Nicolas Rey-Villamizar

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.

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

Liadi, Singh, and colleagues used Timelapse Imaging Microscopy In Nanowell Grids (TIMING) to show that CD4+ CD19-chimeric antigen receptor (CAR+) T cells participate in multikilling of tumor cells with slower kinetics of killing than CD8+CAR+ T cells, but high motility subgroups of both T-cell subsets have similar kinetics. 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. Cancer Immunol Res; 3(5); 473–82. ©2015 AACR. See related commentary by June, p. 470

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.

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.

Why Do They Leave: Modeling Participation in Online Depression Forums.

Depression is a major threat to public health, accounting for almost 12% of all disabilities and claiming the life of 1 out of 5 patients suffering from it. Since depression is often signaled by decreasing social interaction, we explored how analysis of online health forums may help identify such episodes. We collected posts and replies from users of several forums on healthboards.com and analyzed changes in their use of language and activity levels over time. We found that users in the Depression forum use fewer social words, and have some revealing phrases associated with their last posts (e.g., cut myself ). Our models based on these findings achieved 94 F1 for detecting users who will withdraw from a Depression forum by the end of a 1-year observation period.

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.

Analysis of Anxious Word Usage on Online Health Forums.

Online health communities and support groups are a valuable source of information for users suffering from a physical or mental illness. Users turn to these forums for moral support or advice on specific conditions, symptoms, or side effects of medications. This paper describes and studies the linguistic patterns of a community of support forum users over time focused on the used of anxious related words. We introduce a methodology to identify groups of individuals exhibiting linguistic patterns associated with anxiety and the correlations between this linguistic pattern and other word usage. We find some evidence that participation in these groups does yield positive effects on their users by reducing the frequency of anxious related word used over time.

Semi-supervised CLPsych 2016 Shared Task System Submission.

The 2016 CLPsych Shared Task is centered on the automatic triage of posts from a mental health forum, au.reachout.com. In this paper, we describe our method for this shared task. We used four different groups of features. These features are designed to capture stylistic and word patterns, together with psychological insights based on the Linguistic Inquiry and Word Count (LIWC) word list. We used a multinomial naive Bayes classifier as our base system. We were able to boost the accuracy of our approach by extending the number of training samples using a semi-supervised approach, labeling some of the unlabeled data and extending the number training samples.

Quantitative single-cell characterization of CAR+ T cell effector functions

Adoptive cell therapy (ACT) utilizing chimeric antigen receptor (CAR) T cells rendered specific for CD19 have demonstrated significant anti-tumor effects in patients with CD19+ chronic lymphocytic leukemia (CLL). In spite of the clinical promise of ACT in achieving complete responses, their efficacy remains unpredictable and new approaches are needed to address a priori define the therapeutic potential of T-cell based therapies. In our current work, we characterize the in vitro functionality of CD19-specific (CD19RCD28) CAR+ T cells propagated using artificial antigen presenting cells expressing membrane bound IL-21, by employing a novel methodology single-cell nanowell screening that determines their cytotoxic ability and cytokine secretion capability at single-cell resolution. We show that CAR+ T cells exert specific cytotoxicity against NALM6 cells (31 ± 8 %) when co-incubated at a 1:1 ratio in nanowell containers. Furthermore, single CAR+ T cells were capable of engaging and killing multiple targets; 17 ± 8% of T cells killed two target cells and 9 ± 3% killed three target cells within the 6 hour window of observation. In parallel, microengraving was used to determine the cytokine secretion profile of these same cells. Hierarchical clustering of the two functions indicated that interferon-gamma (IFNγ) secretion is not correlated to cytotoxicity or the ability of T cells to kill multiple target cells. Simultaneously, monitoring apoptosis on CAR+ T cells allowed us to quantify their activation-induced cell death (AICD). CAR+ T cells that secreted IFNγ upon target ligation did not undergo AICD whereas T cells that engaged in repeated killing showed an increased propensity to undergo AICD (p = 0.04). Dynamic time-lapse imaging of the interactions between CAR+ T cells and tumor cells indicated that the majority of CAR+ T cells have high basal motility, form long-lived interactions with tumor cells (50 - 100 min) that lead to motility arrest and subsequent tumor-cell apoptosis. However, contact lifetimes or overall contact duration were not reliable predictors of subsequent tumor-cell apoptosis. Finally, kinetics of serial killing suggest that motile CAR+ T cells that form short-lived contacts exhibit rapid killing with very little motility arrest in vitro. In summary, our SNS based methodology allows the deep functional characterization of clinical grade CAR+ T cells and can be used to: (1) determine in vitro functions of CAR+ T cells that correlate with clinical efficacy and (2) inform CAR design to maximize effector functionality while minimizing AICD.