DOI: 10.5061/dryad.pk0p2ngxg
title: A single-cell atlas of circulating immune cells over the first two months of age in extremely premature infants

description: 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.
description: 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.
description: # **A single-cell atlas of circulating immune cells over the first two
 months of age in extremely premature infants**
 [https://doi.org/10.5061/dryad.pk0p2ngxg](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. 1. File: pilotk5.Rdata Description: Exported data from CyTOF analysis. 2. 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. 3. File: All_samples_plots.html. Description: R script that includes all packages used to determine 4. File: [PBMC pilot.ipynb](https://ood-mccleary.ycrc.yale.edu/pun/sys/dashboard/files/fs/gpfs/gibbs/project/konnikova/wg297/PBMC/code/1317_1379_CORRECTED/manscript_code/PBMC%20pilot.ipynb) Description: We used the Scanpy package for the initial clustering analysis, which included quality control, batch correction, and clustering. 5. File: [infant_pbmc_integrate.ipynb](https://ood-mccleary.ycrc.yale.edu/pun/sys/dashboard/files/fs/gpfs/gibbs/project/konnikova/wg297/PBMC/code/1317_1379_CORRECTED/manscript_code/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](https://ood-mccleary.ycrc.yale.edu/node/r813u03n07.mccleary.ycrc.yale.edu/64763/notebooks/project/PBMC/code/1317_1379_CORRECTED/manscript_code/pbmc_cellchat.ipynb) and [pbmc_cellchat.R](https://ood-mccleary.ycrc.yale.edu/pun/sys/dashboard/files/fs/gpfs/gibbs/project/konnikova/wg297/PBMC/code/1317_1379_CORRECTED/manscript_code/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](https://ood-mccleary.ycrc.yale.edu/pun/sys/dashboard/files/fs/gpfs/gibbs/project/konnikova/wg297/PBMC/code/1317_1379_CORRECTED/manscript_code/pbmc_cellchat.R) to perform the analysis for the comparison of cell-cell commutation between different ages. 8\. File: [tcr_update.ipynb](https://ood-mccleary.ycrc.yale.edu/node/r813u03n07.mccleary.ycrc.yale.edu/64763/notebooks/project/PBMC/code/1317_1379_CORRECTED/manscript_code/tcr_update.ipynb) Description: We used the package scirpy for the TCR analysis to define clones. 9\. File: [diversity.R](https://ood-mccleary.ycrc.yale.edu/pun/sys/dashboard/files/fs/gpfs/gibbs/project/konnikova/wg297/PBMC/code/1317_1379_CORRECTED/manscript_code/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](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE271413)