Lind‑Holm Mogensen et al.
Journal of Neuroinflammation(2024) 21:174
https://doi.org/10.1186/s12974-024-03164-x

Journal of Neuroinflammation

Open Access

RESEARCH

PARK7/DJ‑1 deficiency impairs microglial
activation in response to LPS‑induced
inflammation
Frida Lind‑Holm Mogensen1,2, Carole Sousa1,3, Corrado Ameli4, Katja Badanjak5, Sandro L. Pereira5,
Arnaud Muller6,7, Tony Kaoma6, Djalil Coowar8, Andrea Scafidi1,2, Suresh K. Poovathingal4,9, Maria Tziortziou5,
Paul M. A. Antony10, Nathalie Nicot7, Aurélien Ginolhac11, Daniela M. Vogt Weisenhorn12,13,
Wolfgang Wurst12,13,14,15,16, Aurélie Poli1, Petr V. Nazarov6,17, Alexander Skupin4,18,19, Anne Grünewald5,20 and
Alessandro Michelucci1*

Abstract
Background Specific microglia responses are thought to contribute to the development and progression of neu‑rodegenerative diseases, including Parkinson’s disease (PD). However, the phenotypic acquisition of microglial
cells and their role during the underlying neuroinflammatory processes remain largely elusive. Here, according
to the multiple-hit hypothesis, which stipulates that PD etiology is determined by a combination of genetics and vari‑
ous environmental risk factors, we investigate microglial transcriptional programs and morphological adaptations
under PARK7/DJ-1 deficiency, a genetic cause of PD, during lipopolysaccharide (LPS)-induced inflammation.
Methods Using a combination of single-cell RNA-sequencing, bulk RNA-sequencing, multicolor flow cytometry
and immunofluorescence analyses, we comprehensively compared microglial cell phenotypic characteristics
in PARK7/DJ-1 knock-out (KO) with wildtype littermate mice following 6- or 24-h intraperitoneal injection with LPS.
For translational perspectives, we conducted corresponding analyses in human PARK7/DJ-1 mutant induced pluripo‑
tent stem cell (iPSC)-derived microglia and murine bone marrow-derived macrophages (BMDMs).
Results By excluding the contribution of other immune brain resident and peripheral cells, we show that microglia
acutely isolated from PARK7/DJ-1 KO mice display a distinct phenotype, specially related to type II interferon and DNA
damage response signaling, when compared with wildtype microglia, in response to LPS. We also detected discrete
signatures in human PARK7/DJ-1 mutant iPSC-derived microglia and BMDMs from PARK7/DJ-1 KO mice. These specifictranscriptional signatures were reflected at the morphological level, with microglia in LPS-treated PARK7/DJ-1 KOmice showing a less amoeboid cell shape compared to wildtype mice, both at 6 and 24 h after acute inflammation,
as also observed in BMDMs.
Conclusions Taken together, our results show that, under inflammatory conditions, PARK7/DJ-1 deficiency skews
microglia towards a distinct phenotype characterized by downregulation of genes involved in type II interferon
signaling and a less prominent amoeboid morphology compared to wildtype microglia. These findings suggest*Correspondence:
Alessandro Michelucci
alessandro.michelucci@lih.lu
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

Page 2 of 24

that the underlying oxidative stress associated with the lack of PARK7/DJ-1 affects microglia neuroinflammatory
responses, which may play a causative role in PD onset and progression.
Keywords PARK7/DJ-1, Lipopolysaccharide, Microglia, Neuroinflammation, Parkinson’s disease, Microglia morphology

Background
Microglia serve as the central nervous system’s primary
immune effector cells. Under threatening conditions,microglia rapidly react by undergoing transcriptional,
morphological and functional changes, known as “microglia activation”. This process encompasses various stages
and substates, which vary depending on the context andduration of the compromising conditions, seeking toresolve the threat efficiently [52].
Parkinson’s disease (PD) is the second most common
neurodegenerative disease, which involves the accumulation and aggregation of α-synuclein in the form of
Lewy bodies and the progressive loss of dopaminergic
neurons. The main risk factor for PD is aging and, as theworld´s population becomes older, the number of people diagnosed with a neurodegenerative disease continues to increase [17]. Additionally, factors beyond aging,such as pesticide exposure and infections, may contribute significantly to this trend, as the incidence of PD currently outpaces the rate of aging and is disproportionallyon the rise, a tendency that is particularly noticeable innewly industrialized countries [17]. The notion that PD
is caused by a combination of genetics and different environmental risk factors refers to the multiple-hit hypothesis [53]. Among the detected genetic defects, the loss offunction of PARK7, encoding for the protein DJ-1, causes
autosomal recessive early-onset PD in humans. DJ-1 isa multifunctional protein with roles in cellular transformation, transcriptional regulation, and anti-oxidative
stress functions [14]. Although PARK7 mutations leading to DJ-1 loss of function are rare and GWAS studies
failed to show a link between PARK7 promoter polymorphisms and PD risk so far [31], they are causative of Lewy
body pathology and α-synucleinopathy [69]. Further, low
DJ-1 protein levels and over-oxidized forms have been
detected in the cerebrospinal fluid, blood, urine and brainof idiopathic PD patients, suggesting that DJ-1 deficiencies may play a role in a larger spectrum of PD patients[13, 35, 42, 48, 57, 58]. On the other hand, among theenvironmental risk factors, a number of bacterial andviral infections, including Helicobacter pylori [8, 50] andhepatitis C virus [51], have been linked to increased idiopathic PD risk, thus indicating a potential contributingrole of infections in the development or exacerbation of
the disease, at least in certain individuals [20, 64, 80].
Activated microglial phenotypes and neuroinflammatory processes are detected in the early stages of PD

and in the substantia nigra of post-mortem tissues [24,34, 39, 63]. However, the role of microglia in the onset
and progression of PD remains vague. In line with themulti-hit hypothesis, we here postulate that PARK7/
DJ-1 deficiency influences microglial responses under
inflammatory conditions. To address this hypothesis, wetook advantage of the PARK7/DJ-1 KO mouse model,
which shows subtle impairments in cognitive and motor
functions and heightened vulnerability to neurotoxins[10, 26, 37]. How microglia react in those mice under
inflammatory conditions has been addressed in specificmodels, while the majority of the studies have been conducted in vitro using PARK7/DJ-1 deficient immortalized microglial cell lines or primary cells, which show an
exacerbated response to inflammatory stimuli [42]. By
comparing the transcriptional profile of sorted microglial cells isolated from PARK7/DJ-1 KO with wildtype
mice peripherally injected with lipopolysaccharide (LPS),a stimulus we have previously shown to induce a prominent microglia activation [67], we identified distinct signatures of this activated state. Specifically, microglia in
PARK7/DJ-1 KO LPS-treated mice were characterized
by the downregulation of genes involved in immune
responses, especially interferon-related genes, andupregulation of genes related to DNA damage response,when compared to the corresponding cells isolated fromwildtype mice. We further confirmed a dampened transcriptional response towards LPS in human PARK7/DJ-1
mutant compared to isogenic control induced pluripotent stem cell (iPSC)-derived microglia and murine bonemarrow-derived macrophages. Furthermore, in line withthese distinct profiles detected at the transcriptional
level, microglial cells in PARK7/DJ-1 KO mice exhibited a less prominent amoeboid shape when comparedto wildtype mice in response to LPS-induced systemic
inflammation.
Taken together, our results show that, under inflammatory conditions, PARK7/DJ-1 deficiency skews microglia
towards a distinct phenotype, which may have an impact
during neuroinflammatory processes under threatening
conditions, such as during α-synuclein accumulation at
the onset and during the progression of PD.

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

Materials and methods
Mice

PARK7/DJ-1 KO (B6;129P2-Park7Gt(XE726) Byg/Mmucd)
mice were kindly provided by the laboratory of Dr D.M.
Vogt Weisenhorn in Munich (Germany), where they havebeen originally developed and characterized [55]. The
PARK7/DJ-1 KO line used in our laboratory had alreadybeen backcrossed with C57BL/6NCrl mice for at least ten
generations. For all the described experiments, we used
PARK7/DJ-1 KO and wildtype littermate mice that were3- and 4-month age-matched siblings, with the exceptionof the aged cohort where we used 12- and 13-month old
mice, generated from heterozygous breeding pairs. Mice
were bred and housed in a specific pathogen-free animal
research facility at a relative humidity of 40–70%, at 22 °C
and in 12 h light/dark cycles. Mice received food (SAFE
A40) and water ad libitum.
PARK7/DJ‑1 KO mouse genotyping

PARK7/DJ-1 KO, heterozygote and wildtype littermates
were genotyped using a genotyping kit (KAPA Biosystems, KK7352), following the manufacturer’s instructions. Briefly, genomic DNA was extracted from mouseear marking samples or tail pieces. The PCR cycling
program (BioRad 3000) was as follows: 1 cycle of initialdenaturation at 95 °C for 3 min followed by 35 cycles ofdenaturation at 95 °C for 15 s, annealing at 58 °C for 15 s
and extension at 72 °C for 15 s, ended by a final extension at 72 °C for 7 min. Primers used for genotyping
were as follows. Wildtype Park7 forward primer: 5’-AGG​
CAG​TGG​AGA​AGT​CCA​TC-3’; wildtype Park7 reverse
primer: 5’-AAC​ATA​CAG​ACC​CGG​GAT​GA-3’; KO
Park7 reverse primer: 5’-CGG​TAC​CAG​ACT​CTC​CCA​
TC-3’. PCR products loaded on a 2% agarose gel showedbands at 475 bp or 231 bp, corresponding to wildtype or
KO alleles, respectively.
Acute microglia isolation by fluorescence‑activated cellsorting (FACS)

Mice were treated with a single intraperitoneal injection of LPS (4 μg/g body weight) or with PBS (saline) as
vehicle control for 24 h. Processing of the brain, microglial isolation and multicolor flow cytometry preparation
was done as previously described [67]. Briefly, myelin wasremoved from cell suspension with the Myelin removal
kit (130-096-733, Miltenyi Biotec), according to the manufacturer’s protocol. For multicolor staining, cells wereincubated for 15 min with Fc receptor binding inhibitor
to reduce the binding of non-specific Fc-gamma receptors, and then stained with fluorochrome-conjugatedantibodies or their corresponding isotype controls for
45 min at 4 °C in the dark (Table S1). After washing, cells
were pelleted at 300 g for 10 min at 4 °C and resuspended

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in 200 µL of the appropriated buffer. Hoechst (0.1 µg/ml;
Sigma) or Sytox Red (1:1000; Thermo Fisher Scientific)
were added shortly before flow cytometry measurements
for dead cell discrimination. C­ D11b+CD45int living single cells were sorted with FACS Aria™ SORP cytometer(BD Biosciences) fitted with a 640 nm (30 mW) red laser,a 355 nm (60 mW) UV laser, a 405 nm (50 mW) violet
laser, a 488 nm (100 mW) blue laser and a 561 nm (50mW) yellow/green laser. Data were analyzed with FACSDiva (Becton Dickinson) and FlowJo (v7.6.5; Tree Star)
software.
Single‑cell RNA‑sequencing using Drop‑seq,bioinformatics and data analyses

FACS-sorted ­CD11b+CD45int cells were collected in precooled HBSS, 0.5% BSA and transferred directly for subsequent Drop-seq analysis as previously described [67].
Cell calling, normalization and scaling

DropletUtils [44] was employed to distinguish cells fromempty droplets. Subsequently, raw gene counts of theidentified “cells” were converted into Seurat object [30].
Cells expressing less than 100 genes and genes detectedin less than 3 cells were discarded. The resulting object
was subjected to log-normalization and scaling using thestandard functions of the Seurat package (NormalizeData and ScaleData; Seurat v4.3.0.1).
Dimension reduction and clustering

Principal component analysis was performed via singularvalue decomposition (function propack.svd, svd package,
v0.5.4). Correlations between each principal component(PC) and technical/experimental factors, such as the total
number of counts per cell and mouse genotype, were
estimated using linear models. Given the strong correlation between the first PC and the total number of counts
per cell, we selected PCs 2 to 20 for uniform manifold
approximation and projection (UMAP) and clustering
analysis. UMAP was generated using the RunUMAP
function of the Seurat package, while cells were clusteredusing FindNeighbors function followed by FindClusters.
Differential expression analysis

Differential expression analysis was conducted using the
MAST algorithm [21] as implemented in the FindAllMarkers or FindMarkers functions of the Seurat package.
FindAllMarkers was employed to identify marker genes
specific to each cell cluster while FindMarkers was used
for other comparative analyses.
CD11b magnetic enrichment for ex vivo analyses

Mice were treated with a single intraperitoneal injection of lipopolysaccharide (LPS) (Sigma, E.Coli O55:B5,

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

L6529-1MG) (4 μg/g body weight) or with PBS (saline)
as vehicle control for 6 h for ex vivo analyses. Mice were
terminally anaesthetized with a combination of Ketamine(100 mg/mL; Nimatek Vet) / Dorbene (medetomidine/ hydrochloride; 1 mg/mL; Dorbene vet) and perfusedtranscardially with PBS. Brains were rapidly dissected
and stored in PBS on ice. The olfactory bulb and cerebellum were removed and the brain dissected into small
pieces in HBSS (Lonza) in a petri dish. Brain pieces werecollected in HBSS, centrifuged and resuspended in an
enzymatic mix, following the manufacturer’s instructions for the neural dissociation kit (130-092-628, Miltenyi Biotec). After dissociation, cell suspensions wereincubated for 20 min with CD11b beads (130-049-60,
Miltenyi Biotec) and passed through LS columns (130042-401, Miltenyi Biotec) to enrich microglia by magnetic separation. Lastly, ­CD11b+ cells were collected andeither stained for flow cytometry analyses or treated with
RLT lysis buffer for subsequent RNA extraction using the
Qiagen RNA isolation kit following the manufacturer’s
instructions (RNeasy® Mini Kit, 74,104, Qiagen).
Human induced pluripotent stem cell‑derived microglia

Induced pluripotent stem cells (iPSCs) carrying thec.192G > C mutation in PARK7 causing the amino acidchange p.E64D in DJ-1 and leading to aberrant splicing[7, 38] were cultured in mTeSR™ Plus complete medium(Stem Cell Technologies) in 6-well plates. Embryoid bodies (EBs) were generated from iPSCs by supplementing
mTeSR™ medium with specific growth factors (Table S2).
On day 4, EBs were transferred to T75 flasks to form “factories” [76]. The iPSCs were differentiated into microglia
following an established protocol [28, 76]. See Table S2
for medium composition and growth factors/supplements for each differentiation step. Cells were kept at
37 °C in a humidified incubator at 5% ­CO2. Microglia-likecells were cultured in 6-well plates at a density of 1 × ­106
cells/well and treated with 100 ng/mL LPS (Sigma, E.Coli
O55:B5, L6529-1MG) for 6 h. RNA was extracted using
the Qiagen RNA isolation kit following the manufacturer’s instructions (RNeasy® Mini Kit, 74,104 Qiagen).
Bone marrow‑derived macrophage differentiationand treatments

After euthanasia, tibia and femur from PARK7/DJ-1 KOmice and wildtype littermates were dissected out andkept in ice-cold DMEM Glutamax (61960526, Gibco)for 1.5-12 h before the extraction of bone-marrow cells(BMCs). Both extremities of tibia and femur were cut toallow a 26 g needle to flush BMCs according to standardized protocols [74]. The cell pellet was treated with
NH4Cl (0.17 M) for 5 min to remove red blood cells.
BMCs were cultured on low-attachment conditions inPage 4 of 24

DMEM Glutamax (61965026, Gibco), supplemented with
10% FBS (10270-106, Gibco), 1% penicillin-streptavidin(DE17-602E, Lonza), 1 mM sodium pyruvate (11360070,
Gibco) and 20 ng/mL M-CSF (416-ML-010/CF, Biotechne). The complete medium was supplemented on
day 4. On day 8, fully differentiated bone marrow-derived
macrophages (BMDMs) were treated with LPS (100 ng/mL) for 6 h. To investigate their morphological features,
BMDMs were stained with Acti-stain™ 488 Fluorescent
Phalloidin (PHDG1, Cytoskeleton, Inc.) and incubatedfor 1.5 h at room temperature at day 8 of differentiation
with or without prior LPS treatment. Actin cytoskeleton was imaged with a Nikon Eclipse microscope at a
10 × magnification and analyzed using CellProfiler™ [68].
Primary microglia and treatments

CD11b+ cells were collected as previously described. Primary adult microglia were plated overnight in 48-wellplates coated with poly-L-lysine (0.1 mg/mL solution;
Sigma-Aldrich) at a density of 3 × ­105 cells per well andgrown in DMEM-F12 supplemented with 10% FBS (LifeTechnologies) and pen-strep (100 U/mL / 100 μg/ml; Life
Technologies). Cells were stimulated with interferongamma (IFNγ; 315-05, Peprotech) at a final concentration of 50 ng/mL for 6 h.
RNA sequencing and bioinformatics analyses

Cells were pelleted down and lysed in RLT buffer for subsequent RNA extraction using the Qiagen RNA isolation
kit following the manufacturer’s instructions (RNeasy®
Mini Kit, 74104, Qiagen). A DNase treatment followed
by a clean-up has been added using the RNA Clean &Concentrator™-5 kit (Zymo Research) following the
manufacturer’s instructions. Quantification of total RNA
was performed using a N60 Nanophotometer® (ImplenGmbH) and a Qubit 2.0 Fluorometer® (Thermo Fisher
Scientific) with the Qubit RNA HS Assay Kit. The quality
was assessed using a Fragment Analyzer System (Agilent
Technologies) using a RNA Pico Chip (Agilent Technologies) (only samples with RQN ≥ 7 were further analyzed).
The Illumina® stranded Total RNA Prep, Ligation with
Ribo-Zero Plus kit Library preparation has been used toprocess libraries for RNA-sequencing according to Illumina’s Reference Guide # 1000000124514 v00 June 2020,
starting with 25 ng of total RNA. All libraries have been
quantified using a Qubit HS dsDNA kit (Thermo Fisher
Scientific) and the library quality check has been performed using a High Sensitivity NGS Fragment Analysis
Kit on the Fragment Analyzer System (Agilent Technologies). Indexed libraries were normalized and pooled to
be sequenced on an Illumina Novaseq 6000 sequencer
according to manufacturer’s instructions with targeted
conditions of 2 × 75 basepair and 25 M reads/sample. For

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

the murine dataset, raw fastq files were quality checkedusing a combination of FastQC, fastq_screen and RSeQC
tools wrapped in MultiQC v1.11 [19] followed by a preliminary analysis composed of read trimming, mappingand counting using Cutadapt [45], STAR 2.7.9a [16]and the Mouse genome (GRCm39 – Mus_musculus.
GRCm39.108.gtf ). For the human dataset, raw fastq files
were quality checked using a combination of FastQC andfastq_screen tools wrapped in MultiQC v1.11 [19]. Reads
were trimmed using AdapterRemoval v2.3.2 [60], andmapped against the human genome (Ensembl GRCh38.
p14) with the splice-aware STAR 2.7.9a [16]. Resultingalignments were processed by featureCounts (package
RsubRead v2.8.1) [40] to obtain gene counts further usedby DESeq2 for differential gene expression. All of thesesteps were integrated into a local snakemake [49] environment. The template for the human dataset is available
in release 0.3.2: https://​gitlab.​lcsb.​uni.​lu/​aurel​ien.​ginol​
hac/​snake​make-​rna-​seq/-/​relea​ses/​v0.3.2. The software
set used by the template on the ULHPC [77] was bundled in a Docker image available at https://​hub.​docker.​com/r/​ginol​hac/​snake-​rna-​seq tag v0.5. Differential gene
expression analysis was conducted using R statisticalsoftware v4.2.3 [72] and DESeq2 v1.38.3 for the murine
dataset and v1.42.0 for the human dataset [43] using theresulting gene count tables. Heatmaps were created using
ComplexHeatmap v2.18.0 [27].
Gene ontology analysis

Differentially expressed genes were submitted to Database for Annotation, Visualization and Integrated Discovery (DAVID) [62] for further exploration of Gene
Ontology (GO) terms for biological processes (BP) using
overrepresentation analysis. Additionally, we run geneset enrichment analysis (GSEA) by R package clusterProfiler (v.4.10.1) using log fold changes from the group comparisons for gene ordering.
Reverse transcription was performed using SuperScript™
III (18080093, Invitrogen) according to the manufacturer’s instructions. At first, oligo (dT) primers (50 μM) and5 mM dNTP mix were added to the RNA and incubatedat 65 °C for 5 min. Subsequently, tubes were incubatedat 4 °C for 1 min and then 5 × first-strand buffer, 0.1 M
DTT (707265ML, Thermo Fisher Scientific), RNase OUT(10777019, Thermo Fisher Scientific) and 200 units/μl
SuperScript™ III RT (18080051, Thermo Fisher Scientific) were added and incubated for 45 min at 50 °C. The
reaction was lastly inactivated by heating for 15 min at
70 °C. The qPCRs were carried out in 384-well plates(Applied Biosystems QuantStudio5 Real-Time PCR System). All samples were run in three technical triplicates.
Reverse transcription and qPCR

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Diluted cDNA was mixed with 10 μL of Fast SYBR Green
Master Mix (Applied Biosystems) and 0.5 μL of 10 μM
gene-specific forward and reverse primers. All primers
were designed using the Primer BLAST tool with the following parameters: Tm 60 °C ± 3 °C, possibly spanningexon-exon junctions separated by at least one intron on
the corresponding genomic DNA region and a productsize between 75 and 300 bp (Table S3). The qPCRs were
performed using the following standardized program:
20 s at 95 °C followed by 35 cycles of PCR stage of 1 s
at 95 °C (denaturation) and 20 s at 60 °C (annealing) anda melt curve stage of 15 s at 95 °C, 1 min at 60 °C and
10 s at 95 °C. The average threshold cycle (Ct) values wereused to quantify the mRNA product by the ΔΔCt method
normalized to the housekeeping gene Gapdh, whichshowed better stability than other housekeeping genes(e.g. Rpl27) across experimental groups (data not shown).
Protein extraction and Western blotting

Protein samples were prepared by lysing whole brain
tissues or Magnetic-Activated Cell Sorted (MACS)CD11b+ cells from wildtype and PARK7/DJ-1 KO mice,
human fibroblasts and iPSCs in cell lysis buffer (Pierce
RIPA, 89900, Thermo Fisher Scientific), supplemented
with Halt protease and phosphatase inhibitor (78444,
Thermo Fisher Scientific). Protein extracts were then normalized, denatured at 95–100 °C for 6–8 min, resolved
on a 4–12% BisTris gel (NP0323, Lifetech), transferred on
polyvinylidene difluoride (PVDF) membranes (LifeTech)and blocked with 2% milk in Tris-buffered saline (TBS)solution containing 0.1% Triton X-100. Anti-DJ-1 (Cell
Signaling) was used for protein detection, while anti-αactin (Merck Millipore) was used for signal normalization. Details of these antibodies can be found in Table S1.
After washes, the membranes were incubated eitherwith HRP-conjugated secondary antibodies or Rabbit
IgG Cross-Adsorbed Secondary Antibody, DyLight™
800 (SA5-10036, Thermo Fisher Scientific), according
to the manufacturer’s instructions. Signal development
was performed either via addition of Super Signal West
Femto Maximum Sensitivity Substrate (Thermo Fisher
Scientific) followed by image acquisition with ImageQuant LAS4010 imaging station (HRP-conjugated secondary antibodies) or via Odyssey Fc Imaging System(LI-COR Biosciences) (Rabbit IgG Cross-Adsorbed Secondary Antibody, DyLight™ 800).
Cytoplasmic and mitochondrial reactive oxygen species
measurements
Bone marrow‑derived macrophages

BMDMs were differentiated as previously described andplated in 96-well plates high performance #1.5 coverLind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

glass (0.170 ± 0.005 mm) (Cellvis) at a density of 2 × ­105
cells per well. Cells were treated with LPS (L4391, E. Coli
O111:B4, Sigma-Aldrich) at a final concentration of
100 ng/mL at day 7 for overnight treatment or for 6 h on
the day of imaging (day 8 of differentiation). Cells wereplated to have 3 technical replicates per condition.
Primary microglia

Primary microglia were obtained as previously describedand cultured for less than 24 h before imaging in 96-wellplates coated with poly-L-lysine (1 μg/mL solution;
Sigma-Aldrich) at a density of 1.5 × ­105 cells per well.
Two hours after seeding, microglia were treated with LPS(100 ng/mL) overnight. Cells were plated to have 2 technical replicates per condition.
To assess the mitochondrial membrane potential, cells
were incubated with 150 µL/well of full medium with
TMRE (T669, Life Technologies) at a final concentrationof 10 nM for 30 min. Cells were co-stained with Hoechst
33342 (62249, Life Technologies) at 1 µM, MitoTracker
Green-FM (M7514, Life Technologies) at 100 nM toderive the mitochondrial mask, and CellROX Deep Red(C10422, Life Technologies) at 5 µM to assess oxidative
stress levels. Medium was subsequently removed andcells were washed twice following incubation in medium
containing 10 nM of TMRE. Images were acquired
using the Yokogawa CellVoyager CV8000 High-Content
Screening System, a spinning-disk confocal microscope,using a 60 × water immersion objective. The following
bandpass filters were used: BP 445–45, BP 525–50, BP
600–37, and BP 676–29. Z-stacks were acquired witha slicing interval of 0.4 µm and 12 or 13 fields per well
were obtained for BMDMs and microglia respectively.
Automated segmentation of diverse structures (i.e.
nuclei, mitochondria and CellRox Deep Red area) andthe subsequent feature extraction, which included mitochondrial morphometrics, was performed using Matlab(version 2021a, MathWorks) by adapting a previously
used method [2]. The analysis was run on the High Performance Computing (HPC) facilities of the University of
Luxembourg in collaboration with the LCSB-Bioimaging
platform. The code can be shared upon request.
Tissue preparation for immunohistochemistry analyses
Cryosectioning preparation (6‑h LPS time point)

After perfusion with cold PBS, one brain hemisphere permouse was drop-fixed in 4% paraformaldehyde (PFA,
Lonza) for 24 h, washed with PBS and immersed in 30%
sucrose (Sigma, SO389) in PBS until complete permeation. When the brain was at the bottom of the tube, it was
cut via a rodent brain matrix (ASP Instruments, RBM2000C) into 4 sagittal macro-sections per hemisphere,

Page 6 of 24

which were kept at − 80 °C until further processing. Subsequently, the macro-sections were embedded in OCT(Tissue-Tek) and cut on a cryostat into 50 μm sections.
Tissue sections were mounted on glass slides (Epredia
Superfrost™ Plus Adhesion, Epredia™ J1800AMNZ) andwashed in PBS.
Floating section preparation (24‑h LPS time point)

After perfusion with cold PBS, the brain was drop-fixed
in 4% PFA for 48 h. Subsequently, brains were stored at
4 °C in 0.02% sodium azide/PBS and serialized parasagittal free floating 30 µm-thick sections were generated
with a vibratome (Leica; VT-1000S) and collected in
cryoprotective medium, PBS containing 1–1 ethylene
glycol (Sigma-Aldrich) and 1% w/v polyvinylpyrrolidone(Sigma-Aldrich). Sections were stored at − 20 °C until
further processing.
Immunofluorescence staining

Tissue sections were incubated in 3% ­H2O2 + 1.5% Triton
X-100 for 30 min and subsequently washed with PBS andblocked in 5% BSA for 30 min. Tissue sections were incubated with primary antibodies at room temperature overnight and washed with PBS before incubating them withsecondary antibody in 2% BSA 0.3% Triton X-100 for 2 h
at RT (Table S1). After washing three times with PBS
for 10 min, tissue sections were mounted using mounting medium containing 4ʹ,6-diamidino-2-phenylindole(DAPI) (Invitrogen, 00–4959-52) and covered with glass
coverslips. Images were acquired using a LSM 880 confocal microscope (Zeiss). iPSCs were plated on coverslips
and fixed in 4% PFA for 10 min, washed with PBS threetimes for 5 min, subsequently permeabilised and blocked
in 3% BSA and 0.4% Triton-X-100 for 1 h, washed in
PBS and incubated overnight with primary antibodies(Table S1) in 1% BSA and 0.25% Triton-X-100. Finally,
iPSCs were incubated for 2 h with secondary antibodies
and Hoechst and mounted on glass slides.
Image acquisition and application of microglia
and immune‑cells morphological analysis and clustering(MIC‑MAC) 2

Stained slides were imaged on a LSM 880 confocal
microscope (Zeiss) with the 20 × objective (laser andfilters) (the experimenter was blinded for genotype
and treatment during acquisition). Nine 3D confocal
tile scans were captured with a resolution of 0.221 μm
in the x and y dimensions and a resolution of 1 μm inthe z dimension. The average thickness per 3D image
was 30 µm, and between 28 and 31 μm, depending on
the antibody penetration. The resulting 3D stacks of­IBA1+ microglial cells were inspected with Imaris andfiltered to ensure coherent and high standards of image

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

quality. The 20 × 3D images were then processed with
Imaris where slice intensities were corrected using
the Normalizer Layer function, followed by Gaussian Filter (width = 0.3). Segmentation of ­IBA1+ cells
was performed in Imaris by using the Surface Creation Tool (Local Contracts with Smooth enabled andsurface detail of 0.5 μm) with a threshold manually
adjusted to account for small variations in the contrastof the images (threshold parameter between 0.5 and 1).
The Split Touching Object option was disabled, and theresulting 3D structures were filtered by size (between
18,000 and 180,000 voxels) to filter out incomplete
structures or microglia clusters that would inevitably bias the morphological analysis. For the 24-h LPS
time point, images were captured using a 40 × objectivewith a voxel size of: 0.415 × 0.415 × 0.524 μm3 with the
405 nm and 633 nm lasers with a pinhole of 42.6 = 1.47
Airy unit (1 μm section). Digital gain DAPI: 1, gain master 860. Offset − 2. A647, Digital gain: 1, Gain master
846 and offset − 7. The morphological feature extraction of each segmented cell was ultimately performedby using Microglia and Immune-cells Morphological
Analysis and Clustering (MIC-MAC) 2, which allows
for automatic segmentation of microglia from 3D fluorescent microscopy images [22]. The mice included forthese analyses were as follows: wildtype control group:
2 males and 2 females; wildtype LPS group: 1 male and2 females; PARK7/DJ-1 KO control group: 1 male and2 females; PARK7/DJ-1 KO LPS group: 2 males and 2
females.
Statistical analyses

Data was analyzed in Excel and Prism v10.0.3. For
qPCR data, fold change from control condition or ­2−
ΔCT
between groups was calculated and compared via
ANOVA with multiple comparisons. Comparison of
two groups of normally distributed data was performed
using unpaired t-tests. If data did not pass tests fornormality using Kolmogorov–Smirnov (alpha = 0.05),

Page 7 of 24

a non-parametric Kruskal–Wallis test was performed
followed by Dunn’s test for multiple comparisons. All
experiments included at least 3 biological replicates.
All tests were performed on a significance level of 5%
and all graphs depict means with standard error of themean (SEM).

Results
Distinct activation profiles detected by single‑celltranscriptional analysis of acutely isolated ­CD11b+CD45int
microglial cells between wildtype and PARK7/DJ‑1 KO mice
24 h after LPS‑induced systemic inflammation

First, we confirmed the lack of DJ-1 in the brain of thecorresponding KO mice. As expected, while DJ-1 was
expressed in the brain tissue of wildtype mice, it wasneither detected in brain tissue nor in ­CD11b+CD45int
microglia isolated from PARK7/DJ-1 KO mice (Fig.
S1A). In our previous study, we demonstrated that intraperitoneal injection of LPS at a dosage of 4 μg/g bodyweight in C57BL/6N mice led to a significant activationof microglial cells after 24 h [67]. To investigate the transcriptional profile of microglia under the same inflammatory conditions in mice lacking PARK7/DJ-1, we
FACS-isolated ­CD11b+CD45int cells, previously identified as predominantly representing microglia [67], fromboth wildtype and PARK7/DJ-1 KO mice after 24 h of
LPS injection. Immuno-profiling of the brain under these
conditions showed increases in the levels of monocytes­(CD11b+CD45+Ly6C+ cells) and decreases in the amountof lymphocytes ­(CD11b−CD45+ cells) compared to baseline conditions, indicative of established neuroinflammation at similar levels both in wildtype and PARK7/DJ-1
KO mice (Fig. S1B,C). We conducted single-cell RNAsequencing of ­CD11b+CD45int cells using the Dropseq technology (Fig. 1A) and performed subsequentclustering analysis of 2931 microglial cells, with 953
cells isolated from two PARK7/DJ-1 KO mice and 1978
cells from two wildtype mice. This approach enabled
us to identify five distinct clusters in the corresponding
UMAPs (Fig. 1B,C). To verify that the clustering analysis(See figure on next page.)
Fig. 1 Subset of classical activated microglia is mainly composed of cells originated from wildtype mice 24 h after systemic LPS-induced
inflammation. A Schematic representation of scRNA-sequencing analyses of ­CD11b+CD45int microglial cells isolated either from PARK7/DJ-1 KOor wildtype mice 24 h following LPS treatment. B UMAP showing 2931 ­CD11b+CD45int microglial cells from PARK7/DJ-1 KO (yellow) and wildtype(grey) mice 24 h after LPS treatment (n = 2 mice per group). The different size of the dots in the UMAP represents the “read count per cell”,
with large or small dots representing cells with high or low read count, respectively. C UMAP showing clustering analysis of 2931 single microglial
cells. Numbers indicate amount of cells per cluster. D, E Gene ontology terms corresponding to upregulated genes (adjusted p-value < 0.05,
log2FC ≥ 0.5) comparing (D) Cluster 3 or (E) Cluster 4 to the other clusters. F Dot plot showing selected differentially expressed genes (adjusted
p-value < 0.05, |log2FC|≥ 0.5) characterizing each cluster. Color code represents the average log2 Fold Change (FC), while the size of the dot
is proportional to the statistical significance indicated as − log10 (adjusted p-value). G Alluvial plot showing ­CD11b+CD45int microglial cells flowing
into the identified 5 normalized clusters according to their origin, either from wildtype (grey) or PARK7/DJ-1 KO (yellow) mice. H Heatmap showing
percentages of cells deriving either from PARK7/DJ-1 KO or wildtype mice

Lind‑Holm Mogensen et al. Journal of Neuroinflammation

Fig. 1 (See legend on previous page.)(2024) 21:174

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Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

was not driven by the sex of the mouse of origin, we discriminated female and male cells based on the expression
levels of X-inactive specific transcript (Xist), a female
cell-specific nuclear long noncoding RNA crucial for
X-chromosome inactivation [5, 15]. The partition of thecells in “Xist high” and “Xist low” enabled to discriminate
cells, respectively from females (536 cells from PARK7/
DJ-1 KO mouse and 781 cells from wildtype mouse)and males (417 cells from PARK7/DJ-1 KO mouse and1197 cells from wildtype mouse), with cells from both
sexes grouping across the five clusters, suggesting that
our analysis is independent from the mouse sex (Fig.
S1D). After identifying differentially expressed genes(DEGs) that characterize each cluster compared to theothers, we conducted gene ontology (GO) term enrichment analysis by submitting the up-regulated DEGs
from each cluster (adjusted p-value < 0.05, log2FC ≥ 0.5)
to DAVID (Table S4). The resulting GO term for Cluster 1 included “synapse disassembly”, while Cluster 2
was characterized by terms related to transcriptional
regulation, such as “RNA splicing”, “chromatin organization” and “mRNA processing” (Fig. S1E). Cluster 3 wasdefined by GO terms related to classical inflammatory
responses, including “cellular response to interleukin-1”
and “lipopolysaccharide-mediated signalling pathway”,
with upregulation of microglial pro-inflammatory markers (e.g. Gpr84, Lcn2 and Nfkbia) (Fig. 1D), while Cluster 4 was characterized by terms linked to DNA damage
response, such as “DNA replication” and “DNA repair”,
with overexpression of homeostatic microglial genes(e.g. Hexb, Fcrls and Gpr34) and DNA repair markers(e.g. Brca2, Parp1 and Rrm1) (Fig. 1E). Lastly, Cluster 5
was associated with GO terms related to host defense
responses, primarily against viruses, including “defense
response to virus” and “cellular response to interferonbeta” (Fig. S1E) (Table S4). We showed a selection of
DEGs (considering adjusted p-value < 0.05, |log2FC|> 0.5)
characterizing each cluster in a dot plot (Fig. 1F). Whenexamining the composition of each cluster based on theamount of cells per mouse genotype using an alluvial plot
(Fig. 1G) and analyzing the corresponding ratios using aheatmap (Fig. 1H), we observed that clusters 1, 2 and 5
were relatively equally represented in both genotypes. In
contrast, Cluster 3, associated with classical inflammatory responses, was predominantly constituted by microglial cells from wildtype mice (85%), whereas Cluster 4,linked to DNA damage response and cell cycle, primarily
consisted of cells originating from PARK7/DJ-1 KO mice(81%) (Fig. 1G,H). We excluded the presence of proliferating microglial cells in PARK7/DJ-1 KO mice by analyzing the expression of KI67 in ­IBA1+ microglial cells in
cortical brain slices as well as gene expression levels of
Mki67 in C
­ D11b+CD45int microglial cells isolated fromPage 9 of 24

the brain of PARK7/DJ-1 KO and wildtype mice after
24 h of LPS injection. KI67 was not detectable at the protein level (Fig. S1F) and barely expressed at the gene level(Fig. S1G).
Taken together, these results show that microglia in
wildtype mice display a classical inflammatory profile
following 24 h of LPS injection, characterized by substates associated to transcriptional regulation (Cluster
2) and pro-inflammatory responses (Cluster 3), while
microglia in PARK7/DJ-1 KO mice show an enrichment
in DNA damage response signatures (Cluster 4), suggesting a potential involvement in repair and tolerance
mechanisms.
Diverse transcriptional activation profiles of microglia
detectable after 6 h of peripheral LPS‑induced
inflammation in PARK7/DJ‑1 KO compared to wildtype
mice

Next, we sought to investigate whether the distinct
microglial profiles identified after 24 h of LPS-induced
inflammation were also apparent at earlier time points.
With this analysis, we aimed to discern if the impaired
microglial reactivity under PARK7/DJ-1 deficiency
manifested during the early phases of the inflammatory response or if they indicated premature resolution substates. For this, we intraperitoneally injected
PARK7/DJ-1 KO (n = 5) and wildtype (n = 5) mice withLPS (4 μg/g body weight) and MACS-isolated ­CD11b+
cells from dissected brains after 6 h for subsequent transcriptional analyses (Fig. S2A). We verified the validityof our sorting protocol by analyzing the expression levels of oligodendrocyte (Mog and Mobp), astrocyte (Gfap
and Ntsr2) and neuronal (Tubb3 and NeuN) markers in
MACS-isolated ­CD11b+ cells compared to the negative fraction. Those markers were undetectable or only
expressed at very low levels in cells collected within thepositive fraction, which on the contrary expressed highlevels of the microglial markers (Gpr34 and Olfml3)(Fig. S2B). We tested the purity of C
­ D11b+ cells by staining plated sorted cells with IBA1 antibody and detected
90–97% (mean 94.5% ± 1.9%) of ­IBA1+ cells among+­CD11b cells, thus indicating an enrichment of microglial cells (Fig. S2C). By conducting RNA-sequencing
analyses of MACS-isolated C­ D11b+ cells from PARK7/
DJ-1 KO and age-matched wildtype mice following 6 hof LPS injection, we detected 474 DEGs (p-value < 0.05,|log2FC|≥ 0.5) comparing KO versus wildtype. Amongthem, 227 genes were upregulated, while 247 genes were
downregulated (Table S5). GO terms resulting from the
227 upregulated DEGs comparing ­CD11b+ cells isolated
from PARK7/DJ-1 KO and wildtype mice in response
to LPS were “membrane depolarization”, “signal transduction” and “apoptotic signaling pathway” (Fig. 2A).

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

On the other hand, GO terms resulting from the corresponding 247 downregulated DEGs were linked to theimmune response, mainly related to the regulation of
the adaptive immune system, such as “adaptive immune
response” and “negative regulation of T cell proliferation”,
and interferon-gamma-related pathways, exemplified by“interferon-gamma production” and “cellular response
to interferon-gamma” terms (Fig. 2B). Among the related
down-regulated genes, Tnfrsf9, Ciita, Serpina3g, Ifng andH2.Aa were in the top 50 protein-coding DEGs (Fig. 2C).
The characterization of these distinct profiles under
LPS treatment prompted us to investigate whether differences between microglial cells in PARK7/DJ-1 KO
and wildtype mice were already detectable at baseline.
Through RNA-sequencing analysis of MACS-isolated­CD11b+ cells from PARK7/DJ-1 KO (n = 5) and agematched wildtype (n = 4) mice, we detected 308 DEGs
(p-value < 0.05, |log2FC|≥ 0.5) comparing KO versus
wildtype. Among them, 103 genes were upregulated,while 205 genes were downregulated (Table S5). GO
terms resulting from the 103 upregulated DEGs comparing ­CD11b+ cells isolated from PARK7/DJ-1 KO andwildtype mice at baseline were mainly related to developmental processes and morphological adaptation, suchas “epithelial cell differentiation”, “extracellular matrixorganization” and “branching morphogenesis of an epithelial tube” (Fig. 2D), while terms resulting from thecorresponding 205 downregulated DEGs included differentiation and immune response (Fig. 2E). Among therelated down-regulated genes, Cxcl9 and Ifi214, linked tothe interferon-gamma signaling, were in the top 50 protein-coding DEGs (Fig. 2F). We confirmed the decreaseof the expression levels of these genes and the increaseof Mefv by qPCR including additional ­CD11b+ brain
cell samples derived from independent male and female
mice (Fig. S2D). In line with reported results showing an
exaggerated response of PARK7/DJ-1 deficient microglia towards pro-inflammatory stimuli in vitro, microglia

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isolated from PARK7/DJ-1 KO treated with IFNγ (50 ng/mL) for 6 h showed increased expression levels, albeit
not statistically significant, of Cxcl9 and Ifi214 comparedwith cells isolated from wildtype mice (Fig. S2E), suggesting that potential compensatory mechanisms taking place in the brain milieu are not recapitulated whenmicroglia are isolated and cultured in vitro. Further, incontrast with the results we obtained in adult mice, theexpression levels of pro-inflammatory markers, including Il6 and Cxcl9, were up-regulated in MACS-isolated­CD11b+ microglial cells from PARK7/DJ-1 KO aged mice
(12–13 month old) compared to age-matched wildtype
mice treated with LPS (4 μg/g body weight) for 6 h (Fig.
S2F), indicating that compensatory mechanisms underlying the distinct activation profile in adult mice are possibly exhausted with aging.
By conducting a direct comparison of the LPS-induced
genetic responses between microglia isolated fromwildtype and PARK7/DJ-1 KO adult mice, we identified
DEGs comparing LPS and baseline conditions in microglia (adjusted p-value < 0.05, |log2FC|≥ 0.5), both fromwildtype and KO mice (Table S6), and compared thedetected genes. Most of the identified genes (3229 DEGs,
corresponding to 73.7% of total DEGs) were up-regulated
in both LPS-treated conditions and, as expected, weremainly associated with GO terms linked to inflammatory
responses. On the other hand, GO terms corresponding to 336 unique up-regulated DEGs in microglia fromwildtype mice were associated with classical microglial
activation, such as “positive regulation of tumor necrosis
factor production” and “positive regulation of NF-kappaB
TF activity”, while GO terms related to 814 unique upregulated DEGs in microglia from PARK7/DJ-1 KO mice,such as “cellular response to DNA damage stimulus”, werelinked to DNA repair processes (Fig. 2G). Thus, theseresults corroborate the findings obtained by scRNA-seq
analyses after 24 h of LPS treatment. Further, as “cellular response to reactive oxygen species” was among the(See figure on next page.)
Fig. 2 Distinct reaction of microglia in PARK7/DJ-1 KO mice compared to wildtype mice 6 h after systemic LPS-induced inflammation
and at baseline. A, B GO terms corresponding to A upregulated or B downregulated genes comparing microglia in PARK7/DJ-1 KO and wildtype
male mice 6 h after LPS treatment. C Dot plot showing a selection of differentially expressed genes (p-value < 0.05, |log2FC|≥ 0.5) comparing­CD11b+ microglia isolated from PARK7/DJ-1 KO and wildtype mice 6 h after LPS-induced inflammation. Color bar shows log2 Fold Change (FC),while the size of the dot is proportional to the statistical significance indicated as − log10 (p-value). D–E GO terms corresponding to D upregulated
or E downregulated genes comparing microglia in PARK7/DJ-1 KO and wildtype mice at baseline. F Dot plot showing a selection of variable genes(p-value < 0.05, |log2FC|≥ 0.5) comparing ­CD11b+ microglia isolated from PARK7/DJ-1 KO and wildtype mice at baseline. Color bar shows log2 Fold
Change (FC), while the size of the dot is proportional to the statistical significance indicated as –log10 (p-value). G Venn diagram and GO terms
corresponding to upregulated genes matching differentially expressed genes (adjusted p-value < 0.05, log2FC ≥ 0.5) resulting from the comparisonof microglia in wildtype and PARK7/DJ-1 KO mice 6 h after LPS treatment. H Cytoplasmic and mitochondrial ROS levels in microglia isolated
from wildtype and PARK7/DJ-1 KO male mice cultured overnight left untreated or treated with LPS (100 ng/mL) and analyzed by live imaging.
Graphs show normalized CellROX mean of analyzed images (dots). Kruskal–Wallis test with multiple comparisons. ****p < 0.0001; ***p < 0.001;*p < 0.05 (n = mean of 26 images per condition from 3 mice per group)Lind‑Holm Mogensen et al. Journal of Neuroinflammation

Fig. 2 (See legend on previous page.)(2024) 21:174

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Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

terms associated with up-regulated DEGs in microglia
from PARK7/DJ-1 KO mice, we measured the levels of
microglial reactive oxygen species (ROS). For this, we isolated ­CD11b+ microglial cells from PARK7/DJ-1 KO andwildtype mice, cultured overnight with or without LPS(100 ng/mL) and subsequently assessed ROS levels andmitochondrial membrane potential with live imaging.
As expected, ROS tracer intensity in the cytoplasm andmitochondria was higher in microglia from PARK7/DJ-1
KO compared with wildtype mice, both at baseline andfollowing LPS treatment (Fig. 2H). Although there were
no differences in mitochondrial membrane potential
between microglia from PARK7/DJ-1 KO and wildtype
mice at baseline, it was decreased in LPS-treated samples, with a significantly stronger loss of mitochondrial
membrane potential in microglia from PARK7/DJ-1 KOmice (Fig. S2G), indicating that increased ROS amounts
in these cells induce higher levels of mitochondrial clearance to possibly sustain their viability.
At the protein level, in line with the detected transcriptional dampened immune activation of microglia
in PARK7/DJ-1 KO compared to wildtype adult micetreated with LPS, we detected decreased expression levels of CD11b (Fig. S2H).
Taken together, these results show that microglia in
PARK7/DJ-1 KO adult mice downregulate genes associated with inflammatory responses, specially linked tointerferon-gamma signaling, both at the early stages ofinflammatory conditions and at baseline, compared towildtype mice. On the other hand, microglia in PARK7/
DJ-1 KO mice treated for 6 h with LPS upregulate genes
related to “membrane depolarization”, “signal transduction” and “apoptotic signalling pathway”, which, in linewith the described functions of DJ-1 as an important
protector against ROS and the DNA damage response
signatures identified after 24 h of LPS treatment by
scRNA-sequencing (Cluster 4), are related to repair andtolerance mechanisms, such as attenuation of mitochondrial reactive oxygen species generation [56].

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Discrete activated transcriptional profile of microglia
derived from iPSCs harboring the c.192G > C mutation
in PARK7 compared to isogenic controls 6 h after LPS
treatment

Next, to investigate whether these findings hold true inthe human context, we applied a translational approachto verify if the microglial transcriptional phenotype identified in PARK7/DJ-1 KO mice was similar in human
PARK7/DJ-1 mutant iPSC-derived microglia. Briefly,
iPSCs carrying the c.192G > C mutation in PARK7 causing the amino acid change p.E64D in DJ-1 were previously described to lead to undetectable levels of DJ-1
protein in these cells due to aberrant splicing [7]. Bothisogenic control and mutant iPSCs expressed similar levels of the typical pluripotency markers, including
stage-specific embryonic antigen-4 (SSEA4), podocalyxin (TRA-1-60), octamer-binding transcription factor
4 (OCT4) and sex determining region-Y box (SOX2),
thus suggesting that PARK7/DJ-1 deficiency does notaffect their expression (Fig. S3A,B). As expected, while
we measured high levels of PARK7/DJ-1 expression in
isogenic control fibroblasts and iPSCs, both at the protein and mRNA level, its expression was not detectable inthe corresponding mutant cells (Fig. S3C,D). We derived
microglial cells from iPSCs following an establishedprotocol [28, 76]. Analysis of PARK7 in the resultant
iPSC-derived microglia (iMG) confirmed the lack of its
expression in the mutant cells (Fig. S3C). Gene expression analyses of three human microglia homeostatic
genes, GPR34, TREM2 and P2RY12, in both isogenic
control and mutant cultures, showed increased expression levels in iMG when compared to undifferentiated
iPSCs, thus indicating effective differentiation of the twolines into microglia-like cells (Fig. S3E).
As conducted in the mouse model, we carried out RNA
sequencing to compare the transcriptional signatures of
PARK7/DJ-1 mutant and isogenic control iMG, both withor without a LPS (100 ng/mL) treatment for 6 h, which
resulted in relatively distinct clusters according to thefour conditions (Fig. 3A). At baseline, among 197 DEGs
(adjusted p-value < 0.05, |log2FC|≥ 0.5) (Table S7), we(See figure on next page.)
Fig. 3 Discrete iPSC-derived microglia signatures between PARK7/DJ-1 mutant and isogenic control cells under LPS treatment and at baseline.
A Design aware Principal component analysis (PCA) plot showing clustering of 3 replicates of differentiated iPSC-derived microglia (iMG)
per condition: PARK7/DJ-1 mutant and isogenic control iMG untreated or treated with LPS for 6 h. B-C GO terms corresponding to B upregulated
or C downregulated genes comparing PARK7/DJ-1 mutant and isogenic control iMG at baseline. D Dot plot showing selected differentiallyexpressed genes (adjusted p-value < 0.05, |log2FC|≥ 0.5) comparing PARK7/DJ-1 mutant and isogenic control iMG at baseline. Color bar shows
log2 Fold Change (FC) and the size of the dot is proportional to the statistical significance indicated as –log10 (adjusted p-value). E–F GO terms
corresponding to E upregulated or F downregulated genes comparing PARK7/DJ-1 mutant and isogenic control iMG after 6 h LPS treatment. G Dot
plot showing selected differentially expressed genes (adjusted p-value < 0.05, |log2FC|≥ 0.5) comparing PARK7/DJ-1 mutant and isogenic control
iMG after treatment with LPS for 6 h. Color bar shows log2 Fold Change (FC) and the size of the dot is proportional to the statistical significanceindicated as –log10 (adjusted p-value)Lind‑Holm Mogensen et al. Journal of Neuroinflammation

Fig. 3 (See legend on previous page.)(2024) 21:174

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detected 103 upregulated genes in PARK7/DJ-1 mutant
iMG. GO terms associated to those upregulated genes
included “cell adhesion”, “proteolysis” and “extracellularmatrix organization” (Fig. 3B). By contrast, GO analysisof the 94 downregulated genes comprised terms linked toimmune responses, such as “cellular response to lipopolysaccharide” and “positive regulation of interferongamma production” (Fig. 3C), thus indicating an effecton microglial immune surveillance and activation alreadyat baseline. We showed a selection of corresponding up(e.g., CPA4, PCDHGB5, LOXL4) and down- (e.g., NLRP2,
S100A6, ACOD1, IFI44) regulated genes (adjusted
p-value < 0.05, |log2FC|≥ 0.5) characterizing PARK7/DJ-1
mutant iMG in a dot plot (Fig. 3D).
Under LPS conditions, we detected 289 DEGs (adjusted
p-value < 0.05, |log2FC|≥ 0.5), when comparing PARK7/
DJ-1 mutant and isogenic control iMG (Table S7). Amongthem, 83 genes were upregulated, while 206 were downregulated in PARK7/DJ-1 mutant iMG. GO analyses of
up-regulated genes showed terms associated with longchain fatty acid metabolism and cell adhesion (Fig. 3E),
while corresponding analyses of down-regulated genes
resulted in terms related to immune responses, including“defense response to virus”, “cellular response to lipopolysaccharide” and “positive regulation of interferon-beta
production” (Fig. 3F). We displayed a selection of resulting up- (e.g., CLMP, PCDHGB5, HTRA1, ADAM8) anddown-regulated (e.g., S100A6, NLRP2, ACOD1) genes(adjusted p-value < 0.05, |log2FC|≥ 0.5) characterizing
PARK7/DJ-1 mutant iMG treated with LPS using a dot
plot (Fig. 3G). Interestingly, some of these genes werecommonly up- (e.g., CLMP, HTRA1, ADAM8) or downregulated (e.g., ACOD1, S100A6, NLRP2) both at baseline

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and LPS conditions comparing PARK7/DJ-1 mutant andisogenic control iMG (Fig. S3F).
Overall, these results show that PARK7/DJ-1 mutant
iMG, in line with the analogous murine cells, display anattenuated response to LPS compared to corresponding
isogenic control cells. We further verified this analogy byanalyzing GO terms in common between PARK7/DJ-1
mutant iMG and corresponding murine cells under LPS
conditions, which resulted in 23 shared GO terms (Fig.
S4A) associated to a dampened immune response in both
species (Fig. S4B).
Enhanced compactness at baseline and less pronounced
amoeboid morphology of microglia in PARK7/DJ‑1 KO
compared to wildtype mice after 6 and 24 h of LPS
treatment

Since the identified distinct microglial gene expression
patterns may represent morphological phenotypes evident in brain tissues, we investigated microglial morphological changes in PARK7/DJ-1 KO and wildtype miceat baseline and under LPS conditions. To this end, we
stained brain tissues with an antibody against the microglial marker ionized calcium-binding adapter molecule 1(IBA1) and applied MIC-MAC 2 to the resulting 3D confocal images in order to segment and classify microglial
cells based on their morphology [22, 59]. While, homeostatic surveying microglia were highly ramified withmultiple processes (nodes), activated microglia show amore amoeboid and compact shape. Using MIC-MAC
2, we were able to extract morphological features basedon the shape, structure and size of microglia, enabling toscore the cells based on the levels of their roundness or
elongation.

(See figure on next page.)
Fig. 4 Microglia in PARK7/DJ-1 KO mice show a more compact morphology compared to wildtype mice at baseline and a less amoeboid
morphology 6 and 24 h after LPS treatment. A Alluvial plot showing MIC-MAC analysis of ­IBA1+ cortical microglial cells flowing into 5 morphologicalclusters (pictures show 3D reconstructions of a representative microglial cell per cluster, scale bar: 10 μm) based on compactness score (spanningfrom cluster 1, corresponding to homeostatic ramified microglia, to cluster 5, representing amoeboid cells) according to their mouse origin,either from wildtype (baseline in turquoise, 6 h LPS in purple) or PARK7/DJ-1 KO (baseline in red, 6 h LPS in green) (n = 3–4 mice). B Bar graphshowing the ratios of the identified clusters across the four different conditions (wildtype or PARK7/DJ-1 KO 6 h LPS, wildtype or PARK7/DJ-1 KObaseline) (mean = 244 ± 19 cells per cortex region from 3–4 mice per group). Color bar shows compactness score. C Left: representative pictures
showing 3D reconstruction of cortical ­IBA1+ cells in PARK7/DJ-1 KO or wildtype mice with or without 6 h LPS treatment. The shade of red indicatesthe cluster to which the cell belongs. Scale bar: 150 μm. Right: violin plots showing mean compactness (left) or node density (right) (dotted line)of cortical microglial cells in wildtype or PARK7/DJ-1 KO mice with or without 6 h LPS treatment. Kruskal–Wallis test with Dunn’s test for multiple
comparisons. ****p < 0.0001 (mean = 244 ± 19 cells per cortex region from 3–4 mice per group). D Violin plot showing mean of IBA1 intensity(dotted line) divided by the number of ­IBA1+ voxels of cortical microglial cells in wildtype or PARK7/DJ-1 KO mice with or without 6 h LPS treatment.
Kruskal–Wallis test with Dunn’s test for multiple comparisons. ****p < 0.0001 (mean = 244 ± 19 cells per cortex region from 3–4 mice per group).
E Representative pictures showing cortical ­IBA1+ cells in PARK7/DJ-1 KO or wildtype mice with or without 24 h LPS treatment. Scale bar: 50 μm. F
Violin plots showing mean compactness (left) or node density (right) (dotted line) of cortical microglial cells in wildtype or PARK7/DJ-1 KO micewith or without 24 h LPS treatment. Kruskal–Wallis test with Dunn’s test for multiple comparisons. **p < 0.01, *p < 0.05 (mean = 22 ± 2 cells per cortex
region from 3 mice per group). G Representative pictures showing substantia nigra ­IBA1+ cells in PARK7/DJ-1 KO or wildtype mice with or without
24 h LPS treatment. Scale bar: 50 μm. H Violin plots showing mean compactness (left) or node density (right) (dotted line) of substantia nigra
microglial cells in wildtype or PARK7/DJ-1 KO mice with or without 24 h LPS treatment. Kruskal–Wallis test with Dunn’s test for multiple comparisons.
****p < 0.0001, ***p < 0.001, **p < 0.01 (mean = 9 ± 1 cells per substantia nigra region from 3 mice per group)Lind‑Holm Mogensen et al. Journal of Neuroinflammation

Fig. 4 (See legend on previous page.)(2024) 21:174

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Within the analyzed cortical regions, we recognized
five distinct morphological clusters that were differentially represented in PARK7/DJ-1 KO and wildtype miceat baseline and after 6 h of LPS-treatment (Fig. 4A). Corresponding quantifications showed decreased ratios oframified microglia (Cluster 1) and enhanced proportions of amoeboid microglia (Cluster 5) in PARK7/DJ-1
KO compared with wildtype mice at baseline (Fig. 4B).
Under LPS conditions, the ratio of amoeboid microglia(Cluster 5) was increased compared to baseline conditions, although this rise was less prominent in PARK7/
DJ-1 KO compared with wildtype mice (Fig. 4B). We
further confirmed these results by coupling the analysisof relevant features, including compactness and nodedensity, across the various conditions. Indeed, cortical
microglia were more amoeboid and less ramified (fewer
nodes) at baseline, while they were less amoeboid andmore ramified after 6 h LPS-treatment in PARK7/DJ-1
KO compared with wildtype mice (Fig. 4C). In line withthese observations, the intensity of IBA1 expression per
number of I­BA1+ voxels was higher in wildtype compared with PARK7/DJ-1 KO brains 6 h after LPS treatment (Fig. 4D). By similarly investigating morphologicalchanges of microglia 24 h following LPS treatment, we
detected higher compactness and lower node density ofcortical microglia in wildtype compared to PARK7/DJ-1
KO mice (Fig. 4E,F). This trend was even more evident
in the substantia nigra, a brain region that is primarily
affected in PD (Fig. 4G,H), indicating a more prominent
effect of PARK7/DJ-1 KO deficiency in microglial morphological adaptation in PD-related brain regions.
Taken together, our results show that microglia in
PARK7/DJ-1 deficient mice display different morphologies at baseline and exhibit a distinct morphologicalPage 16 of 24

adaptation in response to LPS-induced inflammationcompared to wildtype microglia after 6 and 24 h of systemic LPS treatment.
Type II interferon‑related gene impairment, prominentcompactness at baseline and less evident amoeboid
morphology after LPS treatment detectable in bonemarrow‑derived macrophages comparing precursor cells
isolated from PARK7/DJ‑1 KO and wildtype mice

Lastly, to study if the identified murine ex vivo andhuman in vitro microglial transcriptional profiles under
PARK7/DJ-1 KO deficiency are specific to brain cells or
are also detectable in peripheral myeloid cells, we isolated bone marrow cells from the femurs and tibia of
PARK7/DJ-1 KO and wildtype mice and differentiatedthem for 7 days into macrophages [74]. First, we verified that cells lacking PARK7/DJ-1 were effectively differentiated into macrophages by analyzing the expression
levels of CD11b and F4/80, which indeed did not differ
from wildtype cells and represented over 99% of total
cells (Fig. 5A). Further, the expression levels of Adgre1,
coding for F4/80, did not show differences between thetwo genotypes (Fig. 5B). As detected in microglia at baseline, bone marrow-derived macrophages (BMDMs) fromPARK7/DJ-1 KO mice showed decreased expression levels of Cxcl9 and Ifi214 compared to corresponding cells
isolated from wildtype mice (Fig. 5C). Following exposure of BMDMs to LPS (100 ng/mL) for 6 h, cells fromPARK7/DJ-1 KO mice, as previously demonstrated in
microglia, showed decreased expression levels of Ciita
compared to cells from wildtype mice (Fig. 5D). Notably,in line with the observations we made in mouse brainslices, we detected prominent morphological differences
between macrophages originating from PARK7/DJ-1(See figure on next page.)
Fig. 5 Characterization of PARK7/DJ-1-deficient bone marrow-derived macrophages at baseline and after LPS-treatment. A Representative gating
strategy used to measure percentages of ­CD11b+F4/80+ BMDMs among total cells from bone marrow precursors isolated from wildtype (upper
panel) or PARK7/DJ-1 KO (lower panel) mice (n = 1 mouse per genotype). B Gene expression levels of Adgre1 (F4/80) comparing BMDMs originatingfrom wildtype and PARK7/DJ-1 KO mice analyzed by qPCR. Graph shows mean of fold change (wildtype condition set at 1; Gapdh as housekeepinggene) ± SEM. Mann–Whitney test, ns: not significant (n = 3 mice per group). C Gene expression levels of Park7, Cxcl9 and Ifi214 in wildtype
and PARK7/DJ-1 KO BMDMs analyzed by qPCR. Graphs represent mean of fold change (wildtype condition set at 1; Gapdh as housekeepinggene) ± SEM. Mann–Whitney test, ****p < 0.0001, ***p < 0.001, **p < 0.01 (n = 10 mice per group). D Gene expression levels of Ciita in wildtype
and PARK7/DJ-1 KO BMDMs under 6 h LPS (100 ng/mL) treatment analyzed by qPCR. Graph represents mean of fold change (wildtype condition set
at 1; Gapdh as housekeeping gene) ± SEM. Mann–Whitney test, *p < 0.05 (n = 5 mice per group). E Representative pictures of wildtype and PARK7/
DJ-1 KO BMDMs stained with phalloidin showing actin filaments with and without 6 h LPS (100 ng/mL) treatment (left) and corresponding
quantification of compactness and eccentricity scores (right). Scale bar: 69 μm. Kruskal–Wallis test for multiple comparisons. ****p < 0.0001, ns:
not significant (n = 3 mice per group). F Assessment of cytoplasmic and mitochondrial ROS by live imaging. Left: representative images showing
labeling of ROS in red and mitochondria in green in BMDMs at baseline, after 6 h LPS and LPS-treatment overnight (OVN). Right: graphs show
mean CellROX intensity in cytoplasm and mitochondria per field of view (dots). Kruskal–Wallis test with Dunn’s test for multiple comparisons.
****p < 0.0001, **p < 0.01. G Analysis of mitochondrial membrane potential using the specific marker TMRE. Upper panel: representative pictures
showing mitochondrial membrane potential (in orange) and mitochondria (in green) in BMDMs at baseline and after overnight treatment
with LPS (100 ng/mL). Bottom panel: graph shows mean intensity of TMRE per field of view (dots). Kruskal–Wallis test with Dunn’s test for multiple
comparisons. ****p < 0.0001, **p < 0.01

Lind‑Holm Mogensen et al. Journal of Neuroinflammation

Fig. 5 (See legend on previous page.)(2024) 21:174

Page 17 of 24

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

KO and wildtype mice, with KO macrophages showing enhanced compactness at baseline and a less evident
amoeboid shape following 6 h of LPS exposure comparedto wildtype cells (Fig. 5E). Moreover, we assessed cytoplasmic and mitochondrial ROS levels as well as evaluated the mitochondrial membrane potential with live
imaging on BMDMs derived from PARK7/DJ-1 KO andwildtype mice. At baseline, there were no difference inboth cytoplasmic and mitochondrial ROS levels between
the two genotypes (Fig. 5F). However, after 6 h of LPStreatment (100 ng/mL), ROS levels were significantly
higher in macrophages from PARK7/DJ-1 KO mice inboth compartments. Overnight treatment with LPS led
to a decrease in ROS levels both in wildtype and PARK7/
DJ-1 KO BMDMs compared to the 6-h time point(Fig. 5F). The loss of mitochondrial membrane potential under overnight LPS conditions was more prominent in macrophages from PARK7/DJ-1 KO comparedto wildtype mice (Fig. 5G), in line with our observations
in microglia. These results add on the known scavenging
function of DJ-1 during oxidative stress and correspondto previous findings in DJ-1 KO macrophages [1].
Overall, these results are in agreement with the previously characterized microglial transcriptional signaturesand morphological adaptations detected in the mousebrain, showing that BMDMs under PARK7/DJ-1 deficiency display a reduced IFNrelated signature, both atbaseline and after LPS exposure, as well as display similarmorphological and ROS level alterations.

Discussion
At first PD diagnosis, it has been estimated that 30–50%
of dopaminergic neurons in the substantia nigra havealready degenerated [9, 12]. Therefore, for diagnostic and therapeutic perspectives, it is crucial to explore
occurrences in prodromal PD phases, the period preceding a PD diagnosis, during which neuroinflammatory
processes are already taking place [71, 73]. Thus, in thepresent study we address this challenge by investigatingtranscriptional and morphological changes of microglia
in a prodromal model of PD, the PARK7/DJ-1 KO mouse,
at 3–4 months, an age corresponding to adulthood inhumans [18], where motor symptoms are not manifested
yet [55].
The large majority of studies investigating the functions of DJ-1 in microglia were so far conducted inimmortalized microglial cell lines showing that PARK7/
DJ-1 deficiency sensitizes cells resulting in an exaggerated response to LPS in vitro [42, 75]. In our study, we
conducted in vivo and ex vivo analyses, thus achievinga more comprehensive view of the brain, corroborating
our findings using translational in vitro models. We previously showed that the downregulation of homeostatic

Page 18 of 24

markers and upregulation of classical inflammatory genes
represent a typical transcriptional signature of microglia in a LPS model of neuroinflammation [67]. Here, weshow that microglia in PARK7/DJ-1 KO mice display a
distinct response to LPS, specifically characterized by anattenuated inflammatory transcriptional program, whichis also recapitulated in human PARK7/DJ-1 mutant iPSCderived microglia (iMG). Among the impaired pathways,
we detected a downregulation of genes related to interferon signaling, at baseline and following LPS treatment
in both models. Specifically, in microglia isolated fromPARK7/DJ-1 KO mice, we detected a downregulation ofinterferon-related genes, both at steady state (e.g. Cxcl9,
Tnfrsf9 and Ifi214) and after 6 h (e.g. Ciita, Ifng, H2-Aa,
Oasl2, Gbp8, Ifi213, Ifi27l2a, Tgtp2) or 24 h (e.g. Ifitm3,Irf7) following LPS treatment, aligning with patterns also
observed in long-term/chronic stress models [66, 82].
In the DJ-1 mutant iMG, S100A6, NLRP2 and ACOD1
were among the most downregulated genes, both at baseline and after LPS treatment. Interestingly, S100A6 is agene known to be involved in cellular stress responses
and regulation of cytoskeletal functions [81]. ACOD1 isa key gene involved in immunometabolism coding foran enzyme converting the TCA cycle intermediate cisaconitate into itaconate, which possesses anti-microbial
properties [47]. Of note, ACOD1 expression is induced
by interferon regulating factor 1 (IRF1) [70], thus its
decrease can be linked to the impairment of the interferon signaling pathway. Furthermore, ACOD1 is also an
important mediator of mitochondrial ROS generation[29], hence the downregulation of this gene could be away of adjusting for the loss of DJ-1 in microglia. Previous studies reported excessive inflammatory reactionof DJ-1 deficient astrocytes and microglia to IFN-γ [36]and LPS [41, 46, 79]. Briefly, DJ-1 can bind src-homology
2-domain containing protein phosphatase (SHP-1) andinhibit signal transducer and activator of transcription 1(STAT-1) signaling in microglia and astrocytes, dampening the inflammatory response initiated via STAT-1 [36].
The downregulation of IFN-related genes at baseline andafter LPS-treatment in microglia from PARK7/DJ-1 KO
adult mice may represent a compensatory mechanism
aimed at avoiding an excessive neurotoxic response via
the activation of STAT-1 and NFκB. Along these lines,
higher levels of glutathione (GSH) have been detectedin the medial prefrontal cortex of PARK7/DJ-1 KO
compared to wildtype mice, which might signify a compensatory mechanism to overcome the loss of the antioxidative properties of DJ-1 [11]. Interestingly, changes
in GSH metabolism have been observed in various PD
patients [3]. This is possibly happening in astrocytes, as
those glial cells are known to be important for feeding
neurons with precursors for GSH synthesis [54]. In ourLind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

study, we did not detect upregulation of genes related
to the anti-oxidative machinery, neither in murine nor
in iMG. However, as we aimed to focus on the effect of
the loss of PARK7/DJ-1 in microglia, we cannot exclude
that compensatory mechanisms took place in surrounding CNS cells. We, therefore, hypothesize that the downregulation of IFN-related genes and upregulation of
DNA repair markers, which can be related to tolerogenic
mechanisms [25], keep the loss of DJ-1 and oxidative
stress in check. However, at a certain stage, representing a “tipping point”, the CNS environment and microglial cells are no longer able to compensate for the lossof PARK7/DJ-1 leading to neuroinflammation and neurodegeneration as a consequence of inflammageing andchronic disturbance of the redox balance. This hypothesis
is supported by our findings in aged mice where microglia in PARK7/DJ-1 KO mice show increased expression levels of pro-inflammatory markers compared towildtype mice when injected with LPS.
In addition to the attenuated inflammatory response at
the transcriptional level, we found discrete morphological adaptations comparing the classical amoeboid shapeof microglia after LPS treatment. The understanding of
microglial physiology in relation to its morphology is
generally limited and the shape of microglia only partly
reflect their functions [6]. However, there is consensus
on the overarching patterns and correlations between
the shape of microglia and certain conditions, such asa less ramified and more compact amoeboid shape isconsidered an activated microglia reacting to danger or
damage signals, whereas a highly ramified structure isconsidered a feature of surveying homeostatic microglia[78]. Our study found changes in gene ontologies related
to cytoskeleton and extracellular matrix reorganization,
both in PARK7/DJ-1 KO mice and human iPSC-derived
DJ-1 mutant microglia. A previous study deeply investigated and described reduced microtubules in DJ-1
deficient neurons [61]. More specifically, PARK7/DJ-1
deficient mice exhibited a decreased dendritic complexity with reduced dendritic spine densities in medium
spiny neurons [61]. This was found to be a consequenceof the downregulation of β-tubulin-III, an important
regulator of microtubules, which is regulated by HIF1α.
In our study, we found a decreased complexity of microglial processes under steady state conditions in the cortex
and substantia nigra of PARK7/DJ-1 KO mice, which had
a more compact shape with thicker and less processes aswell as a larger soma. This hyper-ramified morphology
was previously observed in microglia in various chronic
stress models, where stress was shown to increase microglial internal complexity and degree of branching [32,65]. Importantly, these observations are in line with thedetected impaired interferon-related signatures related

Page 19 of 24

to stress conditions at the transcriptional level. As microglial coverage and processes are essential for immunosurveillance, we hypothesize that the reduced complexityof microglia observed in PARK7/DJ-1 KO mice at baseline might affect their functions, including altering theircommunication with other CNS cells and their surveillant features of the brain parenchyma. The pivotal factors
influencing process extensions and cell migration involve
adhesive interactions with the surrounding extracellularmatrix. In this perspective, we found an upregulation ofgenes related to proteolysis, cell adhesion and extracellular matrix organization both in microglia from PARK7/
DJ-1 KO mice (e.g. Adamts13, Elf3, Mefv) and in humanDJ-1 mutant iMG (e.g. CPA4, ADAM8, CAPN3, HTRA1,
CLMP, COL6A3, COL4A1). These findings suggest thatmicroglia under PARK7/DJ-1 deficiency might be more
active in the degradation of the extracellular matrix
and the secretion of proteins involved in adhering andinteracting with the matrix compared to their normal
counterparts.
The distinct microglial phenotypic acquisition under
PARK7/DJ-1 deficiency, both at baseline and following a
LPS treatment, might play a role in fostering dopaminergic cell death and amplifying the damage to those vulnerable neurons over time. In fact, a precise balance between
an efficient and timely response to infections withoutcompromising the fine-tuned neuronal network is essential for a healthy CNS. Following the impairment of IFNrelated pathways that we detected both in the mouse andhuman models, it would be interesting to further investigate the role of DJ-1 in additional infectious paradigms,
such as in viral models. A dampened CNS immune
response towards microbial threats under PARK7/DJ-1
deficiency could render prodromal PD individuals more
susceptible to CNS or systemic infections, thus impacting the brain.

Conclusions
In summary, our investigations provide a comprehensive transcriptional and morphological analysis of
microglia in PARK7/DJ-1 KO mice under steady stateand inflammatory conditions that we also complement for translational prospects in human PARK7/
DJ-1 mutant iMG. Our results suggest that microglial
cells under PARK7/DJ-1 deficiency react differently to
an inflammatory stimulus, specifically dampening theinterferon-related pathway at the transcriptional level
and regulating actin dynamics and cell cytoskeleton
affecting their classical amoeboid activated phenotypic state. Furthermore, we detected an impairmentof the interferon response at baseline, which is associated with a hyper-ramified morphology representing
typical features of microglia under chronic stressful

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

conditions. Taken together, our findings suggest thatthe underlying chronic oxidative stress associated to
the lack of PARK7/DJ-1 at baseline affects microglia
neuroinflammatory responses, which may play a causative role in PD onset and progression. Further studies
are warranted to investigate how the identified molecular and morphological cues underlying PARK7/DJ-1
deficiency affect key microglial functions and how they
will affect the underlying neuronal network also taking
advantage of models of chronic LPS [4] or living microbial infections [33] to assess the triggered neurodegenerative processes.
Abbreviations
BMC	Bone marrow cell
BMDM	Bone marrow-derived macrophage
BP	Biological processes
CNS	Central nervous system
DAPI	4’,6-Diamidino-2-phenylindole
DAVID	Database for Annotation, Visualization and Integrated Discovery
DEGs	Differentially expressed genes
DJ-1	Protein deglycase-1, Parkinsonism associated deglycase-1 (PARK7)EB	Embryoid bodies
FACS	Fluorescence-activated cell sorting
Gfap	
Glial fibrillary acidic protein
GO	Gene ontology
Gpr34	G-protein coupled receptor 34
GSH	Glutathione
HBSS	Hanks’ Balanced Salt Solution
IBA1	Ionized calcium-binding adapter molecule 1 (also known as allo‑
graft inflammatory factor 1)
IFN-γ	Interferon gammaiMG	Induced pluripotent stem cell-derived microglia-like cell
iPSC	Induced pluripotent stem cell
IRF1	Interferon regulating factor 1
KO	Knockout
LPS	Lipopolysaccharide
MACS	Magnetic-Activated Cell Sorting
MIC-MAC	Microglia and Immune Cell Morphologies Analyser and Classifier
Mog	
Myelin oligodendrocytic glycoprotein
Mobp	
Myelin associated oligodendrocyte basic protein
NeuN	
Neuronal nuclei
Ntsr2	Neurotensin receptor 2
OCT4	Octamer-binding transcription factor 4
Olfml3	Olfactomedin like 3
UMAP	Uniform Manifold Approximation and Projection
Park7	Parkinsonism associated deglycase 7
PBS	Phosphate buffered saline
PCA	Principal component analysis
PD	Parkinson’s disease
RT-qPCR	Reverse Transcription Quantitative Polymerase Chain Reaction
SEM	Standard error of the mean
SHP-1	Src-homology 2-domain containing protein phosphatase
SOX2	Sex determining region-Y box
SSEA4	Stage-specific embryonic antigen-4
STAT-1	Signal transducer and activator of transcription 1
TGFβ	Tumor growth factor-β
TNF	Tumor necrosis factor
TRA-1-60	Podocalyxin
Tubb3	Tubulin 3

Page 20 of 24

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12974-​024-​03164-x.
Supplementary Material 1. Fig. 1. Characterization of wildtype and PARK7/
DJ-1 KO mouse brains. A) Western blot analysis of DJ-1 and α-actin inwhole brain tissue and C­ D11b+ MACS-sorted cells from wildtype and
PARK7/DJ-1 KO mice. Amount of loaded proteins: wildtype brain tissue:
10 µg; PARK7/DJ-1 KO brain tissue: 20 µg; wildtype microglia: 10 µg;
PARK7/DJ-1 KO microglia: 10 µg. B) Gating strategy used to discrimi‑nate lymphocytes ­(CD45hiCD11b− cells) and myeloid cells ­(CD11b+
cells). Among the latter, neutrophils were recognized as ­Ly6G+ cells.
From other myeloid cells ­(Ly6G− cells), we identified border-associatedmacrophages (BAMs) ­(CD206+Ly6C− cells), monocytes ­(CD206−Ly6C+
cells) and microglia ­(Ly6C−CD206− cells). C) Percentages of correspond‑
ing cells over ­CD45+ cells extracted from PARK7/DJ-1 KO (circles) andwildtype (triangles) mice at baseline and 24 h following LPS treatment.
Graphs show mean % of C
­ D45+ cells ± SEM. 2-way ANOVA with Tukey’s
multiple comparisons, *p < 0.05 (n = 3–5 mice per condition). D) UMAP
showing 2931 C­ D11b+CD45int microglial cells from female (pink) andmale (green) mice 24 h after LPS treatment (n = 2 mice per group). E)
Gene ontology terms corresponding to upregulated genes (adjusted
p-value < 0.05, log2FC ≥ 0.5) comparing Cluster 1, 2 or 5 to the otherclusters. Dotted line indicates minimum level of significance, i.e. –log10(adjusted p value = 0.05) = 1.3. F) Analysis of KI67 expression in the cortex
of wildtype and PARK7/DJ-1 KO mice (positive control in subventricularzone—SVZ). Scale bars: 50–100 μm. G) Gene expression levels of MKi67 in­CD11b+CD45int microglial cells analyzed by qPCR (Gapdh as housekeepinggene). Bars represent mean ± SEM of analyzed mice (dots). Unpaired t-test,
ns: not significant (n ≥ 3 mice).
Supplementary Material 2 Fig. 2. Characterization of ­CD11b+ cells
isolated with magnetic beads and validation of identified marker genes
discriminating microglia in wildtype and PARK7/DJ-1 KO mouse brains. A)
Schematic representation of transcriptional analyses of ­CD11b+ microglial
cells isolated either from PARK7/DJ-1 KO or wildtype mice at baseline and
6 h following LPS treatment. B) Gene expression levels of brain cell mark‑ers in ­CD11b+ (blue) and ­CD11b− (brown) cells analyzed by qPCR. Graphs
show relative expression levels of oligodendrocytic (Mog, Mobp), astrocytic(Gfap, Ntsr2), neuronal (Tubb3, NeuN) and microglia (Gpr34, Olfml3) markers(Gapdh as housekeeping gene). Bars represent mean ± SEM of analyzed
male mice (dots). Unpaired t-test, ****p < 0.0001, ***p < 0.001, *p < 0.05
(n ≥ 3 mice). C) Representative picture of ­CD11b+ cells plated and stainedwith IBA1 antibody (top image) and quantification (n = 3 technical repli‑
cates from one mouse) of ­IBA1+ cells (% of total cells based on DAPI stain‑ing) (bottom graph). Scale bar: 20 μm. D) Gene expression levels of Cxcl9,
Ifi214 and Mefv in C
­ D11b+ cells isolated from wildtype (n = 7) and PARK7/
DJ-1 KO (n = 7) mice at baseline analyzed by qPCR (pink dots: females;
white dots: males). Graphs represent mean of fold change (wildtype con‑dition set at 1; Gapdh as housekeeping gene) ± SEM. Mann–Whitney test,*p < 0.05. E) Gene expression levels of Cxcl9 and Ifi214 in primary microglia
untreated or treated with IFNγ (50 ng/mL) for 6 h obtained by culturing­CD11b+ cells isolated from wildtype (n = 4) and PARK7/DJ-1 KO (n = 4)
mice analyzed by qPCR (pink dots: females; blue dots: males). Graphs rep‑resent mean of fold change (wildtype untreated condition set at 1; Gapdh
as housekeeping gene) ± SEM. Mann–Whitney test, **p < 0.01, *p < 0.05.
F) Gene expression levels of Cxcl9 and Il6 in ­CD11b+ cells isolated fromaged wildtype (n = 5) and PARK7/DJ-1 KO (n = 5) mice 6 h following LPS
treatment analyzed by qPCR (pink dots: females; blue dots: males). Graphs
represent mean of fold change (wildtype LPS condition set at 1; Gapdh
as housekeeping gene) ± SEM. Mann–Whitney test, **p < 0.01. G) Analysisof mitochondrial membrane potential in microglia at baseline and after
LPS treatment (100 ng/mL) overnight using the specific marker TMRE.
Graph shows normalized mean intensity of TMRE per field of view (dots) ineach group. Kruskal–Wallis test with Dunn’s test for multiple comparisons.
****p < 0.0001 (n = 3 male mice per group). H) Protein expression levelsof CD11b in ­CD11b+ cells analyzed by multicolor flow cytometry. Graphshows normalized geometric mean ± SEM of analyzed mice (dots). 1-way
ANOVA with Tukey’s multiple comparisons, *p < 0.05 (n = 3 mice).

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

Supplementary Material 3 Fig. 3. Quality control of human induced
pluripotent stem cells, their efficient differentiation into microglia-likecells and differentially expressed genes between PARK7/DJ-1 mutant andisogenic control iPSC-derived microglia. A) iPSC colonies stained for nuclei(Hoechst) and specific pluripotent markers, SSEA4, TRA-1–60, OCT4 and
SOX2 (upper panel shows isogenic control iPSCs, bottom panel shows
PARK7/DJ-1 mutant iPSCs). Scale bar: 59 µm. B) OCT4 and SOX2 gene
expression levels in both isogenic control (iCtrl) and mutant (DJ-1 Mut)
iPSC lines analyzed by qPCR. Graphs represent mean of relative expres‑sion (iCtrl iPSC set at 1; Gapdh as housekeeping gene) (n = 1). C) Gene
expression levels of PARK7 and, specifically, PARK7 exon 3 in both isogenic
control (iCtrl) and mutant (DJ-1 Mut) iPSC lines and iPSC-derived microglia(iMG) by qPCR. Graphs represent mean of relative expression (GAPDH ashousekeeping gene) ± SEM (n ≥ 1). D) Western blot showing DJ-1 expres‑sion, both in isogenic control and mutant lines, in fibroblasts and iPSCs. E)
Gene expression levels of microglia homeostatic genes (GPR34, TREM2 andP2RY12) in both isogenic control (iCtrl) and mutant (DJ-1 Mut) iPSC lines,
in undifferentiated iPSCs and iMG analyzed by qPCR. Graphs represent
mean of relative expression (GAPDH as housekeeping gene) ± SEM (n ≥ 1).
F) Differentially expressed genes (adjusted p-value < 0.01, |log2FC|≥ 1)
comparing PARK7/DJ-1 mutant and isogenic control iMG under LPS treat‑ment and at baseline (n = 3 replicates of differentiated iMG per condition).
Color bar shows log2 Fold Change.
Supplementary Material 4 Fig. 4. Comparison of GO terms associatedto genes modulated in DJ-1 deficient microglia under LPS conditions
between human and mouse models. A) Venn diagram showing numbersof unique and shared GO terms resulting from gene set enrichment analy‑sis (GSEA) using log fold changes comparing PARK7/DJ-1 deficiency withwildtype conditions under LPS conditions from both human and mousemodels. B) Dot plot depicting 23 GO terms in common between PARK7/
DJ-1 mutant iMG and corresponding murine cells under LPS conditions.
Color bar shows Normalized Enrichment Score (NES).
Supplementary Material 5.
Supplementary Material 6.
Supplementary Material 7.
Supplementary Material 8.
Supplementary Material 9.
Supplementary Material 10.
Supplementary Material 11.
Acknowledgements
We are grateful to Ms. Amandine Bernard for mouse genotyping and, together
with Mr. Alexandros Pailas, for technical support with Western blotting as wellas to Ms. Virginie Baus for helping with the cryostat tissue cutting. We would
also like to thank Dr Eduardo Rosales Jubal for advices on statistical analysesof qPCR and MIC-MAC results. Part of the analyses were carried out using theHigh Performance Computing (HPC) facilities of the University of Luxembourg(https://​hpc.​uni.​lu).
Author contributions
Conceptualization, FL-HM, and AlM; Methodology, FL-HM, CS, CA, KB, SLP, ArM,
TK, DC, AnS, SKP, MT, PMAA, NC, AuG, DMVW, WW, AP, PVN, AlS, AnG, and AlM;
Formal analysis, FL-HM, CS, CA, KB, SLP, ArM, TK, PMAA, AuG, AlM; Supervision:
PVN, AlS, AnG, and AlM; Writing—original draft preparation, FL-HM, and AlM;
Writing—review, and editing, all authors; Data curation, FL-HM, and AlM;
Funding acquisition, AlM. All authors read and approved the final version of
the manuscript.
Funding
F.L-HM. was supported by the Luxembourg National Research Fund
(FNR) through the FNR-PRIDE program i2TRON for doctoral education(PRIDE/14254520/I2TRON). C.S. was supported by the FNR (AFR/6916713)
and the Fondation du Pélican de Mie et Pierre Hippert-Faber under the aegis
of Fondation de Luxembourg. C.A. and K.B. were supported by the FNRPRIDE program CriTiCS for doctoral education (PRIDE/10907093/CriTiCS). In
addition, C.A. was supported by the CMCM (Caisse Médico-Complémentaire

Page 21 of 24

Mutualiste Luxembourg) matching grant. MT received funding from the
FNR as part of the FNR-PRIDE program NextImmune2 for doctoral educa‑tion (PRIDE21/16749720). W.W. was supported by the Deutsche Zentrum fürPsychische Gesundheit (DZPG), site Munich-Augsburg. PVN was supported
by a FNR CORE grant (C21/BM/15739125/DIOMEDES). Al.S. was supportedby the FNR through an INTER grant (INTER/DFG/17/11583046). AG was
supported by the FNR within the framework of the ATTRACT (Model-IPD,
FNR9631103) program as well as the National Centre of Excellence in Researchon Parkinson’s disease (NCER-PD, FNR/NCER13/BM/11264123). In addition, AG
and SLP received funding from the FNR within the framework of a CORE grant(“CAMeSyn”, C19/BM/13688526). Lastly, we acknowledge financial support
from the Action LIONS « Vaincre le Cancer» Luxembourg. For the purpose ofopen access, and in fulfilment of the obligations arising from the FNR grant
agreement, the author has applied a Creative Commons Attribution 4.0 Inter‑national (CC BY 4.0) license to any Author Accepted Manuscript version arisingfrom this submission.
Availability of data and materials
The single-cell RNA-sequencing and RNA-sequencing murine data supportingthe conclusion of this article have been deposited in the Gene Expression
Omnibus (GEO) repository (www.​ncbi.​nlm.​nih.​gov/​geo/) with accession
numbers GSE115571/GSE256105 and GSE254687, respectively. The humanRNA-sequencing data has been deposited at the European Genome-phe‑nome Archive (EGA), which is hosted by the EBI and the CRG, under accession
number EGAD50000000291. Further information about EGA can be foundon https://​ega-​archi​ve.​org "The European Genome-phenome Archive in 2021"[23].

Declarations
Ethics approval and consent to participate
All animal experiments were performed according to all applicable laws
and regulations (EU Directive 2010/63/EU and Grand-Ducal Regulation of 11
January 2013 on the protection of animals used for scientific purposes), after
receiving approval for the animal license (reference number: LUPA2022/1)
from the Animal Experimentation Ethics Committee at the University of
Luxembourg (AEEC) and the Ministry of Agriculture, Viniculture and Rural
Development. Experiments on iPSCs were conducted in accordance with the
ethics rules at the University of Luxembourg.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Neuro‑Immunology Group, Department of Cancer Research, Luxembourg
Institute of Health, 6A, rue Nicolas‑Ernest Barblé, L‑1210 Luxembourg, Luxem‑
bourg. 2 Faculty of Science, Technology and Medicine, University of Luxem‑
bourg, L‑4365 Esch‑sur‑Alzette, Luxembourg. 3 Present Address: International
Iberian Nanotechnology Laboratory, 4715‑330 Braga, Portugal. 4 Integrative
Cell Signalling Group, Luxembourg Centre for Systems Biomedicine, Universityof Luxembourg, L‑4362 Esch‑sur‑Alzette, Luxembourg. 5 Molecular and Func‑
tional Neurobiology Group, Luxembourg Centre for Systems Biomedicine, Uni‑
versity of Luxembourg, L‑4362 Esch‑sur‑Alzette, Luxembourg. 6 Bioinformatics
Platform, Department of Medical Informatics, Luxembourg Institute of Health,
L‑1445 Strassen, Luxembourg. 7 LuxGen Genome Center, Luxembourg Instituteof Health and Laboratoire National de Santé, L‑3555 Dudelange, Luxembourg.
8
Rodent Platform, Luxembourg Centre for Systems Biomedicine, Universityof Luxembourg, L‑4362 Esch‑sur‑Alzette, Luxembourg. 9 Present Address: Single
Cell Analytics and Microfluidics Core, Vlaams Instituut Voor Biotechnologie-KU
Leuven, 3000 Louvain, Belgium. 10 Bioimaging Platform, Luxembourg Centre
for Systems Biomedicine, University of Luxembourg, L‑4362 Esch‑sur‑Alzette,
Luxembourg. 11 Department of Life Sciences and Medicine, Faculty of Science,
Technology and Medicine, University of Luxembourg, L‑4365 Esch‑sur‑Alzette,
Luxembourg. 12 Institute of Developmental Genetics, Helmholtz Zentrum
München-German Research Center for Environmental Health, 85764 Neuher‑berg, Germany. 13 Technische Universität München-Weihenstephan, 85354 Fre‑
ising, Germany. 14 German Center for Neurodegenerative Diseases (DZNE),

Lind‑Holm Mogensen et al. Journal of Neuroinflammation(2024) 21:174

81377 Munich, Germany. 15 Munich Cluster for Systems Neurology (SyNergy),
81377 Munich, Germany. 16 Deutsche Zentrum für Psychische Gesundheit(DZPG), 80336 Munich, Germany. 17 Multiomics Data Science Group, Depart‑
ment of Cancer Research, Luxembourg Institute of Health, L‑1445 Strassen,
Luxembourg. 18 Department of Neuroscience, University of California San
Diego, La Jolla, CA 92093, USA. 19 Integrative Biophysics, Department of Phys‑
ics and Material Science, University of Luxembourg, L‑1511 Luxembourg,
Luxembourg. 20 Institute of Neurogenetics, University of Lübeck, 23538 Lübeck,Germany.
Received: 5 February 2024 Accepted: 3 July 2024

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