Innovations in cytometry methods and technology

Abstract

of existing methods and development ofnew techniques for upcoming biomedical problems are thecorner stones of scientific discovery. In many cases, these inno-vations are question driven. However, in other cases, the tech-nical advances are the driving force and lay the foundation fornew discoveries. Cytometry as a multi-disciplinary science isby nature the source for both innovative discoveries enabledby new methods and assays as well as by fascinating newinstrumentation (may it be hardware or software).One important approach for innovation is simplification.Kiesel and coworkers (this issue, page 317) developed a simplemicrofluidic cytometer that allows absolute counting of la-beled cells in suspension in the absence of elaborate optics.Their approach is termed ‘‘spatially modulated fluorescenceemission’’ and is based on mathematical correlation of eventsthat move behind a grid of bars of varying wideness that arepositioned between microfluidic channel and a light detectingcharge coupled device (CCD) array. The resulting data displaya high signal-to-noise ratio and yield data comparable to thoseobtained by traditional but more complex (and expensive)flow cytometers. Such cytometers of low complexity and pri-cing are dedicated for use in resource limited places, specia-lized on a limited diagnostic range and underline the effortstaken by cytometrists in this area (1,2).Alternatively, simplifying the preanalytics by the aban-donment of staining or even preparative procedures mayrender an additional insight into biological processes andreduce costs. One such label free approach is made possible byanalyzing the complex patterns of light scattering by singlecells in flow systems as demonstrated for microbes (3). Panand colleagues (this issue, page 284) went a step further andmeasured two-dimensional light scattering patterns of indivi-dual eukaryotic cells sprayed by an ink jet aerosol generator.By using autocorrelation analysis and modeling of cell scatter-ing, patterns can be obtained that may in the future lead tosimplified cell identification and discrimination between celltypes and between different states of cell activation and devel-opment.Both aforementioned studies used complex mathematicaltools to analyze their measurements and identify and charac-terize individual cells. Indeed, the complexity of data pro-duced by new instrumentation is generating the demand fornew ways to view and understand them. The work presentedby Malkassian and coworkers (this issue, page 263) is a verygood example. The authors used a submersible flow cytometer(4) that collects data from phytoplankton in a spatiotemporalmanner. These data provide a rich wealth of informationregarding shape, size, scattering, autofluorescence, amongothers. The mathematical approach proposed enables thefunctional analysis and classification to better understand thecomplexity and population dynamics of complex biocenosisin a changing environment. The application may be furtherextended to the temporal analysis of cytomes in health anddisease, for example, in clinical monitoring.Considering that cytometry is not exclusively restrictedto the analysis of single suspended events in flow but also toimage-based cytometry (alternatively, image cytometry, ima-ging cytometry, slide based cytometry... unfortunately, there isstill no unified idiom for cytometry on the microscope!) ofimmobilized specimens additional challenges arise. One ofthese challenges is the automated recognition of areas that arerelevant for analysis, such as areas covered by tissue fragmentsas in tissue micro arrays (5). This problem is particularly rele-vant for high-throughput image cytometry. Zeder and co-workers (this issue, page 306) addressed a new challenge: theanalysis of microbial communities in aquatic systems by sam-