Source Paper
High-fat diet disrupts a septal control on feeding to promote obesity in male mice
Jiang S, Lai S, Jing H, Wu X, Li F et al.
Nat Commun • 2025
Source Paper
Jiang S, Lai S, Jing H, Wu X, Li F et al.
Nat Commun • 2025
Objective: Population vector analysis to assess how LS GABAergic neuronal ensembles encode different food types (chow vs. high-fat diet) using dimensionality reduction and visualization of neural population activity patterns
This is a Population vector analysis for food type encoding protocol using Mouse as the model organism. The procedure involves 9 procedural steps, 2 equipment items, 3 materials. Extracted from a 2025 paper published in Nat Commun.
Model and subjects
Mouse • Gad2-Cre mice • Male • Adult • Not specified for this analysis
Study window
~6 week study window | ~10 minutes hands-on
Core workflow
Viral injection and GRIN lens implantation • Recovery and baseplate attachment • Adaptive training
Primary readouts
Key equipment and reagents
Verified items
0
Direct vendor links
0
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Inject AAV2/9-hEF1a-DIO-GCaMP6s into LS and implant GRIN lens 100 µm above injection site, secured with dental cement
“unilateral injections of AAV2/9-hEF1a-DIO-GCaMP6s were performed. A GRIN lens was inserted 100 µm above the injection site”
Allow 4-6 weeks recovery, then fix baseplate matching miniscope to skull with dental cement
“After 4-6 weeks of GCaMP6s injection, a baseplate that matched the miniscope was fixed to each mouse's skull”
Provide adaptive training sessions before imaging
“mice received 10-minute adaptive training for at least 3 days”
Record calcium signals while randomly placing food pellets and recording mouse feeding behavior
“we randomly placed a food pellet for each freely moving mouse in turn, and simultaneously recorded video of the process whereby the mouse ate the food. There were at least 10 food intake periods”
Acquire imaging data at 30-Hz frame rate using UCLA Miniscope-DAQ-DT-Software
“Imaging data were acquired at a 30-Hz frame rate and collected using UCLA Miniscope-DAQ-DT-Software”
Process calcium signals using CNMF-E software to extract motion-corrected fluorescence dynamics from individual neurons
“Calcium signal processing was performed using CNMF-E software to extract motion-corrected GCaMP6s fluorescence dynamics from individual neurons”
Quantify neuronal activity traces as Z-scores or ΔF/F values with baseline defined as mean fluorescence during first 2 seconds of each trial
“Neuronal activity traces were quantified as Z-scores or ΔF/F values, with baselines defined as the mean fluorescence during the first 2 seconds of each trial”
Construct n-dimensional activity vectors representing ensemble responses at each timepoint using Z-score normalized signals
“we employed population vector analysis. In brief, this approach constructs n-dimensional activity vectors (n = neuron count) representing ensemble responses at each timepoint through Z score normalized signals”
Apply PCA for dimensionality reduction, projecting high-dimensional vectors onto 2D visualization space
“PCA was subsequently applied for dimensionality reduction, projecting high-dimensional vectors onto a 2D visualization space”
This section explains what the experiment is doing, which readouts matter, what the data artifacts usually look like, and how the analysis should flow from raw capture to reported result.
Population vector analysis to assess how LS GABAergic neuronal ensembles encode different food types (chow vs.
Objective
Population vector analysis to assess how LS GABAergic neuronal ensembles encode different food types (chow vs. high-fat diet) using dimensionality reduction and visualization of neural population activity patterns
Subjects
From paperMouse • Gad2-Cre mice • Male • Adult
Sample count
From paperNot specified for this analysis
Viral injection and GRIN lens implantation (Not specified)
Recovery and baseplate attachment (4-6 weeks)
Adaptive training (10 minutes for at least 3 days)
Calcium imaging during food consumption (At least 10 food intake periods per session)
Population activity vectors encoding food type
From paperThis readout is central to the experiment's endpoint interpretation and should be reviewed before running the analysis.
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Principal component projections of neural ensemble activity
From paperThis readout is central to the experiment's endpoint interpretation and should be reviewed before running the analysis.
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
2D visualization of population-level encoding patterns for chow vs HFD
From paperThis readout is central to the experiment's endpoint interpretation and should be reviewed before running the analysis.
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Population activity vectors encoding food type
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Principal component projections of neural ensemble activity
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
2D visualization of population-level encoding patterns for chow vs HFD
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Acquisition
Collect raw experimental outputs with enough metadata to preserve sample identity, condition, and timing.
Preprocessing / cleaning
Review raw outputs for quality, remove unusable captures, and organize the data into a comparison-ready table or image set.
Scoring or quantification
Quantify the primary readouts for this experiment: Population activity vectors encoding food type; Principal component projections of neural ensemble activity; 2D visualization of population-level encoding patterns for chow vs HFD.
Statistical comparison
Statistical method not yet structured for this page.
Reporting output
Report representative outputs alongside summary comparisons for Population activity vectors encoding food type, Principal component projections of neural ensemble activity, 2D visualization of population-level encoding patterns for chow vs HFD.
Source links and direct wording from the methods section for validation and deeper review.
Citation
Jiang S et al. (2025). High-fat diet disrupts a septal control on feeding to promote obesity in male mice. Nat Commun
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Direct vendor pages are linked from the protocol above. This section stays focused on the full comparison view and the prep checklist.
Gather these items before starting the experiment. Check off items as you prepare.
Open Ephys • UCLA Miniscope V4
Not specified • Not specified
Taitool Bioscience • S0351-9
Shenzhen Ready Biological Medicine Co., Ltd • D12492
Beijing Keao Xieli Feed Co., Ltd • 2252
Use this section as the page quality checkpoint. It keeps section navigation, evidence access, readiness, and verification meaning in one place.
Current status surfaces were computed from experiment data updated Feb 28, 2026.
Source access
Jump back into the original paper or the methods evidence section when you need exact wording, exclusions, or method-specific caveats.
This protocol has structured steps plus evidence quotes, and is ready for canonical sync.
Steps
9
Evidence Quotes
9
Protocol Items
5
Linked Products
0
Canonical Sync
Pending
What this means
The completeness score reflects how much structured protocol data is present: steps, methods evidence, listed materials, linked products, and paper provenance.
Computed from the current experiment record updated Feb 28, 2026.
Canonical Sync shows whether a ConductGraph-backed protocol is available for this experiment route right now. It is a sync-status signal, not a claim that every downstream vendor link or step detail is perfect.
Steps
9
Evidence
9
Specific Products
0/0
Canonical Sync
Pending
What this score means
The verification score reflects evidence coverage, subject detail, paper provenance, step depth, and whether linked products resolve to specific item pages instead of generic searches.
Computed from the current experiment record updated Feb 28, 2026.
A page can have structured steps and still need review when evidence is thin, product links are generic, or canonical protocol coverage is still pending.
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