Lab-chip device analysis often requires high throughput quantification of fluorescent cell images, obtained under different conditions of fluorescent intensity, illumination, focal depth, and optical magnification. Many laboratories still use manual counting - a tedious, expensive process prone to inter-observer variability. The manual counting process can be automated for fast and precise data gathering and reduced manual bias. We present a method to segment and count cells in microfluidic chips that are labeled with a single stain, or multiple stains, using image analysis techniques in Matlab and discuss its advantages over manual counting. Microfluidic based cell capturing devices for HIV monitoring were used to validate our method. Captured CD4+ CD3+ T lymphocytes were stained with DAPI, AF488-anti CD4, and AF647-anti CD3 for cell identification. Altogether 4788 (76 × 3 × 21) gray color images were obtained from devices using discarded 10 HIV infected patient whole blood samples (21 devices). We observed that the automatic method performs similarly to manual counting for a small number of cells. However, automated counting is more accurate and more than 100 times faster than manual counting for multiple-color stained cells, especially when large numbers of cells need to be quantified (>500 cells). The algorithm is fully automatic for subsequent microscope images that cover the full device area. It accounts for problems that generally occur in fluorescent lab-chip cell images such as: uneven background, overlapping cell images and cell detection with multiple stains. This method can be used in laboratories to save time and effort, and to increase cell counting accuracy of lab-chip devices for various applications, such as circulating tumor cell detection, cell detection in biosensors, and HIV monitoring devices, i.e. CD4 counts.
Quantifying the location and/or number of features in a histological section of the brain currently requires one to first, manually register a corresponding section from a tissue atlas onto the experimental section and second, count the features. No automated method exists for the first process (registering), and most automated methods for the second process (feature counting) operate reliably only in a high signal-to-noise regime. To reduce experimenter bias and inconsistencies and increase the speed of these analyses, we developed Atlas Fitter, a semi-automated, open-source MatLab-based software package that assists in rapidly registering atlas panels onto histological sections. We also developed CellCounter, a novel fully-automated cell counting algorithm that is designed to operate on images with non-uniform background intensities and low signal-to-noise ratios.
Histology; Mapping; Atlas; Analysis; Software; Cell Counting; IEG; Arc
The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.
Automated analysis; Cell segmentation; CellSegm; High-throughput; Nucleus staining; Surface staining
An automated system for differential white blood cell (WBC) counting based on morphology can make manual differential leukocyte counts faster and less tedious for pathologists and laboratory professionals. We present an automated system for isolation and classification of WBCs in manually prepared, Wright stained, peripheral blood smears from whole slide images (WSI).
A simple, classification scheme using color information and morphology is proposed. The performance of the algorithm was evaluated by comparing our proposed method with a hematopathologist's visual classification. The isolation algorithm was applied to 1938 subimages of WBCs, 1804 of them were accurately isolated. Then, as the first step of a two-step classification process, WBCs were broadly classified into cells with segmented nuclei and cells with nonsegmented nuclei. The nucleus shape is one of the key factors in deciding how to classify WBCs. Ambiguities associated with connected nuclear lobes are resolved by detecting maximum curvature points and partitioning them using geometric rules. The second step is to define a set of features using the information from the cytoplasm and nuclear regions to classify WBCs using linear discriminant analysis. This two-step classification approach stratifies normal WBC types accurately from a whole slide image.
System evaluation is performed using a 10-fold cross-validation technique. Confusion matrix of the classifier is presented to evaluate the accuracy for each type of WBC detection. Experiments show that the two-step classification implemented achieves a 93.9% overall accuracy in the five subtype classification.
Our methodology achieves a semiautomated system for the detection and classification of normal WBCs from scanned WSI. Further studies will be focused on detecting and segmenting abnormal WBCs, comparison of 20× and 40× data, and expanding the applications for bone marrow aspirates.
Automated white blood cell classification; color channels; linear discriminant analysis; morphology
BACKGROUND: Manual counting of microvessels is subjective and may lead to unacceptable interobserver variability, which may explain conflicting results. AIMS: To develop and test an automated method for microvessel counting and objective selection of the hot spot, based on image processing of whole sections, and to compare this with manual selection of a hot spot and counting of microvessels. METHODS: Microvessels were stained by CD31 immunohistochemistry in 10 cases of invasive breast cancer. The number of microvessels was counted manually in a subjectively selected hot spot, and also in the same complete tumour sections by interactive and automated image processing methods. An algorithm identified the hot spots from microvessel maps of the whole tumour section. RESULTS: No significant difference in manual microvessel counts was found between two observers within the same hot spot, and counts were significantly correlated. However, when the hot spot was reselected, significantly different results were found between repeated counts by the same observer. Counting all microvessels manually within the entire tumour section resulted in significantly different hot spots than manual counts in selected hot spots by the same observer. Within the entire tumour section no significant differences were found between the hot spots of the manual and automated methods using an automated microscope. The hot spot was found using an eight connective path search algorithm, was located at or near the border of the tumour, and (depending on the size of the hot spot) did not always contain the field with the largest number of microvessels. CONCLUSIONS: The automated counting of microvessels is preferable to the manual method because of the reduction in measurement time when the complete tumour is scanned, the greater accuracy and objectivity of hot spot selection, and the possibility of visual inspection and relocation of each measurement field afterwards.
Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer’s disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability.
A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast “gain control”, a “blob analysis” step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined ‘size’ and ‘aspect’ ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating ‘Gold-standard’ mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson’s correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach’s alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: −5.53 to 6.10) was detected between the cell counts of the two methods.
The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly.
Automated analysis; Cell counting; Cell quantification; Blob analysis; Image processing; Image analysis; Retinal ganglion cells; Retinal cell apoptosis; DARC; Glaucoma
Sensing of infectious danger by toll-like receptors (TLRs) on macrophages causes not only a reprogramming of the transcriptome but also changes in the cytoskeleton important for cell spreading and motility. Since manual determination of cell contact areas from fluorescence micrographs is very time-consuming and prone to bias, we have developed and tested algorithms for automated measurement of macrophage spreading. The two-step method combines identification of cells by nuclear staining with DAPI and cell surface staining of the integrin CD11b. Automated image analysis correlated very well with manual annotation in resting macrophages and early after stimulation, whereas at later time points the automated cell segmentation algorithm and manual annotation showed slightly larger variation. The method was applied to investigate the impact of genetic or pharmacological inhibition of known TLR signaling components. Deficiency in the adapter protein Myd88 strongly reduced spreading activity at the late time points, but had no impact early after LPS-stimulation. A similar effect was observed upon pharmacological inhibition of MEK1, the kinase activating the mitogen-activated protein kinases (MAPK) ERK1/2, indicating that ERK1/2 mediates Myd88-dependent macrophages spreading. In contrast, macrophages lacking the MAPK p38 were impaired in the initial spreading response but responded normally 8–24 h after stimulation. The dichotomy of p38 and ERK1/2 MAPK effects on early and late macrophage spreading raises the question which of the respective substrate proteins mediate(s) cytoskeletal remodeling and spreading. The automated measurement of cell spreading described here increases the objectivity and greatly reduces the time required for such investigations and is therefore expected to facilitate larger throughput analysis of macrophage spreading, e.g., in siRNA knockdown screens.
macrophage; spreading; TLR; image analysis
Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.
automated cell segmentation; disector; FARSIGHT; Fractionator; ImageJ; stereology; stem cells
Chlamydiae are obligate intracellular bacteria that propagate in the inclusion, a specific niche inside the host cell. The standard method for counting chlamydiae is immunofluorescent staining and manual counting of chlamydial inclusions. High- or medium-throughput estimation of the reduction in chlamydial inclusions should be the basis of testing antichlamydial compounds and other drugs that positively or negatively influence chlamydial growth, yet low-throughput manual counting is the common approach. To overcome the time-consuming and subjective manual counting, we developed an automatic inclusion-counting system based on a commercially available DNA chip scanner. Fluorescently labeled inclusions are detected by the scanner, and the image is processed by ChlamyCount, a custom plug-in of the ImageJ software environment. ChlamyCount was able to measure the inclusion counts over a 1-log-unit dynamic range with a high correlation to the theoretical counts. ChlamyCount was capable of accurately determining the MICs of the novel antimicrobial compound PCC00213 and the already known antichlamydial antibiotics moxifloxacin and tetracycline. ChlamyCount was also able to measure the chlamydial growth-altering effect of drugs that influence host-bacterium interaction, such as gamma interferon, DEAE-dextran, and cycloheximide. ChlamyCount is an easily adaptable system for testing antichlamydial antimicrobials and other compounds that influence Chlamydia-host interactions.
Manual counting of bacterial colony forming units (CFUs) on agar plates is laborious and error-prone. We therefore implemented a colony counting system with a novel segmentation algorithm to discriminate bacterial colonies from blood and other agar plates.
A colony counter hardware was designed and a novel segmentation algorithm was written in MATLAB. In brief, pre-processing with Top-Hat-filtering to obtain a uniform background was followed by the segmentation step, during which the colony images were extracted from the blood agar and individual colonies were separated. A Bayes classifier was then applied to count the final number of bacterial colonies as some of the colonies could still be concatenated to form larger groups.
To assess accuracy and performance of the colony counter, we tested automated colony counting of different agar plates with known CFU numbers of S. pneumoniae, P. aeruginosa and M. catarrhalis and showed excellent performance.
The White Blood Cell (WBC) differential count yields clinically relevant information about health and disease. Currently, pathologists manually annotate the WBCs, which is time consuming and susceptible to error, due to the tedious nature of the process. This study aims at automation of the Differential Blood Count (DBC) process, so as to increase productivity and eliminate human errors.
Materials and Methods:
The proposed system takes the peripheral Leishman blood stain images as the input and generates a count for each of the WBC subtypes. The digitized microscopic images are stain normalized for the segmentation, to be consistent over a diverse set of slide images. Active contours are employed for robust segmentation of the WBC nucleus and cytoplasm. The seed points are generated by processing the images in Hue-Saturation-Value (HSV) color space. An efficient method for computing a new feature, ‘number of lobes,’ for discrimination of WBC subtypes, is introduced in this article. This method is based on the concept of minimization of the compactness of each lobe. The Naive Bayes classifier, with Laplacian correction, provides a fast, efficient, and robust solution to multiclass categorization problems. This classifier is characterized by incremental learning and can also be embedded within the database systems.
An overall accuracy of 92.45% and 92.72% over the training and testing sets has been obtained, respectively.
Thus, incremental learning is inducted into the Naive Bayes Classifier, to facilitate fast, robust, and efficient classification, which is evident from the high sensitivity achieved for all the subtypes of WBCs.
Incremental learning; naive bayes classifier; number of lobes; white blood cells classification
live cell imaging is a useful tool to monitor cellular activities in living systems. It is often necessary in cancer research or experimental research to quantify the dividing capabilities of cells or the cell proliferation level when investigating manipulations of the cells or their environment. Manual quantification of fluorescence microscopic image is difficult because human is neither sensitive to fine differences in color intensity nor effective to count and average fluorescence level among cells. However, auto-quantification is not a straightforward problem to solve. As the sampling location of the microscopy changes, the amount of cells in individual microscopic images varies, which makes simple measurement methods such as the sum of stain intensity values or the total number of positive stain within each image inapplicable. Thus, automated quantification with robust cell segmentation techniques is required.
An automated quantification system with robust cell segmentation technique are presented. The experimental results in application to monitor cellular replication activities show that the quantitative score is promising to represent the cell replication level, and scores for images from different cell replication groups are demonstrated to be statistically significantly different using ANOVA, LSD and Tukey HSD tests (p-value < 0.01). In addition, the technique is fast and takes less than 0.5 second for high resolution microscopic images (with image dimension 2560 × 1920).
A robust automated quantification method of live cell imaging is built to measure the cell replication level, providing a robust quantitative analysis system in fluorescent live cell imaging. In addition, the presented unsupervised entropy based cell segmentation for live cell images is demonstrated to be also applicable for nuclear segmentation of IHC tissue images.
Leukocyte differentials are an important component of clinical care. Morphologic assessment of peripheral blood smears (PBS) may be required to accurately classify leukocytes. However, manual microscopy is labor intensive. The CellaVision DM96 is an automated system that acquires digital images of leukocytes on PBS, pre-classifies the cell type, and displays them on screen for a Technologist or Pathologist to approve or reclassify. Our study compares the results of the DM96 with manual microscopy.
Three hundred and fifty-nine PBS were selected and assessed by manual microscopy with a 200 leukocyte cell count. They were then reassessed using the CellaVision DM96 with a 115 leukocyte cell count including reclassification when necessary. Correlation between the manual microscopy results and the CellaVision DM96 results was calculated for each cell type.
The correlation coefficients (r2) range from a high of 0.99 for blasts to a low of 0.72 for metamyelocytes.
The correlation between the CellaVision DM96 and manual microscopy was as good or better than the previously published data. The accuracy of leukocyte classification depended on the cell type, and in general, there was lower correlation for rare cell types. However, the correlation is similar to previous studies on the correlation of manual microscopy with an established reference result. Therefore, the CellaVision DM96 is appropriate for clinical implementation.
Digital pathology; hematopathology; peripheral blood smear
The main interest of this research project is to promote automation in performing preoperative planning for hip joint replacement surgery using a special medical image viewing software, ViewPro™. Preoperative planning is performed to carefully prepare the surgery and to accurately select the hip implants. The first step of preoperative planning is to calibrate the x-ray image to adjust the magnification factor. A femoral head implant is used as magnification factor reference. Automation is introduced by developing an algorithm to automatically detect this reference object in the image. The algorithm used for performing the automatic detection of the reference object is gradient vector flow (GVF) snake. A study has been performed to compare the newly developed semiautomatic algorithm to the old manual calibration algorithm. The results show a close relation between the two algorithms (less than 1% of average relative difference). It is concluded that the developed semiautomated algorithm can be used as an alternative for performing the manual calibration.
Preoperative planning; total hip arthroplasty; snake algorithm; gradient vector flow
An automated colony counter was found to readily detect surface and subsurface bacterial colonies of 0.3-mm size or greater with a high degree of precision. On a logarithmic scale, counting efficiency consistently ranged from 89 to 95% of corresponding manual count determinations for plates containing up to 1,000 colonies. In routine application, however, automated plate counts up to approximately 400 colonies were selected as a more practical range for operation. The automated counter was easily interfaced with an automated data acquisition system.
We describe here an automated system for the counting of multiple samples of double-stained microbial cells on sections of membrane filters. The application integrates an epifluorescence microscope equipped with motorized z-axis drive, shutters, and filter wheels with a scanning stage, a digital camera, and image analysis software. The relative abundances of specific microbial taxa are quantified in samples of marine picoplankton, as detected by fluorescence in situ hybridization (FISH) and catalyzed reporter deposition. Pairs of microscopic images are automatically acquired from numerous positions at two wavelengths, and microbial cells with both general DNA and FISH staining are counted after object edge detection and signal-to-background ratio thresholding. Microscopic fields that are inappropriate for cell counting are automatically excluded prior to measurements. Two nested walk paths guide the device across a series of triangular preparations until a user-defined number of total cells has been analyzed per sample. A backup autofocusing routine at incident light allows automated refocusing between individual samples and can reestablish the focal plane after fatal focusing errors at epifluorescence illumination. The system was calibrated to produce relative abundances of FISH-stained cells in North Sea samples that were comparable to results obtained by manual evaluation. Up to 28 preparations could be analyzed within 4 h without operator interference. The device was subsequently applied for the counting of different microbial populations in incubation series of North Sea waters. Automated digital microscopy greatly facilitates the processing of numerous FISH-stained samples and might thus open new perspectives for bacterioplankton population ecology.
Full automation in medical microscopy has been accomplished in the field of clinical determination of the white blood cell differential count. Manufacture of differential counting microscopes commenced in 1974, and approximately 1,000 of these robots are now in the field. They analyze images of human white blood cells, red blood cells, and platelets at the global rate of approximately 100,000 slides per day. This incredible throughout represents automated image analysis and pattern recognition at the rate of 5 billion images per year and represents a major accomplishments in the application of machine vision in medicine. In other areas, such as cytology and cytogenetics, automated computer vision is still in the research phase. This paper discusses the state of the art in blood smear analysis automation and in other related areas including multi-resolution microscopy where images are currently being generated over a 64:1 magnification containing from one-quarter megapixel to one gigapixel in full color.
We present a very simple procedure yielding high-contrast images of adherent, confluent cells such as human neuroblastoma (SH-EP) cells by ordinary bright-field microscopy. Cells are illuminated through a color filter and a pinhole aperture placed between the condenser and the cell culture surface. Refraction by each cell body generates a sharp, bright spot when the image is defocused. The technique allows robust, automatic cell counting from a single bright-field image in a wide range of focal positions; it does this via free, readily available image-analysis tools. Contrast may be enhanced by swelling cell bodies by brief incubation in PBS. The procedure was benchmarked against manual counting and automated counting of fluorescently labeled cell nuclei.. Counts from day-old and freshly seeded plates were compared in a range of densities, from sparse to densely overgrown. On average bright-field images produced the same counts as fluorescent images, with less than 5% error. This method will allow routine cell counting using a plain bright-field microscope, absent cell-line modification or cell staining.
Brightfield; microscopy; fluorescence; cell culture; counting; pinhole; monochomatic; PBS; defocusing
Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs.
The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.
Phase contrast microscopy (PCM) is a widely used analytical method for airborne asbestos, but it is unable to distinguish asbestos from non-asbestos fibers and requires time-consuming and laborious manual counting of fibers. Previously, we developed a high-throughput microscopy (HTM) method that could greatly reduce human intervention and analysis time through automated image acquisition and counting of fibers. In this study, we designed a dual-mode HTM (DM-HTM) device for the combined reflection and fluorescence imaging of asbestos, and automated a series of built-in image processing commands of ImageJ software to test its capabilities. We used DksA, a chrysotile-adhesive protein, for selective detection of chrysotile fibers in the mixed dust-free suspension of crysotile and amosite prepared in the laboratory. We demonstrate that fluorescently-stained chrysotile and total fibers can be identified and enumerated automatically in a high-throughput manner by the DM-HTM system. Combined with more advanced software that can correctly identify overlapping and branching fibers and distinguish between fibers and elongated dust particles, the DM-HTM method should enable fully automated counting of airborne asbestos.
asbestos; chrysotile; DksA; high-throughput microscopy; dual-mode imaging; reflection; fluorescence; image processing and analysis; automated counting
Filopodia are small cellular projections that help cells to move through and sense their environment. Filopodia play crucial roles in processes such as development and wound-healing. Also, increases in filopodia number or size are characteristic of many invasive cancers and are correlated with increased rates of metastasis in mouse experiments. Thus, one possible route to developing anti-metastatic therapies is to target factors that influence the filopodia system. Filopodia can be detected by eye using confocal fluorescence microscopy, and they can be manually annotated in images to quantify filopodia parameters. Although this approach is accurate, it is slow, tedious and not entirely objective. Manual detection is a significant barrier to the discovery and quantification of new factors that influence the filopodia system.
Here, we present FiloDetect, an automated tool for detecting, counting and measuring the length of filopodia in fluorescence microscopy images. The method first segments the cell from the background, using a modified triangle threshold method, and then extracts the filopodia using a series of morphological operations. We verified the accuracy of FiloDetect on Rat2 and B16F1 cell images from three different labs, showing that per-cell filopodia counts and length estimates are highly correlated with the manual annotations. We then used FiloDetect to assess the role of a lipid kinase on filopodia production in breast cancer cells. Experimental results show that PI4KIII β expression leads to an increase in filopodia number and length, suggesting that PI4KIII β is involved in driving filopodia production.
FiloDetect provides accurate and objective quantification of filopodia in microscopy images, and will enable large scale comparative studies to assess the effects of different genetic and chemical perturbations on filopodia production in different cell types, including cancer cell lines.
Filopodia; Morphology; FiloDetect; Microscopy image
Two automated counters, the H1 (Technicon) and the H6000 (Technicon), which count 10,000 cells per sample, were compared and used to examine the clinical relevance of the additional haematological information now provided to the rheumatologist in three groups of patients--38 with rheumatoid arthritis (RA), 41 with ankylosing spondylitis (AS), and 35 with systemic lupus erythematosus (SLE). The two machines agreed in their estimations of the main indices (haemoglobin, red blood cell count, and white blood cell count), but estimations of platelet count and volume were significantly lower on the H6000 machine, as were mean cell haemoglobin and monocyte count, whereas packed cell volume and red cell distribution width were higher. As expected, both machines identified pancytopenia among the group with SLE, while low haemoglobin and high platelet count were found particularly among patients with RA and AS respectively. Additional information available from these counters showed marked variability in red cell size in SLE, and also of haemoglobin content, which is only measured on the newer H1 machine. Flags for microcythaemia, anisochromasis, and white cell noise (usually due to nucleated red cells) were all more common in SLE. Interpretation of results was complicated by the inevitable difference in age and sex distribution among the disease groups, and identification of active disease was also limited by the effect of drugs. In conclusion, the increasingly widespread use of automated counters as part of the routine haematological service may provide the rheumatologist with useful information, but, as always, care should be taken in the interpretation of indices in patients receiving non-steroidal or second line agents, and also in extrapolating results from one machine to another when they are updated or when patients are monitored at more than one centre.
Aims—To arrive at a reproducible sampling technique for counting apoptotic cells in tissue sections of invasive breast cancer that can serve as a protocol for further clinical studies.
Methods—In 4 μm thick haematoxylin and eosin stained tissue sections of 12 breast cancers, apoptotic cells, recognised by strict morphological criteria, were counted in consecutive fields of vision at ×1000 magnification in a marked area in the most poorly differentiated region of tumour. These counts were regarded as the gold standard. Subsequently, in a systematic sampling experiment, the number of fields that had to be counted to derive an acceptable coefficient of variation (CV) was determined. To compare counts at different magnifications, all fields of vision were also counted at ×630 and ×400. The intra- and inter-observer reproducibility was tested by repeated measurements at these magnifications in 10 systematically selected fields of vision.
Results—Apoptosis seemed to be a rare event, affecting, on average, about 1% of tumour cells. Noticeable clustering of apoptotic cells was observed. The systematic sampling experiment showed that at ×1000 magnification, the CV was improved by counting up to 20 fields. When comparing ×400 and ×630 magnifications with the ×1000 magnification, the correlation coefficients were 0.88 and 0.87, respectively. However, the lower magnifications yielded lower counts. With regard to reproducibility, the intra-observer correlation coefficient was 0.91 at ×630 and 0.76 at ×400. The inter-observer correlation coefficient was 0.77 at ×630 and 0.68 at ×400.
Conclusions—Apoptotic cells can be counted readily in haematoxylin and eosin stained tissue sections. However, a systematic sampling protocol must be followed and cells should be counted at a relatively high magnification to obtain acceptable reproducibility. The suggested protocol will permit further correlative and prognostic studies and the monitoring of the effects of treatment.
breast cancer; apoptosis; sampling; reproducibility
Currently established methods to identify viable and non-viable cells of cyanobacteria are either time-consuming (eg. plating) or preparation-intensive (eg. fluorescent staining). In this paper we present a new and fast viability assay for unicellular cyanobacteria, which uses red chlorophyll fluorescence and an unspecific green autofluorescence for the differentiation of viable and non-viable cells without the need of sample preparation.
The viability assay for unicellular cyanobacteria using red and green autofluorescence was established and validated for the model organism Synechocystis sp. PCC 6803. Both autofluorescence signals could be observed simultaneously allowing a direct classification of viable and non-viable cells. The results were confirmed by plating/colony count, absorption spectra and chlorophyll measurements. The use of an automated fluorescence microscope and a novel ImageJ based image analysis plugin allow a semi-automated analysis.
The new method simplifies the process of viability analysis and allows a quick and accurate analysis. Furthermore results indicate that a combination of the new assay with absorption spectra or chlorophyll concentration measurements allows the estimation of the vitality of cells.
The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.