Back

Human bone mesoscale 3D structure revisited by plasma focused ion beam serial sectioning

Binkley, D. M.; Deering, J.; Yuan, H.; Gourrier, A.; Grandfield, K.

2020-07-02 bioengineering
10.1101/2020.07.01.180729 bioRxiv
Show abstract

Visualizing bone mineralization and collagen microfibril organization at intermediate scales between the nanometer and the 100s of microns range, the mesoscale, is still an important challenge. Similarly, visualizing cellular components which locally affect the tissue structure requires a precision of a few tens of nanometers at maximum while spanning several tens of micrometers. To address this issue, we employed a plasma focused ion beam (PFIB) equipped with a scanning electron microscope (SEM) to sequentially section nanometer-scale layers of demineralized and mineralized human femoral lamellar bone over volumes of approximately 46 x 40 x 9 m3, and 29 x 26 x 9 m3, respectively. This large scale view retained high enough resolution to visualize the collagen microfibrils while partly visualizing the lacuno-canalicular network (LCN) in three-dimensions (3D). We showed that serial sectioning can be performed on mineralized sections, and does not require demineralization. Moreover, this method revealed ellipsoidal mineral clusters, noted by others in high resolution studies, as a ubiquitous motif in lamellar bone over tens of microns, suggesting a heterogeneous and yet regular pattern of mineral deposition past the single collagen fibril level. These findings are strong evidence for the need to revisit bone mineralization over multi-length scales. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/180729v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1fe58a0org.highwire.dtl.DTLVardef@16c726corg.highwire.dtl.DTLVardef@cc28c6org.highwire.dtl.DTLVardef@1891d1f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.