THE complex ways that cell analytical technologies allow mixed cell populations to quantize into their fractal elements—the individual cells—starts to enable the determination of whether cells that cannot be split further (with regard to their phenotype and function) exist in a cytome (1). These cells would represent the real elementary building units of cell systems. Some scientists have proposed the term “Quantum Biology” to describe this dissection of cell populations of an organ system (cytome) or an organism. Although, “Quantum Cell Biology” may be the more appropriate term since quantum biology was, back in 1944, coined by Erwin Schrödinger (2) and refers to the use of quantum physics to explain cell biological processes.
Methods to “… and to quantize cell populations are manifold”: novel microfluidic-based flow cytometry and cell sorter systems like the one used by Takao and Takeda (this issue, page 107) can be used to identify extremely rare cells such as circulating tumor cells unequivocally and enrich them in a cross-contamination-free way for further detailed characterization. Additional applications of microfluidic flow systems, expand among others, to label free and comprehensive cell characterization, for example, by multifrequency impedance analysis (3), and cell death processes (4), just to name a few.
Furthermore, multiplexed analysis is expandable to soluble compounds of cellular products particularly in biological fluids and can be applied to the identification of dozens of different analytes simultaneously (5). In this issue, Grossmann and colleagues (this issue, page 118) propose a novel quantitative microscopic microbead assay to detect antinuclear antibodies for autoimmune disease diagnosis. Such advancements help to further add to the knowledge on disease patterns and progression, wherein microscopy is a major supporter.
In fact, quantitative microscopy is a major driving force in multiplexing and cytometry. For example, discrimination of tumor and nontumor cells seems possible by analyzing the 3D nuclear architecture of cells, particularly the distribution of telomeres in interphase nuclei (6). Klewes and colleagues (this issue, page 159) are developing an automated scoring system for quantifying telomeres and their structural organization in nuclei. With their approach, the authors are able to detect tumor cells based on 3D architectural profiles of the genome. In the future, this new tool could assist in patient diagnosis, the detection of minimal residual disease, the analysis of treatment response, and furthermore in treatment decisions.
The authors' approach includes distinction of close-by objects in 3D and automated telomere localization and counting. For such analysis, reliable and the highest possible signal resolution is critical. Leray and colleagues (this issue, page 149) optimized object discrimination in image analysis of data obtained from fluorescence lifetime imaging (FLIM), first in a theoretical approach then in practice. The authors demonstrate that with their new polar approach method, they are able to distinguish closely neighboring objects in a cell beyond the presently possible resolution by FLIM microscopy.
Light scattering is a label free and simple applicable parameter for analysis and quantitation of intracellular objects in flow (7) and in imaging cytometry. Pasternack and colleagues further developed light scattering measurements to detect disruption of mitochondria in apoptotic processes (this issue, page 137). The authors' provide evidence that mitochondrial fission can be monitored in situ within living cells through the use of light scattering signatures that depend on the degree of particle orientation. This method may be significant in applications where sample labeling is difficult or where fluorescence labels may interfere with biological functions. Monitoring at low image resolution would allow for higher cell throughput and to cell systems-wide monitoring.
Unbiased analysis of multiplexed and multivariate data is still a matter of research and development (8). In the present issue, Roederer and colleagues (this issue, page 167) report of their advancements in developing an analytical platform for explorative data analysis for multiparametric data. This is a further step on the way toward nonsupervised investigative approaches to complex data obtained from quantitative single cell analysis. However, one should take the warning and always be aware not to overenthusiastically interpret the generated statistical data (Communication to the Editor by Roederer; this issue, page 95).
The aforementioned examples of recent advances in quantitative cell analysis are of clear novelty and go beyond the previously published advances made by the authors. But let me make at the end of this month's editorial a brief Gedankenspiel.
Transferring the concept of quantizing cell cytomes into their absolute “unsplittable” basic elements, into scientific publication habits, one may rightly develop the idea that scientific results could also be split into their basic units that cannot be further downsized. I suggest naming this interesting approach “smallest publishable unit” (SPIT). Quantizing the novelty from one experiment means that between each of the quanta “(or publishable units) the common or identical parts of different” quanta are identical, and the difference is defined by only a few criteria. (As a simple example from the cytome, mature helper and cytotoxic T-cells have a number of surface antigens, such as CD45, CD3, and so forth, in common but are still different cells as they do either express CD4 or CD8). Similarly, two closely related SPITs may be identical in their major parts but still be faintly distinguishable at a closer look.
The substantial difference is, in my view, that the first example is nature and biology, the second one is purely a human construct, and thus can be influenced by the creator(s). Journal and publication numbers are ever increasing, and, even with the best and most advanced search engines, there is an overwhelming number of articles to publish and retrieve. So I feel it right to ask the question: Is this really a useful approach to raise the knowledge and quality of science? I leave it to the reader and the authors to find the right answer.