I tend to agree with Maryalice on this issue. I do many CD34 determinations over a weeks time. Percentages as low as .02% can be common. what makes me feel confident in the data is the exact phenotype on multiple parameters that give a clustering affect. These cells always fall within certain areas on the parameters involved and do form a significant cluster. Someone many moons ago told me that the confidence in analyzing rare events is due to the number of parameters that are used to determine those events. Thanks Maryalice for your comment. jim houston -----Original Message----- From: Maryalice Stetler-Stevenson [mailto:stetler@box-s.nih.gov] Sent: Monday, December 06, 1999 8:17 AM To: cyto-inbox Subject: RE: rare event Actually, 5 cells can mean something in an analysis, although one has to be cautious. If you are just setting quadrants or gates based upon isotype controls, then it is not meaningful. With distictly abnormal tumor cells, you can have a pattern of staining that is far removed from background. Furthermore there is a cluster, not a spread, and this cluster is seen in multiple parameter analysis, including size and side scatter. I most often seen this with Hairy cell leukemia.If 1)the cells are clustering in an abnormally bright pattern for CD20 and CD22 and positive for kappa but negative for lambda, 2) lying between normal lymphs and monocytes on FSC vrs SSC 3) and others tubes are consistent-e.g. Bright CD25+, Bright CD11c+, Bright CD20 and CD103+ population that forms a nice cluster,this is detection of tumor in a patient with a history of hairy cell leukemia matching this immunophenotype. It is the homogeneous, tight clustering pattern over multiple parameters that allows one to see that this is not background. This is not generally applicable, but does apply in some clinical specimens. I still would cautiously word a written diagnosis on such a case but would verbally convey my impression to the clinician. Now this may not pertain to the discussion at hand, but I felt chatty this morning. Maryalice >Ann - -I am a little puzzled. The statistics of a 0.05% population in 10,000 >total cells is only 5 cells. Even with the best possible background, the >confidence limits of that frequency are statistically pretty poor. In the >paper I gave to Hai Qi (see corrected reference on Flow Cytometry Mailing >List), we explored this issue and showed that accurate frequency prediction >is relatively poor below about 20 cells and only really gets stable at about >50 cells. If you see something that I don't, I would really want to know! >--Jim Leary > >-----Original Message----- >From: Ann Atzberger [mailto:Ann.Atzberger@EMBL-Heidelberg.de] >Sent: Friday, December 03, 1999 7:32 AM >To: cyto-inbox >Subject: Re: rare event > > > >Hallo Hai Qi, > >if I understand correctly, you ran a total of 200 thousand cells of the >same sample through the machine twice. As you say the subpopulation is not >that rare at 0.15%. I have often analysed samples with rare events at >0.05% of the total population with good precision,(granted the signal was >way above background) and only saving 10,000 events. > >If you have a lot of debris or dead cells in the sample the %s can vary; so >you can either: 1.use a ficoll gradient to clean up the sample. >2.reset your threshold value to cut out as much debris as possible. >3.live gate. >4.Run the cells slowly through the machine. >5.Make sure the machine is clean, as bits of junk stuck in the sample probe >can effect your values. > >I might be wrong but I think saving 200,000 cells has to be more than ample >to detect this subpopulation. > >Ann >At 16:49 01.12.99 -0600, you wrote: >> >> >>It is really not too rare, a cell population about 0.04% to 0.15%, while >the >>background is 0.01-0.05%. The problem is the variation among different >>measurements, presumably due to the systematic error. For example, I had >>10e7 total cells and ran 2*10e5 out of them for two times: one time I got >>0.04% and the other time I got 0.15%. In order to have smaller variation, >>obviously I have to increase my sample size, but the last thing I want to >do >>is to run through all 10e7 cells. Can anyone give me a handy statistical >>method for estimating the sample size that makes a cost-effective >>compromise? Thank you very much. >> >>Hai Qi >>Dept. of Pathology, UTMB >> >> Maryalice Stetler-Stevenson Director Flow Cytometry Unit Laboratory of Pathology, NCI, NIH
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