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Biol Reprod. 2012 Aug; 87(2): 42.
Published online 2012 Jun 6. doi:  10.1095/biolreprod.112.101147
PMCID: PMC3431427

Comprehensive Analysis of Genes Expressed by Rare Microchimeric Fetal Cells in the Maternal Mouse Lung1


During pregnancy, cells from each fetus travel into the maternal circulation and organs, resulting in the development of microchimerism. Identification of the cell types in this microchimeric population would permit better understanding of possible mechanisms by which they affect maternal health. However, comprehensive analysis of fetal cells has been hampered by their rarity. In this study, we sought to overcome this obstacle by combining flow cytometry with multidimensional gene expression microarray analysis of fetal cells isolated from the murine maternal lung during late pregnancy. Fetal cells were collected from the lungs of pregnant female mice. cDNA was amplified and hybridized to gene expression microarrays. The resulting fetal cell core transcriptome was interrogated using multiple methods including Ingenuity Pathway Analysis, the BioGPS gene expression database, principal component analysis, the Eurexpress gene expression atlas, and primary literature. Here we report that small numbers of fetal cells can be flow sorted from the maternal lung, facilitating discovery-driven gene expression analysis. We additionally show that gene expression data can provide functional information about fetal cells. Our results suggest that fetal cells in the murine maternal lung are a mixed population, consisting of trophoblasts, mesenchymal stem cells, and cells of the immune system. Detection of trophoblasts and immune cells in the maternal lung may facilitate future mechanistic studies related to the development of immune tolerance and pregnancy-related complications, such as pre-eclampsia. Furthermore, the presence and persistence of mesenchymal stem cells in maternal organs may have implications for long-term postpartum maternal health.

Keywords: mesenchymal stem cells, microarray, microchimerism, reproductive immunology, trophoblasts


In mice and humans, fetal cells enter the maternal circulation early in pregnancy, increase throughout gestation, and sharply decrease in the days following delivery [1, 2]. After birth, a substantial population of fetal cells remains in the maternal organs, resulting in microchimerism [3, 4].

The types of fetal cells that persist in the mother are actively being studied. Some investigators have hypothesized that these microchimeric fetal cells consist of a single population with characteristics between those of embryonic and adult stem cells [5, 6]. The work of other groups has suggested that microchimeric fetal cells consist of multiple, more differentiated cell types, such as lymphocytes [3, 7, 8], hepatocytes [9, 10], neurons [11], cardiomyocytes [12, 13], endothelial cells [7], and thymocytes [14]. Our group has further demonstrated that fetal cells, especially within a single organ, such as the maternal lung, comprise a phenotypically diverse population, as exemplified by expression of surface markers typically found on both immature and mature cell types of multiple lineages [8, 15]. Currently it is unknown whether (1) committed cells enter the maternal circulation and transdifferentiate, (2) a variety of committed cells cross the placenta en masse, (3) multipotent stem progenitor cells differentiate to multiple cell types, or (4) a variety of unipotent stem progenitor cells differentiate along their commitment lineages [16].

It is important to understand the origin and identity of microchimeric fetal cells, as their phenotype and differentiation status may impact long-term maternal health. For example, fetal cells are found at tumor sites in postpartum women [17], but it is not known whether they are helpful or harmful in this context. If the fetal cells show characteristics of hematopoietic and/or immune cells, they may have a role in tumor surveillance and destruction. Alternatively, fetal cells with endothelial cell properties could contribute to tumor angiogenesis and progression [18].

Because fetal cells are relatively rare within maternal organs, their characterization presents a significant challenge. In this study, we sought to overcome this obstacle by combining flow cytometry with comprehensive gene expression microarray analysis of fetal cells isolated from the murine maternal lung during late pregnancy. Our results suggest that fetal cells in the maternal lungs are a mixed population composed mainly of placental, mesenchymal stem and immune cells.



The Institutional Animal Care and Use Committee of Tufts University School of Medicine, Division of Laboratory Animal Medicine, approved all protocols. All institutional and standard guidelines regarding the ethical use of experimental animals were followed. Male mice homozygous for the enhanced green fluorescent protein (Egfp) transgene (C57BL/6-Tg [CAG-EGFP] C14-Y01-FM131Osb [stock no. 267; Riken BioResource Center, Japan], originally provided by Dr. Masaru Okabe; bred in-house) were mated to 10- to 12-week-old wild type C57BL/6J females (stock no. 664; Jackson Laboratory, Bar Harbor, ME). With the homozygous male, all pups inherited one copy of the Egfp transgene. Egfp expression was used as a marker for all fetal cells independent of gender.

Flow Sorting of Fetal Cells in Maternal Lung

Seven female mice were euthanized by CO2 inhalation 18 days after mating. Gestational ages of the pups were 14–17.5 days as determined by Theiler staging [19]. The thoracic cavity was opened, and the pulmonary vasculature was perfused with ice-cold Dulbecco PBS. The lungs were harvested. A single-cell suspension was created separately from each lung pair, using a GentleMACS dissociator (Miltenyi Biotech, Auburn, CA) following the manufacturer's protocol. Briefly, lungs were incubated with collagenase D and DNase I at 37°C and then physically dissociated and filtered. Cell suspensions were centrifuged at 300 × g and resuspended in flow cytometry buffer (Dulbecco PBS with 2% bovine serum albumin and 0.1% sodium azide) and 1.5 μg/ml propidium iodide (PI) to exclude dead cells. Because GFP is sufficiently bright, no antibodies were used [2, 20, 21]. A 488-nm laser was used to excite the fluorophores with a MoFlo high-speed flow cytometer (DAKO, Fort Collins, CO). Green fluorescence was collected with a 530/40 nm filter and PI with a 670/40 nm filter. Fetal liver from euthanized pups was used as a GFP+ control. C57BL/6J virgin female lungs were used as a negative control, and the GFP gate was drawn to exclude all cells in the virgin female lungs (Fig. 1). GFP+, PI− cells from each set of lungs were sorted directly into the lysis buffer provided with the WT-Ovation One-Direct amplification system (NuGEN Technologies, Inc., San Carlos, CA) at a dilution of 10–40 cells/μl. Although the manufacturer of the kit recommends use of the lysis buffer at a maximum concentration of 10 cells/μl, titration experiments in our laboratory previously determined that high quality mRNA could be obtained at concentrations of up to 40 cells/μl. Cell lysates from each female were pipetted repeatedly and stored at −80°C for 1–8 days following sorting.

FIG. 1.
Flow cytometry gates. GFP+, PI− cells were collected using the triangular gate shown in the lower right corner. This gate also eliminated the autofluorescence seen in the lungs.

Fetal Nucleic Acid Amplification

To assess RNA quality and to verify the presence of Egfp, one-step quantitative reverse transcriptase PCR (qRT-PCR) was performed with the total cell lysate for Egfp (forward primer, 5′-ACTACAACAGCCACAACGTCTATATCA-3′; reverse primer, 5′-GGCGGATCTTGAAGTTCACC-3′; and TaqMan probe, 5′-FAM-CCGACAAGCAGAAGAACGGCATCA-TAMRA-3′) and beta-actin (Actb forward primer, 5′-AGGTCATCACTATTGGCAACGA-3′; reverse primer, 5′-CAACGTCACACTTCATGATGGA-3′; and probe, 5′-FAM-AGCCTTCCTTCTTGGGTATGGAATCCTGT-TAMRA-3′). All samples were run on a 7900 model sequence detector with TaqMan One-Step RT-PCR Master Mix reagents kit (Applied Biosystems, Foster City, CA) as previously described [22].

cDNA was converted and amplified from RNA in the cell lysates by using the WT-Ovation One Direct amplification system following the manufacturer's protocol, with the exception that up to 80 cells were used as input material. While the manufacturer of the kit suggests use of no more than 20 cells per reaction, titration experiments performed in our laboratory showed higher quality cDNA using 80 cells. The quantity of cDNA was measured using a Nanodrop 2000 (ThermoScientific, Wilmington, DE), and the quality of cDNA was assessed using a Bioanalyzer 2100 (Agilent, Santa Clara, CA).

Gene Expression Microarrays

Twenty-five micrograms of cDNA were fragmented and biotinylated by using the Encore Biotin module (NuGEN Technologies, Inc.) and hybridized to mouse 430 2.0 arrays (Affymetrix, Santa Clara, CA). Arrays were hybridized in an Affymetrix oven at 45°C and 60 rpm for 18–40 h. Arrays were washed using a GeneChip Fluidics Station 450, stained with streptavidin-phycoerythrin, scanned with the GeneArray Scanner, and analyzed using GeneChip Microarray Suite 5.0 software (Affymetrix). Microarray data were quantile normalized with ideal mismatch background correction and Tukey biweight summarization using the Bioconductor package in the R software environment (http://www.bioconductor.org/). Following normalization, a list was made of probes with a present call [23] on at least six of seven arrays. This was called the “fetal cell core transcriptome.” Raw and normalized data are available in NCBI's Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo) under GEO series accession number GSE38188.

Analysis of the Fetal Cell Core Transcriptome

The list of fetal cell core transcriptome probes was uploaded to NetAffx, a Web-based analytic tool maintained by Affymetrix. NetAffx was used to translate the fetal cell core transcriptome probes into the corresponding genes (www.affymetrix.com/analysis/index.affx). This allowed use of the published literature and other resources for analysis, as well as elimination of genes represented in the dataset by multiple probes.

Ingenuity pathway analysis.

Functional information was obtained from the Web-based software tool Ingenuity Pathway Analysis (IPA; content version 11904312). The fetal cell core transcriptome was used to identify enriched pathways. The categories “Top Canonical Pathways” and “Top Transcription Factors” were used primarily in this analysis. IPA uses the right-tailed Fisher exact test to calculate a P value, which represents the likelihood that a nonrelevant biological function is reported as significant. We applied the Benjamini-Hochberg correction for multiple testing where appropriate.

Tissue specificity.

To gain information about fetal cell function, tissue-specific probes from the fetal cell core transcriptome were identified using the BioGPS Gene Expression Database (Novartis Research Foundation; http://biogps.org) [24, 25]. This publicly available atlas of protein-encoding transcripts uses previously published gene expression data [26]. The database includes information from 71 tissues obtained from healthy C57BL/6 mice, 9 cell types stimulated in vitro, and 11 cell lines. All were analyzed using Affymetrix mouse genome 430 version 2.0 arrays. For this study, BioGPS version was used. For each of the probes within the fetal cell core transcriptome, we defined tissue specificity as expression in one tissue over 30 multiples of the median, and no unrelated tissue with an expression value greater than one-third of the maximum expression value [24]. With the exception of placenta, the tissues used in the BioGPS database were not fetal; hence, the reported expression levels reflect those of adult tissue.

To partially address the gene expression differences between adult and fetal tissues, the Eurexpress transcriptome atlas database (www.eurexpress.org) [27] was also used. The Eurexpress atlas is a free, publicly available collection of gene expression patterns measured by in situ hybridization in mouse fetuses at Gestational Day 14.5. It does not include placenta or other extraembryonic tissues. Gene names were individually searched, and annotations provided by the atlas were used to determine embryonic expression.

Principal component analysis.

Principal component analysis (PCA) is an orthogonal transformation of potentially correlated variables into an equal number of noncorrelated variables. This approach allows representation and visualization of the dominant patterns in large datasets [28]. PCA was performed with two datasets downloaded from GEO: the dataset from Thorrez et al. [29] (GEO accession number GSE9954) and that by Lattin et al. [26] (GEO accession number GSE10246). We chose these datasets because both used C57BL/6 mice and Affymetrix Mouse Genome 430 version 2.0 arrays (as we did in our study). Two independent datasets were used to reduce the influence of the characteristics of an individual dataset. GEO datasets, together with data collected from GFP+ fetal cells, were normalized in R, using quantile normalization with ideal mismatch background correction and Tukey biweight summarization. Principal components 1 versus 2, 2 versus 3, and 3 versus 4 were plotted.

Genes of interest.

We used Ingenuity Pathway Analysis, NCBI Gene, and primary literature to further discover genes in the fetal cell core transcriptome that were associated with the tissue types suggested by the BioGPS database and PCAs.


Flow Sorting of Fetal Cells in Maternal Lung and Fetal Nucleic Acid Amplification

From each set of maternal lungs, 66−420 GFP+, PI− fetal cells were flow sorted (Fig. 1). qRT-PCR was performed with four of the cell lysate samples and results confirmed the presence of the GFP transcript, indicating fetal cells had been collected and there was good quality mRNA present. One of the four samples had a lower mRNA concentration as measured by Actb qRT-PCR amplification, and Egfp did not amplify in that sample. After amplification, the cDNA concentration ranged from 241 to 935 ng/μl.

Analysis of the Fetal Cell Core Transcriptome

A total of 883 probes had a present call on at least 6 of the 7 arrays. As determined through NetAffx, the probes represented 594 genes, 71 predicted genes or loci, and 45 unmapped probes. Approximately one-third of the 594 genes are considered housekeeping genes (e.g., heat shock proteins, mitochondrial respiration proteins such as cytochrome C oxidase, glycolysis enzymes such as citrate synthase, and ribosomal proteins; see Supplementary Table S1 [all Supplemental Data are available online at www.biolreprod.org]).

Ingenuity pathway analysis.

Of the 883 probes in the fetal cell core transcriptome, 655 probes were available for pathway analysis, indicating the IPA database had sufficient information for the gene product to place it in one or more pathways. The top five canonical pathways identified in the core analysis were EIF2 signaling (P = 6.64 × 10−43), regulation of eIF4 and p70S6K signaling (P = 3.39 × 10−14), mTOR signaling (P = 1.87 × 10−9), Sertoli cell-Sertoli cell signaling (P = 2.16 × 10−6), and protein kinase A signaling (P = 5.99 × 10−6). The top five transcription regulators were FOS (P = 1.35 × 10−6), NFE2L2/NRF2 (P = 2.14 × 10−5), E2F1 (P = 2.52 × 10−5), YY1 (P = 4.11 × 10−5), and PPARγ (P = 8.19 × 10−5). Complete results and specific genes in each pathway are presented in Supplementary Tables S2 and S3.

Tissue specificity.

Using the BioGPS database and the fetal cell core transcriptome, we found 52 probes, corresponding to 49 genes, that met criteria for establishing tissue specificity (Supplementary Table S4). These 49 genes derive from several organs and organ systems (Fig. 2). Lungs, nervous tissue, testes, and placenta were represented most often. Using the Eurexpress Transcriptome Database, we found that fetal nervous system, lung, kidney, intestines, bone, thymus, and mesenchymal tissues were most highly represented by the 49 genes (Supplementary Table S4). None of the placenta-specific genes were expressed in the embryos.

FIG. 2.
Organs with tissue-specific genes. Analysis of the fetal cell core transcriptome using the BioGPS Gene Expression database revealed expression of 49 tissue-specific genes from several different organs. The numbers represent the number of tissue-specific ...

Principal component analysis.

Data from the fetal cell microarrays had several key differences from those of reference datasets. For example, fetal cell arrays had higher scale factors and lower percent calls. These differences dominated the first principal component of the data set, which separated fetal cell data from GEO datasets in both cases (data not shown).

In a PCA that compared our data to the Lattin et al. [26] data set, graphing PC3 versus PC4 (Fig. 3a) allowed tissue type relationships to be determined. Data points closest to the GFP+ fetal cells were placenta, cells and tissues of immune origin (bone marrow, mast cells, peripheral macrophages, and lymph nodes), pancreas, and osteoblasts. For a full list of data points and a high-resolution color image, see Supplementary Figs. S1 and S2.

FIG. 3.
Principal component analysis. a) Comparison of our fetal cell data, circled in green, to data from Lattin et al. [26]. b) Comparison of our fetal cell data, circled in green, to data from Thorrez et al. [29].

PCA comparing our data to the Thorrez et al. data [29] revealed slightly different results. PC3 versus PC4 (Fig. 3b) showed the closest relationship of the GFP+ fetal cells to placenta, with additional similarity to lung, ovary, eye, embryonic stem cells, and adrenal gland. Because both versions of the PCA showed similarities to placenta, in addition to the presence of placenta-specific genes as identified using the BioGPS expression atlas, it is likely that many of the fetal cells originated from the placenta (Fig. 4).

FIG. 4.
Analysis overlap. Venn diagram showing overlap of PCA and BioGPS analyses. Each circle contains the overlap of our data with the external datasets analyzed. All three circles converge on placenta.

Genes of interest.

Seventy-nine genes of interest were identified (Fig. 5) that fell into four main categories with some overlap: reproduction (33 genes), immune system (32 genes), endothelial (13 genes), and mesenchymal (12 genes). The large number of reproductive- and immune-specific genes suggests a role for fetal cell trafficking in development of immune tolerance during pregnancy.

FIG. 5.
Genes of interest. The number of genes in each category is shown in parentheses next to the overall category. Alternate gene names are listed in parentheses next to the official gene designation. If a probe targets the mRNA from more than one gene, both ...


Here we report that low numbers of fetal cells can be flow sorted from the maternal lung and that reproducible gene expression information can be obtained from them. We performed a discovery-driven analysis to deduce the cell type(s) of origin as well as the putative function of the fetal cells. Our multidimensional analyses, particularly PCA, suggest that at least some of the fetal cells in the murine maternal lung are placental in origin. This finding is unexpected and novel in the mouse, yet it is supported by a recent report of fetal cells trafficking to infarcted heart tissue in pregnant female mice. The fetal cells differentiated into cardiomyocytes and also expressed Cdx2, an important early regulator of trophoblast differentiation [13, 30]. Cdx2 was not within our fetal cell core transcriptome, suggesting that fetal cells in the maternal lung are at a later differentiation state than those seen in the infarcted heart.

During pregnancy, the maternal immune system must accept the presence of the antigenically foreign fetus while maintaining the ability to fight infection. Several mechanisms have been proposed to facilitate this immunological tolerance, including exposure to paternal antigens on circulating fetal cells [31, 32]. In humans, trophoblast deportation is hypothesized to be important for induction of immune tolerance to the fetus [32] and is elevated in pre-eclampsia [32, 33]. Further evidence comes from previous work in our laboratory showing that allogeneic matings have more fetal cell trafficking [15], suggesting that fetal cell transfer during pregnancy is affected by genetic differences between the mother and fetus. It is possible that fetal cell microchimerism plays a role in the development of maternal immune tolerance. Further support for this hypothesis is the expression of genes involved in modulation of the immune system, including Aicda, Zap70, Cd24a, Psg28, Scgb1a1 (also known as uteroglobin), and Foxp1 (Fig. 5). PCA also showed similarity to many immune system cells and tissues (bone marrow, mast cells, peripheral macrophages, and lymph nodes; Fig. 3). The impact of fetal cell microchimerism on the maternal immune system is an intriguing area for future research.

In addition to placenta and immune cells, the PCA, BioGPS, and Eurexpress Atlas results showed that fetal cells are similar to nervous system, lung, adipose, bone, pancreas, and mesenchyme. Combined with the expression of genes known to be involved in epithelial-mesenchymal transition (such as Vim, Ctnnb1, Notch2, Smad1, and Bmpr2) (Fig. 5), our results suggest that some of the fetal cells are mesenchymal stem cells (MSCs). MSCs are stromal cells characterized by an absence of hematopoietic lineage markers, in vitro adherence to plastic, and their ability to differentiate into adipocytes, chondrocytes, and osteocytes [34]. We have previously suggested that fetal cells in the maternal organs may be MSCs [35]. Earlier work from our laboratory showed expression of surface markers on fetal cells that were characteristic of MSCs [8, 15]. Another group demonstrated the presence of fetal MSCs in the bones and bone marrow of postpartum women [36]. These isolated fetal cells were able to differentiate in vitro into adipocytes and osteocytes. Furthermore, microchimeric fetal cells are known to differentiate in vivo in response to injury in both mice and humans, contributing to repair of injury in multiple maternal organs, demonstrating pluripotency [16, 35]. The possibility that the MSCs and placental cells are the same population cannot be excluded, as the placenta is known to contain MSCs [37]. Future work could incorporate cell surface markers and flow cytometry to sort subpopulations of cells, followed by comparative transcriptomic analysis to determine if these cells are from the same population.

Fetal cells were collected from females at the end of pregnancy but at three different gestational days. A 3-day window represents considerable time during murine fetal development. We acknowledge that there might have been differences in the fetal cell population identified if we collected cells only at one gestational time point. However, the genes we identified were found consistently between samples and so were expressed over the entire three-day period.

Although our sample size was small in this study, the probes identified in the fetal cell core transcriptome were expressed on at least six of seven arrays. A prior report that studied the stability of microarray results with increasing replicates found that a minimum of five replicates was needed to produce stable results. By eight to ten replicates near-maximal levels of stability were achieved [38]. In addition, the GFP+ cell microarrays clustered together in both PCAs. This further supports the reproducibility and validity of our results.

Despite the fact that the two PCAs were performed using similar datasets, the results did not entirely overlap. This is partially due to the way PCA is performed, and the reason why performing a similar analysis using two different datasets provides more information about reproducibility than using only one external dataset or combining all the data together. Another source of variability is that each GEO dataset only included two to three replicates. Moreover, using external datasets that use whole organs made of a mixture of cells may have led to variability between the two datasets. Last, the methods used by each group differ (compare references [29] and [26]), further introducing discrepancies.

Another constraint is that extensive annotation of gene expression and function in murine fetal tissues was not available. Therefore, much of our analysis relies on gene expression patterns from adult tissues. We used the Eurexpress Atlas to partially address this issue, although the atlas does not include any extraembryonic tissues, such as placenta, and tissue was collected only at one time point during embryonic life [27].

The presence of lung transcripts in several of the analyses may suggest contamination with maternal cells. However, our flow cytometry gates were established using a virgin female control and were drawn very conservatively. We also had a gate based on forward scatter in order to eliminate any adherent cells. The placenta expresses surfactant proteins [39] and participates in gas exchange [40], making it somewhat similar in function to the lung. Thus, transcripts annotated as specific for the adult mouse lung may actually be present in the placenta as well. Finally, these results may indicate differentiation of the fetal cells within the lung parenchyma. This possibility will need further investigation.

In conclusion, we report here a comprehensive gene expression analysis of fetal cells within the murine maternal lung during pregnancy. We show that these cells are a mixed population, as previously suggested by cell surface marker experiments in our laboratory. The major contributors to the microchimeric population are trophoblasts, mesenchymal stem cells and cells of the immune system. We propose that trophoblast trafficking occurs in mice, which may allow development of a model to investigate the role of trophoblast deportation in the etiology of pre-eclampsia. Our results have additional implications for the development of immune tolerance during pregnancy, providing a model system for future research. Finally, the persistence of fetal or placental MSCs may impact long-term maternal postpartum health.


The authors thank Steven Kwok and Allen Parmalee for assistance with flow cytometry and Drs. Gordon Huggins and Andrew Hoffman for critical review of the manuscript.


1Supported by National Institutes of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) grants R01 HD-049469-05 to D.W.B., T32 HD-049341-05 to D.W.B., and R01 HD-058880 to D.K.S.. The Provost Fund at Tufts University was awarded to D.W.B.; and S.P. was awarded the Sackler Dean's Fellowship Award.


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