Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Combined Single Cell Flow Cytometry and Imaging Analyses Reveal Immunomodulatory Effects Exerted By Targeted Phospho-SYK Inhibitors with Elevated Sensitivity in NPM1 Mutated AML

The Society of Biomolecular Imaging and Informatics First Annual Conference

A central challenge in the development of drug-encapsulated polymeric nanoparticles is the inability to control the nanoparticle physicochemical properties that affect their biodistribution, drug release, and efficacy. Nanoparticles may be developed by mixing and nanoprecipitation of polymers and drugs dissolved in organic solvents with non-solvents. Inadequate control over this mixing process is a source of variability in the synthesis of these nanoparticles by nanoprecipitation. We demonstrate that rapid and tunable mixing through hydrodynamic flow focusing in a microfluidic device can be used to control nanoprecipitation of poly(lactic-co-glycolic acid)-bpoly(ethylene glycol) (PLGA-PEG) diblock copolymers as a model polymeric biomaterial for drug delivery.

Chapter 7 - Microfluidic mixing and devices for preparing nanoparticulate drug delivery systems

BackgroundIntravital multiphoton microscopy (IMM) provides single-cell imaging within intact living systems. IMM of the autofluorescent metabolic co-enzymes NAD(P)H and FAD, or optical metabolic imaging (OMI), provides in vivo label-free imaging of metabolic changes. The metabolism of tumor cells and immune cells is closely associated with cancer progression,1–3 so we aim to study metabolic trends before and after administration of an established, effective, triple-combination immunotherapy within murine melanoma tumors.4 This therapy includes 12 Gy external beam radiation, intratumoral administration of a hu14.18-IL2 immunocytokine (anti-GD2 mAb fused to IL2), and intraperitoneal administration of anti-CTLA-4 leading to in situ vaccination and cure of GD2+ murine tumors.4 Previous work has shown that a T cell response is critical to the efficacy of this therapy,4 5 so we created mCherry-labeled T cell mouse models to study T cell response. Here, IMM was used to image concurrent tumor cell and T cell metabolic trends, T cell infiltration, and tumor microenvironment composition.MethodsWe created mCherry-labeled T cell mouse models through CRISPR/Cas9 knock-in and Cre- lox genetic modifications. We then implanted syngeneic B78 (GD2+) melanoma cells intradermally into the flanks of C57BL/6 mice to induce measurable tumors. Mice were anesthetized, skin flap surgery performed, and tumors imaged at varying time points. IMM was performed using 750–1040 nm to excite NAD(P)H, FAD, and mCherry through a 40X (1.15 NA) objective. Fluorescence lifetime data was collected using time correlated single photon counting electronics. Murine tissues were later harvested and analyzed via flow cytometry and immunohistochemistry to confirm mCherry expression in mouse models and IMM findings.ResultsHere we demonstrate the feasibility of our IMM platform to perform single-cell resolution imaging in vivo. We establish that our genetically engineered mouse models enable clear identification and tracking of mCherry T cell populations. In addition, we show that label-free OMI provides metabolic trends and structural information in vivo (figure 1). Overall, we demonstrate concurrent imaging of intravital tumor cell and T cell populations within the tumor microenvironment.ConclusionsOur preliminary results suggest that the combination of IMM and our mCherry mouse models with OMI allows for concurrent imaging of T cell infiltration and metabolic trends. With continued work, this imaging platform has the potential to provide dynamic, metabolic information on tumor cell and immune cell populations to inform further immunotherapy development.AcknowledgementsThis work is supported by the Morgridge Institute for Research (Interdisciplinary Fellowship awarded to A.R.H.) and the NIH (R01 CA205101 and R35 CA197078). The authors thank the University of Wisconsin Carbone Cancer Center (UWCCC) Support Grant P30 CA014520, the UWCCC Translational Research Initiatives in Pathology laboratory - supported by the UW Department of Pathology and Laboratory Medicine and the Office of The Director NIH (S10OD023526), the UWCCC Flow Cytometry Laboratory, and the Genome Editing and Animal Models Laboratory for core services.Abstract 42 Figure 1Representative intravital multiphoton microscopy images of a B78 syngeneic melanoma growing in a mouse with mCherry-labeled T cells. (A) Fluorescence intensity image of all fluorophores shows mCherry-labeled T cells (red) infiltrating untreated tumor tissue and vasculature as well as metabolic coenzymes NAD(P)H (blue) and FAD (green) expressed by the tumor. (B) Fluorescence intensity image of NAD(P)H alone shows NAD(P)H landscape as well as tumor boundaries and winding vasculature filled with red blood cells. (C) Fluorescence lifetime image shows mCherry-labeled T cell populations and their corresponding mean lifetime (tau m ) values. Fluorescence lifetime values help distinguish mCherry-labeled T cells (typical tau m = 1,400 ps) from nonspecific red autofluorescence in vivo. (D) Fluorescence lifetime image shows NAD(P)H expression and corresponding mean lifetime values which give insight into tumor metabolism and microenvironment.Ethics ApprovalAll animal work was approved by the University of Wisconsin Institutional Animal Care and Use Committees.ReferencesRenner K, Singer K, Koehl GE, Geissler EK, Peter K, Siska PJ, Kreutz M. Metabolic hallmarks of tumor and immune cells in the tumor microenvironment. Front Immunol. 2017, 8 (MAR), 1–11.Mockler MB, Conroy MJ, Lysaght J, Targeting T. Cell Immunometabolism for cancer immunotherapy; understanding the impact of the tumor microenvironment. Front Oncol. 2014, 4 (May), 1–11.Ghesquière B, Wong BW, Kuchnio A, Carmeliet P. Metabolism of stromal and immune cells in health and disease. Nature 2014, 511 (7508), 167–176.Morris ZS, Guy EI, Francis DM, Gressett MM, Werner LR, Carmichael LL, Yang RK, Armstrong EA, Huang S, Navid F, Gillies SD, Korman A, Hank JA, Rakhmilevich AL, Harari PM, Sondel PM. In situ tumor vaccination by combining local radiation and tumor-specific antibody or immunocytokine treatments. Cancer Res 2016;76 (13):3929–3941.Morris ZS, Guy EI, Werner LR, Carlson PM, Heinze CM, Kler JS, Busche SM, Jaquish A, A, Sriramaneni RN, Carmichael LL, Loibner H, Gillies SD, Korman AJ, Erbe AK, Hank J, A, Rakhmilevich AL, Harari PM, Sondel PM. Tumor-specific inhibition of in situ vaccination by distant untreated tumor sites. Cancer Immunol Res 2018;6 (7):825–834.

Capillary-driven microfluidic device with integrated nanoporous microbeads for ultrarapid biosensing assays

In this paper, we report developing approaches to tuning the optical behavior of microfluidic devices by infusing smart hybrids of liquid crystal and quantum dots into microchannel confinement. We characterize the optical responses of liquid crystal-quantum dot composites to polarized and UV light in single-phase microflows. In the range of flow velocities up to 10 mm/s, the flow modes of microfluidic devices were found to correlate with the orientation of liquid crystals, dispersion of quantum dots in homogeneous microflows and the resulting luminescence response of these dynamic systems to UV excitation. We developed a Matlab algorithm and script to quantify this correlation by performing an automated analysis of microscopy images. Such systems may have application potential as optically responsive sensing microdevices with integrated smart nanostructural components, parts of lab-on-a-chip logic circuits, or diagnostic tools for biomedical instruments.

BackgroundQuantitative co-localization studies strengthen the analysis of fluorescence microscopy-based assays and are essential for illustrating and understanding many cellular processes and interactions. In our earlier study, we presented a rank-based intensity weighting scheme for the quantification of co-localization between structures in fluorescence microscopy images. This method, which uses a combined pixel co-occurrence and intensity correlation approach, is superior to conventional algorithms and provides a more accurate quantification of co-localization.FindingsIn this brief report we provide the source code and implementation of the rank-weighted co-localization (RWC) algorithm in three (two open source and one proprietary) image analysis platforms. The RWC algorithm has been implemented as a plugin for ImageJ, a module for CellProfiler and an Acapella script for Columbus image analysis software tools.ConclusionsWe have provided with a web resource from which users can download plugins and modules implementing the RWC algorithm in various commonly used image analysis platforms. The implementations have been designed for easy incorporation into existing tools in a ‘ready-for-use’ format. The resources can be accessed through the following web link: http://simpsonlab.pbworks.com/w/page/48541482/Bioinformatic_Tools.

We present a simple technique to generate stable hydrodynamically focused flows by driving the flow with hydrostatic pressure from liquid columns connected to the inlets of a microfluidic device. Importantly, we compare the focused flows generated by hydrostatic pressure and classical syringe pump driven flows, and find that the stability of the hydrostatic pressure driven technique is significantly better than the stability achieved via syringe pumps, providing fluctuation-free focused flows that are suitable for sensitive microfluidic flow cytometry applications. We show that the degree of flow focusing with the hydrostatic method can be accurately controlled by the simple tuning of the liquid column heights. We anticipate that this approach to stable flow focusing will find many applications in microfluidic cytometry technologies. Keywords: Microfluidics, hydrodynamic flow focusing, hydrostatic pressure driven flows, flow cytometry

Received: 2018-06-07 | Accepted: 2018-06-19 | Available online: 2018-06-30 https://doi.org/10.15414/afz.2018.21.02.77-83 The aim of this study was to distinguishing between kernels of maize hybrids by the use of image analysis tools. We analyzed 10 registered Zea mays L. hybrids (5 – dent, 2 – semi-flint to flint, and 3 – semi-flint to dent type). Different parameters on ventral, dorsal, corolla side, and lateral side cross section of kernel were measured. Sample per each hybrid comprised 50 maize kernels. Acquired bio-images were processed by software Zeiss AxioVision Rel. 4.8. We analyzed the segmented regions of interest on the kernels. The data for area (mm 2 ), height and width (mm) were gathered from these regions. The hybrid ZE EDOX significantly differed (p < 0.05) from all other hybrids almost in all traits. It is the hybrid with the smallest area of the whole kernel, floury endosperm proportion, and depressed part on corolla. The new trait the area of the depressed part on the kernel corolla was measured. The hybrids with smaller proportion of floury endosperm had smaller area of depressed part, and vice versa. The image analysis methods can usefully contribute to selection of proper hybrids for different types of use. Keywords: maize, Zea mays L., kernel, image analysis References BLASCHKE, T. et al. (2014) Geographic Object-Based Image Analysis – Towards a new paradigm. In ISPRS Journal of Photogrammetry and Remote Sensing, vol. 87, pp. 180–191. https://doi.org/10.1016/j.isprsjprs.2013.09.014 DE CARVALHO, M. L. M., VAN AELST, A. C., VAN ECK, J. W., HOEKSTRA, F. A. (1999) Pre-harvest stress cracks in maize (Zea mays L.) kernels as characterized by visual, X-ray and low temperature scanning electron microscopical analysis: effect on kernel quality. In Seed Science Research, vol. 9, pp. 227–236. DELL´AQUILA, A. (2006) Computerised seed imaging: a new tool to evaluate germination quality. In Communications in Biometry and Crop Science, vol. 1, no. 1, pp. 20–31. ERASMUS, C. (2003) Maize kernel translucency measurement by Image Analysis and its relationship to vitreousness and dry milling performance. PhD thesis. Pretoria: University of Pretoria. FOX, G. and MANLEY, M. (2009) Hardness methods for testing maize kernels. In Journal of Agricultural and Food Chemistry, vol. 57, no. 13, pp. 5647–5657. doi: https://doi.org/10.1021/jf900623w GLASBEY, C. A. and HORGAN, G. W. (2001) Image analysis in agriculture research. In Quantitative Approaches in System Analysis, special issue, vol. 23, pp. 43–54. GUELPA, A., DU PLESIS, A., KIDD, M., MANLEY, M. (2015) Nondestructive estimation of maize (Zea mays L.) kernel hardness by means of an X-ray micro-computed tomography (μCT) density calibration. In Food and Bioprocess Technology, vol. 8, no. 6, pp. 1419–1429. JANDA, J. and MICHALEC, V. (1982) Mayze. Bratislava: Priroda. 408 p. MUTTERER, J. and ZINCK, E. (2013) Quick‐and‐clean article figures with FigureJ. In Journal of Microscopy, vol. 252, pp. 89– 91. doi: https://doi.org/10.1111/jmi.12069 ROBUTTI, J. L., HOSENEY, R. C. and WASSOM, C. E. (1974). Modified opaque-2 corn endosperms. ll. Structure viewed with a scanning electron microscope. In American Association of Cereal Chemists, vol. 51, pp. 173–180. RODRIGUEZ-PULIDO, F. J. et al. (2012) Ripeness estimation of grape berries and seeds by image analysis. In Computers and Electronics in Agriculture, vol. 82, pp. 128–133. SCHINDELIN, J., RUEDEN, C. T., HINER, M. C., ELICEIRI, K. W. (2015) The ImageJ ecosystem: An open platform for biomedical image analysis. In Mol. Reprod. Dev., vol. 82, pp. 518–529. doi: https://doi.org/10.1002/mrd.22489 UCHIDA, S. (2013) Image processing and recognition for biological images. In Development, growth and differentiation, vol. 55, pp. 523–549. doi: https://doi.org/10.1111/dgd.12054 WATSON, S. A. (1987) Structure and composition. In WATSON, S. A. and RAMSTAD, P. E. (Eds.). Corn chemistry and technology. St. Paul: American Association of Cereal Chemists. WIWART M. et al. (2012) Identification of hybrids of spelt and wheat and their parental forms using shape and color descriptors. In Computers and Electronics in Agriculture, vol. 83, pp. 68–76. https://doi.org/10.1016/j.compag.2012.01.015

The tremendous advancement in mobile computing hardware and software changes everything in people including health care. Image review on computer-based workstations has made film-based review outdated. However, with the expansion of needs in portability of digital workstations, we wish a acceptable quality of medical image reviewing in a handled device, which could give radiologists more convenience. In this context, mobile health (m-Health) delivers health care services, overcoming geographical, temporal, and even organizational barriers. This paper presents a collaborative and mobile platform for image analysis which provides medical images shared so that the radiologists can review these data anytime and anywhere. In addition, this platform has the biggest differences with other popular or traditional systems that it supports collaboration and plug-in. It means that all the experts in the different locations can have a research together like in the same conference face to face and programmers can develop their favorite functions as they will. At last, the system also supplies many indispensable medical image analysis auxiliary functions such as image registration and image segmentation, both of which offer radiologists greatly exact diagnosis tools. We evaluated the platform with respect to expert satisfaction, functional characteristics. The experimental results show that our platform outperforms conventional image review systems in these regards.

The Application of Microfluidics in Preparing Nano Drug Delivery Systems

Traditional approaches for measuring cardiac myocyte hypertrophy have been of low throughput and subjective, limiting the scope of experimental studies designed to understand it. Here, we describe an automated image acquisition and analysis platform for studying the dynamics of cardiac myocyte hypertrophy in vitro. Image acquisition scripts record 5 × 5 mosaic images of fluorescent protein-labeled neonatal rat ventricular myocytes from each well of a 96-well plate using the microscope's automated stage and focus. Image analysis algorithms automatically segment myocyte boundaries, track myocytes, and quantify changes in shape. We describe each step of the image acquisition and analysis algorithms and provide specific examples of how to implement them using Metamorph and CellProfiler software. With this system, shape dynamics of thousands of individual cardiac myocytes can be tracked for up to a week. This imaging platform was recently applied to study reversal of cardiac myocyte hypertrophy following withdrawal of the α-adrenergic agonist phenylephrine. Hypertrophy readily reversed at low but not high levels of α-adrenergic signaling, leading to identification of an intracellular population of α-adrenergic receptors responsible for this reversibility delay.

Microfluidic gradient systems offer a very precise means to probe the response of cells to graded biomolecular signals in vitro, for example to model how morphogen proteins affect cell fate during developmental processes. However, existing gradient makers are designed for non-physiological plastic or glass cell culture substrates that are often limited in maintaining the phenotype and function of difficult-to-culture mammalian cell types, such as stem cells. To address this bottleneck, we combine hydrogel engineering and microfluidics to generate tethered protein gradients on the surface of biomimetic poly(ethylene glycol) (PEG) hydrogels. Here we used software-assisted hydrodynamic flow focusing for exposing and rapidly capturing tagged proteins to gels in a step-wise fashion, resulting in immobilized gradients of virtually any desired shape and composition. To render our strategy amenable for high-throughput screening of multifactorial artificial cellular microenvironments, a dedicated microfluidic chip was devised for parallelization and multiplexing, yielding arrays of orthogonally overlapping gradients of up to 4 × 4 proteins. To illustrate the power of the platform for stem cell biology, we assessed how gradients of tethered leukemia inhibitory factor (LIF) influence embryonic stem cell (ESC) behavior. ESC responded to LIF gradients in a binary manner, maintaining the pluripotency marker Rex1/Zfp42 and forming self-renewing colonies above a threshold concentration of 85 ng cm(-2). Our concept should be broadly applicable to probe how complex signaling microenvironments influence stem cell fate in culture.

We demonstrate high-throughput second harmonic generation imaging of single cells in flow at a throughput of 10,000 cells/sec – enabled by an all-optical laser scanning imaging technique, coined free-space angular-chirp-enhanced delay (FACED), combined with a high-speed time-gated photon counting detection scheme.