Polarization imaging and classification of Jurkat T and Ramos B cells using a flow cytometer

Towards a templated reaction that translates RNA in cells into a proaptotic peptide-PNA conjugate

Nucleic acid-templated chemistry opens the intriguing prospect of triggering the synthesis of drugs only in diseased cells. Herein, we explore the feasibility of using RNA-templated chemical reactions for the activation of a known Smac peptidomimetic compound (SMC), which has proapoptotic activity. Two peptide nucleic acid (PNA) conjugates were used to enable conditional activation of a masked SMC by reduction of an azide either by Staudinger reduction or catalytic photoreduction using a ruthenium complex. The latter provided ~135 nM SMC-PNA on as little as 10 nM (0.01 eq.) template. For the evaluation of the templated azido-SMC reduction system in cellulo, a stable HEK 293 cell line was generated, which overexpressed a truncated, non-functional form of the XIAP mRNA target. We furthermore describe the development of electroporation protocols that enable a robust delivery of PNA conjugates into HEK 293 cells. The action of the reactive PNA conjugates was evaluated by viability and flow cytometric apoptosis assays. In addition, electroporated probes were re-isolated and analyzed by ultra-high performance liquid chromatography (UPLC). Unfortunately, the ruthenium-PNA conjugate proved phototoxic, and treatment of cells with PNA-linked reducing agent and the azido-masked SMC conjugate did not result in a greater viability loss than treatment with scrambled sequence controls. Intracellular product formation was not detectable. A control experiment in total cellular RNA isolate indicated that the templated reaction can in principle proceed in a complex system. The results of this first-of-its-kind study reveal the numerous hurdles that must be overcome if RNA molecules are to trigger the synthesis of pro-apoptotic drugs inside cells.

Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes

Measurement of nuclear-to-cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label-free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross-polarized diffraction image (p-DI) pairs divided into three nuclear size groups of OCM_S , OCM_O and OCM_L based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray-level co-occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p-DI was found to be 98.8% and 97.5% respectively for binary classifications of OCM_S vs OCM_O and OCM_O vs OCM_L for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high-order correlations of diffraction patterns are potentially useful for label-free detection of single cells with large N:C ratios.

Deep learning of diffraction image patterns for accurate classification of five cell types

Development of label-free methods for accurate classification of cells with high throughput can yield powerful tools for biological research and clinical applications. We have developed a deep neural network of DINet for extracting features from cross-polarized diffraction image (p-DI) pairs on multiple pixel scales to accurately classify cells in five types. A total of 6185 cells were measured by a polarization diffraction imaging flow cytometry (p-DIFC) method followed by cell classification with DINet on p-DI data. The averaged value and SD of classification accuracy were found to be 98.9% ± 1.00% on test data sets for 5-fold training and test. The invariance of DINet to image translation, rotation, and blurring has been verified with an expanded p-DI data set. To study feature-based classification by DINet, two sets of correctly and incorrectly classified cells were selected and compared for each of two prostate cell types. It has been found that the signature features of large dissimilarities between p-DI data of correctly and incorrectly classified cell sets increase markedly from convolutional layers 1 and 2 to layers 3 and 4. These results clearly demonstrate the importance of high-order correlations extracted at the deep layers for accurate cell classification.

Development and evaluation of realistic optical cell models for rapid and label-free cell assay by diffraction imaging

Methods for rapid and label-free cell assay are highly desired in life science. Single-shot diffraction imaging presents strong potentials to achieve this goal as evidenced by past experimental results using methods such as polarization diffraction imaging flow cytometry. We present here a platform of methods toward solving these problems and results of optical cell model (OCM) evaluations by calculations and analysis of cross-polarized diffraction image (p-DI) pairs. Four types of realistic OCMs have been developed with two prostate cell structures and adjustable refractive index (RI) parameters to investigate the effects of cell morphology and index distribution on calculated p-DI pairs. Image patterns have been characterized by a gray-level co-occurrence matrix (GLCM) algorithm and four GLCM parameters and linear depolarization ratio δ_L have been selected to compare calculated against measured data of prostate cells. Our results show that the irregular shapes of and heterogeneity in RI distributions for organelles play significant roles in the spatial distribution of scattered light by cells in comparison to the average RI values and their differences among the organelles. Discrepancies in GLCM and δ_L parameters between calculated and measured p-DI data provide useful insight for understanding light scattering by single cells and improving OCM.

Quantitative analysis and comparison of 3D morphology between viable and apoptotic MCF-7 breast cancer cells and characterization of nuclear fragmentation

Morphological changes in apoptotic cells provide essential markers for defining and detection of apoptosis as a fundamental mechanism of cell death. Among these changes, the nuclear fragmentation and condensation have been regarded as the important markers but quantitative characterization of these changes is yet to be achieved. We have acquired confocal image stacks of 206 viable and apoptotic MCF-7 cells stained by three fluorescent dyes. Three-dimensional (3D) parameters were extracted to quantify and compare their differences in morphology. To analyze nuclear fragmentation, a new method has been developed to determine clustering of nuclear voxels in the reconstructed cells due to fluorescence intensity changes in nuclei of apoptotic cells. The results of these studies reveal that the 3D morphological changes in cytoplasm and nuclear membranes in apoptotic cells provide sensitive targets for label-free detection and staging of apoptosis. Furthermore, the clustering analysis and morphological data on nuclear fragmentation are highly useful for derivation of optical cell models and simulation of diffraction images to investigate light scattering by early apoptotic cells, which can lead to future development of label-free and rapid methods of apoptosis assay based on cell morphology.

Resolving power of diffraction imaging with an objective: a numerical study

Diffraction imaging in the far-field can detect 3D morphological features of an object for its coherent nature. We describe methods for accurate calculation and analysis of diffraction images of scatterers of single and double spheres by an imaging unit based on microscope objective at non-conjugate positions. A quantitative study of the calculated diffraction imaging in spectral domain has been performed to assess the resolving power of diffraction imaging. It has been shown numerically that with coherent illumination of 532 nm in wavelength the imaging unit can resolve single spheres of 2 μm or larger in diameters and double spheres separated by less than 300 nm between their centers.

In vitro and in vivo assessment of direct effects of simulated solar and galactic cosmic radiation on human hematopoietic stem/progenitor cells

Future deep space missions to Mars and near-Earth asteroids will expose astronauts to chronic solar energetic particles (SEP) and galactic cosmic ray (GCR) radiation, and likely one or more solar particle events (SPEs). Given the inherent radiosensitivity of hematopoietic cells and short latency period of leukemias, space radiation-induced hematopoietic damage poses a particular threat to astronauts on extended missions. We show that exposing human hematopoietic stem/progenitor cells (HSC) to extended mission-relevant doses of accelerated high-energy protons and iron ions leads to the following: (1) introduces mutations that are frequently located within genes involved in hematopoiesis and are distinct from those induced by γ-radiation; (2) markedly reduces in vitro colony formation; (3) markedly alters engraftment and lineage commitment in vivo; and (4) leads to the development, in vivo, of what appears to be T-ALL. Sequential exposure to protons and iron ions (as typically occurs in deep space) proved far more deleterious to HSC genome integrity and function than either particle species alone. Our results represent a critical step for more accurately estimating risks to the human hematopoietic system from space radiation, identifying and better defining molecular mechanisms by which space radiation impairs hematopoiesis and induces leukemogenesis, as well as for developing appropriately targeted countermeasures.

Label-Free Classification of Bax/Bak Expressing vs. Double-Knockout Cells

We combine optical scatter imaging with principal component analysis (PCA) to classify apoptosis-competent Bax/Bak-expressing, and apoptosis resistant Bax/Bak-null immortalized baby mouse kidney cells. We apply PCA to 100 stacks each containing 236 dark-field cell images filtered with an optically implemented Gabor filter with period between 0.3 and 2.9 μm. Each stack yields an "eigencell" image corresponding to the first principal component obtained at one of the 100 Gabor filter periods used. At each filter period, each cell image is multiplied by (projected onto) the eigencell image. A Feature Matrix consisting of 236 × 100 scalar values is thus constructed with significantly reduced dimension compared to the initial dataset. Utilizing this Feature Matrix, we implement a supervised linear discriminant analysis and classify successfully the Bax/Bak-expressing and Bax/Bak-null cells with 94.7% accuracy and an area under the curve (AUC) of 0.993. Applying a feature selection algorithm further reveals that the Gabor filter period ranges most significant for the classification correspond to both large (likely nuclear) features as well as small sized features (likely organelles present in the cytoplasm). Our results suggest that cells with a genetic defect in their apoptosis pathway can be differentiated from their normal counterparts by label-free multi-parametric optical scatter data.

Comparison study of distinguishing cancerous and normal prostate epithelial cells by confocal and polarization diffraction imaging

Accurate classification of malignant cells from benign ones can significantly enhance cancer diagnosis and prognosis by detection of circulating tumor cells (CTCs). We have investigated two approaches of quantitative morphology and polarization diffraction imaging on two prostate cell types to evaluate their feasibility as single-cell assay methods toward CTC detection after cell enrichment. The two cell types have been measured by a confocal imaging method to obtain their three-dimensional morphology parameters and by a polarization diffraction imaging flow cytometry (p-DIFC) method to obtain image texture parameters. The support vector machine algorithm was applied to examine the accuracy of cell classification with the morphology and diffraction image parameters. Despite larger mean values of cell and nuclear sizes of the cancerous prostate cells than the normal ones, it has been shown that the morphologic parameters cannot serve as effective classifiers. In contrast, accurate classification of the two prostate cell types can be achieved with high classification accuracies on measured data acquired separately in three measurements. These results provide strong evidence that the p-DIFC method has the potential to yield morphology-related “fingerprints” for accurate and label-free classification of the two prostate cell types.

Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus

Coherent light scattering presents complex spatial patterns that depend on morphological and molecular features of biological cells. We present a numerical approach to establish realistic optical cell models for generating virtual cells and accurate simulation of diffraction images that are comparable to measured data of prostate cells. With a contourlet transform algorithm, it has been shown that the simulated images and extracted parameters can be used to distinguish virtual cells of different nuclear volumes and refractive indices against the orientation variation. These results demonstrate significance of the new approach for development of rapid cell assay methods through diffraction imaging.

Acquisition of cross-polarized diffraction images and study of blurring effect by one time-delay-integration camera

Blurred diffraction images acquired from flowing particles affect the measurement of fringe patterns and subsequent analysis. An imaging unit with one time-delay-integration (TDI) camera has been developed to acquire two cross-polarized diffraction images. It was shown that selected elements of Mueller matrix of single scatters can be imaged with pixel matching precision in this configuration. With the TDI camera, the effect of blurring on imaging of scattered light propagating along the side directions was found to be much more significant for biological cells than microspheres. Despite blurring, classification of MCF-7 and K562 cells is feasible since the effect has similar influence on extracted image parameters. Furthermore, image blurring can be useful for analysis of the correlations among texture parameters for characterization of diffraction images from single cells. The results demonstrate that with one TDI camera the polarization diffraction imaging flow cytometry can be significantly improved and angular distribution of selected Mueller matrix elements can be accurately measured for rapid and morphology-based assay of particles and cells without fluorescent labeling.

Anticancer/antiviral agent Akt inhibitor-IV massively accumulates in mitochondria and potently disrupts cellular bioenergetics

Inhibitors of the PI3-kinase/Akt (protein kinase B) pathway are under investigation as anticancer and antiviral agents. Akt inhibitor-IV (ChemBridge 5233705, CAS 681281-88-9, AKTIV), a small molecule reported to inhibit this pathway, exhibits potent anticancer and broad-spectrum antiviral activity. However, depending on concentration, this cationic benzimidazole derivative exhibits paradoxical positive or negative effects on the phosphorylation of Akt that are not well understood. To elucidate its mechanism of action, we investigated its spectroscopic properties. This compound proved to be sufficiently fluorescent (excitation λ_max = 388 nm, emission λ_max = 460 nm) to enable examination of its uptake and distribution in living mammalian cells. Despite a low quantum yield of 0.0016, imaging of HeLa cells treated with AKTIV (1 μM, 5 min) by confocal laser scanning microscopy, with excitation at 405 nm, revealed extensive accumulation in mitochondria. Treatment of Jurkat lymphocytes with 1 μM AKTIV for 15 min caused accumulation to over 250 μM in these organelles, whereas treatment with 5 μM AKTIV yielded concentrations of over 1 mM in mitochondria, as analyzed by flow cytometry. This massive loading resulted in swelling of these organelles, followed by their apparent disintegration. These effects were associated with profound disruption of cellular bioenergetics including mitochondrial depolarization, diminished mitochondrial respiration, and release of reactive oxygen species. Because mitochondria play key roles in both cancer proliferation and viral replication, we conclude that the anticancer and antiviral activities of AKTIV predominantly result from its direct and immediate effects on the structure and function of mitochondria.

Analysis of diffraction imaging in non-conjugate configurations

Diffraction imaging of scattered light allows extraction of information on scatterer's morphology. We present a method for accurate simulation of diffraction imaging of single particles by combining rigorous light scattering model with ray-tracing software. The new method has been validated by comparison to measured images of single microspheres. Dependence of fringe patterns on translation of an objective based imager to off-focus positions has been analyzed to clearly understand diffraction imaging with multiple optical elements. The calculated and measured results establish unambiguously that diffraction imaging should be pursued in non-conjugate configurations to ensure accurate sampling of coherent light distribution from the scatterer.