Conventional Co-Housing Modulates Murine Gut Microbiota and Hematopoietic Gene Expression

Abstract

Specific-pathogen-free (SPF) mice have improved hematopoietic characteristics relative to germ-free mice, however, it is not clear whether improvements in hematopoietic traits will continue when the level of microorganism exposure is further increased. We co-housed SPF C57BL/6 mice in a conventional facility (CVT) and found a significant increase in gut microbiota diversity along with increased levels of myeloid cells and T cells, especially effector memory T cells. Through single cell RNA sequencing of sorted KL (c-Kit+Lin−) cells, we imputed a decline in long-term hematopoietic stem cells and an increase in granulocyte-monocyte progenitors in CVT mice with up-regulation of genes associated with cell survival. Bone marrow transplantation through competitive repopulation revealed a significant increase in KSL (c-Kit+Sca-1+Lin−) cell reconstitution in recipients of CVT donor cells which occurred when donors were co-housed for both one and twelve months. However, there was minimal to no gain in mature blood cell engraftment in recipients of CVT donor cells relative to those receiving SPF donor cells. We conclude that co-housing SPF mice with mice born in a conventional facility increased gut microbiota diversity, augmented myeloid cell production and T cell activation, stimulated KSL cell reconstitution, and altered hematopoietic gene expression.

Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse

“…A human dataset contained 15,245 single CD34 + stem/progenitor cells after filtering out cells with small numbers of detected genes, as visualized in UMAP, displayed clear clusters, suggesting distinct cell types at molecular levels ( Figure 2 a). Hematopoietic cell identity was assigned to each cell cluster by examining cluster-specific genes with a reported lineage signature gene list [ 1 , 6 ]. CD34 + cells were grouped into 15 clusters and then computationally assigned to the following cell populations: hematopoietic stem cells and multipotent progenitors (HSCs), granulocyte–monocyte progenitors (GMPs), B megakaryocyte–erythroid progenitors (MEPs), lymphocyte progenitors (ProBs), and early T lineage progenitors (ETPs).…”

“…Network reconstruction with time-series data has become popular because the data capture a more thorough picture of the system than does non-temporal data [ 3 , 4 ]. Recently, single-cell RNA sequencing (scRNA-seq) has provided a powerful method to discover regulatory relationships in hematopoiesis [ 5 , 6 , 7 ]. Pseudo-time ordering places individual cells along a virtual time axis and provides a large amount of complex additional information for network analysis.…”

“…As an illustration, networks of three genes at three time points were not identical ( Figure 1 a) [ 8 ]. It is biologically meaningful to examine the evolution of these patterns over time rather than characterizing a single static graph that represents only the interactions persistent over time [ 6 , 7 ]. Reconstruction and dynamic network analysis with bulk or single-cell data help to understand evolution of biological processes (differentiation, development, and disease onset and progress) at the network level [ 3 , 4 ].…”

Comparative Transcriptomic Analysis of the Hematopoietic System between Human and Mouse by Single Cell RNA Sequencing

“…Lineage − CD117 + cells were sorted from bone marrow of C57BL/6 mice ( Figure 1 a). The Chromium Single Cell 3′ platform (10× Genomics) was used to prepare scRNA-seq cDNA libraries [ 14 , 15 ]. RNA-seq libraries were sequenced with paired-end reads of 75-bp on Illumina HiSeq 3000 System.…”

“…Cells with a high percentage of mitochondrion gene reads (>10%) were also excluded. Raw and processed data from all experiments were deposited in the NCBI Gene Expression Omnibus with GSE135194 and GSE142235 [ 14 , 15 ]. Downstream analyses were performed using the R software package Seurat ( , v2.3.4, accessed on 18 June 2018).…”