Background Iron is necessary for neuronal function but in excess generates neurodegeneration. Although most of the components of the iron homeostasis machinery have been described in neurons, little is known about the particulars of their iron homeostasis. In this work we characterized the response of SH-SY5Y neuroblastoma cells and hippocampal neurons to a model of progressive iron accumulation.
Background
Iron is necessary for neuronal function but in excess generates neurodegeneration. Although most of the components of the iron homeostasis machinery have been described in neurons, little is known about the particulars of their iron homeostasis. In this work we characterized the response of SH-SY5Y neuroblastoma cells and hippocampal neurons to a model of progressive iron accumulation.Results We found that iron accumulation killed a large proportion of cells, but a sub-population became resistant to iron. The surviving cells evoked an adaptative response consisting of increased synthesis of the iron-storage protein ferritin and the iron export transporter IREG1, and decreased synthesis of the iron import transporter DMT1. Increased expression of IREG1 was further substantiated by immunocytochemistry and iron efflux experiments. IREG1 expression directly correlated with iron content in SH-SY5Y and hippocampal cells. Similarly, a high correlation was found between IREG1 expression and the rate of iron efflux from SH-SY5Y cells.
Results
We found that iron accumulation killed a large proportion of cells, but a sub-population became resistant to iron. The surviving cells evoked an adaptative response consisting of increased synthesis of the iron-storage protein ferritin and the iron export transporter IREG1, and decreased synthesis of the iron import transporter DMT1. Increased expression of IREG1 was further substantiated by immunocytochemistry and iron efflux experiments. IREG1 expression directly correlated with iron content in SH-SY5Y and hippocampal cells. Similarly, a high correlation was found between IREG1 expression and the rate of iron efflux from SH-SY5Y cells.Conclusions Neuronal survival of iron accumulation associates with increased expression of the efflux transporter IREG1. Thus, the capacity of neurons to express IREG1 may be one of the clues to iron accumulation survival.
Conclusions
Neuronal survival of iron accumulation associates with increased expression of the efflux transporter IREG1. Thus, the capacity of neurons to express IREG1 may be one of the clues to iron accumulation survival.

Background Because of its intense oxidative metabolism, the brain consumes a high fraction of total oxygen generating large amounts of reactive oxygen species [ 1 , 2 ]. Although brain antioxidant defenses function properly during most of human life, a number of neurodegenerative processes which involve redox-active iron accumulation become evident with age [ 3 - 5 ]. Iron is a pro-oxidant that in the reductive intracellular environment catalyses hydroxyl radical formation through the Fenton reaction [ 6 ]. At present, the crucial components of the iron homeostasis machinery have been identified. Thus, current efforts should be directed to the understanding of the mechanisms that regulate cellular iron levels and antioxidant defenses. This is of primary importance for the development of strategies to ameliorate iron accumulation and oxidative damage in neurons. In vertebrates, cellular iron levels are post-transcriptionally controlled by the activity of iron regulatory proteins (IRP1 and IRP2), cytosolic proteins that bind to structural elements called iron-responsive elements (IREs). IREs are found in the untranslated region of the mRNAs of the major proteins that regulate cellular iron homeostasis: the transferrin receptor, involved in plasma-to-cell iron transport, and the iron-storage protein ferritin. IRP2-/- mice are born normal but in adulthood develop a movement disorder characterized by ataxia, bradykinesia and tremor [ 7 ]. IRP1-/- mice are normal with slight misregulation of iron metabolism in the kidney and brown fat [ 8 ]. Thus, IRP2 seems to dominate the physiological regulation of iron metabolism whereas IRP1 seems to predominate in pathophysiological conditions. Iron is internalized into cells by the import transporter DMT1. Four DMT1 isoforms have been identified that differ in both the N-and the C-termini [ 9 ]. Two of the isoforms have a 3' iron responsive element (IRE) in their mRNA. Additional variation is given by exons 1A and 1B in the 5' end. Expression of DMT1 in response to iron availability follows a pattern similar to transferrin receptor [ 10 ], but its control by the IRE/IRP system is not clear [for review see [ 11 ]]. A new iron transporter, IREG1, also known as ferroportin or MTP1, was recently described [ 12 , 13 ]. The protein is expressed mainly in enterocytes and macrophages [reviewed in [ 14 ]]. In enterocytes IREG1 is responsible for iron efflux during the process of intestinal iron absorption, while in Kupffer cells IREG1 mediates iron export for reutilization by the bone marrow [ 15 ]. The presence of both DMT1 and IREG1 has been described in neurons, glioma cells and astrocytes [ 16 - 18 ]. The presence of IREG1 in neurons opens the possibility that they may be able to down-regulate intracellular iron concentration through its expression. In this study we examined iron homeostasis in SH-SY5Y neuroblastoma cells and hippocampal neurons. We found that iron accumulation killed a large proportion of cells, but a sub-population became resistant to iron accumulation developing an adaptative mechanism intended to decrease intracellular iron content.
Background
Because of its intense oxidative metabolism, the brain consumes a high fraction of total oxygen generating large amounts of reactive oxygen species [ 1 , 2 ]. Although brain antioxidant defenses function properly during most of human life, a number of neurodegenerative processes which involve redox-active iron accumulation become evident with age [ 3 - 5 ]. Iron is a pro-oxidant that in the reductive intracellular environment catalyses hydroxyl radical formation through the Fenton reaction [ 6 ]. At present, the crucial components of the iron homeostasis machinery have been identified. Thus, current efforts should be directed to the understanding of the mechanisms that regulate cellular iron levels and antioxidant defenses. This is of primary importance for the development of strategies to ameliorate iron accumulation and oxidative damage in neurons.In vertebrates, cellular iron levels are post-transcriptionally controlled by the activity of iron regulatory proteins (IRP1 and IRP2), cytosolic proteins that bind to structural elements called iron-responsive elements (IREs). IREs are found in the untranslated region of the mRNAs of the major proteins that regulate cellular iron homeostasis: the transferrin receptor, involved in plasma-to-cell iron transport, and the iron-storage protein ferritin. IRP2-/- mice are born normal but in adulthood develop a movement disorder characterized by ataxia, bradykinesia and tremor [ 7 ]. IRP1-/- mice are normal with slight misregulation of iron metabolism in the kidney and brown fat [ 8 ]. Thus, IRP2 seems to dominate the physiological regulation of iron metabolism whereas IRP1 seems to predominate in pathophysiological conditions.Iron is internalized into cells by the import transporter DMT1. Four DMT1 isoforms have been identified that differ in both the N-and the C-termini [ 9 ]. Two of the isoforms have a 3' iron responsive element (IRE) in their mRNA. Additional variation is given by exons 1A and 1B in the 5' end. Expression of DMT1 in response to iron availability follows a pattern similar to transferrin receptor [ 10 ], but its control by the IRE/IRP system is not clear [for review see [ 11 ]].A new iron transporter, IREG1, also known as ferroportin or MTP1, was recently described [ 12 , 13 ]. The protein is expressed mainly in enterocytes and macrophages [reviewed in [ 14 ]]. In enterocytes IREG1 is responsible for iron efflux during the process of intestinal iron absorption, while in Kupffer cells IREG1 mediates iron export for reutilization by the bone marrow [ 15 ]. The presence of both DMT1 and IREG1 has been described in neurons, glioma cells and astrocytes [ 16 - 18 ]. The presence of IREG1 in neurons opens the possibility that they may be able to down-regulate intracellular iron concentration through its expression.In this study we examined iron homeostasis in SH-SY5Y neuroblastoma cells and hippocampal neurons. We found that iron accumulation killed a large proportion of cells, but a sub-population became resistant to iron accumulation developing an adaptative mechanism intended to decrease intracellular iron content.Results Iron accumulation and cell death Iron accumulation was determined in SH-SY5Y cells grown to confluence and then cultured for two days in media containing from 1.5 to 80 μM iron (Figure  1A ). Total cell iron increased with increasing extracellular iron, reaching a plateau at 40–80 μM Fe (Figure  1B ). The observed increase in cell iron was accompanied by increases in the labile iron pool (Figure  1C ). Iron accumulation indeed caused loss of cell viability, with hippocampal neurons demonstrating higher sensitivity than SH-SY5Y cells to iron treatment (Figure  2 ). Nevertheless, a sub-population of cells survived to high iron concentrations. It was of interest to inquire into the processes underlying this adaptation, since they could help to understand iron accumulation observed in a number of neurodegenerative diseases. Consequently, we characterized the components of the iron homeostasis machine during the process of iron accumulation. Ferritin and DMT1 regulation Ferritin, the main iron-storage protein in mammalian cells, is considered the first line of defense against iron overload. Increasing iron from 1.5 to 5 μM produced a robust 4-fold increase in cell ferritin content (Figure  3A ). Further increases in iron induced additional increases in ferritin. At 80 μM extracellular iron, ferritin increased 11-fold compared to the basal 1.5 μM iron condition. In molar base, ferritin increased more than iron. The iron to ferritin mol : mol ratio decreased from 1500 at 1.5 μM Fe to 400 at 10 μM Fe to 200 at 80 μM Fe (Figure  3B ). We further characterized iron homeostasis in SH-SY5Y cells by examining the expression of the iron importer DMT1 (Figure  4 ). A 3.5-fold decrease in DMT1 protein expression was observed when iron increased from 1.5 to 80 μM. The presence of DMT1 even at high iron concentration explains the sustained iron uptake observed at 80 μM Fe [ 19 ]. Thus, DMT1 activity persisted even under conditions of iron accumulation that preceded cell death. IREG1 expression and functionality Given that the presence of IREG1 in the central nervous system has been   reported [[ 16 ]], it was of interest to examine if it participates in neuronal iron homeostasis. Western blot analysis revealed that SH-SY5Y cells   expressed anti-IREG1 reactive bands of 65.3 and 122.1 KDa molecular weight (Figure  5A ). Densitometric analysis revealed a 10-fold increase in the 122.1 KDa band in the 1.5 to 80 mM Fe range while the 65.3 band had a minor increase (Figure  5A ). Both bands were eliminated if the antibody was   incubated with the immunogenic peptide before the assay (Figure  5B ). A similar pattern was obtained with an independent anti-IREG1 antibody (the kind gift of Dr. David Haile). Thus, it is most likely that the 65.3 and   122.1 KDa bands correspond to the monomer and dimer of IREG1. The stability of the 122.1 KDa band was dependent of the concentration of b-mercaptoethanol in the sample buffer since increasing b-mercaptoethanol produced a shift in the 122.1 KDa /65.3 KDa band ratios (Figure  5C ). It is possible that in neuronal cells Ireg1 tends to form S-S bridged dimers resistant to the electrophoresis conditions. The presence of IREG1 in SH-SY5Y neuroblastoma cells and hippocampal neurons was further documented by immunocytochemistry. IREG1 was detected in both types of cells, with a predominantly cytosolic distribution pattern (Figure  6 ). The levels of IREG1 expression were directly proportional to the amount of iron in the culture. Thus, it was determined by two independent methods that IREG1 expression in neuronal cells increased with cell iron content. Efflux of iron from neurons has never been reported. In view of the presence of IREG1, we tested whether SH-SY5Y cells actually had an iron efflux function. To that end, iron efflux from cells pre-cultured for 2 days with varied iron concentrations was determined by atomic absorption spectrometry. This method was preferred to the use of radioisotopic iron since the latter could underestimate a putative iron efflux because of isotope dilution with the pre-existing iron pool (see Figure  1B ). SH-SY5Y cells had discrete but measurable iron efflux activity (Figure  7A ). The iron efflux rate increased markedly in the 20–80 μM Fe range (Figure  7B ). Interestingly, the efflux rate correlated closely with the presence of the 122.1 KDa band, while the correlation between efflux activity and the 62.5 KDa band was weaker (Figure  7C ).
Results
Iron accumulation and cell death Iron accumulation was determined in SH-SY5Y cells grown to confluence and then cultured for two days in media containing from 1.5 to 80 μM iron (Figure  1A ). Total cell iron increased with increasing extracellular iron, reaching a plateau at 40–80 μM Fe (Figure  1B ). The observed increase in cell iron was accompanied by increases in the labile iron pool (Figure  1C ). Iron accumulation indeed caused loss of cell viability, with hippocampal neurons demonstrating higher sensitivity than SH-SY5Y cells to iron treatment (Figure  2 ). Nevertheless, a sub-population of cells survived to high iron concentrations. It was of interest to inquire into the processes underlying this adaptation, since they could help to understand iron accumulation observed in a number of neurodegenerative diseases. Consequently, we characterized the components of the iron homeostasis machine during the process of iron accumulation.
Iron accumulation and cell death
Iron accumulation was determined in SH-SY5Y cells grown to confluence and then cultured for two days in media containing from 1.5 to 80 μM iron (Figure  1A ). Total cell iron increased with increasing extracellular iron, reaching a plateau at 40–80 μM Fe (Figure  1B ). The observed increase in cell iron was accompanied by increases in the labile iron pool (Figure  1C ). Iron accumulation indeed caused loss of cell viability, with hippocampal neurons demonstrating higher sensitivity than SH-SY5Y cells to iron treatment (Figure  2 ). Nevertheless, a sub-population of cells survived to high iron concentrations. It was of interest to inquire into the processes underlying this adaptation, since they could help to understand iron accumulation observed in a number of neurodegenerative diseases. Consequently, we characterized the components of the iron homeostasis machine during the process of iron accumulation.Ferritin and DMT1 regulation Ferritin, the main iron-storage protein in mammalian cells, is considered the first line of defense against iron overload. Increasing iron from 1.5 to 5 μM produced a robust 4-fold increase in cell ferritin content (Figure  3A ). Further increases in iron induced additional increases in ferritin. At 80 μM extracellular iron, ferritin increased 11-fold compared to the basal 1.5 μM iron condition. In molar base, ferritin increased more than iron. The iron to ferritin mol : mol ratio decreased from 1500 at 1.5 μM Fe to 400 at 10 μM Fe to 200 at 80 μM Fe (Figure  3B ). We further characterized iron homeostasis in SH-SY5Y cells by examining the expression of the iron importer DMT1 (Figure  4 ). A 3.5-fold decrease in DMT1 protein expression was observed when iron increased from 1.5 to 80 μM. The presence of DMT1 even at high iron concentration explains the sustained iron uptake observed at 80 μM Fe [ 19 ]. Thus, DMT1 activity persisted even under conditions of iron accumulation that preceded cell death.
Ferritin and DMT1 regulation
Ferritin, the main iron-storage protein in mammalian cells, is considered the first line of defense against iron overload. Increasing iron from 1.5 to 5 μM produced a robust 4-fold increase in cell ferritin content (Figure  3A ). Further increases in iron induced additional increases in ferritin. At 80 μM extracellular iron, ferritin increased 11-fold compared to the basal 1.5 μM iron condition. In molar base, ferritin increased more than iron. The iron to ferritin mol : mol ratio decreased from 1500 at 1.5 μM Fe to 400 at 10 μM Fe to 200 at 80 μM Fe (Figure  3B ).We further characterized iron homeostasis in SH-SY5Y cells by examining the expression of the iron importer DMT1 (Figure  4 ). A 3.5-fold decrease in DMT1 protein expression was observed when iron increased from 1.5 to 80 μM. The presence of DMT1 even at high iron concentration explains the sustained iron uptake observed at 80 μM Fe [ 19 ]. Thus, DMT1 activity persisted even under conditions of iron accumulation that preceded cell death.IREG1 expression and functionality Given that the presence of IREG1 in the central nervous system has been   reported [[ 16 ]], it was of interest to examine if it participates in neuronal iron homeostasis. Western blot analysis revealed that SH-SY5Y cells   expressed anti-IREG1 reactive bands of 65.3 and 122.1 KDa molecular weight (Figure  5A ). Densitometric analysis revealed a 10-fold increase in the 122.1 KDa band in the 1.5 to 80 mM Fe range while the 65.3 band had a minor increase (Figure  5A ). Both bands were eliminated if the antibody was   incubated with the immunogenic peptide before the assay (Figure  5B ). A similar pattern was obtained with an independent anti-IREG1 antibody (the kind gift of Dr. David Haile). Thus, it is most likely that the 65.3 and   122.1 KDa bands correspond to the monomer and dimer of IREG1. The stability of the 122.1 KDa band was dependent of the concentration of b-mercaptoethanol in the sample buffer since increasing b-mercaptoethanol produced a shift in the 122.1 KDa /65.3 KDa band ratios (Figure  5C ). It is possible that in neuronal cells Ireg1 tends to form S-S bridged dimers resistant to the electrophoresis conditions. The presence of IREG1 in SH-SY5Y neuroblastoma cells and hippocampal neurons was further documented by immunocytochemistry. IREG1 was detected in both types of cells, with a predominantly cytosolic distribution pattern (Figure  6 ). The levels of IREG1 expression were directly proportional to the amount of iron in the culture. Thus, it was determined by two independent methods that IREG1 expression in neuronal cells increased with cell iron content. Efflux of iron from neurons has never been reported. In view of the presence of IREG1, we tested whether SH-SY5Y cells actually had an iron efflux function. To that end, iron efflux from cells pre-cultured for 2 days with varied iron concentrations was determined by atomic absorption spectrometry. This method was preferred to the use of radioisotopic iron since the latter could underestimate a putative iron efflux because of isotope dilution with the pre-existing iron pool (see Figure  1B ). SH-SY5Y cells had discrete but measurable iron efflux activity (Figure  7A ). The iron efflux rate increased markedly in the 20–80 μM Fe range (Figure  7B ). Interestingly, the efflux rate correlated closely with the presence of the 122.1 KDa band, while the correlation between efflux activity and the 62.5 KDa band was weaker (Figure  7C ).
IREG1 expression and functionality
Given that the presence of IREG1 in the central nervous system has been   reported [[ 16 ]], it was of interest to examine if it participates in neuronal iron homeostasis. Western blot analysis revealed that SH-SY5Y cells   expressed anti-IREG1 reactive bands of 65.3 and 122.1 KDa molecular weight (Figure  5A ). Densitometric analysis revealed a 10-fold increase in the 122.1 KDa band in the 1.5 to 80 mM Fe range while the 65.3 band had a minor increase (Figure  5A ). Both bands were eliminated if the antibody was   incubated with the immunogenic peptide before the assay (Figure  5B ). A similar pattern was obtained with an independent anti-IREG1 antibody (the kind gift of Dr. David Haile). Thus, it is most likely that the 65.3 and   122.1 KDa bands correspond to the monomer and dimer of IREG1. The stability of the 122.1 KDa band was dependent of the concentration of b-mercaptoethanol in the sample buffer since increasing b-mercaptoethanol produced a shift in the 122.1 KDa /65.3 KDa band ratios (Figure  5C ). It is possible that in neuronal cells Ireg1 tends to form S-S bridged dimers resistant to the electrophoresis conditions.The presence of IREG1 in SH-SY5Y neuroblastoma cells and hippocampal neurons was further documented by immunocytochemistry. IREG1 was detected in both types of cells, with a predominantly cytosolic distribution pattern (Figure  6 ). The levels of IREG1 expression were directly proportional to the amount of iron in the culture. Thus, it was determined by two independent methods that IREG1 expression in neuronal cells increased with cell iron content.Efflux of iron from neurons has never been reported. In view of the presence of IREG1, we tested whether SH-SY5Y cells actually had an iron efflux function. To that end, iron efflux from cells pre-cultured for 2 days with varied iron concentrations was determined by atomic absorption spectrometry. This method was preferred to the use of radioisotopic iron since the latter could underestimate a putative iron efflux because of isotope dilution with the pre-existing iron pool (see Figure  1B ). SH-SY5Y cells had discrete but measurable iron efflux activity (Figure  7A ). The iron efflux rate increased markedly in the 20–80 μM Fe range (Figure  7B ). Interestingly, the efflux rate correlated closely with the presence of the 122.1 KDa band, while the correlation between efflux activity and the 62.5 KDa band was weaker (Figure  7C ).Discussion The number of neurological diseases associated with iron accumulation in the brain underlines the need for increased knowledge of the mechanisms of brain iron homeostasis. In this study we show that iron accumulation by SH-SY5Y neuroblastoma cells and hippocampal neurons resulted in cell death of part of the population, while another fraction survived by adapting the expression of iron homeostasis proteins. Iron content increased significantly as a function of Fe in the culture up to 20–40 μM Fe, increasing very little thereafter up to 80 μM Fe. Cell iron increase was accompanied by increased ferritin content. The increase in ferritin more than compensated for the increase in iron. Iron to ferritin mol ratios of 1500, 260 and 190 were obtained for 1.5, 20 and 80 μM Fe in the culture media. Thus, the IRE/IRP system of SH-SY5Y cells over-responded to iron accumulation in terms of ferritin expression. Despite the increase in ferritin, the LIP increased between 1.5 and 80 μM Fe. This finding clearly indicates that in SH-SY5Y cells the level of labile iron is a function of total iron, even in the presence of ample ferritin supply. It is possible that ferritin-stored iron contributes to the LIP each time that ferritin undergo lisosomal degradation. Iron accumulation was accompanied by a marked decrease in DMT1 expression. Nevertheless, some DMT1 persisted even at 40–80 μM iron. The persistence of DMT1 at high iron concentrations could underline the continuous iron uptake observed under these conditions [ 19 ]. This is curious because at 40–80 μM Fe cells were dying. Sustained DMT1 expression points to the inability of neuronal cells to shut-off iron uptake and the need for additional defense mechanisms to prevent iron-mediated cell death. The discovery of increased IREG1 expression in response to cell iron accumulation is a major break-through in the understanding of cell survival under conditions of iron accumulation. Total IREG1, and especially a putative IREG1 dimer, increased markedly in the 20–80 μM Fe range. Thus, in SH-SY5Y cells IREG1 is up-regulated by increased cell iron. Expressed IREG1 was functional since it associated with increased iron efflux activity. Iron efflux activity in astrocytes [ 18 ] and neurons (this work) indicate that iron efflux from brain cells is a dynamic process, and highlights the importance of iron transporters as determinants of iron accumulation. The regulation of IREG1 expression is unknown but seems to be cell-specific. In enterocytes, IREG1 expression is induced by iron deficiency [ 13 ] while in macrophages iron increases IREG1 expression [ 20 ]. The findings reported here indicate that in neuronal cells IREG1 has a macrophage-like regulation. This is certainly the case for cells in the 40–80 μM range that survived to iron accumulation. IREG1's predominantly cytosolic distribution pattern is similar to that of Kupffer cells [ 12 ]. Again, this distribution points to macrophage-like behavior of neuronal IREG1. In examining brain biopsies from Alzheimer's patients an intriguing question arises: Why do some neurons die or present evident signs of degeneration while others in the vicinity show a normal phenotype? Extrapolating on the data presented here, it is tempting to hypothesize that surviving neurons induce IREG1 expression while sick neurons do not. Nevertheless, at present we cannot exclude that other regulatory molecules may play a pivotal role under these conditions.
Discussion
The number of neurological diseases associated with iron accumulation in the brain underlines the need for increased knowledge of the mechanisms of brain iron homeostasis. In this study we show that iron accumulation by SH-SY5Y neuroblastoma cells and hippocampal neurons resulted in cell death of part of the population, while another fraction survived by adapting the expression of iron homeostasis proteins.Iron content increased significantly as a function of Fe in the culture up to 20–40 μM Fe, increasing very little thereafter up to 80 μM Fe. Cell iron increase was accompanied by increased ferritin content. The increase in ferritin more than compensated for the increase in iron. Iron to ferritin mol ratios of 1500, 260 and 190 were obtained for 1.5, 20 and 80 μM Fe in the culture media. Thus, the IRE/IRP system of SH-SY5Y cells over-responded to iron accumulation in terms of ferritin expression. Despite the increase in ferritin, the LIP increased between 1.5 and 80 μM Fe. This finding clearly indicates that in SH-SY5Y cells the level of labile iron is a function of total iron, even in the presence of ample ferritin supply. It is possible that ferritin-stored iron contributes to the LIP each time that ferritin undergo lisosomal degradation.Iron accumulation was accompanied by a marked decrease in DMT1 expression. Nevertheless, some DMT1 persisted even at 40–80 μM iron. The persistence of DMT1 at high iron concentrations could underline the continuous iron uptake observed under these conditions [ 19 ]. This is curious because at 40–80 μM Fe cells were dying. Sustained DMT1 expression points to the inability of neuronal cells to shut-off iron uptake and the need for additional defense mechanisms to prevent iron-mediated cell death.The discovery of increased IREG1 expression in response to cell iron accumulation is a major break-through in the understanding of cell survival under conditions of iron accumulation. Total IREG1, and especially a putative IREG1 dimer, increased markedly in the 20–80 μM Fe range. Thus, in SH-SY5Y cells IREG1 is up-regulated by increased cell iron. Expressed IREG1 was functional since it associated with increased iron efflux activity. Iron efflux activity in astrocytes [ 18 ] and neurons (this work) indicate that iron efflux from brain cells is a dynamic process, and highlights the importance of iron transporters as determinants of iron accumulation.The regulation of IREG1 expression is unknown but seems to be cell-specific. In enterocytes, IREG1 expression is induced by iron deficiency [ 13 ] while in macrophages iron increases IREG1 expression [ 20 ]. The findings reported here indicate that in neuronal cells IREG1 has a macrophage-like regulation. This is certainly the case for cells in the 40–80 μM range that survived to iron accumulation. IREG1's predominantly cytosolic distribution pattern is similar to that of Kupffer cells [ 12 ]. Again, this distribution points to macrophage-like behavior of neuronal IREG1. In examining brain biopsies from Alzheimer's patients an intriguing question arises: Why do some neurons die or present evident signs of degeneration while others in the vicinity show a normal phenotype? Extrapolating on the data presented here, it is tempting to hypothesize that surviving neurons induce IREG1 expression while sick neurons do not. Nevertheless, at present we cannot exclude that other regulatory molecules may play a pivotal role under these conditions.Conclusions Hippocampal neurons and SH-SY5Y cells displayed an active system to regulate iron content. Nevertheless, this system was unable to block iron accumulation which resulted in death of part of the cell population. Another fraction of the cell population developed an adaptative mechanism that includes decreased expression of the import transporter DMT1 and increased expression of ferritin and the efflux transporter IREG1. The finding that neurons regulate the expression of functional IREG1 opens new avenues for the understanding and possible treatment of iron-related neurodegenerative processes.
Conclusions
Hippocampal neurons and SH-SY5Y cells displayed an active system to regulate iron content. Nevertheless, this system was unable to block iron accumulation which resulted in death of part of the cell population. Another fraction of the cell population developed an adaptative mechanism that includes decreased expression of the import transporter DMT1 and increased expression of ferritin and the efflux transporter IREG1. The finding that neurons regulate the expression of functional IREG1 opens new avenues for the understanding and possible treatment of iron-related neurodegenerative processes.Methods Antibodies and immunodetection Antibody D-1, prepared against the C-terminal end of the IRE-containing isoform of DMT1 was used as described previously [ 10 ]. Additionally, a rabbit polyclonal antibody against peptide CGPDEKEVTKENQPNTSVV, corresponding to the consensus sequence of human, rat and mouse carboxyl-terminal sequence of IREG1, was obtained from BioSonda, Chile  . Western analysis Cell extracts, cells were prepared treating cells with lysis buffer (50 μl per 1 × 106 cells of 10 mM MOPS, pH 7.5, 3 mM MgCl2, 40 mM KCl, 1 mM phenylmethylsulfonyl fluoride, 10 μg/ml leupeptin, 0.5 μg/ml aprotinin, 0.7 μg/ml pepstatin A, 5% glycerol, 1 mM dithiothreitol, 0.1% Triton X-100). The mixture was incubated for 15 min on ice and centrifuged for 10 min at 5,000 × g. Protein concentrations were determined using the bicinchoninic acid (BCA) protein assay. The supernatant was stored at -70°C. For Western analysis, 30 micrograms of protein from each sample were boiled in Laemmli sample buffer for 5 min and subjected to SDS-PAGE on a 7.5% acrylamide gel. Proteins were transferred to nitrocellulose membrane and blocked for 1 hr at 25°C with 5% nonfat dry milk in blocking saline (20 mM Tris, 0.5 M NaCl, 0.05% Tween-20). Membranes were incubated with primary antibody overnight at 4°C, rinsed with blocking saline and incubated with horseradish peroxidase-conjugated anti-rabbit IgG antibody for 1 hr at 25°C. Transferred proteins were detected with a peroxidase-based chemiluminiscence assay kit (SuperSignal, Pierce Chem. Co., Rockford, IL). Chemiluminiscence was detected using a Molecular Imager FX device (Bio-Rad, Hercules, CA). The bands were quantified by densitometry using the Quantity One (Bio-Rad) software. Cell culture and iron challenge Human neuroblastoma SH-SY5Y cells (CRL-2266, American Type Culture Collection Rockville, MD), were seeded at 1 × 105 cells in 2-cm 2  plastic wells and cultured in a 5 % CO 2  incubator in MEM/F12 medium supplemented with 10 % fetal bovine serum and 5 mM glutamine. The medium was replaced every two days. Under these conditions, doubling time was about 48 hours. After 8 days in culture, the culture reached a steady-state number of cells. At this time, cells were challenged with iron for the next two days as described [ 19 ]. In brief, low-iron culture media was supplemented with either 1, 5, 10, 20, 40 or 80 μM Fe 3+  as the complex FeCl 3 -sodium nitrilotriacetate. Cell viability was quantified by the MTT assay (Molecular Probes, OR) following the manufacturer's instructions. This model of iron loading attempts to replicate neuronal iron accumulation that occurs during life [ 4 ]. Hippocampal neurons were prepared from E18.5 rat embryos [ 21 ]. Neurons were plated over poly-L-lysine coated cover slips at 100,000 cells/cm 2 . Cultures were maintained in 10% bovine serum until 3 hours after plating, when the culture medium was replaced with medium containing B27 supplement [ 22 ]. After 3 days in culture, the cells were challenged with iron as described above. Labile iron pool The intracellular labile or reactive iron pool of neuroblastoma cells was determined as described [ 23 , 24 ]. The increase in fluorescence after the addition of SIH chelator is directly proportional to the iron labile pool, i.e., iron in complexes with affinity constant < 10 6 . Immunocytochemistry Cells grown in cover slips were sequentially fixed with 2% and 4% parafolmaldehyde (PFA) in Eagles' MEM, and then washed three times with phosphate-buffered saline (PBS). The fixed cells were permeabilized with Triton-X-100 (0.2%) in PBS at room temperature for 3 min and blocked with defatted milk (10%) in PBS for more than 1 h. The cells were incubated with anti-IREG1 antibody (1:500) overnight at 4°C, washed with PBS and then incubated with Alexa-546-conjugated goat anti-rabbit IgG. The labeled cells were observed with a Zeiss LSM 510 Meta confocal laser scanning microscope. Data analysis Variables were tested in triplicates, and experiments were repeated at least twice. Variability among experiments was <20%. One-way ANOVA was used to test differences in mean values, and Turkey's post-hoc test was used for comparisons (In Stat program from GraphPad Prism). Differences were considered significant if P < 0.05.
Methods
Antibodies and immunodetection Antibody D-1, prepared against the C-terminal end of the IRE-containing isoform of DMT1 was used as described previously [ 10 ]. Additionally, a rabbit polyclonal antibody against peptide CGPDEKEVTKENQPNTSVV, corresponding to the consensus sequence of human, rat and mouse carboxyl-terminal sequence of IREG1, was obtained from BioSonda, Chile  .
Antibodies and immunodetection
Antibody D-1, prepared against the C-terminal end of the IRE-containing isoform of DMT1 was used as described previously [ 10 ]. Additionally, a rabbit polyclonal antibody against peptide CGPDEKEVTKENQPNTSVV, corresponding to the consensus sequence of human, rat and mouse carboxyl-terminal sequence of IREG1, was obtained from BioSonda, Chile  .Western analysis Cell extracts, cells were prepared treating cells with lysis buffer (50 μl per 1 × 106 cells of 10 mM MOPS, pH 7.5, 3 mM MgCl2, 40 mM KCl, 1 mM phenylmethylsulfonyl fluoride, 10 μg/ml leupeptin, 0.5 μg/ml aprotinin, 0.7 μg/ml pepstatin A, 5% glycerol, 1 mM dithiothreitol, 0.1% Triton X-100). The mixture was incubated for 15 min on ice and centrifuged for 10 min at 5,000 × g. Protein concentrations were determined using the bicinchoninic acid (BCA) protein assay. The supernatant was stored at -70°C. For Western analysis, 30 micrograms of protein from each sample were boiled in Laemmli sample buffer for 5 min and subjected to SDS-PAGE on a 7.5% acrylamide gel. Proteins were transferred to nitrocellulose membrane and blocked for 1 hr at 25°C with 5% nonfat dry milk in blocking saline (20 mM Tris, 0.5 M NaCl, 0.05% Tween-20). Membranes were incubated with primary antibody overnight at 4°C, rinsed with blocking saline and incubated with horseradish peroxidase-conjugated anti-rabbit IgG antibody for 1 hr at 25°C. Transferred proteins were detected with a peroxidase-based chemiluminiscence assay kit (SuperSignal, Pierce Chem. Co., Rockford, IL). Chemiluminiscence was detected using a Molecular Imager FX device (Bio-Rad, Hercules, CA). The bands were quantified by densitometry using the Quantity One (Bio-Rad) software.
Western analysis
Cell extracts, cells were prepared treating cells with lysis buffer (50 μl per 1 × 106 cells of 10 mM MOPS, pH 7.5, 3 mM MgCl2, 40 mM KCl, 1 mM phenylmethylsulfonyl fluoride, 10 μg/ml leupeptin, 0.5 μg/ml aprotinin, 0.7 μg/ml pepstatin A, 5% glycerol, 1 mM dithiothreitol, 0.1% Triton X-100). The mixture was incubated for 15 min on ice and centrifuged for 10 min at 5,000 × g. Protein concentrations were determined using the bicinchoninic acid (BCA) protein assay. The supernatant was stored at -70°C. For Western analysis, 30 micrograms of protein from each sample were boiled in Laemmli sample buffer for 5 min and subjected to SDS-PAGE on a 7.5% acrylamide gel. Proteins were transferred to nitrocellulose membrane and blocked for 1 hr at 25°C with 5% nonfat dry milk in blocking saline (20 mM Tris, 0.5 M NaCl, 0.05% Tween-20). Membranes were incubated with primary antibody overnight at 4°C, rinsed with blocking saline and incubated with horseradish peroxidase-conjugated anti-rabbit IgG antibody for 1 hr at 25°C. Transferred proteins were detected with a peroxidase-based chemiluminiscence assay kit (SuperSignal, Pierce Chem. Co., Rockford, IL). Chemiluminiscence was detected using a Molecular Imager FX device (Bio-Rad, Hercules, CA). The bands were quantified by densitometry using the Quantity One (Bio-Rad) software.Cell culture and iron challenge Human neuroblastoma SH-SY5Y cells (CRL-2266, American Type Culture Collection Rockville, MD), were seeded at 1 × 105 cells in 2-cm 2  plastic wells and cultured in a 5 % CO 2  incubator in MEM/F12 medium supplemented with 10 % fetal bovine serum and 5 mM glutamine. The medium was replaced every two days. Under these conditions, doubling time was about 48 hours. After 8 days in culture, the culture reached a steady-state number of cells. At this time, cells were challenged with iron for the next two days as described [ 19 ]. In brief, low-iron culture media was supplemented with either 1, 5, 10, 20, 40 or 80 μM Fe 3+  as the complex FeCl 3 -sodium nitrilotriacetate. Cell viability was quantified by the MTT assay (Molecular Probes, OR) following the manufacturer's instructions. This model of iron loading attempts to replicate neuronal iron accumulation that occurs during life [ 4 ]. Hippocampal neurons were prepared from E18.5 rat embryos [ 21 ]. Neurons were plated over poly-L-lysine coated cover slips at 100,000 cells/cm 2 . Cultures were maintained in 10% bovine serum until 3 hours after plating, when the culture medium was replaced with medium containing B27 supplement [ 22 ]. After 3 days in culture, the cells were challenged with iron as described above.
Cell culture and iron challenge
Human neuroblastoma SH-SY5Y cells (CRL-2266, American Type Culture Collection Rockville, MD), were seeded at 1 × 105 cells in 2-cm 2  plastic wells and cultured in a 5 % CO 2  incubator in MEM/F12 medium supplemented with 10 % fetal bovine serum and 5 mM glutamine. The medium was replaced every two days. Under these conditions, doubling time was about 48 hours. After 8 days in culture, the culture reached a steady-state number of cells. At this time, cells were challenged with iron for the next two days as described [ 19 ]. In brief, low-iron culture media was supplemented with either 1, 5, 10, 20, 40 or 80 μM Fe 3+  as the complex FeCl 3 -sodium nitrilotriacetate. Cell viability was quantified by the MTT assay (Molecular Probes, OR) following the manufacturer's instructions. This model of iron loading attempts to replicate neuronal iron accumulation that occurs during life [ 4 ].Hippocampal neurons were prepared from E18.5 rat embryos [ 21 ]. Neurons were plated over poly-L-lysine coated cover slips at 100,000 cells/cm 2 . Cultures were maintained in 10% bovine serum until 3 hours after plating, when the culture medium was replaced with medium containing B27 supplement [ 22 ]. After 3 days in culture, the cells were challenged with iron as described above.Labile iron pool The intracellular labile or reactive iron pool of neuroblastoma cells was determined as described [ 23 , 24 ]. The increase in fluorescence after the addition of SIH chelator is directly proportional to the iron labile pool, i.e., iron in complexes with affinity constant < 10 6 .
Labile iron pool
The intracellular labile or reactive iron pool of neuroblastoma cells was determined as described [ 23 , 24 ]. The increase in fluorescence after the addition of SIH chelator is directly proportional to the iron labile pool, i.e., iron in complexes with affinity constant < 10 6 .Immunocytochemistry Cells grown in cover slips were sequentially fixed with 2% and 4% parafolmaldehyde (PFA) in Eagles' MEM, and then washed three times with phosphate-buffered saline (PBS). The fixed cells were permeabilized with Triton-X-100 (0.2%) in PBS at room temperature for 3 min and blocked with defatted milk (10%) in PBS for more than 1 h. The cells were incubated with anti-IREG1 antibody (1:500) overnight at 4°C, washed with PBS and then incubated with Alexa-546-conjugated goat anti-rabbit IgG. The labeled cells were observed with a Zeiss LSM 510 Meta confocal laser scanning microscope.
Immunocytochemistry
Cells grown in cover slips were sequentially fixed with 2% and 4% parafolmaldehyde (PFA) in Eagles' MEM, and then washed three times with phosphate-buffered saline (PBS). The fixed cells were permeabilized with Triton-X-100 (0.2%) in PBS at room temperature for 3 min and blocked with defatted milk (10%) in PBS for more than 1 h. The cells were incubated with anti-IREG1 antibody (1:500) overnight at 4°C, washed with PBS and then incubated with Alexa-546-conjugated goat anti-rabbit IgG. The labeled cells were observed with a Zeiss LSM 510 Meta confocal laser scanning microscope.Data analysis Variables were tested in triplicates, and experiments were repeated at least twice. Variability among experiments was <20%. One-way ANOVA was used to test differences in mean values, and Turkey's post-hoc test was used for comparisons (In Stat program from GraphPad Prism). Differences were considered significant if P < 0.05.
Data analysis
Variables were tested in triplicates, and experiments were repeated at least twice. Variability among experiments was <20%. One-way ANOVA was used to test differences in mean values, and Turkey's post-hoc test was used for comparisons (In Stat program from GraphPad Prism). Differences were considered significant if P < 0.05.Authors' contributions MTN conceived of the study, participated in its design and coordination and drafted the manuscript. PA performed the experiments with hippocampal neurons, did the ferritin assays and participated in the analysis and interpretation of data. MN optimized the immunocytochemistry detection of IREG1, performed the confocal microscope observations and participated in the analysis and interpretation of data. VT did the Western blot, labile iron pool and viability assays and contributed to the discussion of the results. MA set up the method to determine total Fe concentration, did the sample and control measures of iron and participated in the analysis and interpretation of data. All coauthors participated in refining the text.
Authors' contributions
MTN conceived of the study, participated in its design and coordination and drafted the manuscript. PA performed the experiments with hippocampal neurons, did the ferritin assays and participated in the analysis and interpretation of data. MN optimized the immunocytochemistry detection of IREG1, performed the confocal microscope observations and participated in the analysis and interpretation of data. VT did the Western blot, labile iron pool and viability assays and contributed to the discussion of the results. MA set up the method to determine total Fe concentration, did the sample and control measures of iron and participated in the analysis and interpretation of data. All coauthors participated in refining the text.


Background Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor ( SF ), for every gene on an array. The  SF  is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values.
Background
Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor ( SF ), for every gene on an array. The  SF  is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values.SFSFResults Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from  SF  calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal,  SF  showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation.
Results
Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from  SF  calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal,  SF  showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation.SFSFConclusions Eliminating 40% of the signal data during  SF  calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays.
Conclusions
Eliminating 40% of the signal data during  SF  calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays.SF

Background The high-density oligonucleotide microarray, also known as GeneChip ® , made by Affymetrix Inc (Santa Clara, CA), has been widely used in both academic institutions and industrial companies, and is considered as the "standard" of gene expression microarrays among several platforms. A single GeneChip ®  can hold more than 50,000 probe sets for every gene in human genome. A probe set is a collection of probe pairs that interrogates the same sequence, or set of sequences, and typically contains 11 probe pairs of 25-mer oligonucleotides [ 1 - 3 ]. Each pair contains the complementary sequence to the gene of interest, the so-called perfect match (PM), and a specificity control, called the Mismatch (MM) [ 3 ]. Gene expression level is obtained from the calculation of hybridization intensity to the probe pairs and is referred to as the "signal" [ 4 - 10 ]. The normalization method used in GeneChip software is called scaling and is defined as an adjustment of the average signal value of all arrays to a common value, the target signal value in order to make the data from multiple arrays comparable [ 4 , 11 ]. The purpose of data normalization is to minimize the effects of experimental and/or technical variations so that meaningful biological comparisons can be made and true biological changes can be found among multiple experiments. Several approaches have been proposed and shown to be effective and beneficial. They were mostly from studies on two-color spotted microarrays [ 12 - 19 ]. Some authors proposed normalization of the hybridization intensities, while others preferred to normalize the intensity ratios. Some used global, linear methods, while others used local, non-linear methods. Some suggested using the spike-in controls, or house-keeping genes, or invariant genes, while others preferred all the genes on the array. For GeneChip data, some have proposed different models to normalize signal values or normalize probe pair values [ 10 , 20 - 24 ]. Despite the presence of other alternatives, many biologists still use the default scaling method and consider that such method is satisfactory and is useful to identify biological alterations [ 23 , 25 , 26 ]. With the increasing awareness and usage of GeneChip technology and willingness to continue to use GeneChip software among many biologists, it is worth improving the performance or correcting the problems of the software. In this report, the author has demonstrated that in the scaling algorithm excluding 2% of the probe sets with the highest and the lowest values did not have much benefit. However, the logarithmic transformation of signal values prior to scaling proved to be the optimum normalization strategy and is strongly recommended.
Background
The high-density oligonucleotide microarray, also known as GeneChip ® , made by Affymetrix Inc (Santa Clara, CA), has been widely used in both academic institutions and industrial companies, and is considered as the "standard" of gene expression microarrays among several platforms. A single GeneChip ®  can hold more than 50,000 probe sets for every gene in human genome. A probe set is a collection of probe pairs that interrogates the same sequence, or set of sequences, and typically contains 11 probe pairs of 25-mer oligonucleotides [ 1 - 3 ]. Each pair contains the complementary sequence to the gene of interest, the so-called perfect match (PM), and a specificity control, called the Mismatch (MM) [ 3 ]. Gene expression level is obtained from the calculation of hybridization intensity to the probe pairs and is referred to as the "signal" [ 4 - 10 ]. The normalization method used in GeneChip software is called scaling and is defined as an adjustment of the average signal value of all arrays to a common value, the target signal value in order to make the data from multiple arrays comparable [ 4 , 11 ].The purpose of data normalization is to minimize the effects of experimental and/or technical variations so that meaningful biological comparisons can be made and true biological changes can be found among multiple experiments. Several approaches have been proposed and shown to be effective and beneficial. They were mostly from studies on two-color spotted microarrays [ 12 - 19 ]. Some authors proposed normalization of the hybridization intensities, while others preferred to normalize the intensity ratios. Some used global, linear methods, while others used local, non-linear methods. Some suggested using the spike-in controls, or house-keeping genes, or invariant genes, while others preferred all the genes on the array. For GeneChip data, some have proposed different models to normalize signal values or normalize probe pair values [ 10 , 20 - 24 ]. Despite the presence of other alternatives, many biologists still use the default scaling method and consider that such method is satisfactory and is useful to identify biological alterations [ 23 , 25 , 26 ]. With the increasing awareness and usage of GeneChip technology and willingness to continue to use GeneChip software among many biologists, it is worth improving the performance or correcting the problems of the software. In this report, the author has demonstrated that in the scaling algorithm excluding 2% of the probe sets with the highest and the lowest values did not have much benefit. However, the logarithmic transformation of signal values prior to scaling proved to be the optimum normalization strategy and is strongly recommended.Results The statistical algorithm in current GeneChip software (MAS 5 and GCOS 1) for gene expression microarray data has eliminated the negative gene expression values, a problem present in earlier versions of the software [ 5 , 7 ]. It uses a robust averaging method based on the Tukey biweight function to calculate the gene expression level from the logarithm transformed hybridization data [ 3 - 5 , 11 ]. The reported data of a probe set is the antilog of the Tukey biweight mean multiplied by a  SF  and/or a normalization factor ( NF affy ). When both the  SF  and  NF affy  are equal to 1, there is no normalization or manipulation of original data. Both  NF affy  and  SF  are computed in virtually the same way.  NF affy  is calculated in comparison analysis to compare the array average of one experiment with that of a baseline experiment, while  SF  is obtained from the signal average of one experiment comparing with a common value, the target signal in absolute analysis [ 3 - 5 , 11 , 22 ]. The average value used in GeneChip is a trimmed average. It is not calculated from all probe sets, but from 96% of the probe sets after the 2% of the probe sets with the highest and the 2% of the lowest signals were removed. In this report, a total of 76 experiments with rat U34A GeneChip were analyzed. As shown in Table  1 , the total hybridization signals varied although all arrays were hybridized with the same amount of biotin-labeled cRNA and scanned with the same scanner of identical settings. The array of the highest hybridization intensities had 2.8 times more signals than that of the lowest. The average array signals had 25.8% variation in terms of coefficient of variation. The mean signals were significantly greater than the median signals on each array, indicating a non-normal distribution. The density plot showed a long-tailed and skewed distribution (not shown) and the average of such data is known to be sensitive to the larger values in the data set. The rat U34A GeneChip contained 8799 probe sets; hence 2% was about 176 probe sets. The sum of the 2% of the probe sets with the lowest signals accounts for less than 0.1% of the total signals (0.05% ± 0.01%, mean ± SD, n = 76) and its impact on  SF  calculation can be ignored. However, the sum of the 2% of the probe sets with the highest signals, the  TrimTotal  as used in this report, was responsible for about 40% of the total signals (from 34% to 54%, Table  1 ). The remaining 96% of the probe sets used for  SF  calculation, produced only about 60% of the signals. Excluding 4% of the probe sets did not reduce the variation, but rather slightly increased the variation, which in turn resulted in a wider range of  SF s (Table  1 ). It was also found that the  TrimTotal  was highly correlated with total signal (R = 0.928), but less with medians (R = 0.536) and the mean of log signals (R = 0.643). The trimmed percentage ( Tp ) was found to be negatively associated with the median (R = 0.558, b = -1.116) and the mean of log signals (R = 0.495, b = -0.968), but not with the total signal of all probe sets. Among other approaches to global linear normalization, one can also use the median signal or the mean of logarithm transformed signals to calculate the NF.  NFLogMean  showed a higher correlation with  NFMedian  than with  SF . There were larger differences between  NFLogMean  and  SF  than those between  NFLogMean  and  NFMedian  (Fig.  1 ). To test if the larger difference was a result of removing 4% of the probe sets from the calculation, another NF, the  NFTrimLogMean  was obtained using the same data as for  SF , but with a log transformation. There is a very significant correlation between  NFTrimLogMean  and  NFLogMean  (R = 0.9998). The 4% of the probe sets that was removed from  NFTrimLogMean  calculation reduced the total data by only 4% after log transformation. Since it is impossible to obtain the true normalization factor, an average of the four global linear  NF s mentioned above was used instead to estimate the 'true' NF. To compare them with the true NF, a score ( NFscore ) is introduced. Each NF is calculated against the respective 'true' NF to obtain its  NFscore . The average  NFscore  (± SD) is 7.01% (± 6.24%), 4.51% (± 3.48%), 2.25%(± 2.33%) and 1.95% (± 1.61%), and the sum of  NFscore  is 5.33, 3.43, 1.71 and 1.48 for  SF ,  NFMedian ,  NFTrimLogMean  and  NFLogMean , respectively (Fig.  1 ). The sum of  NFscore  indicated an accumulated variation from the true NF, and the larger the number, the larger the accumulated variation. An attempt to add a 5th NF obtained from the arithmetic mean of all probe sets of the array was also made to calculate and compare  NFscore  with each NFs, and the results showed the same conclusion (data not shown). It is fair to conclude that  NFLogMean  produced the least variation.
Results
The statistical algorithm in current GeneChip software (MAS 5 and GCOS 1) for gene expression microarray data has eliminated the negative gene expression values, a problem present in earlier versions of the software [ 5 , 7 ]. It uses a robust averaging method based on the Tukey biweight function to calculate the gene expression level from the logarithm transformed hybridization data [ 3 - 5 , 11 ]. The reported data of a probe set is the antilog of the Tukey biweight mean multiplied by a  SF  and/or a normalization factor ( NF affy ). When both the  SF  and  NF affy  are equal to 1, there is no normalization or manipulation of original data. Both  NF affy  and  SF  are computed in virtually the same way.  NF affy  is calculated in comparison analysis to compare the array average of one experiment with that of a baseline experiment, while  SF  is obtained from the signal average of one experiment comparing with a common value, the target signal in absolute analysis [ 3 - 5 , 11 , 22 ]. The average value used in GeneChip is a trimmed average. It is not calculated from all probe sets, but from 96% of the probe sets after the 2% of the probe sets with the highest and the 2% of the lowest signals were removed.SFNFaffySFNFaffyNFaffySFNFaffySFIn this report, a total of 76 experiments with rat U34A GeneChip were analyzed. As shown in Table  1 , the total hybridization signals varied although all arrays were hybridized with the same amount of biotin-labeled cRNA and scanned with the same scanner of identical settings. The array of the highest hybridization intensities had 2.8 times more signals than that of the lowest. The average array signals had 25.8% variation in terms of coefficient of variation. The mean signals were significantly greater than the median signals on each array, indicating a non-normal distribution. The density plot showed a long-tailed and skewed distribution (not shown) and the average of such data is known to be sensitive to the larger values in the data set.The rat U34A GeneChip contained 8799 probe sets; hence 2% was about 176 probe sets. The sum of the 2% of the probe sets with the lowest signals accounts for less than 0.1% of the total signals (0.05% ± 0.01%, mean ± SD, n = 76) and its impact on  SF  calculation can be ignored. However, the sum of the 2% of the probe sets with the highest signals, the  TrimTotal  as used in this report, was responsible for about 40% of the total signals (from 34% to 54%, Table  1 ). The remaining 96% of the probe sets used for  SF  calculation, produced only about 60% of the signals. Excluding 4% of the probe sets did not reduce the variation, but rather slightly increased the variation, which in turn resulted in a wider range of  SF s (Table  1 ). It was also found that the  TrimTotal  was highly correlated with total signal (R = 0.928), but less with medians (R = 0.536) and the mean of log signals (R = 0.643). The trimmed percentage ( Tp ) was found to be negatively associated with the median (R = 0.558, b = -1.116) and the mean of log signals (R = 0.495, b = -0.968), but not with the total signal of all probe sets.SFTrimTotalSFSFTrimTotalTpAmong other approaches to global linear normalization, one can also use the median signal or the mean of logarithm transformed signals to calculate the NF.  NFLogMean  showed a higher correlation with  NFMedian  than with  SF . There were larger differences between  NFLogMean  and  SF  than those between  NFLogMean  and  NFMedian  (Fig.  1 ). To test if the larger difference was a result of removing 4% of the probe sets from the calculation, another NF, the  NFTrimLogMean  was obtained using the same data as for  SF , but with a log transformation. There is a very significant correlation between  NFTrimLogMean  and  NFLogMean  (R = 0.9998). The 4% of the probe sets that was removed from  NFTrimLogMean  calculation reduced the total data by only 4% after log transformation.NFLogMeanNFMedianSFNFLogMeanSFNFLogMeanNFMedianNFTrimLogMeanSFNFTrimLogMeanNFLogMeanNFTrimLogMeanSince it is impossible to obtain the true normalization factor, an average of the four global linear  NF s mentioned above was used instead to estimate the 'true' NF. To compare them with the true NF, a score ( NFscore ) is introduced. Each NF is calculated against the respective 'true' NF to obtain its  NFscore . The average  NFscore  (± SD) is 7.01% (± 6.24%), 4.51% (± 3.48%), 2.25%(± 2.33%) and 1.95% (± 1.61%), and the sum of  NFscore  is 5.33, 3.43, 1.71 and 1.48 for  SF ,  NFMedian ,  NFTrimLogMean  and  NFLogMean , respectively (Fig.  1 ). The sum of  NFscore  indicated an accumulated variation from the true NF, and the larger the number, the larger the accumulated variation. An attempt to add a 5th NF obtained from the arithmetic mean of all probe sets of the array was also made to calculate and compare  NFscore  with each NFs, and the results showed the same conclusion (data not shown). It is fair to conclude that  NFLogMean  produced the least variation.NFNFscoreNFscoreNFscoreNFscoreSFNFMedianNFTrimLogMeanNFLogMeanNFscoreNFscoreNFLogMeanDiscussion Logarithmic transformation is a well-accepted approach for stabilizing variance and has become a common choice for data transformation and normalization for spotted microarrays [ 12 , 16 ]. Much improvement has been made in GeneChip microarray technology and accompanying software during the past few years. The current version of GeneChip software has improved its performance and is better than the earlier versions that used the Average Difference to express levels of gene expression [ 3 , 4 ]. However, the normalization algorithm was inherited and remains the only and default option for gene expression data processing in both MAS 5 and the newly released GeneChip Operating Software (GCOS) software. They continue to use the arithmetic mean of signals to obtain the  SF  in absolute analysis (single array) and the  NF  in comparison analysis (two arrays) [ 3 - 5 , 7 , 11 , 22 ]. It is clearly shown here that the trimmed average and the resulting  SF  had a larger variance than the median-based NF, or the NF based on the mean of log transformed signals. Similar results were observed in other GeneChip expression arrays, such as mouse U74A and human U133A (data not shown). Elimination of the highest and the lowest 2% of the probe set signals did not stabilize the trimmed means. When intra-array variance was reduced by 40%, this approach cannot be considered to be optimal. The logarithmic transformation of signals stabilized the variation well and made the normalization process much less dependent upon the mean and less affected by the outliers. Although simple and popular, the global linear normalization has its drawbacks, especially when the relationship among multiple experiments or genes is not linear. To address such problems, several methods have been proposed to conduct local and non-linear normalization, [ 12 , 14 - 17 , 20 , 22 , 27 ]. Data normalization is a very critical and important step for microarray data mining process. The use of different approaches to normalization may have a profound impact on the selection of differentially expressed genes and conclusions about the underlying biological processes especially when subtle biological changes are investigated [ 12 , 16 , 28 ].
Discussion
Logarithmic transformation is a well-accepted approach for stabilizing variance and has become a common choice for data transformation and normalization for spotted microarrays [ 12 , 16 ]. Much improvement has been made in GeneChip microarray technology and accompanying software during the past few years. The current version of GeneChip software has improved its performance and is better than the earlier versions that used the Average Difference to express levels of gene expression [ 3 , 4 ]. However, the normalization algorithm was inherited and remains the only and default option for gene expression data processing in both MAS 5 and the newly released GeneChip Operating Software (GCOS) software. They continue to use the arithmetic mean of signals to obtain the  SF  in absolute analysis (single array) and the  NF  in comparison analysis (two arrays) [ 3 - 5 , 7 , 11 , 22 ]. It is clearly shown here that the trimmed average and the resulting  SF  had a larger variance than the median-based NF, or the NF based on the mean of log transformed signals. Similar results were observed in other GeneChip expression arrays, such as mouse U74A and human U133A (data not shown). Elimination of the highest and the lowest 2% of the probe set signals did not stabilize the trimmed means. When intra-array variance was reduced by 40%, this approach cannot be considered to be optimal. The logarithmic transformation of signals stabilized the variation well and made the normalization process much less dependent upon the mean and less affected by the outliers.SFNFSFAlthough simple and popular, the global linear normalization has its drawbacks, especially when the relationship among multiple experiments or genes is not linear. To address such problems, several methods have been proposed to conduct local and non-linear normalization, [ 12 , 14 - 17 , 20 , 22 , 27 ]. Data normalization is a very critical and important step for microarray data mining process. The use of different approaches to normalization may have a profound impact on the selection of differentially expressed genes and conclusions about the underlying biological processes especially when subtle biological changes are investigated [ 12 , 16 , 28 ].Conclusions Normalization of microarray data allows direct comparison of gene expression levels among experiments. A global linear normalization, called scaling has been widely used in GeneChip microarray technology for gene expression analysis. The scaling factor ( SF ) is calculated from a trimmed average of gene expression level after excluding the 2% of the data points of the highest values and the lowest values. It is shown here that the 2% of the probe sets of the highest signals contained from 34% to 54% of the total signals. Elimination of the outliers did not reduce, but increased the variation among multiple arrays. Instead, normalization factors obtained from the mean of the log transformed signals had the best performance. Thus, the current scaling method, although widely used, is not optimal and needs further improvement. The mean of logarithm transformed signals is highly recommended to use for normalization factor calculation.
Conclusions
Normalization of microarray data allows direct comparison of gene expression levels among experiments. A global linear normalization, called scaling has been widely used in GeneChip microarray technology for gene expression analysis. The scaling factor ( SF ) is calculated from a trimmed average of gene expression level after excluding the 2% of the data points of the highest values and the lowest values. It is shown here that the 2% of the probe sets of the highest signals contained from 34% to 54% of the total signals. Elimination of the outliers did not reduce, but increased the variation among multiple arrays. Instead, normalization factors obtained from the mean of the log transformed signals had the best performance. Thus, the current scaling method, although widely used, is not optimal and needs further improvement. The mean of logarithm transformed signals is highly recommended to use for normalization factor calculation.SFMethods GeneChip experiments and data Total RNA was isolated from rat tissues or cells in Trizol reagent and purified with Qiagen Rneasy kit. cDNA was synthesized in presence of oligo(dT)24-T4 (Genset Corp, La Jolla, CA) and biotinlated UTP and CTP were used to generate biotin labeled cRNA according to the recommended protocols [ 29 ]. Rat genome microarray, U34A GeneChip (Affymetrix Inc., Santa Clara, CA) was used and hybridized with 15 μg of gel-verified fragmented cRNA. Hybridization intensity was scanned in GeneArray 2500 scanner (Agilent, Palo Alto, CA) with Microarray Suite (MAS) 5.0 software [ 4 ]. Data from a total of 76 independent GeneChip experiments were used in this study. Normalization factor (NF) Gene expression data exported from MAS 5.0 were submitted to a Perl script to calculate different normalization factors. In the scaling approach, a trimmed average signal is calculated after excluding 2% probe sets with the highest signals and 2% with the lowest signal values. The scaling factor ( SF ) is obtained using equation (1) in comparison with a chosen fixed number, called the target signal ( TS ) and is verified with the results from MAS 5.0 of the same settings [ 3 , 4 , 11 ]. SF j  =  TS  /  S TrimMeanj      (1) Other normalization factors for comparison were obtained by the following: NFMedian j  =  TS  /  S med j      (2) NFLogMean j  = 2  nf j where  i  = 1..., n represents the probe sets,  j  = 1..., J represented the array experiments,  Si  is the signal of the anti-log of a robust average (Tukey biweight) of log(PM-MM) reported from MAS 5.0 [ 5 ],  S med j  is the median signal on the array  j ,  S TrimMeanj  is the trimmed average on array  j  after excluding 2% of the probe sets with the highest and the lowest signals [ 3 , 4 , 11 , 22 ].  NFMedian j  is obtained by using the median signal on array  j , and  NFLogMean j  is obtained by using the mean of log transformed signals.  TS  was set to 150, 38 and 38 for  SF ,  NFMedian  and  NFLogMean , respectively in order to have similar NFs. In comparison with different NFs, a score,  NFscore  is introduced.  NFscore j  = ( NF j  -  TrueNF j )/ TrueNF j , and  TrueNF j  = ( SF j  +  NFMedian j  +  NFLogMean j  +  NFTrimLogMean j )/4, where  NFTrimLogMean j , was calculated from equation (3) excluding the 2% of the probe sets with the highest and lowest signals,  TrueNF j  was used as a 'true' NF. Sum of  . Other analysis Unless otherwise specified, logarithm transformation is carried out with the logarithm base 2. Trimmed total signal  TrimTotal  is the sum of the signals from the 2% of the probe sets with the highest signal values. Total signal  Total  is the sum of the signals of all probe sets in the array, and trimmed percentage  Tp j  = ( TrimTotal j  /  Total j ) × 100%.
Methods
GeneChip experiments and data Total RNA was isolated from rat tissues or cells in Trizol reagent and purified with Qiagen Rneasy kit. cDNA was synthesized in presence of oligo(dT)24-T4 (Genset Corp, La Jolla, CA) and biotinlated UTP and CTP were used to generate biotin labeled cRNA according to the recommended protocols [ 29 ]. Rat genome microarray, U34A GeneChip (Affymetrix Inc., Santa Clara, CA) was used and hybridized with 15 μg of gel-verified fragmented cRNA. Hybridization intensity was scanned in GeneArray 2500 scanner (Agilent, Palo Alto, CA) with Microarray Suite (MAS) 5.0 software [ 4 ]. Data from a total of 76 independent GeneChip experiments were used in this study.
GeneChip experiments and data
Total RNA was isolated from rat tissues or cells in Trizol reagent and purified with Qiagen Rneasy kit. cDNA was synthesized in presence of oligo(dT)24-T4 (Genset Corp, La Jolla, CA) and biotinlated UTP and CTP were used to generate biotin labeled cRNA according to the recommended protocols [ 29 ]. Rat genome microarray, U34A GeneChip (Affymetrix Inc., Santa Clara, CA) was used and hybridized with 15 μg of gel-verified fragmented cRNA. Hybridization intensity was scanned in GeneArray 2500 scanner (Agilent, Palo Alto, CA) with Microarray Suite (MAS) 5.0 software [ 4 ]. Data from a total of 76 independent GeneChip experiments were used in this study.Normalization factor (NF) Gene expression data exported from MAS 5.0 were submitted to a Perl script to calculate different normalization factors. In the scaling approach, a trimmed average signal is calculated after excluding 2% probe sets with the highest signals and 2% with the lowest signal values. The scaling factor ( SF ) is obtained using equation (1) in comparison with a chosen fixed number, called the target signal ( TS ) and is verified with the results from MAS 5.0 of the same settings [ 3 , 4 , 11 ]. SF j  =  TS  /  S TrimMeanj      (1) Other normalization factors for comparison were obtained by the following: NFMedian j  =  TS  /  S med j      (2) NFLogMean j  = 2  nf j where  i  = 1..., n represents the probe sets,  j  = 1..., J represented the array experiments,  Si  is the signal of the anti-log of a robust average (Tukey biweight) of log(PM-MM) reported from MAS 5.0 [ 5 ],  S med j  is the median signal on the array  j ,  S TrimMeanj  is the trimmed average on array  j  after excluding 2% of the probe sets with the highest and the lowest signals [ 3 , 4 , 11 , 22 ].  NFMedian j  is obtained by using the median signal on array  j , and  NFLogMean j  is obtained by using the mean of log transformed signals.  TS  was set to 150, 38 and 38 for  SF ,  NFMedian  and  NFLogMean , respectively in order to have similar NFs. In comparison with different NFs, a score,  NFscore  is introduced.  NFscore j  = ( NF j  -  TrueNF j )/ TrueNF j , and  TrueNF j  = ( SF j  +  NFMedian j  +  NFLogMean j  +  NFTrimLogMean j )/4, where  NFTrimLogMean j , was calculated from equation (3) excluding the 2% of the probe sets with the highest and lowest signals,  TrueNF j  was used as a 'true' NF. Sum of  .
Normalization factor (NF)
Gene expression data exported from MAS 5.0 were submitted to a Perl script to calculate different normalization factors. In the scaling approach, a trimmed average signal is calculated after excluding 2% probe sets with the highest signals and 2% with the lowest signal values. The scaling factor ( SF ) is obtained using equation (1) in comparison with a chosen fixed number, called the target signal ( TS ) and is verified with the results from MAS 5.0 of the same settings [ 3 , 4 , 11 ].SFTSSF j  =  TS  /  S TrimMeanj      (1)SFjTSSTrimMeanjOther normalization factors for comparison were obtained by the following:NFMedian j  =  TS  /  S med j      (2)NFMedianjTSSjNFLogMean j  = 2  nf jNFLogMeanjnfjwhere  i  = 1..., n represents the probe sets,  j  = 1..., J represented the array experiments,  Si  is the signal of the anti-log of a robust average (Tukey biweight) of log(PM-MM) reported from MAS 5.0 [ 5 ],  S med j  is the median signal on the array  j ,  S TrimMeanj  is the trimmed average on array  j  after excluding 2% of the probe sets with the highest and the lowest signals [ 3 , 4 , 11 , 22 ].  NFMedian j  is obtained by using the median signal on array  j , and  NFLogMean j  is obtained by using the mean of log transformed signals.  TS  was set to 150, 38 and 38 for  SF ,  NFMedian  and  NFLogMean , respectively in order to have similar NFs.ijSiSjjSTrimMeanjjNFMedianjjNFLogMeanjTSSFNFMedianNFLogMeanIn comparison with different NFs, a score,  NFscore  is introduced.  NFscore j  = ( NF j  -  TrueNF j )/ TrueNF j , and  TrueNF j  = ( SF j  +  NFMedian j  +  NFLogMean j  +  NFTrimLogMean j )/4, where  NFTrimLogMean j , was calculated from equation (3) excluding the 2% of the probe sets with the highest and lowest signals,  TrueNF j  was used as a 'true' NF. Sum of  .NFscoreNFscorejNFjTrueNFjTrueNFjTrueNFjSFjNFMedianjNFLogMeanjNFTrimLogMeanjNFTrimLogMeanjTrueNFjOther analysis Unless otherwise specified, logarithm transformation is carried out with the logarithm base 2. Trimmed total signal  TrimTotal  is the sum of the signals from the 2% of the probe sets with the highest signal values. Total signal  Total  is the sum of the signals of all probe sets in the array, and trimmed percentage  Tp j  = ( TrimTotal j  /  Total j ) × 100%.
Other analysis
Unless otherwise specified, logarithm transformation is carried out with the logarithm base 2. Trimmed total signal  TrimTotal  is the sum of the signals from the 2% of the probe sets with the highest signal values. Total signal  Total  is the sum of the signals of all probe sets in the array, and trimmed percentage  Tp j  = ( TrimTotal j  /  Total j ) × 100%.TrimTotalTotalTpjTrimTotaljTotaljAbbreviations GeneChip ®  is the registered trademark owned by Affymetrix Inc. PM: perfect Match; MM: mismatch; SF: scaling factor; NF: normalization factor; TS: target signal Short phrase: Normalization of GeneChip microarray data
Abbreviations
GeneChip ®  is the registered trademark owned by Affymetrix Inc.PM: perfect Match; MM: mismatch; SF: scaling factor; NF: normalization factor; TS: target signal Short phrase: Normalization of GeneChip microarray data