Histological processing and neurolucida reconstruction

After recording, slices were histologically processed to enable biocytin-based reconstructions. Slices were fixed in 4% paraformaldehyde/4% sucrose in phosphate-buffered saline (PBS; pH 7.2–7.4) overnight at 4°C. The following day, slices were washed for 3 × 15 mins in PBS. Subsequently, slices were permeabilized in pre-cooled 100% methanol at −20°C for 5–10 mins. Slices were then washed in PBS a further 3 × 10 mins. Endogenous peroxidases were blocked in 1% H₂O₂ for 15 mins at room temp. Further 3 × 5 min PBS washes were performed. Slices were then incubated with Vectastain ABC elite kit (Vector Labs) overnight at 4°C. The next day, slices were washed a further 3 × 10 mins in PBS, and incubated with ImmPact SG Peroxidase substrate (Vector Labs) to initiate staining reaction. The staining was stopped when developed (around 10 mins) with PBS. Further 3 × 5 min PBS washes were performed, and slices were mounted/coverslipped in Mowiol (Sigma-Aldrich). Filled neurons in mounted and coverslipped slices were reconstructed using the Neurolucida system (MBF Bioscience) with a 100× oil-immersion objective. Resulting Neurolucida DAT files were converted to SWC using the freeware NLMorphologyConverter (www.neuronland.org).

2-photon imaging and fluorescence reconstruction

2PLSM (Denk et al., 1990) was performed using a workstation custom built from a SliceScope (Scientifica) microscope fitted with an MDU (Scientifica), with photomultipliers in epifluorescence configuration. Scanners were Thorlabs GVSM002/M 5-mm galvanometric mirrors. A MaiTai BB (Spectraphysics) Ti:Sa laser tuned to 800–820 nm for Alexa 594 excitation was used for excitation. Uniblitz LS6ZM2/VCM-D1 shutters were used to gate the laser, while laser power level was controlled manually using a polarizing beam splitter (Melles Griot PBSH-450-1300-100 with AHWP05M-980 half-wave plate) and monitored using a power meter (Melles Griot 13PEM001/J) after a fraction of the beam was picked off with a glass slide.

PCI-6110 boards (National Instruments) were used to acquire imaging data using custom versions of ScanImage v3.5–3.7 (Pologruto et al., 2003) in Matlab (MathWorks, Natick, MA). 3D image stacks with slices of 512 × 512 pixels were acquired at 2 ms/line with z-steps of 1–2 μm. To reduce noise, each slice of the stack was an average of three frames. Resulting TIFF stacks were subsequently 3D-median filtered for inspection and for figures, but not for the reconstruction process. Stack brightness and contrast were altered in MacBiophotonics ImageJ (www.macbiophotonics.ca). Parameters were chosen to allow visualization and manual tracing of neurites with the least possible artificial enlargement of diameters. Registration of stacks was performed manually in Neuromantic (http://www.reading.ac.uk/neuromantic) and reconstruction of neurons was performed in this environment.

Morphological analysis

Images of reconstructed cells (e.g., Figure 2) were rendered using NEURON. Quantitative analysis of reconstructions in SWC format was performed using either L-measure (Scorcioni et al., 2008), for which details of each function are available at http://cng.gmu.edu:8080/Lm/help/index.htm, or with our custom software qMorph written in Igor Pro, previously described in Buchanan et al. (2012). In L-measure, results are for the entire cell (axons and dendrites pooled together). The L-measure function “Length” refers to average compartment length, so in Table ​Table11 we have referred to this as “Compartment length” for clarity. Custom software was used to create density maps, convex hulls and Sholl analysis (Sholl, 1953). Prior to analysis, morphologies were rotated slightly (16.97 ± 5.36° on average) to align apical dendrite/pial surface directly upward. Morphologies were aligned on the soma for all analyses.

Table 1

Morphometry.

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Morphological measures used for comparison of reconstruction methods (Figures 2, 3) in pyramidal and basket cells. Measures were generated using either in-house software (see Methods) or L-measure (listed as function names from the software to reduce ambiguity). Comparisons with significance levels p < 0.05 and p < 0.01 are highlighted in green and yellow respectively. (–) indicates unit less measures such as counts and ratios.

To create density maps, each compartment of a reconstruction was represented by a 2D Gaussian aligned on its XY center, with its amplitude proportional to compartment length and its sigma fixed to 25 μm. These Gaussians were summed to create a smoothed 2D projection of morphology (density map). Axon and dendrite were treated separately. Individual density maps were peak normalized to enable averaging across reconstructions. Symmetry in density maps is a result of mirroring of reconstructions, however analyses on individual cells were performed on non-mirrored data. Ensemble maps for axon and dendrite were normalized, assigned color lookup tables and merged with a logical OR (e.g., Figure 2). Gamma correction was used to better visualize weak densities.

Convex hulls were created for each reconstruction based on 2D projections of axonal and dendritic arbors, using the gift-wrapping algorithm, also known as the Jarvis march (Jarvis, 1973). Ensemble hulls are convex hulls of all hulls of a certain type, including mirror images. Sholl analysis was performed in radial coordinates, moving in increasing 6.5 μm steps from r = 0, with the origin centered on the cell soma, and counting the number of compartments crossing a given radius. Sholl diagrams are averaged without normalization. Maximum value is the maximum number of crossings, whilst critical radius is the radius at which the maximum number of crossings was found. Maximum Sholl radius is the furthest radius with at least one crossing (the enclosing radius).

Process diameters were calculated using L-measure to obtain averages of cells (axon and dendrite measured separately). Diameters of visually matched locations between reconstructions of the same cells with different methods were measured manually in Neuromantic.

Statistical comparisons

Results are reported as mean ± s.e.m. unless otherwise stated. Comparisons were made using paired samples t-test for equal means, unless otherwise stated. No corrections for multiple comparisons were applied, as for the purposes of this paper we feel it is more important and preferable to highlight potential differences between methods than to overlook them. Statistical tests were carried out in Igor Pro, Microsoft Excel and/or JMP (SAS). At least three animals were used for each group analyzed, and n_cell = n_animal (Aarts et al., 2014). Significance levels p < 0.05, p < 0.01 and p < 0.001 are denoted by one, two, and three stars respectively.

Data clustering

Multidimensional hierarchical data clustering was performed on the first two principal components of standardized data in JMP using Ward's method and the Euclidean distance as linkage metric; or normal mixtures iterative clustering, which is based on the expectation-maximization algorithm (http://www.jmp.com/support/help/Normal_Mixtures.shtml). Prior to clustering, we performed principal component analysis on all variables listed in Table ​Table1.1. In order to achieve fair weighting of morphological features in clustering, we identified pairs of variables in the resulting correlation matrix where r > 0.8, and excluded the variable which had the lower loading value in PCA (Tsiola et al., 2003). Clustering of morphologies was thus performed on the first 2 principal components of 27 measured parameters. From L-measure, we used Diameter, Length, PathDistance, Branch_Order, Taper_1, Contraction, Daughter_Ratio, Parent_Daughter_Ratio, Partition_asymmetry, Bif_ampl_local, Helix, Fractal_Dim. From our custom software qMorph, we used distance to center of axonal cloud, angle to center of axonal cloud, most distal axonal compartment x-coordinate, most distal axonal compartment y-coordinate, most distal dendritic compartment x-coordinate, angle to most distal dendritic compartment, axon hull x-center, axon hull width, dendritic hull x-center, dendritic hull y-center, dendritic hull width, axon Sholl max value, axon Sholl critical radius, dendrite Sholl critical radius, axon Sholl maximum/enclosing radius.

Reconstructions from 2PLSM have larger process diameter

When creating 3D reconstructions of neurons to be used for e.g., computer modeling, it is important for these to be as accurate as possible, as even quite subtle structural differences can have quite dramatic effects on biophysical properties (Vetter et al., 2001; Schaefer et al., 2003). For example, differences in process diameter between reconstructions will affect membrane surface area, process volume, number of ion channels, axial resistance, length constant, and in turn propagation of electrical signals. Changes in laser power during acquisition of fluorescence images and image processing prior to reconstruction when using 2PLSM/FI may have affected reconstructed process diameter. Comparison of reconstructions based on FI or biocytin histology (BH) revealed a significant trend for those created using 2PLSM/FI to have larger process diameter than those based on BH (Figure ​(Figure44).

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Figure 4

FI reconstructions suffer from systematically enlarged process diameters. (A) Two reconstructions of the same cell using FI (top) and BH (bottom). Inset: zoom highlighting differences in diameter for dendrite (black) and axon (red). Arrows in inset: example of matched dendritic locations, quantified in Figures 3C,D. (B) Average differences in process diameter for PCs and BCs using either method. FI reconstruction resulted in consistently larger diameter for PCs (n = 5 cell pairs, FI vs. BH; axon 1.20 ± 0.14 μm vs. 0.67 ± 0.04 μm, p < 0.05; dendrite 1.65 ± 0.17 μm vs. 0.84 ± 0.03 μm, p < 0.01) and BCs (n = 3 cell pairs; axon 0.89 ± 0.04 μm vs. 0.55 ± 0.04 μm, p < 0.05; dendrite 1.40 ± 0.16 μm vs. 0.71 ± 0.03 μm, p < 0.05). Average diameters for entire cells are found in Table ​Table1.1. (C) Differences in diameter for manually matched dendritic locations using either method (see Figure ​Figure3A).3A). All but one matched measurements were plotted above the line of equality, reflecting the tendency of FI reconstructions to have larger process diameter (PCs; n = 5 cell pairs; n = 25 segment pairs; FI vs. BH Diameter; mean 1.80 ± 0.15 μm vs. 0.91 ± 0.09 μm; p < 0.001). (D) The degree of agreement between the two methods is ascertained using a Bland-Altman or Tukey mean-difference plot (Bland and Altman, 1986). FI diameter—BH diameter is plotted against averaged process diameters, (FI+BH diameter)/2. Middle dotted line indicates a positive mean difference (0.89 ± 0.13 μm), showing that FI reconstructions consistently suffer from exaggerated process diameters. The upper and lower dotted lines indicate ±2SD and the 95% limits of agreement (SD = 0.64 μm). Linear regression (not shown) identified a significant slope (0.56; p < 0.05), showing that FI reconstruction overestimates diameters more for larger diameters. ^*p < 0.05, ^**p < 0.01.

We compared differences in average process diameter between the two reconstruction methods using L-measure. Diameter was consistently significantly larger for reconstructions made using FI for axonal and dendritic compartments of both cell types (Figure ​(Figure4B).4B). Differences in process diameter between reconstruction methods were investigated in more detail by comparing the diameter of many individually matched compartments for each PC dendrite using manual measurements (Figures 4C,D). All but one of the matched segments had a larger diameter when reconstructed from 2PLSM stacks (n = 25; n = 5 cells; FI vs. BH, 1.80 ± 0.15 μm vs. 0.91 ± 0.09 μm; p < 0.001). Taken together, these results show that FI reconstructions consistently exaggerate compartment diameter, on average and also typically for individual compartments.

Quantitative morphological analysis and cell-type classification

One of the most common uses of 3D reconstructions such as those compared here is analysis of morphology, particularly in order to establish cell type. For example, axonal morphology is often cited as the most important determinant of cortical IN cell type (Markram et al., 2004; Wang et al., 2004; Toledo-Rodriguez et al., 2005; Ascoli et al., 2008). Increasingly, many properties of neural circuits such as synapse type and ion channel expression are found to be dependent on anatomical cell class (Blackman et al., 2013); therefore it is vital to accurately verify morphological type in any study where there may be cell-type-specific differences.

Our results indicate that FI and BH reconstructions are equal in providing an accurate representation of local morphology, with most morphological measures being indistinguishable between the two (Table ​(Table1).1). Unsupervised clustering results in successful separation of cell type in both methods (Figure ​(Figure3).3). Whilst both methods appear to generate equivalent results for this purpose, FI reconstructions may confer a number of benefits that make them preferable in cell classification. Firstly, FI reconstructions, due to the ability to monitor FI online during electrophysiology experiments, effectively have a 100% yield for most purposes, as compared to the 50-80% yield of BH in our hands, which is dependent on post-recording histology (Figure ​(Figure1).1). The lower yield of BH is highly dependent on the experimenter's experience and training with this state-of-the-art method, as well as on other factors such as cell type and age of the brain tissue. Although the yield can clearly be improved with experience and training, it will never reach 100%. FI-based reconstructions, however, are in our hands quite straightforward and are in fact an excellent training opportunity for volunteering undergraduate students who are just starting working in a lab. In addition, with FI, cell type may also be subjectively identified online whilst recording, increasing the throughput of electrophysiology experiments targeting a particular cell type. Furthermore, the unwanted distortions and shrinkage seen with BH reconstructions are avoided when using FI.

With all methodological comparisons, it is important to consider the costs involved. As FI reconstructions do not require histological processing or a dedicated setup for reconstruction, and image stacks can be acquired at the same time as electrophysiological recording, the time to generate a single reconstruction is much less than with BH, which can translate into saving running costs. Furthermore, FI reconstructions require less auxiliary equipment and use of consumables than BH reconstructions, resulting in lower cost per reconstruction. FI reconstructions do, however, require the initial high setup cost of the laser-scanning microscope, so this reasoning only applies for labs that already have access to 2PLSM or to confocal imaging. In our eyes, these benefits, together with the almost equal performance of FI and BH in revealing local morphology, make FI the preferred method in studies focusing on cell-type classification. This said, some cell types may extend over much larger areas than those described here (Lichtman and Denk, 2011). Whilst increasing fluorophore concentration, fill time and area imaged may increase the visible extent of FI reconstructions (see Figure ​Figure2),2), our results show that BH reconstructions reveal more distal processes (Table ​(Table1;1; e.g., hull width, max. Sholl radius, etc.), and therefore may be preferable if reconstruction over large distances is required. Even so, FI of axonal arborizations ranging several millimeters has successfully been carried out (see for example Pressler and Strowbridge, 2006; Williams et al., 2007), suggesting that this problem is possible to overcome by fine-tuning the FI reconstruction method. Mapping connectivity on larger scales using FI may be possible with whole-brain methods such as serial two-photon tomography (Ragan et al., 2012; Osten and Margrie, 2013).

Multicompartmental computer modeling

Another major use of 3D reconstructions is in single-cell multicompartmental modeling. In this application, accuracy is paramount; even subtle differences in morphology may have considerable effects on both passive and active properties of neurons and models (Segev et al., 1995; Vetter et al., 2001). For example, dendritic morphology is thought to play a key role in the level of coupling in cortical pyramidal cell coincidence detection (Schaefer et al., 2003). Our results reveal that differences in morphology resulting from reconstruction method choice alone have large and significant effects on simulation of EPSP propagation. FI reconstructions consistently exhibit much smaller depolarizations than BH reconstructions (Figure ​(Figure55).

The major contributing factor to these results is likely the large differences in dendritic diameter obtained between the two methods. Differences in measured process diameter alone would affect models of e.g., synaptic efficacy (Holmes, 1989) and voltage attenuation (Stuart and Spruston, 1998). Our results show that FI reconstructions consistently and significantly have larger process diameters, both on average and for matched compartments. As both BH and FI methods allow visualization of spines and axonal varicosities, a lack of spine detection is unlikely to be the cause of the larger diameters seen in FI. This finding is not unexpected, however, since increasing the laser power during acquisition of 2PLSM fluorescence images typically results in an apparent thickening of dendrites and axons. Neurite diameters obtained with 2PLSM are also subjectively affected by brightness/contrast settings during the reconstruction procedure, with a tendency for broadening of diameters when adjusting look-up tables to compensate for weak fluorescence. This problem seems much smaller with BH, presumably because the contrast produced with the histological amplification process is generally quite sufficient in and of itself. Due to the wavelength used, the theoretical resolution limit of light microscopes is also better than that of 2PLSM. This difference is compounded by the typical usage of high numerical aperture oil-immersion objectives with BH.

Although we have not tested this, we suspect that the neurite thickening problem might be considerably smaller with confocal microscopy than with 2PLSM, since its resolution limit is much better. It would be interesting to see a side-by-side comparison of FI reconstructions from 2PLSM and confocal microscopy stacks.

As diameter appears to be the main contributing factor for differences in computer modeling between FI and BH reconstructions of the same cells, it may be possible to correct for this, assuming that the differences are systematic. Preliminary results using a correction factor determined from differences in diameter of matched compartments suggest that it is possible to recover EPSP amplitudes in FI reconstructions to the levels seen with BH by manipulating diameter alone (data not shown). However, whilst it may be possible to determine specific correction parameters for a particular setup and experimenter by directly comparing diameter differences, these parameters may not be the same in alternate situations. For example, wide inter-experimenter differences in diameter and simulation results have been described when reconstructing from multiphoton data (Losavio et al., 2008). Another important factor to consider is that without technically demanding dendritic recordings, it is difficult to ascertain completely the ground truth, i.e., which of BH or FI is closer to reality. This said, the higher resolution and better signal-to-noise ratio found with BH justifies its position as a gold standard and as such BH reconstructions can be considered a benchmark or gold standard.

Because of the factors described above, and the large differences between EPSP modeling with FI and BH reconstructions, we recommend the use of BH in all multicompartmental modeling applications. This is further supported by the greater morphological detail revealed in BH reconstructions; it has been shown that even small differences in dendritic arborization may have large effects on the physiological properties of pyramidal cells (Schaefer et al., 2003), and simulations of such properties should therefore be based on the most accurate and complete morphological reconstructions possible. In contrast to neurite diameters and number of branches, the distortions and shrinkage seen with BH reconstructions are not likely to affect simulations much, and are therefore less of an issue for modeling as opposed to in morphometric applications (Schaefer et al., 2003). Until resolution-limit breaking FI reconstruction methods (see below) become commonplace, BH-based reconstructions are likely to remain state of the art for all multicompartmental computer-modeling applications.

We thank Alanna Watt, Tom Mrsic-Flogel, Sonja Hofer, Simon Schultz, Julia Oyrer and Mark Farrant for help and useful discussions. We thank Michael Häusser and Troy Margrie for lending their Neurolucida setups, and Scientifica for lending electrophysiology and imaging equipment. This work was supported by a BBSRC Industrial CASE studentship BB/H016600/1 (Arne V. Blackman) that was co-funded by Scientifica, EU FP7 Future Emergent Technologies grant 243914 “Brain-i-nets” (Robert Legenstein and P. Jesper Sjöström), European Union project #269921 “BrainScaleS” (Stefan Grabuschnig and Robert Legenstein) CIHR OG 126137 (P. Jesper Sjöström), and NSERC DG 418546-12 (P. Jesper Sjöström).