Single-cell RNA sequencing of intestinal immune cells in neonatal necrotizing enterocolitis

doi:10.21203/rs.3.rs-2491577/v1

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Research Article

Single-cell RNA sequencing of intestinal immune cells in neonatal necrotizing enterocolitis

https://doi.org/10.21203/rs.3.rs-2491577/v1

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Journal Publication

published 11 Apr, 2023

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Purpose

Necrotizing enterocolitis (NEC) causes fatal intestinal necrosis in neonates, but its etiology is unknown. We analyzed the intestinal immune response to NEC.

Methods

Using single-cell RNA sequencing (scRNA-seq), we analyzed the gene-expression profiles of intestinal immune cells from four neonates with intestinal perforation (two with NEC and two without NEC). Target mononuclear cells were extracted from the lamina propria of the resected intestines.

Results

In all four cases, the major immune cells, such as T cells (15.1–47.7%), B cells (3.1–19.0%), monocytes (16.5–31.2%), macrophages (1.6–17.4%), dendritic cells (2.4–12.2%), and natural killer cells (7.5–12.8%), were present in similar proportions to those in the neonatal cord blood. Gene set enrichment analysis showed that the MTOR, TNF-α, and MYC signaling pathways were enriched in T cells of the NEC patients, suggesting upregulated immune responses related to inflammation and cell proliferation. In addition, all four cases exhibited a bias toward cell-mediated inflammation, based on the predominance of T helper 1 cells.

Conclusion

Intestinal immunity in NEC subjects exhibited stronger inflammatory responses compared to non-NEC subjects. Further scRNA-seq and cellular analysis may improve our understanding of the pathogenesis of NEC.

Necrotizing enterocolitis

single-cell sequencing

intestinal immune cells

gene expression

Necrotizing enterocolitis (NEC) causes inflammation and necrosis, and sometimes perforation, of the intestinal tract in neonates[1], resulting in life-threatening peritonitis that requires urgent surgery. With an incidence of 1 in 1,000 births, the mortality rate of NEC is significantly higher than that of diseases that cause neonatal intestinal perforation without necrosis, such as focal intestinal perforation (FIP)[2, 3]. In addition to a high mortality rate (20–30%), 25% of surviving patients with NEC have neurological complications, such as ischemia of the central nervous system and intraventricular hemorrhage, that may be caused by a systemic inflammatory response[4, 5]. If the extent of the intestinal necrosis is extremely large, the patients will have short bowel syndrome and will require long-term intravenous nutrition management after intestinal resection[6]. Improved prevention and treatment of NEC are therefore necessary.

Although several causes of NEC have been proposed, including insufficient intestinal blood flow[1, 7, 8], abnormal intestinal flora[9, 10], or malfunction of the intestinal barrier mechanism and intestinal immunity[11, 12], the exact pathogenesis of this disease is still unclear. One likely hypothesis is that it is related to an abnormality in the development and function of intestinal immunity. This is because NEC is a disease that characteristically occurs during the neonatal period, when the patients are exposed to large numbers of infectious organisms for the first time in their life, mainly through the intestine. The small intestine contains a wide variety of immune cells, such as T cells, B cells, monocytes, macrophages, dendritic cells (DCs), and natural killer (NK) cells[13, 14]. Studies on the relationship between NEC and immunity suggests that T helper 1 (Th1) cells may affect inflammation of the intestinal tract[11] and that a reduction in regulatory T (Treg) cells may be involved in the pathogenesis of NEC[15]. Several recently identified rare immune cell subsets, such as innate lymphoid cells (ILCs), may also be involved[16, 17]. However, it is not known which cells specifically are responsible for this inflammatory reaction. Although some microarray and bulk RNA sequencing studies on the human NEC have revealed links between NEC and proinflammatory cytokines and inflammatory pathways[18–21], few studies have used single-cell RNA sequencing (scRNA-seq), which can address the link between each immune cell subset and NEC in humans[11].

In this study, we aimed to identify immune cell populations that are strongly involved in NEC inflammation and evaluate their gene-expression profiles using scRNA-seq.

Study design and patients

The subjects were neonates who underwent intestinal resection due to intestinal perforation between October 2017 and November 2018 (Supplemental Fig. 1A). We defined cases with intestinal necrosis around the perforation as the NEC group, and cases without intestinal necrosis as the non-NEC group. Before the surgery, we explained to the family that we would use the resected intestine for this research and obtained their consent. This study was approved by the Nagoya University Bioethics Committee (2016-0336-02).

Single-cell isolation

A small bowel segment including the perforated area was resected during surgery. The specimen was immediately submerged in phosphate-buffered saline (PBS; FUJIFILM Waco, Osaka, Japan) and cooled with ice. We transported the segment to our laboratory as soon as possible and removed the intestinal epithelium in PBS at 37°C at a final concentration of 30 mM di-sodium dihydrogen ethylenediaminetetraacetate dihydrate (EDTA-2Na; Nacalai Tesque, Kyoto, Japan) for 10 min. We shredded the intestinal wall (without epithelium) with scissors, and then incubated it several times in Dulbecco's modified Eagle medium (DMEM; Nacalai Tesque) with 0.5 mg ml^− 1 Collagenase D (Roche, Basel, Switzerland) for 15 min at 37°C. Next, we dissociated the cells mechanically using pipette tips and passed them through a 70 µm filter.

Single cells were separated via density gradient centrifugation at 2,700 rpm at 23°C for 12 min using Percoll PLUS (GE Healthcare, IL, USA) 40% and 80%. The single-cell suspensions were stained with Fixable Viability Dye eFluor 780 (eBioscience, CA, USA) to assess cell viability.

Flow sorting was performed on a BD FACSAria Fusion Flow Cytometer (Becton, Dickinson and Company, NJ, USA) to collect viable cells. We gated cells suggestive of lymphocytes from forward scatter (FSC) and side scatter (SSC) (Supplemental Fig. 1B). Cells were collected in PBS and loaded onto the 10× Chromium Single Cell Platform.

Droplet-based single-cell RNA-seq

Viable cells were suspended in PBS with 0.1% bovine serum albumin (BSA) (FUJIFILM Waco, Osaka, Japan) and loaded onto the Chromium 10× Genomics platform (10× Genomics, Pleasanton, CA, USA) to construct an scRNA-seq library using the Chromium Single Cell 3′ Reagent Kits v. 2 (cat. nos. 120267 and 1000009), according to the manufacturer’s instructions. After scRNA-seq library quantification using a Qubit dsDNA HS Assay Kit (Life Technologies, Carlsbad, CA, USA; cat. no. Q32851) and an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA; cat. no. 5067–5592), the library was sequenced on an Illumina HiSeq4000 platform (Illumina, San Diego, CA, USA) with the 2 × 150 bp paired-end-reads option. The data was primarily analyzed using Cell Ranger v. 3.0.2 (10× Genomics), followed by Seurat v. 3.2.2[22] (https://satijalab.org/seurat/) and Monocle3 v. 0.2.3.3[23] (https://cole-trapnell-lab.github.io/monocle3/), together with Rstudio, which is a graphical interface for R (v. 1.2.5042, https://rstudio.com/).

Gene expression analysis

We used principal component analysis (PCA) and uniform manifold approximation and projection for dimension reduction (UMAP) to cluster the cells based on gene-expression data in R, Rstudio, and Seurat. We used singleR[24] to annotate cell types based on the human primary cell atlas[25] and calculated the number of cells in each type. For each cell type, gene data were extracted individually and clustered using PCA and UMAP with Monocle3. The cell subsets were identified based on marker-gene expression.

Gene expression in each cell type was analyzed with GSEA[26] using an average of log-transformed fold-change values (avg_logFC), which were obtained using the FindMarkers function in Seurat, from a comparison of the NEC and non-NEC groups. This analysis allowed us to assess the expression of signaling pathways based on the hallmark pathway sets in the molecular signatures database (MsigDB). Pathways with a nominal (NOM) p-value < 0.05 and an FDR p-value < 0.25 were considered significant.

Study cohort

We enrolled four cases of intestinal perforation in neonates. Two cases were in patients with NEC (identified as NEC1 and NEC2), and the other two were intestinal perforation without necrosis. One of these was a patient with FIP (patient FIP1), and the other was a patient with idiopathic perforation (IDP, IDP1) due to a fecal plug. In all four cases, the perforated and necrotic intestines were surgically resected.

The details of the patients’ clinical history and postoperative course are shown in Table 1. All cases were operated on after abdominal X-rays revealed pneumoperitoneum that suggested intestinal perforation. The time from symptom onset to surgery ranged from a few hours to two days. For NEC1, blood tests conducted a day before surgery revealed abnormalities such as low platelets and low neutrophils. FIP1 exhibited abdominal distension two days before the surgery. For IDP1, enterography for a fecal plug a day before surgery revealed pneumoperitoneum. Puncture drainage was performed initially, but the condition did not improve and laparotomy was performed the next day. NEC2 exhibited sudden abdominal distension and was operated on a few hours later. NEC1 died from hepatic hemorrhage three days postoperatively. NEC2, FIP1, and IDP1 survived the postoperative course.

Table 1

Patient characteristics
Case	Organ	Age at surgery (days)	Gestational age (weeks)	Birth weight (g)	Outcome	Preoperative course
NEC1	Ileum	6	24	699	Died	Low platelets and low neutrophils 1 d before
NEC2	Ileum	2	33	1,690	Survived	Sudden abdominal distension on operative day
FIP1	Jejunum	4	27	707	Survived	Abdominal distension 2 d before
IDP1	Ileum	4	28	737	Survived	Pneumoperitoneum and puncture drained 1 d before
NEC, necrotizing enterocolitis; FIP, focal intestinal perforation; IDP, idiopathic perforation

scRNA-seq library preparation and sequencing

We removed intestinal epithelium from the resected intestine and extracted live mononuclear cells to perform scRNA-seq of the immune cells in the intestinal lamina propria. The scRNA-seq library preparation was performed using the 10× Chromium system. For each case, we sequenced an average of 400 million reads for around 10,000 cells (NEC1: 8,312 cells; NEC2: 9,001 cells; FIP1: 8,295 cells; IDP1: 13,882 cells).

Cell type identification

Using the statistical analysis software R and packages for scRNA-seq (Seurat[22] and singleR[24]), we identified cell types from the gene expression data (Fig. 1A). Since most neutrophils were removed during mononuclear cell isolation, minor neutrophilic contamination was excluded from the analysis. We identified major immune cells such as T cells (15.1–47.7%), B cells (3.1–19.0%), monocytes (16.5–31.2%), macrophages (1.6–17.4%), DCs (2.4–12.2%), and NK cells (7.5–12.8%) among the blood mononuclear cells (BMCs) from each patient (Fig. 1B). They were distributed in similar proportions to those in the neonatal cord blood[27], based on the fact that T cells and monocytes comprised the majority of cells in the non-neutrophil category. The proportion of cell types differed among the patients, however. NEC2 had more T cells and B cells than the other patients, while IDP1 had fewer T cells and more macrophages and DCs. We did not identify any rare immune cells such as ILCs, either using singleR or based on the expression of transcription factors such as GATA3 and RORC. The identification of cell types was consistent with the expression of several marker genes.

We found non-BMCs such as smooth muscle cells, fibroblastic cells, endothelial cells, and other mesenchymal cells that remained after BMC isolation (Fig. 1C). However, we did not analyze these cells because they were essentially contaminants that remained after the isolation of BMCs.

Gene set enrichment analysis

We compared the gene-expression data for T cells, B cells, monocytes, and macrophages between the NEC (NEC1 and NEC2) and non-NEC (FIP1 and IDP1) groups using gene set enrichment analysis (GSEA; Fig. 2). The pathways that were enriched in the NEC group were mainly related to upregulated inflammatory reactions (MTOR signaling: HALLMARK_MTORC1_SIGNALING; TNF-α signaling: HALLMARK_TNFA_SIGNALING_VIA_NFKB) and cell proliferation (MYC signaling: HALLMARK_MYC_TARGETS_V1). Genes associated with the MTOR signaling pathway were upregulated in the NEC group in T cells, B cells, monocytes, and macrophages (normalized enrichment score [NES] = 1.84, 1.91, 1.86, 1.60, respectively; false discovery rate [FDR] = 0.031, 0.031, 0.030, 0.084, respectively).

Subsets of cell types

Within the cell types identified by singleR, we tried to identify cell subsets based on marker-gene expression.

Among T cells, we identified naïve, effector, memory, and Treg cells[28–30] (Fig. 3A, B). We were not able to completely discriminate between CD4⁺ and CD8⁺ naïve T cells because the expression of lineage markers in these cells was either low or negative, based on our scRNA-seq analysis. All four cases exhibited a high proportion of Th1 cells, but we did not identify any T helper 2 (Th2) cells (Fig. 3C). The cell counts of Th1 cells in the NEC group (11.0% and 15.1%) were lower than in the non-NEC group (36.1% and 44.3%). In addition to Th1 cells, we identified T helper 17 (Th17) (0.3–9.0%) and CD8⁺ effector T (Teff) cells (0.15–19.0%), which are usually present in Th1-biased responses[31]. Treg cells were also present (2.6–13.8%). The ratio of Th17 to Treg cell counts[32, 33] varied from 0.11 to 1.40. The proportion of CD8⁺ central memory T (TCM) cells for NEC1 was > 100 times greater than that for NEC2 (41.1% vs 0.36%) and higher than for the non-NEC cases (8.6% and 10.6% in FIP1 and IDP1, respectively), although the reason for this was unclear. The proportions of cell subsets for NEC2 differed substantially from those for the other three patients: the frequency of naïve T cells was higher (81.8%), and the frequencies of CD4⁺ TCM cells (0%), CD8⁺ TCM cells (0.36%), and CD8⁺ Teff cells (0.15%) were lower.

Among the B cells, we identified activated B cells, memory B cells, and long-lived plasma cells[34, 35] (Fig. 4A, B). However, only one of these subsets of B cells was predominant in each patient. There were more activated B cells in NEC2 (93.7%), memory B cells in NEC1 (96.3%) and FIP1 (85.6%), and long-lived plasma cells in IDP1 (93.0%) (Fig. 4C). These differences may be explained by the patient’s stage of inflammation: NEC2 may have been in the earliest phase of inflammation, NEC1 and FIP1 may have been in the middle phases, and IDP1 may have been in the final phase.

We differentiated between classical monocytes and nonclassical monocytes (Fig. 4D, E, F), although we could not identify two clusters using known monocyte markers (e.g., CD14, CD16, CD55, CD115, CD127, and CCR2)[36, 37]. Among the macrophages, we did not identify well-known subsets such as M1 and M2 macrophages, even using typical markers[38–40], possibly because of alterations in gene expression caused by inflammation (Supplemental Fig. 2A, B). We were also unable to clearly identify subsets of DCs and NK cells, possibly because the classification of subsets and markers for these groups is still controversial[41–44] (Supplemental Fig. 2C, D, E, F).

Using the scRNA-seq data for the intestinal BMCs obtained from these four neonatal intestinal perforation cases, we were able to identify the different cell types and subtypes according to their expression of gene markers. We were also able to examine the differences in the proportions of cell subsets and levels of gene expression between the patients with NEC and those without NEC. Although few studies have used scRNA-seq with human specimens, we evaluated this study as an appropriate preliminary research when additional research is done.

Our GSEA results showed that genes associated with MTOR and TNF-α signaling were enriched in the NEC group, suggesting that there is more cellular inflammation in NEC than in perforations without necrosis. Specifically, genes associated with MTOR signaling were enriched in T cells, B cells, monocytes, and macrophages in the NEC group.

Our cell-count data showed that all four cases had a Th1-biased immune response. This indicates that cell-mediated immunity and inflammation were dominant, and that allergic reactions and parasitic infections were not associated with these cases of NEC[45]. It could not be determined that the stronger cellular inflammation in NEC was related to the Th1-biased proportion in this study.

The differences in the numbers of cell subsets we found may reflect the differences in interval between symptom onset and surgery. Based on clinical observations of symptom onset, this interval was briefest for NEC2. In accordance with this, the predominant B cells (activated B cells) in NEC2 were at the earliest stage of differentiation among the four patients. In contrast, memory or plasma cells were predominant in the other patients. Similarly, naïve T cells, which are the most undifferentiated T cells in the periphery, were predominant in NEC2. The low abundance of CD4⁺ TCM, CD8⁺ TCM, and CD8⁺ Teff cells in NEC2 may also be explained by the fact that insufficient time had passed for these immune cell types to differentiate[30, 46, 47].

This study has several limitations. Research using human clinical specimens is very difficult to conduct. For this study, in particular, there was the challenge of selecting cases with similar characteristics, such as the interval between symptom onset and surgery, gestational age at delivery or disease onset, body weight, location of the perforation, and ethnicity. The differences in proportions of immune cells in the NEC and non-NEC groups may thus have been strongly biased by differences in these characteristics. With respect to monocytes and macrophages, the impact of fetal development, classification of cell subsets, and their dynamics during inflammation are still unclear, and this lack of information increases uncertainty in the data interpretation in this study.

In conclusion, we observed that intestinal perforation in the context of NEC was associated with a higher level of cellular inflammation than in cases without NEC. However, we were unable to identify the specific responses or immune cells that caused the inflammation and necrosis in NEC to be more severe because of the different characteristics of each case. Additional analyses with larger numbers of human cases or cellular analysis using animal model to examine the relation between stronger cellular inflammations and Th1-biased responses in NEC are thus warranted.

Acknowledgements

We would like to acknowledge all participating patients, their families, and the doctors who made this study possible. This work was supported by numerous grants and programs: the JSPS Grant-in-Aid for Scientific Research (B) Grant Number JP17H04235 for YT; the Grant-in-Aid for Young Scientists (B) Grant Number JP17K17000 for KO; the Grant-in-Aid for Challenging Research (Exploratory) Grant Number JP18K19503 for SS; the Joint Research Program of the Institute for Genetic Medicine in Hokkaido University for KO; the Cooperative Research Project Program of the Medical Institute of Bioregulation in Kyushu University for YT; and the Joint Research of the Exploratory Research Center on Life and Living Systems (ExCELLS) (program No. 19–308, 20–316) in the National Institutes of Natural Sciences for SS.

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Single-cell RNA sequencing of intestinal immune cells in neonatal necrotizing enterocolitis

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Abstract

Purpose

Methods

Results

Conclusion

Figures

Introduction

Patients And Methods

Study design and patients

Single-cell isolation

Droplet-based single-cell RNA-seq

Gene expression analysis

Results

Study cohort

scRNA-seq library preparation and sequencing

Cell type identification

Gene set enrichment analysis

Subsets of cell types

Discussion

Declarations

Acknowledgements

References

Additional Declarations

Supplementary Files

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