There are many techniques through which neuron morphologies may be imaged. These techniques can be classified based on two factors: how they target neurons for imaging, and how the axons and dendrites of the probed neurons are labeled so they are visible for imaging. Neurons may be targeted through their propensity to take up heavy metals or genetic markers, through immunohistochemistry or by direct injection (Elston et al., 1999; Jacobs et al., 2001; Travis et al., 2005; Donohue and Ascoli, 2011; Parekh and Ascoli, 2013, 2015; Carter and Shieh, 2015). Neurons may be labeled using a variety of heavy metals, fluorescent or chromogenic labels are used to allow imaging. We may expect that both targeting and labeling aspects will introduce biases upon the resulting reconstructions of morphology.

Staining with heavy metals remains one of the key imaging techniques. Golgi staining is the oldest such method. In Golgi staining, silver nitrate is introduced to fixed tissue, and the metal is taken up by a sub-population of neurons through a mechanism that remains largely uncharacterized. Neurons are stained in their entirety and then imaged with light microscopy (Koyama, 2013). The method can be subdivided into Rapid Golgi, Golgi-Kopsch, and Golgi-Cox (Koyama, 2013) and each version labels a subset of neurons. Other heavy metals such as osmium and lead can instead be used for dense labeling, which is popular for EM data (Watson, 1958; Tapia et al., 2012). Or, alternatively, lipophilic dyes such as DiOlistics can be introduced ballistically to neurons and allow for Golgi-like staining (Staffend and Meisel, 2011). Since the biological mechanism of heavy staining method is mainly unknown, the extract morphology may be subject to selection bias (Staffend and Meisel, 2011). Because it is relatively simple to perform compared to other methods, heavy metals are a popular staining method for fixed tissues.

A more recent approach is to target neurons through genetic markers. Fluorescent proteins such as green fluorescent protein (GFP), red fluorescent protein (RFP), and yellow fluorescent protein (YFP) can be introduced transgenically to be expressed in neurons, and then imaged through fluorescent microscopy to reveal morphology (Marshall et al., 1995). The use of fluorescent proteins may be limited to animals for which good genetic tools exist. Fluorescent proteins are introduced under the control of promoter regions that are active for known cell-type markers. They target specific populations of neurons, which may differ from those neurons targeted by other methods. The size of these potential selection biases again remains relatively uncharacterized. Fluorescent techniques are popular because they readily integrate both into genetic and physiological approaches.

Immunostaining has advanced to be a leading staining technique. Immunostaining uses antibodies to target neuronal molecular markers, which can be labeled with fluorescent or chromogenic tags for imaging (Chen et al., 2010; Tanapat, 2013). A common approach relies on biotin variants, such as biocytin or neurobiotin, being conjugated to an antibody (Swietek et al., 2016). A complex of biocytin and its binding partner, avidin, are tagged with a fluorescent or colored label that can then be imaged. The avidin-biotin complex allows imaging through light, fluorescent, or EM microscopy depending on the label. Common fluorescent dyes used with immunostaining include Alexa Fluor (AF) (Carter and Shieh, 2015). Immunostaining targets neurons based on particular molecular markers which allows a broad range of targets. Immunostaining is particularly popular as it readily integrates into the modern molecular approaches.

Finally, neurons can be directly injected (Vaney, 2002; Elston, 2003). Direct injection allows neurons to be labeled in vivo or in slice samples and later imaged in a fixed preparation, meaning electrophysiology can be related to morphology. It is common to directly inject fluorescent dyes such as Lucifer Yellow (Hanani, 2012) or biotin variants such as biocytin or neurobiotin (Klenowski et al., 2017).

Each method comes with idiosyncrasies and methodological steps that can vary across laboratories. For instance, in immunostaining the antibody concentration, length of incubation time, and accessibility to the antigen all must be balanced to produce a good result (Paavilainen et al., 2010; Carter and Shieh, 2015). All these factors may vary from lab to lab and are a known source of variability. For example, it has been shown that hippocampal CA1 neurons measured in rats housed in different labs are not consistent in terms of their morphometry (Scorcioni et al., 2004). Tripathy et al. (2015) have shown similar biases in electrophysiology (Tripathy et al., 2015; Tebaykin et al., 2017). Understanding the effects of staining is thus crucial for the interpretation of downstream analyses.

Each method also targets different neurons and operates through different biochemical processes such that, even if performed within the same lab, morphology measurements can differ by staining method. For instance, during dehydration it is well-known that incubation with different dyes can affect tissue shrinkage which in turn can affect morphology (Grace and Llinás, 1985). Neurobiotin staining is known to affect both electrophysiology and morphology (Xi and Xu, 1996). In comparing morphology obtained by Golgi-Cox staining and neurobiotin electroporation, it has been shown that neurobiotin-filling revealed significantly larger dendritic arbors and different spine densities compared to GolgiCox-stained neurons (Klenowski et al., 2017). Despite these known issues, there are few systematic studies that examine the size and nature of these biases across the many methods used to quantify morphology.

Large databases of neuron morphologies (Ascoli, 2006) collect data from many labs, each employing different methods. This allows the comparison of data across distinct staining methods. While many experimental aspects of neuron quantification will differ, the staining method is a central experimental choice. As such, it is important to ask what large databases can tell us about the biases induced by staining methods.

Here we quantify the variation in measured neuron morphology related to the staining or the fluorescent labeling method used, though we will refer to both of these as staining method. We analyze rodent data that has been uploaded by various labs to the public morphology repository neuromorpho.org (Ascoli, 2006). We group them based on the biological attributes and the staining methods. By matching on biological attributes and comparing the morphometry of each group we identify the variation that can be explained by different staining methods.

We first asked if neurons obtained by distinct staining methods are distinguishable. Within each group, we compared the distribution of each morphological feature between a pair of staining methods (Figure 2). To do this we tested the hypothesis that the reconstructed morphologies are statistically similar within each group. We observed that, for each pairwise comparison between two staining methods, there is at least one group which shows significant differences in at least one morphological feature (Figure 3, Wilcoxon rank-sum test, p < 0.05, corrected). In fact, for most of the pairwise comparisons between staining methods we observed a large proportion of highly significant differences (Figure 3, Wilcoxon rank-sum test, p < 0.001, corrected). This suggests that morphologies obtained by different staining methods seldom agree with each other.

We wondered if the biases in neuronal morphology between staining methods of the same type (e.g., Golgi vs. Golgi-Cox) were less than the biases in morphology between methods of a different type (e.g., Golgi vs. GFP). In fact comparisons between staining methods of the same class showed just as high a proportion of statistically significant differences as comparisons between methods of a different class: in both within-class and between-class comparisons 90% of tests performed were statistically significant (Figure 3). Thus, even morphologies obtained by similar methods can show strong biases due to experimental choices.

Given this preponderance of variability related to the staining method, we sought to understand which morphological features in particular had the strongest biases. In order to examine this we computed the average difference in each feature between each pair of staining methods, averaged over all groups for that comparison. This analysis shows that in general the total length, number of branches, and the length of segments show the strongest biases related to the staining method (Figure 4). Using a permutation test to determine the statistical significance of the average effect, we observed that 76% of average effects within these three features were significant. While features related to angles of the dendritic tree show weaker effects—only 32% of average effects in these features were significant. This suggests that features related to the coverage of the dendritic tree are most affected by the choice of staining method.

An omission from these analyses is the possible confounding effect that the rodent strain may have on neuron morphology (Rodriguez et al., 1999; Routh et al., 2009). There is less available data that we could use for matching. Nonetheless, we also analyzed the effect of the staining method on data matched also by strain. This resulted in six comparison groups. In this analysis the staining method biases are of similar significance and size in comparison to the analysis presented above (Supplementary Figures 1, 2). Thus, the biases we observe in morphology due to the staining method do not appear to be explained by reported differences in neuron morphology between rodent strains.

A caveat of the available data for our analysis is that it cannot fully separate the effect of the staining method from other laboratory-fixed effects. The ideal dataset to uniquely identify the effect of the staining method would be to have one lab perform the same experiments but with different staining methods. There are a few instances in which such a comparison was made. However, while the data we have thus exhibits a threat to causal validity (Pearl, 2009), we do have sufficient data to estimate how many neurons would be required to estimate such an artifact. To do this we performed a power analysis of the Wilcoxon rank-sum test through resampling. For most morphological features, we found that an effect size of d = 0.5 required 50 neurons/data points to detect the effect with probability 0.9 (1−β), assuming a type I error rate (α) of 0.05. For features that are defined for each dendrite segment, a single neuron would generally provide this much data. However, to ensure neuron-neuron variability is taken into account, a safer estimate is to assume at least 50 neurons are required. Thus, it seems likely that future versions of neuromorpho.org will soon be able to answer these questions with more precision.

It is important to accurately characterize neuron morphology for a number of reasons. Dendritic morphology determines the computations a neuron can perform, and has a role in circuit function and neurological disease (Agmon-Snir et al., 1998; Elston and Fujita, 2014; Šišková et al., 2014; Yang et al., 2017). Morphology varies by brain region, cell layer, species, and age (Scheibel and Jacobs, 2003; Elston, 2007; Spruston, 2008; Elston and Fujita, 2014). It thus may provide clues as to the function of the region. For instance, Purkinje cells in the cerebellum and pyramidal cells in cortex may provide striking examples of a structure-function relation (Stein and Glickstein, 1992; Körding and König, 2001; Guerguiev et al., 2017). And distinct morphological features affect functional properties differently. For instance, features to do with dendrite diameter may affect electrophysiological properties more than branching angle. Characterizing the morphology specific to brain region, species, etc. is thus important. This is most cleanly identified when the same experimental methodology is used over different brain regions. For instance, Jacobs and Scheibel studied dendritic variation in primate cortical pyramidal cells with the Golgi technique (Scheibel and Jacobs, 2003). Elston and colleagues studied thousands of individually injected cells from multiple cortical areas in a singe hemisphere, replicating the studies in age/sex/hemisphere matched brains within a species and across species (Elston et al., 2001; Elston, 2007). Yet many brain regions and cell types have not been analyzed in this form. Studying variability in morphology by brain region and cell type must typically be performed with data collected from many different methods. Before conclusions can be made, the bias of the methodology must therefore be established.

Here we focused on the correlation of staining methods on measured neuron morphology. We showed a significant difference between neurons that were extracted from the same region, species, gender distribution, and age but with different staining methods. Although this analysis was focused on the staining method, a similar approach could be taken to study the effect of other methodological details such as the reconstruction software or microscopy method. Understanding the source of these artifacts is necessary for us to have an accurate picture of the variation of neurons in the brain.

There are a number of explanations for biases in morphology related to staining methods. First, there may be procedural differences between laboratories, coming from preferences for particular sub-regions or cell types or other preparation details not reported. Indeed previous studies show this is a large source of variability (Scorcioni et al., 2004). Large differences in morphology can exist within a small region, e.g., visuotopic variation within visual cortex and age (Elston, 2003, 2007; Elston and Fujita, 2014). As such, we may expect significant biases to be related to non-staining related signals.

Second, there are methodological biases related to the 3D or planar reconstruction of neuron morphology. To produce a 3D reconstruction of a neuron, we need to fix a direction for the slicing the specimen and choose the thickness of the slices. The staining methods may set a limit on the slice thickness. For instance, when using GFP, neurons are often imaged through confocal microscopy. This sets a bound for the slice thickness, which may affect morphometrics (Rodriguez et al., 2003; Ke et al., 2013). Shrinkage of the neuron during the fixation can also affect measured morphology (Grace and Llinás, 1985). These biases most likely affect the local morphological features, such as angles; the global features, such as total length, are likely less affected. Yet we observed larger biases in global features. This may suggest that biases related to 3D reconstruction are minimal.

Third, there may exist differences caused by other methodological details that happen to be correlated with the staining method, not because the method goes in hand with the staining method itself, but just by chance (or cultural heritage) in the data we analyzed. For example if the objective type used in the microscopy correlates with different staining methods then that would be a potential confound. However, by performing the same comparison over lab groups and brain regions, we mitigate these confounding effects to some extent, and thus better measure differences that are particular to the staining method. But these other explanations can not be ruled out entirely without more controlled comparisons. This is challenging, even with a large database such as neuromorpho.org. Our power analysis demonstrates how much data would be needed to cleanly address these questions. Ultimately, only a clean experiment with a proper random assignment strategy could produce causal certainty.

Our results are consistent with one mechanistic account by which biases are created from the staining method, and not other potential confounds: different molecule size of agents used in different staining methods target different parts of a neuron. If different staining methods capture different parts of the morphology then we would expect strongest artifacts to be observed between features to do with the amount of dendritic tree described, e.g., total length, dimension, number of branches, etc, and smaller artifacts for more local geometric properties like the branching angles. If, alternatively, the biases observed in morphology were due to staining methods/labs targeting different neuronal subpopulations within a given brain region, then we may expect stronger artifacts to also be observed in local parameters such as branching angle. We do not observe strong artifacts in these parameters. A more careful modeling approach that takes generated neuron morphologies and subsamples them according to a staining model may be able to give a more precise account of the type of biases we may expect due to the staining method, and thus this interpretation could be better tested.

The feature set used here is often used as a basis of cell classification (Vasques et al., 2016). In this regard, our results suggest the need to standardize and carefully characterize these artifacts–after all, such biases could have massive effects on the results of clustering methods used for cell type identification. Alternatively, although some features are affected by different staining method, there are some features that are only weakly affected by the method (Figure 4). It would thus be possible to use features that vary most by cell type and least by staining method as the basis of classification or clustering. This should allow combining our findings with those of previous classification approaches to make the procedures robust to the details of the staining method.

Our analysis tells a cautionary tale about the progressively more popular combination of data sets across labs. Fully characterizing neuronal morphology and its relation to function relies on the generation and analysis of vast amounts of data. Across neuroscience, collaborative efforts across institutions are studying morphology (e.g., Churchland, 2017). Amongst the wealth of datasets available, the need for understanding variability due to the data generation process is important for drawing inferences and analyzing data across disparate sources. This problem is becoming widespread in neuroscience where electrophysiological, molecular, and morphological data are now routinely shared.

The datasets analyzed for this study can be found in repository neuromorpho (version 7.4) http://neuromorpho.org. The code for performing the analysis is available at https://github.com/BonsaiNet/Staining-methods-and-morphologies.

RF and KK designed the study. RF and BL performed the analysis. RF, BL, and KK wrote the manuscript.