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. 2024 Apr 17:18:1360432.
doi: 10.3389/fnins.2024.1360432. eCollection 2024.

A method to analyze gene expression profiles from hippocampal neurons electrophysiologically recorded in vivo

Haruya Yagishita  1   2 , Yasuhiro Go  3   4   5 , Kazuki Okamoto  2   6   7 , Nariko Arimura  1 , Yuji Ikegaya  2   8   9 , Takuya Sasaki  1   2
Affiliations

A method to analyze gene expression profiles from hippocampal neurons electrophysiologically recorded in vivo

Haruya Yagishita et al. Front Neurosci. .

Abstract

Hippocampal pyramidal neurons exhibit diverse spike patterns and gene expression profiles. However, their relationships with single neurons are not fully understood. In this study, we designed an electrophysiology-based experimental procedure to identify gene expression profiles using RNA sequencing of single hippocampal pyramidal neurons whose spike patterns were recorded in living mice. This technique involves a sequence of experiments consisting of in vivo juxtacellular recording and labeling, brain slicing, cell collection, and transcriptome analysis. We demonstrated that the expression levels of a subset of genes in individual hippocampal pyramidal neurons were significantly correlated with their spike burstiness, submillisecond-level spike rise times or spike rates, directly measured by in vivo electrophysiological recordings. Because this methodological approach can be applied across a wide range of brain regions, it is expected to contribute to studies on various neuronal heterogeneities to understand how physiological spike patterns are associated with gene expression profiles.

Keywords: burstiness; firing rate; juxtacellular recording; single cell RNA sequencing; spike rise time.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that NA and TS were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Application of RNA sequencing analysis to juxtacellularly recorded hippocampal neurons from head-fixed mice. (A) Experimental procedures. A fluorescently labeled neuron identified in a slice is indicated by an arrow. (B) Juxtacellularly recorded high-pass-filtered (500 Hz) voltage trace including spikes. (C) Trace showing spikes in response to current injections for labeling of the recorded neuron. (D) Superimpositions of the locations of all recorded neurons (black dots) on the dorsal hippocampal CA1 cell layer in sequential coronal brain sections. (E) Representative sequential images and fluorescent pictures (from top to bottom) for sampling of a labeled neuron in panel (C). After attaching a glass pipette to the soma of the labeled neuron (i), a negative pressure with 20 mbar is applied to the pipette to suck the cell (ii). After sufficient suction, the pressure was released (iii). (F) Log-normalized counts of all 40 neurons sampled, illustrating genes with relevant neuronal markers (housekeeping, excitatory, inhibitory, and non-neuronal cell markers).
Figure 2
Figure 2
Gene expression profiles between bursty and non-bursty cells. (A) (Left) High-pass-filtered (500 Hz) voltage traces of two representative neurons (cells #40 and #4). The rectangle dotted areas are expanded in the right panels. (Right) Histograms of inter-spike intervals computed from the neurons. (B) The distribution of burstiness indices from all recorded neurons (n = 40 neurons classified into 7 bursty and 33 non-bursty neurons). (C) A scatter plot of log2 fold changes against the average expression levels for individual genes. Genes with significant differential expressions between bursty and non-bursty neurons (Padj < 0.05, n = 292 out of 8,462 genes) are marked in red. (D) Violin plots showing the expression levels of Atp5 family genes identified as the differentially expressed genes. Each gray dot represents a single cell. Atp5a1: p = 1.7 × 10−10, q = 5.1 × 10−8; Atp5b: p = 1.2 × 10−18, q = 1.0 × 10−10; Atp5g1: p = 6.0 × 10−4, q = 0.024; Atp5h: p = 2.4 × 10−6, q = 3.1 × 10−4; Atp5l: p = 3.9 × 10−4, q = 0.0017; Atp5o: p = 8.0 × 10−5, q = 6.0 × 10−3.
Figure 3
Figure 3
Gene expression profiles correlated with firing rates. (A) High-pass-filtered (500 Hz) voltage traces of two representative neurons (cells #15 and #24). (B) The distribution of firing rates from all recorded neurons (n = 40 neurons). (C) (Left top) Neurons were aligned according to their firing rates (n = 40 neurons). (Left bottom) A heatmap showing expression levels of metagenes from the individual neurons. Here, the metagenes that used in the multi regression model are shown (see Supplementary Figure S3 and S4 for all 25 metagenes). The metagenes are sorted by their partial regression coefficients (β) computed from the liner regression model (middle). **p = 0.0010, *p = 0.013, #p = 0.0033. (Right) Spearman’s rank correlation coefficients (rs) between their gene expression levels and the firing rates of the individual neurons. $$q = 8.4 × 10−3, $q = 0.18. (D) Relationship between the expression levels of metagene 1 (left), 21 (middle) or 11 (right) and the firing rates. Each dot represents a cell (n = 40 cells; metagene 1: rs = 0.54, p = 3.3 × 10−4, q = 8.4 × 10−3; metagene 21: rs = 0.39, p = 0.014, q = 0.18; metagene 11: rs = −0.12, p = 0.45, q = 0.95).
Figure 4
Figure 4
Gene expression profiles correlated with spike rise times. (A) (Left) A juxtacellularly recorded spike waveform (high-pass filtered at 500 Hz). (Right) Magnified averaged spike waveforms from two representative neurons (cell #19 and #24). (B) The distribution of spike rise times from all recorded neurons (n = 40 neurons). (C) (Left top) Neurons were aligned according to their rise times. (Left bottom) A heatmap showing expression levels of genes related to voltage-gated sodium ion channels. The genes are sorted by partial regression coefficients (b) computed from the multi regression model (middle). **p = 9.8 × 10−3, *p = 0.099, ##p = 0.080. (Right) Spearman’s rank correlation coefficients (rs) between their expression levels and the rise times of the individual neurons. $$$q = 0.041, $$q = 0.10, $q = 0.041. (D) Three genes showing significant positive or negative correlations (Slmap: rs = 0.44, p = 4.6 × 10−3, q = 0.041; Scn1a: rs = −0.35, p = 0.028, q = 0.10; Scn2b: rs = −0.42, p = 7.5 × 10−3, q = 0.041). In each graph, the rise times of the individual neurons are plotted against their gene expression levels (n = 40 neurons).
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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by KAKENHI (20H03545 and 21H05243) from the Japan Society for the Promotion of Science (JSPS) (grants 1041630 and JP21zf0127004), from the Japan Agency for Medical Research and Development (AMED) and grants from the Japan Science and Technology Agency (JST) (JPMJCR21P1 and JPMJMS2292) to TS; KAKENHI (21H05238 and 21H05245) from the JSPS to YG; grants from the JST Exploratory Research for Advanced Technology (JPMJER1801) and Institute for AI and Beyond of the University of Tokyo to YI; and a Grant-in-Aid for JSPS Fellows (22 J22779) to HY.

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