Object-oriented image analysis for high content screening: Detailed quantification of cells and sub cellular structures with the Cellenger software

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

Classifying changes in LN-18 glial cell morphology: a supervised machine learning approach to analyzing cell microscopy data via FIJI and WEKA

In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. With the traditional, end-point assay-based approach being error-prone, and existing computational approaches being complex, we tested existing machine learning frameworks to find methods that are relatively simple, yet powerful enough to accomplish the goal of analyzing cell microscopy data. This paper details the machine learning pipeline for pixel-based classification and object-based classification. Furthermore, it compares the performances of three classifiers. The classifiers evaluated were the fast-random forest (RF), the sequential minimal optimization (SMO), and the Bayesian network (BN). Images were first preprocessed using smoothing and contrast methods found in FIJI. For pixel-based classification, the preprocessed images were fed into the Trainable Waikato Segmentation (TWS). For object-based classification, training and classification were conducted within the Waikato Environment for Knowledge Analysis (WEKA) interface. All classifiers' performance was evaluated using the WEKA experimental explorer. In terms of performance, the BN had the lowest classification accuracy for both the pixel-based and object-based model. The object-based SMO classifier had the best performance with the lowest mean absolute error of 0.05. The TWS and WEKA interface allows users to easily create and train classifiers for image analysis. However, for analyzing large image datasets, they are not ideal. Grapical abstract.

Land Use Changes in the Zoige Plateau Based on the Object-Oriented Method and Their Effects on Landscape Patterns

Land use/land cover change (LUCC) is the most direct driving force of landscape pattern change. The Zoige Plateau is a natural ecosystem with the largest high-altitude swamp wetland in China and its land use pattern has undergone great changes in recent years, but how the changes of each land use type affect the landscape pattern is uncertain. Here, we used the object-oriented method to extract land use information in 2015. Then, combined with land use data, the land use change characteristics from 2000 to 2015 were analyzed. We used the correlation analysis method to analyze the effects of land use changes on landscape pattern systematically. Three key conclusions were reached. (1) Land use information for the Zoige Plateau could be extracted with high accuracy by combining the object-oriented method and support vector machine (SVM). The overall accuracy was 93.2% and the Kappa coefficient was 0.889. (2) The comprehensive dynamic degree of land use was the highest from 2010 to 2015. From 2000 to 2015, the wetland area decreased the fastest because 57.05% of the wetlands were transferred out. Construction land increased the fastest, and the transferred in area from grassland and farmland were the main reason. (3) The effects of unused land, farmland, and construction land on the overall landscape pattern were stronger than that of the other types, among which farmland had the most significant impact (with a correlation coefficient of 0.959, p < 0.001). The change of unused land was the most highly significant factor associated with the landscape area pattern, and both the water body and unused land showed strong correlations with landscape shape pattern change. This suggested that the effects of land use types occupying a relatively small area on the landscape pattern were intensified. This study will provide guidance for the environmental management of local land resources and other natural ecosystem areas.

Assessment of Land Cover Change and Its Impact on Changes in Soil Erosion Risk in Nepal

Land cover change is a critical driver for enhancing the soil erosion risk in Nepal. Loss of the topsoil has a direct and indirect effect on human life and livelihoods. The present study provides an assessment of the decadal land use and land cover (LULC) change and consequent changes in the distribution of soil erosion risk for the years, 1990, 2000, and 2010, for the entire country of Nepal. The study attempted to understand how different land cover types change over the three decades and how it has changed the distribution of soil erosion risks in Nepal that would help in the development of soil conservation priority. The land cover maps were produced using geographic object-based image analysis (GEOBIA) using Landsat images. Soil erosion patterns were assessed using the revised universal soil loss equation (RUSLE) with the land cover as the input. The study shows that the forest cover is the most dominant land cover in Nepal that comprises about 6,200,000 ha forest cover. The estimated annual erosion was 129.30 million tons in 1990 and 110.53 million tons in 2010. The assessment of soil erosion dynamics was presented at the national, provincial, and district level. District wise analysis revealed that Gulmi, Parbat, Syangja, and the Tanahu district require priority for soil conservation.

High-Throughput Screening Raman Spectroscopy Platform for Label-Free Cellomics

We present a high-throughput screening Raman spectroscopy (HTS-RS) platform for a rapid and label-free macromolecular fingerprinting of tens of thousands eukaryotic cells. The newly proposed label-free HTS-RS platform combines automated imaging microscopy with Raman spectroscopy to enable a rapid label-free screening of cells and can be applied to a large number of biomedical and clinical applications. The potential of the new approach is illustrated by two applications. (1) HTS-RS-based differential white blood cell count. A classification model was trained using Raman spectra of 52 218 lymphocytes, 48 220 neutrophils, and 7 294 monocytes from four volunteers. The model was applied to determine a WBC differential for two volunteers and three patients, producing comparable results between HTS-RS and machine counting. (2) HTS-RS-based identification of circulating tumor cells (CTCs) in 1:1, 1:9, and 1:99 mixtures of Panc1 cells and leukocytes yielded ratios of 55:45, 10:90, and 3:97, respectively. Because the newly developed HTS-RS platform can be transferred to many existing Raman devices in all laboratories, the proposed implementation will lead to a significant expansion of Raman spectroscopy as a standard tool in biomedical cell research and clinical diagnostics.

Loss of Npn1 from motor neurons causes postnatal deficits independent from Sema3A signaling

The correct wiring of neuronal circuits is of crucial importance for the function of the vertebrate nervous system. Guidance cues like the neuropilin receptors (Npn) and their ligands, the semaphorins (Sema) provide a tight spatiotemporal control of sensory and motor axon growth and guidance. Among this family of guidance partners the Sema3A-Npn1 interaction has been shown to be of great importance, since defective signaling leads to wiring deficits and defasciculation. For the embryonic stage these defects have been well described, however, also after birth the organism can adapt to new challenges by compensational mechanisms. Therefore, we used the mouse lines Olig2-Cre;Npn1(cond) and Npn1(Sema-) to investigate how postnatal organisms cope with the loss of Npn1 selectively from motor neurons or a systemic dysfunctional Sema3A-Npn1 signaling in the entire organism, respectively. While in Olig2-Cre(+);Npn1(cond-/-) mice clear anatomical deficits in paw posturing, bone structure, as well as muscle and nerve composition became evident, Npn1(Sema-) mutants appeared anatomically normal. Furthermore, Olig2-Cre(+);Npn1(cond) mutants revealed a dysfunctional extensor muscle innervation after single-train stimulation of the N.radial. Interestingly, these mice did not show obvious deficits in voluntary locomotion, however, skilled motor function was affected. In contrast, Npn1(Sema-) mutants were less affected in all behavioral tests and able to improve their performance over time. Our data suggest that loss of Sema3A-Npn1 signaling is not the only cause for the observed deficits in Olig2-Cre(+);Npn1(cond-/-) mice and that additional, yet unknown binding partners for Npn1 may be involved that allow Npn1(Sema-) mutants to compensate for their developmental deficits.

Understanding health and disease with multidimensional single-cell methods

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple-cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple-cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.

Epithelial invasion outcompetes hypha development during Candida albicans infection as revealed by an image-based systems biology approach

Candida albicans is the most common opportunistic fungal pathogen of the human mucosal flora, frequently causing infections. The fungus is responsible for invasive infections in immunocompromised patients that can lead to sepsis. The yeast to hypha transition and invasion of host-tissue represent major determinants in the switch from benign colonizer to invasive pathogen. A comprehensive understanding of the infection process requires analyses at the quantitative level. Utilizing fluorescence microscopy with differential staining, we obtained images of C. albicans undergoing epithelial invasion during a time course of 6 h. An image-based systems biology approach, combining image analysis and mathematical modeling, was applied to quantify the kinetics of hyphae development, hyphal elongation, and epithelial invasion. The automated image analysis facilitates high-throughput screening and provided quantities that allow for the time-resolved characterization of the morphological and invasive state of fungal cells. The interpretation of these data was supported by two mathematical models, a kinetic growth model and a kinetic transition model, that were developed using differential equations. The kinetic growth model describes the increase in hyphal length and revealed that hyphae undergo mass invasion of epithelial cells following primary hypha formation. We also provide evidence that epithelial cells stimulate the production of secondary hyphae by C. albicans. Based on the kinetic transition model, the route of invasion was quantified in the state space of non-invasive and invasive fungal cells depending on their number of hyphae. This analysis revealed that the initiation of hyphae formation represents an ultimate commitment to invasive growth and suggests that in vivo, the yeast to hypha transition must be under exquisitely tight negative regulation to avoid the transition from commensal to pathogen invading the epithelium.

CognitionMaster: an object-based image analysis framework

Background

Automated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired.

Results

In this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose “analysis pipelines“ that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept.

Conclusions

We introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis.

Automated cell identification and tracking using nanoparticle moving-light-displays

An automated technique for the identification, tracking and analysis of biological cells is presented. It is based on the use of nanoparticles, enclosed within intra-cellular vesicles, to produce clusters of discrete, point-like fluorescent, light sources within the cells. Computational analysis of these light ensembles in successive time frames of a movie sequence, using k-means clustering and particle tracking algorithms, provides robust and automated discrimination of live cells and their motion and a quantitative measure of their proliferation. This approach is a cytometric version of the moving light display technique which is widely used for analyzing the biological motion of humans and animals. We use the endocytosis of CdTe/ZnS, core-shell quantum dots to produce the light displays within an A549, epithelial, lung cancer cell line, using time-lapse imaging with frame acquisition every 5 minutes over a 40 hour time period. The nanoparticle moving light displays provide simultaneous collection of cell motility data, resolution of mitotic traversal dynamics and identification of familial relationships allowing construction of multi-parameter lineage trees.

Novel standards in the measurement of rat insulin granules combining electron microscopy, high-content image analysis and in silico modelling

AIMS/HYPOTHESIS

Knowledge of number, size and content of insulin secretory granules is pivotal for understanding the physiology of pancreatic beta cells. Here we re-evaluated key structural features of rat beta cells, including insulin granule size, number and distribution as well as cell size.

METHODS

Electron micrographs of rat beta cells fixed either chemically or by high-pressure freezing were compared using a high-content analysis approach. These data were used to develop three-dimensional in silico beta cell models, the slicing of which would reproduce the experimental datasets.

RESULTS

As previously reported, chemically fixed insulin secretory granules appeared as hollow spheres with a mean diameter of ∼350 nm. Remarkably, most granules fixed by high-pressure freezing lacked the characteristic halo between the dense core and the limiting membrane and were smaller than their chemically fixed counterparts. Based on our analyses, we conclude that the mean diameter of rat insulin secretory granules is 243 nm, corresponding to a surface area of 0.19 μm(2). Rat beta cells have a mean volume of 763 μm(3) and contain 5,000-6,000 granules.

CONCLUSIONS/INTERPRETATION

A major reason for the lower mean granule number/rat beta cell relative to previous accounts is a reduced estimation of the mean beta cell volume. These findings imply that each granule contains about twofold more insulin, while its exocytosis increases membrane capacitance about twofold less than assumed previously. Our integrated approach defines new standards for quantitative image analysis of beta cells and could be applied to other cellular systems.

Image analysis benchmarking methods for high-content screen design

The recent development of complex chemical and small interfering RNA (siRNA) collections has enabled large-scale cell-based phenotypic screening. High-content and high-throughput imaging are widely used methods to record phenotypic data after chemical and small interfering RNA treatment, and numerous image processing and analysis methods have been used to quantify these phenotypes. Currently, there are no standardized methods for evaluating the effectiveness of new and existing image processing and analysis tools for an arbitrary screening problem. We generated a series of benchmarking images that represent commonly encountered variation in high-throughput screening data and used these image standards to evaluate the robustness of five different image analysis methods to changes in signal-to-noise ratio, focal plane, cell density and phenotype strength. The analysis methods that were most reliable, in the presence of experimental variation, required few cells to accurately distinguish phenotypic changes between control and experimental data sets. We conclude that by applying these simple benchmarking principles an a priori estimate of the image acquisition requirements for phenotypic analysis can be made before initiating an image-based screen. Application of this benchmarking methodology provides a mechanism to significantly reduce data acquisition and analysis burdens and to improve data quality and information content.

Deciphering membrane-associated molecular processes in target tissue of autoimmune uveitis by label-free quantitative mass spectrometry

Autoimmune uveitis is a blinding disease presenting with autoantibodies against eye-specific proteins as well as autoagressive T cells invading and attacking the immune-privileged target tissue retina. The molecular events enabling T cells to invade and attack the tissue have remained elusive. Changes in membrane protein expression patterns between diseased and healthy stages are especially interesting because initiating events of disease will most likely occur at membranes. Since disease progression is accompanied with a break-down of the blood-retinal barrier, serum-derived proteins mask the potential target tissue-related changes. To overcome this limitation, we used membrane-enriched fractions derived from retinas of the only available spontaneous animal model for the disease equine recurrent uveitis, and compared expression levels by a label-free LC-MSMS-based strategy to healthy control samples. We could readily identify a total of 893 equine proteins with 57% attributed to the Gene Ontology project term "membrane." Of these, 179 proteins were found differentially expressed in equine recurrent uveitis tissue. Pathway enrichment analyses indicated an increase in proteins related to antigen processing and presentation, TNF receptor signaling, integrin cell surface interactions and focal adhesions. Additionally, loss of retina-specific proteins reflecting decrease of vision was observed as well as an increase in Müller glial cell-specific proteins indicating glial reactivity. Selected protein candidates (caveolin 1, integrin alpha 1 and focal adhesion kinase) were validated by immunohistochemistry and tissue staining pattern pointed to a significant increase of these proteins at the level of the outer limiting membrane which is part of the outer blood-retinal barrier. Taken together, the membrane enrichment in combination with LC-MSMS-based label-free quantification greatly increased the sensitivity of the comparative tissue profiling and resulted in detection of novel molecular pathways related to equine recurrent uveitis.

Safety and efficacy of an inhaled epidermal growth factor receptor inhibitor (BIBW 2948 BS) in chronic obstructive pulmonary disease

RATIONALE

Epidermal growth factor receptor (EGFR) activation is implicated in mucin hypersecretion in chronic obstructive pulmonary disease (COPD).

OBJECTIVES

To investigate the safety and efficacy of an inhaled EGFR antagonist (BIBW 2948) in COPD.

METHODS

Multicenter, double-blind, placebo-controlled trial of 4 weeks of treatment with two doses of BIBW 2948 (15 and 30 mg twice a day) on safety and mucin-related outcomes in 48 patients with COPD. The effect of BIBW 2948 on EGFR activation in airway epithelial cells was assessed using an ex vivo assay. Efficacy measures included the volume of mucin in the airway epithelium (Vs mu,bala) in bronchial biopsies and the expression of mucin genes in bronchial brushings.

MEASUREMENTS AND MAIN RESULTS

Inhaled BIBW 2948 induced a dose-related inhibition of EGFR internalization (reflecting decreased EGFR activation) in epithelial cells from treated subjects. However, BIBW 2948 was associated with a dose-related increase in adverse events, including reversible liver enzyme elevation (n = 2), and reduction in FEV(1). The changes in mucin stores and mucin gene expression were not significantly different in the pooled BIBW 2948 group versus placebo (volume of mucin per surface area of basal lamina = 0.22 +/- 7.11 vs. 0.47 +/- 8.06 microm(3)/microm(2); P = 0.93). However, in the 30 mg twice a day group, the reduction in epithelial mucin stores was greatest in subjects with the greatest degree of EGFR inhibition (Pearson r = 0.98; 95% confidence interval, 0.71-0.99).

CONCLUSIONS

Four-week treatment with BIBW 2948 did not significantly decrease epithelial mucin stores and was poorly tolerated in patients with COPD. Ex vivo analyses suggest that higher doses may be more effective at both EGFR inhibition and decreases in mucin stores but that adverse events should be expected. Clinical trial registered with www.clinicaltrials.gov (NCT00423137).

Imaging-based live cell yeast screen identifies novel factors involved in peroxisome assembly

We describe an imaging-based method in intact cells to systematically screen yeast mutant libraries for abnormal morphology and distribution of fluorescently labeled subcellular structures. In this study, chromosomally expressed green fluorescent protein (GFP) fused to the peroxisomal targeting sequence 1, consisting of serine-lysine-leucine, was introduced into 4740 viable yeast deletion mutants using a modified synthetic genetic array (SGA) technology. A benchtop robot was used to create ordered high-density arrays of GFP-expressing yeast mutants on solid media plates. Immobilized live yeast colonies were subjected to high-resolution, multidimensional confocal imaging. A software tool was designed for automated processing and quantitative analysis of acquired multichannel three-dimensional image data. The study resulted in the identification of two novel proteins, as well as of all previously known proteins required for import of proteins bearing peroxisomal targeting signal PTS1, into yeast peroxisomes. The modular method enables reliable microscopic analysis of live yeast mutant libraries in a universally applicable format on standard microscope slides, and provides a step toward fully automated high-resolution imaging of intact yeast cells.

Profiling distinct mechanisms of tumour invasion for drug discovery: imaging adhesion, signalling and matrix turnover

Recent advances in microscopic imaging technology, fluorescent reporter reagents, 3-dimensional (3D) cell models and multiparametric image analysis have enhanced our ability to model and understand complex cell physiology. Extension of these approaches to live cell, kinetic studies allows further spatial and temporal understanding of a multitude of dynamic functional events, including tumour cell invasion. Recent in vivo and 3D in vitro studies reveal how tumour cells utilize a diverse variety of mechanisms to permit invasion through 3D tissue environments. Such high degrees of diversity and plasticity between invasion mechanisms present a significant challenge to the successful treatment of malignant cancer. This review examines how advances in time-resolved imaging has contributed to the characterization of distinct modes of invasion and their associated molecular mechanisms. Specifically, we highlight the development of fluorescent reporter molecules and their incorporation into more predictive 3D in vitro and in vivo models, to enhance mechanistic analysis of tumour invasion. We also highlight the latest advances in kinetic imaging instrumentation applicable to in vitro and in vivo models of tumour invasion. We discuss how multiparametric image analysis can be used to interpret image data generated by these approaches. We further discuss how these approaches can be integrated into drug discovery pipelines to facilitate evaluation and selection of candidate drugs and novel pharmaceutical compositions, targeting multiple invasive mechanisms.

Image-based chemical screening

Technological advances have made it feasible to conduct high-throughput small-molecule screens based on visual phenotypes of individual cells, using automated imaging and analysis. These screens are rapidly moving from being small, proof-of-principle tests to robust and widespread screens of hundreds of thousands of compounds. Automated imaging screens maximize the information obtained in an initial screen and improve the ability to select high-quality leads. In this Perspective, I highlight the key steps necessary for conducting a high-throughput image-based chemical compound screen.

Cytomics - importance of multimodal analysis of cell function and proliferation in oncology

Cancer is a highly complex and heterogeneous disease involving a succession of genetic changes (frequently caused or accompanied by exogenous trauma), and resulting in a molecular phenotype that in turn results in a malignant specification. The development of malignancy has been described as a multistep process involving self-sufficiency in growth signals, insensitivity to antigrowth signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and finally tissue invasion and metastasis. The quantitative analysis of networking molecules within the cells might be applied to understand native-state tissue signalling biology, complex drug actions and dysfunctional signalling in transformed cells, that is, in cancer cells. High-content and high-throughput single-cell analysis can lead to systems biology and cytomics. The application of cytomics in cancer research and diagnostics is very broad, ranging from the better understanding of the tumour cell biology to the identification of residual tumour cells after treatment, to drug discovery. The ultimate goal is to pinpoint in detail these processes on the molecular, cellular and tissue level. A comprehensive knowledge of these will require tissue analysis, which is multiplex and functional; thus, vast amounts of data are being collected from current genomic and proteomic platforms for integration and interpretation as well as for new varieties of updated cytomics technology. This overview will briefly highlight the most important aspects of this continuously developing field.

High-content siRNA screening

Very recent developments in instrumentation and image analysis have made microscopy applicable to high-throughput screening (HTS). For 'High-Content Screening' modern automated microscopy systems provide a throughput of up to 100,000 (confocal) images, with amazingly high resolution, of cells fluorescently stained using multiple colours that are imaged simultaneously during the screen. Image analysis tools provide multi-parametric pattern extraction and quantification on-the-fly. Big pharmaceutical companies have presented image-based screens of more than 100,000 compounds, while academia has published data on large RNA interference screens for functional genomics. Numerous whole-genome sequencing projects have been completed and published. Gene annotation is still in flux. Nevertheless, about 23,000 human genes have been reliably annotated. Additionally, gene expression array technologies and proteomics have added further data on molecules present in cells and tissues. The major challenge of the present and future is to unravel the detailed function of all these gene products and their interaction. One way to gain insight, is to design oligonucleotides that induce lack-of-function phenotypes by specifically inhibiting protein production.