We would like to thank the members of the Institut Curie Flow Facility for their help with setting up the flow cytometry experiments. We also want to acknowledge the contributions of the other members of the Team Peri&#233;, during multiple discussions. We thank Dr. Julia Marchingo and Prof. Phil Hodgkin (Walter end Eliza Hall Institute of Medical Research) for sharing their protocol of the multiplexing of cell division dyes on lymphocytes. We thank the Saint Louis hospital cord blood biobank for providing the biological resources necessary for the development of this protocol. The study was supported by an ATIP-Avenir grant from CNRS and Bettencourt-Schueller Foundation (to L.P.), grants from the Labex CelTisPhyBio (ANR-10-LBX-0038) (to L.P. and A.D.), Idex Paris-Science-Lettres Program (ANR-10-IDEX-0001-02 PSL) (to L.P.), the Canceropole INCA Emergence (2021-1-EMERG-54b-ICR-1, to L.P.), and the ITMO MIIC grant (21CM044, to L.P.). As well as funding from the European Research Council (ERC) under the European Union&#39;s Horizon 2020 research and innovation program ERC StG 758170-Microbar (to L.P.), A.D. was supported by a fellowship from Fondation de France.

<ol>
	<li>Ginhoux, F., Yalin, A., Dutertre, C. A., Amit, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+immunology:+Past,+present,+and+future.">Single-cell immunology: Past, present, and future.</a> Immunity. 55 (3), 393-404 (2022).</li><li>Ke, M., Elshenawy, B., Sheldon, H., Arora, A., Buffa, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single+cell+RNA-sequencing:+A+powerful+yet+still+challenging+technology+to+study+cellular+heterogeneity.">Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity.</a> Bioessays. 44 (11), 2200084 (2022).</li><li>Regev, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+human+cell+atlas.">The human cell atlas.</a> Elife. 6, 27041 (2017).</li><li>Laurenti, E., G&#246;ttgens, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+haematopoietic+stem+cells+to+complex+differentiation+landscapes.">From haematopoietic stem cells to complex differentiation landscapes.</a> Nature. 553 (7689), 418-426 (2018).</li><li>Haas, S., Trumpp, A., Milsom, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Causes+and+consequences+of+hematopoietic+stem+cell+heterogeneity.">Causes and consequences of hematopoietic stem cell heterogeneity.</a> Cell Stem Cell. 22 (5), 627-638 (2018).</li><li>Loughran, S. J., Haas, S., Wilkinson, A. C., Klein, A. M., Brand, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lineage+commitment+of+hematopoietic+stem+cells+and+progenitors:+insights+from+recent+single+cell+and+lineage+tracing+technologies.">Lineage commitment of hematopoietic stem cells and progenitors: insights from recent single cell and lineage tracing technologies.</a> Experimental Hematology. 88, 1-6 (2020).</li><li>Peri&#233;, L., Duffy, K. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Retracing+the+in+vivo+haematopoietic+tree+using+single-cell+methods.">Retracing the in vivo haematopoietic tree using single-cell methods.</a> FEBS Letters. 590 (22), 4068-4083 (2016).</li><li>Yu, V. W. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epigenetic+memory+underlies+cell-autonomous+heterogeneous+behavior+of+hematopoietic+stem+cells.">Epigenetic memory underlies cell-autonomous heterogeneous behavior of hematopoietic stem cells.</a> Cell. 167 (5), 1310-1322 (2016).</li><li>Ganuza, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lifelong+haematopoiesis+is+established+by+hundreds+of+precursors+throughout+mammalian+ontogeny.">Lifelong haematopoiesis is established by hundreds of precursors throughout mammalian ontogeny.</a> Nature Cell Biology. 19 (10), 1153-1163 (2017).</li><li>Naik, S. H., Schumacher, T. N., Peri&#233;, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellular+barcoding:+A+technical+appraisal.">Cellular barcoding: A technical appraisal.</a> Experimental Hematology. 42 (8), 598-608 (2014).</li><li>Quek, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetically+distinct+leukemic+stem+cells+in+human+CD34+&#8722;+acute+myeloid+leukemia+are+arrested+at+a+hemopoietic+precursor-like+stage.">Genetically distinct leukemic stem cells in human CD34 &#8722; acute myeloid leukemia are arrested at a hemopoietic precursor-like stage.</a> The Journal of Experimental Medicine. 213 (8), 1513-1535 (2016).</li><li>Karamitros, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+analysis+reveals+the+continuum+of+human+lympho-myeloid+progenitor+cells.">Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells.</a> Nature Immunology. 19 (1), 85-97 (2018).</li><li>Boitano, A. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aryl+hydrocarbon+receptor+antagonists+promote+the+expansion+of+human+hematopoietic+stem+cells.">Aryl hydrocarbon receptor antagonists promote the expansion of human hematopoietic stem cells.</a> Science. 329 (5997), 1345-1348 (2010).</li><li>Delaney, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notch-mediated+expansion+of+human+cord+blood+progenitor+cells+capable+of+rapid+myeloid+reconstitution.">Notch-mediated expansion of human cord blood progenitor cells capable of rapid myeloid reconstitution.</a> Nature Medicine. 16 (2), 232-236 (2010).</li><li>Fares, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cord+blood+expansion.+Pyrimidoindole+derivatives+are+agonists+of+human+hematopoietic+stem+cell+self-renewal.">Cord blood expansion. Pyrimidoindole derivatives are agonists of human hematopoietic stem cell self-renewal.</a> Science. 345 (6203), 1509-1512 (2014).</li><li>Guo, B., Huang, X., Lee, M. R., Lee, S. A., Broxmeyer, H. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antagonism+of+PPAR-&#947;+3+signaling+expands+human+hematopoietic+stem+and+progenitor+cells+by+enhancing+glycolysis.">Antagonism of PPAR-&#947; 3 signaling expands human hematopoietic stem and progenitor cells by enhancing glycolysis.</a> Nature Medicine. 24 (3), 360-367 (2018).</li><li>Vannini, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+NAD-booster+nicotinamide+riboside+potently+stimulates+hematopoiesis+through+increased+mitochondrial+clearance.">The NAD-booster nicotinamide riboside potently stimulates hematopoiesis through increased mitochondrial clearance.</a> Cell Stem Cell. 24 (3), 405-418 (2019).</li><li>Gupta, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nov/CCN3+enhances+cord+blood+engraftment+by+rapidly+recruiting+latent+human+stem+cell+activity.">Nov/CCN3 enhances cord blood engraftment by rapidly recruiting latent human stem cell activity.</a> Cell Stem Cell. 26 (4), 527-541 (2020).</li><li>Horwitz, M. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Omidubicel+vs+standard+myeloablative+umbilical+cord+blood+transplantation:+results+of+a+phase+3+randomized+study.">Omidubicel vs standard myeloablative umbilical cord blood transplantation: results of a phase 3 randomized study.</a> Blood. 138 (16), 1429-1440 (2021).</li><li>Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D., Klein, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lineage+tracing+on+transcriptional+landscapes+links+state+to+fate+during+differentiation.">Lineage tracing on transcriptional landscapes links state to fate during differentiation.</a> Science. 367 (6479), 3381 (2020).</li><li>Loeffler, D., Schroeder, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+cell+fate+control+by+continuous+single-cell+quantification.">Understanding cell fate control by continuous single-cell quantification.</a> Blood. 133 (13), 1406-1414 (2019).</li><li>Tario, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimized+staining+and+proliferation+modeling+methods+for+cell+division+monitoring+using+cell+tracking+dyes.">Optimized staining and proliferation modeling methods for cell division monitoring using cell tracking dyes.</a> Journal of Visualized Experiments. (70), e4287 (2012).</li><li>Marchingo, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=T-cell+stimuli+independently+sum+to+regulate+an+inherited+clonal+division+fate.">T-cell stimuli independently sum to regulate an inherited clonal division fate.</a> Nature Communications. 7, 13540 (2016).</li><li>Horton, M. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplexed+division+tracking+dyes+for+proliferation-based+clonal+lineage+tracing.">Multiplexed division tracking dyes for proliferation-based clonal lineage tracing.</a> Journal of Immunology. 201 (3), 1097-1103 (2018).</li><li>Lehmann, E. L., Romano, J. P., Casella, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Testing statistical hypotheses. , 784 (2005).</li><li>Tak, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HSPCs+display+within-family+homogeneity+in+differentiation+and+proliferation+despite+population+heterogeneity.">HSPCs display within-family homogeneity in differentiation and proliferation despite population heterogeneity.</a> Elife. 10, 360624 (2021).</li><li>Sommerkamp, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mouse+multipotent+progenitor+5+cells+are+located+at+the+interphase+between+hematopoietic+stem+and+progenitor+cells.">Mouse multipotent progenitor 5 cells are located at the interphase between hematopoietic stem and progenitor cells.</a> Blood. 137 (23), 3218-3224 (2021).</li><li>Kato, K., Radbruch, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+and+characterization+of+CD34++hematopoietic+stem+cells+from+human+peripheral+blood+by+high-gradient+magnetic+cell+sorting.">Isolation and characterization of CD34+ hematopoietic stem cells from human peripheral blood by high-gradient magnetic cell sorting.</a> Cytometry. 14 (4), 384-392 (1993).</li><li>Miltenyi, S., M&#252;ller, W., Weichel, W., Radbruch, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+gradient+magnetic+cell+separation+with+MACS.">High gradient magnetic cell separation with MACS.</a> Cytometry. 11 (2), 231-238 (1990).</li><li>Doulatov, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revised+map+of+the+human+progenitor+hierarchy+shows+the+origin+of+macrophages+and+dendritic+cells+in+early+lymphoid+development.">Revised map of the human progenitor hierarchy shows the origin of macrophages and dendritic cells in early lymphoid development.</a> Nature Immunology. 11 (7), 585-593 (2010).</li><li>Goardon, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coexistence+of+LMPP-like+and+GMP-like+leukemia+stem+cells+in+acute+myeloid+leukemia.">Coexistence of LMPP-like and GMP-like leukemia stem cells in acute myeloid leukemia.</a> Cancer Cell. 19 (1), 138-152 (2011).</li><li>Laurenti, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CDK6+levels+regulate+quiescence+exit+in+human+hematopoietic+stem+cells.">CDK6 levels regulate quiescence exit in human hematopoietic stem cells.</a> Cell Stem Cell. 16 (3), 302-313 (2015).</li><li>Aiuti, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lentiviral+hematopoietic+stem+cell+gene+therapy+in+patients+with+Wiskott-Aldrich+syndrome.">Lentiviral hematopoietic stem cell gene therapy in patients with Wiskott-Aldrich syndrome.</a> Science. 341 (6148), 1233151 (2013).</li><li>Davison, A. C., Hinkley, D. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Bootstrap Methods and their Application. , (1997).</li><li>Horton, M. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lineage+tracing+reveals+B+cell+antibody+class+switching+is+stochastic,+cell-autonomous,+and+tuneable.">Lineage tracing reveals B cell antibody class switching is stochastic, cell-autonomous, and tuneable.</a> Immunity. 55 (10), 1843-1855 (2022).</li><li>Notta, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinct+routes+of+lineage+development+reshape+the+human+blood+hierarchy+across+ontogeny.">Distinct routes of lineage development reshape the human blood hierarchy across ontogeny.</a> Science. 351 (6269), 2116 (2016).</li><li>Grinenko, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hematopoietic+stem+cells+can+differentiate+into+restricted+myeloid+progenitors+before+cell+division+in+mice.">Hematopoietic stem cells can differentiate into restricted myeloid progenitors before cell division in mice.</a> Nature Communications. 9 (1), 1898 (2018).</li><li>Saeys, Y., Van Gassen, S., Lambrecht, B. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+flow+cytometry:+Helping+to+make+sense+of+high-dimensional+immunology+data.">Computational flow cytometry: Helping to make sense of high-dimensional immunology data.</a> Nature Reviews Immunology. 16 (7), 449-462 (2016).</li></ol>

The authors declare no conflict of interest relevant to this work. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

The MultiGen assay is a high-throughput, easy to perform, and unexpensive assay, that has been instrumental to study lymphocyte23,24,35 and murine hematopoietic cells26,27. Here, we present a new development of the approach that allows to decipher ex vivo the early phase of human HSPC commitment, at the single-cell level using short term culture (Figure 6). Single-cell ex vivo culture systems are typically used to assess the long-term fate of HSPCs into mature cells, but some fates occur earlier than others36, potentially biasing the analysis toward fewer fates. In addition, these culture systems usually miss information about divisions during fate commitment. The first commitment steps have been shown to occur as early as at the start of the culture, sometimes without division26,37, making short term culture and tracking division essential to study early fate commitment. By simultaneously following the fate, division, and kinship, this assay allows to understand the role of the first division and fate decision in human HSPCs. Using the assay, it is possible to infer after how many divisions the commitment process occurs, the balance between self-renewal and differentiation for those early progenitors, and how those properties are inherited across generations. To our knowledge, this is the only assay that allows these types of measurements for human HSPCs, at single-cell resolution. In addition, using different combinations of cell division dyes, we increased the throughput of the analysis, making this assay a valuable tool for generating large datasets quickly. The dye combinations allow to follow several families in the same wells, increasing the number of cells available for analysis in short-term culture. The number of combinations could potentially be increased even more, via the addition of other dyes (e.g., yellow dye) or modifying the ratio of CFSE and CTV. However this reduces the number of other parameters that can be analyzed.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/64918/64918fig06.jpg" /> 
Figure 6: Schematic representation of the protocol. <a href="https://www.jove.com/files/ftp_upload/64918/64918fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To perform the analysis successfully, due to the large number of wells and the reduced number of cells to analyze, it is necessary to run the flow cytometry analysis on an analyzer equipped with a plate reader. The new generation of bench analyzers is particularly adapted to this assay, as most of them have a smaller dead volume to reduce the percentage of cell loss. This in turn guarantees a higher efficiency in recovering the entirety of each well, prompting an efficiency estimated in the 70% range26. Estimating the cell loss during the flow cytometry acquisition is crucial for the analysis of each individual family. For example, assuming no cell death and counting the number of divisions, it is possible to estimate the number of cells per each family. Nevertheless, it is desirable to run some confirmatory experiments, particularly in the estimation of cell death in the tested culture conditions and measuring the recovery rate experimentally using a defined numbers of cells.
One of the crucial steps of this protocol is the peak assignment. As already mentioned, a good quality peak distribution is strongly dependent from the isolation of very narrow peaks at cell sorting. Nevertheless, it is still difficult to assign the correct number of divisions based uniquely on the distribution. As cell sorting and flow cytometry analysis are performed on two different machines, it is not possible to directly compare the intensity of each signal, so it could be difficult to know if the first peak observed on the right end of the histogram is peak 0 or peak 1. In this regard, few solutions are possible; one way is to perform an orthogonal experiment to accurately measure the number of divisions performed by these cells (e.g., live-cell imaging). Another possibility is to simply count the number of cells in the well under an inverted brightfield microscope, before running the flow cytometry analysis. This will infer an average number of divisions (assuming no cell death). Finally, a post-hoc solution for peak assignment is the detection of an unusual number of &#34;impossible families&#34;; these families are composed of a larger than possible number of cells per generation (e.g., five cells in generation 2, or two cells in generation 1 and one cell in generation 2). The possibility to exclude impossible families is coded in the statistical analysis step, and flags the impossible family. If the occurrence of these errors is too high, it is reasonable to assume that the peak assignment needs to be revised.
In this protocol, we included a few examples of data representation and analysis for the assay, as this has become an essential step in the generation and interpretation of large datasets38. The first example is the heatmap showing the totality of all analyzed cells, organized per family. This is an efficient tool to explore the general properties of the data and potential conclusions: are families composed by multiple cell types or do they tend to be homogeneous in composition? Are families spread over multiple generations, or are they mostly dividing the same number of times? This exploratory analysis needs to then be complemented with more specific plots and statistical testing. It can be used to quantitatively assess symmetric and asymmetric fate commitment, differentiation without division, the balance between self-renewal and differentiation, and the number of divisions for a given commitment fate. It is fundamental, during the experimental planning, to set the cell culture length accordingly to the type of question asked; for example, for the first two questions (symmetric/asymmetric balance and differentiation without division), planning very short cultures steps allows the isolation of a large number of families that have performed only one or no divisions at all26. Conversely, longer experiments allow the exploration of the number of divisions required for a specific cell commitment, as they sample families at different stages of differentiation. Nevertheless, this method is not designed for long-term cultures (2-3 weeks), as cell dye dilution is not able to accurately track more than seven or eight divisions22. As a consequence, this tool is mostly adapted to study the early commitment of hematopoietic progenitors, and is not designed to make robust conclusions on the long term differentiation properties of these cells.
The statistical framework was developed specifically for the analysis of this type of data and based on the concept of permutations26. This was necessary because of the observation of a familial dependance on the cell type distribution and on the number of divisions performed. In other words, cells that are part of the same family are also more likely to display similar phenotypes and divide the same number of times. While an in-depth analysis is beyond the scope of this work, the provided set of statistical tests should be sufficient when assessing different conditions.
In conclusion, this protocol constitutes a valuable tool for assessing the cellular dynamics of hematopoietic stem and progenitor cells ex vivo, in a rapid and inexpensive way. Due to its flexibility and versatility regarding time point, culture conditions, and type of HSPCs analyzed, it allows to test a variety of experimental conditions. As a flow cytometry-based assay, it can be implemented in most labs, and it does not require extensive prior knowledge, making it a good candidate for screenings and pilot experiments.

The past decade was marked by the worldwide spreading of single-cell approaches to cellular and molecular biology. Following in the steps of single-cell genomics1,2, nowadays it is possible to study many components of a single cell (e.g., DNA, RNA, proteins), with novel single cell -omics techniques burgeoning every year. These techniques have shed light on old and new questions for the fields of immunology, neurobiology, oncology, and others, both using human and model organism cells3. By highlighting the differences between individual cells, single cell -omics prompted the definition of a new model of hematopoiesis, centered on the heterogeneity of hematopoietic stem and progenitor cells (HSPCs) and moving away from the classical model of discrete homogeneous populations4,5.
One of the few drawbacks of all the -omics techniques is the destruction of the cell of interest, precluding the possibility to assess its functionality. Conversely, other single-cell methods, such as single-cell transplantation assay and lineage tracing technologies, provide a readout of the functionality of the ancestor cell by assessing the fate of individual cells in vivo6,7. Lineage tracing technologies involves labeling the cell of interest with a heritable genetic7 or a fluorescent label8,9, allowing the fate of multiple single cells to be followed at the same time. However, the characterization of the starting cells is typically limited to a restricted number of parameters, such as the expression of a few surface proteins assessed by flow cytometry10. In addition, single-cell lineage tracing technologies require laborious detection of the cellular label, typically via DNA/RNA sequencing or imaging. This last point in particular limits the number of conditions that can be tested in a single experiment.
Another class of methods that are used to study the functionality of single cells are ex vivo cell culturing systems of single HSPCs. Easy to perform, these gold standard assays involve the sorting of individual cells into 96-wells cell culture vessels, and after culture, characterizing the cell progeny phenotype, typically by flow cytometry or morphological analysis. These assays have mostly been used to characterize the long-term differentiation of HSPCs into mature cells, typically after 2-3 weeks of culture11,12. Alternatively, they have been used to try to maintain and expand ex vivo HSPCs13,14,15,16,17,18, with the promise of medical benefit for human stem cell transplantation19. Lastly, they have been used to study the early commitment of HSPCs using short-term culture20, with the low number of cells generated in this culture being the main limiting factor. One drawback of these different kinds of ex vivo assays is that they only partially reflect the in vivo complexity; still, they are one of the rare ways to study human HSPC differentiation.
One missing piece of information from existing single-cell methods (single cell-omics, lineage tracing, and ex vivo culture) is the accurate detection of cell divisions, an essential parameter to consider when studying HSPC dynamics21. A simple way to assess the number of divisions via flow cytometry is the use of soluble &#34;protein dyes&#34;, like the 5-(and 6)-carboxyfluorescein diacetate succinimidyl ester (CFSE)22. These division dyes diffuse inside the cytoplasm of the stained cells, and diluted by half and passed to the two daughter cells at every cell division, allowing to enumerate up to 10 divisions. Combining several division dyes, it is possible to seed multiple individual progenitors in the same well, as each individual dye allows separation of the different descendants. This is the principle behind the use of cell dyes for multiplex clonal and division-tracking that was first introduced for murine lymphocytes23,24.
Here, we present the development of the MultiGen assay for use with murine and human HSPCs. It allows the testing of many single cells simultaneously for their properties of differentiation, division, and kinship ex vivo. This high-throughput, easy to perform, and inexpensive assay allows to measure the cellular phenotype, the number of divisions performed, and the cell kinship and clonal relationship with the other cells in the well, all at the same time. It can be used to quantitatively assess symmetric and asymmetric fate commitment, the balance between self-renewal and differentiation, and the number of divisions necessary for a given commitment fate. The protocol requires a fluorescence activated cell sorter (FACS) and a flow cytometer with a plate reader, plus the equipment necessary to perform cell culture. In addition to the technical protocol for execution of the assay on human HSPCs, we also provide the detailed analysis framework, including the statistical testing necessary to assess cellular properties related to the concept of cell family25. This protocol has already been used successfully to describe the murine HSPC compartment26,27.
The following protocol uses magnetically enriched CD34+ cells as the starting material28. In this way, it is possible to efficiently stain and isolate the human HSPCs from different blood sources (e.g., cord blood, bone marrow, peripheral blood). It is important not to discard the CD34- fraction, as it will be used as part of the protocol to set different types of experimental controls. The mentioned cell amounts and volumes can be scaled up or down, according to the experimental workflow and necessities. Similarly, the protocol can be adapted to the study of different types of progenitors, simply by modifying the antibodies used for the cell sorting and flow cytometry steps.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1.5 mL polypropylene microcentrifuge tubes</td>
			<td>vWR</td>
			<td>87003-294</td>
			<td></td>
		</tr>
		<tr>
			<td>15-mL polypropylene tubes</td>
			<td>vWR</td>
			<td>734-0451</td>
			<td></td>
		</tr>
		<tr>
			<td>50-mL polypropylene tubes</td>
			<td>vWR</td>
			<td>734-0448</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well U-bottom culture plate&#160;</td>
			<td>Falcon</td>
			<td>353077</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-human Lin APC</td>
			<td>Thermo Fisher</td>
			<td>22-7776-72</td>
			<td>Dilution 1/40</td>
		</tr>
		<tr>
			<td>ARIA III</td>
			<td>BD</td>
			<td></td>
			<td>Can be replaced with any FACS sorter able to sort individual cells in 96-wells plate</td>
		</tr>
		<tr>
			<td>Carboxyfluorescein succinimidyl ester (CFSE)</td>
			<td>Life Technologies</td>
			<td>C34570</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell Trace Violet (CTV)&#160;</td>
			<td>Life Technologies</td>
			<td>C34571</td>
			<td></td>
		</tr>
		<tr>
			<td>Compensation beads&#160;</td>
			<td>BD</td>
			<td>552843</td>
			<td></td>
		</tr>
		<tr>
			<td>Dulbecco&#8217;s modified Eagle medium (DMEM)</td>
			<td>Life Technologies</td>
			<td>11320033</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethylenediaminetetraacetic acid (EDTA)</td>
			<td>Thermo Scientific</td>
			<td>J62948-36</td>
			<td>Prepare a solution 0.5 M, in sterilised water</td>
		</tr>
		<tr>
			<td>FC block Fc1.3216</td>
			<td>BD</td>
			<td>564220</td>
			<td>Dilution 1/50</td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum (FBS)</td>
			<td>Dutscher</td>
			<td>S1900-500C</td>
			<td>Batch S00CH</td>
		</tr>
		<tr>
			<td>FlowJo v10.8.1</td>
			<td>BD</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Mouse anti-human CD10 PerCP-5.5, clone HI10a</td>
			<td>Biolegend</td>
			<td>312216</td>
			<td>Dilution 1/20</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD123 BUV395, clone 7G3</td>
			<td>BD</td>
			<td>564195</td>
			<td>Dilution 1/15</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD34 APC-Cy7, clone 581</td>
			<td>Biolegend</td>
			<td>343513</td>
			<td>Dilution 1/40</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD38 BV650, clone HB7</td>
			<td>Biolegend</td>
			<td>356620</td>
			<td>Dilution 1/40</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD45RA AF700, clone HI100</td>
			<td>BD</td>
			<td>560673</td>
			<td>Dilution 1/20</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD45RA PE-Cy7, clone HI100</td>
			<td>BD</td>
			<td>560675</td>
			<td>Dilution 1/20</td>
		</tr>
		<tr>
			<td>Mouse anti-human CD90 PE, clone 5E10</td>
			<td>Biolegend</td>
			<td>328110</td>
			<td>Dilution 1/20</td>
		</tr>
		<tr>
			<td>Phosphate Buffered Saline (PBS) 1X</td>
			<td>Life Technologies</td>
			<td>10010001</td>
			<td></td>
		</tr>
		<tr>
			<td>Python</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>R</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sterile 12x75 mm conical polypropylene tubes</td>
			<td>Falcon</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>ZE5&#160;</td>
			<td>Biorad</td>
			<td></td>
			<td>Can be replaced with any flow cytometry analyzer equipped with a plate reader</td>
		</tr>
		<tr>
			<td>Laboratory prepared</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Cell culture media</td>
			<td></td>
			<td></td>
			<td>Depends from the specific experiment. Prepare fresh daily and store at +4 &#176;C until use</td>
		</tr>
		<tr>
			<td>DMEM + 10% FBS</td>
			<td></td>
			<td></td>
			<td>Can be stored in sterile conditions, at +4 &#176;C up to 1 year. To prepare 500 mL, add 50 mL of FBS to 450 mL DMEM</td>
		</tr>
		<tr>
			<td>PBS 1X + EDTA 0.1%</td>
			<td></td>
			<td></td>
			<td>Can be stored in sterile conditions, at room temperature, up to 1 year. To prepare 500 mL, add 3.42 mL of EDTA 0.5 M to 500 mL PBS 1X</td>
		</tr>
		<tr>
			<td>Staining buffer</td>
			<td></td>
			<td></td>
			<td>Can be stored in sterile conditions, at +4 &#176;C up to 1 year. To prepare 500 mL, add 2 mL of EDTA 0.5 M and 1 mL FBS to 500 mL PBS 1X</td>
		</tr>
	</tbody>
</table>

simultaneous assessment of kinship, division number, and phenotype via flow cytometry for hematopoietic stem and progenitor cells

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able to generate large datasets, necessitate the destruction of the cell of interest, making it impossible to assess its functional fate. Heterogeneous biological differentiating systems like hematopoiesis are therefore difficult to describe. Building on cell division tracking dyes, we further developed a protocol to simultaneously determine kinship, division number, and differentiation status for many single hematopoietic progenitors. This protocol allows the assessment of the ex vivo differentiation potential of murine and human hematopoietic progenitors, isolated from various biological sources. Moreover, as it is based on flow cytometry and a limited number of reagents, it can quickly generate a large amount of data, at the single-cell level, in a relatively inexpensive manner. We also provide the analytical pipeline for single-cell analysis, combined with a robust statistical framework. As this protocol allows the linking of cell division and differentiation at the single-cell level, it can be used to quantitatively assess symmetric and asymmetric fate commitment, the balance between self-renewal and differentiation, and the number of divisions for a given commitment fate. Altogether, this protocol can be used in experimental designs aiming to unravel the biological differences between hematopoietic progenitors, from a single-cell perspective.

Watch this Scientific Journal Video about Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells at JoVE.com

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Presented here is a flow cytometry-based technique that allows for simultaneously measuring the number of cell divisions, surface cell phenotype, and cellular kinship. Those properties can be tested statistically using a permutation-based framework.

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able ...

simultaneous-assessment-kinship-division-number-phenotype-via-flow

Research

JoVE Journal

Immunology and Infection

1.2K Views.  Institut Curie. Presented here is a flow cytometry-based technique that allows for simultaneously measuring the number of cell divisions, surface cell phenotype, and cellular kinship. Those properties can be tested statistically using a permutation-based framework. 

Video: Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Quantitative Assessment of Immune Cells in the Injured Spinal Cord Tissue by Flow Cytometry:  a Novel Use for a Cell Purification Method

Detection of immune cells in the injured central nervous system (CNS) using morphological or histological techniques has not always provided true quantitative analysis of cellular inflammation. Flow cytometry is a quick alternative method to quantify immune cells in the injured brain or spinal cord tissue. Historically, flow cytometry has been used to quantify immune cells collected from blood or dissociated spleen or thymus, and only a few studies have attempted to quantify immune cells in the injured spinal cord by flow cytometry using fresh dissociated cord tissue. However, the dissociated spinal cord tissue is concentrated with myelin debris that can be mistaken for cells and reduce cell count reliability obtained by the flow cytometer. We have advanced a cell preparation method using the OptiPrep gradient system to effectively separate lipid/myelin debris from cells, providing sensitive and reliable quantifications of cellular inflammation in the injured spinal cord by flow cytometry. As described in our recent study (Beck &amp; Nguyen et al., Brain. 2010 Feb; 133 (Pt 2): 433-47), the OptiPrep cell preparation had increased sensitivity to detect cellular inflammation in the injured spinal cord, with counts of specific cell types correlating with injury severity. Critically, novel usage of this method provided the first characterization of acute and chronic cellular inflammation after SCI to include a complete time course for polymorphonuclear leukocytes (PMNs, neutrophils), macrophages/microglia, and T-cells over a period ranging from 2 hours to 180 days post-injury (dpi), identifying a surprising novel second phase of cellular inflammation. Thorough characterization of cellular inflammation using this method may provide a better understanding of neuroinflammation in the injured CNS, and reveal an important multiphasic component of neuroinflammation that may be critical for the design and implementation of rational therapeutic treatment strategies, including both cell-based and pharmacological interventions for SCI.

Quantification of cellular inflammation in the injured/pathological CNS by flow cytometry is complicated by lipid/myelin debris that can have similar size and granulation to cells, decreasing sensitivity/accuracy. We have advanced a cell preparation method to remove myelin debris and improve cell detection by flow cytometry in the injured spinal cord.

Detection of immune cells in the injured central nervous system (CNS) using morphological or histological techniques has not always provided true ...

Flow Cytometry Analysis of Immune Cells Within Murine Aortas

Atherosclerosis is a chronic inflammatory process of medium and large size vessels that is characterized by the formation of plaques consisting of foam cells, immune cells, vascular endothelial and smooth muscle cells, platelets, extracellular matrix, and a lipid-rich core with extensive necrosis and fibrosis of surrounding tissues.1 The innate and adaptive arms of the immune response are involved in the initiation, development and persistence of atherosclerosis.2, 3 There is a significant body of evidence that different subsets of the immune cells, such as macrophages, dendritic cells, T and B lymphocytes, are present within the aortas of healthy and atherosclerosis-prone mice4. Additionally, immune cells are found in the surrounding aortic adventitia which suggests an important role of this tissue in atherogenesis.2
For some time, the quantitative detection of different types of immune cells, their activation status, and the cellular composition within the aortic wall was limited by RT-PCR and immunohistochemical methods for the study of atherosclerosis. Few attempts were made to perform flow cytometry using human aortas, and a number of problems, such as a high autofluorescence, have been reported5,6. Human atherosclerotic plaques were digested with collagenase 1, and free cells were collected and stained for CD14+/CD11c+ to highlight macrophage-derived foam cells. In this study, a "mock" channel was used to avoid false-positive staining.6 Necrotic materials accumulating during the digestion process give rise in a large amount of debris that generates a high autofluorescence in aortic samples. To resolve this problem, a panel of negative and positive controls has been proposed, but only double staining could be applied in these samples. We have developed a new flow cytometry-based method7 to analyze the immune cell composition and characterize the activation, proliferation, differentiation of immune cells in healthy and atherosclerosis-prone aorta. This method allows the investigation of the immune cell composition of the aortic wall and opens possibilities to use a broad spectrum of immunological methods for investigations of immune aspects of this disease.

This paper presents a flow cytometry-based method to investigate the immune composition of aortas. The paper also illustrates an additional technique that allows examining surrounding adventitia and vessel wall separately. This method opens possibilities to perform phenotypical analyses of aortic leukocytes and apply several immunological assays for atherosclerosis studies.

Atherosclerosis is a chronic inflammatory process of medium and large size vessels that is characterized by the formation of plaques consisting of foam ...

Human In Vitro Suppression as Screening Tool for the Recognition of an Early State of Immune Imbalance

Regulatory T cells (Tregs) are critical mediators of immune tolerance to self-antigens. In addition, they are crucial regulators of the immune response following an infection. Despite efforts to identify unique surface marker on Tregs, the only unique feature is their ability to suppress the proliferation and function of effector T cells. While it is clear that only in vitro assays can be used in assessing human Treg function, this becomes problematic when assessing the results from cross-sectional studies where healthy cells and cells isolated from subjects with autoimmune diseases (like Type 1 Diabetes-T1D) need to be compared. There is a great variability among laboratories in the number and type of responder T cells, nature and strength of stimulation, Treg:responder ratios and the number and type of antigen-presenting cells (APC) used in human in vitro suppression assays. This variability makes comparison between studies measuring Treg function difficult. The Treg field needs a standardized suppression assay that will work well with both healthy subjects and those with autoimmune diseases. We have developed an in vitro suppression assay that shows very little intra-assay variability in the stimulation of T cells isolated from healthy volunteers compared to subjects with underlying autoimmune destruction of pancreatic &beta;-cells. The main goal of this piece is to describe an in vitro human suppression assay that allows comparison between different subject groups. Additionally, this assay has the potential to delineate a small loss in nTreg function and anticipate further loss in the future, thus identifying subjects who could benefit from preventive immunomodulatory therapy1. Below, we provide thorough description of the steps involved in this procedure. We hope to contribute to the standardization of the in vitro suppression assay used to measure Treg function. In addition, we offer this assay as a tool to recognize an early state of immune imbalance and a potential functional biomarker for T1D.

Human In Vitro Suppression as Screening Tool for the Recognition of an Early State of Immune Imbalance

Tregs are potent suppressors of the immune system. There is a lack of unique surface markers to define them, hence, definitions of Tregs are primarily functional. Here we describe an optimized in vitro assay capable of identifying immune imbalance in subjects at risk to develop T1D.

Regulatory T cells (Tregs) are critical mediators of immune tolerance to self-antigens. In addition, they are crucial regulators of the immune response ...

Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

In recent years, it has become apparent that genomic instability is tightly related to many developmental disorders, cancers, and aging. Given that stem cells are responsible for ensuring tissue homeostasis and repair throughout life, it is reasonable to hypothesize that the stem cell population is critical for preserving genomic integrity of tissues. Therefore, significant interest has arisen in assessing the impact of endogenous and environmental factors on genomic integrity in stem cells and their progeny, aiming to understand the etiology of stem-cell based diseases.
LacI transgenic mice carry a recoverable &#955;&#160;phage vector encoding the LacI reporter system, in which the LacI gene serves as the mutation reporter. The result of a mutated LacI gene is the production of &#946;-galactosidase that cleaves a chromogenic substrate, turning it blue. The LacI reporter system is carried in all cells, including stem/progenitor cells and can easily be recovered and used to subsequently infect E. coli. After incubating infected E. coli on agarose that contains the correct substrate, plaques can be scored; blue plaques indicate a mutant LacI gene, while clear plaques harbor wild-type. The frequency of blue (among clear) plaques indicates the mutant frequency in the original cell population the DNA was extracted from. Sequencing the mutant LacI gene will show the location of the mutations in the gene and the type of mutation.
The LacI transgenic mouse model is well-established as an in vivo mutagenesis assay. Moreover, the mice and the reagents for the assay are commercially available. Here we describe in detail how this model can be adapted to measure the frequency of spontaneously occurring DNA mutants in stem cell-enriched Lin-IL7R-Sca-1+cKit++(LSK) cells and other subpopulations of the hematopoietic system.

Here we describe an in vivo mutagenesis assay for small numbers of purified hematopoietic cells using the LacI transgenic mouse model.&#160;The LacI gene can be isolated to determine the frequency, location, and type of DNA mutants&#160;spontaneously arisen or after exposure to genotoxins.

In recent years, it has become apparent that genomic instability is tightly related to many developmental disorders, cancers, and aging. Given that stem ...

Flow Cytometry-based Assay for the Monitoring of NK Cell Functions

Natural killer (NK) cells are an important part of the human tumor immune surveillance system. NK cells are able to distinguish between healthy and virus-infected or malignantly transformed cells due to a set of germline encoded inhibitory and activating receptors. Upon virus or tumor cell recognition a variety of different NK cell functions are initiated including cytotoxicity against the target cell as well as cytokine and chemokine production leading to the activation of other immune cells. It has been demonstrated that accurate NK cell functions are crucial for the treatment outcome of different virus infections and malignant diseases. Here a simple and reliable method is described to analyze different NK cell functions using a flow cytometry-based assay. NK cell functions can be evaluated not only for the whole NK cell population, but also for different NK cell subsets. This technique enables scientists to easily study NK cell functions in healthy donors or patients in order to reveal their impact on different malignancies and to further discover new therapeutic strategies.

A simple and reliable method is described here to analyze a set of NK cell functions such as degranulation, cytokine and chemokine production within different NK cell subsets.

Natural killer (NK) cells are an important part of the human tumor immune surveillance system. NK cells are able to distinguish between healthy and ...

Fluorescence-activated Cell Sorting for Purification of Plasmacytoid Dendritic Cells from the Mouse Bone Marrow

Fluorescence-activated cell sorting (FACS) is a technique to purify specific cell populations based on phenotypes detected by flow cytometry. This method enables researchers to better understand the characteristics of a single cell population without the influence of other cells. Compared to other methods of cell enrichment, such as magnetic-activated cell sorting (MCS), FACS is more flexible and accurate for cell separation due to the ability of phenotype detection by flow cytometry. In addition, FACS is usually capable of separating multiple cell populations simultaneously, which improves the efficiency and diversity of experiments. Although FACS has some limitations, it has been broadly used to purify cells for functional studies in both in vitro and in vivo settings. Here we report a protocol using fluorescence-activated cell sorting to isolate a very rare population of immune cells, plasmacytoid dendritic cells (pDC), with high purity from the bone marrow of lupus-prone mice for in vitro functional studies of pDC.

We report a protocol using fluorescence-activated cell sorting to isolate plasmacytoid dendritic cells (pDC) with high purity from the bone marrow of lupus-prone mice for functional studies of pDC.

Fluorescence-activated cell sorting (FACS) is a technique to purify specific cell populations based on phenotypes detected by flow cytometry. This method ...

A Flow Cytometry-Based Cytotoxicity Assay for the Assessment of Human NK Cell Activity

Within the innate immune system, effector lymphocytes known as natural killer (NK) cells play an essential role in host defense against aberrant cells, specifically eliminating tumoral and virally infected cells. Approximately 30 known monogenic defects, together with a host of other pathological conditions, cause either functional or classic NK cell deficiency, manifesting in reduced or absent cytotoxic activity. Historically, cytotoxicity has been investigated with radioactive methods, which are cumbersome, expensive and potentially hazardous. This article describes a streamlined, clinically applicable flow cytometry-based method to quantify NK cell cytotoxic activity. In this assay, peripheral blood mononuclear cells (PBMCs) or purified NK cell preparations are co-incubated at different ratios with a target tumor cell line known to be sensitive to NK cell-mediated cytotoxicity (NKCC). The target cells are pre-labeled with a fluorescent dye to allow their discrimination from the effector cells (NK cells). After the incubation period, killed target cells are identified by a nucleic acid stain, which specifically permeates dead cells. This method is amenable to both diagnostic and research applications and, thanks to the multi-parameter capabilities of flow cytometry, has the added advantage of potentially enabling a deeper analysis of NK cell phenotype and function.

A flow cytometry-based method to quantitatively determine the cytotoxic activity of human natural killer cells is shown here.

Within the innate immune system, effector lymphocytes known as natural killer (NK) cells play an essential role in host defense against aberrant cells, ...

Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages

Alveolar macrophages are terminally differentiated, lung-resident macrophages of prenatal origin. Alveolar macrophages are unique in their long life and their important role in lung development and function, as well as their lung-localized responses to infection and inflammation. To date, no unified method for identification, isolation, and handling of alveolar macrophages from humans and mice exists. Such a method is needed for studies on these important innate immune cells in various experimental settings. The method described here, which can be easily adopted by any laboratory, is a simplified approach to harvesting alveolar macrophages from bronchoalveolar lavage fluid or from lung tissue and maintaining them in vitro. Because alveolar macrophages primarily occur as adherent cells in the alveoli, the focus of this method is on dislodging them prior to harvest and identification. The lung is a highly vascularized organ, and various cell types of myeloid and lymphoid origin inhabit, interact, and are influenced by the lung microenvironment. By using the set of surface markers described here, researchers can easily and unambiguously distinguish alveolar macrophages from other leukocytes, and purify them for downstream applications. The culture method developed herein supports both human and mouse alveolar macrophages for in vitro growth, and is compatible with cellular and molecular studies.

Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages

This communication describes methodologies for isolation and culture of alveolar macrophages from humans and murine models for experimental purposes.

Alveolar macrophages are terminally differentiated, lung-resident macrophages of prenatal origin. Alveolar macrophages are unique in their long life and ...

Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells

In this article, we present a protocol that is optimized to preserve neutrophil-lineage cells in fresh BM for whole BM CyTOF analysis. We utilized a myeloid-biased 39-antibody CyTOF panel to evaluate the hematopoietic system with a focus on the neutrophil-lineage cells by using this protocol. The CyTOF result was analyzed with an open-resource dimensional reduction algorithm, viSNE, and the data was presented to demonstrate the outcome of this protocol. We have discovered new neutrophil-lineage cell populations based on this protocol. This protocol of fresh whole BM preparation may be used for 1), CyTOF analysis to discover unidentified cell populations from whole BM, 2), investigating whole BM defects for patients with blood disorders such as leukemia, 3), assisting optimization of fluorescence-activated flow cytometry protocols that utilize fresh whole BM.

Here, we present a protocol to process fresh bone marrow (BM) isolated from mouse or human for high-dimensional mass cytometry (Cytometry by Time-Of-Flight, CyTOF) analysis of neutrophil-lineage cells.

In this article, we present a protocol that is optimized to preserve neutrophil-lineage cells in fresh BM for whole BM CyTOF analysis. We utilized a ...

Isolation of Uterine Innate Lymphoid Cells for Analysis by Flow Cytometry

Described here is a simple method to isolate and phenotype mouse group 1 uterine innate lymphoid cells (g1 uILCs) from individual pregnant uterus by flow cytometry. The protocol describes how to set up time mating to obtain multiple synchronous dams, the mechanical and enzymatic digestion of the pregnant uterus, the staining of single-cell suspensions, and a FACS strategy to phenotype and discriminate g1 uILCs. Although this method inevitably loses the spatial information of cellular distribution within the tissue, the protocol has been successfully applied to determine uILC heterogeneity, their response to maternal and foetal factors affecting pregnancy, their gene expression profile, and their functions.

This is a method to isolate uterine lymphoid cells from both pregnant and non-pregnant mice. This method can be used for multiple downstream applications such as FACS phenotyping, cell sorting, functional assays, RNA-seq, and proteomics. The protocol here demonstrates how to phenotype group 1 uterine innate lymphoid cells by flow cytometry.

Described here is a simple method to isolate and phenotype mouse group 1 uterine innate lymphoid cells (g1 uILCs) from individual pregnant uterus by flow ...