Computational Cytometry (2019)

For this month's Editorial Choice, I selected the manuscript by Baidyuk and colleagues (this issue, pages 730–736) on the analysis of the remodeling process of the myocardium after infarction in a rat model. This topic is of high general interest as myocardial infarction is a global threat and for me since I was working in the field of cardiology for several decades. The authors quantified the remodeling of the myocardium after surgical induction of infarction using different techniques including interference microscopy and image analysis. An important result is that even though the tissue regenerated after the infarction, the number of lost cardiomyocytes was not completely replaced. This may result in vulnerability of the postinfarction tissue and regenerative therapies could be useful future options. This study underlines the importance of reliable quantitative single cell analysis in clinical and clinical-related research. Before I move to a set of interesting manuscripts in the present issue, I wanted to inform you about changes of the editorial board team. This roll-on/roll-off program is a regular process of renewing the Editorial Board team that started 2 years ago 1. Thus, I would like to express my thanks to the outgoing editors Donna Arndt Jovin, Bela Molnar, Robert Murphy, and Herbert Schneckenburger for their excellent and diligent work for the journal over many years as reviewers and guest editors. Let me welcome the new editors Sara De Biasi and Genny Del Zotto. Both are already well-known in the cytometry community. Dr. De Biasi participated successfully in the Cytometry Part A mentoring program 2 and is now part of the team. Dr. Del Zotto initiated a new manuscript type, the “Phenotype Reports,” a most promising type of minireview. Read more about it and see the prototype manuscript in the June issue of Cytometry Part A. Due to the ever increasing volume of data obtained by OMICS technologies, computational tools become indispensable to generate a systematic understanding of these data and break down highest dimensionality into few so that they become comprehensible for our mind that is quite limited in imagining multidimensional fields. In cytometry, various clustering approaches are in use and also since recently complementary data display of Boolean gating in 2D dot-plots (see as examples 3 and Buus and colleagues (this issue, pages 726–729)). Therefore, it was time to recognize computational developments in biomedicine as an area of research and science of its own. Consequently, Dr. Greg Finak from Fred Hutchinson cancer research center, Seattle, WA, developed a new manuscript category termed “Computational Articles” 4 with a prototype article published last December 5. Going further, he is coeditingas guest editor with Dr. Yvan Saeys 6, University of Gent, an upcoming special issue of Cytometry Part A on “Machine learning for single cell data.” To emphasize these developments, I have compiled four research articles that focus on computational approaches in cytometry. Lam and colleagues (this issue, pages 757–768) used images of adherent cells obtained by digital holographic microscopy to develop a classifier that differentiates cell types by their phase maps. Using machine learning, the algorithms were able to accurately recognize different cell types and were useful to quantify images from scratch wound healing and Boyden chamber migration assays. So far, mass cytometry produces the highest data dimensionality that can be obtained by flow cytometry. With the present mathematical tools, it is difficult to predict the number of discrete cell populations with unique extracellular and intracellular antigen expression patterns by automated data analysis. Therefore, substantial manual annotation is required limiting the reproducibility of data analysis. To overcome this obstacle, Abdelah and colleagues (this issue, pages 769–781) went new ways and applied linear discriminant analysis (LDA) classifiers for the analysis of mass cytometry data. They report that LDA was able to cluster complex data sets and to identify previously unknown cell populations. In automated cell populations identification techniques, Euclidian distance analysis is mostly employed. Nguyen and colleagues (this issue, pages 782–791) tested as an alternative Mahalanobis distance metric for this approach. The authors applied their alternative distance metric learning to synthetical microbial community data and to mass cytometry data sets. Mahalanobis distance metric improves the performance of automated cell population identification using both classification and clustering algorithms. The last example by Hall and colleagues (this issue, pages 797–802) is a practical demonstration of implementing and combining computer scripts to further automate routine processes in cytometry SRL facilities. Such implementation can serve as time savers and improve work efficiency and the service provided by the SRL. I will now head off to the upcoming CYTO 2019 conference in Vancouver where I hope to meet many of you.