游客,您好!欢迎您进入蓝细菌领域动态监测平台平台!登录 | 注册  科技信息监测服务平台  帮助中心  RSS
  您当前的位置: 首页 > 网页快照
Maternal-fetal immune responses in pregnant women infected with SARS-CoV-2 - Nature Communications
Methods . Human participants, clinical specimens, and definitions . Human maternal peripheral blood, umbilical cord blood, and placental tissues were obtained at the Perinatology Research Branch, an intramural program of the Eunice Kennedy Shriver National Institute of Child Health and Human In Development (NICHD), National Institutes of Health, U.S. Department of Health and Human Services, Wayne State University (Detroit, MI, USA), and the Detroit Medical Center (DMC) (Detroit, MI, USA). The collection and use of human materials for research purposes were approved by the Institutional Review Boards of Wayne State University School of Medicine and the Detroit Medical Center. All participating women provided written informed consent prior to sample collection. The study groups were divided into pregnant women who had a positive RT-PCR test for SARS-CoV-2 (nasopharyngeal test provided by the Detroit Medical Center) and healthy gestational age-matched controls. The demographic and clinical characteristics of the study groups are shown in Supplementary Table? 1 . The maternal peripheral blood was collected at admission, prior to the administration of any medication, and the umbilical cord blood and placental tissues were collected immediately after delivery. Gestational age was established based on the last menstrual period and confirmed by ultrasound examination. Labor was defined as the presence of regular uterine contractions with a frequency of ≥2 times every 10?min and cervical ripening. Term delivery was defined as birth ≥37 weeks of gestation. Preeclampsia was defined as new-onset hypertension that developed ≥20 weeks of gestation and proteinuria. Other clinical and demographic characteristics were obtained by review of medical records. Placental histopathological examination . Placentas were examined histologically by perinatal pathologists according to standardized DMC protocols. Briefly, three to nine sections of the placenta were examined, and at least one full-thickness section was taken from the center of the placenta; others were taken randomly from the placental disc. Acute and chronic inflammatory lesions of the placenta (maternal inflammatory response and fetal inflammatory response), as well as other placental lesions, were diagnosed according to established criteria 83 , as shown in Supplementary Table? 1 . Immunoassays . Immunoglobulin (Ig) M and G determination in the maternal blood and umbilical cord blood . Maternal peripheral blood and umbilical cord blood were collected into tubes without an anticoagulant, and the tubes were stored at room temperature for 30–60?min prior to centrifugation for 10?min at 1600× g and 4?°C. After centrifugation, the serum was collected and stored at ?80?°C. The serum concentrations of SARS-CoV-2 IgM and IgG were determined using the human anti-SARS-CoV-2 IgM and human anti-SARS-CoV-2 IgG ELISA kits (LifeSpan BioSciences, Inc., Seattle, WA, USA), according to the manufacturer’s instructions. Plates were read using the SpectraMax iD5 (Molecular Devices, San Jose, CA, USA) and analyte concentrations were calculated with the SoftMax Pro 7 (Molecular Devices). The sensitivities of the assays were 0.469?ng/mL (human anti-SARS-CoV-2 IgM) and 2.344?ng/mL (human anti-SARS-CoV-2 IgG). Determination of cytokine and chemokine concentrations in the maternal blood and umbilical cord blood . Maternal peripheral blood and umbilical cord blood were collected into tubes with an anticoagulant (EDTA or citrate), which were centrifuged for 10?min at 1600× g and 4?°C. Upon centrifugation, the plasma was collected and stored at ?80?°C prior to cytokine/chemokine determination. The V-PLEX Pro-Inflammatory Panel 1 (human) and Cytokine Panel 1 (human) immunoassays (Meso Scale Discovery, Rockville, MD, USA) were used to measure the concentrations of IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, and TNF (Pro-inflammatory Panel 1) or GM-CSF, IL-1α, IL-5, IL-7, IL-12/IL-23p40, IL-15, IL-16, IL-17A, TNF-β, and VEGF-A (Cytokine Panel 1) in the maternal and cord blood plasma, according to the manufacturer’s instructions. Plates were read using the MESO QuickPlex SQ 120 (Meso Scale Discovery) and analyte concentrations were calculated with the Discovery Workbench 4.0 (Meso Scale Discovery). The sensitivities of the assays were 0.21–0.62?pg/mL (IFN-γ), 0.01–0.17?pg/mL (IL-1β), 0.01–0.29?pg/mL (IL-2), 0.01–0.03?pg/mL (IL-4), 0.05-0.09?pg/mL (IL-6), 0.03-0.14?pg/mL (IL-8), 0.02-0.08?pg/mL (IL-10), 0.02-0.89?pg/mL (IL-12p70), 0.03-0.73?pg/mL (IL-13), 0.01–0.13?pg/mL (TNF), 0.08–0.19?pg/mL (GM-CSF), 0.05–2.40?pg/mL (IL-1α), 0.04–0.46?pg/mL (IL-5), 0.08–0.17?pg/mL (IL-7), 0.25–0.42?pg/mL (IL-12/IL-23p40), 0.09–0.25?pg/mL (IL-15), 0.88–9.33?pg/mL (IL-16), 0.19–0.55?pg/mL (IL-17A), 0.04–0.17?pg/mL (TNF-β), 0.55–6.06?pg/mL (VEGF-A). Immunophenotyping of maternal and cord blood leukocytes . Maternal peripheral blood and umbilical cord blood were collected into tubes containing EDTA. Fifty microlitres of whole blood were incubated with fluorochrome-conjugated anti-human mAbs (Supplementary Table? 5 ) for 30?min at 4?°C in the dark. After incubation, erythrocytes were lysed using BD FACS lysing solution (BD Biosciences, San Jose, CA, USA). For intracellular staining, erythrocyte lysis was not performed and the cells were instead fixed and permeabilized using the BD Cytofix/Cytoperm kit (BD Biosciences) prior to staining with intracellular fluorochrome-conjugated anti-human mAbs (Supplementary Table 5). Finally, leukocytes were washed and resuspended in 0.5?mL of FACS staining buffer (BD Biosciences) and acquired using the BD LSRFortessa flow cytometer and FACSDiva 6.0 software. The absolute number of cells was determined using CountBright absolute counting beads (Thermo Fisher Scientific/Molecular Probes, Eugene, OR, USA). The analysis and figures were performed using the FlowJo software version 10 (FlowJo, Ashland, OR, USA). Immunophenotyping included the identification of general leukocyte populations (neutrophils, monocytes, T cells, B cells, and NK cells), monocyte subsets, neutrophil subsets, T-cell subsets, and B-cell subsets. Specifically, the numbers of effector memory T cells (T EM ; CD3 + CD4 + /CD8 + CD45RA ? CCR7 ? ), na?ve T cells (T N ; CD3 + CD4 + /CD8 + CD45RA + CCR7 + ), central memory T cells (T CM ; CD3 + CD4 + /CD8 + CD45RA ? CCR7 + ), terminally differentiated effector memory T cells (T EMRA ; CD3 + CD4 + /CD8 + CD45RA + CCR7 ? ), Th1/Tc1-like T cells (CD3 + CD4 + /CD8 + CXCR3 + CCR6 + /CCR6 - ), Th2/Tc2-like T cells (CD3 + CD4 + /CD8 + CXCR3 ? CCR6 ? ), and Th17/Tc17-like T cells (CD3 + CD4 + /CD8 + CXCR3 - CCR6 + ) in maternal and cord blood are presented in Fig.? 3 . ROS production by neutrophils and monocytes . Fifty microlitres of maternal peripheral blood and cord blood were stimulated with 50??L of ROS assay mix containing 1:250 of ROS assay stain and ROS assay buffer [both from the ROS assay kit (eBioscience, San Diego, CA, USA)] and 1??L of phorbol myristate acetate (PMA; 3??g/mL) (Millipore Sigma, Burlington, MA, USA). The unstimulated group received 1:250 ROS assay mix and 1× phosphate-buffered saline (PBS) (Thermo Fisher Scientific/Gibco, Grand Island, NY, USA). The cells were incubated at 37?°C with 5% CO 2 for 60?min. Following incubation, erythrocytes were lysed using ammonium–chloride–potassium (ACK) lysing buffer (Lonza, Walkersville, MD, USA), and the resulting leukocytes were collected after centrifugation at 300× g for 5?min. Next, leukocytes were resuspended in 0.5?mL of 1× PBS and acquired using the BD LSRFortessa flow cytometer and FACSDiva 6.0 software to measure ROS production by neutrophils and monocytes. The analysis and figures were performed using the FlowJo software version 10. Single-cell RNA sequencing . Preparation of single-cell suspensions . Single-cell suspensions were prepared from the BP, PV, and CAM, as previously described with modifications 36 . Digestion of placental tissues was performed using collagenase A (Sigma Aldrich, St. Louis, MO, USA) or the enzyme cocktail from the Umbilical Cord Dissociation Kit (Miltenyi Biotec, San Diego, CA, USA). Next, tissue suspensions were washed with 1× PBS and filtered through a cell strainer (Miltenyi Biotec). Cell pellets were collected after centrifugation at 300× g for 10?min at 20?°C. Erythrocytes were lysed using ACK lysing buffer and the reaction was stopped by washing with 0.04% bovine serum albumin (Sigma Aldrich) in 1× PBS. Then, the cell pellets were collected after centrifugation at 300× g for 10?min at 20?°C and resuspended in 1× PBS for cell counting using an automatic cell counter (Cellometer Auto 2000; Nexcelom Bioscience, Lawrence, MA). Dead cells were removed from the cell suspensions using the Dead Cell Removal Kit (Miltenyi Biotec) to obtain a final cell viability of ≥80%. Single-cell library preparation using the 10× Genomics platform . Viable cells were used for single-cell RNA-seq library preparation following the protocol for the 10× Genomics Chromium Single Cell 3′ Gene Expression Version 3 Kit (10× Genomics, Pleasanton, CA, USA). Briefly, cell suspensions were loaded into the Chromium Controller to generate gel beads in emulsion (GEM), each containing a single cell and a single Gel Bead with barcoded oligonucleotides. Reverse transcription of mRNA into complementary (c)DNA was performed using the Veriti 96-well Thermal Cycler (Thermo Fisher Scientific, Wilmington, DE, USA). The resulting cDNA was purified using Dynabeads MyOne SILANE (10× Genomics) and the SPRIselect Reagent (Beckman Coulter, Indianapolis, IN, USA). cDNA amplicons were optimized via enzymatic fragmentation, end-repair, and A-tailing followed by the incorporation of adapters and sample index by ligation. The sample index PCR product was amplified using the Veriti 96-well Thermal Cycler. The Agilent Bioanalyzer High Sensitivity Chip (Agilent Santa Clara, CA, USA) was used to analyze and quantify the final library construct. The Kapa DNA Quantification Kit for Illumina platforms (Kapa Biosystems, Wilmington, MA, USA) was used to quantify the DNA libraries, following the manufacturer’s instructions. Sequencing . 10× scRNA-seq libraries were sequenced on the Illumina NextSeq 500 in the Genomics Services Center (GSC) of the Center for Molecular Medicine and Genetics (Wayne State University School of Medicine, Detroit, MI, USA). The Illumina 75 Cycle Sequencing Kit (Illumina, San Diego, CA, USA) was used with 58 cycles for R2, 26 for R1, and 8 for I1. Genotyping . DNA was extracted from maternal peripheral blood and umbilical cord blood/tissue using DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany), following manufacturer’s instructions modified with the addition of 4??l RNase A (100?mg/mL) (Qiagen) and incubation in 56?°C. Purified DNA samples were quantified using Qubit TM dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA). Two platforms were used for genotyping: (i) low-coverage (~0.4×) whole-genome sequencing imputed to 37.5?M variants using the 1000 Genomes database (Gencove, New York, NY, USA); and (ii) Infinium Global Diversity Array-8 v1.0 Kit microarrays processed by the Advanced Genomics Core of University of Michigan (Ann Arbor, MI, USA). For the array platform, genotype information was converted to vcf format using “iaap-cli gencall” and “gtc_to_vcf.py” from Illumina, and imputation to 37.5?M variants using the 1000 Genomes haplotype references was done using the University of Michigan Imputation Server ( https://imputationserver.sph.umich.edu/ ). The maternal/fetal relationship of the genotyped samples was ascertained using plink2 KING-robust kinship analysis 84 . The vcf files from the two platforms were then merged together and filtered for high-quality imputation and coverage for at least 10 scRNA-seq transcripts using bcftools. scRNA-seq data analysis . Sequencing data were processed using Cell Ranger version 4.0.0 from 10× Genomics for de-multiplexing. The fastq files were then aligned using kallisto 85 , and bustools 86 summarized the cell/gene transcript counts in a matrix for each sample using the “lamanno” workflow for scRNA-seq. Each library was then processed using DIEM 87 to eliminate debris and empty droplets. In parallel, “cellranger counts” were also used to align the scRNA-seq reads using the STAR 88 aligner to produce the bam files necessary for demultiplexing the individual of origin, based on the genotype information using souporcell 89 and demuxlet 90 . We removed any droplet/GEM barcode that was assigned to doublet or ambiguous cells in demuxlet or souporcell, and only those cells that could be assigned a pregnancy case and maternal/fetal origin were kept. All count data matrices were then normalized and combined using the “NormalizeData,” “FindVariableFeatures,” and “ScaleData” methods implemented in the Seurat package in R (Seurat version 3.1, R version 4.0.0) 91 , 92 . Next, the Seurat “RunPCA” function was applied to obtain the first 100 principal components, and the different libraries were integrated and harmonized using the Harmony package in R version 1.0 93 . The top 30 harmony components were then processed using the Seurat “runUMAP” function to embed and visualize the cells in a two-dimensional map via the Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) algorithm 94 , 95 . To label the cells, the SingleR 96 package in R version 1.3.8 was used to assign a cell-type identity based on our previously labeled data as a reference panel (as performed in ref. 36 ). Cell type abbreviations used are STB, syncytiotrophoblast; EVT, extravillous trophoblast; CTB, cytotrophoblast; npiCTB, non-proliferative interstitial cytotrophoblast; LED, lymphoid endothelial decidual cell; and NK, natural killer cell. Genes utilized to distinguish Macrophage-1, Macrophage-2, Stromal-1, Stromal-2, and Stromal-3 cell types are provided in Supplementary Data? 7 . Differential gene expression for scRNA-seq data . To identify differentially expressed genes, we created a pseudo-bulk aggregate of all the cells of the same cell type/location/origin. For each combination, we only used samples with more than 20 cells. The negative binomial model implemented in the DESeq2 R package version 1.28.1 97 was used to calculate the log 2 FC between SARS-CoV-2 (+) and healthy pregnant women. A term for each library was added to the DESeq2 model to correct for technical batch effects. We also evaluated the contribution of additional covariates, but their impact was minimal when compared to the model that adjusts for batch effects only. The p -values were adjusted using the Benjamini-Hochberg false-discovery rate method (FDR) 98 , and the DEGs were selected based on an adjusted p -value?highest log 2 FC across T-cell, Macrophage-2, and Stromal-1 cell types were further illustrated using violin plots representing the single-cell gene expression data in log 10 [transcripts per million]. Comparison with previous scRNA-seq SARS-CoV-2 studies . Single-cell RNA-seq data showing the effects of SARS-CoV-2 on peripheral T cells were obtained from a previous study 37 . The log 2 FCs from this previous study were compared to those obtained here in maternal T cells from the PV and BP (PVBP) and the CAM. The comparison was visualized with scatter plots using the ggplot2 R package version 3.3.2 and Spearman’s correlation analysis. Additionally, this previously generated set of SARS-CoV-2-associated genes in T cells was used to repeat the FDR p -value adjustment to reduce the burden of multiple testing in CAM-derived maternal T cells and provided a long list of genes. This list of genes was further analyzed with the clusterProfiler in R version 4.0.1 to perform over-representation analysis (ORA). GO and pathway enrichment analysis for scRNA-seq data . The clusterProfiler in R version 4.0.1 99 was used to perform ORA based on the GO, KEGG (release 90.0+?/05-29 100 , 101 ), and Reactome databases. P-values were adjusted for multiple comparisons using the FDR method 98 . The functions “enrichPathway” and “enrichKEGG” from “clusterProfiler” were used to perform ORA separately for each list of genes obtained as differentially expressed for each cell type, placental compartment, and maternal/fetal origin. Only results that were significant after correction were reported with q ?computational pipeline Viral-Track was used to study viruses in raw scRNA-seq data (github.com/PierreBSC/Viral-Track) 38 . A combined index of both the host GRCH37(hg19) and viral reference genomes was constructed in Viral-Track. The viral genomes were downloaded from the Virusite database version 2020.3 103 that includes all published viruses, viroids, and satellites (NCBI RefSeq). Afterward, the STAR aligner was used to map reads to the indexed host and viral genome. Viral genomes were filtered based on read-map quality, nucleotide composition, sequence complexity, and genome coverage. Sequence complexity was calculated by computing the average nucleotide frequency and Shannon’s entropy. Reads with a sequence entropy above 1.2, genome coverage greater than 5%, and longest contig longer than three times the mean read length are required for a viral segment to be considered present (default thresholds empirically defined by Viral-Track). As no viral reads were detected in our PVBP and CAM libraries, the correct implementation of the Viral-Track pipeline was validated by reanalyzing the data of bronchoalveolar lavage samples of patients with severe and mild SARS-CoV-2 38 and reproducing the detection of SARS-CoV-2 and human metapneumovirus. RNA sequencing of maternal blood and cord blood . RNA isolation and sequencing . Total RNA was isolated from the maternal blood and cord blood collected into PAXgene Blood RNA tubes using the PAXgene Blood RNA Kit (Qiagen), following the manufacturer’s instructions. The purity, concentration, and integrity of the RNA samples were assessed using the NanoDrop 1000 spectrophotometer (Thermo Scientific, Wilmington, Delaware, USA) and the Bioanalyzer 2100 (Agilent Technologies, Wilmington, Delaware, USA). The RNA-seq library was prepared by BGI Genomics (BGI ShenZhen, China) using the Oligo dT Stranded mRNA library preparation protocol (BGI, Hong Kong). Paired-end sequence reads (at least 50 million paired-end reads) of 150?bp length were generated using DNBseq (MGI-G400), and the raw data were provided by BGI. Bulk RNA sequencing data analysis . Transcript abundance from RNA-seq reads was quantified with Salmon 104 . The differential expression of genes between groups was tested by fitting a negative binomial distribution model implemented in DESeq2. The model included maternal age, BMI, nulliparity, labor status, and delivery route as covariates. Genes were filtered out if not detected in at least two samples regardless of the infection status. Genes with a minimum FC of 1.25-fold and an adjusted p- value ( q -value) of <0.1 were considered differentially expressed. The DEGs for each group comparison were used as input in iPathwayGuide (ADVAITA Bioinformatics, Ann Arbor, MI, USA) 105 , 106 to identify the significantly impacted biological processes and pathways. Volcano plots were used to display the evidence of differential expression for each comparison. The differences in SARS-CoV-2-associated gene expression changes between maternal and cord blood were tested using negative binomial models implemented in the DESeq2 package. These models included the main effects of disease status, sample type (maternal blood vs. cord blood), and their interactions. A minimum difference in FC of 1.25 and a FDR-adjusted p -value ( q -value)?umbilical cord blood . The log 2 FC associated with SARS-CoV-2 in the maternal blood and cord blood was compared to those obtained from immune cell types in scRNA-seq of the placental tissues. For each tissue and/or cell type, the FC was obtained from the DESeq2 model as described above by comparing SARS-CoV-2 (+) pregnancies to healthy controls. The FC were standardized by dividing by the standard error provided by the DESeq2 model, given the difference in the two technologies. Only immune-relevant cell types that had differentially expressed genes in the single-cell analysis were used for comparison: maternal and fetal T cell (PVBP and CAM), maternal Macrophage-2 (CAM), and maternal Monocyte (CAM). The comparisons of the effects of SARS-CoV-2 were based on the Spearman correlation and visualized with scatter plots using the ggplot2. Detection of SARS-CoV-2 RNA/proteins in the placenta . Detection of SARS-CoV-2 RNA in the placenta . Total RNA was isolated from the BP, PV, and CAM using QIAshredders and the RNeasy Mini Kit (both from Qiagen), according to the manufacturer’s instructions. Positive and negative controls were SARS-Related Coronavirus 2 (SARS-CoV-2) External Run Control and Negative Control (both from ZeptoMetrix, Buffalo, NY, USA). Following the instructions from the CDC-2019 Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel, cDNA was synthesized using TaqPath TM 1-Step RT-qPCR Master Mix, CG (Thermo Fisher Scientific/Applied Biosystems, Frederick, MD, USA) and primers from the 2019-nCoV RUO Kit (Integrated DNA Technologies, Newark, NJ, USA). Reactions were incubated at 25?°C for 2?min followed by 50?°C for 15?min. Initial denaturation was set for 2?min at 95?°C followed by 45 amplification cycles at 95?°C for 3?s and 55?°C for 30?s. A cycle of quantification ( C q value) less than 45 indicates a positive result. Two positive PCR controls were used: 2019-nCoV_N (virus) and Hs_RPP30 (human) (both from Integrated DNA Technologies). Each PCR sample was run in duplicate. RNA extractions were also performed using QIAamp Viral RNA Mini Kit (Qiagen) and results were comparable to those generated using the RNeasy Mini Kit. SARS-CoV-2 viral particle sensitivity assay . For each experiment ( n ?=?3), nine pieces of freshly collected PV were used. Eight of the tissue pieces were spiked with increasing numbers of viral particles [SARS-Related Coronavirus 2 (SARS-CoV-2) External Run Control] (0, 5, 10, 50, 100, 500, 1000, or 5000 particles/homogenate) and a piece of tissue was spiked with SARS-Related Coronavirus 2 (SARS-CoV-2) Negative Control prior to mechanical digestion. Total RNA was isolated using the RNeasy Mini Kit, according to the manufacturer’s instructions. Detection of SARS-CoV-2 RNA was performed as described above. Detection of SARS-CoV-2 proteins in the placenta . Five-?m-thick tissue sections of formalin-fixed, paraffin-embedded PV, BP, and the CAM were cut, mounted on SuperFrost ? Plus microscope slides (Erie Scientific LLC, Portsmouth, NH, USA), and subjected to immunohistochemistry using SARS-CoV/SARS-CoV-2 (COVID-19) spike antibody [1A9] [Catalog. No: GTX632604 (IHC-P application), dilution 1:100] (GeneTex, Irvine, CA, USA) and SARS-CoV-2 (COVID-19) nucleocapsid antibody [Catalog. No. GTX135357 (IHC-P application), dilution 1:100] (GeneTex). To serve as a positive control, placental tissues from pregnant women were spiked with SARS-CoV-2 (Isolate: USA/WA1/2020) (ZeptoMetrix) Culture Fluid (heat-inactivated). Spiked tissues were subjected to immunohistochemistry using SARS-CoV/SARS-CoV-2 (COVID-19) spike antibody [1A9] and SARS-CoV-2 (COVID-19) nucleocapsid antibody. Staining was performed using the Leica Bond-Max automatic staining system (Leica Microsystems, Wetzlar, Germany) with the Bond Polymer Refine Detection Kit (Leica Microsystems). The mouse isotype [Catalog. No: IS75061-2 (IHC-P application), no dilution needed] (Agilent) and rabbit isotype [Catalog. No: IS60061-2 (IHC-P application), no dilution needed] (Agilent) were used as negative controls. Tissue slides were then scanned using the Vectra Polaris Multispectral Imaging System (Akoya Biosciences, Marlborough, MA, USA) and images were analyzed using the Phenochart v1.0.8 image software (Akoya Biosciences). Supplementary Table? 6 summarizes the number of slides included in this study. Detection of SARS-CoV-2 viral RNA in formalin-fixed paraffin-embedded placental tissues . Formalin-fixed paraffin-embedded (FFPE) placental tissues that showed a positive signal for SARS-CoV-2 spike or nucleocapsid proteins as indicated by immunohistochemistry were used for detection of SARS-CoV-2 viral RNA. Total RNA was isolated from 6-14 sections of 10-?m-thick FFPE BP, PV, and the CAM using the PureLink TM FFPE Total RNA Isolation Kit (Invitrogen), according to the manufacturer’s instructions. Total RNA was also isolated from spiked tissues as described above. Following the instructions from the CDC-2019 Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel, cDNA was synthesized using TaqPath TM 1-Step RT-qPCR Master Mix, CG, and primers from the 2019-nCoV RUO Kit. Reactions were incubated at 25?°C for 2?min followed by 50?°C for 15?min. Initial denaturation was set for 2?min at 95?°C followed by 45 amplification cycles at 95?°C for 3?s and 55?°C for 30?s. A cycle of quantification ( C q value) less than 45 indicates a positive result. Two positive PCR controls were used: 2019-nCoV_N (virus) and Hs_RPP30 (human). Each PCR sample was run in duplicate. Molecular microbiology . Sample collection . Swabs (FLOQSwabs; COPAN, Murrieta, CA, USA) for molecular microbiology were collected from the CAM, the amnion–chorion interface of the placental disc, and the placental villous tree. These swabs were stored at ?80?°C until DNA extractions were performed. DNA extraction . All DNA extractions were completed within a biological safety cabinet using a DNeasy Powerlyzer Powersoil Kit (Qiagen, Germantown, MD, USA), with minor modifications to the manufacturer’s instructions as previously described 80 , 81 . Personnel wore sterile surgical masks, gowns, and gloves during the procedure. Briefly, following UV treatment, 400??L of Powerbead solution, 200??L of phenol:chloroform:isoamyl alcohol (pH 7–8), and 60??L of pre-heated solution C1 were added to the bead tubes. The swab samples were added to the tubes, incubated for 10?min, and then mechanically lysed for two rounds of 30?s each using a bead beater. Following a 1?min centrifugation and transferring of the supernatant to new tubes, 1.0??L of PureLink ? RNase A (20?mg/mL) (Invitrogen), 100??L of solution C2, and 100??L of solution C3 were added. The tubes were incubated at 4?°C for 5?min and centrifuged for 1?min. After transferring the lysates to new tubes, 650??L of solution C4 and 650??L of 100% ethanol were added. Next, 635??L of the lysate were loaded onto the filter columns and centrifuged for 1?min, discarding the flowthrough. This wash step was repeated three times to ensure all the lysates passed through the columns. Following the washes, 500??L of solution C5 were added to the filter columns. After a 1?min centrifugation, the flowthrough was discarded and the tubes were centrifuged again for 2?min to dry the spin columns. The spin columns themselves were transferred to a clean 2.0?mL collection tube, and 60??L of pre-heated solution C6 were added directly to the center of the spin columns. After a 5?min incubation at room temperature, the DNA was eluted via a 1?min centrifugation. Purified DNA was then transferred to clean 2.0?mL collection tubes and immediately stored at ?20?°C. Twelve extractions of sterile FLOQSwabs were included as technical controls for potential background DNA contamination. 16s rRNA gene quantitative real-time PCR . Total bacterial DNA abundance within samples was measured via amplification of the V1–V2 region of the 16S rRNA gene according to the protocol of Dickson et al. 107 with minor modifications, as previously described 80 , 81 . These modifications included the use of a degenerative forward primer (27f-CM: 5′-AGA GTT TGA TCM TGG CTC AG-3′) and a degenerate probe containing locked nucleic acids (+) (BSR65/17: 5′-56FAM-TAA?+?YA?+?C ATG?+?CA?+?A GT?+?C GA-BHQ1-3′). Each 20?μL reaction contained 0.6?μM of 27f-CM primer, 0.6?μM of 357R primer (5′-CTG CTG CCT YCC GTA G-3′), 0.25?μM of BSR65/17 probe, 10.0?μL of 2× TaqMan Environmental Master Mix 2.0 (Invitrogen), and 3.0?μL of either purified DNA or nuclease-free water. The total bacterial DNA qPCR was performed using the following conditions: 95?°C for 10?min, followed by 40 cycles of 95?°C for 30?s, 50?°C for 30?s, and 72?°C for 30?s. Duplicate reactions were run for all samples. Raw amplification data were normalized to the ROX passive reference dye and analyzed using the 7500 Software version 2.3 (Applied Biosystems, Foster City, CA, USA) with automatic threshold and baseline settings. The cycle of quantification (Cq) values were calculated for samples based on the mean number of cycles required for normalized fluorescence to exponentially increase. 16S rRNA gene sequencing and processing . Amplification and sequencing of the V4 region of the 16S rRNA gene were performed using the dual indexing sequencing strategy developed by Kozich et al. 108 . The forward primer was 515F: 5′-GTGCCAGCMGCCGCGGTAA-3′ and the reverse primer was 806R: 5′-GGACTACHVGGGTWTCTAAT-3′. Each PCR reaction contained 0.75??M of each primer, 3.0??L template DNA, 10.0?μL of 2× TaqMan Environmental Master Mix 2.0, and DNase-free water to produce a final volume of 20??L. Reactions were performed using the following conditions: 95?°C for 10?min, followed by 40 cycles of 95?°C for 20?s, 55?°C for 15?s, and 72?°C for 5?min, with an additional elongation at 72?°C for 10?min. All PCR reactions were run in duplicate and products from duplicate reactions were pooled prior to purification and sequencing. 16S rRNA gene sequencing libraries were prepared according to Illumina’s protocol for Preparing Libraries for Sequencing on the MiSeq (15039740 Rev. D) for 2 or 4?nM libraries. Sequencing was conducted using the Illumina MiSeq platform (V2 500 cycles, Illumina MS102-2003), according to the manufacturer’s instructions with modifications found in 108 . All samples were quantified using the Qubit dsDNA HS assay and pooled in equimolar concentration prior to sequencing. 16S rRNA gene sequences were clustered into amplicon sequence variants (ASVs) defined by 100% sequence similarity using DADA2 version 1.12 109 in R version 3.6.1 110 according to the online MiSeq protocol ( https://benjjneb.github.io/dada2/tutorial.html ) with minor modifications, as previously described 81 . These modifications included allowing truncation lengths of 250 and 150 bases, and a maximum number of expected errors of 2 and 7 bases, for forward and reverse reads, respectively. To increase power for detecting rare variants, sample inference allowed for the pooling of samples. In addition, samples in the resulting sequence table were pooled prior to the removal of chimeric sequences. Sequences were then classified using the silva_nr_v132_train_set database with a minimum bootstrap value of 80%, and sequences that were derived from Archaea, chloroplast, or Eukaryota were removed. The R package decontam version 1.6.0 111 was used to identify ASVs that were potential background DNA contaminants based on their pattern of occurrence in biological vs. technical control samples using the “IsNotContaminant” function. An ASV was determined to be a contaminant, and was thus removed from the entire dataset, if it had a p score?≥?0.4, had a higher mean relative abundance in technical controls than biological samples, and was present in more than one-third of technical control samples. Although one ASV, which was classified as Lactobacillus , met all the criteria for being defined as a contaminant, it was highly abundant in all three positive control vaginal samples and was therefore not removed from the dataset. Ultimately, a total of 148 ASVs determined to be contaminants were removed from the dataset prior to analysis. The vast majority of these ASVs were classified as Staphylococcus (138/148 ASVs; 93.2%). 16S rRNA gene profile statistical analyses . Prior to analyses, the dataset was randomly subsampled to 5426 sequences per sample. Heatmaps of the 16S rRNA gene profiles of samples, including all prominent ASVs (i.e., those ASVs with an average relative abundance ≥2% for any placental site and/or mode of delivery combination) were generated using the open-source software program Morpheus ( https://software.broadinstitute.org/morpheus ). Differences in the structure of 16S rRNA gene profiles of samples were assessed using the Bray–Curtis dissimilarity index. Variation in the 16S rRNA gene profiles of the placental samples from different study groups was visualized through principal coordinates analyses using the R package vegan version 2.5-6 112 . Statistical evaluation of 16S rRNA gene profile differences between study groups was completed using permutational multivariate analysis of variance (PERMANOVA) 113 through the “adonis” function in the R package vegan version 2.5-6. Statistical analysis . Statistical analyses were performed using SPSS v19.0 (IBM, Armonk, NY, USA) or the R package (as described above). For human demographic data, the group comparisons were performed using the Fisher’s exact test for proportions and the Mann–Whitney U -test for non-normally distributed continuous variables. Immunoglobulin concentrations were compared using Mann–Whitney U -tests. Since cytokine data were obtained in duplicates, and one of the two measurements could have been below the detection limit, we used linear mixed-effects models to compare concentrations between groups while accounting for the number of measurements available in each sample. In these models, the fixed effects were the infection status, maternal age, BMI, and nulliparity, while a random effect was assigned to each patient. The significance of the group coefficient was assessed using the likelihood ratio test between a model with and without the infection status. Before fitting the models, cytokines with levels below the detection limit in both duplicates in a given sample were imputed with 99% of the smallest detected value across all samples. PCA of cytokine data after imputation of values below detection limit was performed using the R package PCAtools after separately normalizing the data from maternal and cord blood. A logistic regression model was used to test the association between the infection status and up to three principal components. Significant p -values were based on a likelihood ratio test. For the comparison of flow cytometry data between study groups, Mann–Whitney U -tests were performed. p ?flow cytometry data were transformed into Z -scores by subtracting the mean and dividing by the standard deviation, which were both calculated from the control group. The Z -scores were visualized as a heat map and compared between SARS-CoV-2 (+) and control groups using two-sample t -tests. p -values were adjusted for multiple comparisons using the FDR method to obtain q -values. A q -value?flow cytometry data were also determined, and PC1–PC3 were plotted on a 3D scatter plot. Single-cell RNA-seq and MiSeq data analyses were performed as described in their respective sections. Reporting summary . Further information on research design is available in the? Nature Research Reporting Summary linked to this article. .
From:
监测目标主题     
(1)  
(1)  
(1)  
(1)  
(1)  
系统抽取主题     
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)  
(1)