In cases where cells do not have clear positive population rare events or low expression level or the amount of sample is limiting e. These beads bind antibodies through their constant regions light chain or Fc part and are a universal reagent to generate strong positive signal for each of your markers.
Resulting flow data must be corrected using compensation or the situation can be avoided in the first place by moving PE to yellow-green laser line. FITC single stain control before and after compensation. Note that compensation does not alter the existing data spread but can make it more visible by bringing a population downwards on a log scale. Like the logarithmic visualization methods that have been used for many years for flow cytometry data, Logicle visualization methods do not alter raw data in any way.
They simply provide a method of visualizing data that enables display of data points that are obscured by logarithmic visualization methods and a more intuitive way for visualizing data in the region around zero. However, Logicle visualization offers an additional benefit: it provides a flexible scan that can be alters to enable the best visualization for a population of interest.
However, in Logicle displays, it serves to increase the ability to resolve populations at the low end of the scale. However, this default visualization setting is often sub-optimal as fluorescence values for some cells may fall below the default, forcing pile-ups at the low end of the scale during the initial input into FlowJo that cannot be corrected except by re-importing the data.
Once the data is imported, we then do an initial series of gatings to select non-clumped, live, size-gated lymphocytes, and to view this population in a window of its own see Figure 2.
This allows us to define a broad population that can be used to reset the Logicle visualization scales in a way that will put all cells in the population on scale in all channels. In these cases, the values surrounding zero are highly compressed and it is difficult to resolve populations that fall within that regions.
Figure 4 shows a data set before and after transformation. Note that the logicle transformation may increase or decrease length of the linear region that surround zero on the logicle scale.
Scale transformations such as these are more familiar in settings where a scale is changed from log to linear to separate points that would otherwise be crowded together. We developed the logicle scale to serve the same purpose in a way that is well suited to the types of data collected in flow cytometry. This initial transformation resets the lodicle scales in all data dimensions to eliminate regions where no data points exist.
However, for individual subsets, the region below well zero may be very sparsely populated even if data points exist in this region for other subset. Therefore, we sometimes do retransformations to obtain the best views for these subsets. For example, in the upper panels in the figure, the region between and on the CD11a axis shown in the figure as a shaded region is compressed such that it is difficult to resolve subset details in this region. A better view of these data can be obtained by calling for a retransformation of the displayed data.
This results in a new display in which the CD11a data for all cells in the subset being viewed are now appropriately distributed along the CD11a axis. In other analyses, retransformation often resolves subsets that have low cell-associated fluorescence from those with autofluorescence levels not shown. Importantly, retransformation based on the one subset e. Thus, additional retransformation may be required based specifically on individual subsets.
Note that any transformation, including the original one, can be recovered by gating on the appropriate subset a calling for another retransformation. A combination of cell surface markers can be used to identify various human lymphocyte and leukocyte populations. The gating path strategy that is followed can make a great difference in the ease with which individual subsets can be teased out of the overall data set. We start by gating out dead cells and scatter gating to remove small debris and large clumps of cells.
After this, we routinely try several strategies before deciding on one that is useful. Common procedures for the analysis of human blood include gradient density centrifugation-based or magnetic separation of subsets of interest.
However, multiparameter flow cytometry enables examination of leukocytes subsets in whole blood, without any prior purification or manipulation. This figure illustrates stepwise gating, based on 6-color staining, to resolve neutrophils and other leukocyte populations in human peripheral blood.
Other markers can be added to this 6-color combination to investigate functional properties e. Six of the colors are routinely used to identify the subsets; the remaining colors are used for experiment purposes. Figure 5 shows the color stain combination and gating strategy that we routinely use to identify human peripheral blood eosinophils, neutrophils, basophils, NK cells, monocytes, T cells, and B cells; 6 colors are used for subset discrimination The reagents in the stain sets used for each of the staining combinations are presented in Table 1.
The fluorescence detection channel denotes the fluorescence of the primary fluorochrome being detected. Reagents such as PerCPCy5. Note that, although quadrant gating is used quite commonly, this method most often forces the inclusion of unwanted cells in one or another of the gates.
As we have indicated above, FMO controls are far more appropriate and rewarding for such purposes see Figure 2B. In essence, unless the populations in all four quadrants are well separated, use of the quadrant gating method should be avoided. Fortunately, FACS technology development, and the emergence of new software support for various aspects of this technology, are now cooperating in this effort.
We look forward to seeing more and better FACS data in the future, and hope that our readers join us in helping to achieve this goal. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication.
As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. National Center for Biotechnology Information , U. Clin Lab Med. Author manuscript; available in PMC Sep 1. James W.
Parks , Leonard A. Herzenberg , and Leonore A. Author information Copyright and License information Disclaimer. Corresponding author. Tung: ude. Herzenberg: ude. Copyright notice. The publisher's final edited version of this article is available at Clin Lab Med. See other articles in PMC that cite the published article. Fluorescence compensation Most FACS instruments have fluorescence compensation hardware that can be set to correct for spill-over.
Logicle vs. Log visualization Logarithmic scales have been used for years to visualize FACS data, both for data collection and data analysis. Open in a separate window. Figure 1. Thus, we suggest: 1. Choose the appropriate reagent combination for your study Even when they have access to FACS instruments that can collect data for 12 or more fluorescence colors, many investigators continue to do 2-or 3-color FACS analysis. Figure 2. Memory T cell subsets are best resolved by stains that include CD62L Compensated data are shown for freshly isolated human peripheral blood mononuclear cells Ficoll-isolated PBMC stained with a 9-color reagent combination.
Add the correct compensation controls Fluorescence compensation, which corrects for spectral overlap spillover of one fluorescence color into the channel in which another color is detected, is paramount to correct analysis of FACS data. Figure 3. Include Fluorescence-minus-one FMO controls when needed Defining the boundary between positive and negative cells has always been a challenge when dully-staining subsets need to be resolved.
Use automated protocol design tools if available Engineers and architects routinely use Computer Aided Design CAD tools that provide the information and infrastructure necessary for the efficient planning of both simple and highly complex buildings. Collect uncompensated data with a well-standardized instrument 1. Set the instrument up correctly The hardware design for FACS instruments has progressed much in the last few years. Collect uncompensated data FACS data should always be collected prior to application of the fluorescence compensation correction.
Safely store and archive the data once collected If FACS data is worth collecting, it is most likely worth saving. Use Logicle bi-exponential data displays to view compensated data 1. Compute the fluorescence compensation matrix and apply it to the data Choose a data analysis program that has a compensation utility. When you are ready to proceed to a more complete overview of the new compensation work flow, please see this document. Here is a collection of external links about Compensation.
Search for:. Compensation has undergone a major redesign for FlowJo Version 10!
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