{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:34:59Z","timestamp":1787240099305,"version":"build-2736575974"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Res Notes"],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>RT-qPCR is a common tool for quantification of gene expression, but its accuracy is dependent on the choice and stability (steady state expression levels) of the reference gene\/s used for normalization. To date, in the bone field, there have been few studies to determine the most stable reference genes and, usually, RT-qPCR data is normalised to non-validated reference genes, most commonly GAPDH, ACTB and 18\u2009S rRNA. Here we draw attention to the potential deleterious impact of using classical reference genes to normalise expression data for bone studies without prior validation of their stability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using the geNorm and Normfinder programs, panels of mouse and human genes were assessed for their stability under three different experimental conditions: 1) disease progression of Crouzon syndrome (craniosynostosis) in a mouse model, 2) proliferative culture of cranial suture cells isolated from craniosynostosis patients and 3) osteogenesis of a mouse bone marrow stromal cell line. We demonstrate that classical reference genes are not always the most \u2018stable\u2019 genes and that gene \u2018stability\u2019 is highly dependent on experimental conditions. Selected stable genes, individually or in combination, were then used to normalise osteocalcin and alkaline phosphatase gene expression data during cranial suture fusion in the craniosynostosis mouse model and strategies compared. Strikingly, the expression trends of alkaline phosphatase and osteocalcin varied significantly when normalised to the least stable, the most stable or the three most stable genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      To minimise errors in evaluating gene expression levels, analysis of a reference panel and subsequent normalization to several stable genes is strongly recommended over normalization to a single gene. In particular, we conclude that use of single, non-validated \u201chousekeeping\u201d genes such as\n                      <jats:italic>GAPDH<\/jats:italic>\n                      ,\n                      <jats:italic>ACTB<\/jats:italic>\n                      and\n                      <jats:italic>18\u2009S rRNA<\/jats:italic>\n                      , currently a widespread practice by researchers in the bone field, is likely to produce data of questionable reliability when changes are 2 fold or less, and such data should be interpreted with due caution.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/1756-0500-5-222","type":"journal-article","created":{"date-parts":[[2012,5,8]],"date-time":"2012-05-08T03:07:00Z","timestamp":1336446420000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Bone to pick: the importance of evaluating reference genes for RT-qPCR quantification of gene expression in craniosynostosis and bone-related tissues and cells"],"prefix":"10.1186","volume":"5","author":[{"given":"Xianxian","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jodie T","family":"Hatfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan J","family":"Hinze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongzheng","family":"Mu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J","family":"Anderson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barry C","family":"Powell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2012,5,8]]},"reference":[{"key":"1682_CR1","first-page":"441","volume-title":"In A-Z of Quantitative PCR","author":"SA Bustin","year":"2004","unstructured":"Bustin SA, Nolan T: Data analysis and interpretation. 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