Spatial Transcriptomics Revealed Alzheimer’s Disease Associated Molecular Markers in Parvalbumin Interneurons.

Updated on October 07, 2024

Introduction
Materials
Methods
Results
Discussion
Data Availability
Code Availability
References
Abbreviations

Color key

Introduction

Aim of the study

Utilized GeoMx DSP and Xenium In Situ for spatial transcriptomic analysis of PV+ and PV− neurons in the retrosplenial cortex (RSC) of a mouse model of Alzheimer’s Disease (AD).
Discovered distinct disease-associated molecular markers specifically in PV+ neuron populations.
Integrated datasets from GeoMx and Xenium for comprehensive spatial transcriptomic insights.
Established the methodological coherence between GeoMx DSP and Xenium In Situ platforms, setting a framework for future integrated studies.

Materials

5xFAD transgenic (TG) and wild-type (WT) mice
Spatial transcriptomics datasets

[GeoMx] Discovery cohort
1. Sample, N=7:
2. Platform: Nanostring GeoMx Digital Spatial Profiler (DSP)
3. Panel: Whole Transcriptome Atlas (WTA)
4. Segment, N=4:
5. ROI (Region of interest): 14
6. AOI (Area of illumination): 46

[Xenium] Validation cohort
1. Sample, (consecutive sections): N=6:
2. Platform: 10X Genomics Xenium In Situ
3. Panel: 247 genes
4. FOV (Field of view): 349
5. Number of cells: 303,158 cells

Number of samples:

Platform	TG	WT	Total
GeoMx	4	3	7
Xenium	3	3	6
Total	7	6	13

Sample naming convention for GeoMx DSP data

Group|No|Sex - Segment - ROI

Group: [TG | WT]
No (moude model ID): as is
Sex: [M | F]
Segment: [PV | NeuN | Amyloid | TN]
ROI: three-digit number
Example: TG1M-PV-010

Methods

Non-negative Matix Factorization (NMF) ^{Lee and Seung (1999)}

Identification of metagenes ^{Brunet et al. (2004)}

InSituType ^{Danaher et al. (2022)}

Cell-typing with the reference datasets (supervised)

SOTK (Spatial Omics Toolkit): Select the best rank from NMF based on correlation network in a data-driven way

SOTK GitHub

Results

Preprocessing, QC, and XeniumRanger

[GeoMx] Normalization

Variance Stabilizing Transformation (VST) was applied using DESeq2 ^{Love et al. (2014)}

RNA-seq tends to follow the negative-binomial distribution, their SD tends to increase with mean
A shifting and scaling of the data to stabilize the variance (generalised logarithm) for each cohort/profile
In this study, Count matrix was fed in to VST and upper quartile [UQ] normalization was not applied

Segment-level boxplots:

Count	VST

Individual-level boxplots:

Distribution

[GeoMx] Identify Interneuron- and Neuron-specific genes

Differential Expression (DE) analysis between PV and NeuN segment in WT

Welch's t-test, FDR <0.05 and | Log2 fold-change | > 0.2

Results:

Stats results in XLSX

[GeoMx] Feature selection

Selects a subset of relevant features while keeping the original feature space intact
Profiles often too big for downstream analysis
Highly variable genes, N=2000:

Coefficient of variation < 0.01
Order by standard deviation

[GeoMx] Transcriptome deconvolution using NMF

NMF parameters

Method: brunet
Rank: {2, 3, 4, 5, ..., 10}
Iteration: 1000
Seed: 123456

Input

PV: 2,000 features X 12 samples
NeuN: 2,000 features X 14 samples

Output

Segment	Rank survey	Consensus matrix	Coefficient H matrix (Metagene X Sample)	Basis W matrix (Gene X Metagene)
PV
NeuN

[GeoMx] Identify overrepresented metagenes

Workflow:

Include a subset of the NMF results: max rank at half size of the population
Concatenate basis W matrices column-wide from NMF results
Calculate all pairwise coefficient correlations using Spearman
Generate a correlation network with positive correlations (Spearman rho >0.5)
Identify communities using the fast greedy algorithm

Overrepresented communities, or biological modules, identified:

[GeoMx] Select an optimal Rank from NMF using SOTK

Mark and label metagenes at each rank:

Rank = 3

Rank = 2	Rank = 4

Rank = 5	Rank = 6

Select the best rank: the minimum rank that covers the most communities

Sample-metagene relationships: based on the maximum coefficient values

Metagene-associated genes ^{Tsukahara et al. (2021)}

The top 100 genes based on basis values for a given metagene
Correlation between the expression level and coefficient values for a given gene

PV	NeuN

Metagene 1 (97) Metagene 2 (94) Metagene 3 (79)	Metagene 1 (83) Metagene 2 (49) Metagene 3 (88) Metagene 4 (72)

[GeoMx] Metagene functional annotations

Hypergeometric distribution - g:Profiler results:

PV
NeuN

Top 10 enriched terms:

[GeoMx] Variability among samples

Metagene-sample relationships on the correlation network per sample:

PV
NeuN

Each sample shows different contribution of the metagenes

[Xenium] Regions of Interest (ROIs)

The number of cells in each ROI

Section	WT1F	WT2F	WT3F	TG2F	TG3F	TG4F	Total
RSC	3,415	2,335	1,498	2,085	391	187	9,911
SUB	293	142	161	297	N/A	N/A	893
VIS	1,483	1,223	637	1,247	N/A	258	4,848
ENT	682	430	738	426	566	264	3,106
CA1	663	807	1,140	907	1,172	753	5,442
Total	6,536	4,937	4,174	4,962	2,129	1,462	24,200

RSC, retrosplenial cortex SUB, subiculum VIS, visual cortex ENT, entorhinal cortex CA1 region of the hippocampu

[Xenium and GeoMx] Correlation between and across consecutive sections in RSC

between sections:

across sections:

[Xenium] Unsupervised clustering using k-means clustering

k = 7 (Assumption: at least seven cell types based on the NMF results [three PV and four NueN metagenes])
Results - import the following CSV files to the Xenium Explorer:

WT1F	WT2F	WT3F

TG2F	TG3F	TG4F

[Xenium] Deconvolution using NMF results

Cell typing of Xenium data with the GeoMx data (NMF results) as a reference using InSituType ^{Danaher et al. (2022)}
Results - import the following CSV files to the Xenium Explorer:

[Xenium] t-distributed Stochastic Neighbor Embedding (tSNE)

RSC section

All cells from five sections

[Xenium] Differential expression analysis

Wilcoxon rank sum test results in EXCEL

Discussion/Limitations

Deveploed a framework to deconvolute spatial transcriptomics data and identified TG- and WT-associated metagenes and features/genes.
Dner, Gad1, and Pvalb are significantly down-regulated in TG compared to the WT in female mice.
There is a limited intersection between the mouse whole transcriptomics atlas and the xenium 250 mouse brain panel.
An arbitrary threshold value, i.e., Spearman rho >0.5, was used in the network construction. However, higher coefficient threshold filters in a limited number of nodes and lower values include doubtful biological modules.
Xenium internally checks and filters out out-of-focus FOV (field-of-view). However, it is necessary to filter data at the FOV level since each FOV captures different morphology or regions that should be normalized across FOVs.