A versatile platform for comprehensive chip-based explorative cytometry


  • Christian Hennig and Gesine Hansen developed the methodology for iterative cytometry; Nico Adams and Christian Hennig developed the model ontology; Christian Hennig developed and programmed the software used for automated microscopy, image recognition, and data mining.


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 μl, 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. © 2008 International Society for Advancement of Cytometry

Flow cytometry (FCM) is currently the gold standard for the characterization of cells in suspension using fluorescent antibodies and other dye-tagged probes. In today's laboratory routine, five to eight markers can be detected simultaneously. However, it has become clear that understanding the high complexity of our immune system requires the differentiation of many more cell subtypes and activation states, naïve, memory and effector cell types as well as different regulatory cells. Complicating this, many of these cells belong to rare side populations or are prepared from sample sources that yield only very low numbers of cells. Therefore, simple and robust systems that can distinguish 30 and more markers on a small sample are highly desirable. Because of the combinatorial explosion of possible detectable marker combinations, a less hypothesis-driven, more explorative method that allows a stepwise exploration of a sample would be very useful. The hypothesis-free analysis of a cytometric sample is the fundamental concept of ‘Cytomics’ as introduced by Valet (1). Additionally, ex vivo manipulation on living cells that allow observation of regulation of proteins before and after stimulation of the very same cell are preferred features of future cytometric technologies.

Previous work has demonstrated that it is possible to analyze up to 17 fluorescent markers per cell using FCM (2). Translation of this technology into laboratory routine proved to be difficult, since the availability of ‘exotic’ antibody–dye combinations that are necessary for this multicolor approach are very limited, and the establishment of such a setup is time-consuming because of the multiple compensation steps for the spillover of the different fluorescence spectra of the dyes. Furthermore, stepwise manipulation and explorative investigation of samples is not possible using FCM. Slide-based cytometry (SBC) is an emerging complementary technology, which overcomes some of these limitations by analyzing cells bound to a solid surface (3, 4). Iterative cycles of staining, imaging, and bleaching of dye-tagged probes have the potential to enhance the applications of this approach even further by enabling detection of a high number of parameters per cell (5, 6). We were interested in evaluating whether such an approach could be used to manipulate and quantitatively and qualitatively analyze immune cells with results comparable to current flow-based methods while additionally allowing the analysis of small samples with an unlimited set of markers. Ideally, an implementation of iterative chip-based cytometry (iCBC) for comprehensive immunophenotyping should fulfil the following requirements:

  • 1Simple sample preparation and immobilization of cells, using even minute amounts of sample (e.g., material obtained by biopsies or lavages).
  • 2No modification of cellular composition or quantitative and qualitative expression of markers due to the immobilization procedure.
  • 3Short cycle times for iterative analysis.
  • 4Unlimited set of surface and intracellular markers detectable.
  • 5Low cell-activating property of the surface for functional assays.
  • 6High correlation of results obtained by crossplatform validation (iCBC vs. FCM).

We developed and evaluated an implementation of iCBC for the comprehensive immunophenotyping and even functional analysis of small numbers of immune cells immobilized within microfluidic chips by automated conventional epifluorescence wide field microscopy that achieves the listed requirements. Because of the potentially large space of possible marker combinations associated with each cell, we furthermore discuss the combination of our experimental methodology with up-to-date informatics tools for knowledge capture and knowledge generation.


Microfluidic Chips

Microfluidic chips or slides containing cell-adhesive surfaces for unselective self-immobilization of cells and microfluidics that allow the application/exchange of buffers and staining solutions were obtained from NanoSky (Germany) or Carl Roth (Germany) and used according to manufacturer's recommendations.

Preparation of Cells

For the detection of antigen-specific cells, BALB/c mice were immunized twice intranasally with ovalbumin /LPS (12.5 mg/ml, 40 μl within 3 days). Three days later, spleen cells were harvested and restimulated in culture plates with ovalbumin (200 μg/ml) or mock control for 72 h. For intracellular staining of cytokine production, 1 μl/ml Brefeldin A (GolgiBlock, BD Biosciences, Heidelberg, Germany) was added 2 h before harvesting the cells. For analysis of BAL fluid of asthmatic and nonasthmatic mice, experimental asthma was induced and BAL fluid was obtained using an established protocol (7). For the functional human B-cell assay, an established protocol for CD40L- and IL-4/IL-10-induced germinal center reaction of human PBMC in a cell culture system was used (8). For iCBC, cells were subsequently harvested from the culture, washed in phosphate-buffered saline (PBS), and resuspended in 1–10 μl of PBS. Upon introduction of the suspension into the microfluidic channel, cells distributed randomly over the channel surface. After an incubation time of 5 min for binding of the cells to the surface, the chip was rinsed with PBS. Cells were either fixed in CellFix (BD Biosciences) or subjected directly to the respective staining protocol.

Staining Protocol iCBC

Prior to staining, the chip was scanned for autofluorescence of dead cells or macrophages. Subsequently, 10 μl of the respective PE- or FITC-labeled antibody was added to the microfluidic chip at concentrations of 2 μg/ml to 200 ng/ml (dependent on labeling strength of the respective antibody). After an incubation time of 5 min at room temperature, the chip was rinsed with 200 μl PBS and applied to the imaging system for data acquisition. For the staining of intracellular molecules, immobilized cells were fixed and permeabilized using FixPerm solution (BD Biosciences). For viability testing, the chip was filled with 10 μl of trypan blue and immediately scanned on the imaging system. Afterwards, the chip was rinsed again with PBS to avoid any cytotoxic effects caused by trypan blue itself.

Staining Protocol FCM

For crossvalidation with FCM, harvested cells were resuspended in 50 μl PBS/1% BSA and incubated with 1 μl of the respective dye-labeled antibodies (0.2 μg/ml) for 15 min at RT in the dark. After washing, the cells were subjected to flow-cytometric analysis or permeabilized for intracellular cytokine staining using FixPerm solution (BD Biosciences) and a staining protocol as supplied by the manufacturer.

Imaging System

The optical setup we used for iCBC consisted of an upright microscope (Zeiss axioplan 2e, motorized z-drive, electronic control of fluorescence light- and transmitted light-shutter, Goettingen, Germany), equipped with a mercury lamp (HBO 100) for excitation, suitable filter-sets for the used dyes (PE or FITC), a Plan-Neofluar 16×/0.50 immersion objective (Zeiss), and a motorized scanning table (Merzhaeuser, Wetzlar, Germany). Signals were detected using a sensitive monochromatic 12-bit CCD device (Zeiss AxioCam MRm). Typically, 4–10 different positions were scanned and two pictures were taken for every position (picture 1: 7-s-exposure per position in the fluorescence light mode, picture 2: 100-ms-exposure in the transmitted light mode). The cumulative spatial resolution was 0.3 μm/pixel.

Iterative Staining–Imaging–Bleaching cycles

After staining, the cells bound within the microfluidic chip were scanned by the imaging system as described before. To enable iterative staining of the cells, remaining signals were bleached using the same excitation filter as for imaging by extending illumination by 30 s per position. After bleaching, a second fluorescent-light image was acquired from the same position for background correction. Afterwards, the chip was removed from the microscope and stained for the next cycle. Precise (re-)positioning, which is a prerequisite for tracking each cell as an individual over the cycles, was accomplished by using the transmitted light picture of the pattern formed by the cells at the respective position.

Image Recognition and Processing

Cell recognition

Applying Laplace filtering and two-fold smoothing of the picture, 95% of the cells could be automatically separated from the background in the transmitted-light-mode picture. The remaining 5% of cells like agglomerates were tagged manually or excluded from further analysis. For each of the cells, coordinates of its center (corrected by the offset calculated by the readjustment procedure) and size were recorded.

Fluorescence intensity

Mean fluorescence intensity per cell was calculated by collecting the gray-scale values of each pixel within a cell divided by its cell area [expressed in arbitrary units (AU)]. Since illumination by the fluorescence lamp is relatively constant during the restaining cycles but not totally homogeneous for every part of the scanned position, we decided to subtract the calculated fluorescence intensity of the cell after the bleaching procedure from the unbleached cell to account for possible local inhomogeneity of illumination.

Data analysis

The information associated with each cell (e.g., cell size, xy coordinates on the chip, relative fluorescence intensity) was stored in list-mode format. For further analysis, a bivariate gating was performed manually. Subsequently, each cell was grouped by hierarchical clustering by our in-house software. The resulting groups were then compared with a model-cytometry-ontology for the purposes of cell identification, knowledge capture, and structuring.

Ontology Development

There is currently no agreed-upon methodology for the development of ontologies and a number of methods have been described in the literature (9). We used a methontology approach (10), which consists of the phases requirement specification, conceptualization, integration, and implementation. The integration stage in the methontology approach normally aims to identify existing ontologies, which already describe some of the required concepts and to integrate these into the new ontology. At this stage, we considered several ontologies such as the cell ontology (11), the gene ontology (GO) (12) and the foundational model of anatomy (13), but found that these ontologies were either at the wrong level of granularity or contained concept definitions, which were unsatisfactory for our purposes and hence decided against integration of these resources. During the implementation phase, the ontology was constructed with the official W3C standard web ontology language (OWL) [McGuiness D, van Hamelen F, OWL Web Ontology Language Overview, http://www.w3.org/TR/owl-features/ (last accessed June 2008)] using the Protégé Ontology Editor and Knowledge Acquisition System [The Protégé Ontology Editor and Knowledge Acquisition System, http://protege.stanford.edu (last accessed June 2008)] (Fig. 6).

Software for viewing iCBC files, further sample archives, and the ontology file (OWL) can be downloaded from the SkyDrive directory http://cid-950aa2a18eff465c.skydrive. live.com/self.aspx/iSBC-examples.



As shown in Figure 1a, iCBC generates raw data images (I) that are subsequently processed by image recognition methods: cells are separated by software from the background (II) and mean fluorescence intensity for each cell is calculated for every tested marker (histogram: III). This was repeated for each tested marker in a cyclic setup (Fig. 1b). After bivariate gating (positive/negative), each cell is assigned automatically to a ‘fingerprint’ of the tested marker set. To further analyze the quality of the experimental data, extensive validation of the system was performed:

Figure 1.

General setup for iterative chip-based cytometry. (a) Image recognition and gating. On the left, a fluorescent light picture of immobilized and specifically stained cells (CD4-PE on mouse spleen cells) is shown (I). Cells are detected by software and a fluorescence intensity value is calculated for each single cell (II) and depicted in a dotplot or histogram allowing bivariate gating (III). (b) Example for a cyclic analysis for four different markers. Shown are cut-outs of raw images of four cycles of staining–imaging–bleaching of bronchio-alveolar-lavage (BAL) cells of a mouse with an asthmatic phenotype. In the first cycle, the cells were stained with anti-CD11b-PE and imaged. Remaining fluorescence was bleached. Afterwards, the cells were stained with anti-B7H1-PE, and the same positions as before were scanned [repeated afterwards for CD11c, CD3, and subsequently for all desired other markers (data not shown)]. Four different cell types are marked (according to published marker combinations—I: Alveolar macrophage (large, autofluorescent, CD11c+, CD11b-, MHCII-, B7-H1+, Gr1-), II: Eosinophil (small, autofluorescent, CD11b+, CD44+, B7-H1-, Gr1+), III: monocyte (medium size, CD11c-, CD11b+ MHCII-, B7-H1+, Gr1-), and IV: T-helper-cell (small, no autofluorescence, CD3+, CD4+, TCR2+, Gr1-, CD11c-).

Stability of the imaging system/background correction

In laser-based cell scanning systems, fluctuations of the laser beam may result in instability of the readout (14). Although illumination by the wide-field epifluorescence approach used in our setup does not lead to strong pixel-wise fluctuations, any local inhomogeneity of illumination could also lead to the generation of artifacts. Although local inhomogeneity was already acceptable, we subtracted pixel-wise a background image of the same position after bleaching. This led to a further improvement of data quality (Fig. 2a).

Figure 2.

Validation of the system. (a) Influence of local background correction. Pixel-wise subtraction of the local background results in a further enhanced quality of the data. The graph shows an overlay of histograms for the same data before and after background correction (mouse spleen cells stained for CD8-PE; black graph: before background subtraction; red graph: after background subtraction). The big arrows indicate the improved homogeneity of the values for the CD8-negative subpopulation, which in turn enables a better separation of CD8+ from CD8- cells. In this example, background correction reduced the standard deviation (SD) of calculated fluorescence intensity of the CD8- fraction from 365 to 300 arbitrary fluorescence intensity units (AU). (b) Influence of the cell number per sample volume on the surface cell density. The graph shows typical resulting cell densities on the chip surface (cell-number/position, 1 position = 0.15 μm2) in dependence from the number of cells applied to the chip. The sample volume was adjusted to the two different available chip geometries (2 and 10 μl channel volume). ♦ data for 10 μl sample volume; ▴ data for 2 μl sample volume. (c) Analysis of living cells. Freshly isolated mouse spleen cells were immobilized on a chip and living cells were stained with PE-labeled anti-CD62L. Afterwards, the cells were left either untouched (□) or were stimulated with PMA/Ionomycin (▴) as described in Materials and Methods. During an 8-h period, cells were repeatedly scanned for CD62L shedding or for viability using trypan-blue staining (♦). Immobilization of otherwise unstimulated cells led to a gradual activation (about 25% of initially CD62L-positive cells shedded the molecule during an 8-h period). In contrast, activation with PMA/Ionomycin led to a rapid activation and shedding of CD62L within 2 h (▴). Viability was relatively constant within the observation period as shown by the constant number of trypan-blue negative cells (♦). (d) Sensitivity of the method. Freshly isolated human PBMCs were stained with different concentrations of anti-CD4 PE antibodies for 15 min at room temperature in the dark. Afterwards, fluorescence intensity of the stained cells was measured within both flow cytometry (FCM) and iCBC. Note that different scaling of the axis was used for better comparison of the results. Antibody concentrations: U: unstained; 1: 100 ng/ml; 2: 500 ng/ml; 3: 1 μg/ml; 4: 2 μg/ml; 5: 3 μg/ml; 6: 10 μg/ml; 7: 20 μg/ml. The red arrows indicate the difference of signal-to-background ratio between CD4-negative cells and CD4-positive cells stained with 100 ng/ml.

Generation of ‘artificial’ phenotypes

The validity of a chip- or slide-based system also depends on the generation of ‘artificial’ immunophenotypes either by the detachment of cells from the surface during subsequent cycles of analysis or by software-induced merging of phenotypes from two or more adjacent cells. We therefore analyzed chips with a medium cell density (700 cells/position, 1 position = 0.15 μm2). Cell loss was measured by setting a trigger-point in Cycle 1 (100%), afterwards following these cells in transmitted light mode over six cycles (immobilized living cells) or 21 cycles (fixed and permeabilized cells), respectively. With immobilized living cells, 4% of all cells were lost, mainly occurring between Cycles 1 and 2, whereas fixed cells showed no detectable cell loss (<0.05%). Erroneous software-induced merging of phenotypes did not occur in this setting (see Table 1, CD4+CD19+ cells), since software recognizes and excludes cells that show a strong nonhomogenous staining pattern (moon-phase like).

Table 1. Crossvalidation FCM versus iCBC
 FCM (%)iCBC (%)
  1. Comparison of iCBC versus FCM with regard to specificity of stainings. Mouse spleen cells (n = 5 animals) were either stained iteratively with CD3-PE, CD4-PE, CD8-PE, and CD19-PE for analysis by iCBC or stained with CD3-PerCP, CD4-PE, and CD8-FITC or CD19-PerCP, CD4-PE, and CD3-FITC for flow cytometric analysis. Relative numbers of cells in each compartment show comparable results for both technologies.

CD19+60 ± 160 ± 0.7
CD3+39 ± 2.540 ± 3
CD4+24.5 ± 227 ± 1.5
CD8+15 ± 214.5 ± 1.5
CD4+CD3+17 ± 319 ± 2

Sample size

One of the most desired features of improved cytometric methods is the comprehensive analysis of small samples containing only few cells. In slide-based systems, these cells, when immobilized on the surface, should form a homogeneous layer with a high cell density although allowing an unambiguous separation between single cells. To evaluate this, two geometries of microfluidic channels (2 and 10 μl sample volume) were tested for cell densities on the chip surface resulting from different concentrations of cells within the sample volume. As shown in Figure 2b, maximum surface cell density was about 2,500 cells/scanned position (1 position = 0.15 μm2). Optimum cell density ranges between 400 and 1,800 cells/position. We therefore concluded that 10,000–100,000 cells should be applied in a final sample volume of about 2 μl, whereas larger number of cells should be resolved in 10 μl final sample volume.

Analysis of living cells

Since it is known that some markers show significant differences when measured with immunofluorescence methods prior- versus postfixation (15), some sensitive markers like CD62L or CD27 have to be measured on living cells. Viability of immobilized cells was therefore evaluated by repeated staining of the cells using trypan blue and recording the number of living cells. Eight hours after binding of the cells to the chip surface, the number of viable cells was above 90%. To evaluate changes of markers due to on-chip activation, we tested repeatedly for CD62L shedding, one of the earliest activation markers (16), during this 8-h period. The number of CD62L-positive mouse spleen cells was 42% directly after preparation from blood as well as after immobilization on the surface. Without further stimulation, the number slowly decreased reaching 30% after 8 h. In contrast, additional stimulation with 250 pg/ml PMA and 250 pg/ml Ionomycin on the chip surface led to a rapid activation and CD62L-shedding of the cells (Fig. 2c).

Crossplatform validation

To evaluate comparability of iCBC with FCM, we performed a crossplatform validation using a standard flow cytometer (FACScan, Becton Dickinson).


As shown in Table 1, readout of main cellular components of spleen cell homogenizates of Balb/C mice was nearly identical between the two methods. All other markers tested in murine and human cells showed comparable results and were not dependent on the time-point of staining after binding of the cells to the chip (data not shown).


As shown in Figure 2d, sensitivity of iCBC is enhanced 10-fold in regard to the used flow cytometer. This mainly depends on signal artifacts of the detector of the flow cytometer that broadens the signal of the unstained population (background signal). Since image recognition of iCBC is not influenced by such artifacts, the signal of negative cells is more homogeneous enabling more sensitive separation of weak signals from the background. Furthermore, dynamic range of both FCM and iCBC allows separation into high, medium, and low expression signals with comparable results (Fig. 2d).


To demonstrate the usefulness of this method for both research and clinical applications, we chose three different settings: (1) identification of rare cells, (2) comprehensive analysis of a small sample size, and (3) immunophenotyping of human B-cell differentiation. Additional example data and a data-viewer can be downloaded (see Material and Methods).

Research I: Rare cells—Detection of antigen-specific memory T-cells in mouse spleen homogenizates

Rare antigen-specific circulating memory cells (e.g., <0.01% of total spleen cells) producing cytokines after immunization and in vitro restimulation were detected and phenotyped in spleens of nasally immunized mice and control mice using the following marker set: IFNy, IL-4, IL-10, TCR2, TCR1, CD19, B220, CD49b, CD3, CD4, CD8, CD11c, CD11b, CD44, CD62L. As shown in Figure 3, nasal application of antigen resulted in the generation of IFN-gamma producing, CD3+CD4+CD11c-IL4- Th1 memory T-cells (additionally CD62L-CD44+ TCR2+CD8-CD11b-B220-, data not shown). The frequency of those cells was about three in 10,000 cells, analyzed in five independent experiments with five mice per group in each experiment. No such cells were observed in controls (AG-specific restimulated spleen cells of nonimmunized mice or nonrestimulated spleen cells of immunized mice).

Figure 3.

Example 1: Phenotyping of rare cells. iCBC can be used to detect and extensively phenotype rare antigen-specific cells. Ovalbumin-restimulated spleen cells of mice nasally immunized with ovalbumin/LPS contained larger, IFN-gamma producing, activated memory CD4+CD3+IL-4-CD8- cells (Th1-cells, rate 3/10,000 spleen cells, two representative cells shown). These cells are additionally characterized as CD62L-CD44+TCR2+CD8-CD11b-B220- (data not shown). Interestingly, due to blocking of the Golgi apparatus, most of the specific fluorescence from intracellular IFN-gamma staining was confined to this organelle. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com]

Research II: Comprehensive analysis of small sample sizes—Bronchial lavage (BAL) of mouse lungs

Cells obtained by bronchial lavage (100 μl) of lungs of ‘asthmatic’ mice that had been immunized to ovalbumin by systemic and local antigen application (7) were stained using the following markers: Autofluorescence, CD3, CD4, CD8, TCR2, TCR1, CD49b, CD44, CD62L, CD19, B220, MHCII, CD11b, CD11c, F4/80, Gr-1, CD103, CD27, CD28, CTLA, B7-1, B7-2, B7-H1, Fox-P3, GITR, CXCR4, CCR3, CCR5, CCR7, IFNy, IL-4, IL-10 (all PE labeled). As shown in Figure 4, a variety of different cell types and differentiation states that are not accessible by conventional FCM can be observed by iCBC.

Figure 4.

Example 2: Deep phenotyping of small samples. iCBC can be used to comprehensively phenotype small cell numbers from body fluids or needle aspirates. As an example, we compared cellular composition of 100 μl of bronchial lavage fluid (BALF) of ova-immunized ‘asthmatic’ mice. Cells were immobilized on the chip surface and subjected to comprehensive iCBC using 31 PE-labeled antibodies (CD3, CD4, CD8, TCRa/b, TCRy/d, CD49b, CD44, CD62L, CD19, B220, MHCII, CD11b, CD11c, F4/80, Gr-1, CD103, CD27, CD28, CTLA, B7-1, B7-2, B7-H1, Fox-P3, GITR, CXCR4, CCR3, CCR5, CCR7, IFNy, IL-4, IL-10). The diagram shows the relative proportion of each cell type within BALF as well as the ‘fingerprint’ of the individual cell types and subtypes. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com]

Clinical setting: Immunophenotyping of human B-cell differentiation in an artificial lymph-node model

Primary humoral immunodeficiencies (PHID) can be grouped by their ability to produce specific antibodies or by the activation of B-cells after antigenic stimulation (e.g., Hyper-IgM syndrome, CVID, ALPS). By using CD40L and IL-4 as stimulus, we performed a comprehensive phenotyping of differentiation steps of in vitro-activated human B-cells of a patient with humoral immunodeficiency and lymphoproliferative syndrome and a control (Fig. 5). In healthy individuals, all stages of B-cell activation (naïve B-cells, extrafollicular activated, germinal center B-cells, plasmablasts, and memory cells) are distinguishable. In the patient, B-cells can be activated and enter the germinal stage but are unable to proceed to formation of plasmablasts or memory cells. This is in congruence to the clinical appearance of the patient with hypogammaglobulinemia and generalized lymphadenopathia, thus underlining the usefulness of iCBC for diagnostic purposes.

Figure 5.

Example 3: Clinical setting. The capacity of iCBC to differentiate many cell types and activation states can be used for novel diagnostic approaches. As an example, we used an artificial lymph node model (as described in the text) to diagnose functional defects of humoral immune responses by CD40L + IL-4 stimulation. We used a panel of eight markers (columns) to distinguish differentiation states of B-cells after a 6-day in vitro stimulation. Data from a patient suffering from an inherited humoral immunodeficiency and generalized lymphadenopathy of unknown etiology and a healthy control are compared. Each row shows a representative cell for the respective differentiation step of either control (a) or patient (b). The patient readily activates his B-cells, but has a block during differentiation into centrocytes/centroblasts and is unable to generate plasmablasts and memory cells. In contrast, the healthy control is able to generate IgG+ plasmablasts and memory cells. Additionally, CD3 was stained as a negative control (not shown). Note that CD19 and CD80 are not consistently expressed by germinal center B-cells of patient and control probably reflecting the genetic block at this developmental stage (abbreviations: ex. act, extrafollicular activated B cell; cb/cc, centrocyte/centroblast; pb, plasmablast; mem, memory cell). [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com]


The growing knowledge of the immense complexity of the immune system is challenging and calls for enhanced technologies that allow for a multiparameter analysis of its components under different conditions. Until now, more than 100 different subpopulations of cells and activation states can be detected within a single sample of blood, lymph node, spleen, or bone marrow (17). None of the existing flow cytometric technologies is able to perform a comprehensive analysis of this complexity in every-day laboratory routine (deep immunophenotyping), especially with regard to small sample sizes and stepwise, explorative cytometry.

Immobilizing cells on a two-dimensional support and iterative staining–imaging–bleaching cycles are evolving as an alternative concept for comprehensive analysis of surface and intracellular markers of cells (5, 6). Until now, however, the implementation of this approach for extensive immunophenotyping of small samples was hampered by relatively long sample preparation and analysis times as well as the requirement of relatively large amounts of sample. Furthermore, the choice of (often harsh) sample preparation and fixation methods (spin fixation, formaldehyde treatment, or drying) led to modification of cellular composition and/or changes in quantitative and/or qualitative expression of markers within the sample.

We developed and evaluated a microfluidic-chip-based system for comprehensive analysis of immune cells (iCBC) using extremely small sample sizes. To avoid ex vivo modification of the sample composition or marker expression due to sample preparation, we used commercially available cell-adhesive surfaces within microfluidic chips. This allowed the unspecific self-immobilization of statistical numbers of living cells (e.g., lymphocytes, granulocytes, monocytes/DC, platelets, or bacteria) in a homogenous manner and subsequent iterative analysis by simple fluid exchange. Crossvalidation of the same samples between iCBC and FCM revealed a high degree of correlation in specificity (Table 1). In contrast to parallel marker detection by FCM, no spillover/compensation problems occur, since only one antibody is detected per cycle. It is noteworthy that, because every marker can be used with the same tagged dye (e.g. FITC, PE), it is possible to easily extend the set of usable commercial available antibodies. Using a different signal detection approach, sensitivity is enhanced 10-fold compared to FCM (Fig. 2d). The quality of results is further enhanced as more markers (negative and positive) can be combined to ensure validity of the observed cell type or activation state.

Several methodological approaches have been developed that in principle allow in combination the detection of a wide variety of markers per cell using SBC (18). In comparison to previously reported slide-based immunophenotyping methods, where the number of staining cycles was limited to four (6), or where markers can only be measured after fixation of cells destroying fixation-sensitive markers (5), we developed a simple, robust, and reproducible setup that extends the number of possible manipulations to a theoretically unlimited number of parameters. In the first cycles, fixation-sensible markers like CD62L, CD27, and chemokines are detected. As shown, even in vitro activation of cells can be performed and monitored on-chip (Fig. 2c). Afterwards, the cells are fixed and stained for fixation-stable or intracellular markers. This generates the possibility of storing the chips with fixed cells. After initial analysis of the results, these chips can then be used later for evaluating additional markers. This explorative cytometry in turn leads to shorter periods of knowledge generation, since an experiment can be carried out once and then analyzed successively by repeated rounds of staining over several weeks for several parameters as new questions arise from initial data mining.

The method presented in this paper currently has two bottlenecks: Although we have considerably simplified and shortened sample preparation and staining time (5 min per marker), there is still a relatively long bleaching time (30 s per position) to remove the remaining fluorescence prior to the subsequent staining. This is acceptable for research, because the usefulness and uniqueness of the obtained results outweigh the relatively long analysis time of cumulative 10–20 markers/8 h. However, for routine diagnostic purposes, it would be helpful to develop novel dye-tags that allow a shortening of the signal-removal procedure.

The second bottleneck is the handling of the combinatorial explosion of information resulting from the use of large marker sets: The large and even increasing number of differentiable cell types requires the detection of a larger number of markers per cell for unambiguous cell identification and classification. Moreover, the experimental setup and the nature of the sample source have a significant influence on the composition, activation- and differentiation states of cells within the sample, thus increasing the complexity of the system even further. For this reason, the combination of high content cytometry methods with modern informatics tools such as ontologies for knowledge capture and structuring as well as for comparison of obtained data with other experiments and the published state of the art is mandatory. Ontologies are fundamental tools for knowledge management in a machine-comprehensible format. Although there is ongoing work directed toward extending existing ontologies like the gene ontology (12) for immunological work (19), currently no publicly available ontology exists, which, in our view, could be used or extended for our purposes. We have therefore developed a draft for the cytometry domain, which covers the concepts and workflows needed for the evaluation of our example experiments and models selected concepts such experimental protocols and cell definitions (Fig. 6). It is important to note that this workflow also represents a general model of the way in which cytometric information is communicated in immunological research papers and allows the extraction of knowledge from such papers and its storage in a unified format. Our initial experiences with using this ontology approach suggest that a cytometry ontology is a valuable extension of cellular analysis methods that produce high content cytometry information.

Figure 6.

Scheme of the model cytometry ontology. Overview of the model cytometry ontology used to capture and structure data from the described experiments and its comparison with preceding experiments or existing literature (detailed ontology can be downloaded from http://cid-950aa2a18eff465c.skydrive.live.com/self.aspx/iSBC-examples). This ontology describes cell types not only on the basis of expressed markers, but also on the basis of the experimental setup used to obtain these cells, which has an important impact on sample composition and marker expression. The diagram illustrates both the hierarchical classification of concepts (i.e. BALF is a CellSource, black arrows) as well as selected relationships between them (grey arrows with dashed boxes). The flow from CellSource to FinalCellCollection models the laboratory workflow from initially setting up the experiment (e.g., immunizing mice with antigen) to the final instrumental analysis of a collection of cells: The FinalCellCollection is an assembly of different cell types that evolve from the stepwise combination of ExperimentalProcedures like preparational methods (OrganPreparation, CellCulturePreparation, e.g. lung tissue homogenization, MACS sorting) and experimental protocols [ExperimentalModificationInVivo (e.g., experimental asthma induction, use of gene knock-outs or knock-ins) or ExperimentalModifcationInVitro (e.g., cell culture stimulation)] acting on a (primary) CellSource derived from an unmodified organ (e.g., Spleen, Blood). Cell-types are further defined by a distinct set of markers (Marker) each consisting of its name, ExpressionLevel, and the reference paper that described the cell–marker relation (e.g., PubMed ID, DOI). [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com]

Taken together, the new platform method for cytometry presented here combines the advantages of simple sample preparation and immobilization of cells, using even minute amounts of sample, detectability of an unlimited set of surface and intracellular markers, and low cell activating properties of the surface enabling functional assays for an explorative, less hypothesis-driven approach for cytometric analysis.

We believe that explorative cytometry by combination of iCBC with appropriate informatics tools is very useful for further unrevealing the complexity of our immune system. Besides the presented examples, future applications of this technology are phenotyping procedures and especially functional studies in immunological research, oncology, pathology, pharmacology, and hematology. Therefore, chip-based cytometry approaches could become valuable tools for achieving the goals of a human cytome project and systems biology (20).