Phasic oxygen dynamics confounds fast choline-sensitive biosensor signals in the brain of behaving rodents
Authors: Anton Sirota
Ricardo M Santos
Journal: eLife (2021)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: amperometry; biochemistry; chemical biology; cholinergic; in vivo
Affiliations: Bernstein Center for Computational Neuroscience, Faculty of Medicine, Ludwig-Maximilians Universit√§t M√ºnchen, Planegg-Martinsried, Germany

Abstract:  Cholinergic fast time-scale modulation of cortical physiology is critical for cognition, but direct local measurement of neuromodulators in vivo is challenging. Choline oxidase (ChOx)-based electrochemical biosensors have been used to capture fast cholinergic signals in behaving animals. However, these transients might be biased by local field potential and O2-evoked enzymatic responses. Using a novel Tetrode-based Amperometric ChOx (TACO) sensor, we performed highly sensitive and selective simultaneous measurement of ChOx activity (COA) and O2. In vitro and in vivo experiments, supported by mathematical modeling, revealed that non-steady-state enzyme responses to O2 give rise to phasic COA dynamics. This mechanism accounts for most of COA transients in the hippocampus, including those following locomotion bouts and sharp-wave/ripples. Our results suggest that it is unfeasible to probe phasic cholinergic signals under most behavioral paradigms with current ChOx biosensors. This confound is generalizable to any oxidase-based biosensor, entailing rigorous controls and new biosensor designs. 

Retracted: No

PubPeer comments:
 In the ANOVA and post hoc tests of Fig. 1D, Fig. 1S1, Fig. 3HI, were any precautions taken for the unequal variances? Assuming there were none, the significant difference in Fig. 3I looks like it won t survive in a direct test with correction for multiple comparisons. I suspect the ANOVAs in Fig. 3HI should be repeated-measures tests. Tests of normality (like Anderson-Darling) probably have power that is too low to detect non-normality for the small sample sizes where that would be useful. , 
 #1 Dear Hemigephyra Atra, Thank you for raising such questions on statistical analysis. The ANOVA tests you refer to did not explicitly control for unequal variances. However, ANOVA is relatively robust to deviations from the assumption of equal variances, provided the groups being compared have the same or approximately the same size (which is the case). Additionally, the differences between groups in Figures 1 and 3 are highly significant, assuring that variations in the statistical approach used wonu2019t affect the inference of significance. This includes the tests relative to Figures 3H and I, for which we have also done a non-parametric Wilcoxon rank-sum test followed by a false discovery rate Benjamini Hochberg correction for multiple comparisons (not included in the last version of the paper). Noteworthy, this still results in a significant difference between groups at 10 uM cumulative O2 concentration in Figure 3I. The ANOVAs in Figure 3H and I are not repeated measures since values come from calibrations of different sensors. In general, the power of normality tests decreases with sample size. However, it is feasible to consider that for small sample sizes, the non-rejected normality of the data still warrants an adequately unbiased assessment of the data using parametric tests. , 
 #2 Thank you for the rapid reply. The rule of thumb I know is that ANOVA is robust to variances differing within a factor of 4 (and this also requires groups to be equal), which implies a factor of 2 for the standard deviations. The problem as I see it arises from the habitual use of the pooled variance in the post hoc tests; I don t believe the Tukey-Kramer test adjusts for different group variances? In any case, the factor of two doesn t appear to be satisfied in Fig. 1D (although I don t believe that result would be called into question) and more importantly for Fig. 3I. In the latter, the only point of difference between the blue and orange curves is at 10 uM O2, yet the blue point has a confidence interval that is much larger than those of all the other points, especially the other blue ones. I don t have matlab and I m not sure I can extract the data easily to check, but my feeling is that the difference at 10 uM would be much more weakly significant if a direct test were performed and correction made for 5 multiple comparisons. I apologise if I misunderstood the experimental design. What I understood was that several electrodes were each exposed to different concentrations of O2 (as in panel 3B) and that its cumulative concentration is what is plotted on the x-axis of panel 3I. If the same electrodes were tested at the different O2 concentrations and the O2 was one of the factors of your ANOVA, then I believe they would be repeated measures because the same electrodes appear in each. Regarding tests for normality, eLife s own instructions regarding statistical errors state:  Another problem related to small sample size is that the distribution of the sample is more likely to deviate from normality, and the limited sample size makes it often impossible to rigorously test the assumption of normality (Ghasemi and Zahediasl, 2012). https: elifesciences.org articles 48175 ,