We used a technique originally devised for single-cell transcriptomics to capture live neurons from acutely dissected mouse hippocampal slices. The slices were prepared with a vibratome and perfused with artificial cerebrospinal fluid (ACSF) in a submerged chamber (Fig. 1a). Neurons were identified by shape and location, and patched with the aid of a micromanipulator without rupturing the cell membrane (cell-attached patch mode). To demonstrate the method we selected two classes of neurons – dentate gyrus (DG) granule cells and CA1 pyramidal cells – because they (i) differ from one another in size, morphology and biophysical properties17; (ii) undergo profound structural and functional changes in response to increased excitatory afferent activity18, 19; and (iii) are part of a reentrant neural circuit that underpins long-term memory formation20, 21. Using the micromanipulator, the patched neurons were gently pulled away from the slices while keeping the giga-ohm seal intact and were visualized to ensure that a single cell had been captured and no glial contamination was present (Fig. 1b). The neurons were aspirated into the pipette tip, and expelled into a glass vial kept on dry ice. Samples were subjected to single-phase isopropanol extraction and analyzed by nLC/MS using on-column injection (Fig. 1c). Special precautions were taken to overcome problems generated by the presence of inorganic salts, as described in the Materials and Methods section.

Figure 1 Patch clamp-assisted single neuron lipidomics. (a) Mouse brain slices are prepared using a vibratome and transferred to the perfusion chamber of a standard electrophysiology setup. A target neuron is visually identified and patched in the cell-attached configuration using a standard manipulator. A patched granule cell of the dentate gyrus and the attached pipette are shown in pseudocolor. (b) The neuronal soma is carefully pulled away from the slice while maintaining the giga-ohm seal. Visual inspection is used to confirm that the neuron is free of extraneous contaminating material such as glial cells; calibration bar: 10 μm (c) The patched neuron is aspirated into the pipette tip and transferred to a sterile vial kept in dry ice. Stimulated cells are collected from the slice within 3 min of stimulation. Samples are subjected to single-phase isopropanol extraction and lipids are analyzed by nanoflow liquid chromatography/high-resolution time-of-flight mass spectrometry (nLC-MS). Full size image

Total ion current (TIC) was monitored in the positive-ion mode and spectra were acquired in the m/z range = 100–1000. TIC tracings of single-neuron extracts were uninformative. Nevertheless, considering that relevant signals could be hidden by matrix-derived noise, we extracted ions for a representative set of 236 lipids known to be present in neural tissue (Supplementary Table S1 ). We focused on lipid-related signals that met the following criteria: (i) signal-to-noise ratio >3; (ii) presence in ≥75% of single-neuron samples; (iii) peak area at least 3x higher than two sets of controls including freshly prepared ACSF (5 μL) and ‘sham-capture’ controls, in which a patch pipette was lowered into the slice but no cell was collected. Table 1 lists 41 analytes that met the filtering criteria. These included abundant membrane constituents such as cholesterol (Fig. 2a), but also less-represented species such as hexosylceramide (d18:1/24:0) and cholesteryl ester (CE) 16:0 (total number of carbons: total number of double bonds) (Fig. 2b,c). For a few neuronal lipids we were able to obtain full mass spectra (e.g., cholesterol, Fig. 2d ). In most cases, however, identification relied on the comparison with retention time (Rt) and accurate mass/charge (m/z) values (with 5 ppm accuracy) of lipids represented in highly diluted extracts (1:50,000) of whole mouse hippocampus analyzed under identical nLC/MS conditions (Supplementary Figs. S1–40).

Table 1 Lipid species detected in individual hippocampal neurons. Full size table

Figure 2 Lipidomics analysis of individual neurons from mouse hippocampus. Representative nLC/MS tracings for (a) cholesterol ([M−H 2 O + H]+, m/z = 369.35, Rt = 15.12 min), (b) hexosylceramide (d18:1/24:0) ([M + H]+, m/z = 812.69, Rt = 20.96), and (c) cholesterol ester 16:0 ([M + NH 4 ]+, m/z = 642.61, Rt = 25.22) obtained from a single DG granule cell. Black tracings: neurons; red tracings: artificial cerebrospinal fluid (ACSF). (d) Mass spectrum of cholesterol from a single DG granule cell. (e) Principal component analysis of lipids present in whole hippocampal tissue (orange triangles), single DG granule cells (blue triangles), single CA1 pyramidal cells (green triangles) and ACSF (yellow triangles). (f) Relative quantification of main lipid classes from individual neurons (blue circles; granule and pyramidal cells combined) and whole hippocampal tissue (orange circles). Abbreviations: CE, cholesteryl esters; DAG?, a lipid tentatively identified as diacylglycerol; GPL, glycerophospholipids; HexCer, hexosylceramides; SM, sphingomyelins. (g) Relative quantification of lipid species from individual neurons (blue bars) and whole hippocampal tissue (orange bars). Results are represented as mean ± s.e.m (n = 20 single neurons and 5 punches from different slices per group); *P < 0.05; **P < 0.005; ***P < 0.001; Mann-Whitney U test. Full size image

We normalized the abundance of individual lipid species by the total abundance of all identified lipids and used principal component analysis to compare neurons to whole hippocampal tissue, as well as DG granule cells to CA1 pyramidal cells. The analyses did not reveal significant differences between neuron types (Supplementary Fig. S41), but clearly separated neurons (granule and pyramidal cells, separated or combined) from tissue (Fig. 2e). The two latter datasets differed in three main lipid classes – CE (P = 0.0007), sphingomyelins (SM) (P = 0.0451) and hexosylceramides (HexCer) (P = 0.0383) (Fig. 2f) – but also in specific members of all classes (Fig. 2g). These included, among others, phosphatidylcholine (PC) 32:0 (P = 0.0008), PC 34:1 (P = 0.0273); phosphatidylethanolamine (PE) 38:6 (P = 0.0006), PE 40:6 (P = 0.0001); CE 16:0 (P = 0.0007), CE 24:0 (P = 0.0007), phosphatidylinositol (PI) 38:5 (P = 0.0013), SM 20:0 (P = 0.0001), SM 24:0 (P = 0.0001), HexCer (d18:1/24:1) (P = 0.0013) and a lipid tentatively identified as diacylglycerol (DAG?) 42:10 (P = 0.0446; Supplementary Fig. S39). Most notably, neurons contained substantially higher levels of CE 16:0, CE 24:0, PI 38:5 and SM 24:0 than did whole hippocampal tissue (Fig. 2g). While the biological significance of these differences is presently unclear, the finding that the lipid profile of adult brain neurons is distinguishable from that of brain parenchyma emphasizes the need to investigate lipid homeostasis at cellular resolution.

Stimulation of excitatory afferent fibers causes long-lasting morphological and functional changes in hippocampal neurons17, 20,21,22. To test whether neural activity also influences the cells’ lipid profile, we placed electrodes either in the lateral perforant path (LPP) or the Schaffer-commissural (SC) systems, projections that provide excitatory input to DG granule cells and CA1 pyramidal cells, respectively17, 20, 21. In both cases, we used a stimulation pattern that mimicked high-frequency gamma waves, which are generated during cognitive processing23 and elicit long-term potentiation (LTP) of synaptic transmission21, 22, 24. Neuronal cell bodies were collected within 3 min of stimulation, snap-frozen and subjected to analysis. In parallel experiments, to assess the impact of stimulation on whole DG tissue, we used a manual puncher to remove cylinders (0.86 mm diameter) from the DG of control and stimulated slices.

LPP stimulation substantially altered the lipid profile of DG granule cells (Fig. 3a). The changes were bidirectional and involved multiple lipid species. As shown in Fig. 3c, significant increases were seen with PC 36:0 (P = 0.0127), PC 38:1 (P = 0.0133), PC 38:5 (P = 0.0042), PE 36:2 (P = 0.0115), PE 40:1 (P = 0.0001), phosphatidylserine (PS) 40:7 (P = 0.0452), HexCer (d18:1/18:0) (P = 0.0098), HexCer (d18:1/18:1) (P = 0.0172), CE 16:0 (P = 0.0042), CE 24:0 (P = 0.0076), and SM 24:0 (P = 0.0076). Decreases were observed instead with PC 34:1 (P = 0.0004), tentatively identified DAG 42:10 (P = 0.0001) and DAG 44:11 (P = 0.0004), PE 40:6 (P = 0.0071) and non-esterified cholesterol (P = 0.0041). No significant changes were seen in other detectable lipids. Compared with granule cells, enhanced afferent activity had more restricted effects on pyramidal neurons (Fig. 3b), in which levels of PC 36:1 (P = 0.0031), PC 40:6 (P = 0.0380) and SM 24:0 (P = 0.0172) were elevated following stimulation, whereas those of PC 38:5 were lowered (P = 0.0457) (Fig. 3d). In sharp contrast with single granule cell neurons, and underscoring the cellular specificity of the response, we found that the lipid profile of DG punches was not significantly affected by LPP stimulation (Supplementary Fig. S42). Collectively, the results of these experiments suggest that physiologically relevant patterns of neural activity cause marked lipidome-wide alterations in individual hippocampal neurons, which are measurable by the present method. Many of these modifications may be attributed to accelerated phospholipid remodeling and cholesterol esterification, and might reflect therefore an activity-dependent reorganization of the neuronal membrane. This conclusion is consistent with evidence indicating that high-frequency afferent activity promotes cholesterol redistribution25 and initiates postsynaptic spine enlargement and new spine generation in hippocampal neurons26, 27. Similarly, accumulation of SM 24:0 might reflect an expansion of SM-rich liquid-ordered domains (‘lipid rafts’)28 – possibly linked to accelerated trafficking of ionotropic glutamate receptors29,30,31 – and increased coupling between inner and outer membrane leaflets32. Testing these hypotheses will require, of course, dedicated experiments.

Figure 3 Effects of physiological stimulation on the lipidome of individual hippocampal neurons. Principal component analysis of lipids from resting and stimulated (a) DG granule cells (blue: resting; magenta: stimulated) or (b) CA1 pyramidal cells (green: resting; orange: stimulated). Relative quantification of individual lipid species from (c) resting (blue bars) or stimulated (magenta bars) granule cells; and (d) resting (green bars) or stimulated (orange bars) pyramidal cells. Results are represented as mean ± s.e.m (n = 10 single neurons per group); *P < 0.05; **P < 0.005; ***P < 0.001; Mann-Whitney U test. Full size image

In sum, the patch clamp-assisted lipidomics method described here allowed us to detect lipids in individual neurons freshly isolated from live mammalian brains. Because of its sensitivity, flexibility and ease of implementation, the method may be used, alone or in combination with transgenic expression of fluorescent lineage-specific proteins, to profile the lipidome of most, if not all, neurons in the central nervous system. Furthermore, the method can be easily adapted for use in targeted lipidomics, with potential gains in sensitivity and ability to quantify, or combined with RT-PCR to link transcriptomics and lipidomics data at the single neuron level. Its main current weakness – a still limited coverage of the neuronal lipidome – should be progressively mitigated by improvements in LC-MS/MS technology. Thus, the method is applicable to a broad variety of studies of lipid homeostasis in the healthy and diseased brain.