNA-seq (see the experimental outline in Figure 4A). Bulk RNA-seq of differentiated and undifferentiated colonoids (n=3) cultured at 20% showed that genes characteristic for stem and progenitor cells (ASPM, AXIN2, WPHB2, LGR5, MKI67, MYC, OLFM4, PCNA, BIRC5, SLC12A2, SMOC, ASCL2) were upregulated in the undifferentiated colonoids. Genes characteristic for absorptive colonocytes (ALP1, ANEP, CA1, CA2, FABP2, SLC26A3, FABP1), goblet- (FCGBP, MUCs 1, 2, 5, 3B, 13, 3A, PHGR1, PLA2G10, ZG16, TPSG1, SPINK), enteroendocrine- (CHGA, CHGAB, AFP, ENPP2, INSM1, SYP, ENO3, GCG),- and tuft cells (ALOX5, CDHR2, ESPN, RGS2, TRPM6, POU2F3) were upregulated in the differentiated colonoids (Figure 4B).
Figure 4
For the scRNA-seq, four samples of colonoids (2% undifferentiated, 2% differentiated, 20% differentiated, and 20% differentiated) were analyzed in separate batches (Figure 4A). The cell marker gene list confirmed by the bulk RNA-seq (Figure 4B) was utilized to separately characterize the cell clusters present in each UMAP-plot (Figure 4C). Furthermore, a supervised comparison of the genes regulated within each cluster was performed. In both 2% and 20% oxygen, differentiated cell clusters were present in the differentiated colonoids (first column of Figure 4C) and stem cell clusters and progenitor cell clusters were present in the undifferentiated colonoids (second column of Figure 4C). Within the differentiated clusters, the cells overlapped in their expression of cell type genes for absorptive-, goblet-, enteroendocrine- and tuft cells. Thus, making it difficult to further divide the cluster into, e.g., absorptive- and goblet cells. Consequently, we annotated the cluster as differentiated cells. Undifferentiated colonoids cultivated at 20% oxygen had more cells sharing gene expression with differentiated cells (i.e., late progenitor and differentiated clusters in the second column of Figure 4C) than colonoids cultivated in 2% oxygen, indicating that auto-differentiation was more prominent at high than low oxygen level. In undifferentiated colonoids cultured at 20% oxygen we also observed some cells (“unassigned cluster”) that appear to be slightly more differentiated than late progenitor cells within the same culture (Figure 4D and Figures 5A-D). Every condition had an mt-cluster representing dead or severely stressed cells. The mt-clusters and stressed clusters were more numerous in the differentiated conditions compared to the undifferentiated conditions in both 2% and 20% oxygen.
Figure 5

Cell cluster specific transcriptome in 2% and 20% oxygen. Enrichment analysis of upregulated genes in the stem, progenitor, and differentiated cell clusters from 2% vs. 20% oxygen culture (adjusted P <0.05). were generated with MetaCore+MetaDrug™ version 21.3 build 70600 (SF1, sheets 12-15). The dot plots display how (A) genes related to cell cycle and mitosis (A), genes associated with cell adhesions and cell junctions (B), hypoxia-related genes (C) and barrier genes (D) were expressed in the different cell type clusters. Cell clusters are listed along the x-axis, while genes are listed along the y-axis. The color intensity represents mean expression within a cluster, and the radius of the dot represents the fraction of cells in the cluster expressing a gene. (E, F) Bars indicate –Log10 false discovery rate (FDR), while the y-axis lists top three GO processes upregulated in stem-like cell clusters (E) and differentiated cell cluster (F) in 2% compared to 20% oxygen culture (Supplementary file SF1, sheets 14 and 15, respectively).
Each cell type specific cluster shared the same signature genes when looking at the fraction of cells that expressed the gene and the mean expression in the group (visualized by dot-blot in Figure 4D). For instance, the differentiated cell clusters had similar expressions of FABP1, MUC13, or PHGR1. The stem cell clusters had similar expressions of BIRC5, ASPM, and MKI67. Mean expression and the fraction of cells expressing specific genes (e.g., FABP1, BIRC5, and MUC13) were equal in cell clusters originating from 2% and 20% oxygen environments. The main difference between stem cell clusters and progenitor cell clusters was that progenitor cell clusters had a lower expression of classical stem cell genes (e.g., PCNA, SLC12A2, and MYC) while also sharing gene expression characteristics for differentiated cells (e.g., FABP1, CA1 or ENPP2). After each cluster was annotated, the cells were combined in a joint UMAP plot, where clusters with equal annotations in 2% and 20% oxygen merged as illustrated by the circles, rectangles, and oval shapes in Figure 4E. This observation suggests that the equal cell type clusters across oxygen environments share common characteristics. Overall, and corresponding to immunofluorescence data, the results from the scRNA-seq indicate that the oxygen environment does not alter the cell type composition of the colonoids.
Transcriptomic differences among cell clusters in 2% and 20% oxygen
Distinct cell types are exposed to different oxygen concentrations within the colon, with stem cells at the bottom of the crypts being exposed to a higher concentration than apical differentiated IECs adapted to thrive in an oxygen environment close to 0% (
Enrichment analysis showed that the stem-like cell clusters in both 2% oxygen (SF1, sheet 12) and 20% oxygen (SF1, sheet 13) had significant upregulation of gene network related to cell cycle and mitosis compared to progenitor and differentiated cells Figure 5A). Differentiated cell-clusters had upregulated networks related to e.g., cytoskeleton and cell junctions compared to stem and progenitor cells (Figure 5B and SF1, sheets 12-13). Thus, the cell clusters identified based on cell marker genes (Figure 4) showed molecular signatures (Figure 5) that characterize stem cell function and features in differentiated cells of the human colon epithelium (
Differentiated colonoids respond to a pro-inflammatory signal in both 2% and 20% oxygen but are hyperresponsive in supraphysiological oxygen
Upon exposure to signals from microbiota and immune cells, or DAMPs from, e.g., dying and infected cells, the colonic epithelium secrete immunomodulators such as chemokines and NGAL that regulate homeostasis and inflammation in the gut (
Figure 6

TNF+ Poly(I:C) induced chemokines and NGAL release from colonoids grown at high and low oxygen concentrations. Chemokines (A) and NGAL (B) in conditioned medium from differentiated untreated colonoids and after 24 hours of treatment with TNF + Poly(I:C). The treatment groups are represented on the x-axis, while the concentration of the target molecule is represented on the y-axis. In (A), CXCL2, CXCL5, CXCL10, CXCL12, CX3CL1, and CCL25 concentrations (pg/mL) in conditioned medium were detected by Bio-plex Pro-Human Chemokine Panel analysis. In (B) NGAL was detected by ELISA. In each panel, the violin plots show the differences between untreated and TNF+Poly(I:C) treated colonoids cultivated at 2% oxygen (blue) and 20% oxygen (red). Each independent experimental replicate is plotted as individual values, and the graph to the right in each panel shows paired data for each donor treated with TNF+Poly(I:C) at 2% (blue circles) and 20% (red triangles). Statistical analyses were performed using RM one-way ANOVA followed by Šídák’s multiple comparisons test. In left panel (B), NGAL was plotted and analyzed on log2 transformed data. Right panel (B) shows paired NGAL concentrations as pg/ml for each donor. See Table 1 for colonoid donor characteristics * < 0.05, ** < 0.005, *** < 0.001, and **** < 0.0001. ns = non-significant.
Short-time reduced oxygen levels alter the expression of genes related to differentiation, metabolism, mucus lining, and immune networks in differentiated colonoids
To examine how general functions in differentiated colonoids are directly affected by physiological hypoxia, we analyzed an in-house bulk RNA-seq dataset comparing fully differentiated, untreated colonoids cultured continuously at 20% oxygen with colonoids cultured at 2% oxygen for the last 40 hours [(
Figure 7

Significant changes in epithelial gene expression after a short time reduction of oxygen. The panels show data from bulk RNA-seq analysis of differentiated colonoids grown continuously at 20% or at 2% for the last 40 hours. (A) PCA plot of PC2 vs. PC3 for the complete dataset from GSE172404 (
The differentially expressed genes (Figure 7) were involved in all levels of cellular signaling networks, such as receptors and protein-tyrosine kinases (IL10RB, IL22RA, IFNAR2, NOD2, CXCR4, IFNGR2, JAK1, JAK3), transcription factors (FOXO3, FOSL2, JUN, JUNB, JUND), histone demethylases (KDM2A, KDM3A, KDM4B, KDM5B) and regulators of translation (NARS, WARS). Thus, a short-term (40 hours) reduction in oxygen level was associated with gene expression and translation alterations that may impact a broad spectrum of cellular responses. Indeed, among effector genes differentially expressed between 20% and 2% oxygen, we found genes related to stemness and differentiation (EFNA1, EFNA3, EFNA4, PCNA, MYCBP, E2F2, E2F3, E2F4, KLF4, TFF1, TFF2, TFF3, MUC17, MUC20, MUC20-OT1), genes involved in epithelial cell junctions (TJP-3, CLDN1, CLDN2, CLDN4, GJB3, CEACAM1, CTNNB1), genes involved in inflammation (IL1A, IL1B, TGFB1, IL18) and genes related to antigen presentation through MHC class I (CIITA, HLA-A, HLA-B, HLA-C) (Figures 7D, E).
Regarding the hypoxia response, we found, in line with previous observations, an increase in classical HIF-1 target genes CA9 and VEGF-A (
Discussion
This study is the first aiming to evaluate whether IEOs from the human colon can be cultured from single cells to fully differentiated colonoids in a continuously low oxygen environment close to in vivo conditions (physioxia). We examined how colonoids grew, differentiated, and responded to extracellular signals at supraphysiological 20% oxygen and an oxygen environment resembling physiological hypoxia with the 2% concentration based on our previous study showing that human differentiated colonoids adapted well to short time low (2%) oxygen (
To identify cells with proliferation potential, we stained with KI67 which was significantly more expressed in undifferentiated colonoids in both 2% and 20% oxygen. Protein expression of MUC2 and CK20 was significantly higher in differentiated colonoids than in undifferentiated colonoids, with the same tendency for CGA. There were no significant differences in 2% compared to 20% oxygen for any cell marker. Thus, human colonoids can be established from single stem cells which proliferate (KI67) and differentiate into colonoids with goblet cells (MUC2), absorptive (MUC2 negative, CK20 positive), and enteroendocrine (CGA) cells in both 2% and 20% oxygen. Some CK20-positive differentiated cells retained proliferating potential (KI67 positive) at 2% and 20% oxygen. A recent study determined that a Wnt-3A-, R-spondin- and Nogging-free medium is optimal for colonoid differentiation (57). Our differentiation media had less Wnt-3A than the growth medium (5% vs. 50%) but was not Wnt-3A free and may have stimulated more growth with colonoids having a higher proportion of cells with proliferation potential. Since the colonic epithelium has a proliferation niche at the crypt bottom (58), it may be beneficial to retain some proliferating cells during colonoid differentiation to resemble in vivo characteristics. Further studies are needed to evaluate the effects of media compositions, as well as oxygen gradients within the organoid cultures (59) for various experimental approaches.
To capture transcriptome-wide effects of oxygen at single-cell resolution, we performed a scRNA-seq on colonoids from four different conditions: undifferentiated and differentiated at 2% or 20% oxygen. Others have used scRNA-seq to discover novel cell types within the intestinal epithelium, heterogeneity in colorectal tumors, and functional cell alterations in IBD (
Enrichment analysis of genes in the differentiated cell clusters in 2% and 20% oxygen showed that “Responses to hypoxia” and “Responses to reduced oxygen level” were highly upregulated in 2%, although hypoxia-related genes could be detected in 20% oxygen as well. One of the upregulated HIF-1 targets was Gal-1, which has anti-inflammatory effects by modulating innate and adaptive immune cells’ fate and function (67). Gal-1 is upregulated in active ulcerative colitis, and Crohn’s disease, which the authors hypothesize is due to Gal-1 aiding in inflammation resolution (68). Treatment with Gal-1 in 2,4,6-trinitrobenzenesulfonic acid-induced colitis models has shown improved inflammation resolution (69). HIF-1α is degraded in the presence of high oxygen while it is stabilized in lower oxygen levels. Physiological hypoxia in the colonic epithelium stabilizes HIF-1, which is important for barrier integrity, xenobiotic clearance, and cellular metabolism (
Intestinal epithelium expresses multiple cytokine receptors and pattern recognition receptors (PRRs) providing crosstalk between microbiota, immune cells, and cell types of the epithelial lining (
To further examine the direct effects of physoxia vs supraphysiological oxygen level, we did an enrichment analysis of our in-house bulk RNA-seq dataset comparing differentiated, untreated colonoids cultured continuously at 20% oxygen with colonoids cultured at 2% oxygen for the last 40 hours (
This study aimed to assess whether human 3D colonoids could be cultivated in a 2% oxygen environment. A possible limitation is that the pericellular oxygen environment was not measured. Correlation between the environmental and pericellular oxygen environment is ruled by the physics of gas diffusion and oxygen distribution in cell cultures (89). Factors influencing this include media thickness, media mixing, connective forces, and cellular oxygen consumption. Okkelmann et al. imaged mouse small intestinal organoids in Matrigel, finding homogeneous oxygen distribution but with pericellular oxygen variations, perhaps dependent on inter-organoid variation in oxygen consumption (90). The optimal culturing condition to mimic in vivo conditions is an ongoing debate with several studies supporting physiological hypoxia (56). However, in3certain situations, a low oxygen environment could be disadvantageous. Wang et al. identified a Hopx+ colitis-associated regenerative stem cell contributing to mucosal repair in mice, showing that regeneration after mucosal injury is impeded in a hypoxic environment (2% oxygen) mediated by endoplasmic reticulum stress (91). In the present study, with human colonoids in a homeostatic environment, physiological hypoxia appeared beneficial for stem cell proliferation. The studies were performed in different species and different situations (homeostasis vs. injury repair), complicating comparison. Sabui et al. showed that hypoxia (1% oxygen or chemically induced) inhibited thiamin pyrophosphate and free thiamin uptake in colonic NCM460 cells. Dietary thiamin is primarily absorbed in the proximal part of the small intestine (92), while microbiota-derived thiamin can be absorbed by colonic epithelial cells (93). Contrarily, several studies show that physiological hypoxia is beneficial for colonic epithelium barrier integrity, cell metabolism, nutrient absorption, and maintaining a communalistic microbiome (
In summary, refined patient-derived organoid model systems are effective tools for understanding the interplay between oxygen level, microbiota, and innate immune signals in health and disease. IEOs are also valuable models for, e.g., colon cancer (94) and studies of enteric infections (95, 96). However, the relatively hypoxic gut environment influencing barrier function and inflammatory tone is not appropriately modeled by standard atmospheric oxygen culture conditions. Our data further support that colonoid studies can and should be performed in physioxia if physiological resemblance to the in vivo conditions is important. Stabilizing HIF-1α in the colonoids mimics HIF-1α stabilization in vivo, increasing the colonoid model’s translational value. Culturing colonoids in a low oxygen environment might benefit co-culturing studies with bacteria. In addition, culturing IEOs at various oxygen concentrations can give valuable information about the effects of oxygen on different cell types in health and diseases.
Statements
Data availability statement
The original contributions presented in the study are publicly available. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/- Accession Numbers - GSE218623, GSE217663, GSE172404.
Ethics statement
The studies involving human participants were reviewed and approved by Central Norway Regional Committee for Medical and Health Research Ethics. The patients/participants provided their written informed consent to participate in this study.
Author contributions
TB and AKS supervised the study. GAW, SG, IB, HKS, and TB contributed to the experimental design, generated, and analyzed data. AEØ and AKS collected and characterized patient samples. AF performed bioinformatic analysis. GAW, SG, HSK, AF, and TB made figure panels. GAW and TB drafted the manuscript. All authors contributed to the article and approved the submitted version.
Funding
This study was funded by the Faculty of Medicine and Health Science, NTNU (GAW, SG, IB, HKS, AF, AEØ, AKS, and TB), the Liaison Committee between the Central Norway Regional Health Authority, and NTNU (SG, IB, AF, AEØ, AS, and TB), the Liaison committee between St. Olav’s University Hospital and Faculty of Medicine and Health Sciences at NTNU (TB). The scRNA-seq was partly supported by 10X Genomic and NTNU Genomic Core Grant Program. The authors work within the Clinical Academic Group for Precision Medicine in Inflammatory Bowel Disease (CAG-IBD https://www.ntnu.edu/cag-ibd/), which is supported by The Liaison Committee for Education, Research and Innovation in Central Norway (Project no. 90545800).
Acknowledgments
This work was performed in collaboration with the Gastrointestinal Endoscopy Unit at the Department of Gastroenterology and Hepatology, St. Olav’s University Hospital. We thank Bjørn Munkvold, Zekarias Ginbot, Claire Louet, Ingrid Aass Roseth, and Liv Ryan for their technical assistance. The RNA-seq method and bioinformatics analyses were carried out in collaboration with the Genomics Core Facility (GCF) at the Norwegian University of Science and Technology (NTNU). We thank Geir Amund Hasle for valuable support with bioinformatics and data analysis. We thank Turid Follestad for advising with the Linear Mixed Model. Confocal imaging was carried out at the Cellular and Molecular Imaging Core Facility (CMIC), NTNU. Both GCF and CMIC are funded by the Faculty of Medicine and Health Sciences at NTNU, and the Central Norway Regional Health Authority. We thank Alexandre Gideon, Anders Hagen Jarmund, Cristoffer Sakshaug, and Bjørnar Sporsheim for valuable discussion and help with image analysis.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1095812/full#supplementary-material
Abbreviations
IEOs, Intestinal epithelial organoids; NGAL, Neutrophile gelatinase-associated lipocalin; MUC2, Mucin 2; CK20, Cytokeratin 20; CGA, Chromogranin A; EEC, Enteroendocrine cell; IBD, Inflammatory bowel disease; HIF-1, Hypoxia-inducible factor 1; TNF, Tumor necrosis factor; CGM, Complete growth medium; LMM, Linear mixed model; CI, Confidence interval; PRR, Pattern recognition receptor; TLR, Toll-like receptor; IEC, Intestinal epithelial cell; CTCF, Corrected total cell fluorescence; UMAP, Uniform Manifold Approximation and Projection; PCA, Principal Component Analysis; TFF, Trefoil factor family; TBS, Tris-Buffered Salin; PBS, Phosphate-Buffered Salin; RNA-seq, RNA sequencing; GO processes, Gene ontology processes.
References
1
LitvakYByndlossMXBäumlerAJ. Colonocyte metabolism shapes the gut microbiota. Science (2018) 362(6418):eaat9076. doi: 10.1126/science.aat9076
2
AllaireJMCrowleySMLawHTChangS-YKoH-JVallanceBA. The intestinal epithelium: Central coordinator of mucosal immunity. Trends Immunol (2018) 39(9):677–96. doi: 10.1016/j.it.2018.04.002
3
CrosnierCStamatakiDLewisJ. Organizing cell renewal in the intestine: stem cells, signals and combinatorial control. Nat Rev Genet (2006) 7(5):349–59. doi: 10.1038/nrg1840
4
GehartHCleversH. Tales from the crypt: new insights into intestinal stem cells. Nat Rev Gastroenterol Hepatology (2019) 16(1):19–34. doi: 10.1038/s41575-018-0081-y
5
PetersonLWArtisD. Intestinal epithelial cells: regulators of barrier function and immune homeostasis. Nat Rev Immunol (2014) 14(3):nri3608. doi: 10.1038/nri3608
6
DarwichASAslamUAshcroftDMRostami-HodjeganA. Meta-analysis of the turnover of intestinal epithelia in preclinical animal species and humans. Drug Metab Disposition (2014) 42(12):2016–22. doi: 10.1124/dmd.114.058404
7
BeumerJCleversH. Cell fate specification and differentiation in the adult mammalian intestine. Nat Rev Mol Cell Bio (2020) 22(1):39–53. doi: 10.1038/s41580-020-0278-0
8
BakkeIWalaasGABrulandTRøysetESvan Beelen GranlundAEscudero-HernándezCet al. Mucosal and faecal neutrophil gelatinase-associated lipocalin as potential biomarkers for collagenous colitis. J Gastroenterol (2021) 56(10):914–27. doi: 10.1007/s00535-021-01814-y
9
FearonERVogelsteinB. A genetic model for colorectal tumorigenesis. Cell (1990) 61(5):759–67. doi: 10.1016/0092-8674(90)90186-I
10
ØstvikAEGranlundAVBTorpSHFlatbergABeisvågVWaldumHLet al. Expression of toll-like receptor-3 is enhanced in active inflammatory bowel disease and mediates the excessive release of lipocalin 2. Clin Exp Immunol (2013) 173(3):502–11. doi: 10.1111/cei.12136
11
SungHFerlayJSiegelRLLaversanneMSoerjomataramIJemalAet al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin (2021) 71(3):209–49. doi: 10.3322/caac.21660
12
SatoTVriesRGSnippertHJvan de WeteringMBarkerNStangeDEet al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature (2009) 459(7244):262–5. doi: 10.1038/nature07935
13
BarkerNHuchMKujalaPvan de WeteringMSnippertHJvan EsJHet al. Lgr5(+ve) stem cells drive self-renewal in the stomach and build long-lived gastric units in vitro. Cell Stem Cell (2010) 6(1):25–36. doi: 10.1016/j.stem.2009.11.013
14
SchütteMRischTAbdavi-AzarNBoehnkeKSchumacherDKeilMet al. Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors. Nat Commun (2017) 8:14262. doi: 10.1038/ncomms14262
15
GopalakrishnanSHansenMDSkovdahlHKRosethIAvan Beelen GranlundAØstvikAEet al. Tofacitinib downregulates TNF and Poly(I:C)-dependent MHC-II expression in the colonic epithelium. Front Immunol (2022) 13. doi: 10.3389/fimmu.2022.882277
16
YooJHDonowitzM. Intestinal enteroids/organoids: A novel platform for drug discovery in inflammatory bowel diseases. World J Gastroenterol (2019) 25(30):4125–47. doi: 10.3748/wjg.v25.i30.4125
17
Sayoc-BecerraAKrishnanMFanSJimenezJHernandezRGibsonKet al. The JAK-inhibitor tofacitinib rescues human intestinal epithelial cells and colonoids from cytokine-induced barrier dysfunction. Inflammation Bowel Dis (2020) 26(3):407–22. doi: 10.1093/ibd/izz266
18
BarbáchanoAFernández-BarralABustamante-MadridPPrietoIRodríguez-SalasNLarribaMJet al. Organoids and colorectal cancer. Cancers (Basel) (2021) 13(11):2657. doi: 10.3390/cancers13112657
19
WakisakaYSugimotoSSatoT. Organoid medicine for inflammatory bowel disease. Stem Cells (2022) 40(2):123–32. doi: 10.1093/stmcls/sxab020
20
LiuLSaitz-RojasWSmithRGonyarLInJGKovbasnjukOet al. Mucus layer modeling of human colonoids during infection with enteroaggragative e. coli Sci Rep (2020) 10(1):10533. doi: 10.1038/s41598-020-67104-4
21
LehmannRLeeCMShugartECBenedettiMCharoRAGartnerZet al. Human organoids: a new dimension in cell biology. Mol Biol Cell (2019) 30(10):1129–37. doi: 10.1091/mbc.E19-03-0135
22
KeeleyTPMannGE. Defining physiological normoxia for improved translation of cell physiology to animal models and humans. Physiol Rev (2019) 99(1):161–234. doi: 10.1152/physrev.00041.2017
23
ZhengLKellyCJColganSP. Physiologic hypoxia and oxygen homeostasis in the healthy intestine. a review in the theme: Cellular responses to hypoxia. Am J Physiol-cell Ph (2015) 309(6):C350–C60.
24
SemenzaGL. Oxygen sensing, homeostasis, and disease. N Engl J Med (2011) 365(6):537–47. doi: 10.1056/NEJMra1011165
25
Kelly CalebJZhengLCampbell EricLSaeediBScholz CarstenCBayless AmandaJet al. Crosstalk between microbiota-derived short-chain fatty acids and intestinal epithelial HIF augments tissue barrier function. Cell Host Microbe (2015) 17(5):662–71.
26
HamerHMJonkersDVenemaKVanhoutvinSTroostFJBrummerRJ. Review article: the role of butyrate on colonic function. Aliment Pharmacol Ther (2008) 27(2):104–19. doi: 10.1111/j.1365-2036.2007.03562.x
27
KumarTPandeyRChauhanNS. Hypoxia inducible factor-1α: The curator of gut homeostasis. Front Cell Infect Mi (2020) 10.
28
MuenchauSDeutschRde CastroIJHielscherTHeberNNieslerBet al. Hypoxic environment promotes barrier formation in human intestinal epithelial cells through regulation of MicroRNA 320a expression. Mol Cell Biol (2019) 39(14):e00553–18. doi: 10.1128/MCB.00553-18
29
LouisNAHamiltonKECannyGShekelsLLHoSBColganSP. Selective induction of mucin-3 by hypoxia in intestinal epithelia. J Cell Biochem (2006) 99(6):1616–27. doi: 10.1002/jcb.20947
30
KellyCJGloverLECampbellELKominskyDJEhrentrautSFBowersBEet al. Fundamental role for HIF-1α in constitutive expression of human β defensin-1. Mucosal Immunol (2013) 6(6):1110–8. doi: 10.1038/mi.2013.6
31
ManresaMCTaylorCT. Hypoxia inducible factor (HIF) hydroxylases as regulators of intestinal epithelial barrier function. Cell Mol Gastroenterol Hepatol (2017) 3(3):303–15. doi: 10.1016/j.jcmgh.2017.02.004
32
DaneseSLevesqueBGFeaganBGJucovABhandariBRPaiRKet al. Randomised clinical trial: a phase 1b study of GB004, an oral HIF-1alpha stabiliser, for treatment of ulcerative colitis. Aliment Pharmacol Ther (2022) 55(4):401–11. doi: 10.1111/apt.16753
33
KumarBAdebayoAKPrasadMCapitanoMLWangRBhat-NakshatriPet al. Tumor collection/processing under physioxia uncovers highly relevant signaling networks and drug sensitivity. Sci Adv (2022) 8(2):eabh3375. doi: 10.1126/sciadv.abh3375
34
ChenCTangQZhangYYuMJingWTianW. Physioxia: a more effective approach for culturing human adipose-derived stem cells for cell transplantation. Stem Cell Res Ther (2018) 9(1):148. doi: 10.1186/s13287-018-0891-4
35
PiossekFBenekeSSchlichenmaierNMucicGDrewitzSDietrichDR. Physiological oxygen and co-culture with human fibroblasts facilitate in vivo-like properties in human renal proximal tubular epithelial cells. Chem Biol Interact (2022) 361:109959. doi: 10.1016/j.cbi.2022.109959
36
ShinDYHuangXGilCHAljoufiARopaJBroxmeyerHE. Physioxia enhances T-cell development ex vivo from human hematopoietic stem and progenitor cells. Stem Cells (2020) 38(11):1454–66. doi: 10.1002/stem.3259
37
PatelSPCalle GonzalezBPaoneNMuellerCFlossJCSousaMEet al. Effect of physiological oxygen on primary human corneal endothelial cell cultures. Transl Vis Sci Technol (2022) 11(2):33. doi: 10.1167/tvst.11.2.33
38
SkovdahlHKGopalakrishnanSSvendsenTDGranlundABakkeIGinbotZGet al. Patient derived colonoids as drug testing platforms–critical importance of oxygen concentration. Front Pharmacol (2021) 12. doi: 10.3389/fphar.2021.679741
39
SchindelinJArganda-CarrerasIFriseEKaynigVLongairMPietzschTet al. Fiji: an open-source platform for biological-image analysis. Nat Methods (2012) 9(7):676–82. doi: 10.1038/nmeth.2019
40
BatesDMächlerMBolkerBWalkerS. Fitting linear mixed-effects models using lme4. J Stat Software (2015) 67(1):1–48.
41
KuznetsovaABrockhoffPBChristensenRHB. lmerTest package: Tests in linear mixed effects models. J Stat Software (2017) 82(13):1–26. doi: 10.18637/jss.v082.i13
42
ØstvikAESvendsenTDGranlundADosethBSkovdahlHKBakkeIet al. Intestinal epithelial cells express immunomodulatory ISG15 during active ulcerative colitis and crohn’s disease. J Crohn’s Colitis (2020). doi: 10.1093/ecco-jcc/jjaa022
43
FranzénOGanL-MBjörkegrenJLM. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database (2019) 2019:baz046. doi: 10.1093/database/baz046
44
The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res (2019) 47(D1):D330–d8. doi: 10.1093/nar/gky1055
45
AshburnerMBallCABlakeJABotsteinDButlerHCherryJMet al. Gene ontology: tool for the unification of biology. Nat Genet (2000) 25(1):25–9. doi: 10.1038/75556
46
CarbonSIrelandAMungallCJShuSMarshallBLewisS. AmiGO: online access to ontology and annotation data. Bioinformatics (2009) 25(2):288–9. doi: 10.1093/bioinformatics/btn615
47
SlemcLKunejT. Transcription factor HIF1A: downstream targets, associated pathways, polymorphic hypoxia response element (HRE) sites, and initiative for standardization of reporting in scientific literature. Tumour Biol (2016) 37(11):14851–61. doi: 10.1007/s13277-016-5331-4
48
StuartTButlerAHoffmanPHafemeisterCPapalexiEMauckWM3rdet al. Comprehensive integration of single-cell data. Cell (2019) 177(7):1888–902.e21. doi: 10.1016/j.cell.2019.05.031
49
HippenAAFalcoMMWeberLMErkanEPZhangKDohertyJAet al. miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data. PloS Comput Biol (2021) 17(8):e1009290. doi: 10.1371/journal.pcbi.1009290
50
WickhamH. ggplot2: Elegant graphics for data analysis. Springer-Verlag New York (2016).
51
ThanasupawatTHammjeKAdhamIGhiaJ-EDel BigioMRKrcekJet al. INSL5 is a novel marker for human enteroendocrine cells of the large intestine and neuroendocrine tumours. The Gene Ontology Consortium (2013) 29(1):149–54. doi: 10.3892/or.2012.2119
52
ParikhKAntanaviciuteAFawkner-CorbettDJagielowiczMAulicinoALagerholmCet al. Colonic epithelial cell diversity in health and inflammatory bowel disease. Nature (2019) 567(7746):49–55. doi: 10.1038/s41586-019-0992-y
53
PawlowskiJKraftAS. Bax-induced apoptotic cell death. Proc Natl Acad Sci U S A (2000) 97(2):529–31. doi: 10.1073/pnas.97.2.529
54
ImHKGamazonERStarkALHuangRSCoxNJDolanME. Mixed effects modeling of proliferation rates in cell-based models: Consequence for pharmacogenomics and cancer. PloS Genet (2012) 8(2):e1002525. doi: 10.1371/journal.pgen.1002525
55
Mas-BarguesCSanz-RosJRomán-DomínguezAInglésMGimeno-MallenchLEl AlamiMet al. Relevance of oxygen concentration in stem cell culture for regenerative medicine. Int J Mol Sci (2019) 20(5):1195. doi: 10.3390/ijms20051195
56
AlvaRGardnerGLLiangPStuartJA. Supraphysiological oxygen levels in mammalian cell culture: Current state and future perspectives. Cells (2022) 11(19):3123. doi: 10.3390/cells11193123
57
WilsonSSMayoMMelimTKnightHPatnaudeLWuXet al. Optimized culture conditions for improved growth and functional differentiation of mouse and human colon organoids. Front Immunol (2021) 11. doi: 10.3389/fimmu.2020.547102
58
BarkerN. Adult intestinal stem cells: critical drivers of epithelial homeostasis and regeneration. Nat Rev Mol Cell Biol (2014) 15(1):19–33. doi: 10.1038/nrm3721
59
OkkelmanIANetoNPapkovskyDBMonaghanMGDmitrievRI. A deeper understanding of intestinal organoid metabolism revealed by combining fluorescence lifetime imaging microscopy (FLIM) and extracellular flux analyses. Redox Biol (2020) 30:101420. doi: 10.1016/j.redox.2019.101420
60
LiHCourtoisETSenguptaDTanYChenKHGohJJLet al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat Genet (2017) 49(5):708–18. doi: 10.1038/ng.3818
61
MouTDengWGuFPawitanYVuTN. Reproducibility of methods to detect differentially expressed genes from single-cell RNA sequencing. Front Genet (2019) 10:1331. doi: 10.3389/fgene.2019.01331
62
HigginsJMidgleyCBerghA-MBellSMAskhamJMRobertsEet al. Human ASPM participates in spindle organisation, spindle orientation and cytokinesis. BMC Cell Biol (2010) 11(1):85. doi: 10.1186/1471-2121-11-85
63
MartiniEWittkopfNGüntherCLeppkesMOkadaHWatson AlastairJet al. Loss of survivin in intestinal epithelial progenitor cells leads to mitotic catastrophe and breakdown of gut immune homeostasis. Cell Rep (2016) 14(5):1062–73. doi: 10.1016/j.celrep.2016.01.010
64
ScholzenTGerdesJ. The ki-67 protein: From the known and the unknown. J Cell Physiol (2000) 182(3):311–22. doi: 10.1002/(SICI)1097-4652(200003)182:3<311::AID-JCP1>3.0.CO;2-9
65
EstradaJCAlboCBenguríaADopazoALópez-RomeroPCarrera-QuintanarLet al. Culture of human mesenchymal stem cells at low oxygen tension improves growth and genetic stability by activating glycolysis. Cell Death Differentiation (2012) 19(5):743–55. doi: 10.1038/cdd.2011.172
66
TsaiC-CChenY-JYewT-LChenL-LWangJ-YChiuC-Het al. Hypoxia inhibits senescence and maintains mesenchymal stem cell properties through down-regulation of E2A-p21 by HIF-TWIST. Blood (2011) 117(2):459–69. doi: 10.1182/blood-2010-05-287508
67
SundbladVMorosiLGGeffnerJRRabinovichGA. Galectin-1: A jack-of-All-Trades in the resolution of acute and chronic inflammation. J Immunol (2017) 199(11):3721–30. doi: 10.4049/jimmunol.1701172
68
Papa GobbiRDe FrancescoNBondarCMugliaCChirdoFRumboMet al. A galectin-specific signature in the gut delineates crohn’s disease and ulcerative colitis from other human inflammatory intestinal disorders. BioFactors (2016) 42(1):93–105. doi: 10.1002/biof.1252
69
SantucciLFiorucciSRubinsteinNMencarelliAPalazzettiBFedericiBet al. Galectin-1 suppresses experimental colitis in mice. Gastroenterology (2003) 124(5):1381–94. doi: 10.1016/S0016-5085(03)00267-1
70
FurutaGTTurnerJRTaylorCTHershbergRMComerfordKNarravulaSet al. Hypoxia-inducible factor 1–dependent induction of intestinal trefoil factor protects barrier function during hypoxia. J Exp Med (2001) 193(9):1027–34. doi: 10.1084/jem.193.9.1027
71
PralLPFachiJLCorreaROColonnaMVinoloMAR. Hypoxia and HIF-1 as key regulators of gut microbiota and host interactions. Trends Immunol (2021) 42(7):604–21. doi: 10.1016/j.it.2021.05.004
72
GloverLEBowersBESaeediBEhrentrautSFCampbellELBaylessAJet al. Control of creatine metabolism by HIF is an endogenous mechanism of barrier regulation in colitis. Proc Natl Acad Sci (2013) 110(49):19820–5. doi: 10.1073/pnas.1302840110
73
HallCHTLeeJSMurphyEMGerichMEDranRGloverLEet al. Creatine transporter, reduced in colon tissues from patients with inflammatory bowel diseases, regulates energy balance in intestinal epithelial cells, epithelial integrity, and barrier function. Gastroenterology (2020) 159(3):984–98.e1. doi: 10.1053/j.gastro.2020.05.033
74
LeeJSWangRXAlexeevEELanisJMBattistaKDGloverLEet al. Hypoxanthine is a checkpoint stress metabolite in colonic epithelial energy modulation and barrier function. J Biol Chem (2018) 293(16):6039–51. doi: 10.1074/jbc.RA117.000269
75
ØstvikAEGranlundAvBuggeMNilsenNJTorpSHWaldumHLet al. Enhanced expression of CXCL10 in inflammatory bowel disease. Inflammation Bowel Dis (2013) 19(2):265–74. doi: 10.1002/ibd.23034
76
SkovdahlHKGranlundAØstvikAEBrulandTBakkeITorpSHet al. Expression of CCL20 and its corresponding receptor CCR6 is enhanced in active inflammatory bowel disease, and TLR3 mediates CCL20 expression in colonic epithelial cells. PloS One (2015) 10(11):e0141710. doi: 10.1371/journal.pone.0141710
77
ThorsvikSBakkeIGranlundARøysetESDamåsJKØstvikAEet al. Expression of neutrophil gelatinase-associated lipocalin (NGAL) in the gut in crohn’s disease. Cell Tissue Res (2018) 374(2):339–48. doi: 10.1007/s00441-018-2860-8
78
ThorsvikSDamåsJGranlundAFloTBerghKØstvikAet al. Fecal neutrophil gelatinase-associated lipocalin as a biomarker for inflammatory bowel disease. J Gastroenterol Hepatology (2017) 32(1):128–35. doi: 10.1111/jgh.13598
79
ThorsvikSGranlundASvendsenTDBakkeIRøysetESFloTHet al. Ulcer-associated cell lineage expresses genes involved in regeneration and is hallmarked by high neutrophil gelatinase-associated lipocalin (NGAL) levels. J Pathol (2019). doi: 10.1002/path.5258
80
BaggioliniM. Chemokines and leukocyte traffic. Nature (1998) 392(6676):565–8. doi: 10.1038/33340
81
Camba-GómezMArosaLGualilloOConde-ArandaJ. Chemokines and chemokine receptors in inflammatory bowel disease: Recent findings and future perspectives. Drug Discovery Today (2022) 27(4):1167–75. doi: 10.1016/j.drudis.2021.12.004
82
BonecchiRBianchiGBordignonPPD’AmbrosioDLangRBorsattiAet al. Differential expression of chemokine receptors and chemotactic responsiveness of type 1 T helper cells (Th1s) and Th2s. J Exp Med (1998) 187(1):129–34. doi: 10.1084/jem.187.1.129
83
TaubDDLloydARConlonKWangJMOrtaldoJRHaradaAet al. Recombinant human interferon-inducible protein 10 is a chemoattractant for human monocytes and T lymphocytes and promotes T cell adhesion to endothelial cells. J Exp Med (1993) 177(6):1809–14. doi: 10.1084/jem.177.6.1809
84
SunLLiTTangHYuKMaYYuMet al. Intestinal epithelial cells-derived hypoxia-inducible factor-1alpha is essential for the homeostasis of intestinal intraepithelial lymphocytes. Front Immunol (2019) 10:806. doi: 10.3389/fimmu.2019.00806
85
ColganSPFurutaGTTaylorCT. Hypoxia and innate immunity: Keeping up with the HIFsters. Annu Rev Immunol (2020) 38:341–63. doi: 10.1146/annurev-immunol-100819-121537
86
PavlidisPTsakmakiATreveilALiKCozzettoDYangFet al. Cytokine responsive networks in human colonic epithelial organoids unveil a molecular classification of inflammatory bowel disease. Cell Rep (2022) 40(13):111439. doi: 10.1016/j.celrep.2022.111439
87
KarhausenJIblaJCColganSP. Implications of hypoxia on mucosal barrier function. Cell Mol Biol (Noisy-le-grand) (2003) 49(1):77–87.
88
MatthijsenRADerikxJPKuipersDvan DamRMDejongCHBuurmanWA. Enterocyte shedding and epithelial lining repair following ischemia of the human small intestine attenuate inflammation. PloS One (2009) 4(9):e7045. doi: 10.1371/journal.pone.0007045
89
PavlackyJPolakJ. Technical feasibility and physiological relevance of hypoxic cell culture models. Front Endocrinol (2020) 11. doi: 10.3389/fendo.2020.00057
90
OkkelmanIAFoleyTPapkovskyDBDmitrievRI. Live cell imaging of mouse intestinal organoids reveals heterogeneity in their oxygenation. Biomaterials (2017) 146:86–96. doi: 10.1016/j.biomaterials.2017.08.043
91
WangYChiangILOharaTEFujiiSChengJMueggeBDet al. Long-term culture captures injury-repair cycles of colonic stem cells. Cell (2019) 179(5):1144–59.e15. doi: 10.1016/j.cell.2019.10.015
92
HoyumpaAMJr.StricklandRSheehanJJYarboroughGNicholsS. Dual system of intestinal thiamine transport in humans. J Lab Clin Med (1982) 99(5):701–8.
93
SaidHMOrtizASubramanianVSNeufeldEJMoyerMPDudejaPK. Mechanism of thiamine uptake by human colonocytes: studies with cultured colonic epithelial cell line NCM460. Am J Physiology-Gastrointestinal Liver Physiol (2001) 281(1):G144–G50. doi: 10.1152/ajpgi.2001.281.1.G144
94
BetgeJRindtorffNSauerJRauscherBDingertCGaitantziHet al. The drug-induced phenotypic landscape of colorectal cancer organoids. Nat Commun (2022) 13(1):3135. doi: 10.1038/s41467-022-30722-9
95
MartinsFHRajanACarterHEBaniasadiHRMaressoAWSperandioV. Interactions between enterohemorrhagic escherichia coli (EHEC) and gut commensals at the interface of human colonoids. mBio (2022) 13(3):e0132122. doi: 10.1128/mbio.01321-22
96
JangKKKaczmarekMEDallariSChenYHTadaTAxelradJet al. Variable susceptibility of intestinal organoid-derived monolayers to SARS-CoV-2 infection. PloS Biol (2022) 20(3):e3001592. doi: 10.1371/journal.pbio.3001592
Summary
Keywords
oxygen, intestinal epithelial cells (IECs), differentiation, proliferation, inflammatory bowel disease, transcriptome, single-cell RNA-sequencing (scRNAseq), chemokines (cytokines)
Citation
Walaas GA, Gopalakrishnan S, Bakke I, Skovdahl HK, Flatberg A, Østvik AE, Sandvik AK and Bruland T (2023) Physiological hypoxia improves growth and functional differentiation of human intestinal epithelial organoids. Front. Immunol. 14:1095812. doi: 10.3389/fimmu.2023.1095812
Received
11 November 2022
Accepted
09 January 2023
Published
27 January 2023
Volume
14 - 2023
Edited by
Hong Wan, Queen Mary University of London, United Kingdom
Reviewed by
Manuela Buettner, Hannover Medical School, Germany; Fernando Gabriel Chirdo, CONICET Instituto de Estudios Inmunológicos y Fisiopatalógicos (IIFP), Argentina; Ted S. Steiner, University of British Columbia, Canada
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Copyright
© 2023 Walaas, Gopalakrishnan, Bakke, Skovdahl, Flatberg, Østvik, Sandvik and Bruland.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Torunn Bruland, torunn.bruland@ntnu.no
This article was submitted to Mucosal Immunity, a section of the journal Frontiers in Immunology
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