Automated profiling of individual cell-cell interactions from high-throughput time-lapse imaging microscopy in nanowell grids (TIMING).

MOTIVATION: There is a need for effective automated methods for profiling dynamic cell-cell interactions with single-cell resolution from high-throughput time-lapse imaging data, especially, the interactions between immune effector cells and tumor cells in adoptive immunotherapy. RESULTS: Fluorescently labeled human T cells, natural killer cells (NK), and various target cells (NALM6, K562, EL4) were co-incubated on polydimethylsiloxane arrays of sub-nanoliter wells (nanowells), and imaged using multi-channel time-lapse microscopy. The proposed cell segmentation and tracking algorithms account for cell variability and exploit the nanowell confinement property to increase the yield of correctly analyzed nanowells from 45% (existing algorithms) to 98% for wells containing one effector and a single target, enabling automated quantification of cell locations, morphologies, movements, interactions, and deaths without the need for manual proofreading. Automated analysis of recordings from 12 different experiments demonstrated automated nanowell delineation accuracy >99%, automated cell segmentation accuracy >95%, and automated cell tracking accuracy of 90%, with default parameters, despite variations in illumination, staining, imaging noise, cell morphology, and cell clustering. An example analysis revealed that NK cells efficiently discriminate between live and dead targets by altering the duration of conjugation. The data also demonstrated that cytotoxic cells display higher motility than non-killers, both before and during contact. CONTACT: broysam@central.uh.edu or nvaradar@central.uh.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors.

MOTIVATION: The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions. RESULTS: Thick rat brain sections (100-300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.g. 5903 × 9874 × 229 voxels). An over-complete dictionary-based model was learned for the image-specific local structure of microglial processes. The microglial arbors were reconstructed seamlessly using an automated and scalable algorithm that exploits microglia-specific constraints. This method detected 80.1 and 92.8% more centered arbor points, and 53.5 and 55.5% fewer spurious points than existing vesselness and LoG-based methods, respectively, and the traces were 13.1 and 15.5% more accurate based on the DIADEM metric. The arbor morphologies were quantified using Scorcioni's L-measure. Coifman's harmonic co-clustering revealed four morphologically distinct classes that concord with known microglia activation patterns. This enabled us to map spatial distributions of microglial activation and cell abundances. AVAILABILITY AND IMPLEMENTATION: Experimental protocols, sample datasets, scalable open-source multi-threaded software implementation (C++, MATLAB) in the electronic supplement, and website (www.farsight-toolkit.org). http://www.farsight-toolkit.org/wiki/Population-scale_Three-dimensional_Reconstruction_and_Quanti-tative_Profiling_of_Microglia_Arbors CONTACT: broysam@central.uh.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Individual Motile CD4(+) T Cells Can Participate in Efficient Multikilling through Conjugation to Multiple Tumor Cells.

T cells genetically modified to express a CD19-specific chimeric antigen receptor (CAR) for the investigational treatment of B-cell malignancies comprise a heterogeneous population, and their ability to persist and participate in serial killing of tumor cells is a predictor of therapeutic success. We implemented Timelapse Imaging Microscopy in Nanowell Grids (TIMING) to provide direct evidence that CD4(+)CAR(+) T cells (CAR4 cells) can engage in multikilling via simultaneous conjugation to multiple tumor cells. Comparisons of the CAR4 cells and CD8(+)CAR(+) T cells (CAR8 cells) demonstrate that, although CAR4 cells can participate in killing and multikilling, they do so at slower rates, likely due to the lower granzyme B content. Significantly, in both sets of T cells, a minor subpopulation of individual T cells identified by their high motility demonstrated efficient killing of single tumor cells. A comparison of the multikiller and single-killer CAR(+) T cells revealed that the propensity and kinetics of T-cell apoptosis were modulated by the number of functional conjugations. T cells underwent rapid apoptosis, and at higher frequencies, when conjugated to single tumor cells in isolation, and this effect was more pronounced on CAR8 cells. Our results suggest that the ability of CAR(+) T cells to participate in multikilling should be evaluated in the context of their ability to resist activation-induced cell death. We anticipate that TIMING may be used to rapidly determine the potency of T-cell populations and may facilitate the design and manufacture of next-generation CAR(+) T cells with improved efficacy.

Algorithms for improved 3-D reconstruction of live mammalian embryo vasculature from optical coherence tomography data.

BACKGROUND: Robust reconstructions of the three-dimensional network of blood vessels in developing embryos imaged by optical coherence tomography (OCT) are needed for quantifying the longitudinal development of vascular networks in live mammalian embryos, in support of developmental cardiovascular research. Past computational methods [such as speckle variance (SV)] have demonstrated the feasibility of vascular reconstruction, but multiple challenges remain including: the presence of vessel structures at multiple spatial scales, thin blood vessels with weak flow, and artifacts resulting from bulk tissue motion (BTM). METHODS: In order to overcome these challenges, this paper introduces a robust and scalable reconstruction algorithm based on a combination of anomaly detection algorithms and a parametric dictionary based sparse representation of blood vessels from structural OCT data. RESULTS: Validation results using confocal data as the baseline demonstrate that the proposed method enables the detection of vessel segments that are either partially missed or weakly reconstructed using the SV method. Finally, quantitative measurements of vessel reconstruction quality indicate an overall higher quality of vessel reconstruction with the proposed method. CONCLUSIONS: Results suggest that sparsity-integrated speckle anomaly detection (SSAD) is potentially a valuable tool for performing accurate quantification of the progression of vascular development in the mammalian embryonic yolk sac as imaged using OCT.

Antibody Fc engineering improves frequency and promotes kinetic boosting of serial killing mediated by NK cells.

The efficacy of most therapeutic monoclonal antibodies (mAbs) targeting tumor antigens results primarily from their ability to elicit potent cytotoxicity through effector-mediated functions. We have engineered the fragment crystallizable (Fc) region of the immunoglobulin G (IgG) mAb, HuM195, targeting the leukemic antigen CD33, by introducing the triple mutation Ser293Asp/Ala330Leu/Ile332Glu (DLE), and developed Time-lapse Imaging Microscopy in Nanowell Grids to analyze antibody-dependent cell-mediated cytotoxicity kinetics of thousands of individual natural killer (NK) cells and mAb-coated target cells. We demonstrate that the DLE-HuM195 antibody increases both the quality and the quantity of NK cell-mediated antibody-dependent cytotoxicity by endowing more NK cells to participate in cytotoxicity via accrued CD16-mediated signaling and by increasing serial killing of target cells. NK cells encountering targets coated with DLE-HuM195 induce rapid target cell apoptosis by promoting simultaneous conjugates to multiple target cells and induce apoptosis in twice the number of target cells within the same period as the wild-type mAb. Enhanced target killing was also associated with increased frequency of NK cells undergoing apoptosis, but this effect was donor-dependent. Antibody-based therapies targeting tumor antigens will benefit from a better understanding of cell-mediated tumor elimination, and our work opens further opportunities for the therapeutic targeting of CD33 in the treatment of acute myeloid leukemia.

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.