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Single osteocyte gene expression in an in vivo model for load-induced bone adaptation
Robin Wilson1, Andreas Trüssel1, Duncan Webster1, Felix Kurth2, Petra Dittrich2, Ralph Müller1
1. Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
2. Bioanalytics Group, ETH Zürich, Zürich, Switzerland
Osteocytes, cells embedded within bone matrix, have been shown to regulate of bone adaptation,
signaling bone formation or resorption based on mechanical cues from their microenvironments.
However, studies thus far have only investigated the collective cellular behavior of osteocytes. Because
bone is anisotropic, osteocytes experience different strains under mechanical loading. Thus, to
accurately determine the relationship between mechanical strain, osteocyte behavior, and bone
adaptation, it is crucial to utilize a single-cell approach. Using an in vivo model for bone adaptation and
in vivo µCT, we can locate and quantify bone formation and resorption. Furthermore, we can isolate
individual osteocytes using laser capture microdissection for gene expression analysis via qRT-PCR.
Mapping these single-cell gene expression profiles back to their in vivo locations in the original µCT
volume will give us great insight osteocyte behavior. To date, we have successfully analyzed gene
expression in small groups of microdissected osteocytes (3-10 cells) using qRT-PCR. Briefly, female
C57BL/6 mice vertebrae were cryosectioned (12µm thickness), stained in 1% Cresyl Violet in 75%
ethanol, dehydrated in an ethanol gradient, and microdissected using a P.A.L.M. laser microscope into
PCR tube caps. A two-step Taqman qRT-PCR protocol was used for gene expression analysis. Lysis,
reverse transcription, and pre-amplification were performed using the CellsDirect™ One-Step qRT-PCR
kit. The resulting cDNA was diluted and analyzed using a standard Taqman qPCR protocol. Our results
show that we can detect expression of HPRT-1 in 3 cells (Ct=24.8±0.2) and 10 cells (CT=23.1±0.8). These
results prove the feasibility of gene expression analysis of individually microdissected osteocytes. By the
end of the project, we hope to improve our protocol sensitivity to detect single cell gene expression.
Then, we will map the gene expression to the in vivo mechanical strain and local bone remodeling. This
will be the first study that is able to map single cell gene expression to local, in vivo conditions.