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作者：Jane Synnergrena,e, Theresa L. Gieslerb, Sudeshna Adakc, Reeti Tandonc, Karin Noakssond, Anders Lindahle, Patric Nilssonf, Deirdre Nelsong, Bjrn Olssona, Mikael C.O. Englundd, Stewart Abbotg, Peter Sartipyd作者单位：aSchool of Humanities and Informatics, University of Skvde, Skvde, Sweden;
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【摘要】
$ h3 U$ _: O \: R' E+ Q! h( `0 ~3 D Housekeeping genes (HKGs) are involved in basic functions needed for the sustenance of the cell and are assumed to be constitutively expressed at a constant level. Based on these features, HKGs are frequently used for normalization of gene expression data. In the present study, we used the CodeLink Gene Expression Bioarray system to interrogate changes in gene expression occurring during differentiation of human ESCs (hESCs). Notably, in the three hESC lines used for the study, we observed that the RNA levels of 56 frequently used HKGs varied to a degree that rendered them inappropriate as reference genes. Therefore, we defined a novel set of HKGs specifically for hESCs. Here we present a comprehensive list of 292 genes that are stably expressed (coefficient of variation <20%) in differentiating hESCs. These genes were further grouped into high-, medium-, and low-expressed genes. The expression patterns of these novel HKGs show very little overlap with results obtained from somatic cells and tissues. We further explored the stability of this novel set of HKGs in independent, publicly available gene expression data from hESCs and observed substantial similarities with our results. Gene expression was confirmed by real-time quantitative polymerase chain reaction analysis. Taken together, these results suggest that differentiating hESCs have a unique HKG signature and underscore the necessity to validate the expression profiles of putative HKGs. In addition, this novel set of HKGs can preferentially be used as controls in gene expression analyses of differentiating hESCs.
( b x- J P/ n1 {- { 【关键词】 Gene expression Microarray In vitro differentiation Human embryonic stem cells Housekeeping gene Normalization, ~) k9 W* r$ `) n6 f- l
INTRODUCTION
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Housekeeping genes (HKGs) are required for basal cellular function and maintenance and are assumed to be expressed at relatively stable levels across different cell types and experimental conditions .
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Human ESCs represent populations of pluripotent undifferentiated cells with unlimited replication capacity that can be coaxed to differentiate into a variety of specialized cells . To realize the potential of hESCs, it is necessary to gain much deeper knowledge about the processes that govern differentiation of these cells.
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In recent years, significant progress toward understanding cellular differentiation has been fueled, in part, by studying gene expression using microarrays .
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4 l" B b6 D5 `6 U, F5 F H0 t# KMicroarrays are very useful for identification and evaluation of HKGs, and many adult and fetal tissues have been analyzed . However, no studies to date that have included hESCs or early derivatives thereof have specifically investigated stably expressed genes in these cell populations. In the present study, we performed whole-genome expression profiling of undifferentiated and differentiating hESCs using CodeLink Bioarrays targeting approximately 54,000 transcripts and expressed sequence tags (ESTs). Of 56 genes that have previously been used as HKGs, we observed that the vast majority varied considerably in differentiating hESCs. Hence, we have identified a novel set of stably expressed genes that can be used for normalization of gene expression data from hESCs. We propose that these novel HKGs are more reliable as reference genes in studies of early differentiation of hESCs compared with many of the previously used HKGs.
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MATERIALS AND METHODS8 y$ v5 K% \, w
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Human ESC Culture and In Vitro Differentiation
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The hESC lines SA001, SA002, and SA002.5 (Cellartis AB, Göteborg, Sweden, http://www.cellartis.com) were propagated as previously described . Undifferentiated hESCs were passaged every 4¨C5 days by mechanical dissociation using a Stem Cell Cutting Tool (Swemed Lab International AB, Billdal, Sweden, http://www.swemed.com).
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Spontaneously differentiating hESC cultures were obtained using two different protocols (Fig. 1), using serum-containing medium (KnockOut-Dulbecco's modified Eagle's medium, 20% fetal calf serum, 1% penicillin-streptomycin (PEST), 1% GlutaMAX, 1% nonessential amino acids, and 0.1 mM ß-mercaptoethanol) (Invitrogen, Carlsbad, CA, http://www.invitrogen.com). In protocol 1 (the high density . After an additional 6 days, the suspended EBs were either harvested for RNA extraction or plated onto gelatin-coated culture dishes to allow further differentiation. At day 14 after plating of the EBs, the cells were harvested for RNA extraction.5 y; m" `3 w6 h X9 i* N! g5 L/ X
1 a! B6 A8 Z* wFigure 1. Schematic of the experimental design. Undifferentiated human ESCs were either harvested at day 5 after passage or subcultured further to induce differentiation through the high density or EB protocol, as described in Materials and Methods. Abbreviations: EB11d, EBs cultured for 6 days in suspension; EB25d, EBs plated after 6 days in suspension and maintained for additional 14 days in culture; HD11d, differentiated cells maintained on mouse embryonic fibroblast until day 11 after passage; HD25d, differentiated cells maintained on mouse embryonic fibroblast until day 25 after passage; UD5d, undifferentiated cells at day 5 after passage.7 ~0 Z; J; P; u' D0 @, j4 l
% o$ }+ Q; s$ E* eRNA Extraction
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8 F) t& P5 ?4 ?. V# r5 V% N3 iTotal RNA was extracted from undifferentiated or differentiated hESCs using the Qiagen RNeasy Mini Kit (Hilden, Germany, http://www1.qiagen.com) according to the manufacturer's instructions. DNase treatment was performed on-column using the RNase-free DNase Kit (Qiagen).
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Microarray Analysis Using the CodeLink Bioarrays Platform
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Total RNA was used to generate cRNA using the CodeLink Expression Assay Reagent kit (GE Healthcare, Little Chalfont, Buckinghamshire, U.K., http://www.gehealthcare.com). Total RNA and final cRNA concentrations and quality were assessed using an Agilent Bioanalyzer and UV spectrophotometry (PerkinElmer Life and Analytical Sciences, Boston, http://www.perkinelmer.com).. f9 [- r$ N5 ^2 F
" k: \! J* C; _1 sFragmented cRNA was hybridized at 37¡ãC for 18 hours to CodeLink Human Whole Genome Bioarrays (GE Healthcare) targeting approximately 57,000 transcripts and ESTs. All experiments were performed in triplicate. For detection and quantization, an Axon GenePix 4000B scanner (Molecular Devices Corp., Union City, CA, http://www.moleculardevices.com) and the CodeLink Scanning and Expression Analysis software (version 4.2) (GE Healthcare) were used.2 Y; D$ c0 k) F9 m8 o) u
. `" _% c N* T" P% oIdentification of Stably Expressed Genes in Differentiating hESCs
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6 n+ \2 Y5 l" \0 e2 _: GAll subsequent analyses were based on average intensity measurements from three technical replicates, where only probes flagged as "good" in all arrays were included. For each hESC line, genes expressed at a stable level were identified by computing the coefficient of variation (CV) using the mean expression from undifferentiated and differentiated hESCs. The threshold was set to CV = 20%, since the number of probes below this threshold matched the number of probes that were within 1.5-fold change in all pairwise comparisons. It has been shown that the CodeLink platform can detect a fold change of 1.5 with 90% power using three technical replicates . The low expression threshold was selected based on the expression range of the positive control gene LEUB. The threshold of two was selected for high expression as it is close to the median of the good-flagged probes. For all bioinformatics analyses, the R software package (http://www.r-project.org) was used (scripts are available upon request).
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0 H- L: A( O; M4 mReal-Time Quantitative PCR Analysis
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2 ]8 r) p+ E) ^( |. PIn separate experiments, hESC lines SA001 and SA002 were maintained and differentiated according to the HD protocol . Quantitation of gene expression was based on the cycle threshold (Ct) value of each sample.- R: S/ b4 F) V, n3 g+ ]0 V, H
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RESULTS; q: N3 T5 ]% }% p& c5 y+ X
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To obtain populations of differentiating hESCs in vitro, we used two different protocols (Fig. 1). In the first, hESCs were maintained in the absence of bFGF on MEF without passaging of the cells (HD protocol). In the second, the hESCs were differentiated through EB formation (EB protocol). Cells were harvested at matched time points and used for subsequent gene expression analysis. To verify that differentiation of the hESCs occurred under the experimental conditions used in this study, the RNA levels of known pluripotency and differentiation markers were monitored. Figure 2 summarizes the results obtained from cells cultured according to the HD protocol and shows the kinetics of the downregulation of OCT4, SOX2, TERT, and DNMT3B and upregulation of FN1, ACTC, and ISLT1 RNA levels during hESC differentiation. Notably, these marker genes cover the range of low-expressed genes (DNMT3B) up to highly expressed genes (FN1). Similar results were obtained for cells differentiated through EB formation (data not shown).
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Figure 2. RNA levels of pluripotent markers OCT4, SOX2, TERT, and DNMT3B are presented, as are levels for FN1, ACTC, and ISLT1, which are markers for early differentiation. Intensity units (y-axis) are proportional to the mRNA level. Differentiation of the human ESCs (hESCs) was induced according to the high-density protocol described in Materials and Methods. Undifferentiated hESCs were harvested at day 5 after passage (light gray bars) or cultured for a total of 11 days (dark gray bars) or 25 days (solid bars) after passage.' ~) ]* P+ c" K" {' T
1 Z) N; k8 E+ {2 P, Y1 kUsing published reports, we assembled a list of 56 commonly used HKGs and investigated the stability of these genes in differentiating hESCs. The CV was calculated for each gene and hESC line (Table 1). The threshold for detection of differentially expressed genes using CodeLink Bioarrays is a fold change of 1.5 , and this corresponds to a CV of 20% in the experiments used in the present study. Strikingly, only 4 of the 56 genes were stably expressed (CV
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& R6 u/ J8 z3 x$ ^& @Table 1. CVs for 56 frequently used housekeeping genes (HKGs) in undifferentiated and early differentiating human ESCs (hESCs)
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3 Y2 R8 _0 I" k0 X, }This observation prompted us to perform whole-genome investigations to define a novel set of genes that could be used as HKGs in differentiating hESCs. For each probe and cell line, we used only probes that were good-flagged in all replicates and calculated the CV. Figure 3 shows the number of probes identified and the overlap among the different cell lines. For SA001, SA002, and SA002.5, we obtained 2,308, 3,100, and 1,873 unique genes, respectively, that were good-flagged and displayed a CV below 20%, indicating a stable expression during differentiation. Of these, only 292 genes were common to all cell lines, which illustrates that there are substantial differences in the gene expression profiles among different lines. We further divided these 292 genes into three groups (high-, medium-, and low-expressed genes) based on their absolute expression levels. Table 2 shows a subset of the eight most stable genes from each of the three groups (total of 24 genes), and a complete list of all of the 292 HKGs is available in supplemental online Table 1.- B% v) o1 |1 a/ C8 ^
( \5 ?: o4 z5 s& L F- h3 F1 B4 uFigure 3. Venn diagram depicting the number of stably expressed genes in the different human ESC lines. Genes stably expressed across both differentiation stages and differentiation protocols were identified using only good-flagged probes with CVs below 20%. The number of genes in each intersection between cell lines is also indicated.
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4 z; i/ J6 X$ P; ^- l2 L8 d" KTable 2. Selection of stably expressed genes in undifferentiated and early differentiating human ESCs (hESCs). \- A% x9 J6 n
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Our initial analysis (Table 1) suggested that a majority of the commonly used HKGs were not stably expressed in differentiating hESCs. To further explore differences and similarities across tissues and cell types in terms of stably expressed genes, we compared our results with previously published data. Eisenberg and Levanon presented a comprehensive panel of 575 HKG candidates for non-stem cell studies, selected from 47 different adult tissues and cell lines. However, only three of these genes (PPP1R11, PPP2CB, and RASSF1) were also observed to be stably expressed in the differentiating hESCs studied here.
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& y; a$ o) `) n+ u* ^7 U- g6 m+ @' j7 k6 _To validate our results, we accessed independently generated publicly available hESC gene expression data . Finally, the intersection of stably expressed genes among all three studies, comprising a total of 11 different hESC lines, contained a total of six genes (19%), which are listed in Table 3.; t1 [$ ^. H6 Z6 W. P
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Table 3. Intersection of stably expressed genes in this study and previous work of undifferentiated and early differentiating human ESCs (hESCs)
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2 |5 A4 T" q, u* }+ uFurther validation of a subgroup of the genes listed in Tables 2 and 3 using QPCR demonstrated that most of these genes are indeed relatively stably expressed during early differentiation of hESCs (Fig. 4). These experiments were performed using RNA collected from hESC lines SA001 and SA002 cultured and differentiated independently of the cells used to derive the original set of genes (supplemental online Table 1). The genes NRPS998 and ELN appeared to be the most stable ones showing a Ct of approximately 0.4 when comparing cells harvested at day 5 and day 21 after passage. For comparison, we also measured the RNA levels of GAPDH, a commonly used HKG. It is clear that the expression of GAPDH varies substantially, showing a Ct of approximately 3, thus making it inappropriate to use as a reference gene in similar experimental settings., x# O8 l$ ~+ b3 L/ o/ D/ m1 H/ ?
! N F" M) F+ |5 N& z0 CFigure 4. Real-time quantitative polymerase chain reaction (QPCR) analysis of differentiating human ESCs. The cells (SA001 and SA002) were cultured as described in Materials and Methods. Undifferentiated cells were harvested at day 5 (light gray bars) after passage. Differentiated cells were harvested at day 14 (dark gray bars) and day 21 (black bars) after passage. Total RNA was extracted, and the mRNA levels of NRPS998, FBXL12, RNF7, SRP72, SLC4A1AP, NUBP1, RND1, ELN, CREBBP, and GAPDH were subsequently determined using QPCR. The data are presented as Ct, and the bars represent the mean SD (n = 2). Abbreviations: Ct, threshold cycle; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.2 s( W3 X! S( O9 [% V2 ^7 D# S5 \
6 v7 A* e+ h& t# f5 L8 GTo explore the biology of the novel potential HKGs for differentiating hESCs, we used FatiGO . Supplemental online Figure 2 summarizes the results from these comparisons and illustrates the differences between the two sets of HKGs in terms of biological function.4 F q4 ]5 q9 c6 J
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DISCUSSION$ `, w5 d) \3 S
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Global gene expression analysis has become a widely used tool for assessing the molecular state of various cells and tissues. Most investigators report the differential up- and downregulation of genes in relation to a control or basal state. Less reported but equally important is the identification of genes that remain constant during the experimental conditions used. Together, these genes can provide information on the basal activities and states of the cells. In addition, stably expressed genes (i.e., HKGs) represent markers that can be used for normalization of gene expression data across various samples. However, previous studies have shown that the expression patterns of commonly used HKGs can vary extensively ./ I2 i+ M, v! o
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In the present study, we investigated for the first time the stability of commonly used HKGs in differentiating hESCs. We observed that most of them are inappropriate to use as reference genes because of their highly variable expression levels (Table 1). Using global gene expression data obtained from three hESC lines, including a clonally derived line, we identified a new group of genes (supplemental online Table 1) that remained unchanged during early differentiation of the cells. Independent QPCR analysis confirmed the expression profiles of a subset of these genes (Fig. 4). One important aspect of this study is the design of the algorithm for identification of stable genes. Notably, the variation in gene expression is dependent on the absolute RNA level . Taking this into consideration, we divided the expression range into three groups and selected stable genes from each of these groups (details given in Materials and Methods). Thus, these genes are novel candidate reference genes for normalization of gene expression data obtained from hESCs and their early progenies.0 F+ F q, X3 V4 F3 |1 r* s# Z5 W
* q; }3 m! |: P3 ^ D/ oOn a molecular level, the variation among individual hESC lines appears to be due to the genetic diversity in the source material and differences in the initial culture of each line . In the present study, we took steps to minimize the contribution of these and other possible variables and thus selected only genes that are stable in different cell lines and passages. However, it is still important to validate the expression profile of any HKG before using it for normalization in any given experimental setup.6 |6 R- w. P. e J8 u# Q8 o
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Previous studies have identified ubiquitously expressed genes in human cells and tissues . We observed a very poor overlap between these and our novel list of candidate HKGs in differentiating hESCs. This is not very surprising since, to the best of our knowledge, no study so far has actually included hESC-derived material. We also compared the groups of genes with respect to GO annotation and observed significant differences (supplemental online Fig. 2), suggesting that some of the basal maintenance functions in differentiating hESCs may be slightly different from those in specialized cell types. Additional studies are necessary to evaluate the biological significance of these observations.! ?) w9 p; {/ ^; H' ]
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To validate our HKG set in other independent hESC lines, we analyzed publicly available gene expression data obtained from eight additional hESC lines . Two genes associated with protein transport and ion exchange (SRP72 and SLC4A1AP) are also among the stably expressed genes. Although potentially interesting, the exact functions of these genes in hESCs remain to be investigated.6 _) ?$ p% J, m+ h) \6 |
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CONCLUSION
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+ K4 \& A0 t* I. ]9 X/ fIn summary, the results from this study demonstrate that hESCs and early progenies thereof are quite different from other cell types in terms of their HKG expression. Furthermore, the importance of validating candidate reference genes for subsequent normalization of gene expression data is also highlighted. Our novel group of 292 HKGs for differentiating hESCs represents an important step toward the identification of reliable reference genes for early differentiating hESCs.
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DISCLOSURES
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4 Z9 A9 [! j0 c$ f: S8 bThe authors indicate no potential conflicts of interest.3 t& C u4 M* N2 _' ]
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ACKNOWLEDGMENTS6 G# F" o; n. L' o# t. u
' l7 J6 A m+ ?5 O! D5 Z1 HThis work was supported by Cellartis AB (Göteborg, Sweden), GE Healthcare (Niskayuna, NY), and the Information Fusion Research Program (University of Skövde, Sweden) under Grant 2003/0104 from the Knowledge Foundation. Cellartis is the recipient of NIH Grant R24RR019514-01.
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