Multiparametric Image Analysis Research Articles

Morphological profiling in human neural progenitor cells classifies hits in a pilot drug screen for Alzheimer's disease.

Alzheimer's disease accounts for 60-70% of dementia cases. Current treatments are inadequate and there is a need to develop new approaches to drug discovery. Recently, in cancer, morphological profiling has been used in combination with high-throughput screening of small-molecule libraries in human cells in vitro. To test feasibility of this approach for Alzheimer's disease, we developed a cell morphology-based drug screen centred on the risk gene, SORL1 (which encodes the protein SORLA). Increased Alzheimer's disease risk has been repeatedly linked to variants in SORL1, particularly those conferring loss or decreased expression of SORLA, and lower SORL1 levels are observed in post-mortem brain samples from individuals with Alzheimer's disease. Consistent with its role in the endolysosomal pathway, SORL1 deletion is associated with enlarged endosomes in neural progenitor cells and neurons. We, therefore, hypothesized that multi-parametric, image-based cell phenotyping would identify features characteristic of SORL1 deletion. An automated morphological profiling method (Cell Painting) was adapted to neural progenitor cells and used to determine the phenotypic response of SORL1-/- neural progenitor cells to treatment with compounds from a small internationally approved drug library (TargetMol, 330 compounds). We detected distinct phenotypic signatures for SORL1-/- neural progenitor cells compared to isogenic wild-type controls. Furthermore, we identified 16 compounds (representing 14 drugs) that reversed the mutant morphological signatures in neural progenitor cells derived from three SORL1-/- induced pluripotent stem cell sub-clones. Network pharmacology analysis revealed the 16 compounds belonged to five mechanistic groups: 20S proteasome, aldehyde dehydrogenase, topoisomerase I and II, and DNA synthesis inhibitors. Enrichment analysis identified DNA synthesis/damage/repair, proteases/proteasome and metabolism as key pathways/biological processes. Prediction of novel targets revealed enrichment in pathways associated with neural cell function and Alzheimer's disease. Overall, this work suggests that (i) a quantitative phenotypic metric can distinguish induced pluripotent stem cell-derived SORL1-/- neural progenitor cells from isogenic wild-type controls and (ii) phenotypic screening combined with multi-parametric high-content image analysis is a viable option for drug repurposing and discovery in this human neural cell model of Alzheimer's disease.

Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM

The increasing exploration and development of complex oil and gas fields pose challenges to drilling efficiency and safety due to the presence of formations with varying hardness, abrasiveness, and rigidity. Consequently, there is a growing demand for drilling parameter optimization and speed-up technologies. However, existing models based on expert experience can only achieve single-objective optimization with limited accuracy, making real-time adaptation to changing drilling conditions and formation environments challenging. The emergence of artificial intelligence provides a new approach for optimizing drilling parameters. In this study, we introduce the Bi-directional Long Short-Term Memory (Bi-LSTM) deep learning algorithm with the attention mechanism to predict the rate of penetration (ROP). This algorithm improves the ROP prediction accuracy to 98.33%, ensuring reliable subsequent optimization results. Additionally, we propose a coupling optimization algorithm that combines Bi-LSTM with the particle swarm optimization algorithm (PSO) to enhance drilling efficiency through parameter optimization. Our approach aims to maximize drilling footage while maintaining the highest ROP. The optimal solutions obtained are verified through multi-parameter cloud image analysis, yielding consistent results. The application of our approach demonstrates an 81% increase in drilling speed and a 28% reduction in drill bit energy losses. Moreover, the real-time optimization results effectively guide field operations.

Profiling alveolar macrophage responses to inhaled compounds using in vitro high content image analysis

One of the main hurdles in the development of new inhaled medicines is the frequent observation of foamy macrophage (FM) responses in non-clinical studies in experimental animals, which raises safety concerns and hinders progress into clinical trials. We have investigated the potential of a novel multi-parameter high content image analysis (HCIA) assay as an in vitro safety screening tool to predict drug induced FM.Rat (NR8383) and human U937-derived alveolar macrophages were exposed in vitro to a panel of model compounds with different biological activity, including inhaled bronchodilators, inhaled corticosteroids (ICS), phospholipidosis inducers and proapoptotic agents. An HCIA was utilized to produce drug-induced cell response profiles based on individual cell health, morphology and lipid content parameters.The profiles of both rat and human macrophage cell lines differentiated between cell responses to marketed inhaled drugs and compounds known to induce phospholipidosis and apoptosis. Hierarchical clustering of the aggregated data allowed identification of distinct cell profiles in response to exposure to phospholipidosis and apoptosis inducers. Additionally, in NR8383 cell responses formed two distinct clusters, associated with increased vacuolation with or without lipid accumulation. U937 cells presented a similar trend but appeared less sensitive to drug exposure and presented a narrower range of responses.These results indicate that our multi-parameter HCIA assay is suitable to generate characteristic drug-induced macrophage response profiles, thus enabling differentiation of foamy macrophage phenotypes associated with phospholipidosis and apoptosis. This approach shows great potential as pre-clinical in vitro screening tool for safety assessment of candidate inhaled medicines.

The effect of procedural end-tidal CO2 on infarct expansion during anterior circulation thrombectomy.

Carbon dioxide is a potent cerebral vasodilator that may influence outcomes after ischemic stroke. The objective of this study was to investigate the effect of intraprocedural mean end-tidal CO2 (ETCO2) levels on core infarct expansion and neurologic outcome following thrombectomy for anterior circulation ischemic stroke. A retrospective review was conducted of consecutive patients from March 2020 to June 2021 who underwent mechanical thrombectomy for acute anterior circulation ischemic stroke under general anesthesia and achieved successful recanalization (Thrombolysis in Cerebral Infarction [TICI] ≥ 2b). Only patients with CT perfusion, procedural ETCO2, and postoperative MRI data were included. Segmentation software was used for multi-parametric image analysis. Normocarbia defined as mean ETCO2 of 35 mmHg was used to dichotomize subjects. Univariate and multivariate statistics were applied. Fifty-eight patients met criteria for analysis. Of these, 44 had TICI 3 recanalization, 9 had TICI 2c, and 5 had TICI 2b. Within this combined recanalization group, patients with mean ETCO2 > 35 had significantly higher rates of functional independence at 90 days. Although patients tended to salvage more penumbra and experience smaller final infarcts when ETCO2 exceeded 35 mmHg, this did not reach statistical significance. Stroke patients who underwent successful thrombectomy with general anesthesia achieved higher rates of functional independence when procedural ETCO2 exceeded 35 mmHg. Further studies to confirm this effect and investigate optimal ETCO2 parameters should be considered.

Fabricated AIE-Based Probe to Detect the Resistance to Anoikis of Cancer Cells Detached from Tumor Tissue.

(1) Background: Resisting anoikis is a vital and necessary characteristic of malignant cancer cells, but there is no existing quantification method. Herein, a sensitive probe for assessing anoikis resistance of cancer cells detached from the extracellular matrix was developed based on the aggregation-induced emission (AIE) of AIEgens. It has been reported that detached cancer cell endocytose activated integrin clusters, and in the endosome these clusters recruit and activate phosphorylate focal adhesion kinase (pFAK) in the cytoplasm to induce signaling that supports the growth of detached cancer cells. (2) Methods: We established a lost nest cell model of cancer cells and determined their ability to resist anoikis. The colocalization of the activated integrin, pFAK, and endosomes in model cells was observed and calculated. (3) Results: The fluorescence signal intensity of the probe was significantly higher than that of the integrin antibody in the model cells and the fluorescence signal of probe signal was better overlapped with labeled pFAK by fluorescence in endosomes in model cells. (4) Conclusions: We developed a quantitative multi-parametric image analysis program to calculate fluorescent intensity of the probe and antibodies against pFAK and Rab5 in the areas of colocalization. A positive correlation of fluorescence signal intensity between the probe and pFAK on the endosome was observed. Therefore, the probe was used to quantitatively evaluate resisting anoikis of different cancer cell lines under the lost nest condition.

Multiparameter image analysis to evaluate dentinal tubules patency after using different irrigation protocols

Background: Scoring systems have been used to evaluate dentinal tubule patency in several papers; however, these conventional systems are time-consuming and rely primarily on evaluator estimation with a wide range of subjective variations. Objectives: A multiparameter, simple, and objective method is introduced to evaluate the ability of different irrigation protocols for smear layer removal and dentinal tubule patency enhancement. To show the applicability of this method, the effect of different irrigation regimens was evaluated. Methods: Ninety extracted single-rooted teeth were decapitated and shaped with ProTaper to size F4. According to the final irrigation protocol, specimens were divided into three groups: Group 1 (final irrigation of SmearOFF), Group 2 (17% EDTA), and Group 3 (negative control). Groups 1 and 2 were subdivided into two subgroups: Subgroup (A) was activated by an ultrasonic file, whereas samples in Subgroup (B) did not receive any activation. Samples were examined by scanning electron microscopy. Images were analyzed using ImageJ software, and the multiparameter image analysis method (relative total surface area, average size, and the number of opened dentinal tubules) was applied. One-way analysis of variance (ANOVA), followed by Tukey’s post hoc test, was applied to reveal any significant differences (P < 0.05) among groups, activation techniques, and root thirds. Results: Passive ultrasonic activation led to a significant increase in the patency of dentinal tubules especially at the apical third, which was measured and shown by the triple parameter method. Conclusion: Digital image analysis shows potential and advantages for the objective evaluation of dentinal tubules and should replace the traditional subjective scoring system method.

Morpho\u2010metabotyping the oxidative stress response

Oxidative stress and reactive oxygen species (ROS) are central to many physiological and pathophysiological processes. However, due to multiple technical challenges, it is hard to capture a comprehensive readout of the cell, involving both biochemical and functional status. We addressed this problem by developing a fully parallelized workflow for metabolomics (providing absolute quantities for > 100 metabolites including TCA cycle, pentose phosphate pathway, purine metabolism, glutathione metabolism, cysteine and methionine metabolism, glycolysis and gluconeogenesis) and live cell imaging microscopy. The correlative imaging strategy was applied to study morphological and metabolic adaptation of cancer cells upon short-term hydrogen peroxide (H2O2) exposure in vitro. The combination provided rich metabolic information at the endpoint of exposure together with imaging of mitochondrial effects. As a response, superoxide concentrations were elevated with a strong mitochondrial localization, and multi-parametric image analysis revealed a shift towards fragmentation. In line with this, metabolism reflected both the impaired mitochondrial function and shifts to support the first-line cellular defense and compensate for energy loss. The presented workflow combining high-end technologies demonstrates the applicability for the study of short-term oxidative stress, but it can be suitable for the in-depth study of various short-term oxidative and other cellular stress-related phenomena.

Transforming early pharmaceutical assessment of genotoxicity: applying statistical learning to a high throughput, multi end point in vitro micronucleus assay

To provide a comprehensive analysis of small molecule genotoxic potential we have developed and validated an automated, high-content, high throughput, image-based in vitro Micronucleus (IVM) assay. This assay simultaneously assesses micronuclei and multiple additional cellular markers associated with genotoxicity. Acoustic dosing (≤ 2 mg) of compound is followed by a 24-h treatment and a 24-h recovery period. Confocal images are captured [Cell Voyager CV7000 (Yokogawa, Japan)] and analysed using Columbus software (PerkinElmer). As standard the assay detects micronuclei (MN), cytotoxicity and cell-cycle profiles from Hoechst phenotypes. Mode of action information is primarily determined by kinetochore labelling in MN (aneugencity) and γH2AX foci analysis (a marker of DNA damage). Applying computational approaches and implementing machine learning models alongside Bayesian classifiers allows the identification of, with 95% accuracy, the aneugenic, clastogenic and negative compounds within the data set (Matthews correlation coefficient: 0.9), reducing analysis time by 80% whilst concurrently minimising human bias. Combining high throughput screening, multiparametric image analysis and machine learning approaches has provided the opportunity to revolutionise early Genetic Toxicology assessment within AstraZeneca. By multiplexing assay endpoints and minimising data generation and analysis time this assay enables complex genotoxicity safety assessments to be made sooner aiding the development of safer drug candidates.

High-Content Analysis-Based Sensitivity Prediction and Novel Therapeutics Screening for c-Met-Addicted Glioblastoma.

Simple SummaryReal-time ex vivo drug testing tailors individual therapeutics based on predicted drug responses. Most technologies to date rely on conventional drug screening that provides low confidence data. Here, we present high-content analysis-based drug testing of glioblastoma patients to identify the right glioblastoma patients for a given drug. This generates multi-parameter biomarker and phenotype readouts providing a better reliability of the assay. Additionally, we showed a high-content drug repurposing screen and defined a new c-Met-inhibiting function of the CDK4/6 inhibitor Abemaciclib. Large-scale high throughput screening results demonstrate that Abemaciclib sensitivity in glioblastoma patients is highly correlated with the c-Met inhibitors sensitivity, further supporting the accuracy of the platform and important new clinical implications regarding multiple functions of Abemaciclib.(1) Background: Recent advances in precision oncology research rely on indicating specific genetic alterations associated with treatment sensitivity. Developing ex vivo systems to identify cancer patients who will respond to a specific drug remains important. (2) Methods: cells from 12 patients with glioblastoma were isolated, cultured, and subjected to high-content screening. Multi-parameter analyses assessed the c-Met level, cell viability, apoptosis, cell motility, and migration. A drug repurposing screen and large-scale drug sensitivity screening data across 59 cancer cell lines and patient-derived cells were obtained from 125 glioblastoma samples. (3) Results: High-content analysis of patient-derived cells provided robust and accurate drug responses to c-Met-targeted agents. Only the cells of one glioblastoma patient (PDC6) showed elevated c-Met level and high susceptibility to the c-Met inhibitors. Multi-parameter image analysis also reflected a decreased c-Met expression and reduced cell growth and motility by a c-Met-targeting antibody. In addition, a drug repurposing screen identified Abemaciclib as a distinct CDK4/6 inhibitor with a potent c-Met-inhibitory function. Consistent with this, we present large-scale drug sensitivity screening data showing that the Abemaciclib response correlates with the response to c-Met inhibitors. (4) Conclusions: Our study provides a new insight into high-content screening platforms supporting drug sensitivity prediction and novel therapeutics screening.

Abstract 6658: Deciphering anti-PDL1 effect in a syngeneic mouse model of sarcoma

Abstract Background: In the context of novel immuno-oncology therapeutics development at preclinical stage, it's crucial to understand the primary features of the in-vivo model to be used and to delineate mechanisms underlying response to a drug candidate. While PDL1 blockade figures as a gold standard immunotherapy its exact mechanism of action in a mouse model of sarcoma remains to be elucidated. Methods: Using a preclinical syngeneic mouse model of sarcoma based on the inoculation of MCA205 cell line we investigated anti-tumor effect of anti-PDL1 antibody through tumor growth and survival assessment. Using the same experimental setting, and by mean of intratumoral microdialysis, immune-related metabolic pathways at both tumor and contralateral sites were assessed. Tumor samples were also collected for gene expression analysis by RNA sequencing. Also, immune landscape of the tumor was addressed by both flow cytometry analysis and by multiparametric immunohistofluorescence and image analysis. Finally, participation of CD8 T cells was investigated using a specific depleting antibody-based strategy. Results: As expected, administration of anti-PDL1 antibody led to a strong anti-tumor response. With respect to the immunometabolism features of this model, it displays within the tumor - and compared to the non-tumor bearing area - i) a slight Kynurenine pathway activation, ii) a strong Arginase activity, and iii) a high Adenosine production. Interestingly, anti-PDL1 effect was associated with a limitation of Adenosine level within the tumor thus arguing for an important role of the Adenosine axis in the control of the anti-tumor immune response. Along with this effect, PDL1 blockade led to an intratumoral enrichment of leukocytes (CD45+), with a higher abundance of lymphocytes also displaying increased level of IFNgamma. In contrast, macrophages - delineated as CD11b+/F4:80+ - were limited upon treatment, especially the immunosuppressive CD11b/Gr1Low/Int cell subsets while concomitantly promoting a M1/M2 macrophages ratio. Along with these data, RNAseq analysis revealed an enrichment in genes from immune-related datasets including interferon signaling. Interestingly, CD8 depletion fully abrogated anti-PDL1 anti-tumor effect thus confirming the critical role of this population. Conclusions: Altogether, this new multiparametric dataset collected on a preclinical model of sarcoma allows the highlighting of specific intrinsic features of the model, and certainly provides a deeper understanding of the mechanism of action of an effective and approved drug. Such a comprehensive assessment-based strategy can therefore serve as a basis for further exploration of novel candidates. Citation Format: Imane Nafia, Assia Chaibi, Doriane Bortolotto, Christophe Rey, Antoine Italiano, Alban Bessede. Deciphering anti-PDL1 effect in a syngeneic mouse model of sarcoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6658.

A flexible high content imaging assay for profiling macrophage efferocytosis

Macrophages are a diverse population of cells originating from the myeloid lineage, which form an important component of the innate immune system, helping to regulate immune response through secretion of pro/anti-inflammatory cytokines. However they also have an important homeostatic role – helping to remove cellular debris and apoptotic cells from the body (a phagocytic process known as efferocytosis). Here we describe a robust 384 well microplate based imaging assay, using apoptotic target cells for the specific quantification of efferocytosis in human primary monocyte derived macrophages. The methodology described allows for the assay to run in either fixed end-point or live-cell format (the former offering multiple morphological and intensity-based readouts, whilst the latter opens the possibility for future expansion of the methodology to encompass kinetic profiling). Within the methodology described we couple high content image acquisition (on the Cell Voyager 7000S) with multi-parametric image analysis - using Perkin Elmer Columbus combined with GeneData Screener.

APPLICATION OF IMAGE-BASED HIGH CONTENT ANALYSIS FOR THE SCREENING OF BIOACTIVE NATURAL PRODUCTS

Each bioactive compound induces phenotypic changes in target cells that can be made visible by labelling selected molecules of the cells with fluorescent dyes and/or directly observed under the high-throughput microscope. A comparison of the cellular phenotype induced by a compound of interest with known cellular targets allows predicting its mode of action. Over the past 15 years, high-throughput microscopy has been one of the fastest growing fields in cell biology. When combined with automated multiparametric image and data analysis, it is referred to as high-content screening (HCS). Whilst HCS has been successfully applied to the bioactivity characterization of natural products, recent studies used automated microscopy and software to increase speed and to reduce subjective interpretation. In 2017, Institute of Natural Products Chemistry (INPC-VAST) has been equipped with a HCS platform (Olympus Scan^R) that designed for fully automated image acquisition and analysis of biological samples to visually inspect the cellular morphology induced by hit compounds as well as to discriminate from false positives. Accordingly, this short review covers the concepts of HCS and its application in screening of biologically active natural products whose molecular targets could be identified through such approaches.

Abstract 1559: Detection and retrieval of multiple myeloma cells with single-cell molecular confirmation from peripheral blood by multiparameter immunofluorescence, automated scanning, and image analysis

Abstract Background: The detection of multiple myeloma (MM) cells is currently performed by analysis of bone marrow aspirates. Detection of MM cells from the peripheral blood would allow for less invasive and more frequent assessments to monitor residual disease. Using the RareCyte platform, we developed a 5-parameter assay to visualize and retrieve MM cells from peripheral blood and applied it to MM patient samples under an IRB-approved protocol. We also used this to identify plasma cells from the peripheral blood of healthy volunteer donors. Materials and Methods: Healthy volunteer or MM patient peripheral blood was collected into RareCyte blood collection tubes. RPMI8226 cells spiked into normal donor blood samples were used as a model for MM cell assay development. Buffy coats isolated from 7.5mL of blood by AccuCyte® separation were spread onto slides. Slides were stained with a 5-marker panel that included antibodies to CD138, CD38, CD56, CD45, and a nuclear dye on the Leica Bond Rx auto-stainer. Slides were scanned with CyteFinder® and MM cells identified by positive CD138 and CD56 staining and negative CD45 staining. Identified MM cells and WBC control cells were mechanically retrieved with the integrated CytePicker module, whole genome amplified, and Sanger sequencing was performed for specific mutations in four genes (IDH1, NRAS, PHLDA1, RPH3AL) identified by genomic analysis of a bone marrow sample. Plasma cells from normal donors were identified by co-expression of CD138 and CD38. Results: In a pretreatment MM sample, the 5-parameter assay identified 55 MM cells per mL of whole blood. In isolated single cells with at least one successful sequencing reaction, 0 of 13 WBCs contained mutations in at least one of the genes. Mutations were found in 9 of 15 MM cells (defined as CD138+, CD56+, CD38-, and CD45-). In healthy volunteer samples, 5 plasma cells (defined as CD138+, CD38+, and CD45 dim/negative) were identified per mL of whole blood. Conclusions: The 5-parameter assay successfully detected rare cells with an MM phenotype from the peripheral blood of an MM patient. Individually retrieved MM cells were demonstrated to contain mutations specific to the patient's tumor, confirming that the cells identified were MM cells. This approach may allow for less invasive assessment of MM and monitoring of residual disease. Citation Format: Lance U'Ren, Nolan Ericson, Daniel Campton, Arturo Ramirez, C. Anthony Blau, Eric Kaldljian. Detection and retrieval of multiple myeloma cells with single-cell molecular confirmation from peripheral blood by multiparameter immunofluorescence, automated scanning, and image analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1559.

Early postoperative delineation of residual tumor after low-grade glioma resection by probabilistic quantification of diffusion-weighted imaging.

In WHO grade II low-grade gliomas (LGGs), early postoperative MRI (epMRI) may overestimate residual tumor on FLAIR sequences. Consequently, MRI at 3-6 months follow-up (fuMRI) is used for delineation of residual tumor. This study sought to evaluate if integration of apparent diffusion coefficient (ADC) maps permits an accurate estimation of residual tumor early on epMRI. From a consecutive cohort, 43 cases with an initial surgery for an LGG, and complete epMRI (< 72 hours after resection) and fuMRI including ADC maps, were retrospectively identified. Residual FLAIR hyperintense tumor was manually segmented on epMRI and corresponding ADC maps were coregistered. Using an expectation maximization algorithm, residual tumor segments were probabilistically clustered into areas of residual tumor, ischemia, or normal white matter (NWM) by fitting a mixture model of superimposed Gaussian curves to the ADC histogram. Tumor volumes from epMRI, clustering, and fuMRI were statistically compared and agreement analysis was performed. Mean FLAIR hyperintensity suggesting residual tumor was significantly larger on epMRI compared to fuMRI (19.4 ± 16.5 ml vs 8.4 ± 10.2 ml, p < 0.0001). Probabilistic clustering of corresponding ADC histograms on epMRI identified subsegments that were interpreted as mean residual tumor (7.6 ± 10.2 ml), ischemia (8.1 ± 5.9 ml), and NWM (3.7 ± 4.9 ml). Therefore, mean tumor quantification error between epMRI and fuMRI was significantly reduced (11.0 ± 10.6 ml vs -0.8 ± 3.7 ml, p < 0.0001). Mean clustered tumor volumes on epMRI were no longer significantly different from the fuMRI reference (7.6 ± 10.2 ml vs 8.4 ± 10.2 ml, p = 0.16). Correlation (Pearson r = 0.96, p < 0.0001), concordance correlation coefficient (0.89, 95% confidence interval 0.83), and Bland-Altman analysis suggested strong agreement between both measures after clustering. Probabilistic segmentation of ADC maps facilitates accurate assessment of residual tumor within 72 hours after LGG resection. Multiparametric image analysis detected FLAIR signal alterations attributable to surgical trauma, which led to overestimation of residual LGG on epMRI compared to fuMRI. The prognostic value and clinical impact of this method has to be evaluated in larger case series in the future.

In Vivo Characterization of Spontaneous Retinal Neovascularization in the Mouse Eye by Multifunctional Optical Coherence Tomography.

To investigate the early development of spontaneous retinal neovascularization in the murine retina by a multifunctional optical coherence tomography approach. To characterize involved tissue changes in vivo and describe structural and functional changes over time. A multifunctional optical coherence tomography (OCT) system providing 3-fold contrast comprising reflectivity, polarization sensitivity, and OCT angiography (OCTA) was utilized to image very-low-density lipoprotein receptor (VLDLR) knockout mice. Baseline measurements were acquired as early as postnatal day 14 and a follow-up of neovascularization development was performed until the age of 3 months. Control mice were imaged accordingly and a multiparametric image analysis was performed to characterize different stages of pathologic vascular growth. Histology was conducted at the endpoint of the experiment. An interventional pilot experiment was conducted to investigate the effect of the anti-vascular endothelial growth factor (VEGF) agent aflibercept on the development of retinal neovascularization. Onset of neovascularization was imaged at baseline, and significant changes were encountered in the retina over time, including reduced retinal thickness, increase of lesion volume, migration of pigmented structures, and presence of abnormal blood flow in the outer retina. Multifunctional image contrast was correlated to ex vivo histology. Microscopic analysis of retinal flat mounts and cross-sectional samples confirmed the changes observed in in vivo structural and functional OCT images. Administration of an anti-VEGF agent resulted in a significantly reduced lesion volume. Longitudinal, multifunctional OCT imaging of infant VLDLR-/- mouse retinas enabled a multiparametric, in vivo staging of neovascularization formation from before lesion onset until their manifestation.

A high-throughput all-optical laser-scanning imaging flow cytometer with biomolecular specificity and subcellular resolution.

Image-based cellular assay advances approaches to dissect complex cellular characteristics through direct visualization of cellular functional structures. However, available technologies face a common challenge, especially when it comes to the unmet need for unraveling population heterogeneity at single-cell precision: higher imaging resolution (and thus content) comes at the expense of lower throughput, or vice versa. To overcome this challenge, a new type of imaging flow cytometer based upon an all-optical ultrafast laser-scanning imaging technique, called free-space angular-chirp-enhanced delay (FACED) is reported. It enables an imaging throughput (>20 000 cells s-1 ) 1 to 2 orders of magnitude higher than the camera-based imaging flow cytometers. It also has 2 critical advantages over optical time-stretch imaging flow cytometry, which achieves a similar throughput: (1) it is widely compatible to the repertoire of biochemical contrast agents, favoring biomolecular-specific cellular assay and (2) it enables high-throughput visualization of functional morphology of individual cells with subcellular resolution. These capabilities enable multiparametric single-cell image analysis which reveals cellular heterogeneity, for example, in the cell-death processes demonstrated in this work-the information generally masked in non-imaging flow cytometry. Therefore, this platform empowers not only efficient large-scale single-cell measurements, but also detailed mechanistic analysis of complex cellular processes.

Holography microscopy as an artifact-free alternative to phase-contrast.

Artifact-free microscopic images represent a key requirement of multi-parametric image analysis in modern biomedical research. Holography microscopy (HM) is one of the quantitative phase imaging techniques, which has been finding new applications in life science, especially in morphological screening, cell migration, and cancer research. Rather than the classical imaging of absorbing (typically stained) specimens by bright-field microscopy, the information about the light-wave's phase shifts induced by the biological sample is employed for final image reconstruction. In this comparative study, we investigated the usability and the reported advantage of the holography imaging. The claimed halo-free imaging was analyzed compared to the widely used Zernike phase-contrast microscopy. The intensity and phase cross-membrane profiles at the periphery of the cell were quantified. The intensity profile for cells in the phase-contrast images suffers from the significant increase in intensity values around the cell border. On the contrary, no distorted profile is present outside the cell membrane in holography images. The gradual increase in phase shift values is present in the internal part of the cell body projection in holography image. This increase may be related to the increase in the cell internal material according to the dry mass theory. Our experimental data proved the halo-free nature of the holography imaging, which is an important prerequisite of the correct thresholding and cell segmentation, nowadays frequently required in high-content screening and other image-based analysis. Consequently, HM is a method of choice whenever the image analysis relies on the accurate data on cell boundaries.

Detecting ordered small molecule drug aggregates in live macrophages: a multi-parameter microscope image data acquisition and analysis strategy.

Following prolonged administration, certain orally bioavailable but poorly soluble small molecule drugs are prone to precipitate out and form crystal-like drug inclusions (CLDIs) within the cells of living organisms. In this research, we present a quantitative multi-parameter imaging platform for measuring the fluorescence and polarization diattenuation signals of cells harboring intracellular CLDIs. To validate the imaging system, the FDA-approved drug clofazimine (CFZ) was used as a model compound. Our results demonstrated that a quantitative multi-parameter microscopy image analysis platform can be used to study drug sequestering macrophages, and to detect the formation of ordered molecular aggregates formed by poorly soluble small molecule drugs in animals.

Quantitative assessment of retinopathy using multi-parameter image analysis

A multi-parameter quantification method was implemented to quantify retinal vascular injuries in microscopic images of clinically relevant eye diseases. This method was applied to wholemount retinal trypsin digest images of diabetic Akita/+, and bcl-2 knocked out mice models. Five unique features of retinal vasculature were extracted to monitor early structural changes and retinopathy, as well as quantifying the disease progression. Our approach was validated through simulations of retinal images. Results showed fewer number of cells (P = 5.1205e-05), greater population ratios of endothelial cells to pericytes (PCs) (P = 5.1772e-04; an indicator of PC loss), higher fractal dimension (P = 8.2202e-05), smaller vessel coverage (P = 1.4214e-05), and greater number of acellular capillaries (P = 7.0414e-04) for diabetic retina as compared to normal retina. Quantification using the present method would be helpful in evaluating physiological and pathological retinopathy in a high-throughput and reproducible manner.

Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke.

Cerebral small vessel disease (SVD) is common in ageing and patients with dementia and stroke. Its manifestations on magnetic resonance imaging (MRI) include white matter hyperintensities, lacunes, microbleeds, perivascular spaces, small subcortical infarcts, and brain atrophy. Many studies focus only on one of these manifestations. A protocol for the differential assessment of all these features is, therefore, needed. To identify ways of quantifying imaging markers in research of patients with SVD and operationalize the recommendations from the STandards for ReportIng Vascular changes on nEuroimaging guidelines. Here, we report the rationale, design, and methodology of a brain image analysis protocol based on our experience from observational longitudinal studies of patients with nondisabling stroke. The MRI analysis protocol is designed to provide quantitative and qualitative measures of disease evolution including: acute and old stroke lesions, lacunes, tissue loss due to stroke, perivascular spaces, microbleeds, macrohemorrhages, iron deposition in basal ganglia, substantia nigra and brain stem, brain atrophy, and white matter hyperintensities, with the latter separated into intense and less intense. Quantitative measures of tissue integrity such as diffusion fractional anisotropy, mean diffusivity, and the longitudinal relaxation time are assessed in regions of interest manually placed in anatomically and functionally relevant locations, and in others derived from feature extraction pipelines and tissue segmentation methods. Morphological changes that relate to cognitive deficits after stroke, analyzed through shape models of subcortical structures, complete the multiparametric image analysis protocol. Final outcomes include guidance for identifying ways to minimize bias and confounds in the assessment of SVD and stroke imaging biomarkers. It is intended that this information will inform the design of studies to examine the underlying pathophysiology of SVD and stroke, and to provide reliable, quantitative outcomes in trials of new therapies and preventative strategies.