Many basic and clinical studies, particularly in the HIV arena, rely on flow cytometry for collecting and analysizing the study data needed. Recent advances in flow cytometry instrumentation and reagent availability have opened the way to processing larger numbers of samples and to using the technology to address broader questions at the single cell level, e.g., examination of protein phosphorylation, cytokine, chemokine and enzyme production, a measure of T cell activation at the """"""""fine"""""""" subset level, in stimulated and non-stimulated peripheral blood lymphocyte samples from HIV-infected and non-infected subjects. However, having invented/developed much of the basic technology incorporated in today's flow cytometry instruments, and also having run a major basic and clinical research laboratory for many years, and also having led a major basic and clinical research laboratory and the main flow cytometry service center at Stanford, we are keenly aware keenly aware that the capabilities of the new FACS instruments (and the older ones) far outstrip the current data handling capabilites of most laboratories employing these instruments. This is a particular problem in HIV research, an area of special interest in our laboratory and one in which we have direct experience with the difficulties involved in processing flow data. Recognizing this problem, we propose here to develop software that will fully automate the standardization and fluorescence compensation of flow cytometry data. These initial computation steps, which prepare raw flow data for analysis, are very time consuming and require substantial skill However, our preliminary studies indicate that they can be executed reliably and consistently by software, and with greater attention to data quality, by software that completes these operations without user intervention. We propose to develop this software and, once developed, to make it rapidly available to the HIV research community and beyond as """"""""freeware"""""""" and through cooperating commercial sources. This project will involve development and verification of a new method for evaluating fluorescence compensation based on a comprehensive data model. In addition to full automation this method will have the advantage over the current methods of providing error estimates and other quality assurance information to confirm that the results of the automated analysis are reliable.
The studies we propose focus on development of methods for pre-processing flow cytometry data. To be successful, these tools must perform three basic functions without investigator intervention: 1) they must """"""""standardize"""""""" the data so that it can be compared when collected on different days or at different sites;2) they must """"""""compensate"""""""" the data to correct the values obtained in each fluorescence channel for spectral overlap from dyes read in other channels;and, 3) because data quality is central, they must evaluate the raw and processed data and warn the user if data quality has been compromised. Finally, when analyses involve distinguishing between dully stained and unstained cells, the tools should be able to compute virtual FMO distributions that allow data for a stained sample to be used to determine appropriate thresholds for making this distinction. Basically, at the close of the data preparation phase, the data should be ready for analysis and the investigator should either be confident that the processed data is reliable or know why it is not!
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