A single-cell atlas of circulating immune cells over the first two months of age in extremely premature infants
Data files
Feb 27, 2025 version files 15.03 MB
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pilotk5.RData
15.03 MB
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README.md
3.66 KB
Abstract
Extremely premature infants (EPI) born prior to 30 weeks gestation are highly susceptible to infection. The trajectory of peripheral immunity in EPIs is poorly understood. Longitudinal analysis of immune cells from 250mL of whole blood at 1 week (n=7), 1 month (n=7), and 2 months (n=5) from 10 EPI was compared to healthy adults (n=6) and to neonatal cord blood (n=13). Single-cell suspensions from individual samples were split to perform single-cell(sc) RNA-, T- and B-cell receptor sequencing (seq), and phosphoprotein mass cytometry. Our scRNAseq data was integrated with existing data from full-term infants at 2 months of age. The trajectory of circulating T-, B-, myeloid, and natural killer cells in EPI infants over the first two months of life is distinct from full-term infants. Peripheral T cell development rapidly progressed over the first month of EPI life with an increase in the proportion of naïve CD4, regulatory, and cycling T cells, accompanied by increased STAT5 signaling compared to all other samples. Simultaneously, the transcription of IL2, which is essential for T cell growth and proliferation, increased in the lymphocytes, while IL7 and IL15 were highest in B cells and myeloid cells in EPI samples at 2 months of age. EPI memory CD4 T cells were dominated by ZBTB16 expression with a Th1 predominance compared to Th2 skewing of central memory-like T cells in full-term infants. Similarly, B cells from 2-month-old EPIs exhibited increased signatures of activation, BCR signaling, and differentiation compared to all other samples. Both B and T cells from 2-month-old EPIs had increased IFN signatures compared to full-term infants. Together, we demonstrate the feasibility of a robust multi-omic longitudinal analysis in EPIs from minute amounts of blood, developing a resource for studying early-life immune development.
https://doi.org/10.5061/dryad.pk0p2ngxg
Description of the data and file structure
Here we have included data from mass cytometry by time of flight (CyTOF) analysis of 15,000 leukocytes from 28 individual samples. Furthermore, there are the major scripts we used for our scRNAseq, TCR, and BCR data analysis. We uploaded 9 files of Python and R scripts. One file is a .Rdata file exported from cytofkit2 of CyTOF data analysis.
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File: pilotk5.Rdata
Description: Exported data from CyTOF analysis.
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File: gene-usage.html
Description: R script that was used to determine the BCR gene usage across samples for heavy and light-chain based upon the identified B cell populations in the B cell analysis from the scRNAseq data set.
- File: All_samples_plots.html. Description: R script that includes all packages used to determine
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File: PBMC pilot.ipynb
Description: We used the Scanpy package for the initial clustering analysis, which included quality control, batch correction, and clustering.
- File: infant_pbmc_integrate.ipynb
Description: The ipynb scripts we used to integrate, and batch correct our pbmc with published full-term 2 months old samples.
6. File: DEGs C.ipynb
Description: The scripts we used to find the differentially expressed genes between chorioamnionitis and no chorioamnionitis samples in selected cell types.
7. File: pbmc_cellchat.ipynb and pbmc_cellchat.R
Description: The cellchat analysis scripts. We used pbmc_cellchat.ipynb to extract matrix and metadata from the h5ad file, that used as the input data for cellchat analysis. Then we used pbmc_cellchat.R to perform the analysis for the comparison of cell-cell commutation between different ages.
8. File: tcr_update.ipynb
Description: We used the package scirpy for the TCR analysis to define clones.
9. File: diversity.R
Description: The R file was used to analyze the TCR clone diversity using the package alakazam.
Code/Software
- The R analysis was performed using R version 4.2.2, and the Python analysis was performed using Python 3.12.3. All the packages required had been listed in the scripts
Sharing/Access Information
- The raw data from the scRNAseq, BCR, and TCR analysis can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE271413
In this study, we performed multi-omic analysis including mass cytometry by time of flight (CyTOF), single-cell RNA sequencing (scRNAseq), T cell and B cell receptor sequencing (TCR and BCR-seq) on circulating immune cells that were isolated from 100 to 250 microliters of blood obtained from extremely premature infants (EPI, n=10) and compared to cord blood from premature (n=5) and full-term infants (n=5) as well as adult blood. The samples from EPI were obtained at 1 week, 1 month, and 2 months of life.
Here we have included CyTOF data (.Rdata file format) that has been gated on live, DNA+, CD45+ circulating leukocytes. Data was demultiplexed using Premessa (https://github.com/ParkerICI/premessa) and automated RPhenograph clustering with k=30 in Cytofkit2 (https://github.com/JinmiaoChenLab/cytofkit2) and included here is the Rdata file that includes analysis of ~15,000 cells that have been clustered based upon surface marker expression to identify 28 distinct populations of T cells, B cells, NK cells, innate lymphoid cells, Myeloid cells, and neutrophils from Adult blood n=3, full-term cord blood n=4, preterm cord blood n=5, serial samples from 9 EPI infants collected at 1 week n=7, 1 month n=7, 2 months n=4. Using cytofkit2 this data can be visualized in a tsne format showing clusters as well as the heatmap expression of surface and phosphoprotein markers in each sample and among the groups.
We also included the code used to analyze scRNAseq, TCR, and BCR files that were analyzed in R and Python using various packages that are outlined in the description of each file. The raw data and processed data matrices are available for download at Gene Expression Omnibus (accession GSE271413). We performed quality control, batch correction, and clustering. Our scRNAseq data set was integrated with samples from full-term infants at 2 months (GSE204716). We performed a differentially expressed gene analysis based on infant exposure to in utero inflammation, and cell-to-cell communication based on age/timepoint of sample collection and determined TCR/BCR clonality and diversity among samples.