Background Understanding cellular structure and organization, which plays an important role in biological systems ranging from mechanosensation to neural organization, is usually a complicated multifactorial problem depending on genetics, environmental factors, and stochastic processes. from a F2 cross experiment in mice without the growth hormone (which can confound many of the buy Astragaloside III smaller structural differences between strains) and characterized more than 50 million osteocyte lacunae (cell-sized hollows in the bone). The results were then correlated with genetic markers in a process called quantitative trait localization (QTL). Our findings provide a mapping between regions of the genome (all 19 autosomes) and observable phenotypes which could explain between 8C40 % of the variance using between 2C10 loci for each trait. This map shows 4 areas of overlap with previous studies looking at bone strength and 3 areas not previously associated with bone. Conclusions The mapping of microstructural phenotypes provides a starting point for both structure-function and genetic studies on murine bone structure and the specific loci can be investigated in more detail to identify single gene candidates which can then be translated to human investigations. The flexible infrastructure offers a full spectrum of shape, distribution, and connectivity metrics for cellular networks and can be adapted to a wide variety of materials ranging from herb roots to lung tissue in studies requiring high sample counts and sensitive metrics such as the drug-gene interactions and high-throughput screening. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1617-y) contains supplementary material, which is usually available to authorized users. Keywords: Phenotyping, Automated 3D imaging, 3D morphology, Quantitative trait loci, Osteocyte lacunae, 3D morphology, Cortical bone, cell shape, Cell distribution, cell alignment Background In recent years, sequencing genomes has been accelerated manyfold [1, 2]. With a wide availability of reliable genomic information, understanding of complex biological systems is now limited by the ability to develop new and measure subtle buy Astragaloside III changes in phenotypes in an equally rapid rate . In some areas, phenotyping has kept pace through improvements in techniques like high-throughput fluorescent  and optical computed tomography screening . Both of which allow for hundreds of individuals and phenotypes to be screened in quick succession and/or in parallel. Particularly in the field of herb genetics, automation in phenotyping has greatly increased the throughput and reliability of genetic studies . However, for high-resolution analyses such as SEM, confocal microscopy, and Synchrotron-based X-ray Tomographic Microscopy (SRXTM), dealing with large numbers of samples (>10) is usually difficult to impossible for a number of reasons. The first factor is preparation time, since many of these techniques require careful, individual, often human-intensive sample preparation which takes time and scales linearly with the sample count. The second factor is the acquisition time, which on many systems extends above several hours. Consequently entire Rabbit Polyclonal to XRCC3 studies require thousands of hours of real acquisition, which is tedious and often long enough that imaging characteristics of the involved components can change significantly. Finally once all of the data are collected, the task of extracting meaningful metrics from your images can be even more difficult and time consuming than the initial two tasks. In particular for hierarchical systems with thousands of substructures few requirements exist for meaningfully characterizing either the ensemble behavior or the complex relationships between levels in the hierarchy. Complicating the huge time requirements are the importance of reproducibility, which is usually impossible when too many human elements are involved. Finally management of all the samples, data, and results, which easily exceed the capacities of analysis tools like Excel (Microsoft,Redmond, USA), R , and MATLAB (Mathworks, Natick, USA), make data analysis difficult and time consuming and make data exploration all but impossible. Even more scalable tools like buy Astragaloside III SQLite are unable to deliver results quickly. Thus, up until now, most large-scale analysis of phenotype on micro- and nanoscale systems has been a gigantic starting [8C10]. While much of the work carried out has provided interesting insights.