Cell-type specific gene expression profiles of leukocytes in human peripheral blood
© Palmer et al; licensee BioMed Central Ltd. 2006
Received: 21 October 2005
Accepted: 16 May 2006
Published: 16 May 2006
Blood is a complex tissue comprising numerous cell types with distinct functions and corresponding gene expression profiles. We attempted to define the cell type specific gene expression patterns for the major constituent cells of blood, including B-cells, CD4+ T-cells, CD8+ T-cells, lymphocytes and granulocytes. We did this by comparing the global gene expression profiles of purified B-cells, CD4+ T-cells, CD8+ T-cells, granulocytes, and lymphocytes using cDNA microarrays.
Unsupervised clustering analysis showed that similar cell populations from different donors share common gene expression profiles. Supervised analyses identified gene expression signatures for B-cells (427 genes), T-cells (222 genes), CD8+ T-cells (23 genes), granulocytes (411 genes), and lymphocytes (67 genes). No statistically significant gene expression signature was identified for CD4+ cells. Genes encoding cell surface proteins were disproportionately represented among the genes that distinguished among the lymphocyte subpopulations. Lymphocytes were distinguishable from granulocytes based on their higher levels of expression of genes encoding ribosomal proteins, while granulocytes exhibited characteristic expression of various cell surface and inflammatory proteins.
The genes comprising the cell-type specific signatures encompassed many of the genes already known to be involved in cell-type specific processes, and provided clues that may prove useful in discovering the functions of many still unannotated genes. The most prominent feature of the cell type signature genes was the enrichment of genes encoding cell surface proteins, perhaps reflecting the importance of specialized systems for sensing the environment to the physiology of resting leukocytes.
Circulating leukocytes are a rich and readily accessible source of information about the health and physiological state of an individual. A procedure as simple as light microscopy-based quantitation of morphologically distinguishable blood cell types is so broadly useful that it has been a mainstay of clinical diagnosis for decades. Methods that might resolve more subtle variations in leukocytes could have correspondingly greater diagnostic power . To explore and develop this potential, gene expression profiling of peripheral blood cells has become an increasingly popular means of addressing a wide variety of questions about health and disease. This approach has been used to study numerous states of health including multiple sclerosis, renal cell carcinoma, stroke, smallpox, neurofibromatosis type 1, and responses to various stresses [2–8] in the hopes of developing easily assayable prognostic or diagnostic markers, and gaining insight into disease mechanisms, as well as to the study of natural variation and individuality in gene expression [9–11]. While many of these studies have been successful in identifying gene expression patterns that differentiate control and disease groups, their interpretation is often confounded by variation in relative proportions of the cell populations that make up whole blood.
Blood is a complex tissue, containing a variety of cell types – including T-cells, B-cells, monocytes, NK cells, and granulocytes, each of which can be further subdivided. The relative proportion of each of these cell types can vary greatly between individuals and with states of health and disease, and in response to stimuli. In whole blood, neutrophils are usually the most abundant cell type, normally varying in abundance from 30–70% of white blood cells in healthy adults [12, 13] and even more (or less) in disease. Neutrophils are often excluded from analyses of gene expression in human blood, but the remaining mixture of peripheral blood mononuclear cells (PBMC), can also vary greatly in its composition. In healthy adults, monocytes can vary from 2 to 10% of PBMCs , and within the lymphocyte subset, the relative proportion of T-lymphocytes and B-lymphocytes can range from 61–85% and 7–23% respectively ; furthermore, the ratio of CD4+ T-cells to CD8+ T-cells can vary from <1.0 to 2.0 . The relative proportions of the contributing cell types inevitably affect the composite gene expression profiles of whole blood or unfractionated PBMCs. Variation in the relative proportions of distinct cell types provides valuable clinical information in its own right. The ability to distinguish the effects of variation in cellular "demographics" from the signatures of physiological responses, in global gene expression profiles of peripheral blood samples, would thus undoubtedly improve our ability to extract physiological and clinical insights from these signatures.
By comparing gene expression profiles of homogeneous cell populations, it is possible to identify genes with cell-type-specific gene expression patterns. These sets of genes can serve as "biomarkers" for estimating the abundance of specific cell types, and can provide insights into cellular functioning. By sorting peripheral blood from healthy donors based on cell surface markers, we obtained purified populations of CD4+ T-cells, CD8+ T-cells, and B-cells. We then compared the global gene expression profiles of these purified populations, PBMCs and whole blood samples, and attempted to identify cell-type-specific signatures for B-cells, T-cells, CD4+ T-cells, CD8+ T-cells, lymphocytes, and granulocytes.
Results and discussion
We used DNA microarrays containing 37,632 elements representing ~18,000 genes to characterize the global gene expression profiles for 9 B-cell, 6 CD8+ T-cell, 5 CD4+ T-cell, 5 PBMC and 3 whole blood samples, representing 3 female and 4 male healthy adults. In order to obtain an overview of the major sources of variation in this data set, we selected a set of variably expressed genes (741 genes/1109 clones whose transcripts levels varied 3.0 fold or more from the mean across all samples, in at least 3 of the 28 samples) and clustered the samples and genes using these genes.
For each cell population of interest (B-cells, T-cells, CD4+ T-cells, CD8+ T-cells, granulocytes and lymphocytes), we also performed a supervised search for genes that are characteristically expressed by that cell type. Specifically, for each cell type, we searched for genes whose expression was significantly positively correlated with the estimated relative abundance of the cell type across the set of 28 samples of varying cell type composition (p < 0.005 by permutation). We refined these gene lists by also requiring that a signature gene discriminate between the target cell and a closely related cell population (T-cells versus B-cells; CD4+ T-cells versus CD8+ T-cells; whole-blood versus PBMC), using a two-class Significance Analysis of Microarrays (SAM)  (False Discovery Rate < = 5%). We analyzed the final cell type specific gene lists for functional and structural themes using EASE , a program for calculating the statistical enrichment of Gene Ontology (GO)  annotations in a query list relative to a background list (Bonferroni p < 0.05 for Fisher's Exact Test).
We compared our list of 222 T-cell enriched genes with a recently published list of 92 "T-cell enriched" genes , and found that 33 of the 81 genes on this list that were also measured in our study, were also identified as T-cell enriched in this study (P < 10-41, hypergeometric distribution probability of > = 33 T-cell genes/81 genes given population ratio of 222 T-cell genes/16865 genes measured). The genes identified in common by the two studies tended to be classical T-cell associated genes with high discrimination rankings in both studies. This overlap is especially striking in view of the fact that the two studies used different microarray platforms (two color comparative cDNA hybridization versus one color Affymetrix oligonucleotide arrays) and, more importantly, the different selection criteria for T-cell specificity – we required discrimination of T-cells from B-cells as well as a significantly positive correlation between a gene's expression and T-cell abundance across all mixed and purified samples, whereas Cobb et al. required only discrimination between of T-cells and mixed blood cells. The complete list of T-cell specific genes and the results of EASE analysis are available as supplemental data (see Additional file 2 for gene list and Additional file 3 for EASE results).
The majority of T-lymphocytes are CD4+ "helper" T-cells (the average CD4+/CD8+ T-cell ratio is 1.8) , and function primarily in the activation of B-cells and macrophages. We attempted to define CD4+ T-cell specific genes by searching for genes that met the following criteria: I) their expression was significantly positively correlated with relative abundance of CD4+ T-cells across all samples II) their expression was significantly different between CD4+ T-cells and CD8+ T-cells (by SAM) and III) they were at least 2 fold more highly expressed in CD4+ T-cells than in CD8+ T-cells. There were no genes that met all three criteria. Notably, SAM analysis (CD4+ T-cells versus CD8+ T-cells) alone only yielded 4 genes: CD4, ANK3, MXI1, and CTSB. All four genes were more than 2 fold enriched in CD4+ T-cells relative to CD8+ T-cells but only two of these genes (CD4 and ANK3) were also significantly positively correlated with the relative abundance of CD4+ T-cells across all samples. However, since the majority of clones representing CD4 and ANK3 (3 of 4 clones and 2 of 3 clones respectively) did not meet all of the above criteria, these genes did not meet our final criterion of clone consistency (see Methods), and thus did not qualify as CD4+ T-cell "signature" genes (although 2 of the 4 "discordant" CD4 clones did meet the correlation criteria alone). The failure of most CD4 clones to meet our selection criteria may be explained by the fact that CD4 itself is not exclusively expressed by CD4+ T-cells – it is also expressed by monocytes (at lower levels)  and by some neutrophils . The one-sidedness of the CD8+ T-cell: CD4+ T-cell comparison suggests that CD8+ and CD4+ T-cells share much of their cellular machinery, and that CD4+ T-cells begin transcription of their effector molecules only upon stimulation while CD8+ T-cells appear to prepare some of their cytotoxic artillery in advance. This result is in contrast to a study of the gene expression profiles of CD4+ and CD8+ T-cells in response to activation, in which as many as 518 genes were found to be preferentially expressed by either CD8+ T-cells or CD4+ T-cells .
We compared our results to those of a previously published study that also compared whole blood and PBMC samples, and found significant overlap between the two studies. Whitney et al.  found 704 "granulocyte enriched" genes with mean differences of greater than 2 fold in expression between whole blood and PBMC samples from the same individual. Of the 660 genes in this set that were measured in our study, 171 were also classified as granulocyte signature genes in this study (P < 10-135, hypergeometric distribution probability of > = 171 granulocyte genes/660 genes given population ratio of 411 granulocyte genes/16865 genes measured). The concordant genes tended to be those with higher discrimination scores in both studies. The imperfect overlap of the two gene lists is not unexpected because different filtering criteria were used – the 2 fold change required by Whitney et al. was one of three criteria in our study. We tested the applicability of our "granulocyte enriched" gene list in an independent data set of 77 whole blood samples from 75 subjects for which the relative abundance of neutrophils was known . For each of the 17046 well-measured clones in this independent data set, we calculated the Pearson correlation of gene expression to neutrophils relative abundance, and converted these correlations to percentile rank correlations. We found that the genes in our granulocyte signature had a median Pearson correlation of 0.17 and a median percentile rank of 0.91 in the other data set, suggesting that the genes in our granulocyte signature are among the most highly correlated with the true neutrophil abundance. The complete granulocyte signature gene list and corresponding EASE analysis results are available as supplemental data (see Additional file 2 for gene list and Additional file 3 for EASE results).
Individual specific gene expression
Despite the small number of individuals sampled in this study, we carried out a low power search for genes that vary more in expression between individuals than between cell types within an individual. The methods and results of this analysis are available as supplemental data (see Additional file 5 for description, Additional file 6 for text results, and Additional file 7 for figure of results).
Gene list summary statistics "Contrasting Cell Pop." is the cell population to which the query cell population was compared in order to define the cell type signature list. "Genes" reports the number of genes that make up the gene expression signature that we defined for each cell type. "Correlation " reports the range and median correlation between expression levels of the signature genes and the relative abundance of the cognate cell type. "Fold Enrichment" reports the range and median ratio between the expression level of the genes in each cell type's signature and the "contrasting cell population" to which it was compared.
Contrasting cell pop.
Granulocytes (whole blood)
We found that, not surprisingly, the best gene expression based surrogates for the abundance of a particular cell subset were the expression of the genes associated with the characteristic cell surface phenotype (e.g. CD8, CD3D/G, CD20), or other genes known to be associated with cell-type specific functions (such as cytotoxicity or immunoglobulin secretion). While genes previously known to be cell-type specific dominated the cell-type signatures, they also contained numerous unannotated genes, and genes not previously associated with the known functional characteristics of the cells. It is interesting to speculate on the significance of the finding that the lymphocyte subset signatures (B-cells, T-cells, and CD8+ T-cells) were all dominated by genes encoding plasma- membrane associated proteins. This may indicate that, in their resting state, lymphocytes are specialized primarily in the way they sense and monitor their environment. This finding, together with the observation that lymphocytes have elevated levels of transcripts encoding translational machinery, paints a picture of peripheral blood lymphocytes spending the majority of their lives in watchful waiting, while upon activation they respond rapidly are distinguished by their diverse effector roles.
While classical, direct methods of enumerating cell populations remain the simplest, and most accurate method of characterizing the components of mixed cell populations, the cell-type specific gene lists presented here provide an alternative means of characterizing mixed cell populations that could be useful for profiling of archived RNAs and heterogeneous clinical samples for which direct counting is not possible. Furthermore, these cell type signatures contribute significantly to functional annotations of a substantial group of uncharacterized genes. Further studies of the gene expression profiles of other purified leukocyte populations such as NK cells and monocytes will help establish the specialized gene expression programs that enable each cell type to perform its unique function, and will further improve our ability to reconstruct the cellular composition of heterogeneous samples.
Blood samples from 3 female and 4 male volunteers were obtained after informed consent. All volunteers were Caucasians, ranging in age from 22–45 years and self reported as free of chronic and acute infections. Complete blood counts were determined at the Stanford University Hospital Clinical Laboratory by automated procedures and have been made available as supplemental data (see Additional file 4). All subjects fell within normal ranges for the major cell populations (% neutrophils, % lymphocytes, % monocytes, % basophils, % eosinophils).
Isolation and purification of cell types
Sample purity Number of samples and median purity of each purified cell type as a percentage of all cells, measured by FACS. (+) or (-) refer to positive and negative selection respectively.
Total RNA was linearly amplified twice as previously described  with minor modifications. Fluorescent (Cy5-labeled) cDNA probes were prepared from the amplified RNA samples as described and hybridized to cDNA microarrays containing 37,632 array elements, representing approximately 18,000 unique human genes . The cDNA microarrays were manufactured and hybridized as previously described [38, 40] (also see ). A common reference RNA (Cy3-labeled) was mixed with the Cy5-labeled experimental sample before hybridization to provide a common internal reference standard for comparison of relative gene expression levels across arrays [38, 40]. The reference was Stratagene Universal Human Reference RNA – a mixture of RNA from 11 human cell lines (Stratagene, La Jolla, CA). Fluorescence images of the hybridized arrays were obtained using a GenePix 4000B scanner (Axon Instruments, Union City, CA).
Data extraction and filtering
Gene expression measurements were extracted from the fluorescent array images using GenePix Pro 5.0. Spots with coefficients of variation greater than 0.95 in the Cy3 or the Cy5 channel were excluded from analysis, as were spots that had >15% saturated pixels or that contained fewer than 20 total pixels in either channel. Each array was then subjected to normalization for dye effects: the fluorescence ratios were multiplied by a constant such that the average ratio of Cy5/Cy3 across a subset of high-confidence spots was 1. The criterion for inclusion of data in the initial dataset was a normalized intensity/background ratio of at least 2.5 in the reference channel or the sample channel. Prior to statistical analysis, data analysis was restricted to genes for which at least 75% of the samples had well measured data (as defined by the previously described criteria). This filtering yielded 30,320 clones, corresponding to ~16,865 unique genes (collapsed by gene symbol). For pairwise cell type comparisons with SAM, genes were further required to have 75% good data in each of the relevant cell types. For intrinsic analysis, genes were further required to have well measured data for 2 of the 3 cell types in each of the 5 individuals. For each array, the log ratios were transformed such that the mean log expression ratio of all elements on the array was zero. For all clustering, the measurements for each gene (as log ratios) were centered, by subtracting the mean across all samples, in order to emphasize relative expression within the experimental dataset.
Clones were collapsed to unique genes based on gene symbol whenever possible, and otherwise using IMAGE or LCP (lymphochip) clone numbers. Gene names were converted to gene names and symbols using SOURCE . When reporting "gene" based results for gene lists, the highest scoring (most correlated) clone is reported.
For the signature gene lists and intrinsic gene lists our final filter was the removal of "outlier" genes: genes for which there were 3 or more clones available on the microarray, but only 1 gene was in the results list.
Significance analysis of microarrays
We used Significance Analysis of Microarrays (SAM) (Version 1) to identify genes whose expression differed significantly between pairs of related cell types . We reported genes that differed between samples at median false discovery rates of 5%. The lymphocyte versus granulocyte and B-cell versus T-cell comparisons employed the two class unpaired analysis, while the CD4 versus CD8 analysis was for five paired samples from the same individual. All comparisons used K-nearest neighbors to impute missing data.
Relative abundance estimates used for correlation calculations Estimates of relative abundance of each cell type in each sample were based on perfect purifications for purified cell populations and on normal adult values for mixed populations . We used these estimates in order to identify genes whose measured levels of expression were positively correlated with the estimated relative abundance of each cell type. (Abbrev: WB = whole blood).
EASE GO term annotation
We used EASE  to identify themes in the function and cellular localization of genes comprising the signature gene lists for each cell subset. We compared each gene list to the same background file, which consisted of all genes (16,865) that passed the 75% good data filter (described in "Data Filtering" above) for the entire data set. All analyses were done using gene symbol as the unique identifier. In the supplemental data, we report all results for which the Bonferroni corrected Fisher's Exact Probability was less than 0.05 for each of the three ontologies -Molecular Function, Biological Process, and Cellular Component.
peripheral blood mononuclear cell
Significance Analysis of Microarrays
We thank the volunteers who donated and drew blood for this study, Tor Stuge for help with FACS, Alok Saldanha for help with permutation analysis, and Stephen Popper for helpful discussions. This work was supported by the Stanford Genome Training Grant (CP), National Institute of General Medical Sciences Training Grant GM07365 (to AAA and MD) and by the Howard Hughes Medical Institute (POB). POB is an Investigator of the HHMI.
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