Müller, S. & Nebe-von-Caron, G. Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiol. Rev. 34, 554–587 (2010).
Günther, S. et al. Species-sorting and mass-transfer paradigms control managed natural metacommunities. Environ. Microbiol. 18, 4862–4877 (2016).
Props, R., Monsieurs, P., Mysara, M., Clement, L. & Boon, N. Measuring the biodiversity of microbial communities by flow cytometry. Methods Ecol. Evol. 7, 1376–1385 (2016).
Liu, Z. et al. Ecological stability properties of microbial communities assessed by flow cytometry. mSphere 3, e00564–17 (2018).
Liu, Z. et al. Neutral mechanisms and niche differentiation in steady-state insular microbial communities revealed by single cell analysis. Environ. Microbiol. 21, 164–181 (2019).
De Vrieze, J., Boon, N. & Verstrate, W. Taking the technical microbiome into the next decade. Environ. Microbiol. 20, 1991–2000 (2018).
Koch, C. et al. Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function. Nat. Protoc. 8, 190–202 (2013).
Mage, L. M. et al. Shape-based separation of synthetic microparticles. Nat. Mater. 18, 82–89 (2019).
Müller, S. Modes of cytometric bacterial DNA pattern: a tool for pursuing growth. Cell Prolif. 40, 621–639 (2007).
Ludwig, J., Höner zu Siederdissen, C., Liu, Z., Stadler, P. F. & Müller, S. flowEMMi: an automated model-based clustering tool for microbial cytometric data. BMC Bioinforma. 20, 643 (2019).
Koch, C., Fetzer, I., Harms, H. & Müller, S. CHIC-an automated approach for the detection of dynamic variations in complex microbial communities. Cytom. A 83, 561–567 (2013).
Liu, Z. & Müller, S. Bacterial community diversity dynamics highlight degrees of nestedness and turnover patterns. Cytom. Part A https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23965 (2020)
Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 228–238 (2013).
Peters, J. M. & Ansari, M. Q. Multiparameter flow cytometry in the diagnosis and management of acute leukemia. Arch. Pathol. Lab. Med. 135, 44–54 (2011).
Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
Spitzer, H. M. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).
Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annu. Rev. Microbiol. 8, 711–730 (2017).
Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).
Roesch, L. F. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).
Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).
Hallmaier-Wacker, L. K., Lueert, S., Roos, C. & Knauf, S. The impact of storage buffer, DNA extraction method, and polymerase on microbial analysis. Sci. Rep. 8, 6292 (2018).
Hardwick, S. A. et al. Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis. Nat. Commun. 9, 3096 (2018).
Hornung, B. V. H., Zwittink, R. D. & Kuijper, E. J. Issues and current standards of controls in microbiome research. FEMS Microbiol. Ecol. 95, fiz045 (2019).
Sze, M. A. & Schloss, P. D. The impact of DNA polymerase and number of rounds of amplification in PCR on 16S rRNA gene sequence data. mSphere 4, e00163–19 (2019).
Clingenpeel, S., Clum, A., Schwientel, P., Rinke, C. & Woyke, T. Reconstructing each cell’s genome within complex communities—dream or reality? Front. Microbiol. 8, 771 (2015).
Stepanauskas, R. et al. Improved genome recovery and intergrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat. Commun. 8, 84 (2017).
De Bruin, O. M. & Birnboim, H. C. A method for assessing efficiency of bacterial cell disruption and DNA release. BMC Microbiol. 16, 197 (2016).
Mie, G. Beiträge zur optik trüber medien, speziell kolloidaler metallösungen. Ann. Phys. 25, 377–445 (1908).
Woyke, T., Doud, D. F. R. & Schulz, F. The trajectory of microbial single-cell sequencing. Nat. Methods 14, 1045–1054 (2017).
Jahn, M. et al. Subpopulation-proteomics in prokaryotic populations. Curr. Opin. Biotech. 24, 79–87 (2013).
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds. Stackebrandt, E. & Goodfellow, M.) 115–175 (Wiley, 1991).
Lambrecht, J. et al. Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F420 autofluorescence. Microb. Cell Fact. 16, 180 (2017).
Besmer, M. D. et al. The feasibility of automated online flow cytometry for in-situ monitoring of microbial dynamics in aquatic ecosystems. Front. Microbiol 5, 265 (2014).
Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of bacteria and archaea using next-generation sequencing. PLoS ONE 9, e105592 (2014).
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).
Source: Ecology - nature.com
