There are at least three subclasses of monocytes in human blood based on their phenotypic receptors. In the Blood Atlas we have cell lineage enriched genes and 27 of these genes has the highest expression in blood or lymphoid tissues when comparing all tissues and organs analysed. In addition, genes are cell lineage group enriched and an additional 11 genes are enhanced in this cell lineage.
Altogether, genes are elevated and among these also show highest expression in blood or lymphoid tissues when comparing all tissues and organs analysed. Figure 1. The distribution of all genes across the five specificity categories based on transcript abundance in monocyte celll lineage as well as in the other 5 cell lineages. Table 1. Number of genes in the subdivided categories of elevated expression in monocyte cell lineage. Distribution in the six cell lineages Detected in single Detected in some Detected in many Detected in all Total Specificity Lineage enriched 0 70 7 Group enriched 0 0 Lineage enhanced 9 0 2 0 11 Total 0 Table 2.
The genes with the highest level of enriched expression in monocyte cell lineage. Specificity-score corresponds to the score calculated as the fold change of monocyte cell lineage expression to the second highest cell lineage. In the Blood Atlas we have 18 classical monocyte enriched genes and 1 of these genes has the highest expression in blood or lymphoid tissues when comparing all tissues and organs analysed. In addition, genes are cell type group enriched and an additional genes are enhanced in this cell type.
The classical monocytes were isolated from PBMCs. Debris, cell aggregates and most of lymphocytes were eliminated based on scatter profiles. Figure 2. The distribution of all genes across the five specificity categories based on transcript abundance in classical monocyte cells as well as in the other 17 cell types.
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One likely reason for these ambiguities is in the heterogeneity of these monocyte subsets regrouping cells with divergent functions. To better define monocyte populations, we have analysed expression of 17 markers by multicolour flow cytometry in samples obtained from 28 control donors.
Data acquisition was tailored to detect populations present at low frequencies. These large monocytes differed from regular, smaller monocytes with respect to expression of various cell surface molecules, such as FcR, chemokine receptors, and adhesion molecules.
Unsupervised multidimensional analysis confirmed the existence of large monocytes and revealed interindividual variations that were grouped according to unique patterns of expression of adhesion molecules CD62L, CD49d, and CD Distinct inflammatory responses to TLR agonists were found in small and large monocytes.
Overall, refining the definition of monocyte subsets should lead to the identification of populations with specific functions. Monocytes, which are mostly precursors of some macrophage and dendritic cell populations, have been hard to divide into populations with clear-cut inflammatory and immune functions.
This may be due in part to the high number and complexity of phenotypes present in monocyte populations. A classification of human blood monocyte subsets based on the expression of CD14 and CD16 cell surface receptors was proposed 1 and refined over the years 2 , 3. Evidence for this third subset was confirmed in transcriptome analysis 6 , 7 , 8 , 9 , Despite progress in phenotypic analysis, immune functions associated with monocyte subpopulations in the steady state remained ill defined.
Marked functional redundancies between the sub-populations were found, and contradicting results in the literature added to the puzzling difficulties in assigning functions to specific populations 11 , 12 , Thus, intermediate monocytes were described as the major source of pro-inflammatory cytokines upon stimulation 14 , In contrast, non-classical monocytes were also described as the most inflammatory monocytes 3 , 13 , Similarly, anti-inflammatory cytokine secretion was alternatively found high in intermediate 13 or in classical monocytes 6 , 11 , Therefore, production of pro-inflammatory and anti-inflammatory cytokines upon activation, a hallmark property of monocytes, remains hard to ascribe unambiguously to given subsets.
In this study, we stringently assessed the phenotypic heterogeneity of human blood monocytes by multicolour cell surface labelling, flow cytometry analysis, and unsupervised detection of clusters and analysis of their phenotypes. To look for conserved phenotypes, the analysis was extended to 28 healthy Caucasian donors. Results identified novel populations of monocytes with unique morphologic and phenotypic characteristics, and with distinct inflammatory responses to TLR agonists.
Although monocyte populations had heterogeneous phenotypes among healthy donors they could nonetheless be resolved into phenotypic groups based on interindividual variations of expression. In order to further define human blood monocyte populations, we have analysed labelled PBMC from 28 healthy individuals by flow cytometry Supplementary Table S1. CD14 expression was considered a necessary condition for inclusion in monocyte populations Fig.
In addition to a main cluster representing This clearly visible set of larger cells was present in all donors and constituted 8. After exclusion of doublets in each subset Fig. To show that large monocytes did not result from density gradient separation with Ficoll, monocytes were analysed in whole blood.
Gating strategy and identification of monocyte subpopulations. Forward and side scatter identified small and large clusters of monocytes c. After exclusion of doublets from each cluster 49 d , e , the expressions of CD14 and CD16 were analysed in gated cells f , g. The higher expression of CD14 in large monocytes is shown in panel h where the profiles of CD14 and CD16 expressions in large blue contours and small monocytes red contours were overlaid.
Data presented were obtained from one representative donor. Large monocytes Fig. Thus, this newly described set of large monocytes differed from small monocytes with respect to FSC scatter and levels of CD14 expression.
To dismiss the possibility that large monocytes were doublets, PBMC were analysed by imaging flow cytometry. Large monocytes appeared as single cells Fig. Cell size was also analysed in imaging flow cytometry using a mask delimited by CD14 expression. Results showed that large monocytes had a significantly larger size than small monocytes Fig.
Cell diameters were inferred from cell size and were determined to be Imaging flow cytometry analysis of monocyte subpopulations. After exclusion of lymphocytes, selection of CDpositive cells, and doublet exclusion, small and large monocytes were visualized.
The ratio between the minor and major axis of each event in monocyte gates was calculated and compared to the corresponding ratio of cells in the doublet gate. A vertical bar drawn at the nadir between singlet and doublet curves Aspect Ratio Intensity around 0.
Therefore, a total of six populations of monocytes were distinguished according to our gating strategy based on sequential use of lineage selection, SSC and FSC cluster analysis, doublet exclusion in gated monocyte populations, and levels of CD14 and CD16 expression. To further characterize the identified monocyte populations, the expression of 15 cell surface receptors associated with important functions of monocytes was analysed in cells obtained from our panel of 28 donors.
The expressions of the scavenger receptor CD 19 , 20 and the immunoglobulin superfamily CD7 molecule 21 were also determined. Marker expression for each of the six monocyte populations is presented in individual panels Fig. A globally lower expression of most markers characterized sm14 dim 16 neg monocytes Fig.
Individual variation between donors was however one of the salient feature of the analysis of expression for many markers. Remarkably, similar variations were identified in donors, which were unrelated, allowing the identification of groups among our panel of Caucasian donors. The remainder was named OP Five donors from this panel could not be categorized along a unique phenotype.
Phenotypes of monocyte subpopulations as analysed with our typing platform. The expressions of scavenger receptor CD 19 , 20 and immunoglobulin superfamily molecule CD7 21 were also determined y-axis and Supplementary Table S2.
Cells were analysed as described in Fig. Fluorochrome-matched isotype controls were used to determine specific MFI and percentage of positive cells. Expression levels are presented as dots of colour and size reflecting MFI and percentage of positive cells, respectively, according to colour and size scales shown in legend. Variations in the number of donors analysed in each monocyte population were due to the inability to assess the expression of markers when cell numbers were too low.
For each sub-population, donors were grouped according to similar expression of markers as noted at the top of the panels and recapitulated with the OP nomenclature at the bottom of the panels. Similarly, the other monocyte populations could also be split according to the patterns of expression of adhesion molecules. Six donors had distinct patterns of expressions of these markers and were not grouped. These groups were termed OP to OP Two donors with atypical combinations of expression of adhesion molecules were left ungrouped numbers 26 and The sm14 dim 16 neg monocyte population Fig.
Five donors remained without classification. From these results, it appears that variations in the expression of selected markers CD49d, CD43, CD, and CD62L did not occur randomly in the monocyte subpopulations and may correspond to a concerted profile of gene expression. In an attempt to further define monocyte phenotypes across the sub-populations, we set out to determine which profiles detected in small and large monocytes were more likely to be associated in healthy donors.
We used the network analysis tool Gephi 23 , 24 to map the connectivity between profiles OP to OP based on the 28 donors analysed. As shown in Fig. Four clusters were identified. They consisted of profiles from each monocyte sub-populations that are more likely to be associated in a whole monocyte population Table 1. Overall, identification of these monocyte phenotypes in donors further reinforces the notion that monocyte expression of adhesion molecules best reflects the phenotypes of these cells in subsets within subpopulations.
Connectivity map between donors and defined phenotypic profiles. To determine which phenotypic profiles identified in subpopulations of monocytes were more likely to be associated in a given donor, a network analysis was performed using Gephi Green dots represent phenotypic profiles OP, Fig. The size of the dots is proportional to the number of links.
Thus, monocyte phenotype I was composed of profiles OP, , or , , or , and and was present in 5 donors see Table 1. However, in other monocyte sub-populations, these donors shared profiles OP or , , , , and Donors 17 and 25 had profile OP in sub-population sm14 dim 16 neg whereas no other donor had distinctive profiles in this sub-population.
Monocyte phenotype IV was much less defined and included profiles OP and found in only two donors 5 and 8. Conspicuous in these two donors was the paucity of CD16 positive monocytes, a feature also found in donor 9. Monocyte phenotypes II and III were also linked by donors 7, 15, and 24 that shared parts of their profiles. We then looked for associations between Gephi-defined clusters and donor characteristics such as age and sex. Interestingly, as shown in Supplementary Fig.
No other age difference between clusters reached statistical significance. Sex ratios in most clusters were unremarkable Supplementary Fig. These results suggest that age and sex may be contributing factors in the variations of monocyte phenotypes.
Having established the overall expression of selected markers in newly defined monocyte populations, we sought to analyse the combined expression of the markers at a single cell level and to identify cell clusters with similar profiles of expression in an unsupervised manner. We used SPADE, a hierarchical analysis generating branched tree structure of related cells, followed by analysis with viSNE, which allows visualization of high-dimensional single-cell data.
We used SPADE 25 for 20 markers at high resolution and specificity to generate a hierarchy of cell clusters, represented as a tree, for each donor. Analysis of side and forward scatter properties of each branch of the tree together with CD14 expression allowed us to identify branches corresponding to small and large monocytes Supplementary Fig. This unsupervised analysis validated the individualization of small and large monocytes as subpopulations.
Analysis of these profiles showed a large extent of variations between donors, with multiple clusters of monocytes detected. Strikingly, among these interindividual variations, common patterns were distinguishable in groups of unrelated donors. S5 were analysed with viSNE and specific expression profiles of adhesion molecules were identified in donors.
Profiles shown in a—e correspond to monocyte sub-populations a, b, c, d, e identified in Table 2. In panel a , a schematic representation of monocyte populations identified in profile a and that are found in various combinations in profiles b, c, d, e is shown. Plots shown are from representative donors expressing a to e profiles. From these results, it appeared that numerous populations of monocytes could be distinguished and some were shared between donors.
Most importantly, many monocyte populations were present in a fraction of the donors, defining complex intertwined phenotypic groups a, b, c, d, and e in our panel of 28 Caucasian healthy donors. The existence of such phenotypic groups among a population of healthy donors may further refine the definition of monocytes subsets.
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