DOI: 10.5061/dryad.sbcc2frgp
title: Data from: Pathways underlying selective neuronal vulnerability in Alzheimer's disease: Contrasting the vulnerable locus coeruleus to the resilient substantia nigra

description: Identifying factors underlying selective neuronal vulnerability is crucial
 for understanding Alzheimer's disease (AD) pathophysiology. The
 Neuromodulatory Subcortical System (NSS) includes nuclei that exhibit
 early, but varied vulnerability to tau accumulation and neuronal loss.
 This varied vulnerability represents a valuable opportunity to explore the
 underlying mechanisms of AD. In this study, we investigated factors
 contributing to selective neuronal vulnerability by comparing
 transcriptomic profiles of two similar NSS nuclei with differing
 vulnerabilities to AD, the locus coeruleus and substantia nigra. Using
 paired samples from well-characterized postmortem human tissue from
 individuals in early Braak stages and free of comorbid neuropathologic
 diagnoses, we identified pathways related to cholesterol homeostasis and
 antioxidant pathways response as key potential drivers of vulnerability.
description: We leveraged transcriptomics and immunohistochemistry in paired
 samples from human postmortem tissue representing a vulnerable and
 resilient region – the locus coeruleus (LC) and substantia nigra (SN).
 These regions have comparable anatomical features but distinct
 vulnerability to AD.  <strong>Participant
 selection and neuropathologic assessment</strong>
 Cases were sourced from the Biobank for Aging Studies at the
 University of São Paulo  and the Neurodegenerative Disease Brain Bank at
 the University of California, San Francisco Memory and Aging Center which
 is an ADRC. Consent for brain donation was obtained from subjects or next
 of kin following the site-specific protocol approved by the relevant
 Institutional Review Board and the study was performed in accordance with
 the ethical standards as laid down in the 1964 Declaration of Helsinki. In
 both brain banks, brain tissue was sampled for neuropathological diagnosis
 following NACC guidelines. Basic histological and immunohistochemical
 stains were made with the antibodies for phospho-Ser202 Tau (CP13, 1:250,
 courtesy of P. Davies), TDP-43 (1:2000, 10782-2-AP, Proteintech),
 β-amyloid (1:500, MAB5206, Millipore), α-Synuclein (1:5000, LB509,
 courtesy of J. Trojanowsky and V. Lee). Final neuropathologic diagnosis
 was made by following current guidelines. For this
 study, we included cases with Braak stages 0-III that had available frozen
 LC and SN tissue (Supplementary Data 1). We excluded cases with Lewy body
 inclusions, TDP-43 proteinopathy, a primary or contributing diagnosis of
 chronic traumatic encephalopathy, a primary or contributing diagnosis of a
 primary tauopathy, an Axis I psychiatric disorder, a post-mortem interval
 over 24 hours, or gross non-neurodegenerative structural neuropathology.
 Initial case selection sought equal proportions of males and females and
 informant-reported race within Braak stages, as well as a similar age
 distribution between Braak stages.
 <strong>Sample collection and
 processing</strong> The frozen half of the
 brainstem for selected cases was kept on dry ice during the dissection. A
 scalpel was used to shave down the midbrain until the pigmented SN was
 exposed. An additional 3-5mm of midbrain was shaved down around the
 rostral portion of the SN, with borders defined by the pigmented area. The
 protruding portion of the SN was sliced off of the shaved-down face of the
 midbrain and put into RNAlater (AM7020, Invitrogen) to protect RNA in case
 of thawing during transport for processing. The sample in RNAlater was
 frozen down at -80<sup>o</sup>C and transported on dry
 ice. The LC was isolated by excising a tissue block
 approximately 5-10mm in length along the rostrocaudal axis and about 10mm
 in depth from the fourth ventricle near the medial eminence. After
 removal, the pigmented area of the LC was identified. Tissue outside the
 pigmented LC border was then discarded. The isolated LC tissue was
 subsequently preserved in RNAlater and frozen. Tissue
 homogenization and cell lysis were performed in TRIzol. Following cell
 lysis, impurities were removed, and RNAse inhibition, total RNA was
 extracted using phase separation. RNA quality and concentration were
 assessed using an Agilent Bioanalyzer 2100. Samples with an RNA integrity
 number (RIN) greater than 4 and at least 0.1 µg of RNA were advanced to
 library preparation and sequencing. Libraries were prepared from RNA using
 the NEBNext Ultra II RNA Library Prep Kit for Illumina, which included
 poly-A enrichment using poly-T oligo-attached magnetic beads. mRNA was
 fragmented, and first-strand cDNA synthesis was performed using random
 hexamer primers, followed by second-strand cDNA synthesis. Sequencing was
 performed on the Illumina NovaSeq 6000 platform, generating 150 bp
 paired-end (PE150) reads for each sample. Extraction, library preparation,
 and sequencing was performed by Novogene Inc. (Davis, CA).
 <strong>Transcriptomic analysis</strong>
 The quality of sequence files was assessed using the FastQC
 package before and after trimming steps. Trimmomatic (ILLUMINACLIP:
 TruSeq3-PE.fa:2:30:10:2:True, LEADING: 3, TRAILING: 3, SLIDINGWINDOW:
 4:15, and MINLEN: 36) was used to remove adapter sequences and any
 sequences with low mean quality scores. Sequences were aligned to GRCh38
 using STAR alignment and count matrices were generated using
 featureCounts. The count matrices were normalized using the counts per
 million (CPM) function in the edgeR package. Expression levels of positive
 control genes (<em>DBH</em>, <em>SLC6A2</em>, and
 <em>SLC6A3</em>) were checked to confirm accurate sampling.
 Cases with less than 100 CPM of <em>DBH</em> or 10 CPM of
 <em>SLC6A2</em> in the LC, or less than 100 CPM or
 <em>SLC6A3</em> in the SN were excluded from subsequent
 analyses. Principal component analyses (PCA) were
 performed using the RunPCA command from the Seurat package in R with 20
 principal components computed. Differential expression analyses were
 performed using the edgeR package. Only genes with at least 5 counts
 present in at least 25% of samples were included in differential
 expression analyses. Multiple comparison correction was done using the
 Benhamini-Hochberg method and the upper bound of the expected false
 discovery rate (FDR) is reported in analyses as the FDR. Differential
 expression analyses set cutoffs at 0.05 FDR and +/- 0.5 log-fold change
 (logFC) to balance statistical significance with the need to detect
 changes limited to small cell populations in the bulk sample. The design
 matrix for differential expression analyses is formed from an additive
 model formula including the case. Differential expression analyses were
 done both with (corrected) and without (uncorrected) RIN as a covariate in
 the design formula. Gene set enrichment analysis (GSEA)
 was done using the fgsea package in R. The Hallmark gene set collection
 was downloaded from the GSEA Molecular Signatures Database. The gene set
 files were filtered to contain only genes present in the differential
 expression table (5% FDR and +/- 0.5 log-fold change) and then reformatted
 to the specifications required by the fgsea package. The differentially
 expressed genes (DEGs) were ranked based on the negative
 log<sub>10</sub>(p-value) such that the genes with the
 smallest p-values were ranked at the very top of the ranking list. The
 fgsea function was run on the filtered gene set files with the ranked
 genes using a minimum gene set size of 10 and maximum of 500.
 <strong>Immunohistochemistry
 (IHC)</strong> Formalin-fixed paraffin embedded
 tissue cut at 8µm was cut from the pons at the level of the LC and the
 midbrain at the level of the SN for 13 postmortem cases (Table 2). 
 Sections were deparaffinized and underwent antigen retrieval using the
 Discovery CC1 solution (950-500) for 40 minutes at
 95<sup>o</sup>C. Sections were labeled with HMGCS1 (1:400,
 ab155787), ABCA1 (1:200, NB400-105), and LDLR (1:200, 10785-1-AP)
 developed with the Discovery Purple kit (760-229) and counterstained with
 hematoxylin, MYLIP (1:50, 15455-1-AP) developed with the Discovery Green
 HRP kit (760-721) and counterstained with hematoxylin, and SREBP2 (1:200,
 PA1338) developed with the Discovery Purple kit with no counterstaining.
 The OmniMap anti-Rabbit HRP (760-4311) was used for secondary detection of
 all primary antibodies. A set of serial LC slides run without primary
 antibody or counterstain were treated with the anti-Rabbit HRP secondary
 antibody and purple chromogen to verify SREBP2 labeling in the
 LC. Stained sections were scanned on a Zeiss Axioscan
 slide scanner. Staining was assessed semi-quantitatively by selecting
 500x500 µm regions of interest and scoring all the neurons in the frame on
 a scale from 0 to +++. A score of “+” corresponds to a neuron with
 staining that differentiated the cell from being completely devoid of
 signal, but without more than very faint diffuse signal or, in the case of
 granular staining, several speckles. A score of “++” corresponds to a cell
 with moderate-to-strong diffuse staining or a speckled granular staining
 occupying most, but not all, of the cell. A score of “+++” corresponds to
 a cell with very strong diffuse staining or granular staining occupying
 the entirety of the cell represented in the section. These scores were
 converted to numbers (0-3, respectively), the proportions of neurons with
 each score were determined based on the total neuron numbers counted, and
 an average score was computed for each nucleus for each case. Scores were
 analyzed using a paired Wilcox test to compare average scores between
 nuclei for cases at Braak stage 0-II and Braak stage VI.
description: # Data from: Pathways underlying selective neuronal vulnerability in
 Alzheimer's disease: Contrasting the vulnerable locus coeruleus to
 the resilient substantia nigra **Immunohistochemistry data, supplementary
 data, and supplementary methods**
 [https://doi.org/10.5061/dryad.sbcc2frgp](https://doi.org/10.5061/dryad.sbcc2frgp) ## Trancriptomic analysis ### Description of the data and file structure The frozen half of the brainstem for selected cases was kept on dry ice during the dissection. A scalpel was used to shave down the midbrain until the pigmented SN was exposed. An additional 3-5mm of midbrain was shaved down around the rostral portion of the SN, with borders defined by the pigmented area. The protruding portion of the SN was sliced off of the shaved-down face of the midbrain and put into RNAlater (AM7020, Invitrogen) to protect RNA in case of thawing during transport for processing. The sample in RNAlater was frozen down at -80C and transported on dry ice. The LC was isolated by excising a tissue block approximately 5-10mm in length along the rostrocaudal axis and about 10mm in depth from the fourth ventricle near the medial eminence. After removal, the pigmented area of the LC was identified. Tissue outside the pigmented LC border was then discarded. The isolated LC tissue was subsequently preserved in RNAlater and frozen. The LC and SN samples were sent to a vendor (Novogene Inc., Davis, CA) for RNA extraction and sequencing. Tissue homogenization and cell lysis were performed in TRIzol. Following cell lysis, impurities were removed, and RNAse activity was inhibited. Total RNA was then extracted using a phase separation method to remove cell debris. RNA quality and concentration were assessed using an Agilent Bioanalyzer 2100. Samples with an RNA integrity number (RIN) greater than 4 and at least 0.1 µg of RNA were advanced to library preparation and sequencing. Library preparation, including poly-A enrichment, was conducted using the NEBNext Ultra II RNA Library Prep Kit for Illumina. Sequencing was performed on the Illumina NovaSeq 6000 platform, generating 150 bp paired-end (PE150) reads for each sample. The quality of sequence files was assessed using the FastQC package before and after trimming steps. Trimmomatic (ILLUMINACLIP: TruSeq3-PE.fa:2:30:10:2:True, LEADING: 3, TRAILING: 3, SLIDINGWINDOW: 4:15, and MINLEN: 36) was used to remove adapter sequences and any sequences with low mean quality scores. Sequences were aligned to GRCh38 using STAR alignment and count matrices were generated using featureCounts. The count matrices were converted to counts per million (CPM) using the edgeR package. Expression levels of positive control genes (*DBH*, *SLC6A2*, and *SLC6A3*) were checked to confirm accurate sampling. Cases with less than 100 CPM of *DBH* or 10 CPM of *SLC6A2* in the LC, or less than 100 CPM or *SLC6A3* in the SN were excluded from subsequent analyses. Principal component analyses (PCA) were performed using the RunPCA command from the Seurat package in R with 20 principal components computed. Differential expression analyses were performed using the edgeR package [34]. Only genes with at least 5 counts present in at least 25% of samples were included in differential expression analyses. Multiple comparison correction was done using the Benhamini-Hochberg method and the upper bound of the expected false discovery rate (FDR) is reported in analyses as the FDR. Differential expression analyses set cutoffs at 0.05 FDR and +/- 0.5 log-fold change (logFC). The design matrix for differential expression analyses is formed from an additive model formula including the case. We ran models that were uncorrected and corrected for RNA Integrity Number. Gene set enrichment analysis (GSEA) was done using the fgsea package in R. The Hallmark gene set collection was downloaded from the GSEA Molecular Signatures Database. The gene set files were filtered to contain only genes present in the differential expression table (5% FDR and +/- 0.5 log-fold change) and then reformatted to the specifications required by the fgsea package. The differentially expressed genes (DEGs) were ranked based on the negative log10(p-value) such that the genes with the smallest p-values were ranked at the very top of the ranking list. The fgsea function was run on the filtered gene set files with the ranked genes using a minimum gene set size of 10 and maximum of 500. Scripts used to process and analyze the transcriptomic data are provided here and referenced to in the manuscript as "Supplementary Methods 1". The raw transcriptomic data are available on GEO ([https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273787](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273787)) and the processed data are provided here. ## Scripts used *"Supplementary methods 1" in published manuscript.* #### File: trim\_and\_qc.sh **Description:** Trimming and quality control script #### File: STAR\_index\_exec.sh **Description:** Generation of index genome using STAR #### File: STAR\_align.sh **Description:** STAR alignment script #### File: featureCounts\_exec.sh **Description:** Feature counts execution script #### File: featureCounts.R **Description:** Feature counts script #### File: NSS\_Vulerability\_pub.R **Description:** Statistical analysis script of processed transcriptomic data ### R Package Software Versions | **Package** | **Version** | | :------------ | :---------- | | gridExtra | 2.3 | | stringr | 1.5.1 | | ggpubr | 0.6.0 | | corrplot | 0.95 | | data.table | 1.16.4 | | fgsea | 1.32.0 | | ggVennDiagram | 1.5.2 | | ggbeeswarm | 0.7.2 | | ggrepel | 0.9.6 | | org.Hs.eg.db | 3.20.0 | | AnnotationDbi | 1.68.0 | | IRanges | 2.40.1 | | S4Vectors | 0.44.0 | | Biobase | 2.66.0 | | BiocGenerics | 0.52.0 | | Seurat | 5.1.0 | | SeuratObject | 5.0.2 | | sp | 2.1-4 | | edgeR | 4.4.1 | | limma | 3.62.1 | | tibble | 3.2.1 | | forcats | 1.0.0 | | tidyr | 1.3.1 | | dplyr | 1.1.4 | | ggplot2 | 3.5.1 | | ggridges | 0.5.6 | | readxl | 1.4.3 | ### Data and metadata Raw transcriptomic data from this paper are available at GEO ([https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273787](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273787)). #### File: SD1\_Metadata\_Dryad.xlsx **Supplementary data 1:** Metadata for cases used in transcriptomics analysis #### File: SD2\_count\_matrix.csv **Supplementary data 2:** Processed transcript counts #### File: SD3\_cpm\_matrix.csv **Supplementary data 3:** Processed counts per million #### File: SD4\_PCA\_feature\_loadings.csv **Supplementary data 4:** Feature loadings for principal component analysis #### File: SD5\_DEG\_Braak0.csv **Supplementary data 5:** Differentially expressed genes between the locus coeruleus and substantia nigra in Braak stage 0 cases with RIN correction built into the paired model (~case+RIN+Nucleus). Fields: logFC, log fold change of differential expression; logCPM, log of the counts per mission of the transcripts; F, F-statistic; PValue, unadjusted p-value; FDR, Benjamini-Hochberg corrected p-value; Ensembl, Ensembl transcript identifier; Gene, Gene name. #### File: SD6\_DEG\_Braak1.csv **Supplementary data 6:** Differentially expressed genes between the locus coeruleus and substantia nigra in Braak stage I cases with RIN correction built into the paired model (~case+RIN+Nucleus). Fields: logFC, log fold change of differential expression; logCPM, log of the counts per mission of the transcripts; F, F-statistic; PValue, unadjusted p-value; FDR, Benjamini-Hochberg corrected p-value; Ensembl, Ensembl transcript identifier; Gene, Gene name. #### File: SD7\_DEG\_Braak2.csv **Supplementary data 7:** Differentially expressed genes between the locus coeruleus and substantia nigra in Braak stage II cases with RIN correction built into the paired model (~case+RIN+Nucleus). Fields: logFC, log fold change of differential expression; logCPM, log of the counts per mission of the transcripts; F, F-statistic; PValue, unadjusted p-value; FDR, Benjamini-Hochberg corrected p-value; Ensembl, Ensembl transcript identifier; Gene, Gene name. #### File: SD8\_DEG\_Braak3.csv **Supplementary data 8:** Differentially expressed genes between the locus coeruleus and substantia nigra in Braak stage III cases with RIN correction built into the paired model (~case+RIN+Nucleus). Fields: logFC, log fold change of differential expression; logCPM, log of the counts per mission of the transcripts; F, F-statistic; PValue, unadjusted p-value; FDR, Benjamini-Hochberg corrected p-value; Ensembl, Ensembl transcript identifier; Gene, Gene name. #### File: SD9\_DEG\_Braak0\_uncor.csv **Supplementary data 9:** Differentially expressed genes between the locus coeruleus and substantia nigra in Braak stage 0 cases without RIN correction built into the paired model (~case+Nucleus). Fields: logFC, log fold change of differential expression; logCPM, log of the counts per mission of the transcripts; F, F-statistic; PValue, unadjusted p-value; FDR, Benjamini-Hochberg corrected p-value; Ensembl, Ensembl transcript identifier; Gene, Gene name. #### File: SD10\_DEG\_Braak1\_uncor.csv **Supplementary data 10:** Differentially expressed genes between the locus coeruleus and substantia nigra in Braak stage I cases without RIN correction built into the paired model (~case+Nucleus). Fields: logFC, log fold change of differential expression; logCPM, log of the counts per mission of the transcripts; F, F-statistic; PValue, unadjusted p-value; FDR, Benjamini-Hochberg corrected p-value; Ensembl, Ensembl transcript identifier; Gene, Gene name. #### File: SD11\_DEG\_Braak2\_uncor.csv **Supplementary data 11:** Differentially expressed genes between the locus coeruleus and substantia nigra in Braak stage II cases without RIN correction built into the paired model (~case+Nucleus). Fields: logFC, log fold change of differential expression; logCPM, log of the counts per mission of the transcripts; F, F-statistic; PValue, unadjusted p-value; FDR, Benjamini-Hochberg corrected p-value; Ensembl, Ensembl transcript identifier; Gene, Gene name. #### File: SD12\_DEG\_Braak3\_uncor.csv **Supplementary data 12:** Differentially expressed genes between the locus coeruleus and substantia nigra in Braak stage III cases without RIN correction built into the paired model (~case+Nucleus). Fields: logFC, log fold change of differential expression; logCPM, log of the counts per mission of the transcripts; F, F-statistic; PValue, unadjusted p-value; FDR, Benjamini-Hochberg corrected p-value; Ensembl, Ensembl transcript identifier; Gene, Gene name. ## Immunohistochemistry analysis ### Description of the data and file structure Formalin-fixed paraffin embedded tissue cut at 8µm was cut from the pons at the level of the LC and the midbrain at the level of the SN for 13 postmortem cases (Table 2).  Sections were deparaffinized and underwent antigen retrieval using the Discovery CC1 solution (950-500) for 40 minutes at 95oC. Sections were labeled with HMGCS1 (1:400, ab155787), ABCA1 (1:200, NB400-105), and LDLR (1:200, 10785-1-AP) developed with the Discovery Purple kit (760-229) and counterstained with hematoxylin, MYLIP (1:50, 15455-1-AP) developed with the Discovery Green HRP kit (760-721) and counterstained with hematoxylin, and SREBP2 (1:200, PA1338) developed with the Discovery Purple kit with no counterstaining. The OmniMap anti-Rabbit HRP (760-4311) was used for secondary detection of all primary antibodies. A set of serial LC slides run without primary antibody or counterstain were treated with the anti-Rabbit HRP secondary antibody and purple chromogen to verify SREBP2 labeling in the LC. Stained sections were scanned on a Zeiss Axioscan slide scanner. Staining was assessed semi-quantitatively by selecting 500x500 µm regions of interest and scoring all the neurons in the frame on a scale from 0 to +++. A score of “+” corresponds to a neuron with staining that differentiated the cell from being completely devoid of signal, but without more than very faint diffuse signal or, in the case of granular staining, several speckles. A score of “++” corresponds to a cell with moderate-to-strong diffuse staining or a speckled granular staining occupying most, but not all, of the cell. A score of “+++” corresponds to a cell with very strong diffuse staining or granular staining occupying the entirety of the cell represented in the section. These scores were converted to numbers (0-3, respectively), the proportions of neurons with each score were determined based on the total neuron numbers counted, and an average score was computed for each nucleus for each case. Scores were analyzed using a paired Wilcox test to compare average scores between nuclei for cases at Braak stage 0-II and Braak stage VI. Each zipped folder contains scans of files in the Zeiss file format (.czi) which is a slide scan for one region per stain per case. The scores assigned to each case are provided in tabular format. ## Antibodies used * HMGCS1 (1:400, ab155787) * ABCA1 (1:200, NB400-105) * LDLR (1:200, 10785-1-AP) * MYLIP (1:50, 15455-1-AP) * SREBP2 (1:200, PA1338) ## Regions * LC: Pons section featuring the locus coeruleus * SN: Midbrain section featuring the substantia nigra ## Code/software Zeiss ZEN lite: [https://www.zeiss.com/microscopy/de/produkte/software/zeiss-zen-lite.html](https://www.zeiss.com/microscopy/de/produkte/software/zeiss-zen-lite.html) #### File: IHC analysis script.R **Description:** Script used to generate figures in the manuscript ## Scores and Files #### File: IHC Analyzed ROIs.zip **Description:** Analyzed regions of interest for all scanned slides #### File: SD13\_Cholesterol\_IHC.xlsx **Description:** Quantification of IHC staining. #### File: ABCA1\_IHC.zip **Description:** IHC staining for ABCA1HMGCS1 #### File: HMGCS1\_IHC.zip **Description:** IHC staining for HMGCS1 #### File: LDLR\_IHC.zip **Description:** IHC staining for LDLR #### File: MYLIP\_IHC.zip **Description:** IHC staining for MYLIP #### File: SREBP2\_IHC.zip **Description:** IHC staining for SREBP2 ## Human subjects data This dataset is derived from human research subjects who enrolled in a research study approved by a site-specific IRB. Participants, or a next-of-kin, provided informed consent permitting the use of and sharing of de-identified data. All personally identifiable information are removed when data are shared using technical controls approved by the relevant IRB's.