Learning Morphological, Spatial, and Dynamic Models of Cellular Components.

In this chapter, we describe protocols for using the CellOrganizer software on the Jupyter Notebook platform to analyze and model cell and organelle shape and spatial arrangement. CellOrganizer is an open-source system for using microscope images to learn statistical models of the structure of cell components and how those components are organized relative to each other. Such models capture the statistical variation in the organization of cellular components by jointly modeling the distributions of their number, shape, and spatial distributions. These models can be created for different cell types or conditions and compared to reflect differences in their spatial organizations. The models are also generative, in that they can be used to synthesize new cell instances reflecting what a model learned and to provide well-structured cell geometries that can be used for biochemical simulations.

Expanding the coverage of spatial proteomics: a machine learning approach.

MOTIVATION: Multiplexed protein imaging methods use a chosen set of markers and provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is inherently limited. RESULTS: In this paper, we present an efficient method to choose a minimal predictive subset of markers that for the first time allows the prediction of full images for a much larger set of markers. We demonstrate that our approach also outperforms previous methods for predicting cell-level protein composition. Most importantly, we demonstrate that our approach can be used to select a marker set that enables prediction of a much larger set than could be measured concurrently. AVAILABILITY AND IMPLEMENTATION: All code and intermediate results are available in a Reproducible Research Archive at https://github.com/murphygroup/CODEXPanelOptimization.

Basal body organization and cell geometry during the cell cycle in Tetrahymena thermophila.

Tetrahymena thermophila possesses arrays of motile cilia that promote fluid flow for cell motility. These consist of intricately organized basal bodies (BBs) that nucleate and position cilia at the cell cortex. Tetrahymena cell geometry and spatial organization of BBs play important roles in cell size, swimming, feeding, and division. How cell geometry and BB organization are established and maintained remains poorly understood, and prior studies have been limited due to difficulties in accurate BB identification and small sample size. We therefore developed an automated image processing pipeline that segments single cells, distinguishes unique BB populations, assigns BBs into distinct ciliary rows, and distinguishes new from mature BBs. We identified unique features to describe the variation of cell shape and BB spatial organization in unsynchronized single-cell images. The results reveal asymmetries in BB distribution and ingression of the cytokinetic furrow within the cell. Moreover, we establish novel spatial and temporal waves in new BB assembly through the cell cycle. Finally, we used measurements from single cells across the cell cycle to construct a generative model that allows synthesis of movies depicting single cells progressing through the cell cycle. Our approach is expected to be of particular value for characterizing Tetrahymena mutants.

Improving and evaluating deep learning models of cellular organization.

MOTIVATION: Cells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both traditional and deep learning methods to build accurate models of cell organization. Most cellular organelles are distinct objects with defined boundaries that do not overlap, while the pixel resolution of most imaging methods is n sufficient to resolve these boundaries. Thus while cell organization is conceptually object-based, most current methods are pixel-based. Using extensive image collections in which particular organelles were fluorescently labeled, deep learning methods can be used to build conditional autoencoder models for particular organelles. A major advance occurred with the use of a U-net approach to make multiple models all conditional upon a common reference, unlabeled image, allowing the relationships between different organelles to be at least partially inferred. RESULTS: We have developed improved Generative Adversarial Networks-based approaches for learning these models and have also developed novel criteria for evaluating how well synthetic cell images reflect the properties of real images. The first set of criteria measure how well models preserve the expected property that organelles do not overlap. We also developed a modified loss function that allows retraining of the models to minimize that overlap. The second set of criteria uses object-based modeling to compare object shape and spatial distribution between synthetic and real images. Our work provides the first demonstration that, at least for some organelles, deep learning models can capture object-level properties of cell images. AVAILABILITY AND IMPLEMENTATION: http://murphylab.cbd.cmu.edu/Software/2022_insilico. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of categorical matrix completion algorithms: toward improved active learning for drug discovery.

MOTIVATION: High throughput and high content screening are extensively used to determine the effect of small molecule compounds and other potential therapeutics upon particular targets as part of the early drug development process. However, screening is typically used to find compounds that have a desired effect but not to identify potential undesirable side effects. This is because the size of the search space precludes measuring the potential effect of all compounds on all targets. Active machine learning has been proposed as a solution to this problem. RESULTS: In this article, we describe an improved imputation method, Impute by Committee, for completion of matrices containing categorical values. We compare this method to existing approaches in the context of modeling the effects of many compounds on many targets using latent similarities between compounds and conditions. We also compare these methods for the task of driving active learning in well-characterized settings for synthetic and real datasets. Our new approach performed the best overall both in the accuracy of matrix completion itself and in the number of experiments needed to train an accurate predictive model compared to random selection of experiments. We further improved upon the performance of our new method by developing an adaptive switching strategy for active learning that iteratively chooses between different matrix completion methods. AVAILABILITY AND IMPLEMENTATION: A Reproducible Research Archive containing all data and code is available at http://murphylab.cbd.cmu.edu/software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A MEMS Variable Optical Attenuator with Ultra-Low Wavelength-Dependent Loss and Polarization-Dependent Loss.

Applications in broadband optical fiber communication system need variable optical attenuators (VOAs) with low wavelength-dependent loss (WDL). Based on analysis on the dispersion of the optical system of a MEMS-based VOA, we provide a method to reduce the WDL significantly with minor revision on the end-face angle of the collimating lens. Two samples are assembled, and the measured WDL is <0.4 dB over the C-band (1.53⁻1.57 µm) at a 0⁻20 dB attenuation range. Meanwhile, the new structure helps to reduce the polarization-dependent loss (PDL) to <0.15 dB, which is only half that of conventional devices.