Comparison of immunophenotyping by slide-based cytometry and by flow 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.

Immunophenotyping of cerebrospinal fluid cells by Chipcytometry

Background

The gold standard in cerebrospinal fluid (CSF) cell immunophenotyping is flow cytometry. Nevertheless, the small amount of CSF cells and the invasive character of lumbar puncture limit the spectrum of possible investigation. Chipcytometry, a modified approach to slide-based cytometry, might be a useful tool for CSF analysis due to the possibility of iterative staining, imaging, and bleaching cycles. The aim of this study was to compare flow cytometric leukocyte subset analysis with Chipcytometry comparing the percentage distribution of distinct cell populations and the T-cell CD4:CD8 ratio. Moreover, this study investigated the interpretability of chips loaded with CSF cells and examined the applicability of Chipcytometry in clinical practice.

Methods

375 CSF samples from 364 patients were analyzed by Chipcytometry using an automated upright microscope. Cell surface molecules were stained using fluorescence-labeled monoclonal antibodies. For cross-validation experiments, flow cytometry data of six patients were analyzed and matched with Chipcytometry data.

Results

Our experiments showed a better agreement examined by Bland-Altman analysis for samples with CSF pleocytosis than for normocellular CSF samples. Data were more consistent for B cells and CD4:CD8 ratio than for T cells and monocytes. Advantages of Chipcytometry compared to flow cytometry are that cells once fixated can be analyzed for up to 20 months with additional markers at any time. The clinical application of Chipcytometry is demonstrated by two illustrative case reports. However, the low amount of CSF cells limits the analysis of normocellular CSF samples, as in our cohort only 11.7% of respectively loaded chips had sufficient cell density for further investigation compared to 59.8% of all chips loaded with samples with elevated cell counts (≥ 5/μl). Varying centrifuge settings, tube materials and resuspension technique were not able to increase the cell yield.

Conclusion

In summary, the results demonstrate the great potential of Chipcytometry of CSF cells for both scientific questions and routine diagnostic. A new chip design optimized to meet the requirements of CSF would greatly enhance the value of this method. Cross-validation results need to be confirmed in a larger cohort.

Fast and simple tool for the quantification of biofilm-embedded cells sub-populations from fluorescent microscopic images

Fluorescent staining is a common tool for both quantitative and qualitative assessment of pro- and eukaryotic cells sub-population fractions by using microscopy and flow cytometry. However, direct cell counting by flow cytometry is often limited, for example when working with cells rigidly adhered either to each other or to external surfaces like bacterial biofilms or adherent cell lines and tissue samples. An alternative approach is provided by using fluorescent microscopy and confocal laser scanning microscopy (CLSM), which enables the evaluation of fractions of cells subpopulations in a given sample. For the quantitative assessment of cell fractions in microphotographs, we suggest a simple two-step algorithm that combines single cells selection and the statistical analysis. To facilitate the first step, we suggest a simple procedure that supports finding the balance between the detection threshold and the typical size of single cells based on objective cell size distribution analysis. Based on a series of experimental measurements performed on bacterial and eukaryotic cells under various conditions, we show explicitly that the suggested approach effectively accounts for the fractions of different cell sub-populations (like the live/dead staining in our samples) in all studied cases that are in good agreement with manual cell counting on microphotographs and flow cytometry data. This algorithm is implemented as a simple software tool that includes an intuitive and user-friendly graphical interface for the initial adjustment of algorithm parameters to the microphotographs analysis as well as for the sequential analysis of homogeneous series of similar microscopic images without further user intervention. The software tool entitled BioFilmAnalyzer is freely available online at https://bitbucket.org/rogex/biofilmanalyzer/downloads/.

Considerations for point-of-care diagnostics: evaluation of acridine orange staining and postprocessing methods for a three-part leukocyte differential test

There exists a broad range of techniques that can be used to classify and count white blood cells in a point-of-care (POC) three-part leukocyte differential test. Improvements in lenses, light sources, and cameras for image-based POC systems have renewed interest in acridine orange (AO) as a contrast agent, whereby subpopulations of leukocytes can be differentiated by colorimetric analysis of AO fluorescence emission. We evaluated the effect on test accuracy using different AO staining and postprocessing methods in the context of an image-based POC colorimetric cell classification scheme. Thirty blood specimens were measured for percent cell counts using our POC system and a conventional hematology analyzer for comparison. Controlling the AO concentration used during whole-blood staining, the incubation time with AO, and the colorimetric ratios among the three population of leukocytes yielded a percent deviation of 0.706%, ? 1.534 % , and ? 0.645 % for the lymphocytes, monocytes, and granulocytes, respectively. Overall, we demonstrated that a redshift in AO fluorescence was observed at elevated AO concentrations, which lead to reproducible inaccuracy of cell counts. This study demonstrates there is a need for a strict control of the AO staining and postprocessing methods to improve test accuracy in these POC systems.

Assessment of Cell Viability with Single-, Dual-, and Multi-Staining Methods Using Image Cytometry

The ability to accurately measure cell viability is important for any cell-based assay. Traditionally, viability measurements have been performed using the trypan blue exclusion method on a hemacytometer, which allows researchers to visually distinguish viable from nonviable cells. While the trypan blue method can work for cell lines or primary cells that have been rigorously purified, in more complex samples such as PBMCs, bone marrow, whole blood, or any sample with low viability, this method can lead to errors. In recent years, advances in optics and fluorescent dyes have led to the development of automated benchtop image-based cell counters for rapid cell concentration and viability measurement. In this work, we demonstrate the use of image-based cytometry for cell viability detection using single-, dual-, or multi-stain techniques. Single-staining methods using nucleic acid stains such as EB, PI, 7-AAD, DAPI, SYTOX Green, and SYTOX Red, and enzymatic stains such as CFDA and Calcein AM, were performed. Dual-staining methods using AO/PI, CFDA/PI, Calcein AM/PI, Hoechst/PI, Hoechst/DRAQ7, and DRAQ5/DAPI that enumerate viable and nonviable cells were also performed. Finally, Hoechst/Calcein AM/PI was used for a multi-staining method. Fluorescent viability staining allows exclusion of cellular debris and nonnucleated cells from analysis, which can eliminate the need to perform purification steps during sample preparation and improve efficiency. Image cytometers increase speed and throughput, capture images for visual confirmation of results, and can greatly simplify cell count and viability measurements.

Objective detection of apoptosis in rat renal tissue sections using light microscopy and free image analysis software with subsequent machine learning: Detection of apoptosis in renal tissue

OBJECTIVE

The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation.

METHODS

A total of 17 images of kidney histological slide samples from male Wistar rats were used. The slides were photographed using an Axio Zeiss Vert.A1 microscope with a 40x objective lens coupled with an Axio Cam MRC Zeiss camera and Zen 2012 software. The images were analyzed using CellProfiler (version 2.1.1) and CellProfiler Analyst open-source software.

RESULTS

Out of the 10,378 objects, 4970 (47,9%) were identified as TUNEL positive, and 5408 (52,1%) were identified as TUNEL negative. On average, the sensitivity and specificity values of the machine learning approach were 0.80 and 0.77, respectively.

CONCLUSION

Image cytometry provides a quantitative analytical alternative to the more traditional qualitative methods more commonly used in studies.

Rapid image-based cytometry for comparison of fluorescent viability staining methods

The ability to accurately measure cell viability is important for any cell-based research. Traditionally, viability measurements have been performed using trypan blue exclusion method on hemacytometer, which allowed researchers to visually distinguish viable from nonviable cells. However, the trypan blue method is often limited to only cell lines or primary cells that have been rigorously purified. In the recent years, small desktop image-based cell counters have been developed for rapid cell concentration and viability measurement due to advances in imaging and optics technologies as well as novel fluorescent stains. In this work, we employed the Cellometer image-based cytometer to demonstrate the ability to simplify viability detection compared to the current methods. We compared various fluorescence viability detection methods using single- or dual-staining technique. Single-staining method using nucleic acid stains including ethidium bromide, propidium iodide, 7AAD, DAPI, Sytox Green and Sytox Red, and enzymatic stains including CFDA and Calcein AM were performed. All stains produced comparable results to trypan blue exclusion method for cell line samples. Dual-staining method using AO/PI, CFDA/PI, Calcein AM/PI and Hoechst 33342/PI that enumerates viable and non-viable cells was tested on primary cell samples with high debris contents. This method allowed exclusion of cellular debris and non-nucleated cells from analysis, which can eliminate the need to perform purification step during sample preparation, and improves the efficiency of viability detection method. Overall, these image-based fluorescent cell counters can simplify assay procedures as well as capture images for visual confirmation.

An automated microfluidic sample preparation system for laser scanning cytometry

Laser scanning cytometry (LSC) is emerging as a clinical tool. In one application a "Clatch" slide, named after the inventor, is used in conjunction with LSC for cell surface marker immunophenotyping of patient samples. The slide requires time consuming and laborious pipetting steps, making a test tedious and prone to handling errors. The Clatch slide also uses a significant number of cells, limiting the number of analyses on paucicellular samples. This paper presents an automated microfluidic system consisting of a control circuit, a microfluidic system, and an aluminum frame, capable of performing immunophenotyping procedures. This prototype system reduces 36 pipetting steps to 1, reduces the amount of cell sample from 180 μL to 56 μL, and shortens the time used by technicians.

Cellometer vision as an alternative to flow cytometry for cell cycle analysis, mitochondrial potential, and immunophenotyping

Cell phenotyping and cell cycle analysis are two commonly used assays in both clinical diagnosis and biomedical research. Cell phenotyping by identifying different biomarkers is essential for the diagnosis of hematologic malignancy, sub-classifying diseases, monitoring response to treatment, predicting prognosis, detecting rare cell populations and residual malignant cells. Cell cycle analysis distinguishes cells in different phases of cell cycle and is often used to determine the cellular response to drugs and biological stimulations. These assays have been traditionally carried out by sensitive fluorescence detection methods such as flow cytometry and laser scanning cytometry for fluorescence-based cell population analysis. However, these instruments remain relatively expensive, large in size, and require a considerable amount of maintenance, which may not be feasible for smaller research groups that do not have access to these equipments or field clinics that require quick diagnostic results on site. Recently, a small portable imaging cytometry system (Cellometer Vision) has been developed by Nexcelom Bioscience LLC (Lawrence, MA) for automated cell concentration and viability measurement using bright-field and fluorescent imaging methods. Here we report new applications of the Cellometer imaging cytometry for fluorescence-based cell population analysis and compared them with conventional flow cytometry. Cell population analysis assays such as immunophenotyping, cell cycle, and mitochondrial membrane potential detection methods have not yet been reported for the Cellometer Vision system. Using this imaging cytometry method for fluorescence-based assays that are typically done by flow cytometry offers a quick, simple, and inexpensive alternative method for biomedical research, which may be beneficial for smaller research laboratories and clinics.

Cellular analysis by open-source software for affordable cytometry

Image cytometry is an important technique in affordable healthcare and cellular research. Some efforts toward establishing a personal, low-cost cytometer have been described in the literature. However, a self-assembled fluorescence microscope requires software for cytometric analysis. There are some open-source image-based software analysis applications available. However, for a quantitative analysis of images, software that can generate data comparable to those of previously evaluated cytometric analyses programs is required. Hence, the aim of this study is to compare results of a commercially available image cytometry program to data obtained using the open-source software CellProfiler (CP). Leukocytes and fluorescent bead images obtained using a Laser Scanning Cytometer were analyzed by CP and the results compared with those of conventional cytometric analyses' programs. Algorithms were developed enabling the analysis of leukocytes and beads by CP. CP provided similar results to those obtained by the cytometer software. Hallmark parameters, including cell count and fluorescence intensity, revealed a high correlation in the analysis of both programs. Therefore, CP is appropriate for cellular analysis on a self-assembled microscope, thereby enabling affordable cytometry.

Laser scanning cytometry: capturing the immune system in situ

Until recently, it has not been possible to image and functionally correlate the key molecular and cellular events underpinning immunity and tolerance in the intact immune system. Certainly, the field has been revolutionized by the advent of tetramers to identify physiologically relevant specificities of T cells, and the introduction of models in which transgenic T-cell receptor and/or B-cell receptor-bearing lymphocytes are adoptively transferred into normal mice and can then be identified by clonotype-specific antibodies using flow cytometry in vitro, or immunohistochemistry ex vivo. However, these approaches do not allow for quantitative analysis of the precise anatomical, phenotypic, signaling, and functional parameters required for dissecting the development of immune responses in health and disease in vivo. Traditionally, assessment of signal transduction pathways has required biochemical or molecular biological analysis of isolated and highly purified subsets of immune system cells. Inevitably, this creates potential artifacts and does not allow identification of the key signaling events for individual cells present in their microenvironment in situ. These difficulties have now been overcome by new methodologies in cell signaling analysis that are sufficiently sensitive to detect signaling events occurring in individual cells in situ and the development of technologies such as laser scanning cytometry that provide the tools to analyze physiologically relevant interactions between molecules and cells of the innate and the adaptive immune system within their natural environmental niche in vivo.

Clinical applications of slide-based cytometry--an update

Slide-based cytometric approaches open the possibility to obtain quantitative and objective data from specimens that so far have not been accessible to this kind of analysis. In this review, we will highlight the specific advantages of slide-based cytometry (SBC) and show the applications that have been established for clinical samples. Focuses are cytomic analyses of oncological and hematological samples where the slide-based concept turned out to open new dimensions in understanding underlying cellular networks. We review the recent literature and point out future applications.

A versatile platform for comprehensive chip-based explorative cytometry

Analysis of the immense complexity of the immune system is increasingly hampered by technical limitations of current methodologies, especially for multiparameter- and functional analysis of samples containing small numbers of cells. We here present a method, which is based on the stepwise functional manipulation and analysis of living immune cells that are self-immobilized within microfluidic chips using automated epifluorescence microscopy overcoming current limitations for comprehensive immunophenotyping. Crossvalidation with flow cytometry revealed a 10-fold increased sensitivity and a comparable specificity. By using small sample volumes and cell numbers (2-10 microl, down to 20,000 cells), we were able to analyze a virtually unlimited number of intracellular and surface markers even on living immune cells. We exemplify the scientific and diagnostic potential of this method by (1) identification and phenotyping of rare cells, (2) comprehensive analysis of very limited sample volume, and (3) deep immunophenotyping of human B-cells after in vitro differentiation. Finally, we propose an informatic model for annotation and comparison of cytometric data by using an ontology-based approach. The chip-based cytometry introduced here turned out to be a very useful tool to enable a stepwise exploration of precious, small cell-containing samples with an virtually unlimited number of surface- and intracellular markers.

[Cytomics and predictive medicine for oncology]

One hundred and fifty years after Virchow introduced his fundamental concept of cellular pathology, we now have tools that allow us to unravel the mechanisms of single living cells on a previously unprecedented level of detail. By exploring the molecular cellular phenotype, multiparametric cytometry not only detects specific cellular functions in general but also offers insights into the interaction of single subunits of proteins (e.g., growth factor receptors). Several quantitative and objective techniques allow analysis of single-cell preparations as well as tissue sections to obtain data on different cellular parameters. This opens the way to quantitative and objective histology, which in the future may be possible even without blood or the need to make an incision. To use this huge amount of data for treatment decisions in an individual patient, novel bioinformatic concepts are needed in order to predict the individual course of a disease. The concept of cytomics centers on the cell as the integral unit of all life and explores diseases starting from the cell and going to subcellular units (top-down analysis).

“Decoding the Dots”: The ImageStream system (ISS) as a novel and powerful tool for flow cytometric analysis

The aim of this article is to provide a brief review of the ImageStream system (ISS). The ISS technology was developed as a novel method for multiparameter cell analysis and subsequently as a supportive tool for flow cytometry (FC). ISS integrates the features of FC and fluorescent microscopy collecting images of acquired cells for offline digital image analysis. The article presents an overview of the main characteristics of ISS and a comparison between ISS, FC and the laser scanning cytometer (LSC). We reviewed ISS applications focusing on those involved in cellular phenotyping and provide our own experience with using ISS as a supportive tool to classical FC and demonstrate the compatibility between FC and ISS photometric analysis as well as the advantages of using ISS to confirm FC results.

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.

Simultaneous analysis of steady-state intracellular pH and cell morphology by automated laser scanning cytometry

BACKGROUND

Cytosolic pH (pHi) changes are critical in cellular response to diverse stimuli, including cell survival and death signaling. The potential drawback in flow-based analysis is the inability to simultaneously visualize the cells during pHi measurements. Here, the suitability of laser scanning cytometer (LSC) in pHi measurement was investigated.

AIM

Using the two extensively reported pH-sensitive fluorescent probes, 2,7-bis(2-Carboxyethyl)-5(6)-carboxyfluorescein acetoxymethyl ester (BCECF-AM) and 5-(and-6)-carboxy SNARF-1 acetoxymethyl ester, we evaluated the potential of automated LSC as a platform for simultaneous determination of pHi and cell morphology. The effect of a variety of buffer systems-commonly employed for pHi measurements-on cell morphology before pH clamping with the ionophore, nigericin, was also assessed.

METHODS

Measurement of cytosolic pH was performed using pH-sensitive fluorescent probes BCECF-AM and SNARF-1. pH clamping was carried out using nigericin and samples were analyzed on the LSC or CyAn ADP Flow Cytometer.

RESULTS

The pHi clamping conditions were optimized as 140 mM potassium and 10 microM nigericin. The suitable buffers used for pH clamping: 140 mM KCl, 1 mM MgCl2, 2 mM CaCl(2).2H2O, 5 mM glucose, 20 mM MES and 140 mM KCl, 1 mM MgCl2, 2 mM CaCl(2).2H2O, 5 mM glucose, and 20 mM Tris. Results obtained with the LSC strongly correlated with those obtained by flow cytometry.

CONCLUSION

We report here that LSC is an excellent and highly reproducible platform for pHi determination, and provides the added advantage of simultaneous imaging of cells before, during, and after pH measurements.

Laser scanning cytometry in the characterization of the proapoptotic effects of transiently transfected genes in cerebellar granule neurons

BACKGROUND

Low transient transfection efficiency limits the ability to characterize putative proapoptotic gene function in neurons. Laser scanning cytometry (LSC), with its high capacity, medium throughput means of collecting fluorescent emissions from cultured cells, offers an effective technology for scoring cell death in neuronal transfectants.

METHODS

Cerebellar granule neurons (CGNs) were transfected with EGFP-fusion constructs of Caspase-3 and Caspase-9 using a DNA-calcium phosphate coprecipitation method. CGNs were fixed, permeablized, and stained with propidium iodide (PI) nuclear dye. An LSC method, based on a combination of Long Red Max Pixel, Long Red Integral, and Green Integral fluorescence parameters was validated for the scoring of apoptotic cell death in CGNs.

RESULTS

In Caspase-3 and Caspase-9 transfected CGNs, cell death was scored both in transfectants and nontransfected culture-mates. The cell death phenotype was found to be independent of transfection efficiency. LSC scoring of Caspase-9 transfectants was compared with visual scoring following Hoechst 33342 staining, yielding results that were similar qualitatively, but not quantitatively, likely owing to the greater sensitivity to green fluorescence of laser scanning compared to human vision.

CONCLUSION

LSC scoring of transiently transfected CGNs offers a rapid and reliable means of characterizing proapoptotic gene effects.

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.