In situ background estimation in quantitative fluorescence imaging.
Fluorescence imaging of bulk-stained tissue is a popular technique for monitoring the activities in a large population of cells. However, a precise quantification of such experiments is often compromised by an ambiguity of background estimation. Although, in single-cell-staining experiments, background can be measured from a neighboring nonstained region, such a region often does not exist in bulk-stained tissue. Here we describe a novel method that overcomes this problem. In contrast to previous methods, we determined the background of a given region of interest (ROI) using the information contained in the temporal dynamics of its individual pixels. Since no information outside the ROI is needed, the method can be used regardless of the staining profile in the surrounding tissue. Moreover, we extend the method to deal with background inhomogeneities within a single ROI, a problem not yet solved by any of the currently available tools. We performed computer simulations to demonstrate the accuracy of our method and give example applications in ratiometric calcium imaging of bulk-stained olfactory bulb slices. Converting the fluorescence signals into [Ca2+] gives resting values consistent with earlier single-cell staining results, and odorant-induced [Ca2+] transients can be quantitatively compared in different cells. Using these examples we show that inaccurate background subtraction introduces large errors (easily in the range of 100%) in the assessment of both resting [Ca2+] and [Ca2+] dynamics. The proposed method allows us to avoid such errors.
Chen
Tsai-Wen
TW
Institute of Physiology, and Department of Medical Statistics, University of Göttingen, Göttingen, Germany.
Lin
Bei-Jung
BJ
Brunner
Edgar
E
Schild
Detlev
D
D000465:Algorithms; D000818:Animals; D001703:Biophysics / Q000379:methods*; D001921:Brain / Q000378:metabolism; D002118:Calcium / Q000737:chemistry / Q000378:metabolism; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted; D016013:Likelihood Functions; D018613:Microscopy, Confocal; D008856:Microscopy, Fluorescence / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D009830:Olfactory Bulb / Q000378:metabolism; D009832:Olfactory Nerve / Q000378:metabolism; D012044:Regression Analysis; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors; D014982:Xenopus laevis
D002118:Calcium