[
Anal Bioanal Chem,
2012]
To accurately determine the quantitative change of peptides and proteins in complex proteomics samples requires knowledge of how well each ion has been measured. The precision of each ions' calculated area is predicated on how uniquely it occupies its own space in m/z and elution time. Given an initial assumption that prior to the addition of the "heavy" label, all other ion detections are unique, which is arguably untrue, an initial attempt at quantifying the pervasiveness of ion interference events in a representative binary SILAC experiment was made by comparing the centered m/z and retention time of the ion detections from the "light" variant to its "heavy" companion. Ion interference rates were determined for LC-MS data acquired at mass resolving powers of 20 and 40 K with and without ion mobility separation activated. An ion interference event was recorded, if present in the companion dataset was an ion within +/- its mass at half-height, +/-15 s of its apex retention time and if utilized by +/-1 drift bin. Data are presented illustrating a definitive decrease in the frequency of ion interference events with each additional increase in selectivity of the analytical workflow. Regardless of whether the quantitative experiment is a composite of labeled samples or label free, how well each ion is measured can be determined given knowledge of the instruments mass resolving power across the entire m/z scale and the ion detection algorithm reporting both the centered m/z and mass at half-height for each detected ion. Given these measurements, an effective resolution can be calculated and compared with the expected instrument performance value providing a purity score for the calculated ions' area based on mass resolution. Similarly, chromatographic and drift purity scores can be calculated. In these instances, the error associated to an ions' calculated peak area is estimated by examining the variation in each measured width to that of their respective experimental median. Detail will be disclosed as to how a final ion purity score was established, providing a first measure of how accurately each ions' area was determined as well as how precise the calculated quantitative change between labeled or unlabelled pairs were determined. Presented is how common ion interference events are in quantitative proteomics LC-MS experiments and how ion purity filters can be utilized to overcome and address them, providing ultimately more accurate and precise quantification results across a wider dynamic range.
[
Methods Mol Biol,
2015]
Optogenetics was introduced as a new technology in the neurosciences about a decade ago (Zemelman et al., Neuron 33:15-22, 2002; Boyden et al., Nat Neurosci 8:1263-1268, 2005; Nagel et al., Curr Biol 15:2279-2284, 2005; Zemelman et al., Proc Natl Acad Sci USA 100:1352-1357, 2003). It combines optics, genetics, and bioengineering to render neurons sensitive to light, in order to achieve a precise, exogenous, and noninvasive control of membrane potential, intracellular signaling, network activity, or behavior (Rein and Deussing, Mol Genet Genomics 287:95-109, 2012; Yizhar et al., Neuron 71:9-34, 2011). As C. elegans is transparent, genetically amenable, has a small nervous system mapped with synapse resolution, and exhibits a rich behavioral repertoire, it is especially open to optogenetic methods (White et al., Philos Trans R Soc Lond B Biol Sci 314:1-340, 1986; De Bono et al., Optogenetic actuation, inhibition, modulation and readout for neuronal networks generating behavior in the nematode Caenorhabditis elegans, In: Hegemann P, Sigrist SJ (eds) Optogenetics, De Gruyter, Berlin, 2013; Husson et al., Biol Cell 105:235-250, 2013; Xu and Kim, Nat Rev Genet 12:793-801, 2011). Optogenetics, by now an "exploding" field, comprises a repertoire of different tools ranging from transgenically expressed photo-sensor proteins (Boyden et al., Nat Neurosci 8:1263-1268, 2005; Nagel et al., Curr Biol 15:2279-2284, 2005) or cascades (Zemelman et al., Neuron 33:15-22, 2002) to chemical biology approaches, using photochromic ligands of endogenous channels (Szobota et al., Neuron 54:535-545, 2007). Here, we will focus only on optogenetics utilizing microbial rhodopsins, as these are most easily and most widely applied in C. elegans. For other optogenetic tools, for example the photoactivated adenylyl cyclases (PACs, that drive neuronal activity by increasing synaptic vesicle priming, thus exaggerating rather than overriding the intrinsic activity of a neuron, as occurs with rhodopsins), we refer to other literature (Weissenberger et al., J Neurochem 116:616-625, 2011; Steuer Costa et al., Photoactivated adenylyl cyclases as optogenetic modulators of neuronal activity, In: Cambridge S (ed) Photswitching proteins, Springer, New York, 2014). In this chapter, we will give an overview of rhodopsin-based optogenetic tools, their properties and function, as well as their combination with genetically encoded indicators of neuronal activity. As there is not "the" single optogenetic experiment we could describe here, we will focus more on general concepts and "dos and don'ts" when designing an optogenetic experiment. We will also give some guidelines on which hardware to use, and then describe a typical example of an optogenetic experiment to analyze the function of the neuromuscular junction, and another application, which is Ca(2+) imaging in body wall muscle, with upstream neuronal excitation using optogenetic stimulation. To obtain a more general overview of optogenetics and optogenetic tools, we refer the reader to an extensive collection of review articles, and in particular to volume 1148 of this book series, "Photoswitching Proteins."