Effect of stretching on inflammation in a subcutaneous carrageenan mouse model analyzed at single-cell resolution

Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE2 and PGD2) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (Nos2) and pro-resolution (Arg1) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis.

Methods
All ultrasound data acquisition and measurements were performed by investigators blinded to intervention condition. Ultrasound images of the back were acquired under isoflurane anesthesia.  A high-frequency ultrasound scanner (Vevo 2100, Fujifilm VisualSonics, Toronto, Canada) in B mode with a 21 MHz transducer (MS 250) was used for optimal spatial resolution. A conductive gel was centrifuged for 5 minutes to remove air bubbles and spread over the skin. The transducer was stabilized with a clamp and mounted into an articulated arm to control the distance and the angle between the transducer and the skin surface. the transducer was oriented transversal or sagittal perpendicular to the skin of the back and centered on the lesion area. Total lesion area was calculated by averaging the lesion area measured at transversal and sagittal positions.

Flow cytometry

Inflammatory lesions were excised and minced in 5% FBS-DMEM, using a scalpel, then the suspension was filtered through a 70mm filter.  Isolated cells were counted using an automated cell counter TC20 (Bio-Rad, CA). For surface receptors, cells at 1 × 106/mL were stained with a mix of mouse monoclonal antibodies: To detect neutrophils (N=92) we used  the antibodies: APC anti-mouse Ly-6/Ly-6c(Gr1) and FITC anti-mouse CD45;   for macrophage populations (N=32): we used the following combination of antibodies: APC cy7 anti-mouse CD45,  APC anti-mouse F4/80, FITC anti-mouse Nos2 (iNOS), PE anti-mouse CD206. Stained cells were examined using a FACSCanto II Flow Cytometer (BD Biosciences, San Jose, CA) with FlowJo single cell analysis software.

Single cell and bulk library preparation and sequencing

Single cell library preparation was performed according to the manufacturer’s instructions for the 10× Chromium single cell kit (10x Genomics). The libraries were then pooled and sequenced on a NextSeq 2000 sequencer (Illumina). Single cell RNA seq data processing and quality control Read processing was performed using the 10x Genomics workflow (Zheng et al. 2017). Briefly, the Cell Ranger Single Cell Software Suite (v3.0.1) was used for demultiplexing, barcode assignment, and unique molecular identifier (UMI) quantification (http://software.10xgenomics.com/single cell/overview/welcome). The reads were aligned to a custom mm10 reference genome  (Genome Reference Consortium Mouse Build 38) extended with additional annotation for several frequently used mouse transgenes.  Both lanes per sample were merged using the ‘cellranger mkfastq’ function and processed using the ‘cellranger count’ function. In total, the Cell Ranger software detected 7,921 cells  per sample, sequenced at 23,326 reads and identifying 1,611 genes derived from 4,375 UMIs per cell on average across all samples. The following metrics were used to flag poor quality cells: number of genes detected, total number of UMIs, and percentage of molecules mapped to mitochondrial genes. Within the Seurat workflow, low quality and artifact cells were excluded by removing any cells that expressed fewer than 200 genes, and removed genes expressed in less than 3 cells. Gene expression matrices were transformed for better interpretability using the Seurat function ‘NormalizeData’. A total of 51,943 cells were included in the subsequent clustering and pseudo time analyses.

For cross-condition data integration and batch correction, ‘FindIntegrationAnchors’ and ‘IntegrateData’ were applied to data in Seurat, following the data integration vignette (https://satijalab.org/seurat/archive/v3.2/immune_alignment. html)

Targeted LC-MS/MS

Supernatants from the inflammatory lesions were placed in ice cold methanol containing deuterated internal standards (d8-5S-hydroxyeicosatetraenoic acid (5-HETE), d4-leukotriene B4 (LTB4), d4-prostaglandin E2 (PGE2) and d5-lipoxin A4 (LXA4); 500pg each) and homogenized using a PTFE Dounce (Kimble Chase). Proteins were allowed to precipitate (4oC), and lipid mediators were extracted using C18 solid-phase cartridges as described before (Dalli et al., 2018) Measurement of lipid mediators was carried out by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a QTrap 5500 (ABSciex, Framingham, MA) equipped with a Shimadzu LC-20AD HPLC and a Shimadzu SIL-20AC autoinjector (Shimadzu, Kyoto, Japan). An Agilent Eclipse Plus C18 column (100mm x 4.6 mm x 1.8 mm) maintained at 50oC was used with a gradient of methanol/water/acetic acid of 55:45:0.01 (v/v/v) to 100:0:0.01 at 0.4 ml/min flow rate. Multiple reaction monitoring (MRM) transitions were used to identify and quantify lipid mediators in samples, as compared with retention times of authentic standards run in parallel. Quantification was achieved using calibration curves constructed with synthetic standards for each mediator, after normalization to extraction recovery based on internal standards and followed by normalization to tissue weight.