Recent Advances in Focused Ion Beam Methodologies for 3D Analysis of Biomineralizing Tissues across Multiple Length Scales
Bibliographic record
Abstract
Biomineralizing tissues such as bone, cartilage, and tendon exhibit a remarkable hierarchical organization, with structural features spanning the macro- to nanometer length scales [1,2]. Historically, capturing these features in 3D posed a significant challenge because conventional methods such as transmission electron microscopy (TEM) and laboratory micro-CT cannot simultaneously provide ultrastructural details and a sufficiently large volume to encompass entire cells and their surrounding matrix. Focused ion beam-scanning electron microscopy (FIB-SEM), originally developed in the semiconductor industry, has emerged as a powerful solution to bridge this gap, enabling researchers to examine how minerals and organic matrices interrelate at multiple length scales – a key step for understanding tissue formation, growth, adaptation, and disease progression. Early FIB tomography studies of bone often involved partial or complete demineralization, following approaches pioneered by Reznikov et al., to circumvent difficulties in milling the hard mineral phase of the tissue [3–5]. While these studies yielded valuable insights into collagen fibril organization [4], the required sample processing risked introducing artifacts or altering the native bone ultrastructure. More recently, direct FIB tomography of fully mineralized bone has been demonstrated using Ga FIB, providing deeper understanding of lamellar organization, cement sheaths, and osteocyte canaliculi within the mineralized matrix [6]. Among the most notable findings from these direct 3D studies is the discovery of an extensive network of nanochannels, each roughly an order of magnitude smaller in diameter than osteocyte canaliculi yet the network possessing a much higher overall volume fraction [7–9]. The nanochannels appear to serve as alternative pathways for ion and small molecule transport within the bone extracellular matrices, a result supported by subsequent work on human cortical bone showing an inverse correlation between nanochannel volume and local calcium content [8]. In addition, unpublished data from lactation mouse models suggest that their nanochannels may participate in osteocytic osteolysis, enabling rapid mineral turnover in the pericellular bone matrix. Despite these advances, the milling volume achievable with Ga FIB remains a key limitation to this technique, generally restricting the field of view to tens of micrometers. Plasma FIB (PFIB) systems – often employing xenon ions – have overcome this barrier, allowing faster material removal and the reconstruction of larger volumes. This capability has allowed the capture of more complete aspects of osteocyte lacunocanalicular networks (LCN) and mineral ellipsoids across mesoscale volumes [10]. By integrating PFIB with X-ray microscopy (XRM), subsurface features of interest at the scale of hundreds of micrometers or more can be identified and selectively milled, an approach recently applied to human trabecular bone [11]. An emerging next step is femtosecond laser FIB (LaserFIB), which uses ultra-short laser pulses to ablate millimeter-scale volumes rapidly before final polishing or tomography with either Ga or Xe FIB. This correlative pipeline has already been shown to expose deeply buried trabeculae in human bone with minimal damage [11], and the application of direct LaserFIB serial sectioning – though still under development – could further accelerate throughput and expand sampling volumes for 3D structural analysis. Meanwhile, other ion species (e.g., oxygen, argon, helium, and neon) have seen specialized use in semiconductor and materials science for milling with low level damage or enhanced chemical contrast [12], but their utility remains largely unexplored for biomineralizing tissues. Some preliminary work has explored oxygen PFIB to mill resin-embedded brain tissue [13] and argon PFIB to prepare cryogenic cellular samples [14], but comprehensive 3D tomography with oxygen, argon, or neon beams for mineralized tissues is largely uncharted. Nonetheless, the distinct sputtering properties and reduced sample damage profiles of these ions suggest potential advantages in future studies of calcified tissues. Altogether, these FIB-based innovations from conventional Ga FIB to PFIB and now LaserFIB are transforming our ability to study 3D mineral-organic interactions in biomineralizing tissues. As these technologies continue to evolve, combining high-resolution volume imaging with targeted site-specific sample preparation should further elucidate how nanoscale pathways, mineral distributions, and structural hierarchies converge to determine tissue function in health and disease [15].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".