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In recent years there has been increasing interest in using cryo TEM tomography to study cells in close to their "native" environment. One limitation of this technique is the relatively low signal to noise ratio in each of the TEM images, since the total electron dose through the sample must be constrained to limit structure damage to the cell. Even with gold markers added to the sample, robust automatic alignment of the TEM slice data for reconstruction remains difficult. We have tried to address this problem by leveraging recent work in probabilistic analysis, and have constructed a prototype alignment system using Markov random fields (MRF s) for alignment, and robust optimization methods for projective model estimation. With markers, there are three basic steps required to align the TEM dataset: marker feature identification, correspondence and tracking of these features throughout the image set, and projective model estimation from these feature tracks. In our framework, features are extracted initially using standard template matching techniques like cross correlation. Feature correspondence and tracking is accomplished by constructing a Markov random field (MRF) probabilistic model where contour labels are random variables which take on values of candidate marker feature locations. We use mutual information and the relative geometric positions to estimate a priori marker correspondence probabilities between two images. An approximate probabilistic inference technique called loopy belief propagation (LBP) is then used to calculate the maximum a posteriori assignment of features to contours in the image set. In this technique, rather than a joint distribution (whose complexity is exponential in the number of random variables), a collection of singleton and pairwise distributions is maintained in a special data structure. This data structure contains cycles, and is called a cluster graph. The a priori estimates for these distributions (initial beliefs) are refined by belief propagation, until they converge to roughly the true pairwise distributions (final beliefs). The correspondences of candidate markers to contours are taken directly from these beliefs. Errors in the correspondence are possible due to feature location mistakes as well as inaccurate inference results. Therefore, the projective model estimation uses a robust fitting method as opposed to least squares (the traditionally applied fitting) and is tolerant to outliers. Once we have an estimate of the projective model, the model is iterated using expectation maximization (EM) to re-estimate perceived outliers with improved reprojection data from the current model. This iteration is performed as many times as necessary before a stopping criterion is satisfied, but in our example a small number of iterations is needed (often only one).This robust framework has allowed us to fully automatically recover dozens of contours (both complete and piecewise) with subpixel accuracy from several challenging cryo datasets of bacteria Caulobacter crescentus. The results were used to create 3D reconstructions comparable to results previously obtainable only by extensive manual intervention.

We present a color calibration pipeline for large camera arrays. Weassume static lighting conditions for each camera, such as studio lighting or astationary array outdoors. We also assume we can place a planar calibrationtarget so it is visible from every camera. Our goal is uniform camera colorresponses, not absolute color accuracy, so we match the cameras to each otherinstead of to a color standard. We first iteratively adjust the color channelgains and offsets for each camera to make their responses as similar aspossible. This step white balances the cameras, and for studio applications,ensures that the range of intensities in the scene are mapped to the usableoutput range of the cameras. Residual errors are then calibrated inpost-processing. We present results calibrating an array of 100 CMOS imagesensors in different physical configurations, including closely or widelyspaced cameras with overlapping fields of views, and tightly packed cameraswith non-overlapping fields of view. The process is entirely automatic, andthe camera configuration runs in less than five minutes on the 100 cameraarray.Pre-2018 CSE ID: CS2005-0821

Genome‐wide association studies (GWASs) have advanced our understanding of the genetics of osteoporosis; however, the challenge has been converting associations to causal genes. Studies have utilized transcriptomics data to link disease‐associated variants to genes, but few population transcriptomics data sets have been generated on bone at the single‐cell level. To address this challenge, we profiled the transcriptomes of bone marrow–derived stromal cells (BMSCs) cultured under osteogenic conditions from five diversity outbred (DO) mice using single‐cell RNA‐seq (scRNA‐seq). The goal of the study was to determine if BMSCs could serve as a model to generate cell type–specific transcriptomic profiles of mesenchymal lineage cells from large populations of mice to inform genetic studies. By enriching for mesenchymal lineage cells in vitro, coupled with pooling of multiple samples and downstream genotype deconvolution, we demonstrate the scalability of this model for population‐level studies. We demonstrate that dissociation of BMSCs from a heavily mineralized matrix had little effect on viability or their transcriptomic signatures. Furthermore, we show that BMSCs cultured under osteogenic conditions are diverse and consist of cells with characteristics of mesenchymal progenitors, marrow adipogenic lineage precursors (MALPs), osteoblasts, osteocyte‐like cells, and immune cells. Importantly, all cells were similar from a transcriptomic perspective to cells isolated in vivo. We employed scRNA‐seq analytical tools to confirm the biological identity of profiled cell types. SCENIC was used to reconstruct gene regulatory networks (GRNs), and we observed that cell types show GRNs expected of osteogenic and pre‐adipogenic lineage cells. Further, CELLECT analysis showed that osteoblasts, osteocyte‐like cells, and MALPs captured a significant component of bone mineral density (BMD) heritability. Together, these data suggest that BMSCs cultured under osteogenic conditions coupled with scRNA‐seq can be used as a scalable and biologically informative model to generate cell type–specific transcriptomic profiles of mesenchymal lineage cells in large populations. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR). [ABSTRACT FROM AUTHOR]