Regenerative and predictive medicine of cardiovascular disease: The 9th Leipziger workshop and the 2nd international workshop on slide based cytometry

Comparison between two programs for image analysis, machine learning and subsequent classification

In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cytometry was considered the gold standard, various studies found that the results of flow cytometry and LSC generated comparable results. One of the first programs for image analysis that included morphological parameters was ImageJ, published in 1997. One of the newer programs for image analysis that is not limited to fluorescence images is the free software CellProfiler. In 2008, the same group published a new software, CellProfiler Analyst. One part of CellProfiler Analyst is a supervised machine-learning-based classifier that allows users to conduct imaging-based diagnoses, e.g., cellular diagnosis based on morphology. Another relatively new, free software for image analysis is QuPath. The aim of the present study was to compare two free programs for conducting image analysis, CellProfiler and QuPath, and the subsequent classification based on machine learning. For this study, images of renal tissue were analyzed, and the identified objects were classified. The same images were loaded in both software programs. Advanced statistical analysis was used to compare the two methods. The Bland-Altman assay showed that all of the differences were within the mean ± 1.96 * standard deviation, i.e., the differences are normally distributed, and the software programs are comparable. For the analyzed samples (renal tissue stained with HIF and TUNEL), the use of QuPath was easier because it offers image analysis without a previous processing of the images (e.g., conversion to grayscale, inverted intensities) and an unsupervised machine learning process.

Automatic detection of hypoxia in renal tissue stained with HIF-1alpha

OBJECTIVE

The objective of this study was the identification of the stain HIF-alpha using the Image Cytometry, and to help to count the positive cells (with HIF-alpha) and the negative cells (without HIF-alpha) from the same sample.

METHOD

17 images of renal tissues from male rats of Winstar lineage; overall, there were 12.587 objects (cells) in the images for analysis. The acquired images were then analyzed through the free softwares CellProfiler (version 2.1.1) and CellProfiler Analyst (version 2.0). In the software CellProfiler Anlyst, there was a separation with the classes of the object, using a classifier, and the classes were: 1) class with HIF-alpha and 2) class without HIF-alpha.

RESULTS

With the data obtained through Score All, it was possible to calculate the percentage of cells that had HIF-alpha; out of 12.587 objects of the sample, 6.773 (54%) had HIF-alpha and 5.814 (46%) did not have HIF-alpha. Data of sensibility 0.90, specificity 0.84 and standard deviation 0.10 and 0.12.

CONCLUSION

The research shows that the free software CellProfiler, through the light microscope, was able to identify the stains, perform the machine's learning, and subsequently count and separate cells from distinct classes (with and without the stain of HIF-alpha).

Cytomics and nanobioengineering

The finding that an individual's genome differs as much as by many million variants from that of the human reference assembly diminished the great enthusiasm that every disease could be predicted based on nucleotide polymorphisms. Even individual cells of an organ may be specifically equipped to perform specific tasks and that the information of individual cells in a cell system is key information to understand function or dysfunction. Therefore, cytomics received great attention during the last years as it allows to quantitatively and qualitatively analyzing great number of individual cells, cell constituents, and of their intracellular and functional interactions in a cellular system and also giving the concept of analysis of these data.Exhaustive data extraction from multiparametric assays and multiple tests are the prerequisite for prediction of drug toxicity. Cytomics, as novel approach for unsupervised data analysis give a chance to find the most predictive parameters, which describe best the toxicity of a chemical. Cytomics is intrinsically connected to drug development and drug discovery.Focused on small structures, nanobioengineering is the ideal partner of cytomics, the systems biological discipline for cell population analysis. Realizing the idea "from the molecule to the patient" develops and offers chemical compounds, proteins, and other biomolecules, cells as well as tissues as instruments and products for a wide variety of biotechnological and biomedical applications.The integrative nanobioengineering combining different disciplines of nanotechnology will promote the development of innovative therapies and diagnostic methods. It can improve the precision of the measurements with focus on single cell analysis. By nanobioengineering and whole body imaging techniques, cytomics covers the field from molecules through bacterial cells, eukaryotic tissues, and organs to small animal live analysis. Toxicological testing and medical drug development are currently strongly broadening. It harbors the promise to substantially impact on various fields of biomedicine, drug discovery, and predictive medicine.As the number of scientific data is rising exponentially, new data analysis tools and strategies like cytomics and nanobioengineering take a lead and get closer to application. Bionanoengineering may strongly support the quantitative data supply, thus strengthening the rational for cytomics approach.

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.

Slide-based cytometry for cytomics--a minireview

In the postgenomic era, to gain the most detailed quantitative data from biological specimens has become increasingly important in the emerging new fields of high-content and high-throughput single-cell analysis for systems biology and cytomics. Areas of research and diagnosis with the demand to virtually measure "anything" in the cell include immunophenotyping, rare cell detection and characterization in the case of stem cells and residual tumor cells, tissue analysis, and drug discovery. Systemic analysis is also a prerequisite for predictive medicine by genomics, proteomics, and cytomics. This issue of Cytometry Part A is dedicated to innovative concepts of system wide single cells analysis and manipulation, new technologies, data analysis and display, and, finally, quality assessment. The manuscripts to these chapters are provided by cutting edge experts in the fields. This overview will briefly highlight the most important aspects of this continuously developing field.

Systems biology and clinical cytomics: The 10th Leipziger Workshop and the 3rd International Workshop on Slide-Based Cytometry, Leipzig, Germany, April 2005

Despite very significant technical and software improvements in flow cytometry (FCM) since the 1980's, the demand for a cytometric technology combining both quantitative cell analysis and morphological documentation in Cytomics became evident. Improvements in microtechnology and computing permit nowadays similar quantitative and stoichiometric single cell-based high-throughput analyses by microscopic instruments, like Slide-Based Cytometry (SBC). SBC and related techniques offer unique tools to perform complex immunophenotyping, thereby enabling diagnostic procedures during early disease stages. Multicolor or polychromatic analysis of cells by SBC is of special importance not only as a cytomics technology platform but also because of low quantities of required reagents and biological material. The exact knowledge of the location of each cell on the slide permits repetitive restaining and reanalysis of specimens. Various separate measurements of the same specimen can be ultimately fused to one database increasing the information obtained per cell. Relocation and optical evaluation of cells as typical SBC feature, can be of integral importance for cytometric analysis, since artifacts can be excluded and morphology of measured cells can be documented. Progress in cell analytic: In the SBC, new horizons can be opened by the new techniques of structural and functional analysis with the high resolution from intracellular and membrane (confocal microscopy, nanoscopy, total internal fluorescence microscopy (TIRFM), and tissue level (tissomics), to organ and organism level (in vivo cytometry, optical whole body imaging). Predictive medicine aims at the detection of changes in patient's state prior to the manifestation of the disease or the complication. Such instances concern immune consequences of surgeries or noninfectious posttraumatic shock in intensive care patients or the pretherapeutic identification of high risk patients in cancer cytostatic therapy. Preventive anti-infectious or anti-shock therapy as well as curative chemotherapy in combination with stem cell transplantation may provide better survival chances for patient at concomitant cost containment. Predictive medicine-guided optimization of therapy could lead to individualized medicine that gives significant therapeutic effect and may lower or abrogate potential therapeutic side effects. The 10th Leipziger Workshop combined with the 3rd International Workshop on SBC aimed to offer new methods in Image- and Slide-Based Cytometry for solutions in clinical research. It moved towards practical applications in the clinics and the clinical laboratory. This development will be continued in 2006 at the upcoming Leipziger Workshop and the International Workshop on Slide-Based Cytometry.

Novel aspects of systems biology and clinical cytomics

The area of Cytomics and Systems Biology became of great impact during the last years. In some fields of the leading cytometric techniques it represents the cutting edge today. Many different applications/variations of multicolor staining were developed for flow- or slide-based cytometric analysis of suspensions and sections to whole animal analysis. Multispectral optical imaging can be used for studying immunological and tumorigenic processes. New methods resulted in the establishment of lipidomics as the systemic research of lipids and their behavior. All of these development push the systemic approach of the analysis of biological specimens to enhance the outcome in the clinic and in drug discovery programs.